Launching Tech Ventures

Posses, Posers, and Pushers – An Entrepreneur’s First Tradeshow

2012-02-26T18:46:00.001-08:00

by Julia Kastner

The hardest thing about being the little guy (or girl) at a big, bad trade show is that you have no idea who is who. I went to the MAGIC trade show in Las Vegas – one of the biggest events for fashion and retail in the world. The way I’ve been describing it is that it’s a department store for department stores – a place where buyers for Macy’s, Nordstrom’s, Saks, and thousands of boutiques come to see what brands are offering for the season a year from now - Spring/Summer 2013. As an entrepreneur with no sample (or prototype) yet, I went to observe, learn, find producers for the denim company I am building, and hopefully meet some customers (as per our note on Customer Visits). I wanted to meet some retail buyers who could give me feedback on denim designs I outsourced. I achieved many of my goals, but the buyers proved elusive. These were my observations:

People seemed to travel in packs – each company moved in its own little posse – making it hard to introduce myself or start a conversation.
More than half the crowd were trying to sell something – their own products, services, or agenda. As I walked around, I couldn’t pay attention to the actual content of the show because I had to keep deflecting flyers and pamphlets and business cards. Even at my hotel at night, while getting onto the elevator, a fabric supplier from China insisted I take his business card and visit his booth the next day!
Most of all, though, it was impossible to tell who was who. Everyone (and I mean everyone) was hunting for SWAG. Students visiting from a fashion institute were pretending to be buyers just to walk away with a few extra tote bags and cosmetics pouches. The fancier brands, like AG or Seven Jeans or French Connection, had their own VIP areas for buyers with free alcohol – there were crowds outside trying to get past the model/bodyguards outside – probably not the real buyers. They were inside the VIP areas getting sales done.

So, given this hostile environment, what should an entrepreneur do? These are my main takeaways:

First and foremost, have a booth. Even if it’s cheap and tucked in a corner, a booth of any kind is better than nothing, because it makes you an “exhibitor” instead of a nobody. I’d plan to be away from my booth as much as possible to mingle at seminars, etc., but I think having a location written on your business card is invaluable as proof that you matter.
On the note of seminars, I found workshops and speaker panels to be great places to actually mingle. I actually met one potential supplier at a seminar – a session on the future of denim. It was a room full of self-selected people, almost everyone there was either a supplier or a brand like me in the denim space. During the session, one man asked a question and introduced himself as an “organic cotton producer”. It was great – I knew who he was and could easily approach him. Once I had a booth, I would do that too – ask a question while introducing myself.

Product Market Fit in Health IT Training

2012-02-24T18:57:00.000-08:00

By Dan Gebremedhin, M.D.

Several studies have shown that when healthcare workers implement new forms of Health IT (Electronic Health Records and other systems) their productivity drops as they are learning to use these new systems. (Click here to learn more about EHRs.)

In attempting to solve this problem, our company has created a robust online training platform. The platform has proven effective in returning users to full productivity faster than the industry standard. Despite this evidence, customers have been reluctant to purchase access to the platform. In this blog entry, I’ll outline the evidence for the effectiveness of our product and begin to examine whether or not there is product market fit.

The Research
There have been a handful of studies that have looked at productivity drop with Health IT adoption. Bhangrava et al, found that providers uniformly dropped in full productivity by 25-33% for at least one month after EHR implementation. After 3 months out, internal medicine physicians returned to full productivity but a large subset of physicians never returned to full productivity during the remainder of the study.

A report by Clayton et al. out of Intermountain Health corroborates this data, by showing that their subset physicians did not return to full productivity until 6 months. Despite the drop in productivity, the physicians in the study increased their level of data entry significantly. This supports the argument that although EHR implementation decreases productivity, EHR use leads to more comprehensive care.

iHIT Results
With the use of our platform technology and methodology, our customers return to full productivity within 4 weeks. Some customers have achieved full productivity within 7 days. From this superior margin of improvement compared to the industry standard, our clients have achieved an ROI of 4:1 and savings of > $1M. These results have been published separately at HIMSS 2011 and ACE 2011. (Click here to learn more.)

Proven Results = PM FIT?
It would seem natural that with proven, published results, there would be significant demand for our product. With 13 healthcare organizations using our product, we have achieved initial traction. In reality, many of these sites were beta-sites that were upgraded to paid subscriptions to our platform. These existing customers did not have to face the same purchase decision that our prospective customers are currently facing.

From our initial market launch in Q32010, we’ve had to adjust our pricing to find the willingness to pay (See below for a chart of our pricing trajectory over the last 10 quarters). We quickly learned that the ROI calculation alone was not enough to convince HCO’s to pay > $150k/year for access to training software.

In focusing on unit economics, we’ve found that HCOs are willing to invest between $100 - 200 per physician per year in total IT online training spend. Further, we know we need to communicate the long term benefits and ROI of the product as opposed to implementation only. As this message is crafted, careful attention will be focused on our conversion rate which sits around 15%. In my estimation, if we can double this to > 30%, our CAC will drop significantly. I think then, we can say that we’ve hit PM Fit and are ready to scale the business.

Source: Interal iHIT research

References
Bhangrava, et al. Electronic Medical Records and Physician Productivity: Evidence from Panel Data Analysis. December 2010. http://www.news.ucdavis.edu/search/news_detail.lasso?id=9665

http://www.healthcareitnews.com/news/study-emrs-effect-docs-productivity-depends-needs-workflow

Clayton et al. Physician use of electronic medical records: Issues and successes with direct data entry and physician productivity. AMIA Annu Symp Proc. 2005; 2005: 141–145. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1560588/

Reflections on leaning a business idea

2012-02-23T07:46:00.002-08:00

By Aditya Dhanrajani

Last week, our class spent 2 hours in a business model exercise where we explored some of our classmates’ startup ideas/products. The idea was to elaborate on the business models of these startups, identify the key hypotheses that the startup was hinged on and explore potential tests that could be run to (dis)prove those hypotheses. Given this was the first time I went through this process I realized that some parts of the process were surprisingly more consuming and had larger implications than I would have anticipated.

Nailing down the business model is key and it drives everything.

Being able to articulate a crisp business model that encompasses, the customer value proposition, got-to-market strategy, technology/operations and the profit formula is a challenging but necessary task. The progression of the four elements of the business model feed into the next. The customer value proposition defines the customer which dictates how we get to them, which influences how we setup the product which defines how the startup makes money. This ensures that at the end of the definition exercise the business idea is elaborate beyond a few bullet points and more importantly consistent.

Identifying key assumption and fleshing out corresponding hypotheses is difficult.

I think the devil is in the detail here. It is easy to say that the key assumptions behind a business model are that the product specifications are sufficient for customers and users are willing to pay for the product. But given limited resources which one does an entrepreneur test. Defining the hypothesis to test is the next challenge. Simply saying the product specifications are sufficient is not testable or falsifiable. It needs to include a clear metric that can be used to declare a hypotheses true or false. I think fleshing out hypotheses is a discipline that an entrepreneur should force upon oneself as sometimes it provides feedback for the business model definition.

Avoid over-thinking tests?

In the team exercise I found myself complicating some of the tests needed. I realize it was because I was too fearful of the hypothesis breaking down under boundary conditions. It was like writing a for loop with several if-elses in it; it just increases the processing time. This is something I am still not clear on but I think initially the test should be simple and should aim at testing the mean. Later if doubts arrive boundary conditions should be tested. The risk in this line of thought is that the boundary condition might invalidate the business model completely. For example do we test our servers to handle our expected volume of 1000 users or max it out at 2000 users. In the beginning the entrepreneur has no clue how many users are going to be using the product.

I think this exercise would be useful to entrepreneurs even after their startup is off and running as it helps act as a touchstone. Getting a team around a white board to go through this helps reinforce the teams vision and provides an opportunity to consciously refine ideas. The whole concept of doing it in a team ensures that the ideas are pushed by each others varying perspectives. What is key is for the team to hold each responsible and drive discipline in the process and dig deep into some of the key questions.

Reckless Cloners or Innovation Adopters?

2012-02-20T15:02:00.000-08:00

By Andrej Rusakov

The criticism of technology start-up “cloners”, who take innovative but proven business ideas from the U.S. and copy them in Europe, Latin America, and China is raging. Silicon Valley entrepreneurs claim that “me too” companies discourage innovation as they limit initial innovator’s upside and thus prevent true entrepreneurs from launching new ventures. Venture Capitalists, however, do not seem to mind, and continue to pour increasingly large funds into copy-cats (e.g. Samwer brothers recently raising $700 million), viewing them as lower risk compared to “new-idea” start-ups as the product/market fit has already been validated in “me too” companies. Who is right here? Do “me too” businesses add value to the society? Can their founders be called entrepreneurs or are they simply executors?

Innovation Adopters?

From a societal point of view, copy-cats do add value. They foster competition and bring innovative products to new markets fast. Why should a billion people in India wait years for paypal.com to eventually enter the market if they can get the same service from someone else today? Moreover, many (but not all) “me too” companies improve the original business concept and adopt it to the localities of the market – something many incumbents lack the abilities, knowledge or resources to do.

Some choose to clone products and concepts in their entireties, however, and not bother with any localization or concept improvement. Such companies may hardly be called innovation adopters. However, they do promote competition. Examples are ample:

Pinterest.com (original)	Pinspire.com (copy)

Fab.com (original)	Bamarang.co.uk (copy)

Entrepreneurs or Executors?

Because copy-cats often do not need to spend much time or money discovering the product/market fit, and the go-to-market approach is already tested by the incumbent, “me too” businesses tend to be “start-ups on steroids” growing fast and offering very lucrative risk adjusted returns to their founders. Indeed, the rumor has it that 90% of the Samwer brothers cloning machine’s start-ups become profitable after 1 year from launch!

But can we call these guys entrepreneurs? Is an MBA launching a “me too” venture anything more than an executor of somebody else’s ideas? I would argue that despite him using other people’s idea, he is still building something from scratch, and thus he is an entrepreneur. Getting the right people together, incentivizing them, securing funding, adopting the product to the local market – what is it if not entrepreneurship? We still call a restaurant owner in London who “borrowed” an interesting concept from New York an entrepreneur. Why tech industry should be any different? Ideas are worth a nickel a bucket; execution is what makes all the difference. In this sense, even Samwer brothers are entrepreneurs – many of the companies they have replicated are more successful than the original once.

Conclusion.

It is a very personal choice between taking on more risk, but having a moral satisfaction of having invented something entirely new, and taking a potentially less risky path of replicating an existing business in a new geography. If you lean towards “true innovation”, strong network effects and high barriers to entry may deter bold replicators to copy your “brain child”. However, there is no guaranty – facebook has been successfully copied in (and adopted to) local markets such as Russia, where it is still having hard time fighting with copy-cats. Something worth keeping in mind when choosing a business to start.

Being in control versus being a control freak in a lean startup

2012-02-16T04:39:00.000-08:00

by Jennifer Hepworth

I’ve been thinking a lot recently about the notion of control. I’m generally a person who feels most comfortable in the driving seat, having decided on a destination, figured out the best way to get there and chosen the sound track. This attitude has gotten me a lot of places, and I think a strong sense of personal responsibility for outcomes is an important quality and a Good Thing.

However.

There’s a line. On one side of the line is being in control and on the other is being a control freak. I’m not saying I’ve been there and made large and messy mistakes and learned the hard way that control-freakery must be avoided, but I’m fortunate enough to have met some very smart people with relevant things to say on the topic which will hopefully prevent me from heading down that path.

1. Great entrepreneurs know what they’re bad at, and hire awesome people to do it for them (Mike Cassidy).

In some ways, Steve Jobs was the consummate control freak (just check out Apple’s wheels within wheels org chart), and he certainly liked being in control. However, though he was clear on the engineering goals he set for his products (extend the battery life! EXTEND THE GOD DAMNED BATTERY LIFE!!!!!) he didn’t care how those goals were achieved. Despite being able to code, his gift was in marketing and design and from the very start of his career he left the engineering to other more capable people, like Steve Wozniak.

2. Take on the tasks dictated by your comparative advantage (Jeffery Bussgang)

This is subtly different advice from point 1. There’s a chance you already know what you suck at, in which case you may follow point 1. and stop reading now. However, just in case you’re one of the tiny number of people who are demonstrably great at everything, or a member of the far larger group of people who believe they’re great at everything, read on. Say you’re better than everyone in your company at identifying new product features, UX design, marketing and sales. First, I would say someone’s done a pretty crappy job at hiring. Second, I would say IT DOESN’T MATTER. It only matters where your relative strength lies. If you’re great at everything, but really really great at sales, you should devote as much of your time to this as possible, off-loading those tasks where you don’t have a comparative advantage to someone else. In a world of finite resources (in this case your time), you maximize your productivity by focusing your efforts on those areas where you’re, you know, most productive.

A few days ago we had a great class with a successful entrepreneur and class guest who readily admits to falling victim to some aspects of control-freakery (though let’s call him a Hero CEO out of respect). In 2010, with a decent sales model and a customer lifetime value easily in excess of 3x the cost of customer acquisition, he was reticent about accepting an unsolicited offer of additional funding from one of their investors to ramp up their sales force. The reason? He felt uncomfortable with the loss of control which inevitably comes as you grow your organization (in his case, to a VP of Sales). He did end up taking the funding, and the company has grown successfully a result, but the fact that decision was delayed and an opportunity could have been missed provides a salutary lesson to anyone who catches themselves trying to dip their fingers too deep into too many pies.

Easier said than done, but maybe next time I’m driving I’ll let someone else chose the radio station.

When Managing Salespeople, Stage Matters

2012-02-15T19:43:00.000-08:00

by McCann, Erin

Does sales management really come down to pushing back on excuses? Several founders have asked me this question in response to Mark Suster’s sales management advice. As a former tech sales executive, I agree with many of his lessons -- when applied to later-stage, post-traction point startups [1]. However, I advocate a more nuanced approach for early-stage startup teams, and suggest the following exceptions when managing your earliest sales hires.

Feedback isn’t always an excuse
Early sales hires understand your clients best. While product managers often obtain direct client feedback [2], your salespeople touch far more accounts at a higher frequency, and thus frequently receive the most candid feedback. While I support Suster’s claim that clients don’t buy features, they can provide valuable information, as we saw with RentJuice’s pricing model [3]. I’m not suggesting accepting justification for missing targets – just disaggregate the feedback from the sale. Salespeople shouldn’t be off the hook for targets, but their insights from clients often prove valuable to product managers and may improve your overall product-market fit.

Equity & team dynamics matter
Cash certainly incentivizes salespeople (every sales executive I know has some commission-based pay), but given the non-sales responsibilities and risk taken on by early-stage hires, they’re likely motivated by more than cash. Talented sales executives could easily choose more mature companies with higher cash opportunities, and avoid difficult early challenges like those at RentJuice, such as navigating significant ambiguity and building sales collateral. What’s more, early-stage startups likely aren’t in a position to offer all-cash compensation. While equity and team dynamics may matter less to traditional salespeople, in my experience they factor heavily into attracting true “renaissance reps” who can balance the complex, changing dynamics of early sales.

Support requests may mean it’s time to scale
Requests for sales support may seem like excuses, but could also indicate the need to hire an account manager. Sure, sometimes reps just don’t like the grunt work – for me, creating proposals always felt like a huge waste of time compared to closing more deals. However, support requests might not be entirely driven by ego or disinterest, and can present an opportunity to drive higher revenue growth by managing and up-selling through existing relationships. Certainly managers need to ensure that they have a repeatable and scalable business model whose metrics justify adding team members and splitting roles. However, once this occurs, these perceived support excuses may in fact drive huge efficiency and monetary gains.

Ultimately, while I appreciate Suster’s experience, candor and self-admitted use of “broad generalizations,” I urge some caution before lumping all salespeople into a “class.” While founders benefit from understanding common incentives and excuses, early hires likely differ tremendously in terms of motivations, responsibilities, and requests. As a founder, what has been your experience with early hires?

1 Leslie, Mark and Charles A. Holloway. The Sales Learning Curve. Boston: Harvard Business Publishing, 2006. Print.

2 Bussgang, Jeffrey, Thomas Eisenmann and Robert Go. The Product Manager. Boston: Harvard Business Publishing, 2011. Print.

3 Eisenmann, Thomas and Liz Kind. RentJuice. Boston: Harvard Business Publishing, 2011. Print.

How much can you cheat?

2012-02-15T19:34:00.000-08:00

by Julien Hagege

For many entrepreneurs network effects sound like a distant promise of a $100 billion jackpot. In a crowded space with only a finite span of user attention, it is no surprise that most will fail and that some will try anything to succeed. Triangulate struggled whether to ‘play dirty’ and use ‘sleazy’ marketing tactics to boost user trials. Last week Path – a social media app - has been dragged through the mud when a hacker revealed that the app was uploading the user’s address book to Path’s servers, without his/her consent. It was indeed very tempting to snatch such data to generate leads and to foster growth, but at what cost?

Today the consensus around private information appears clear: one should not gather private information without the user’s consent, or at least without making it public to the blogosphere (so that bloggers / twitters can denounce any abuse). Path, Carrier IQ and others played ‘dirty’ and paid a hefty price: losing customer’s trust. Path’s CEO has issued public excuses and switched personal data transfer to become an opt-in feature, but only time will tell if they will recover from such a fiasco. LCA (HBS ethics class) taught us the danger of aiming for the lines, and how value could still be created when playing in the center of the court. This lesson can be applied to the tech startup world as well, where facts are easier to conceal and theft doesn’t always leave a trace.

But are some lies OK? Last week a section mate advertised for a new dating social network that leverages facebook friends. I checked it out (as it sounded like a good LTV candidate, not because I’m dating) and became intrigued by the user counter on the main page. I went through the JavaScript code - I can’t hide my engineering roots - and I was only half surprised to discover that the counter was faked. The variable behind the counter – notably called “wow_factor” - was incremented by a random number from 1 to 5 every 1.2 seconds. While there is here no clear harm done to the user, it sends a very negative signal: if you are willing to cheat on this to make the site more attractive, where else are you going to take shortcuts? Following the ‘tyranny of incrementalism’ concept, an entrepreneur should strive to take no shortcut and build trust from day 1.

Yet a lean entrepreneur needs to run experiments that feel realistic and might be compelled to make up numbers. It would even be quite depressing for an entrepreneur to put a counter of the true number of users in the early days (“Come in and sign up to discover 23 people”), and the temptation must be great to boost numbers to kick-start user adoption. I would rather advocate for staying away from such sleazy tactics and focusing on managing your customers’ expectations and getting a great product out there – ie one that achieves product/market fit.

When the first beta comes out you should focus on getting users that are willing to test your product and give feedback. Those early adopters will not buy the concept on a ‘wow factor counter’ alone, but rather on the overall value proposition (being big is not a sufficient value proposition, especially if you’re not big in reality). Expectations matter: say it is a limited beta when that’s the case, and don’t put a fake counter as a cheap trick to boost kick off. When you decide to launch, and if you got the recipe right, traditional marketing campaigns and PR events (like Techcrunch events we discussed in class) will start the machine, and word-of-mouth and direct/indirect network effects will do the rest. If there is a true market out there and your product has a compelling value proposition, users will understand that you have to be small before you become big and will reward your transparency with their trust.

How Sleazy Is Too Sleazy In Acquiring Customers: The Three Product Qualities That Matter

2012-02-15T17:28:00.000-08:00

by Michael Schrader

We’ve all seen the methods:

Spam emails
Free trials that automatically convert into paid subscriptions
Installation of products or spyware without sufficient warning
Overhyping services and under-delivering once installed/purchased
Relentless posts to your Facebook profile

It’s obvious that some companies believe that these are acceptable, and may even be in users’ best interests. So when is this behavior acceptable and when will customers push back? While we don’t have clear data, we can infer from recent examples that there are four strong variables that decide just how much sleazy behavior customers will put up with:

1) How much true value does your product or service offer? Facebook has shown the world that a valuable platform can get away with extremes in questionable practices. Examples such as Beacon, which tracked user’s online purchases and posted the updates online, have drawn uproar from users, yet Facebook continues to grow, with more than 845 million users.
2) Are you seen as an exclusive community? Gilt Group has shown the power of exclusivity. By creating a product with a high end, exclusive feel, Gilt avoided the typical stereotypes that accompany the “Invite Your Friends” sales pitch. People who had been admitted to the site felt as though they were doing a favor for their friends by sending an invite.
3) Do you have strong positive brand recognition? Google has launched a ton of different products over the years, ranging from home runs like Gmail to far less successful products like Wave. The one constant has been Google’s ability to maintain a strong overall brand, which makes invitations to these services exciting to receive.
4) Will people be excited to invite their friends? Zynga tested many Facebook users’ patience with their relentless stream of Farmville invitations. The irony was that nearly all of these posts were initiated by actual users playing the games. Many people thought that their friends would actually want to help them feed their fake sheep, hence they supported the posts.

Let’s walk through each of these insights and see how it can be utilized to help you grow your startup.

1) Value: While value can be tricky to gauge, smoke tests can be a quick and easy method to evaluate your customers’ perception of value. If you don’t like the results, find a way to add more value.
2) Exclusivity: Past examples such as Gilt, Gmail, Facebook, etc. show that a feeling of exclusivity is possible with a variety of online enterprises. The tradeoff is limiting early signups by excluding those that don’t fit your target profile. You will have to determine if you’re the benefits of exclusivity outweigh the potentially slower initial growth.
3) Brand: Most startups can’t count on brand until later. Find another way.
4) Excitement to Invite: This can typically be created by adding a gaming aspect to your site or creating a model that revolves around social activities such as sports or parties.

So if you want to grow through slimy methods, make sure your product has the right elements to overcome the negative backlash that may result.

Pennywise, but dollar foolish – What pitfalls can happen when trying to be “lean”

2012-02-15T17:24:00.000-08:00

by Felipe Arias

The lean mentality has allowed startups to identify key issues and potential pitfalls at an early (and cheaper) stage in their lifecycle and has provided entrepreneurs with the data necessary to pursue ideas or pivot accordingly. Certainly, putting into place rigorous thinking that drives an entrepreneur to develop a vision, translate that vision into a set of testable hypotheses, identify what part of the vision is required to be tested through a minimum viable product, prioritize the tests, and then iterate through learning before spending on scaling and optimization makes intuitive sense. In practice, the drive to be “lean” can push entrepreneurs to miss opportunities, avoid key tests due to difficulty and cost, as well as misattribute the competitive advantage that will be developed by their business. Below, I include three pitfalls uncovered by those brave enough to go before us. In these pitfalls, entrepreneurs may feel that they are wisely conserving resources, but, in doing so, they are avoiding key hypotheses that need testing.

Not scaling sales force when appropriate. Sean Ellis introduces this pitfall in his blog post on “Bringing a Network Effect Business to Market,” wherein companies with network effects need to speed to market in order to grow the value of their network based product. David Skok hammers the point further in his series of posts on SaaS economics and building a successful sales force. However, we still see entrepreneurs who feel that they need to test one more hypothesis before scaling up. Not only do they miss the opportunity for addition revenue and margin, they may be missing out on testing other key hypotheses and gaining customer feedback.
Hero CEOs. Jeff Bussgang pointed out the potential to increase the efficiency frontier for an organization through applying the economic principle of comparative advantage. Sure, an aspiring CEO may be the absolute best person to gather every piece of data or even write every line of code, but leveraging the relative talents of others frees up the CEO to work on the things only he can do. One of our class guests was quick (and proud) to identify himself as a hero CEO. His talents are obvious. However, his “lean” drive to close every deal not only might have slowed down his company's growth, but also delayed him from developing a sustainable business model. As a company scales, a CEO cannot run every department, and a key part of launching a new venture is understanding when to leverage new resources.
Not leveraging available data and partners. The example of Mint using Yodlee vs. Cake Financial building a data resource themselves is certainly not the only one. In this example, Mint was able to move forward, test other hypotheses, and build a successful business. Cake was stuck plowing resources into developing a back end system while missing the opportunity to develop a product that truly inspired the market. In this pitfall, entrepreneurs may be trying to conserve cash, equity, or favors, but end up draining resources and missing the opportunity to build a sustainable competitive advantage. If a piece of your product is available on the market, it makes sense to not have to re-invent the wheel. In Cake’s position, they may have had to go back later to build the backend to get all the data they needed, but they could have pieced together an MVP to test quicker using the available data. Companies may feel that if I am eventually going to have to build it, I might as well avoid spending twice and start now, but, in reality, a more lean approach would be to test that hypothesis before building for scale.

Entrepreneurs in the field will certainly have more examples of when they tried to pursue a “lean” approach, but it ended up costing more in terms of time, cash, resources, and opportunities. For those looking to start ventures, it will be important to try to learn from their pitfalls. Ultimately, being lean is not about conserving resources as long as possible, it is about using them to test hypotheses as efficiently as possible.

Understanding When to Launch

2012-02-15T06:43:00.003-08:00

by Katherine Nadler

For an entrepreneur, one of the most consuming decisions is determining when to launch. Arguably, a product is never complete; therefore, it is up to founders to determine what level of incomplete is acceptable. To do so they must allow their business model and market context to drive their decision making. Asking the right questions is very important.

Does the business model rely on virality? When a product anticipates customer acquisition through virality it makes sense to launch early, sacrificing some of the quality that extra time would afford. As much as entrepreneurs may convince themselves that their product is perfectly positioned to generate “word-of-mouth” or network effects, the best way to know for sure is to test that hypothesis in practice (often first through a beta launch). While the world’s become inherently social as a result of game changers like Facebook, Zynga, Twitter and Foursquare, it does not mean that products will effortlessly generate the same social response.

How damaging is a “buggy” product to my company’s value proposition? Within different businesses, the same mistakes can have very different impacts. Using Airbnb as an example, a glitch in their website where a message to an owner got lost would be annoying but would not impact the value proposition of the company. Users would still be able to find places to stay and base their opinions on that experience. On the other hand, lost files on Dropbox would be nowhere near as innocuous a mistake. This is because Dropbox draws attracts users with its claim of simplicity, reliability and safety. Therefore, when thinking about when to launch, it is important to consider the deal breakers for customers and prioritize those above all else.

What is the competitive landscape? Ventures that are entering an existing market have a completely different set of challenges from one looking to enter a new market. In an existing market, the product must be superior to those offered by established players; this can be very difficult for a resource-constrained young startup. However, in a relatively new market there might be some benefit to actually letting others enter the market first. While letting competitors grab user attention first seems counter intuitive, looking at how financial account aggregator Wesabe lost its market to Mint.com provides some interesting perspective. In a blog written by the former co-founder of Wesabe, he reflects on how “There's a lot to be said for not rushing to market, and learning from the mistakes the first entrants make. Shipping a ‘minimum viable product’ immediately and learning from the market directly makes good sense to me, but engaging with and supporting users is anything but free. Observation can be cheaper.” When entering a market where someone has gotten there first, rushing into the market prematurely with a subpar product can be very damaging while waiting can offer great rewards.

Every situation will likely have many considerations to weigh when deciding when to launch. In my opinion, founders must be careful not to jump to conclusions. Just because a founder believes in the lean startup methodology does not mean he or she should introduce a flawed product. Additionally, founders must consider all different stages of launching and realize that they have the power to separate the launch of their product from the launch of their venture in the press. At the end of the day, the best that anyone can do is make an informed decision and hope for the best.

Smoke (and other) tests: smoke in your face? Pitfalls of lean start-up testing

2012-02-15T06:43:00.002-08:00

by Lauren Miller

The lean start-up approach has the advantage of using genuine customer feedback as a means of improving the entrepreneur’s product specifically for those perceived as the niche consumer, but numerous stories from entrepreneurs and case protagonists about acting on false positives and negatives has caused me to wonder: can smoke and other lean start-up tests just end up being smoke in the entrepreneur’s face?” I question whether an entrepreneur should take the results of his or her tests as “gospel” since pitfalls in the experiment design and interpretation of results can be numerous. I will focus on a few of these potential pitfalls.

Focus groups
Steven Carpenter shared in the Cake Financial case that he has “learned that focus groups… can be unreliable because people can’t always say what they want until they can actually see it and play around with it.” This highlights the fact that customers often can’t or won’t tell you what they want or need and are often limited in what they can even think to ask for. It further underscores the important lessons revealed in Dropbox and Aardvark. Be careful in design of focus groups to utilize the most valuable feedback, as it is not all created equal. Also, make sure to listen to your customers but don’t simply obey them.

Usability tests
Martin Kessner’s study reveals the necessity of running usability tests iteratively. In his study, six usability teams could not find a single common usability problem when independently testing the same product, and no team found 50% or more of the total problems found by all. This should be a sign of caution to the entrepreneur. Though there are a plethora of studies and start-up case examples that document the improvement achieved from usability testing, Kessner’s work could show that in testing products or usability, the iterations make the test more reliable and decrease the likelihood of wide variations in responses. My takeaway: the repetition of usability tests allows an entrepreneur to better qualify the feedback he or she receives and in turn use it as a better determinant of when and how to pivot in hopes of achieving product-market fit.

Feature lists
I am working on an education software start-up, and we currently have a survey on our site that asks teachers which features they would like. Results have been great, as teachers seem to want all of the features we plan to create. Nevertheless, I have recently begun to question the reliability of their responses. I hypothesize that if we added fifteen more features, teachers would likely indicate that they want all of those as well, but the famous Columbia University marketing study implies that products with too many features often overwhelm customers. So how much should an entrepreneur trust customers when they say they want everything? Cindy Alvarez, formerly of KISSmetrics, says that customers want everything because “wanting” is free. Testing how much value features provide by adding a cost to additional features may yield more accurate results. If entrepreneurship is an art and a science, utilizing results of tests like this is the science and the decision of whether to follow them and how is the art.

I advocate that in the lean start-up model, focus should be placed as highly on test design and accuracy in feedback interpretation as it is on running tests. This way, entrepreneurs will avoid smoke-hazed and unreliable results that end up leading to pivots that could destroy their ability to connect with and address the needs of their potential consumer base. However, I’m interested in your thoughts. What other reliability traps have you seen entrepreneurs fall into when using various mechanisms to get customer feedback?

What is the role of an MBA at a technology startup?

2012-02-15T06:43:00.001-08:00

by Michael Quinn

Many skeptics are questioning the value that an MBA brings to an early-stage venture. These concerns have only increased as some of the most successful recent technology startups, such as Facebook, Google, and Dropbox, were started by engineers. Has the MBA lost its value in the fast-paced world of technology?
I don’t think so. In fact, I believe that the lean startup methodology may increase the value of an MBA. Lean principles encourage entrepreneurs to utilize hypothesis-driven development to avoid building a product that no one wants. In the early stages of a venture, fast development iterations and frequent customer feedback is vital. While engineers obviously play a pivotal role in executing fast software iterations, hypothesis testing is only as good as the hypothesis being tested. This is where an MBA can add value.

Engineers are taught to objectively look at a well-defined problem and attempt everything they can to try to solve it. After all, it was Thomas Edison that said, “I didn’t fail; I found out 2000 ways not to make a light bulb but I only needed to find one way to make it work.” Most engineers pay minimal attention to the “user experience,” as long as they have “solved” the problem. If users can’t figure it out, that’s their problem. However, MBAs are trained to handle uncertainty and ambiguity. The “case method” trains an MBA student to dig deeper to understand all aspects of a situation from multiple perspectives. Therefore, MBAs are equipped to ensure that engineers are prioritizing and solving the right kind of problems, those that are the most meaningful to their potential customers.

Engineers enjoy solving new and difficult challenges, as opposed to monitoring metrics. On the other hand, Wall Street MBAs pore over annual reports to extract as much data as possible to produce accurate financial forecasts. That same Wall Street analyst, with a little training in the lean startup methodology, would be equally adept at poring over metrics such as the customer conversion funnel, CAC, and LTV that are so critical to understanding and predicting user behavior.

So what does this all mean for a graduating MBA? One way to improve the perception of MBAs in startup circles is to demonstrate the true value created by a cross-disciplinary approach. Utilize strategy frameworks to identify the best position within the value chain for market entry. Conduct customer interviews and receive user feedback. Exploit your operations background to identify the process bottlenecks and use the Toyota Production System’s “5 Whys” to diagnose the root cause of customer frustration. Leverage your marketing skill set to analyze the customer purchase decision process to ensure proper customer segmentation and understand their willingness-to-pay. Employ your leadership skills to grow the organization with a culture from the start that people want to be a part of.

Doing only one of these things adds minimal value to an early-stage venture. However, combining the complete MBA toolkit with the lean startup methodology can greatly accelerate the process of reaching product-market fit, which even the most relentless engineer will appreciate. That being said, once you have defined the problem to solve, the most important thing you can do to earn an engineer’s respect is to get out of the way and let them do what they do best.

We’ve seen a lot of companies struggle to achieve viral growth: Aardvark, Triangulate, Cake.

2012-02-15T06:43:00.000-08:00

What’s going on? Does LTV have a skewed sample, or is it hard to harness viral growth?

by Jeanne Hwang

After three afternoons of work, we got an abysmal 10 sign-ups. The challenge was to create a viral campaign to get as many new members as possible for Gilt’s JetSetter website. Our campaign was to draw travelers into a contest to match photos with a destination, share their own photos and enter a raffle for becoming a member. We knew virality wouldn’t be high, but we thought, “boy, don’t you want others to see your awesome travel photos and share this great opportunity with your best friends?”

The answer was NO. Our viral coefficient was effectively zero (virality (K) = number of invitations sent (i) * conversion %), as new members had no incentive to increase the number of raffle participants, which decreased their chances of winning. It seems obvious now, but we hoped that we could attract most of the new members through our own outbound marketing (Twitter, Facebook, blogs) supplemented by friends who would share with friends because of course you’d want to help out your friends!

In Cake’s case, much of it was due to the inherent nature of the target market (“men in their 40’s just don’t want to share,” as Steve said at lunch). But Jetsetter’s target market and Triangulate’s target market are young, internet savvy consumers. In fact, in Triangulate’s case, dating is an inherently social business, yet I suspect that virality ended with the first cycle of outreach. Sunil said in class that non-single people didn’t want to be associated with a dating site – the wingmen had very little incentive to send out invites to their friends to join Triangulate, as they wanted minimal association with it.

It seems viral growth is only possible when it’s at the juncture point of three scenarios:

If a user shared a raffle link with a friend, their value to the friend would increase, but it would decrease their own chance and thus, the value to themselves.

Asking a friend to rate a hot-potential-date’s-photo on Triangulate would increase the value to the dater, but the friend risks being associated with the dating site by sharing that photo one more time.

This is another way to think of what David Skok states as the key to viral growth, which is to entice them and address their concerns. So the question is, in businesses that have inherent contradictions, is there a way to decrease the effort or the risk, in order to achieve virality?

In Winner-Takes-All Markets, You Want To Be The 800-Pound Gorilla

2012-02-15T06:42:00.000-08:00

by George Levitte

Startups often face difficult decisions surrounding when and how to grow, particularly regarding whether or not it’s necessary to fully validate a business model prior to scaling up. Under most circumstances it’s best to test and iterate over and over again until the model has been validated and the entrepreneur is confident that the startup can scale and eventually achieve profitability.

More specifically, in markets that do not have “winner-takes-all” dynamics (e.g. strong network effects or significant scale economics) it’s important to have validated the model prior to ramping up. Note that this does not require being profitable, but rather just proving that profitability could be turned on if necessary. Startups often refer to “flipping a switch” on profitability, which might mean adding more ads, charging fees for premium offerings, or any of several other avenues for generating revenue. It’s important to test and iterate sufficiently to prove that you have this switch, even if you decide not to flip it on immediately. That is what validation means.

Scaling up before validating the business model is dangerous for various reasons, and in markets without “winner-takes-all” dynamics it’s rarely worth the risk. Hiring more employees, investing in marketing, and building out a large user base all increase overhead expenses. And because these require formalizing business processes such that they can be replicated over and over throughout the company, it reduces the organization’s flexibility and constrains its ability to pivot quickly. So getting big can dramatically increase both expenses and risk of failure. Furthermore, scaling up in this situation heightens the chances that, in the event that business falters, the startup becomes unable to cover its bills by modifying its operations or finding additional venture capital.

However, in markets with “winner-takes-all” dynamics – particularly those due to network effects – scaling before validating the business model amounts to making a bet on the potential value of dominating that market and on the odds of being able to create new revenue sources sometime in the future. Like any educated bet, sometimes this works out and sometimes it does not. For Facebook, Twitter, Quora, YouTube, and several others it worked. Undoubtedly there are many more who tried and failed. But this is not unexpected given the high mortality rate among startups. The real question is whether scaling before validating increases one’s odds of success, and the answer is that it almost always comes down to an educated bet that could go either way. It depends on your specific market, product, customers, competitors, investors, and frankly – a lot of luck. So if your startup competes in a space where the endgame will likely involve one company dominating and all others failing, then getting big quickly can be a huge competitive advantage. In this situation you want to be the 800-pound gorilla, even if you are not yet sure of how profits might materialize in the future. But if your startup is not in a “winner-takes-all” market then keep testing and iterating. Wait until validating the business model before getting big.

Does “Passion” Always Make for Good Entrepreneurship?

2012-02-15T06:41:00.001-08:00

by Om L. Lala

In a recent HBS class, a firm called “KNP,” specializing in “speaker training and executive communication,” shared their advice on presentation skills. After seeing thousands of VC pitches and interviewing hundreds of VC principals, KNP noted that despite the quantitative nature of the evaluation process, the characteristic that often ends up making the difference between getting funded and not is “passion.” Similarly, entrepreneurs often describe how their “passion” for an idea is what motivates them. But in the journey of most startups, passion can be both an asset and a liability.

Hypothesis-driven entrepreneurship is the best approach a startup can employ to make sure their business model is viable. But this process is an inherently unbiased analysis that should not be influenced by an entrepreneur’s personal attachment to his idea. As such, there can be an inherent conflict between the dispassion required for hypothesis-testing and the entrepreneur’s passion for his idea. As a startup evolves, hypothesis-testing may reveal that a) a business model is simply not viable or b) it requires a dramatic pivot that would fundamentally change the product being offered. Rationally, the entrepreneur should have an interest in determining if either of these scenarios is true for his startup. However, if an entrepreneur is so attached to his business continuing or to his original product vision, he can be blinded by his own passion. He might either not initiate necessary hypothesis testing or ignore its conclusions.

This phenomenon seems to have been at work in the case of Steve Carpenter and Cake. Steve started his business because he was passionate about “applications of social networking for adult consumers” and wanted to relate it to things he cared about. Very late in the evolution of his startup, well after A-2 financing, Steve was forced to realize that social networking did not have a place in a viable Cake business model. In his words, “It turned out that active investors wanted ideas, but they didn’t necessarily want to share them because they viewed the stock market as a zero-sum game.” Why was such a fundamental and important realization made so late? Clearly, Steve was extremely intelligent. But perhaps his original passion for and attachment to the idea of creating a social-networking-based business prevented him from rationally investigating and testing his business model earlier. While some products need to be launched before they can truly be tested, a basic factor like willingness to share investment strategies could have easily been examined through a variety of survey methods.

Reflecting on my own experience, whenever I dismiss an idea I have or decide against a startup position, my most frequent reasoning is “I’m not passionate enough about this product.” But it may be much more sensible to first ask myself whether I am passionate enough about hypothesis-driven entrepreneurship itself, because it is the insistence on conducting rigorous tests and fully accepting their results that is the biggest determinant of success. It is far too easy to let attachment to an original vision inadvertently undermine one’s prospects for success.

Even Visionaries Listen to Their Data

2012-02-15T06:41:00.000-08:00

by Wei Lien Dang

All eyes were on Facebook last week as it filed for a long-awaited IPO. In his letter to shareholders, Mark Zuckerberg commented on the company’s approach to building products:

“The Hacker Way is an approach to building that involves continuous improvement and iteration...Hackers try to build the best services over the long term by quickly releasing and learning from smaller iterations rather than trying to get everything right all at once. To support this, we have built a testing framework that at any given time can try out thousands of versions of Facebook.”

Perhaps surprisingly, that description sounds a lot like the underlying principles of lean startup methodology. I say it’s surprising because Zuckerberg is a visionary who hasn’t shied away from relying on instinct to ship new products even in the face of large protests among Facebook’s users. Features like News Feed and Timeline all generated significant backlash upon their introduction yet this didn’t deter Zuckerberg from pursuing his overall vision. This hardly sounds like an example of a company that is learning by listening to feedback from its users.

This anecdote is symptomatic of the tension that often arises as a startup is searching for product-market fit: how much of product development should be based on vision and instinct vs. customer feedback and what their data is telling you. There are several examples of companies who have gravitated towards the latter approach: Dropbox and Aardvark are two good examples of companies who actively iterated based on a user-centric model for product design. Yet even these teams at times opted to not implement features requested by users and instead chose to pursue product development based on vision and instinct. Do founders and product managers always need to choose one or the other?

The answer is surely no—as Facebook’s success shows, finding product-market fit often requires a combination of both vision/instinct and user-centric design. But what combination is the magic formula? Though there’s likely to be significant variance among companies, at the risk of oversimplifying, the optimal combination might be deconstructed into the two following principles:

1. Initial hypotheses should reflect visionary product development
2. User-centric development should test that vision and adapt it to the market

Vision is what sets a startup on its initial trajectory and sustains its quest to find product-market fit. Product development with no vision is susceptible to producing a poor product: different users have different needs, customers have a hard time articulating what it is they actually want and their behavior may contradict what they say. Trying to please everyone can mean pleasing no one at the same time. Vision is what pushes products forward to make them better by differentiating them from competitors or, in rare cases, creating entirely new markets. But vision alone is also a dangerous design path to take because as Marc Andreessen points out, you may end up designing a product for a non-existent market. Finding product-market fit requires a balance between these two approaches. It means coming up with ideas to make a better product and relying on user feedback to adapt that better product to the existing market.

An interesting issue to explore is whether the optimal combination of vision and user-centric design is a function of how innovative the company’s business model is. For example, consider two categories of market opportunities. The first category is made up of startups that enter an existing market that is well understood. These companies seek to provide an improved product or service that is cheaper, faster, or incrementally differentiated from competitors. The second category consists of companies who are entering entirely new markets and essentially defining how the market takes shape. I would argue that the first category requires a greater amount of user-centric development than vision and that the second category lends itself to a vision-heavy approach. But some combination of the two approaches is still necessary to succeed.

Facebook and Dropbox are testaments to how an optimal combination of vision and user-centric product development can result in big payoffs. Knowing when to stay the course versus abandoning a pre-planned vision can be a harrowing decision for any entrepreneur. Customers don’t always know what they want, and sometimes what they actually do is more important than what they tell you. It’s hard to imagine that Facebook wouldn’t have abandoned News Feed or Timeline if user churn rate suddenly skyrocketed dramatically so as to have a detrimental impact on the company. Facebook ultimately kept these features but tweaked them to better address user concerns. Dropbox continues to think about how it can address user requests without abandoning its core product philosophy of keeping things simple. Both of these companies have passed the product-market fit milestone and yet they continue to execute on their vision while iterating based on user feedback. This goes to show that “lean” is not a mantra that should be confined only to startups searching for product-market fit but one that should also be extended to some of the largest innovative companies in technology today.

Can large corporations be “Lean”?

2012-02-15T06:40:00.001-08:00

by Anonymous

I have constantly found myself thinking through the new lean startup concepts throughout this class and wondering how they could be successfully applied to large organizations.

The definition of a startup and a large corporation are completely different in every critical way: access to capital, human resources, brand awareness etc. And yet, large corporations are constantly trying to find ways to “incubate”, develop, promote and support lean startup methodologies. Over the past few years, I have seen how two large companies have tried to create a “lean” environment within their companies. The common trade-offs are:

Uncertainty vs. Scaling: Startups ideally try to reduce the amount of uncertainty through minimum viable products (MVPs), while corporations require accountable business plans and projections that force premature scaling and disincentives pivoting. Effectively there are rarely corporate structures or environments that even permit anything beyond setting a vision in the Hypothesis Driven Entrepreneurship Process.

Testing vs. Brand: Does testing hurt a brand? As companies grow, so does their customer base, as does the size of their funnel, and ultimately their brand presence. This tension can be seen in multiple ways:
Smoke tests are a wonderful inexpensive way to gauge customer demand for a new product, although large companies are hesitant to endorse false advertisement.
Constrained functionality can tarnish a brand and corporations are often unwilling to engage customers with a half baked product.
Frequent pivoting after a product idea has been launched at a large corporation often indicates weakness to the public and investors. In short, corporations feel they can’t afford to “fail”.

Large companies have constantly failed at creating lean environments. As a result, they have resorted to purchasing companies for a huge premium after ideas or concepts have already proven a “product-market fit” and fail to continue encouraging lean methodology. That’s when you start seeing write-offs for early stage acquisitions that failed to grow, pivot or adapt after acquisition. Does that mean corporations can’t be lean? I would still like to believe there is hope. Corporations would need to constantly test unbranded products and cut off their reliance on branding to drive hype and validation. Most importantly, corporations must create an environment that allows and encourages failure.

Identifying the “Right” Startup for You

2012-02-15T06:40:00.000-08:00

by Jeff Buening

I’ve spent the better part of 2 years now at HBS developing a sense for what it takes to succeed as an entrepreneur. Whether through attending speaker series, joining tech/start-up clubs, pursuing start-up ideas of my own, or extensive coursework I’ve taken (The Entrepreneurial Manager, Social Entrepreneurship in the Biz Sector, Entrepreneurship and Global Capitalism, Entrepreneurial Finance, Launching Tech Ventures, Digital Marketing Strategy, etc.), I’ve covered countless data points and case studies on startups that have both succeeded and failed. And I’ve been fortunate enough to listen firsthand in class to many of these entrepreneurs discuss and diagnose the reasons for their successes and failures.

One topic that consistently pops up unprompted is how and why the entrepreneur decided to start her particular business and, in some cases, leave that startup to pursue the next one – effectively, it’s the decision criteria an entrepreneur applies to start (and stay in) a particular business. Many times, these criteria come in the form of industry focus, nature of the target customer, product-market fit, ability to cheaply test and iterate the product, etc. The nature of these criteria typically varies across a wide spectrum of factors, ranging from personal interests to more tried-and-true pragmatic business needs. In every case, the discussion leaves me pondering the appropriate decision criteria I should apply to my own startup ideas. In essence, what are the critical components that a start-up must meet in order for me to pursue it?

I’ve been actively brainstorming and pressure testing numerous startup ideas of my own since coming to business school. Needless to say, I’ve spent countless hours banging my head against the wall trying to come up with the perfect idea. That process in itself has been somewhat rewarding in that it’s helped me better identify problems and needs in the marketplace that could benefit from business; but it’s also forced me to think about my criteria in a more formal and organized way. I’ve effectively developed a handful of criteria in my head over the past two years, and I’ve been fortunate enough to come up with two separate startup ideas that I’ve been able to pursue, but I’ve yet to notate my criteria down on paper… So why not use this blog as that avenue?

Listed below are some of the specific criteria I currently maintain for evaluating whether to pursue a startup idea… I separate the factors between ‘must-have (MH)’ and ‘nice-to-have (NH)’, as well as classifying them by type of type of factor. I encourage all readers of this blog to comment with additions, challenges, edits, etc.

Personal/General:

The business is not exploitative or slimy in its customer value proposition (MH)
The business ultimately serves a broader social need that makes a very positive impact on some sector of the world (NH)
The company enables me to get my hands dirty doing, building, and managing a lot of different things across the business/ops/mgt functions (NH initially – MH long-term potential)
It creates something disruptive/innovative/Not yet done – as opposed to incrementally improve an already existing product (MH)
It solves a complex, difficult (and potentially messy) problem in the world that requires a lot of time & sweat equity and creates barriers to entry (NH)

Market

Huge potential customer base (MH)
Huge $ market $10B+ - (NH)
Limited competition (NH)

Business Model

For-profit (MH)
Can scale through existing distribution channels on the web, etc – Not dependent on strategic partner (NH)

Viral component (NH)

Easy to test cheaply, early, and often (MH)
Recurring revenue model (NH)
Negative Working Capital model (NH)
Low upfront capital requirements (NH)
Limited ‘network’ dependence (NH)
Cash-flow generation from the outset (NH)

Stage/Team/People:

Funding - Seed or Series A (NH)
Revenue – from 0-$10M (NH)
Between 5-50 employees (NH)
Talented, & social people with strong experience, connections, and intentions (MH)

Industry:

Consumer web/tech company that does the following:

Solves an annoying problem, makes life easier, and/or makes something valuable more affordable, accessible, and achievable (MH)
Solves a problem/need that I personally have as a consumer (NH)
Relates to a field/service/product that I am naturally passionate about (NH)
Pure play web/tech service or Web/tech-enabled service (MH)

Potential sub-categories:

Digital Media, Travel, Health, Sports/Athletics, Education, Food/Dining, Local Deals, etc. (NH)

Location:

Fairly flexible but aiming for one of the below locations - in no particular order (NH):

SF/Bay Area, LA, Chicago, NYC, Somewhere abroad

The Case Against Non-Technical CEOs

2012-02-15T06:39:00.000-08:00

by Michael Belkin

Thousands of ideas are envisioned each day, but only a select few come to fruition. Despite a CEO’s wide-eyed optimism for finding the perfect idea, early stage entrepreneurship is largely about execution. Consider Facebook vs. MySpace, Google vs. AltaVista or Dropbox vs. every previous online backup solution. One doesn’t have to look far for companies who have established themselves not by inventing new business models, but by excelling on superior execution. While some businesses can outsource IT or cordon it off to a skilled CTO, the more a company relies on its technology the more important it is that the founders have technical backgrounds. I propose a simple test, if your business can’t serve a single user or customer without software, the founders should know at least the basics of coding.

As access to capital and business advisors increase, the relevance of non-technical CEOs continues to diminish. With the seed of a good idea and a couple all nighters, an engineer can create a beta, iterate, and have thousands of users (plus usage analytics) before an MBA can say, “pitch deck.” It’s really like an architect who doesn’t know structural engineering. Though one can create beautiful pictures, an architect’s success comes from creating landmark buildings, not drawings. Further, the artistic vision is often influenced by what is physically possible, and knowing the practical implications of seemingly minute decisions can make the difference between a project being scrapped or built.

This isn’t to say that business training is useless to a startup. The ability to critically think about a business model, problem solve, form partnerships, and focus on a greater and profitable vision is essential and something that MBAs are more equipped to accomplish than their tech savvy cohorts. While a group of engineers can often fall into the trap of locking themselves in a room and over-engineering, the businessman’s “voice of reason” often helps a young company define its hypotheses, stay lean, and become profitable.

Commonly, bidding CEOs hope to find the right technical co-founder to implement their vision. However, this can prove to be difficult for an early stage venture with little funding and an untested concept. Engineers can create amazing products because they code projects they are passionate about. Inevitably some of that excitement is lost when one is implementing another’s vision.

An MBA who knows how to code can quickly apply his business acumen to define a business model, create an MVP, iterate, attract users and get investors. Further, understanding the software developer’s mindset is critical to a CEO’s ability to manage a technical team and grants her respect in the technical community. Given these benefits, one would expect an into computer science course to be on every tech-entrepreneur focused MBA’s checklist. However, only recently have business schools started to embrace this view. Instead of asking students to fight against the grain, business schools should support and encourage students to supplement their learning with such practical training and thus regain the relevance of the MBA as technical entrepreneur.

Are We There Yet: Thinking Through Product/Market Fit (LTV)

2012-02-15T06:30:00.001-08:00

by Colin Barry

"Startups occasionally ask me to help them evaluate whether they have achieved product/market fit. It’s easy to answer: if you are asking, you’re not there yet.” – Eric Ries, “The Lean Startup” (pg 220)

The Context

Hypothesis-driven entrepreneurship — epitomized by Eric Ries’ Lean Startup methodology — has become all the rage among aspiring tech founders. It’s not hard to see why. Ries’ focus on customer discovery and iterative development addresses a dangerous problem in the two established paradigms for building software.

Waterfall product development presumes that the problem and the solution are known, and we just have to build the solution in an efficient, staged manner. Agile product development admits that the solution is unknown, but still presumes that the problem is known — the “voice of the customer” (usually the product manager) will recognize useful software when she sees it. But actually, Lean Startup tells us, the problem is usually unknown, too: it takes ingenuity, guts, and contact with customers <shudder> to determine whether software is useful or not.

The Problem

Okay, so we admit that we’re in the land of the blind, and we need to be disciplined and systematic about figuring out what problem we should be solving. But how do we know when we’ve got the right problem paired with the right solution?

Ries tells us that the watershed condition is product/market fit — we’ve looped through the Build-Measure-Learn process, improving all the while, until we’ve built an “engine of growth” (by which Eric appears to mean a product with rapidly accelerating user acquisition). Then, we just add fuel and watch the engine go.

The trouble is that a whole bunch of phenomenally successful and (purportedly) Lean startups slowed down rapid product development iteration — basically, decided that the product was mostly done — well before they had anything resembling an engine of growth.

Why It Matters

The lack of rigor around what constitutes product/market fit makes successfully applying Lean methodology much harder. One of our class guests, David Skok, has written brilliant expositions on constructing a repeatable, scalable sales model. He opened our class discussion with a graphic (roughly):

But wait, my business only has product/market fit if I already have a sustainable user acquisition model — ideally an engine of growth so powerful that just a small quantity of “fuel” causes users to crash my webservers and beat down the front door to my startup’s office in desperate mad dash to consume my product.

But it seems to me that my venture has probably already figured out the marketing and sales part of the equation if that is occurring.

And many (if not most) successful startups do appear to delineate building a product from building a sales model.

Two examples in brief (one cribbed from David Skok):

- Airbnb enters YCombinator with a product they have already trialled at SXSW and the Democratic National Convention. They expect to do a ton of development in YC. Paul Graham tells the founders to stop building product, hop a plane to New York City (where they have the most adoption), and personally snap prettier photos of the apartments currently listed on Airbnb.
Scalable? No way.
Did the market tell them to do this? Nope (at least I don’t think so).
Did it work? Yes. Prospective renters and couch-surfers had been turned off by ugly descriptions of the listed apartments. Success.

- Constant Contact completes product development (in the words of David Skok, has found product/market fit). But prospective customers aren’t buying.
In fact, prospective customers (small businesses in Atlanta) aren’t sure why they would need mass-mailing software in the first place. Constant Contact distributes wire-bound books for small businesses to use as visitor books to record shoppers’ e-mail addresses.
A month later, Constant Contact’s prospective customers have hundreds of e-mail addresses, and now they need mass-mailing software. Success.

We can tell similar stories for startups like JBoss, RentJuice, and Dropbox.

To be clear, I don’t think David is wrong in making a distinction in the startup lifecycle between product development and marketing/sales. I think Lean Startup makes it difficult to tell when to stop focusing primarily on building product and start focusing primarily on selling product.

Conclusions

One possibility (maybe what Eric would argue) is that I’ve defined the “product” part of “product/market fit” too narrowly. The product is not just a piece of software but rather the startup as a whole — a product-marketing-sales-PR conglomeration that must be tuned and operating in harmony.

I’m not so sure. There is a natural tension between Lean-style product development and marketing/sales.

In Lean product development, we tirelessly labor to determine what the customer will use and then build something viable and desirable. If the customer tells us (by his or her behavior) that a feature should be different, we change. We pull the product out of the market.

In sales and marketing, we tirelessly labor to convince the customer that they should use or buy what we already have. If the customer tells us that a feature should be different, we generally try to convince him or her that we know better. We push the product onto the market. And ideally, the customer doesn’t require much convincing.

Lean Startup methodology needs a better definition of product/market fit than “I know it when I see it.” Maybe arbitrary standards like 40% net promoter score make the most sense. Perhaps we just admit that user acquisition growth is not the right metric for many startups, especially those that require higher-touch sales models. Regardless, I think we need to take a deeper look at what goal we’re actually chasing if we’re running lean.

Lean Startup Tradeoffs

2012-02-15T06:30:00.000-08:00

by Sarah Elliot

The primary objective for most entrepreneurs is to create a product that customers want and are willing to pay for. The process by which this objective is reached often requires frequent product iterations and customer interactions. It is through this process that the entrepreneur identifies which aspects of the product vision are incommensurate with the wants and needs of the customer. Closing the communication gap with the customer is critical to the success of a venture. However, there are real and substantial consequences to exposing a customer to a product that isn’t “right”. Therefore, there seems to be an inherent tension between getting the product “right” and getting the product out the door and into the hands of customer. So the question remains,

When does it make sense to launch early with a buggy product, and when is it important to perfect the product?

The answer to this question varies and is contingent upon several contextual factors such as the nature of the product, the degree of exposure, and the core objectives of the entrepreneur. Entrepreneurs that face this question should approach this decision by asking themselves: (1) What is the minimal viable product necessary to reach my core objectives?; (2) What are the trade-offs between launching now and iterating further?; and (3) Who bears the brunt of the risk of failure --the entrepreneur or the customer?

1. What is the minimal viable product necessary to reach core objectives?

One of the core components of Eric Ries’ lean start-up methodology (http://theleanstartup.com/) is the concept of the minimal viable product (MVP). At its most basic level, the MVP is the minimum product and associated feature-set that enables an entrepreneur to validate/test her core hypothesis regarding product/market fit. The MVP process begins with a clearly defined hypothesis about the customer pain-point (problem) and product vision (solution). The next step in the MVP process is to test this hypothesis through the design and execution of a barebones “experiment” whose results demonstrate cause-and-effect relationships. The MVP methodology suggests that a product should be launched upon the creation of the absolute minimum product (or experiment). It is important to highlight that the MVP doesn’t need to be a proprietary product. The objective of the MVP is to quickly validate the core hypothesis; therefore, the use of pre-packaged solutions is ideal if it allows you to quickly and efficiently collect valuable information about the customer and your hypothesis.

As we saw in the case of Cake Financials, Steven Carpenter created a web-based solution that addressed his hypothesis of the customer pain-points: (1) a lack of transparency; (2) distrust of advisors; and (3) complex investment offerings. He believed that his solution would address these concerns by (1) aggregating financial information; (2) comparing portfolios; and (3) automating the buy/sell process. Steve and his team invested a considerable amount of time and resources building out the back-end of his product at the expense of rapid MVP tests. Had he taken an MVP approach, rather than perfecting the product in the absence of reliable customer feedback, he would have learned that his hypothesis was incorrect much sooner – a realization that could have saved his company.

2. What are the trade-offs between launching now and iterating further?

When evaluating the trade-offs between launching an imperfect product now vs. launching a more perfect product later, a two factors should be considered. First, the degree of exposure should be considered. A PR induced broad product launch will magnify the reputational risks associated with product imperfections. One key prescriptive of the MVP method is the use of early adopters as the core test group. The rationale being that early adopters are more likely to be forgiving in the event that the product isn’t quite right. One critical question to ask is, “With whom should I launch this product?” Second, one should consider contextual factors when deciding when to launch. In the case of ScoreBig (Ent Fin), Adam Kanner, made the conscious decision to wait and perfect his product for nearly two years before launch. In his case, he felt that the key stakeholders (sport and media executives) would be reluctant to supply him with ticket inventory (core element of b-model) in the absence of a nearly perfect algorithm and an aesthetically appealing front-end. While he presumably could have tested his hypothesis with an MVP approach much sooner, contextual factors prohibited him from doing so.

3. Who bears the brunt of the risk of failure --the entrepreneur or the customer?

One final consideration when deciding when to launch is the degree of risk shared by the customer. If the launch of an imperfect product exposes the customer to the brunt of the risk, think twice. Integrity must be upheld in all stages of the product development process as trust is not easily restored once broken. If the product could expose a customer to harm (i.e. exposing confidential information or jeopardizing physical safety), keep iterating! The additional information you receive is not worth the risk and the reputational damage is irreversible.

In conclusion, as an entrepreneur, every interaction with the customer is an opportunity to learn important information that ultimately drives product development. Like the time value of money theory, information today is better than information in the future. Entrepreneurs should air on the side of quick iterations to capture information as quickly and efficiently as possible. However, it is important to understand that there are consequences to “getting it wrong”, and entrepreneurs should be mindful of the impact of a failed attempt. While failure is unavoidable, entrepreneurs can adopt key risk mitigation strategies to increase the risk/reward ratio.

Screw being scalable

2012-02-15T06:28:00.000-08:00

by Jake Cusack

I do economic development and occasionally investment work in a number of frontier markets and conflict zones: Afghanistan, Iraq, the West Bank, North Africa, et cetera. I enjoy this work. But, as I have oft said to others, I am exploring a variety of tech-enabled solutions and platform to bring scale to it. After all – my deluded subconscious might think -- if I can only make as much money as hours I work, how will I afford the private jet that could allow me to spend time 5% more efficiently? Where will I get that crucial "leverage" so I can continue to make money without actually doing anything?

Wait, here are some good things in life that you cannot scale: Giving personal advice to a friend. Playing with kids. A great bartender at a local bar. A sustainable artisan NGO in Afghanistan, that only survives because of the intimate approval of the local community. A genius, tailored piece of enterprise software. Doing an inconvenient favor. A ski run in fresh powder. Teaching through the socratic method. Making something with your hands. Your time. Your life.

Four years ago I left the military because I thought private sector development in conflict zones was the missing piece of our national security. Now I get paid quite well to fly around the world to interesting places and work on exactly this. If you told me this is where I'd be when I first showed up at Harvard I would have been pretty pleased. But now, I'm always scheming on the ways to go bigger. I think -- if only I could come up with an idea that would require at least $2M of seed funding before I could even test it! Business school truly is transformational.

I love the massive egoism present in the thought that the truly bright among us must be involved in something scalable -- because after all there is only one of me, and how will the world cope if I'm only dispensing my time and influence on a few small projects and on my close friends.

I do not think there is deep validation from the size of a funding round or number of subscribers. The search for scale means constant comparison, condemnation to inadequateness, against other people’s big numbers. Conversion metrics do not provide proof of soul: evil things can go the most viral of all.

The digital age brings efficiency and easy reach. But an idea that refuses to scale is not necessarily a waste of time – and just because you can go global, does not mean you should.

Postscript: I initially titled this post with stronger words. But I'm a bit worried it will come back to haunt me when I launch my scalable software-as-service frontier market investment analytics platform in three months.

Always be User-Centric!

2012-02-15T06:27:00.002-08:00

by Lucas Suzuki

A couple of companies —Dropbox and Aardvark— that otherwise pride themselves on user-centric design were adamant about not offering the features most frequently requested by their users. What’s going on here?When should vision override user feedback in a lean startup?

All great products and technologies are created following a user-centric approach. But does that mean that companies should always listen to their customers? The answer to that question is no, especially in a startup environment where resources are limited and every bad decision shortens the runway. Startups must always exercise judgment and know whom they should listen to and when.

The root of the problem lies in how people interpret the term “user-centric.” Those who believe that user-centric means only listening to the customer will develop products and features that customers say they want, but can’t use, don’t need, or are not willing to pay for. Instead, companies should think of the term user-centric as understanding the customer and catering to his needs. In order to truly understand the customer and develop a killer solution, a company needs not only to listen, but also pay attention to customers’ behavior and needs.

This misunderstanding of the concept is more common in situations where startups are trying to develop totally new and disruptive products. These products tend to fill an unmet need that oftentimes even customers are unaware of (think of all the cool new products and services that didn’t exist 10 years ago but that you couldn’t live without anymore.) In this case, customers tend to provide genuine but misleading feedback, and they tend to ask for more of the same. For example, according to Henry Ford on the invention of cars: “If I asked people what they wanted, they would have said faster horses.”

Even for simpler product improvement projects, and assuming that customers knew exactly what they wanted, they might not understand how their preferred features will interact with other features. This is because users don’t see the whole picture, and one change in design might have a ripple effect in the final product’s functionalities. For example, in Dropbox, while many users answered that they would like to have a “My folder” synch feature, they probably would have changed their minds if they knew the consequences of their request: “would you like a my folder synch feature that will make the product much more complex and heavy and can cause your system to crash and lose your files?” Therefore, entrepreneurs must again exercise judgment when balancing user requests and system experts’ opinions.

Finally, even when users have a deep understanding of systems and can provide technically sound suggestions, entrepreneurs must exercise judgment. Mainstream and heavy users oftentimes have different needs and values. While it is true that heavy users will likely drive early adoption, only mainstream users can create a homerun. Companies must have clearly in their minds their value proposition and their target customer or risk failing to cross the chasm. Only then can they decide whether suggestions and stated needs are coming from the right source.

In summary, companies should always be user-centric. But that does not mean always listening to your customers. It means that you must always exercise judgment, strive to understand your customers, and know who they are and what they need.

Don’t code: 5 steps to an outsourced MVP

2012-02-15T06:27:00.001-08:00

by Matthew Thurmond

Don’t spend months learning to code or looking for a technical co-founder. My interviews with Harvard Business School entrepreneurs and outsourcing industry experts reveal a new playbook for early-stage ventures that replaces the “shut the door and learn to code” ethos with an “outsource early and hire late” strategy. A handful of web-based tools and services are enabling this shift and savvy entrepreneurs are using them to quickly and cheaply bring minimum viable products (MVP) to market.

The 5 steps below describe an easier way to create MVPs:

1) Mock it up
First things first. What, specifically, do you want to build? If it’s a website, skim similar sites and draw up the main pages with your desired logo. After a few iterations on paper, you should have a general outline. Now use Balsamiq to do a more formal mock-up. The end result will be a shareable pdf that looks like a pencil sketch.

2) Design the graphics & specs
Now you want to add color and interactivity. Use Adobe Photoshop or Fireworks for this. You can download a trial version and can take lessons on Lynda.com if needed. The end result is a pdf that looks like the actual site or app and has clickable links that allow you to experience the navigation.

3) OR, skip 2 and outsource the design
If you have no interest in design you can just skip step 2 and outsource the process entirely. DesignCrowd and 99designs let you run “design contests” based on the product specifications you set. Multiple designers will submit graphical ideas and you pick the best. The process can take 3 days – 2 weeks and costs $250-$750. In return, you get a professional pdf prototype of your MVP that is colorful and clickable.

4) Hire a freelance developer
Now go to oDesk or Elance and post a job with your design linked to the description. Expect 10-20 developers to apply and proactively send your job to 5-10 others on the site. Each developer will have feedback from previous buyers and you can conduct a few interviews to narrow it down. Most of the bids will come from off-shore developers and the good ones will charge $20+/hr. You can also outsource complex MVPs to a U.S. development agency like Elm City Labs. The work quality is higher but these agencies cost more or want equity.

5) Manage the developer
You will need to manage the development process and constantly convey what features/bug fixes you want the developer to accomplish. Use Pivotal Tracker to manage the work queue and use Word and/or Photoshop to send visual aids with clear instructions. Make sure you have ownership of the code, hosting service and domain and set up bonus payments that line up with quality targets and deadlines.

That’s it. If you’ve done the process right you should have a functional web site or application. Now you can test the product with customers and, if successful, start approaching technical co-founders and angel investors. Expect the entire outsourced MVP process for basic websites to take 1-3 months and $1,000-$5,000. Expect more complex apps to take 3-6 months and $5,000-$15,000.

For examples of recent HBS startups that have outsourced development via oDesk and Elance, check out Vaiad and Northwestern University Store.

Launching Ovuline: 4 weeks, 4 cheap lessons and 1 expensive learning

2012-02-15T06:27:00.000-08:00

by Vasile Tofan

Ovuline, a web based fertility monitoring application, went live on January 10, following the HBS MVP Fund sponsored alpha phase. Four weeks, 2,000+ signups and 70,000+ pageviews later I’m attempting to summarize some of our learnings.

A lot of this is very tactical: cheap tips and practical notes – I’ll leave the grand strategizing for the next post. In the spirit of lean thinking – maximizing learning per dollar spent – I will thus start with the dollar side of the equation. If you want to take a shortcut, go directly to the conclusion, then decide for yourself if you want to read the entire post.

1. A viable product – cutting the right corners

As a consumer health product, dealing with a very sensitive issue, we could not allow ourselves drastic shortcuts. Many ‘smoke screens’ would have been damaging to our users who are already under significant stress because of their fertility concerns. For example, when considering to test a Polycystic Ovary Syndrome (PCOS – a condition affecting up to 10% of women) module in our application we could in principle have created a smoke option saying – ‘Monitor for PCOS condition’. But we did not feel comfortable about the ethical (and medical!) implications of some of these ‘smoke tests’. As a team we explicitly decided not to blatantly ‘burn’ through our users to test one or another hypothesis and treat any experiment with outmost respect. Being cheap on ethics is never a good choice.

This still left us with plenty of shortcuts to take in the non crucial aspects: from our Prezi based intro video to the very raw design on some of the pages to the numerous ‘coming soon’ inner tabs – we had enough reasons to be a little embarrassed by our first release. Luckily, our users did not seem to mind too much.

2. Reducing traffic cost – my 2 cents

With 50% of couples taking more than six months to conceive, there is a lot of search for conception related topics on-line. In fact, there are as many as 4 million Google searches each month for ‘how to get pregnant’ only. The Keyword tool was extremely helpful to estimate the specific inventory for all relevant terms. As somewhat unsophisticated AdWords users, we did get fooled initially by the Google-recommended cost-per-click (CPC) bids. ‘How to get pregnant’ for instance, had a suggested bid of $1.15 CPC. A dozen of iterations downwards, we reduced our bids to … $0.01, the absolute minimum! Even at this level we managed to get a steady stream of 300-500 clicks per day. Notes for ourselves:

Always start with a ridiculously low bid first and adjust upwards until you reach the desired stream of clicks per day
Consider expanding search criteria to cover English speaking developing markets which command much lower CPCs (Philippines, India, Pakistan) – even if these are not your target, the data can be still valuable for testing certain hypotheses.

3. Increasing conversions – before you move to A/B testing

Our conversion rate has evolved from the initial ~5% to as high as 13%, implying a user acquisition cost (CAC) of about $0.10 at our CPC of $0.01. We managed to gradually increase our conversion by applying simple tweaks even before jumping to home page A/B testing:

Link your AdWords and Analytics accounts to optimize the keywords employed for Conversions as opposed to Click Through Rate. Google optimizes for CTR by default, which is not always helpful
Monitor your Analytics for obvious conversion outliers – by country, browser type, keyword etc. For example, early on we noticed a pathetically low conversion rate from mobile users (~2% vs the site average ~10%), so we promptly excluded mobile browsing from our AdWords target at this stage
Experiment with non-obvious traffic sources. I was very skeptical of Facebook ads at first – there is no way they can result in lower CAC than AdWords, I was reasoning. To our surprise though, Facebook traffic showed signup conversions of 15%-20% (despite the abysmally low CTR), which, at $0.015 average CPC gave us a CAC of $0.09 (and almost 300 Likes along the way!). Yahoo and Bing (which I personally never use) are next in line.

4. User feedback – cheap advice

Determined to seek early feedback, we made sure we had our Uservoice button prominently displayed on the side. The only problem: there was not much specific feedback coming in (we did receive a lot of praising messages, which, while flattering, did not tell us what we can do better). We felt qualitative interviews or focus groups would be too time consuming and expensive at this stage so decided to integrate a simple, unobtrusively displayed poll into the site. For instance, the ‘Which feature do you value most?’ question drew in more than 200 votes in a matter of days (again, unexpectedly for us it was the Calendar feature – 56% of votes, when we expected Dashboard to win – only 17%). Interestingly, the blog and forum, which took a lot of our time to maintain and moderate, were voted by only 5%. Needless to say, the blog will not be our priority in the following couple of months.

The one expensive learning – an unlikely conclusion

The most important learning however, is somewhat at odds with the above mentioned tips. The abundance of data and the tyranny of Real-Time Google Analytics made our team the slave of minutia detail, often at the expense of big picture. We would frenetically monitor the results, on daily, even hourly basis, not unlike obsessive day traders monitor live stock performance. This over-focus on TODAY and NOW, the obsession with lowering the CAC by a couple of cents and increasing the Conversion by a percentage point was extremely time consuming. More importantly, it was detrimental to our ability to distill the more important learnings. Who are our users? Do they love our product? The ones that come back frequently, why do they? The ones that don’t, why don’t they?

In short, I feel we overly focused on the dollar aspect, as opposed to focusing on the learning per dollar. Luckily, we are only four weeks in.

Launching Disruptive Technology in Public Education

2012-02-15T06:26:00.002-08:00

by Serge Vartanov

I recently attended a presentation by Harvard Business School professor Clayton Christensen on the topic of innovation in health care. Professor Christenson discussed a number of theories as to how health care can become more affordable, but underlying his arguments was a belief that complex capabilities start off in an expensive and centralized form, and inevitably become commoditized and flow to the general population. In the realm of technology, complex processing power first manifested itself in mainframes, then over time diffused out as mini-computers, then desktops, then laptops, and now smartphones – each manifestation less expensive and more accessible. While this inevitability has tremendous implications for health care reform, I couldn’t help but wonder what kind of opportunities this dissemination of technology could have for entrepreneurs who dream of disrupting the realm of public education.

Anyone who has taught in a classroom knows that no two children are created equal – each student comes equipped with a unique learning style and a unique set of capabilities. Schools and teachers spend countless resources testing, analyzing and diagnosing their students so that they can differentiate instruction – for example, giving the 5th grader who is reading at the 5nd grade level less rigorous reading assignments than a 5th grader reading at the 7th grade level. Even when a student’s skills and weaknesses are identified, teachers have to put in a lot of hours to create differentiated curriculum so that every child gets appropriate instruction. Here is an inefficiency in a very large industry that can be corrected through the proper application of technology.

What if there was a website that could diagnose students’ reading levels, and deliver common content at different levels of rigor – that is to say, if a teacher is teaching about Greek Mythology, the more advanced students would get a story with more vocabulary and complex grammar. What if there was an app that could identify a student’s math competencies, and deliver games that would help that student develop critical skills to make school easier? What if there was software that could diagnose a student’s learning style (visual, auditory, tactile) and present social studies curriculum most appropriate to that style (video, article, audio). A lot of these programs exist – but they take the form of enterprise software centralized in only a few schools, creating an opportunity for a lean start-up to market similar capabilities through more affordable and accessible channels directly into the classroom and the home.

The market is huge, but the pitfalls are great. The science behind a lot of this technology is really cool, so it’s easy for a passionate young entrepreneur to fall into the trap of developing the capabilities, coding out a responsive and adaptive program, before making sure that there is demand in the parent and teacher market and product market fit. To disrupt this market, a wise entrepreneur should start with a minimum viable product and shadow her target market (be it parents or teachers). But when working with kids, to whom User Interface is critical for engagement, how would an entrepreneur avoid a false-negative on interest?

Using Lean Startup Practices for New Business Development in Big Corporations*

2012-02-15T06:26:00.001-08:00

by Qingxi Wang

The core of lean startup practices is hypothesis testing process, a systematic approach for validating a new business’ proposed business model. With appropriate adaption, this systematic approach could and should be used for new business development in big corporations. Meanwhile, there is likely to be significant extra challenges during the process.

There are generally three main stages in developing a new business in a big corporation – concept stage, design stage and launching stage. During the concept stage, a new idea is proposed and basic technical and economic analysis is done. In the design stage, a business plan is drawn, which includes the team structure, operational process and solutions, sales model and detailed economic analysis. After winning some buy-in of the business plan, the new business might be launched. During the launching stage, hypothesis for the new business model need to be tested. A systematic approach for validating and revising assumptions in order to find a viable business model, the lean startup practices, would shorten the time to reach break-even and increase the probability of the new business’ success.

So what are the major differences between lean startup and developing new business in big corporations? I will focus on the resource constraints and organizational heritage here.

On the one hand, the key feature of startups is constraint of resources such as capital, talents, production capacity, distribution channels, etc. A big corporation may be well equipped with many of these, if not all, and may be able to ramp up any shortcomings of these in a comparatively short period of time. Unfortunately, these endowments oftentimes disguise a big corporation to waste a lot of investments unnecessarily before finding a viable business model. However, in certain circumstances, big corporations should leverage their rich resources. If there is strong first mover advantage, large benefit of economy of scale, large potential for network effect, or potential incoming competitor, a big corporation may want to invest fast and heavily in a new business, on the condition that there is decent chance of finding a viable business model or the potential long term profit increase for the corporation is large. Admittedly, there is risk for the big corporation to incur losses. However, a big corporation is able to take such risk. Even if the big corporation loses, it would still have another chance to continue to play; if it wins, the gain is big. At a casino, a rich player with more chips can make a bigger bet than a poor player with same cards. Besides, a big corporation can also manage this risk by investing in a portfolio of new businesses and diversify the risk.

On the other hand, because of the existing culture, organizational structures, process, and talent pool, big corporations may encounter many challenges to have new businesses emerge and develop. The established priorities, budgeting process, performance metrics, incentive structures, short term focus, and lack of experiences and skills oftentimes kill new businesses in big corporations. One solution is to have a separate unit, which is free from the existing system, to incubate new businesses. During the incubating process, systematic hypothesis testing approach of lean startup can be used. After a competitive business model is found by this separate unit, the new business may be returned to the corporation system.

*Reference: materials from classes of Launching Technology Ventures, General Management: Processes and Action, and Building and Sustaining Successful Businesses.

Customer Feedback Testing: A Quick Guide to When & What to Use

2012-02-15T06:26:00.000-08:00

by Xing Wang

In lean startups, customer feedback is vitally important because it informs product development. However, customer feedback is equally as important on the customer discovery side as it is for product development: for determining the customer value proposition, likely demand, purchase behavior, and overall attributes of the target consumer.

Value Proposition: To validate if initial hypotheses on your product’s customer value proposition, surveys are an inexpensive but relatively reliable methodology to use. It is vitally important to define the right audience to send the survey to (are your friends really the right target market?) and also choose wording carefully in surveys. The structure and wording of questions in an online survey can skew results incredibly. For example, asking “Would you be interested in Product X? Yes or No” will lead to a very difference outcome than “pick the most interesting product from the list below.” Watch out not to mislead the consumer or else surveying becomes an unreliable benchmark to use!

Product Demand: Simple smoke tests are a cheap way to test product demand. In smoke tests, the product tested is not operational/functional, and instead users are responding with demand to a simple description or other mechanism that approximates the eventual product. Similar to what we see in the Aaadvark case, one can send users to a landing page to see if they would respond to an invitation, or, as in Triangulate, test out to see how many users would click on a dummy “photo” button.

Target Customer: To define a target customer, focus groups or one-on-one interviews are powerful tools through which an entrepreneur can understand the consumer psyche, particularly if he or she is still trying to define who to target as a customer. This technique should be used carefully. While qualitative research is incredibly informative, it can be misleading because customers do not always behave in reality the way that they indicate in focus groups. What one “takes away” from a focus group is also incredibly subjective and vulnerable to biases or multiple interpretations. As we see in the Cake Financial case, timing on when in the life cycle of your startup to rely on focus groups is also very important – focusing too much on what the customer is saying in the very beginning is another potential trap.

Customer Behavior: Some of the best tests for how a customer will interact are through usability tests. By observing what customers are doing, an entrepreneur can quickly pinpoint potential pain points in the customer experience with the product. Drew Houston in Dropbox uses this to great effect when he was able to learn from watching recruits from Craigslist struggle at how to use the Dropbox product. These tests are particularly useful because unlike other types of qualitative research where the customer TELLS you what he or she thinks, you are able to observe actual behaviors, giving a more accurate read on what is going on.

Product Development: Customer feedback on the product development side is extremely tricky to handle. While “the customer is always king”, startups must beware that customers don’t always REALLY know what they want. In particular, in tests like focus groups or one-on-one interviews, what the customer indicates as their wants in that particular setting and moment is not necessarily reflective of how they will actually act in the spur of the moment. This is especially the case with B2C products where a purchase decision is heavily driven by emotions of the moment, i.e. a fashion startup. In fact, as we see in the case of Dropbox, oftentimes what the consumer wants may not align with the ultimate vision of the founder or what the founder believes to be best for the company or product (i.e. syncing Dropbox with My documents). There is a fundamental balance that a founder must carefully consider between user-centric design and top-down product vision.

Make users happy first, then help them

2012-02-15T06:23:00.000-08:00

by Helen Weng

There are two kinds of startups- those attempt to change customer behavior in new or merely unexpected ways (e.g. Facebook, Triangulate, Pinterest, Cake Financial) and those that operate within current infrastructure to solve existing problems or customer pain points (e.g. Dropbox, RentJuice, Quora). Successful entrepreneurs in the second category use the lean startup methodology to form small hypotheses, test, and learn/validate from customers, increasing their chances of success. Changing customer behavior is a much harder task, yet the importance of applying lean principles, particularly around understanding the customer, can be even more crucial.

For me, the lean process and ultimate success requires an obsessive focus on the user. Marc Hedlund, in talking about why Wesabe lost to Mint distinguishes between products that make a user happy quickly and products that actually help people, making them better in some fashion. Ideally, a product should have both characteristics, but when forced to choose at the early stages of a company, go with building something that makes a user happy first. It is much harder for companies that seek to change behavior to draw a user in with something will help them if they do not gain early satisfaction.

Dropbox started off with the simple premise of building a product that will allow users to access their files from any computer. Rather than trying to change behavior, Dropbox built its platform into the existing way users store files in a folder. After perfecting this core value proposition, Dropbox was later able to choose specific new features based on both user feedback and founder vision. Triangulate, on the other hand, attempted to create a new kind of user profile generated from web behaviors and applied matching algorithms using this data towards online dating. This loss of control over a user’s online persona fundamentally changes the way online dating is thought about today. If Triangulate had initially tested this fundamental aspect of user behavior, the company may have been able to apply its implicit user profile creation towards another industry vertical.

Cake Financial and Wesabe also attempted to change customer behavior first with regards to financial investing before perfecting a product that made users happy. Both sought to help users change their financial behavior for the better, whether it was through close understanding of financial data or harnessing the power of social networks. While noble visions are to be lauded, they did not focus on the primary user priority when visiting their site, that of a simple way to upload and make sense of financial data. Mint on the other hand, made the upload process simple, provided tangible benefits from the beginning, and can now slowly change financial behavior by improving financial literacy and understanding.

Lean startup tests force entrepreneurs to harshly examine the realities of user actions. To change behavior, a startup must first build a product that fits into the frame of existing, accepted user behaviors before adding features that can begin to push the boundaries. In other words, the primary goal is to lure the loyal users in first. Then, do with them what you will.

Pivot or Perish? When does it make sense to pull the plug?

2012-02-15T06:22:00.001-08:00

by Jocelyn Whittenburg

The lean startup philosophy recognizes the high likelihood that an original idea will not be perfect and may need major adjustments as a startup grows. Recognizing that pivoting is often necessary allows many startups and their investors to view pivoting positively and encourage healthy changes to the direction of the business. However, pivoting is not always the right answer. Startups sometimes pivot when they should have pulled the plug. Inherent human biases dictate that we naturally lean towards pivoting instead of perishing. There are four types of biases that can lead to a tendency to pivot instead of pull the plug (from HBS Note - Hypothesis-Drive Entrepreneurship: The Lean Startup):

Optimism bias: People have a systematic tendency to overestimate the likelihood of positive events and underestimate the likelihood of negative events. This bias is likely to lead the entrepreneur to be overly optimistic regarding the likelihood of a successful pivot.

Planning fallacy: People also have a tendency to overestimate the benefits of a task and to underestimate the time and money required to complete that task. This bias can be particularly harmful when an entrepreneur is evaluating the pivot opportunity. A pivot can easily be attractive given the entrepreneur’s estimate of the benefit, time, and costs required by the pivot. If, however, the entrepreneur is suffering from planning fallacy (and many are), the benefits are likely to be lower and the time and money required likely to be higher, which will significantly reduce the attractiveness of the pivot.

Confirmation bias: People have a tendency to interpret information in ways that validate their hypotheses. Therefore, when evaluating an MVP test, an entrepreneur may be more likely to interpret the information as favorable to pivoting instead of as a signal to pull the plug.

Sunk cost fallacy: People tend to consider expenses that have been incurred and cannot be recovered when making business decisions. This may lead an entrepreneur to lean towards pivoting and persisting (in order not to waste the time and money already poured into the company) instead of evaluating the business going forward and pulling the plug.

Given these biases, entrepreneurs need to be careful when evaluating the decisions to pivot or perish. In my opinion, an entrepreneur should pivot if:

- MVP hypothesis test results indicate a viable pivot option. The option must hold up under scrutiny (what if the rewards are lower? what if more time and money are required than initially expected?)
- The entrepreneur has enough current funding to pivot or can raise more funding on reasonable terms.

In short, the entrepreneur must identify a specific opportunity, think through the viability of the potential pivot, and test the pivot for robustness against natural cognitive biases. Additionally, the entrepreneur must maintain employee motivation by ensuring that the financing terms for new money mean that the employee will still realize upside upon the success of the company.

On the other hand, the entrepreneur should pull the plug if:

- The MVP hypothesis test did not reveal any viable pivot options or if the pivot options do not stand up to scrutiny.
- Financing cannot be raised on acceptable terms.
- Employees will be in a worse position if they stay on for a pivot than if the company pulls the plug.

Basically, if the entrepreneur does not have a specific viable pivot option, cannot preserve the upside for employees in terms for new financing, and could put his employees in a worse position by pivoting; the entrepreneur should resist the temptations of cognitive biases and pull the plug.

Some entrepreneurs would argue that there is value in pivoting even if the company will ultimately fail. It’s a signal to future investors that the entrepreneur does not give up easily and will remain dedicated to his / her company. Although it is clear that reputation is paramount as an entrepreneur, founders should remember that they are playing with people’s hard-earned money and should think carefully about pouring money into a poorly conceived pivot instead of failing gracefully. There are as many reputational dangers in not knowing when to quit as there are in quitting too quickly.

Using Computers to Bridge the Physician-Patient Interaction

2012-02-15T06:22:00.000-08:00

by Vijay Yanamadala

We are currently facing a shortage of more than 90,000 doctors within the next 10 years, with Healthcare Reform projected to exacerbate this shortage as 30-40 million Americans are brought into the health care system. This problem will affect all areas of medicine but will likely hit emergency room and primary care medicine the hardest, and these are the areas where we already face substantial shortages today. According to the American College of Emergency Physicians, in 2007, patients spent an average of 4 hours and 5 minutes waiting in emergency rooms throughout the US, and this number has since steadily increased. Thus, physician-assisting technology will become imperative if we are to meet the healthcare demands of the near and distant future.

With new Healthcare Reform legislation and the American Recovery and Investment Act, hospitals and physicians will adopt electronic health records by 2015 or face financial penalties. Computers will thus fundamentally redesign the way physicians deliver healthcare to patients. Existing electronic medical records software focuses on storing patient information in electronic format with some added features such as scheduling, automatic reminders, and checks of medication incompatibility. Large scale implementation of such systems at places like Kaiser Permanente Healthcare have demonstrated that computers have substantially improved outcomes and decreased medical errors by ensuring that certain standards of care are met for all patients across the board.

However, with the recent advent of IBM’s Watson, supercomputers are beginning to participate in clinical decision making and guidance at esteemed medical centers like Columbia University and the University of Maryland. Computers will actually assist physicians in ways that increase their time with patients, free them to engage in the more demanding and challenging aspects of medical care, and reduce the amount of time needed per a patient. Now it will be essential to write software that utilizes these new computer capabilities to maximize their value to the healthcare system.

Every physician-patient encounter, whether it is in an emergency room, primary care setting, or specialty care clinic, involves four basic tasks: 1) history taking; 2) physical examination; 3) assessment and diagnosis; and 4) developing a comprehensive plan of action. During clinical history taking, physicians ask the patient to lay out their chief complaints and use carefully worded probing questions to analyze a patient’s symptoms to arrive at a diagnosis efficiently and accurately. The history is the most important part of the diagnostic process, and it is well established that over 80% of diagnoses are made based on the history alone.

A history can take 46% of a physician’s time. However, because of modern time constraints, most physicians are not able to elicit a complete medical history, without which the probability of medical errors increases dramatically – recent studies have shown that time constraints only allow emergency room physicians to ask 55% of essential questions and primary care physicians to ask 59% of essential questions.

We are now in an era where software can effectively ask history questions in a less time constrained manner, thus reducing medical errors and improving patient outcomes. Indeed, the majority of a medical history can be accurately and efficiently obtained by a computer. A 2007 study showed that effective computer software nearly doubles the number of important history questions asked of patients in the emergency room (> 90% of essential questions were asked by the computer) with a mean completion time of only 5.5 minutes. Furthermore, the vast majority of patients enjoyed or even preferred the ability to give a thorough health history by using a computer and 92% of patients would use a computer again in this manner. Indeed, numerous studies have shown similar outcomes with computerized histories. Thus, computerized history taking provides a power new medium in medicine.

Don’t Scale Until You Have Validated Your Business Model?

2012-02-15T06:21:00.001-08:00

by Jonathan Lo

A common theme in the Launching Technology Ventures class is how to utilize a “lean” model until one has validated the business model. Companies such as Dropbox and RentJuice are examples that did precisely that to achieve success, while a company like Cake Financial is openly criticized for not following those guidelines. While I do generally believe in the lean start-up principle, some of the most successful start-ups such as Google, Youtube, Facebook, and Twitter, blatantly ignored this principle to become the companies that they are today. These companies focused on achieving strong network effects before they had any clear plans for monetization. Is this the right approach?

I am involved in a start-up called SaferTaxi, a company that is developing a smartphone application to allow for the booking, paying and rating of taxis in Latin America. While SaferTaxi plans on being one of the first movers in Latin America, this is by no means a new concept in other more developed regions of the world. Companies such as Uber in the US, gettaxi and mytaxi in Europe, have all received lots of Venture Capital funding to achieve scale within their respective regions. While there has yet to be a dominant player in the taxi booking space, initial data provided to investors have indicated that there is a lot of potential for monetization. Are these company’s successes enough to validate our own business model?

A good number of Latin American start-ups were able to achieve success by taking concepts from “developed” markets and implementing the same concepts faster than the incumbents. A great example of this is Mercadolibre, an Argentine company that implemented and scaled the eBay model throughout Latin America. While there were other local competitors that launched around the same time, Mercadolibre was able to scale quickly and gain traction before any of the other players (including eBay). Is this the model we should be following in Latin America?

SaferTaxi is currently faced with many of these questions. While following the lean start-up model is an intuitive path to validate an entirely new business model, are the successes of the likes of Uber and gettaxi enough to validate the SaferTaxi business model? Other start-ups with the same vision as SaferTaxi have already started to emerge in Argentina, Brazil and Chile. Should SaferTaxi be focusing on refining the business model using lean principles and worry about competition after a superior product has been created? Or should SaferTaxi be focusing more on a land grab before it is too late to enter certain markets within Latin America?

Thinking About Virality Before It's Too Late

2012-02-15T06:21:00.000-08:00

by Jesse Garcia

While generating viral growth may be hard, it is not difficult to understand ways to increase the likeliness of a product going viral. Some of the companies discussed in Launching Technology Ventures, including Triangulate and Cake Financial, struggled with the challenge of generating viral growth. For these companies, virality became a priority after having developed a business model and begun product development. However, there are opportunities to increase the likeliness of viral growth when developing the company’s value proposition, and so entrepreneurs should think about designing for virality from day one.

The first consideration in determining the virality of a product is the potential benefit from sharing it. Very simply, sharing occurs between the sender and the recipient. These benefits can include social currency, enhancement of social status, entertainment, education, and potentially economic rewards, and must be experienced by both sides. Social currency, which we can define as the utility derived by a sender from benefitting the recipient, is the common denominator present in all cases where viral growth occurs. Triangulate struggled to achieve virality because in dating, an individual can experience both successes and failures, leading to limited opportunities for social currency. Similarly, Cake Financial failed to achieve virality because investing is also an activity where an individual can experience failures. Cake Financial’s struggles were exacerbated by the fact that there was no economic reward to sweeten the sharing opportunity, as there is ample evidence that the investment performance of active retail investors cannot consistently beat the market.

The other variable in the virality equation is the costs associated with sharing. These can include time and effort. Startups can minimize these costs in many ways, such as integrating with Facebook and other existing social networks. Triangulate and Cake Financial both took advantage of opportunities to minimize the cost of sharing, leaving limited room for improving virality by lowering the cost of sharing.

The result is a cost-benefit analysis of sharing, and it is abundantly clear that there must be a net benefit to sharing for a product to go viral. As explained above, Triangulate and Cake Financial failed to solve the benefit part of the equation. Importantly, the options available to either company to provide this benefit were most numerous before the business model was in place. To increase the likeliness of viral growth for a product, a startup should consider accounting for virality, and the necessary positive net sharing benefit, in its value proposition.

By thinking about it earlier, the virality dilemma becomes easier to solve. For example, trying to change users’ behaviors will not likely lead to a positive net sharing benefit. By simply observing existing human behaviors and ascertaining whether a particular activity is inherently social, a startup can begin solving the dilemma. Moreover, a study of human emotions and heuristics could also provide important insights into whether a NEW activity can truly become social. The over-confidence bias, which is based on numerous surveys where more than 50% of a population believes that he or she is better than average, suggests that users do not tend to see themselves as failures and may not want failures associated with them (as in dating or investing). This bias helps us understand why Triangulate and Cake Financial struggled to go viral.

Virality Challenges

2012-02-15T06:19:00.000-08:00

by Kara Yu

We’ve seen a lot of companies struggle to achieve viral growth: Aardvark, Triangulate, Cake. What’s going on? Does LTV have a skewed sample, or is it hard to harness viral growth?

It’s rare at HBS for us to see failures, but in LTV, we were fortunate to see not only one but several cases where a little bit of founder exuberance and false positives resulted in a lot of work on a product was not inherently viral. In all these cases, the founders created a product that they thought they would

Aardvark had issues with its product/market fit. While I was always a fan of Aardvark and thought it was a great idea to connect users with experts in their social group, it seems like most people thought otherwise. Even though it was popular with initial users, it was never able to cross the chasm. Ultimately, Aardvark solved a problem that users did not have. Some people found it helpful, but no one found it crucial to their lives. Aardvard also made assumptions about people’s tendencies to give back to a community, and share information. By not adequately using lean startup methodology for its customer development process, Aardvark had created a great product that people did not need. The virality of this product was way overestimated because people just did not find it useful.

Cake also had a great idea in a market that was ripe for innovation. While a similar product achieved great popularity (mint.com), growth for Cake stagnated. Even though Steven been diligent about getting validation from industry experts and top Angels, he neglected to receive feedback from his most important audience about the actual product. While conceptually, the idea was great, implementation depended a lot on the user interface. Due to his focus on the backend and neglect for the front end, the product was just simply not useful.

In the end, while virality is very difficult to predict, there is a necessary but not sufficient condition that in order to achieve virality, the product has to be either “cool” or useful to the user. I would never recommend anything to my friends that they don’t find useful. While I am a huge fan of Buzz and Google Latitude, I never mentioned them to my friends because I knew that they wouldn’t have appreciated them. People tend to only make product recommendations that improve or at least maintain their social standing with their friends. Dropbox managed to keep fairly low CAC because it was able to be useful enough to users that they told all their friends. The benefit that both parties received (in terms of extra storage) was a great motivator but had it been a less helpful product, it would not have mattered.

In short, virality is hard to achieve but at least in the cases of Cake and Aardvark, the main problem was product market fit and not their word of mouth marketing strategy.

How do you know when to stop pivoting and pull the plug on your venture?

2012-02-15T06:18:00.000-08:00

by Anonymous

As an entrepreneur following the lean startup philosophy, how do you know when to stop pivoting and pull the plug on your venture? Is this decision easier or harder in a lean startup?

The simple answer is you don’t know when to stop pivoting and pull the plug. It’s impossible to be 100% certain without the benefit of hindsight. You can, however, make a rational decision based on the best available data from your venture. If you follow the lean startup philosophy, and carefully build your business based on tests and data, each successive iteration and product roll-out should bring you closer and closer to having a conclusion to “is there a market that my product fits into?”

When you reach a step where the data disproves your hypothesis you need to carefully proceed. Make sure you didn’t get a false negative, and if confident, think of ways to test another related hypothesis. If not possible, it might make sense to think more broadly. What other products could one build based on the available skill sets, technologies, and resources available? If you are testing these different hypotheses in a “lean” fashion, you should have more resources, and thus time, to conduct more tests before you have to pull the plug.

I would imagine that in most companies there is a momentum that is hard to stop. Once a team is built, a vision communicated, and investors on board for a particular idea it can be tough to realistically assess the situation and make a rational decision of whether to pull the plug. The benefit of building the company “leanly” till this point is that the inertia, and by this I mean primarily money spent, to get you here is the least amount needed and would make the decision less difficult. On the other hand, if you spent $10mm to conduct a test that fails you would have a pretty high bar (obligation) to keep pushing on the path towards the original vision.

So while I do see the lean startup being in an easier position to decide whether to pull the plug or not, we cant overlook the simple truth of human emotion. No matter how closely you follow lean principles, and are data driven, you will always have some ego/emotion tied into a business. Being able to overcome these feelings and look clearly at the data is probably more important than the “leanness” of an organization to making the “pivot or pull-the-plug” decision.

Entrepreneurs Beware: You May Need To Pull The Plug

2012-02-12T12:37:00.000-08:00

by Daniel B.

Exactly seven months ago you changed the world. It was on that magical day that you envisioned the greatest online start-up ever—better than Facebook, Google, and Twitter…combined. Your flawlessly written business plan received accolades from all the top VCs, and your all-star development team finished coding weeks ahead of schedule. As you prepared for launch, you checked repeatedly that the servers could handle the horde of users that were destined for your site. When you went live though, no one came. Days later, still no traffic. Dumbfounded by the inactivity, you contemplated your next move: keep fighting, or pull the plug?

It’s unfortunate, but most of the tech ventures we create will end in failure (yes, this includes your business). We think through all of the ways we’re going to build a successful company, yet we neglect to consider what we’ll do when the business deteriorates. In some cases, the decision to pull the plug is easy. For example, if your bank account is empty, your investors won’t call you back, and your site is the laughingstock of TechCrunch, you should probably bow out. In most other cases though, the decision is not straightforward—especially if you’re building a lean startup.

The lean startup process seeks to avoid waste and optimize resources. The process is loosely characterized by five steps: envision, hypothesize, test, learn, and decide. Speed is critical. The more tests you can run, the more you can learn. A core component of the lean startup process is the ability to pivot. In other words, if you learn that a test rejects your original hypothesis, you can decide to modify your vision (pivot) and then test a new hypothesis. This fast, iterative approach can help you hone in on a more promising outcome, but it’s a double-edged sword. While you may think you’re just one more pivot away from success, you could already be five pivots past failure. It is because of the pivot that the decision to pull the plug is harder in a lean startup.

The ability to pivot is a naïve optimist’s best friend, since an entrepreneur could hypothetically tweak his vision forever. In fact, according to Eisenmann, et al., an entrepreneur does not need to shut down his business until a “test decisively rejects a crucial business model hypothesis, and the entrepreneur cannot identify a plausible pivot.” This can be dangerous though, since a red flag that typically signals disaster for a non-lean startup can be identified as a pivot point for a lean startup. Where do you draw the line?

I support the “never say die” spirit implied by the lean startup process, but I think it needs to be tempered. There are cases when an entrepreneur should pull the plug, even if he can identify a pivot. For example, if co-founders possess irreconcilable differences, or if a founder loses his passion, a pivot may solve the short-term problem, but it won’t help in the long run. As lean entrepreneurs, we need to be cognizant of the pivot trap. Passion is a critical ingredient of any successful startup, but it shouldn’t prevent us from pulling the plug when we need to.

Google, Where Startups Go To Die

2012-02-12T12:35:00.000-08:00

by Amy Chan

Aardvark, Dodgeball, Jaiku, Slide. What do they all have in common? They were successful startups that subsequently failed or shutdown after being acquired by Google.

So why do so many startups fail inside the search giant? Isn’t getting bought by a large corporation with seemingly infinite resources (a “startup sandbox” if you will) a good thing? Below is an analysis of how Google’s often-praised culture and internal processes conflict with Lean Startup principles.

Building Phase: User-Centric Designs Lose

Google’s emphasis on engineering makes it so that their products are technical-centric rather than user-centric. Engineers inherently want to work on the most challenging projects, preferably at the forefront of technology. In fact, Google’s very foundation is based on a highly complex algorithm that effectively matches human searches. But unlike its predecessors that competed mostly on the basis of technology, Web 2.0 companies are not technically difficult applications (think Groupon, Linkedin, Foursquare, Twitter). They excel because of other dimensions such as ease-of-use and virality.

Google Wave is a good example of how the need to build something technical overshadowed a user-centric approach. The team built a product that was ill-defined and attempted to do too many things, coupled with an incredibly complex UI that no user wanted to learn.

Funding Phase: The Fight for Resources

With thousands of projects in development at Google, there is intense competition for funding. In an ideal situation, competition is good because the products that gain the most user-traction should win. Unfortunately, Google is not a free market. The products that win resources are the ones that have the most internal backing (i.e. pet project of an executive) or charismatic leaders that understand “the system” (i.e. PMs that can convince the best engineers to work for them). Entrepreneurs, often young, are great at visioning, working with little-to-no structure and building products; but they are less equipped at navigating through structured and bureaucratic resource-allocation processes. Precisely what makes founders so successful as entrepreneurs is what makes them unsuccessful as product managers.

This fight for resources is what frustrated Dodgeball founders Dennis Crowley and Alex Reinhart. After only two years inside Google and getting no attention, they left to create Dodgeball 2.0, also known as Foursquare.

Testing & Measuring Phase: False Positives

In a resource-rich venture, the Minimum Viable Product approach makes very little sense. Why would a product team test minimally when it has access to millions of Google users and thousands of employees? Moreover, Google tends to beta test within itself before releasing to the public. This type of internal testing creates a biased picture of what works and what doesn’t work. Products that cater to the highly educated, early adopters and computer-savvy end up producing great test results, but may not necessarily appeal to the masses.

Learning Phase: Bias Toward Not Pivoting

After a product is built, funded and tested successfully, there is still a long launch pipeline. With only 365 days of a year and thousands of projects, Google has to somehow artificially spread out its product launches. There is no way to go through fast iterative learning cycles and hypothesis-driven planning when you can’t even launch your product for several months. And in terms of pivoting, forget it – the team will lose credibility and end up in the back of the line. Thus if your options are to preserve, pivot or perish, the bias is toward either preserving or perishing. Unable to pivot, an entrepreneur’s learnings are limited from one venture to the next.

Prediction

Note that the internal processes that Google employ aren’t bad in and of themselves. They are actually quite good at sustaining existing revenue sources. But in terms of harnessing emerging opportunities, unless Google finds a way to reconcile its product development process and the Lean Startup methodology, the Google graveyard will only continue to grow.

Two Lessons Learned from RentJuice

2012-02-12T12:34:00.002-08:00

By Jason Brein

The RentJuice case provides two insights applicable to Excelegrade, my own startup.

Numerous product, market, and business model similarities allow RentJuice to serve as a paradigm for Excelegrade. First, both are SaaS products monetized through a “freemium” model with monthly subscriptions priced at $30-$40 per user and average customers purchasing 5-6 seat licenses. Second, both address industries – residential real estate and public education – with vast amounts of uncollected data and predominantly manual workflow processes. Third, both attack fragmented ecosystems – the 12M owners of over 30M residential rental units and the 7M teachers spread across almost 100k schools – through their respective “densest hubs” – brokers and teachers. Finally, the education and residential real estate markets depend on local economic trends and regulations, so both companies launched city-by-city anticipating that achieving high densities of users in particular metros would create word-of-mouth and other viral effects. These similarities have allowed me to draw lessons from David Vivero’s success building RentJuice.

Lesson #1: Sales Model

Both RentJuice and Excelegrade offer most features for free but charge for customization, analytics, and reporting capabilities that become more valuable as usage increases. Whereas RentJuice always relied on telesales, we intended to hire a direct sales force to visit schools with particularly high concentrations of free users and sell them reporting and analytics tools to allow them to capitalize on the vast amounts of data that their teachers have been collecting.

One problem: Our unit economics cannot support such a high-touch model. Our sales reps command ~$100k annually, so achieving David Skok's suggested multiple of 6x on-target earnings (“OTE”) requires reps to generate almost 240 new customers each year, or almost one new customer per workday. Even if each rep visits four schools daily – an aggressive assumption given teacher schedules, school check-in procedures, travel time, etc. – sales reps would need to close almost 25% of leads to meet quota. And this already-aggressive assumption ignores the “top of the funnel” – the time required to initiate contact with leads, qualify them as likely buyers, and forge relationships with decision-makers.

Instead, we should follow the RentJuice model and develop a strong telesales force that contacts 50-80 customers per day instead of three or four. This requires developing a compelling web demo emphasizing a few widely-used features rather than customizing demos for individual teachers based on their individual feedback. Which brings me to Lesson #2…

Lesson #2: Product Development
RentJuice and Excelegrade are innovative not because they introduce new functionalities never conceived previously, but, rather, because they improve users’ lives by seamlessly integrating multiple important features. Our feature sets are more valuable than the sum of their parts; just as MLS data integration increases the precision of KPIs on the performance dashboard, student performance monitoring becomes a more attractive feature if test are automatically graded and the results easily tracked.

Because the value of the each feature does not become fully apparent in the absence of the others, I previously thought we needed to rely on “vision” up until the point at which we could present users with full feature sets. The RentJuice case, however, has convinced me that Excelegrade should immediately and aggressively begin soliciting user feedback. Given the heightened importance of product simplicity in a lighter-touch sales model, the features we prioritize must be crucial and the click patterns required intuitive. David and Kunal spent their days soliciting feedback from residential real estate agents and their nights incorporating these lessons. We should do the same with educators.

To the class: Do you think RentJuice is a reasonable comp for Excelegrade and do you agree with the two conclusions?

¹ Excelegrade replaces paper-based tests in K-12 classrooms with assessments on laptops and tablet PCs. Our online software allows instructors to design standards-based assessments, administer tests on mobile devices, automatically grade and track student performance, conduct rigorous data analysis, and create individualized progress reports for students, parents, and administrators – features that save teachers’ time and improve educational outcomes. (See our website and product demo for more information.)

Optimism Bias or Persistent Vision?

2012-02-12T12:34:00.001-08:00

by George Audi

Product-market “mis”fit tends to be the most common source of failure for new startups. A key reason why entrepreneurs fail to see the misfit, despite MVP launches, senior advice, or lack of customer interest can be linked back to their optimism bias. Optimism bias as discussed in class refers to risk-takers underestimating the odds they face and overestimating the likelihood of positive events. Such behavior is witnessed not just with startups, but also in mature companies. We have repeatedly read of overconfident CEOs paying excessive premiums for an acquisition and later dealing with post-merger integration dilemmas.

But when does an entrepreneur’s persistence in the face of obstacles become naiveté rather than the source of the next big thing? In hindsight, Mark Zuckerberg was a visionary to introduce a platform on which users can “Poke” their friends and “Like” their “Statuses” – terms which, if asked to users at the time, would have raised eyebrows and generated smirks. On the other hand, Steven Carpenter was considered too “optimistic” in his belief that users of his Cake Financial software would share their portfolio activity with friends and family. The former persisted in his vision and created a multi-billion-dollar social network powered by advertisers’ seek for a “Like”. The latter should have listened less to his gut and shut the curtains on his 4-year-old venture much earlier than he did.

I believe there are 3 main reasons why entrepreneurs are optimistically biased:

Egocentrism: new business ideas frequently arise from an entrepreneur’s own frustration with a specific situation, prompting him/her to find a solution. But such solutions may be considered nice-to-have’s rather than must-have’s by mainstream users.
Pressure to impress: entrepreneurs face a tremendous pressure to perform according to specific milestones, especially after they receive VC funding. This may encourage them to block out negative feedback and/or suboptimal test results.
Lack of adequate industry knowledge: when entrepreneurs do not master the specificities of an industry (or are not surrounded by people who do), they may fully understand its ecosystem. They neglect failed precedents or current competitors, assuming no one else has thought (or is thinking) of the same idea.

While by no means an expert on optimistic bias (I am trying hard to avoid it in my current startup!), I have some thoughts on how one could try to mitigate it.

Identify your vocal customers and engage with them regularly

Whether a few hundred in B2C ventures, or a handful in B2B ones, those customers who are willing to give feedback on your product can become your reality check, and help you calibrate your value proposition to address the true pain points they face

Build an advisory team of topic experts you trust and give them “halt” power!

You can set your own “halt” rules, but for example, your advisors can force a pivot after three failed attempts from your part to prove a certain hypothesis that you are reluctant to let go of

Discuss the feedback you collect from some customers with others

Doing so allows you to separate idiosyncratic criticism/excitement from systemic one. If a certain negative/positive feedback is expressed by some users, bring it up with other users and gauge their reaction. If that new pool has opposing feedback, it’s an indication for you to refute/validate your hypothesis

Offer a free trial to naysayers

If they remain unconvinced after using your service, you would have learned a valuable lesson. If they value it, you’ve earned yourself a paying customer.

This list is by no means exhaustive! I invite you to share your own comments as well and validate/discredit mine. It would be great to have a practical list of Do’s and Don’t’s for us current and future entrepreneurs to transcend to persistent visionaries rather than failed optimists.

George Audi
Twitter: @audi_george

Lean Corporation – An Oxymoron?

2012-02-12T12:34:00.000-08:00

by Paul Chong

Can Lean Startup principles be applied in a Fortune 500 company? The benefits from applying Lean Startup principles (‘Lean’), such as efficient use of resources and rapid learning and innovation, are desired by most large corporations. Perhaps, it then seems odd that so few large corporations have adopted Lean in their product development process. In this blog post, I argue that several key organizational capabilities and characteristics are necessary for Lean to be effective. As large corporations tend to lack many of these capabilities, it comes as no surprise that Lean is rarely effective in such organizations.

Eric Ries recently published a blog post (http://www.startuplessonslearned.com/2011/10/case-study-nordstrom-innovation-lab.html) illustrating how Nordstrom, a Fortune 500 company, has successfully adopted Lean in its internal innovation incubator, the Nordstrom Innovation Lab. He highlights three key features of the Innovation Lab that shows successful application of Lean, namely, rapid iterations (tests conducted weekly), Genchi Gembutsu (direct customer interactions), and simple, rapid, experiments.

Notwithstanding the apparent application of Lean at Nordstrom, I question how effective and sustainable these activities are. My argument is based on four organizational capabilities that appear necessary for Lean to be effective:

1. People:
The obsessive attention and commitment required by the people executing Lean is rarely found within large corporations. It appears that Lean works best when there is direct ownership of the product (and process) by the team. For large corporations, in which accountability is diffused, it is difficult to develop incentive programs that can motivate such levels of ownership. Additionally, large corporations may find it difficult to recruit people with the drive and product obsession required to make Lean really work.

2. Resources:
The efficient use of resources, as espoused by Lean, works in startups often because there is no other alternative. Lean startups efficiently allocate their limited resources as a matter of survival. Corporations, by comparison, have ample resources, which fosters a sense of complacency and waste. Without the threat of failure due to resource limitations, Lean cannot be properly executed in corporations.

3. Willingness for Disruption
Lean works so well for startups because they are able to adapt and pivot in response to new information and learning. Corporations, on the other hand, are constrained by existing businesses and product lines. This is a classical case of The Innovator’s Dilemma.

4. Process
The effective execution of Lean means short cycle times and quick iterations. Large corporations may struggle with this, due to the inherent bureaucracy and red-tape encountered at every decision point. For example, conducting a simple smoke test on customers poses potential risks to a corporation’s brand, necessitating approvals from marketing etc. Startups have significantly less to worry about.

Based on my arguments above, I believe that the effectiveness of Lean is significantly reduced in a corporate environment. This means that startups effectively adopting Lean principles will continue to out-innovate their corporate counterparts. However, as the Nordstrom example shows, corporations have adapted to create internal innovation labs which autonomously operate outside of the corporate bureaucracy. Will Lean remain an innovative edge used exclusively by startups? The Nordstrom Innovation Lab suggests maybe not.

Warning Label

2012-02-12T12:33:00.002-08:00

by Joshua Chuang

Hypothesis-driven, lean entrepreneurship can be the difference between a successful venture and a failed one. This strategy requires one to propose a hypothesis, develop a test around the hypothesis, test it, and learn from it. At its core, hypothesis-driven, entrepreneurship strives to reduce the biggest risk startups face: building a product that no one wants. A powerful tool for entrepreneurs, but potentially dangerous for those who don’t fully understand it or who overlook certain dangers.

The following serves as my warning label to the entrepreneurs out there following the lean startup methodology:

1. BEWARE of AssumptionsPeople tend to presume they understand how things work, when in fact they often don’t. For example, with Cake Financial, the founder and CEO, Steve Carpenter, assumed that the user-interface wasn’t what people cared about and focused his attention on the back-end. He also assumed that Yodlee would be a bad business partner, based on his prior experience. Both are completely valid and, seemingly, reasonable points. However, other plays (like Mint) were able to capitalize on Yodlee’s abilities to create a strong product. Don’t make assumptions/presumptions! Whenever possible, test your assumptions.

2. BEWARE of false positives and negativesConsider the following scenario. You’re friends with many people in the VC and tech industries. You make an assumption that people in this world want a product that automatically folds your laundry (I hate folding clothes, one of you should really make this product). Your friends all love it, and you’re receiving some good press from some famous blogs. All signs point towards making the product, right? WRONG! This is a false positive! While it could be true, it’s not a definitive positive (and you should never read it as one). One might begin building an expensive prototype when, in fact, there was no real demand. So what should you do? When designing your test, create a checklist of potential outcomes and their associated implications. Could I be receiving good press simply because I’m connected to the right VC firm or right advisory board? Are my friends supportive because they believe in me or because they believe in the product? Next time you hypothesis test something, make a checklist first.

3. BEWARE of the “Customer knows best” mentalityPart of the lean startup methodology requires creating minimal viable products, testing them, and improving them based on feedback. Learn as much as you can as quickly as you can. However, what happens when people start asking for numerous improvements/features? What happens when users fundamentally dislike one of your key features? I wish there were a simple rule as to when you listen to the customers and when you stick to your beliefs. Like many things in life, the answer is “it depends”.

In short, my advice is to take your “belief” and test the crap out of it. If the feedback says make changes, but you still wholeheartedly believe you’re right, then try and figure out why they’re wrong. Ask those you trust whether your reasoning makes sense. As I’ve already established, every entrepreneur likes to believe they know best. What I’m telling you is to never trust your gut alone. Prove it if you can. And if all else fails, then make a choice and pray it works out.

Good luck entrepreneurs. You’ve been warned!

Testing the Attractiveness of Dynamic Pricing as an Entrepreneur

2012-02-12T12:33:00.001-08:00

by Will Clayton

Dynamic pricing, in which different groups of customers are charged different prices for the same service based on their varying willingness-to-pay, is very attractive to businesses due to its potential to increase profitability. However, there are situations when adopting dynamic pricing can push potential customers away or damage relationships with your existing customers. So, how can an entrepreneur determine the right course for his or her new product?

First, it is worth differentiating between dynamic pricing driven by real-time variations in supply and demand, such the spike in demand that caused Uber’s rates to fluctuate radically during New Years Eve, and dynamic pricing based on customer profiling, such as an approach in women and men could be charged different prices for the same dress on some online site.

While demand-driven pricing fluctuations might inspire short-term customer frustrations, as Uber experienced, ultimately Brenden Mulligan is likely correct in suggesting that customer unhappiness was driven by a perceived lack of clarity about how much an Uber ride on New Year’s Eve would cost – not that prices were fluctuating based on demand in general. Put another way, the consumer uproar was driven more by (perceived) information asymmetry than by a disagreement about the reasonableness of charging differential prices based on demand fluctuations over time. Most people would abstractly understand that Uber has a limited supply of cars and could be reasonably expected to charge top dollar for its prices.

The second form of dynamic pricing, in which a company determines that certain customer groups can be charged different prices for the same good at the same time, is more likely to generate customer dissatisfaction over the long term. Because this “profile-driven pricing” involves profiting from an information asymmetry, it is likely to cause customers to feel they were taken advantage of – as likely happened to many drunken Uber customers when they saw their bill. Profile-driven pricing is likely to provoke a backlash particularly when customers have a high likelihood of uncovering such practices, a customer’s consequences for paying a higher price are substantive, or there is a high level of communication within your customer base. Consequences are likely to be particularly high if someone paying a higher price is accountable to someone else, a boss for example, who might become aware of their missteps during pricing discussions and penalize them on the basis of that poor performance. High interconnectivity within your customer base makes it more likely that customers will unintentionally discover differential pricing due to frequent interaction across your segmented profile groups. Furthermore, high levels of communication make it more likely that any backlash will grow rapidly beyond a small fraction of your customers who observe the policy first-hand.

When entrepreneurs consider whether dynamic pricing would be appropriate to their product or service, they would do well to conduct some hypothesis testing with potential customers to understand how such a pricing strategy would be perceived. To asses the viability of time-based pricing, one could survey target customers and ask them if they would prefer higher prices that were stable at, for example, $10 or prefer prices that would vary between $5-$15 based on demand. Changing the suggested variable range might provide some clues about potential customers’ tolerance for different ranges. It may even be possible to pose a series of head-to-head choices, e.g. $5-$20 vs. $9 – $18, to receive clearer customer preferences for a variety of different pricing permutations. It would also be important to understand whether the potential customers perceive the logical link between demand and supply – something that would probably be more convincing with a car service than with a web-based software application. In a situation where the product is already available, then the purchasing process could include a prompt for each purchaser to chose between a fixed price and a demand-driven variable price within a given range and assess the breakdown in preferences expressed through customer decisions compared to the initial survey results.

Assessing customer tolerance for profile-driven pricing could be assessed using an A-B smoke test in which potential customers were asked to fill out a short survey on relevant profiling parameters prior to receiving a price. The page displaying the pricing information would state either A) “Based on the information you provided, the price will be X. Please provide your e-mail address to be notified when we launch” or B) “Thank you for helping us understand our potential customer based. Our product will be Y when it is available. Please provide your e-mail address to be notified when we launch”. Comparing the sign-up yield rates for each path would provide some indication of what, if any, negative reaction customers might have to profile-driven pricing. This initial survey may also offer an opportunity to probe how interconnected different profile groups are and thus the probability of your variable pricing being uncovered after launch.

Ultimately, customer tolerance for dynamic pricing seems to be driven by perceptions of information transparency with customers revolting against situations where they perceive pricing to be “unfair”. While the economic benefits of dynamic pricing could be substantial, entrepreneurs would do well to assess customer comfort levels with dynamic pricing prior to launching it and also be cautious with its surrounding messaging to ensure that a firestorm on Twitter doesn’t damage a nascent product or brand. While dynamic pricing could potentially be used effectively in secret, entrepreneurs would be well advised to understand the social networks and communication flows within their customer base to determine how likely it would be that pricing differences might be exposed. In highly networked customer bases, nothing is likely to stay secret forever and the risk of a public backlash should not be ignored. Taking careful steps to understand your customers’ perceptions and values before instituting dynamic pricing can mean the difference between success and failure in your venture.

Does Lean work at big corporations?

2012-02-12T12:33:00.000-08:00

by Ashvini Thammaiah

As someone who’s not going to spend much time at a startup in the near future, I find it interesting to think about how well Lean methodologies can be applied at big corporations. From my experience at larger companies and my understanding of the methodologies, I think it would be difficult to implement Lean methodologies well given the current systems in place at larger companies.

Product Development
Large corporations may try to apply the iterative short work cycles of the Agile methodology in their product development. However, the decision making processes at large corporations severely mute the power of this methodology. Given the financial planning systems and the need to product quarterly results, large corporations tend to be risk averse and resist deviating from plans made during their annual planning period. Any changes that are attempted go through numerous meetings to get buy in from all the different key decision-makers across divisions. Just the mere delays brought about by the amount of time it takes to get the players in the same room, can cause the product development to slow down.

Talent
Unlike startups, employees at large corporations tend to have more of a fixed salary compensation structure rather than one heavily dependent on equity so employees may not be as aligned with the needs of the company as they would be at a startup. Also, since big corporations tend to be more stable than startups, there may be a feeling that an employee’s individual work doesn’t have a significant impact on the overall sustainability of the company given the sheer size of the corporation (free-rider problem). Finally, since most people who come from other companies will be used to traditional corporate budgets, they may not be scrappy with resources. Thus, the incentives and cultures at big corporations are different than those at startups and so may attract people who aren’t as effective with being lean.

Customer Interaction
While many large corporations do surveys and talk to current customers, it’s tough for them to use more accurate information gathering methods, such as smoke tests. Large companies have to be concerned with their image and the effect that any actions may have on their existing business. To get around this, companies can ask their employees to test out new products but that can lead to imperfect information. An example of this is Google Buzz failing with mainstream consumers even after being tweaked in Google’s Sandbox. Also, given that many firms have established sales divisions, talking to customers directly can be difficult. If the sales team is involved, additional time and effort is expended coordinating additional people. However, if they are left out of the conversation, it can be seen as stepping on their toes.

While large corporations have much they could benefit from the Lean methodology, I don’t think that most will be able to implement it in an effective manner without changes to how they run their business currently. Given the challenge, it seems like some companies are creating separate internal innovation groups or investing in startups, which can be more lean given their independence from the large corporation.

When Does It Make Sense To Launch Early With A Buggy Product, And When Is It Important To Perfect The Product?

2012-02-12T12:31:00.000-08:00

by Daniele Diab

Bugs, Bugs, and more Bugs!
Just let me raise the curtain…

You might wonder whether you should go out there after three weeks with a buggy product or wait nine months until your product is perfect. Although it depends on the nature of your product, I would favor launching early and imperfectly.

First, when being a first mover gives you an advantage, it might be very valuable to launch as soon as possible, even with some bugs. Time is money and one of your scarcest resources as a startup is time. Twitter, for instance, was notorious for being a buggy product in its early days, adding users aggressively at all costs and at the risk of going down more often. In my view, it was the right choice not to limit the amount of new users and postpone work on the servers’ stability given the first mover advantage and the importance of network effects.

Second, when your probability of failing is very high and you have limited resources, releasing a product quickly, albeit imperfect, will actually decrease your overall risk. In line with the concept of lean startup where you “test then invest”, having a buggy prototype means having a cheap and quickly available minimum viable product useful to test key hypotheses. The quicker your prototype is in users’ hands, the quicker you receive feedback, which ultimately means the quicker your product gains market traction through responsive design.

And, finally, when it comes to bugs of internal architecture nature, users do not really care. From their perspective, the technology is completely irrelevant. This is further mitigated when you state upfront that your product is in beta version; users will be patient. In the end, it's entirely about the user experience and what really counts is whether users love your product and your concept.

However, let’s not fool ourselves. Launching early can, in some cases, hurt you.

In fact, when sequencing is very likely to be misleading, launching an imperfect and incomplete version of your product might not be the optimal approach. For instance, when you issue a new feature, its success might be dependent on another feature that you scheduled to add later. You might drop the first feature not knowing that it is a valuable feature only if bundled with the feature you were planning to add in a later stage. Launching imperfectly might thus lead to false conclusions about specific hypotheses given their interdependencies – a problem that is not encountered if you go all the way.

Additionally, when the product is providing a mission critical service, launching a buggy product might destroy your reputation, especially when you still have no customer goodwill bandwidth. Some for data sensitive ventures (such as Gmail and Dropbox) or for platforms that are heavily dependent on building a strong relationship of trust (such as Paypal), it is critical for the product to be perfect at least at the software functionality level before being released since the impact of errors in these applications could be fatal.

To conclude, it is important to incorporate the trade-offs in your decision making process when choosing to launch a buggy product early versus waiting for the product to be perfect.

Take Control! End to End MVP Development

2012-02-12T12:30:00.001-08:00

by Rob Uhlemann

Would the original Nintendo Entertainment System been as successful if Nintendo hadn’t produced the ideal controller for that little plumber? Would you trust half your customer experience in the hands of another company? While MVP has focused thus far on software, more product driven companies need to take a page from Apple and Nintendo and push for end to end software and hardware integration to provide customers with the best experience and the MVP framework needs to be tweaked to account for hardware integration. MVP for end-to-end integration needs to focus even more attention on the initial vision of the team, funding & forecasting resources, and seeking out hardware relevant network effects.

Map out the Future! – The founder’s initial vision is even more critical when an integrated product is being developed. Initially the founder needs to recruit a software and a hardware team and provide both with a cohesive vision of how they need to combine for an optimal user experience. The founder will also need an in-depth understanding of what minimum software and hardware specifications are needed to provide the desired customer experience. It may be helpful for the founder to layout a 5 year product roadmap to push towards the MVP and incorporate required team learning into future product iterations. The iPhone wasn’t created overnight. The team started with numerous iPod evolutions before integrating a cellular network, touch screen, and the App store.

Funding, Forecasting, & Cost Analysis oh my! – No getting around it, hardware costs money! A hardware MVP can only survive a short time on smoke tests and prototypes, and eventually the team will need to invest in designers, tooling, and production equipment that may be bear significant costs. The founder should do an early cost analysis (LTV and CAC) to determine the sales price, predicted costs, and worst-case sales volume to verify that the product will be profitable. If not initially profitable, the founder should have a vision on generating additional profit through software or necessary hardware cost reductions required to make the product profitable.

Get Sticky! Building in Network Effects – Think Blackberry Messenger with hardware network wide free messaging or Xbox Live with hardware network wide friend gaming, both provide owners of the hardware with valuable tools that make leaving the ecosystem socially isolating and costly. The founder should spend a significant portion of their time brainstorming network effects and evaluating if they are truly effective. The stickiness will only be in place as long as the entire ecosystem has an advantage. BBM was eclipsed by superior Apple and Android ecosystems, while Microsoft Live has maintained an advantage over competing Sony and Nintendo game networks by providing relevant updates and superior social content such as the Halo series.

While much of the MVP mentality can be transferred to hardware and software integration, the founder should spend more time solidifying their vision, drilling down meticulously on LTV and CAC, and brainstorming unique hardware based network effects.

Help Me Help You: Getting Great Consumer Insights

2012-02-12T12:30:00.000-08:00

by Tiffany Niver

We don’t know what we want. Decades of psychology, economics, and sociology research has shown that people are quite poor at predicting what they want, how it will make them feel, and the long-term implications of gratification. This makes ground-breaking product innovations all the more complicated as understanding users becomes an exercise of mind-reading…and then some. Knowing the limitations of consumer insights and going into the process with a healthy sense of skepticism can empower creativity in coming up with the most impactful tests and analyses. The key to getting great consumer information is focusing on obtaining as much behavioral insight as possible, while following the lean start-up principle of “maximizing learning for unit of time and effort expended.”

Many entrepreneurs and researchers follow a fairly standard path of gaining customer insights.

Baselining Through Consumer Self-Reporting: Before any development or investment begins, they rely on surveys, focus groups, and interviews to learn about markets and consumers. These self-reported methods can be extremely useful in understanding purchasing behaviors, usage patterns, pain points, and customer needs. Oftentimes consumers can accurately articulate their frustrations and desires, and these become cheap and easy ways to get validation for business ideas. However, consumers base their recommendations or experiences on the status quo and thus have difficulty imagining a world which is much different from their current situation. Thus for incremental changes to existing products, these techniques can be extremely useful, but for dramatic deviations from the status quo, the reliability of these methodologies decreases.

Piecemeal / Feature Testing: Once entrepreneurs have their concept finalized and some features or interactions available, they complete smoke tests and feature tests to see how consumers interact with their concept or feature. These tests can further analyze one or two hypotheses and can be helpful in understanding how much appetite there is for a concept or how users react to pieces of a product. A major limitation is that the consumer (and the entrepreneur) still doesn’t know how the set of features or hypotheses will interact together.

Full Product Testing / Behavioral Insights: Much further down the road, a full product is available and a consumer can test every aspect of the experience. At this point, entrepreneurs have access to a tremendous amount of information provided by every click or every piece of feedback that the consumer gives. This helps get into the mind of the consumer and is usually the first time where an entrepreneur can get the actions rather than just the words or intention of consumers.

Each phase of consumer feedback is extremely important, but - with the limitations of self-reported information - oftentimes behavioral feedback is the ideal methodology. A key obstacle, however, is that this necessitates actually having something for users to test which requires time and money. The “holy grail” of consumer testing would be a low-development behavioral test where one could get the benefits of usage data without the costs. While this is clearly difficult to do, entrepreneurs should push themselves to be as creative as possible with testing by doing things like:

Using existing data on consumer behavior more thoroughly (i.e. Wings using Facebook data to launch a product based on consumer behavior)

Creating quantitative data / observing consumers in similar environments (i.e. Cake Financial observing consumers on Quicken, on their trading platforms, or doing research)

Continuing to build the most minimally viable products to test consumer behavior (i.e. Rent the Runway putting together trunk shows)

A key learning I’ve come away from my own experiences and LTV thus far is that actions speak louder than words and until you have something in front of the consumer, you don’t know exactly how they will react. So get out there and do some behavioral testing!!

Is leaner always better?

2012-02-12T12:23:00.002-08:00

by Maya Farah

The concept of the lean startup has been on everyone’s mind for some time, and seems to be one of the key “entrepreneurship themes” HBS is focused on this year – from this LTV course, to Eric Ries’ talk, to the MVP competition.

We all obviously buy into it – I am even in the process of trying it out, as I am working on building a Minimum Viable Product for my own startup as we speak.

However, as we discuss new ventures each week, and interact with their founders and CEOs, I can’t help but wonder whether lean really is always the way to go. I was relieved to know I am not the only skeptical, when I came across an article written by Ben Horowitz in 2010, where he pitches the case for the fat startup.

My main concern with being lean is the following: while it definitely minimizes your downside – you are likely to fail cheaper and less often when you’re lean – it might also lower the maxima that you can achieve, for the following reasons:

You don’t give yourself enough runway – if you’re introducing a new product that you need to educate the market about (or even create a market for), you’re likely to get a lot of false negatives at the beginning. With a process of fast iteration, this might make entrepreneurs discard a lot of ideas that are viable in reality.

Too many tests slow down your progression – you launch too late, you scale up too slowly, you hesitate to hire a sales force, etc. and therefore give up market share or your first mover advantage. (a counter-argument to that is that on the aggregate, by replacing lengthy testing cycles by short ones, you end up launching faster and wasting less time)

You release sub-par products that are not good enough to generate demand – while an MVP is likely to provide you with insights 9 times over 10, some products need to be at more than a “minimum viable” stage to be released to testers and early adopters. Think Dropbox, when Drew Houston waited long enough to make sure the big glitches were ironed out before he committed to storing users’ data.

You can get stuck in what Horowitz calls startup purgatory – yes, you made sure that your Customer Acquisition Cost is less than a third of your Customer Lifetime Value, therefore your startup is viable and maybe cash flow positive. But you’re not likely to hit it big anytime soon, and you’re stuck in mediocre-land – at a time of your life where your opportunity cost is very high.

If you were investing in one of the big visionaries, the Steve Jobs of this world, would you advise them to be lean in their product development? Most probably not. You would want them to step on the gas and capture as big a part of the pie as possible.

However, chances are, based on startup figures, that you’re actually going to be in the 90% who do not achieve home runs, which means that, statistically, leaner is better for you.

Therefore, when deciding on which end of the lean spectrum you would want to be, some criteria to consider are opportunity related:

Are you starting a business in a market growing super fast? (high opportunity cost of starting slow). Are you trying to create a market that doesn’t exist? How fast is the existing/potential competition likely to move?
What will your success most likely depend on? Perfect product, customer reach, other?

But others are more personal:

Are you a first time entrepreneur? Do you still have a much higher failure risk/cost and therefore need to minimize your downside?
How much can you trust your intuition? Have you been in the industry/function you are building a business in for a long time? (And have you therefore recognized and internalized a lot of the trends and needs in an accurate way?)
What are your personal objectives? Are you more afraid of failing or of being stuck in an average venture? Are you going for a positive average, or for a homerun-or-nothing?

Which leads me to my last question: Do lean CEOs know when to hit the gas?

Observing some of the “leaner” CEOs who came to class, I was always impressed with their ability to take their company off the ground so seamlessly, but also skeptical about their aptitude to scale it up once the model is proven. In fact, I personally think there might be a positive correlation between a CEO’s score on the Lean Scale and his/her probability of being replaced by the VCs when the company takes off. One more thing to consider as we are refining our entrepreneurial DNA…

Sales Partnerships- a lean way to scale startups?

2012-02-12T12:23:00.001-08:00

By Aditya Dhanrajani

Recently we have been discussing the challenges startups face managing sales as they scale. We have covered some of the theory developed by David Skok on the cash flow trough created and the effect that hiring sales people have on it.
In hiring salespeople startups put immense pressure on their business model as returns are delayed if they actually are realized. So is setting partnerships another potential model for startups to ease this pressure? Partnerships as used here refer to startups collaborating with a large corporate which has access to the target customer base through their sales force to sell product. An example of this is how a startup called AppDirect partners with telcos . AppDirect provides cloud service marketplace and management platform for SMBs. In order to reach the fragmented SMB customer space the startup is partnering with telcos and using their sales force to get distribution.
Partnerships (if structured accordingly) can provide startups with some benefits:

Flexible sales force : The distribution network can be scaled up or down based on product evolution and testing different sell pitches
Reduce risk profile: As mentioned hiring a sales person is risky as it often might not result in product sales. In case of a partnership agreements can be crafted such that there is guaranteed revenue that the startup gets from the corporate (eg for licensing)
Outsource after sales support : The support infrastructure for the startups products can also be handled by the corporate call centers freeing the startup from investing in its own support center
Potential network effects : If a startup can muster a quorum of partnerships, there is potential for network effects. For example if AppDirect partners with two telcos then the other telcos are incetivised to partner as well to be able to offer the same SMB product offering.

From a lean perspective, a partnership model provides latitude for an entrepreneur to access a large customer base to test hypotheses in parallel amongst a wide variety of customers. These hypotheses can go beyond product to the sales model itself.
At the same time a partnership poses some interesting challenges to a startup :

Securing a partner : There is a catch 22, a corporate would only tie up with a startup after it has reached product market fit and is selling product, while a startup might want to partner when it not yet reached this stage to achieve reach and test product.
Sharing IP : Startups need to share substantial chunks of their IP with their partner who in the long run can decide to create a similar product and eliminate the startup from the value chain
Erode business model : Becoming dependant on a partner can put a startup’s business model at risk as the dominant partner(s) can demand “unreasonable” pivots/changes.
Sales “airtime” : A partners salesperson will be incentivized to sell the partners core product vs the startups add on. As a result there is a scenario where the startup’s product doesn’t get sufficient exposure to customers and therefore yields no revenue.

In my opinion there still are two questions that still need to be explored on this topic. First is cost. Like David Skok’s model lays out the direct sales model, it would be interesting to compare it to data partnerships to see how the costs and revenue stack up. Second is product. This model requires a startup to have a product that provides a core functionality such that a corporate is incentivized to invest in selling it. In the end however sales partnerships need to be evaluated on a case yb case basis to determine if the marriage can generate value for both the startup and the partner in a given industry structure.

Filtering out the noise

2012-02-12T12:23:00.000-08:00

by Samantika Gokhale

Last year, The Sims creator, Will Wright, launched a new online TV show “Bar Karma” which invited a community of fans to drive the show’s content. Users could pitch one-line story concepts using an online tool, members would vote on the ideas, and the most popular plots would be made into a 30-minute episode produced by a professional cast and crew. A start-up which focused so strongly on user-centric design must have been a hit series, right? Wrong. The show flopped and is now ancient history.

Lean start-up theory encourages entrepreneurs to launch early, put a minimum viable product into the hands of real customers, learn quickly about how they use it, and iterate the business model based on their feedback. However, the problem with listening to your users is that they can often be unreliable. They may not actually know what they want until you show it to them. They may tell you what they think you want to hear. Their suggestions maybe skewed towards their experiences with existing products. And finally, their opinions may not be representative of the majority of your target consumers, particularly if they are early adopters. So how do you stop yourself diluting your product vision with hundreds of erroneous features? Should a true visionary simply ignore consumer testing altogether? How do you filter out the noise and understand what you truly need to change about your product?

I have a few ideas for how for a budding entrepreneur can make user testing more useful:

Ask users to make trade-offs: User feedback can often lead companies astray because the users make product suggestions without understanding what the trade-off would be, either in terms of increased complexity, reduced functionality or higher prices. A good example here is Drew Houston’s refusal to incorporate Dropbox’s most requested feature, the synchronisation of files outside of the Dropbox folder, because of the conflict with the product’s simplicity and the safety of the user’s data. An entrepreneur could create surveys which ask users to make trade-offs between pairs of features, and hence isolate modifications that are truly valued by users.

Craft tests to reveal actual preferences rather than stated preferences: Landing page smoke tests which ask users to ‘click to create an account’ or even ‘click to pay a sign-up fee’ can reveal an actual intent to join a service. These tests which attract users using ads can also produce more genuine data as users do not know they are ‘testers’, and so will not feel compelled to provide feedback for feedback’s sake or fall victim to group think, as can often be the case in focus group settings. A great case here is Facebook’s introduction of the Newsfeed feature which received a negative consumer backlash. However, the company chose to keep it as actual user data revealed that the feature was being heavily used.

Listen for the user’s underlying problem, not their recommended solution: When a user asks for a product modification, the entrepreneur must try to distil what the underlying problem is that they are trying to solve. By listening to suggested solutions, the entrepreneur risks making adjustments that bring the product closer to existing solutions in the market. Instead, simply understanding the problem and brainstorming creative solutions can lead to a better product.

Pick a diverse group of users: Early adopters of beta product versions tend to be power users who have very different needs to mainstream consumers. Whilst they are important advocates for the product, they will request too much functionality and so product refinement based on their feedback must be tempered. It may make sense to pick a diverse set of users with whom to test your product and solicit feedback. Furthermore, involving creative types (musicians, advertisers, artists) may lead to ideas that surprise you.

Rely more on tests further down the road: User feedback can be incredibly insightful when refining product features and improving the usability of a product, once the key elements of the business model have been nailed down.

User feedback definitely has a role to play in product development, but the smart entrepreneur will make sure that he filters out the noise.

Lean Startup Methodologies In Mobile Gaming

2012-02-12T12:22:00.000-08:00

by Terrence Gong

The gaming industry has demonstrated a full range of lean startup methodologies adoption. On one end, traditional console and pc games rarely use lean methods: games are hit-driven products with large budgets and develop periods with minimum customer feedback before release (for example, the recent online game, Star Wars: The Old Republic, is rumored to have cost around $200 million to develop). On the flipside, social gaming company, Zynga, utilizes lean-start up methodologies on a daily basis to test new product features by AB testing its large existing user-base (founder Mark Pincus even talks about “ghetto testing” new game ideas in this video). But, many gaming start-ups today focus on mobile gaming (games on iOS and Android platforms, such as Angry Birds and Tiny Tower). How can mobile games benefit from lean startup practices?

A major lean startup hurdle for mobile games is the difficulty in launching quickly to hypothesis test. The Apple iOS store, the market leader for paid apps, has a stringent quality control approval system for new apps. Thus, minimum viable product (mvp) testing on iOS devices is difficult since early game models might be rejected for quality reasons, or the approval process might waste valuable time and make it difficult to pivot and reiterate. Further, less rigorous mvp tests, like surveys, might not be helpful since game mechanics are difficult to convey to users without product videos or demos.

When visiting Silicon Valley in January, one interesting idea came up: mobile game makers can leverage the Android store’s less stringent quality control measures as a means to mvp test before releasing on the more lucrative iOS store. Since developers can put up as well as remove games quickly on the Android platform, game developers can release apps that test game mechanics to see if players find the premise of the game as fun. Then, once they have received enough feedback (metrics like number of levels played, or community score boards), they can then work to invest on building a story to wrap around the proven game mechanics before releasing on both iOS and Android platforms under a new name.

For example, let’s imagine how lean startup methodologies would have worked for the popular “Where’s My Water?” game made by Disney Mobile. This game involves complex water physics where you use touch gestures to navigate streams of water to fill a bathtub for a fictional alligator called “Swampy.” To make this game efficiently, developers could have released a mvp product on the Android platform that tested if people enjoyed trying to manipulate a water stream into a bucket. Through updates that could iteratively unlock new levels (to test different obstacles), the developers could have continued to pivot until game mechanics and level design were a product-market fit. Once reaching a point where the game mechanics have been proven, they could then hire designers to create a protagonist and story that makes sense in light of the game mechanics (the “Swampy” character and his desire to be clean). Then, finally they can submit the game through the iOS quality control pipeline and re-release their new game on the Android marketplace under a new branded title.

Overall, mobile gaming may not be as easy as social games for mvp testing and iteration since the go-to-market channels for the tests are less open, but current loopholes in the app store eco-system provide effective means to make lean startup methodologies economical and practical.

Not too much, not too little, just the right amount of lean. Use lean approaches to perfect lean approaches.

2012-02-12T12:21:00.003-08:00

by Meng Jiang

In my opinion, the worst nightmare for entrepreneurs would be that, after spending countless hours and consuming all resources, they have successfully completed a dream product, but only to find out that no one actually wants it. The lean startup approach is designed to make sure that this doesn’t happen. The minimal viable products are designed to test product market fit earlier on in the game to leave enough time and resources for the entrepreneurs to pivot to a “better” version if needed. The problems, however, are that the word minimal implies personal judgment and the fact that entrepreneurial processes are inherently buried with hurdles.

For teams that are experimenting with MVP for the first time, it is difficult to design a product that is truly both minimal and viable. If the products are too minimal, it would not serve the purpose of testing out the product’s concepts. Without a meaningful mockup, customers would not be able to understand the product’s real intention and may not provide feedbacks that are beneficial to the entrepreneur. Worse yet, these feedbacks may lead the entrepreneur down the wrong path.

On the other hand, if the products are too viable, then it would defeat the purpose of the MVP concept of fail early and fail often. External resources are extremely valuable to entrepreneurs and often makes the difference in either a certain product can cross the chasm or fall short during the last mile. It is therefore important that these resources are not wasted on purely learning exercises. This is preferred, however, to MVP being too minimal, when the downsides are compared.

The other deathtrap of leans startup approaches is forgetting that starting a company is not an easy feat. The MVP concepts are designed to inform the entrepreneurs if there are product-market misfits and if the business model need modified, but it is important not to take every hiccup along the way as a sign that it is time to pivot. Pivoting too fast and too often would also result in the startup burning through resources too quickly without creating any viable products. When time becomes tough, it doesn’t necessarily always mean that the concepts are flawed. It could be a range of other factors that need adjusted. Therefore It is important to spend necessary amount of energy trying to solve the problems before deciding it’ time to move on.

Things are easier said than done. In the end, these approaches require a significant amount of market understanding and self awareness to have a feeling of what is right. And just like everything in life, practice makes perfect. By the fourth time the entrepreneur uses MVP approaches, he/she would have already mastered the perfect balance, but this doesn’t mean he/she has to fail three times first. They could apply the MVP process to the MVP process itself when using it for the first time, stop often and ask themselves if the MVP they have just designed is too minimal or too viable and quickly come to the perfect amount of lean.

Referral Marketing is Sick - Use it to Go Viral

2012-02-12T12:21:00.002-08:00

by Jonathan Krieger

In startup circles Viral Marketing is often discussed as a low cost way to acquire new customers and rapidly scale a business.

David Skok of Matrix Partners uses this blog post to break viral marketing into two components:

Viral coefficient – Number of new customers acquired for each existing customer

Viral cycle-time – Length of time required to acquire a new customer

The higher the viral coefficient and lower the cycle time, the more “viral” the company and the faster it will grow.

One way that companies can impact their viral coefficient is through referral marketing. Referral marketing uses incentives to encourage specific customer behavior- in this case new customer acquisition. Referrals occur when an advocate recommends your product or service to a friend who becomes a new customer. A 2010 study found that 83% of satisfied customers reported a willingness to recommend services to others, however only 29% of customers actually do so. Referral marketing aims to close this gap, providing incentives that motivate the remaining 54% of satisfied customers to become advocates.

Referral incentives are the rewards offered to motivate advocates, and can come in a variety of forms:

Monetary incentives are easy-to-quantify rewards that can be directly linked to financial gain. These include cash, gift certificates, company credit, and future or retroactive rebates and discounts.

Non-monetary incentives are more opaque rewards that are still desired by your advocates. These may include special access to products, experiences (i.e. consultations/training) and information or public recognition and credit (e.g. badges/certificates).

Companies should use incentives that are:

Tailored to their target advocates
Encourage customer engagement and
Allow for profitable customer acquisition cost (CAC)

According to David Skok, best practice for recurring revenue businesses is to target CAC that is no more than 1/3rd of customer lifetime value (LTV)*

A University of Chicago study found that non-cash incentives were 24% more effective at boosting performance than cash incentives.* While cash may be a simple, and cost effective way to encourage referrals, companies should think critically about how they can move beyond cash to reward advocates in meaningful ways.

Cloud based storage service Dropbox provides a best-in-class example of how referrals can drive business growth. In April 2010 an impressive 35% of new Dropbox signups came through referrals.*** Their referral program offers an additional 250 MB of storage space to both the advocate and friend for each new signup. This reward meet’s all the aforementioned criteria. It is highly desired by Dropbox users who quickly fill-up the 2 GB included in their free trial. It also encourages deeper customer engagement; making Dropbox more “sticky” as customer use it to store and share more data, and it is extremely low-cost (approx. $0.014 per month – based on Amazon S3 pricing).

Dropbox also uses a very effective two-sided (win-win) reward system to give extra storage space to both advocates and friends. One of the main factors contributing to the success of referral programs is their ability to leverage pre-existing trust between advocates and friends. Introducing referral incentives to this relationship can create skepticism, distrust and misalignment. “Are you referring me to Dropbox because I’ll love it, or because they’re paying you to do so, and what does this say about our relationship?” Transparently rewarding both parties, resolves this problem, and according to a 2007 study in the Journal of Marketing this two-sided system is the preferred means of close-tie referrals.

By using incentives Dropbox increases the number of successful referrals and, thereby, it’s viral coefficient. Several other internet startups including Fab.com, Birchbox and Gilt Groupe also use effective referral marketing to drive business growth. While the success of referrals will vary across companies and industries, referral marketing can be a highly effective customer acquisition tool that deserves a place in any company’s marketing mix.

* Skok, D. “Startup Killer: the Cost of Customer Acquisition,” For Entrepreneurs, 2009.
** Scott, J., “The Benefits of Tangible Non-Monetary Incentives” University of Chicago, 2004. http://www.forentrepreneurs.com/startup-killer/
***Eisenmann, T. Pao, M, Barley, L. “Dropbox: “It Jut Works,” Harvard Business School, 2011.

The challenge of doing business development

2012-02-12T12:21:00.001-08:00

Two case protagonists (e.g., Dropbox, Cake) pointed out the challenge of doing business development deals with big corporations. Can we generalize about when this challenge is greatest? Do lean startups face special difficulties on this front?

by Krizia Li

“No significant tech company has been built solely through distribution deals without having a strong brand of its own.” – Drew Houston, Co-Founder and CEO of Dropbox

After surviving two stages of evolution in Dropbox’s lifecycle, Drew Houston pinpoints “brand” as one of the critical junctures where lean tech start-ups begin to tip the balance of power as they strike business development deals with large corporations. This key differentiator is public identity; the stronger your perceived brand equity, the more robust your bargaining leverage and protection vis-à-vis partners.

During early development stages for a lean startup, the desire to acquire user growth and operational scale via distribution deals using market-share dominant and resource-rich third parties can lead to increasing risk of loss of control during its structured “hypothesis-driven” entrepreneurship process. To illustrate such special difficulties, let’s use Dropbox’s experience as an unknown startup, as it pitches established PC security software providers to bundle-in its application:

Setting an overarching vision: Clients demand high levels of feature customization, which distracts a lean startup from perfecting its individual core processes, as dominant partners draw focus away from Dropbox’s broader user base towards separate brand-specific needs.

MVP testing: During the customization process, Dropbox cannot sequence accurate MVP tests to research its unique product-market fit, as its team focuses on supporting tangential industries or targeted demographic agendas set by dominant partners. As a consequence, Dropbox’s core business model can be led astray by initial hypothesis testing fallacies or bias.

Operational efficiency: Invaluable time, cash burn and human capital are wasted on evolving negotiations with debatable ROI, where it remains likely suboptimal financial and technological investments will be made for Dropbox as dominant partners usually contribute the lion’s share of operational advantages in the relationship.

Persevere, pivot or perish: Dominant partners control budget allocations and manage startups as units of a portfolio of brands, leading to ultimate dictation of lifecycle and strategic direction of Dropbox. Volatility caused by over-reliance on dominant partners amplifying shifting fundamentals may inflate, or restrict, vital alternatives lean startups face.

Idea theft, reputational risks: During the collaboration period, dominant partners can learn about proprietary IP, eventually negotiating to bury Dropbox in white label deals, which will destroy its nascent brand and commoditize future services.

In contrast, as Dropbox matures, PC and smartphone manufacturers propose to distribute its application via their sales channels. OEMs have switched to consider Dropbox as an essential tool for creating customer value and expanding customer acquisition funnels, thus revenues. Although Google / Android might move to crush Dropbox by duplicating online storage technology in the future, in our current state of play, active protection of Dropbox’s now respected trademark and defensible internal business development model is considered synergistic to its partners’ success.

Dropbox’s simple but proven takeaway on “branding” is one lesson Cake could have profited from if they had foresight to build such assumptions into its growth trajectory. Without decisive, rapid and focused reinforcement of a distinctive value proposition and coveted brand equity, early-stage startups are perceived as secondary priority for big firms and the weaker party during negotiations. By staying a steadfast course for intelligent and calculated sacrifice of early revenues, it is possible to buck the trend that “startups are bought, not sold”!

Lean Startup Movement Is Changing the Way Investors Think

2012-02-12T12:21:00.000-08:00

by Doris Lin

It has been difficult for entrepreneurs to break away from following the footsteps of a “visionary” entrepreneur to become a lean entrepreneur, because investors have been really attracted to backing individuals who possess visionary characteristics. Investors favored individuals who were passionate, optimistic and bold, many of which were qualities that exemplified the most successful entrepreneurs. The large emphasis investors placed on strength of the vision/idea in conjunction with the entrepreneur’s traits may be a function of the lack of a more scientific way to evaluate the likelihood of success of a venture - investors had to take more of a leap of faith.

The lean startup movement has now paved a way for investors to scientifically evaluate a start-up’s potential for success by shifting the focus from the vision to the execution/process. Thus, investors can evaluate the likelihood of success based on early evidence in the company’s ability to discover, learn and iterate through early, inexpensive tests focused on testing hypotheses. It becomes more about trusting the entrepreneur’s ability to execute the discovery/learning process and evaluating them based on the incremental progress made towards refining a vision rather than assessing the probability of achieving a stated vision delivered in a sales pitch.

Flexibility and openness to pivoting are essential in a lean startup, so investors will also need to adopt this in order for entrepreneurs to truly embrace the lean startup methodology. The benefit of having a flexible end vision is the opportunity for the company to focus on what matters most and quickly shift to a new strategy without the pressure to stay the course in fear of destroying credibility. If investors continue to focus primarily on the entrepreneur’s initial stated vision, there is more pressure for the entrepreneur to deliver that at all costs. This increases the likelihood that the entrepreneur falls into a distorted reality that could lead to failure.

This was evident in Steve Carpenter’s early mistake with Cake Financial where he failed to solicit user feedback and focused on obtaining comprehensive data for investment recommendations since he focused on proving his original value proposition. In contrast, Mint.com’s user-centric, flexible approach enabled them to hone in on the other features that proved to be more valuable to users. Even though Mint may not have delivered fully on their original value proposition, they were able to succeed by delivering on another vision that mattered more to users.

We see signs that investors will be focusing on investing in entrepreneurs mastering the lean process based on Carpenter’s recent raise of $4.25M for his latest startup Endorse (tested out in classic lean fashion). Investors can take more comfort in investing in lean entrepreneurs because even if the original vision is not achieved, the entrepreneurs are equipped with the framework/tools to adapt and achieve the right vision. As investors change their evaluation criteria in favor of lean, entrepreneurs should have an easier time adopting the lean methodology since they are forced to demonstrate traits of being both a visionary and a design-driven executor.

The Five Factors of Virality

2012-02-12T12:20:00.002-08:00

by Louis Beryl

One of the most difficult, and most prized, characteristics of new technology products is to achieve viral growth. Many entrepreneurs believe that the cleverness and value that their product delivers will allow the product to literally “sell itself” and spread quickly with low marketing and customer acquisition costs. However, this is not often the case. Reality suggests virality is quite rare and much more difficult to achieve than originally anticipated. I will explore what characteristics of the products themselves allow new products to diffuse rapidly and become viral based on Roger’s Five Factors.

Everett Rogers suggested five product-based factors that determine the rate of adoption in innovations, which are discussed in his book, “Diffusion of Innovations.” (brief summary) The five factors are:

Relative advantage – the degree to which a product is better than the product it replaces

Compatibility – the degree to which a product is consistent with existing values and experiences

Complexity – the degree to which a product is difficult to understand and use

Trialability – the degree to which a product may be experimented with on a limited basis

Observability – the degree to which product usage and impact are visible to others

Let’s look at the example of Dropbox and see how the five factors are able to explain their product’s ability to achieve viral growth.

Relative advantage: Dropbox made a clear value proposition to its customers over existing substitutes that your files will be with you all the time, organized, wherever you are. No more remembering your flash drive or emailing files to yourself. Dropbox wasn’t the first to have the idea of cloud file storage, but they were the first to fully integrate the product cleanly and simply across multiple operating systems and devices and insure that it worked all the time without ever corrupting or losing your files.

Compatibility: Dropbox worked the way people already worked by creating a simple file folder right on your computer, where you stored files in the exact same way you already did. Anyone familiar with a computer found this system naturally intuitive.

Complexity: The Dropbox team spent significant time insuring the product was incredibly simple. They studied user behavior rigorously, making sure that every step of the process, from finding where to download the application on the website, to the installation process on your computer, and ultimately to using the product itself was straightforward and naturally intuitive. “It Just Works.”

Trialability: Dropbox’s use of a freemium model, offering significant free storage to initial users before charging fees for heavy users, allowed user experimentation to be fast, cheap, and painless. Their offer to increase free storage with referrals helped new users experiment further with the sharing characteristics of the product that ultimately helped it achieve viral growth.

Observability: Although all of the other factors were influential in Dropbox’s rapid diffusion among users, the observable nature of the Dropbox product is what ultimately allowed it to achieve high virality. More impressive is that observability is not an inherent characteristic of the product they were selling. Before Dropbox how many people knew what type of storage someone else used on their computer? However, the ability to easily share files with other users on the Dropbox platform quickly exposed many other new users to the product giving it very high observability and important validation from friends and colleagues.

Conclusion

When thinking about the design of any new technology product it is important to remember all of these five factors. In Dropbox’s case it would have been easy to have high relative advantage and compatibility, easy trialability, and low complexity and still miss the importance of observability. This would have made a cool product that only a limited number of savvy tech users would have known about. The genius of the design was to enable non-users to experience the Dropbox platform and observe it for themselves. By doing this, along with nailing all the other factors, they were able to create a product with significant viral growth that quickly diffused to mainstream users.

What consumers want – do they even know?

2012-02-12T12:20:00.001-08:00

by Eilidh MacDonald

What is the best way to get feedback on your product? We all know what Steve Jobs thinks on this, with his famous BusinessWeek quote, “people don't know what they want until you show it to them.” But was he right? There is definitely mixed reviews on the topic of the most effective customer feedback mechanism. With the internet and mobile device breaking down geographical and time barriers it is much easier to reach your target customer. So with your new tech venture, where do you start in this outreach? Focus groups, one-on-one interviews, usability tests, surveys, website feedback forms, website blogs, smoke tests, etc. For a two main reasons, I think the most effective form of consumer feedback for tech ventures is the minimum-viable-product (MVP) and/or usability testing.

One: Time is money. In Triangulate, Sunil Nagaraj tried to enable dating using data collected from social networks. His premise that people would get better love matches if the information used to match is from a more accurate data baseline seems to make sense. And so Sunil set about building an algorithm to provide this service to consumers – the algorithm took time and money. It was only when that time and money ran out did Sunil realize one other factor that bubbles beneath his original premise, when it comes to dating people are vulnerable. As a result, people are simply not comfortable signing up for dating applications without serious consideration. Since his business model depended on viral network effects, he didn’t have time for this serious consideration after he had already spent so much time and money building the product. So could he have got to this outcome quicker? I think so. By building a minimum-viable-product and asking people to sign up and actually submit his/her facebook profile information could have been very revealing on people’s unwillingness to put him or herself out there. Sunil did use a smoke-test to test hits but he didn’t ask for any personal information. Had he done this, I think people’s vulnerability would have started to show earlier.

Two: You only get one chance. In the Cake Financial case the founder had a great idea in a large, underserved market. He put together a great team of financial backers and savvy advisors, and took part in TechCrunch40. But the first product he took to market, with a big, press-covered launch, wasn’t what the consumer wanted. As in the Triangulate case, a lot of time and money had already been spent. A big pivot was in order. By the second product launch, consumers were confused and it was too late. Steve Carpenter had used focus groups to get a grasp on what consumers wanted, but talk is cheap. Technology has enabled the western world to efficiently use time irrespective of locale, and as a result we have become used to doing everything through touch. Had he taken a MVP to market he would have learned some important lessons on what consumers actually wanted to see or have access to when making financial decisions, or what they are or are not willing to share with friends and family. Spending time on data aggregation rather than the end-consumer was a fatal mistake and one that could have been avoided if he had skipped a few focus groups and headed straight to the MVP stage. Let's hope the next entrant to the market does not make the same mistake.

How to bake a CAKE. When to enter a start up competition.

2012-02-12T12:20:00.000-08:00

by Funa Maduka

The protagonist of the CAKE financial case, Steve Carpenter, was presented with what many entrepreneurs would deem as a spectacular opportunity: to be selected for the first TechCrunch 40 competition. A major selection by a nationally recognized entity would undoubtedly bring media attention, expertise and future funders to CAKE’s doorstep. The “stamp of approval” would possibly also facilitate the fundraising process; after an uphill battle to secure Series A funding, Carpenter the competition proved a difficult temptation to resist.

What was most interesting about this opportunity was that the competition prohibited startups from releasing a public beta before entering the competition. Prior to considering the competition, Steve had already planned a series of phased releases of the CAKE product, specifically – “a private alpha site in in March 2007, a public beta in June 2007, and version 1.0 in November 2007.” Therefore Carpenter would ultimately have to scrap this plan and work on private iterations until TechCrunch judges could consider the product in September 2007.

So Carpenter scrapped the plan and entered the TechCrunch 40. CAKE ultimately did pretty well and received the media attention it had desired. Tens of thousands flooded its website as articles were written up about the technology in ZDnet tech blog and others. The problem was that none of these visitors stayed and as time went on, Carpenter began to realize more problems with his business model as the public beta began its “road test,” now after almost a year of intensive work. Because of the amount of time spent on building up the back end, it was not incredibly easy to go back and change the technology with the new features – in fact iterations could only be released on a monthly basis as opposed to every week. In the end CAKE crumbled. Albeit a valiant effort, but one that could have surely benefited from sticking to the original plan.

You can’t bake a cake in the microwave…

It’s common knowledge (well save anthropological evidence in a few college dormitories) that you can’t bake a cake in the microwave. By entering the TechCrunch competition, which hamstrung his ability to iterate, Carpenter essentially did just this.

The lesson we take from this case is that entrepreneurs should look at competitions as critically as they look at their other business decisions. Just because it has a big name, it does not mean that it is the right fit for your organization. In many ways the TechCrunch40 was not right for CAKE at all. Carpenter needed to go through the various phases to understand certain components about customer behavior, network effects and the opportunity to explore front-end development sooner. By releasing it into the market place after so much work had been done, he faced the unenviable challenge of turning a chocolate cake into a vanilla one.

In doing research for this piece, I came across a post by Linda, who writes for the blog Plano and Simple, she had awesome advice about the questions entrepreneurs should ask themselves to ensure that the competition and the business are the right match (at the right time).

Link here! http://www.planoandsimple.com/lindas-blog/are-business-plan-competitions-worth-entering-part-2-6-questions-ask-yourself

Is there such a thing as bad publicity?

2012-02-12T12:13:00.000-08:00

By Sahar Meghani

Just this month, a new study at the Stanford GSB looked at book reviews in the New York Times and found that even when reviews were unfavorable, authors that were previously unknown saw a 30% - 50% bump in sales, simply because the PR generated product awareness. In a startup context, though it is often argued that obscurity can be an asset for entrepreneurs (allowing for learning and failure in private), I generally tend to agree with the saying that all publicity is good publicity!

Of course, startups must focus on their core competencies, refine and test their hypotheses and iterate after learning from customers and I am not advocating shortchanging any of those steps. However, from a marketing and customer acquisition standpoint, the benefits that a high-profile launch at SXSW or at TechCrunch50 can provide are unparalleled and extremely rare. Startups can see a dramatic spike in early adopters, get investor attention and gain legitimacy which can be critical for attracting talent and developing relationships with partners, vendor and suppliers. The resulting momentum can also serve as a powerful motivator for the team members.

Today, when pivots are almost a necessary step in the startup process, I question the assertion that startups only have one real round of PR. When Path, the social sharing startup, released its second-iteration app which included a significant shift in its product, it received tremendous press and experienced a 30x growth in its user base. In fact, the TechCrunch reporter wrote: “There is something about waking up day after day to write about people who take risks; You end up rooting for some of them.” Scrolling through some of the user comments in that article revealed sentiments such as: “[I] didn't care for Path when it first came out, but it's much better and prettier now.” And “I hated the first version, I love this one!” In fact, most of the press about Path is around its improvement. Consumers are being conditioned to be forgiving. I feel that as long as startups have a personality, are transparent, promise only what they can reliably deliver, learn from mistakes, and truly provide value to their consumers, the rounds of PR will follow.

The one instance where I think a public launch strategy could potentially be tough is when a startup is entering a space with an established incumbent and is seen to be provoking or breaking the rules. LinkedIn’s decision last summer to cut off API access to startups like BranchOut, a professional networking startup built on the Facebook platform, that launched very publicly and then violated its Terms of Use agreement, isn’t surprising. Neither is Twitter’s debacle with UberMedia in which Twitter shut down API access. There is absolutely something to be said about operating in stealth mode. However, in the case of BranchOut, I would argue that by publicly launching, the startup has benefited tremendously – monthly users grew 25X soon after launching even though the product in my opinion is buggy and lacking critical functionality. Most importantly, the startup has claimed competitor status and virtually guaranteed a mention in most articles that cover LinkedIn - the results of which could never be matched by operating under the radar.

The Value of a Twitter Follower

2011-05-19T17:33:00.000-07:00

by Puja Ramani and Sampada Telang

What we learned from applying the life time value analysis as well as engagement funnel analysis techniques for Twitter.

(Note: if the graphics below are difficult to read, you can download a PDF version of this post here).

Our goal was to estimate the value of a Twitter follower for various publishers on Twitter. ‘Publisher’ refers to any entity with a business objective on Twitter (examples: Amazon, TechCrunch, celebrities or even local businesses). Given that Twitter primarily earns its revenues by charging publishers (via promoted tweets) as opposed to individual profile owners, we will refer to Twitter’s publishers as its ‘customers’.

Our focus was applying the tool not to Twitter’s customers (i.e., publishers) directly, but to Twitter’s customers’ customers (i.e., publishers' followers) in an attempt to quantify how much value Twitter was creating for publishers. We studied a very small set of publishers, so these findings shouldn’t be taken as definitive averages but rather, used directionally.

Value of a tweet: A key source of interaction between the publisher and its Twitter followers is the ongoing stream of tweets that the publisher sends out via its profile. After numerous conversations with Twitter’s publishers, we discovered that the primary source of value created on the Twitter platform is via ongoing tweets, not promoted tweets. Thus, value of a tweet is central to determining value of a follower.

Hence, our analysis was anchored around determining the life-cycle of a tweet, the engagement that an average tweet creates and the total clicks it drives.

1. Summary of key learnings

1.1 Engagement Funnel

For Twitter’s publishers we define the engagement funnel as the total engagement created by a tweet — through follower retweets, favorites etc. and the rate at which that engagement drives clicks.

Exhibit 1: Engagement funnel

We analyzed the conversion funnel for e-commerce and content publishers and discovered the following:

Tweet click-through rates are defined based on engagement instead of impressions: The standard definition for ‘click-through rate’ is Total number of clicks divided by Total number of impressions. However, it is difficult to reliably estimate tweet impressions due to the chronological ordering of tweets on a follower’s Twitter home page. The only evidence of impression is the engagement or interaction a follower has with a tweet. Thus, click through for tweets = Total clicks divided by Total tweet engagement.
Most engagement on Twitter is in form of clicks and not retweets, favorites etc.: Clicks comprise ~90% of total engagement on Twitter. Surprisingly, this is true not only for e-commerce but also for content publishers.
Content publishers get a higher total number of tweet clicks: This is not surprising, because most followers of content sites want to click on their tweets to stay abreast on news etc., but might be less likely to click on a “deal” tweet from an ecommerce publisher, unless they are really interested in purchasing that item.
Follower quality matters more than quantity: We noticed that a larger number of followers doesn’t necessarily result in more clicks. In fact, a lower number of high quality, relevant followers could generate a higher click rate.
Clicks per tweet are dependent on a number of factors such follower quality, tweet quality, and tweet frequency.
Publishers range from sophisticated (eg- Amazon) with separate handles for each category of products) to barely Twitter literate -- companies that use the same handle for customer service, marketing and specific deals — resulting in poor targeting, low clicks and high unfollows.
The key to improving click-through is effective targeting i.e. create specific handles with a homogenous set of tweets targeted at a specific set of followers (based on interests eg- tech tribe/early adopters or demographics – eg- young women

1.2 Life time value analysis

Life time value of a follower depends on the monetization technique of the publisher – ecommerce, SaaS or ad impressions.
For each publisher, life time value isn’t as simple as 1/churn rate x value per day, but as the methodology below highlights, needs to take into account several other factors.
There is a lot of information asymmetry in social media. There is an opportunity for Twitter and publishers to collaborate, sharing data and analytics, to jointly understand the value of Twitter. More clarity on value creation will certainly enhance Twitter’s ability to capture value (i.e., monetize its user base). At the same time, it will help publishers to use Twitter’s platform much more effectively, thereby increasing their returns.
Similarly, an opportunity exists to extend the Twitter API further to understand vital engagement stats. However, currently the Twitter search API is limited and can only provide a subset of tweet data, making it difficult to build tools on top of it.

2. Methodology and analysis conducted

2.1 Publisher classification

We classified publishers into four buckets based on goals on Twitter. For the purposes of our study we only focused on E-commerce and Content publishers.

Exhibit 2: Publisher classification

2.2 Value of a tweet for e-commerce and content

We defined the value for e-commerce players as shown below.

Exhibit 3: Value of a tweet

2.3 Sample engagement funnels

We worked with Twitter to obtain tweet engagement data for select set of publishers. Below are conversion funnels for e-commerce and content. We obtained click data from bitly wherever possible, but this analysis hinges on the validity of that click data. Based on our observation, these funnels are representative of the category. For example, independent of number of followers, the 4 e-commerce publishers we studied, on an average, generate less than 50 clicks per deal tweet.

Exhibit 4: Sample engagement funnels

2.4 Value per deal tweet for E-commerce publishers

To determine the value for e-commerce players – we deconstructed the value per tweet as follows. We vetted the math below by speaking with multiple publishers.

We realized that publishers capture the click and conversion data via Google Analytics but were unwilling to share it, thus we obtained click data from publicly available source such as bitly.

Exhibit 5: Value from a deal tweet

Below is a sample analysis for a daily deals site. For each item above, we used a combination of various inputs – example we obtained click data from bitly, conversion data from analytics company presentation at Web 2.0 and margin data from company 10 Ks. Deal size and Tweet rate were obtained from tracking the publicly available tweet data.

Note: Estimates below are conservative as we’ve only looked at deal tweets and value created through immediate deal fulfillment. What we haven’t included is as follows –
Purchase of additional items beyond those linked to the deal on the tweet
Additional followers obtained due to retweets, favorites etc.
Value obtained through marketing campaign tweets and customer service tweets

Exhibit 6: Sample analysis for a daily deal site

The value is different for different e-commerce players and local businesses due to differences in business models. Example: For every dollar of sale, Amazon needs to factor in inventory management and delivery costs but Groupon does not. Thus, their margins are very different.

Hence, we built out sensitivity tables for different e-commerce players

Exhibit 7: Value from deal tweets to different types of e-commerce publishers on Twitter

Prototyping: Just Get It Out There

2011-05-05T18:58:00.000-07:00

by Stirling Cox

The idea of a product that would enable commuters in a given city to access information on the location of their buses, has been occupying my thoughts since the summer of last year. With a background in Banking and Consulting, what had been bashed into my head through various training programs and countless deals and projects is that you need to be 100% sure of your facts before opening your mouth. As such I was convinced that it was only through significant primary research and presentation that I would ever be able to prove the viability of my idea and start a business.

I spent much time during the end of the summer and fall term of EC year researching number of commuters, their habits, Smartphone proliferation, looking at different markets that product could be applied to, producing possible revenue models in Excel, preparing financial statements and fancy PowerPoint slides explaining the business. Years of “bad” entrepreneurial practice had me convinced that I needed to have all research completely covered before even approaching possible users or potential funders.

By Christmas time, whilst I had actually spent hours on this idea, I still actually had nothing to show for it. Everytime I discussed the idea with someone and showed them my swanky PowerPoint deck, they loved it, but this was no different from 5 months prior when I voiced over the idea in 30 seconds.

Getting to grasps with the concepts of “just get it out there” or “just get on and do it” was new to me. It seemed irrational that I should start off with a half baked product, a product that wasn’t perfect, a product that may not work in its first version. But countless tales and case examples of Millionaire entrepreneurs taking this approach made me think there was a faint chance that I may be round. After much internal kicking and screaming to stick to what I knew how to do, I decided to give it a go, and developed what I viewed as a first iteration of the product.

Not having any development skills, I built with some assistance (and a tool called SWiSH) a flash live video demonstration of the product. Whilst this was not a formal website or application, nor was it plugged in to any data, this demo now allowed me to showcase to whoever I was discussing the concept with, really what the product would look like, what it would feel like, and demonstrate various capabilities through playing with the video.

Whilst this first step was definitely a step in the right direction, I now realize this was barely a prototype. People saw this and said: “Awesome, can I use it?” This was not a product, merely an enhanced graphical representation of what I was imagining.

My next step is to actually build the first real live version of the prototype. Having learned that I actually really needed to have something usable, I scraped the initial inclination to get a developer to build an application that was not plugged in to any real time data. Whilst this would be a real product, it would not be something that users could use and give feedback on. I also scraped my next idea which was endless discussions with London to open up their data so that I could build the product on their systems.

Instead, after what is now seeming like a no brainer, and a 1 year after the initial idea, I have finally found what may be a useful direction to take the business forward: leverage what is already available. As it turns out, the MBTA in Boston opened up at the start of 2011 all their data on bus positioning to the development community. This provides the perfect testing ground for the product. I am presently in talks with a developer, who has built similar geopositioning applications, about finally getting the first real prototype up and running for Boston. Whilst there are other players out there in the market, Boston provides a perfect testing ground for the first prototype. We are hoping within the next month or so to have an application out there that customers can really play with, try, and critique; an application upon which we can iterate and draw conclusions as to the viability of the idea.

Talking with people who have not taking the course, I realize that most people face this same problem. Lots of people have business ideas, and 90% of them don’t do anything at all with them; they just horde the idea. Of the remaining 10%, the most ambitious 9% spend time researching a market, preparing fancy documents and excel models, but never really get the business launched. The 1% that launch a product are not afraid of it failing, are not afraid of getting it wrong, are not afraid of criticism. They have realized that their business will never get off the ground sat in front of their computer researching the internet or pulling together fancy presentations. Ex-Consultants and Bankers who want to be entrepreneurs… you are going to have to undo a lot of bad practices.

Beware of the Word "Interesting"

2011-05-05T15:15:00.000-07:00

by Evan Shore (republished from his personal blog)

Towards the end of our conversation, Jeff Immelt patted me on the back and said, “Interesting.” After I probed further, he continued, “It’s a good idea, and I think I can see the benefit. I just need to think more about it.” I wondered, as I did during the past several conversations with potential customers, how I could advance from hearing “interesting” to “interested.”

CONTEXT: CUSTOMER DISCOVERY INTERVIEWS

Several months ago, I set out on a quest to understand the specific pain-points that corporations face when it comes to Innovation. I interviewed the CEOs and C-Suite executives of over 40 companies. Most executives identified the same problem: “The single greatest challenge for us is identifying and executing innovations outside the boundaries of our business models.”

I had a vision for a new type of open innovation intermediary that stemmed from my exposure to IDEO, Innocentive, Gen3 Partners, contests like GE’s Ecomagination Challenge, and other platforms that enable corporations to externally-source ideas and IP/technology. I hypothesized that implementation, not ideas, was the main hurdle that corporations faced. I envisioned an intermediary that would enable corporations to use open innovation techniques to effectively crowd-source the execution of new businesses (not just ideas) that fall outside the scope of their capabilities and focus. This is the idea that Jeff Immelt thought was so “interesting.”

WHAT DOES THE WORD “INTERESTING” MEAN?

Interesting means you have not yet found product-market fit. Someone on the GE team commented, “Interesting means it just passed the laugh test.” It means “intellectually stimulating, but I don’t feel in my gut exactly how this applies to me.” If you hear the word, your idea resonates logically with your audience and is likely a novel idea, but they are still processing the information in their heads. You are not speaking with a “mission-critical” customer desperate for your solution, and the value proposition of your offering is not yet specific, tangible, and easy to blend into the audience’s current way of doing things.

WHEN POTENTIAL CUSTOMERS SAY INTERESTING…

1) Check to see if you are speaking with the right person.

In some cases, I was not speaking with the people directly responsible for innovation or growth strategy. Therefore, they did not feel pain points directly, and our conversations occurred just on an intellectual level.

2) Pivot using the Innovator’s Cube.

Three things must be aligned to achieve product-market fit:

A firm understanding of the problem-to-solve (pain points)
Specific selection of customers (ranked on degree of problem severity)
Products/services with a clear value proposition specifically addressing these problems

The word “interesting” indicates that you have not yet found the right box on the Innovator’s Cube:

3) Target different customers or segment your customers more specifically, looking for indications of “must-have” rather than “nice-to-have.”

At first, I did not know who my initial customers would be. I actually started to characterize firms based on whether the word “interesting” was said in the interview.

I discovered that the degree to which customers 1) have a problem, 2) realize they have the problem, 3) are actively looking for a solution, or 4) have access to a solution depends largely on both their size and stage in the lifecycle of their product(s). Fast-growing entrepreneurial firms were so focused on growing their core that they did not recognize any problems. Larger firms generally had the capabilities to manage multiple business units and therefore execute out-of-the-box businesses through Clayton Christensen’s “Innovator’s Solution” (create a separate business unit to explore new territories). The only potential customers who did not say “Interesting,” but rather “Got it. I need this” were those who managed mid-sized firms with declining growth. I hypothesized that because these firms did not have the experience or bandwidth to set up their own “separate group,” these firms would be the “earlyvangelists” for whom Crowd-Sourcing the Innovator’s Solution would be “mission critical.”

4) Probe deeper for customers’ pain points.

“Interesting” means the person does not see how your product fits with their needs. I found that asking CEOs to tell stories about their experiences with out-of-the-box innovation was a helpful way to initially define pain points, and later to qualify potential customers. I usually heard “interesting” when customer needs did not match up with my product vision. After hearing “interesting,” I knew I could gain valuable information that could be useful not only in selling to them later on, but also in determining if they had any “must have” pain points that I could address with a different product.

5) Ask for their concerns.

Even if the product does address their specific pain points, potential customers might say the word “interesting” if they perceive that the proposed solution may create secondary, undesirable consequences.

Some sources of resistance that I encountered involved: IP protection, cultural and organizational barriers to open innovation (e.g., not-invented-here syndrome), the potential to reveal strategically-sensitive information to competitors, an unwillingness to allocate time or resources on initiatives outside the core businesses, an insular sense that “we are already innovative,” a value-claiming mindset resistant to sharing some value with other players in order to grow the pie, and whether corporations have the vision to tightly define out-of-the-box problems for others to solve. All of these issues were usually resolved after additional discussion but may have accounted for the high frequency of the word “interesting.”

6) Refine your product vision, and build a prototype.

Eric Ries said, “You should sell your product before you build it.” However, simply describing my product verbally resulted in a number of “interesting” responses, as people had difficulty envisioning the product. I built prototypes to communicate the vision more tangibly. As I learned more about potential customers’ pain points and probed into their objections (i.e., why my product does not solve their problems), I pivoted around different product permutations.

Three of the four iterations of my product vision included:

An incubator for corporate innovations,
A website that aggregates corporate contests (such as GE’s Ecomagination Challenge and Avery Dennison’s website for idea submissions), and
A consulting firm that facilitates Collaborative Innovation among corporate clients, entrepreneurs, and other institutions (discussed below).

Presenting these ideas using websites and PowerPoint decks provided valuable conversation pieces for my interviews.

Each product iteration had its own set of hypotheses about how to solve problems for particular customers. For example, the contest aggregator solves the problem that smaller corporations may face of not garnering enough participation/awareness for their innovation challenges (in addition to the overarching Innovator’s Dilemma problem). GE does not feel this pain point acutely because it has the prestige and size to garner enough submissions for its Ecomagination Challenge. Therefore, Jeff Immelt’s reaction to this particular idea was that it is “interesting.” By contrast, some of the smaller firms I spoke with said, “My firm is facing this exact issue. I like this a lot.”

WHEN POTENTIAL CORPORATE PARTNERS SAY INTERESTING…

The hurdle to overcome “interesting” when pursuing partnerships is much higher than attracting initial customers, because it is not their problem that you are trying to solve. Instead, potential partners view the partnership as a potential opportunity, rather than the resolution of a pain point.

1) The proposed business model must fit snugly into the operations of the potential partner.

I pitched one of the large consulting companies to start its Collaborative Innovation Practice based on this concept. The business model I had initially envisioned conflicted with its fee-for-service consulting model, and I heard the word “interesting” in my initial meeting. It was tempting to remain firm with my product vision, but I felt it was important to leverage the firm’s client base and saw alternative models for implementation. I pivoted the product concept to still deliver on the desired customer value proposition, but in line with the consulting firm’s business model. I did not hear the word “interesting” in my next meeting and the idea is now gaining internal momentum.

2) There is a higher hurdle for minimum viable product quality. The final presentation to senior management of the consulting firm included a fully-baked plan for implementation, including exact service offerings, points of accountability within the firm, and expected client pricing structures. This hurdle could potentially limit the ability to pivot once approved internally but was necessary in order to have a chance of implementation.

3) Involve a champion at the partner firm whose role and personal motivations are aligned with your business.

I involved someone very early on in the process who had credibility with senior management, knew how to navigate the politics of the firm, and whose interests were fully aligned.

I would appreciate your feedback on this blog post, as long as you do not tell me it is “interesting.”

Bridging Two Classes

2011-05-05T10:04:00.000-07:00

by Douglas Romanoff

My final semester at HBS was a unique experience studying the business models of technology‐based ventures from two different perspectives. From late March to May, Competing Through Business Models (CTBM) class with Professor Hanna Halaburda equipped me with frameworks for how firms create and sustain competitive advantage through their business model designs. From January to early March, Launching Tech Ventures (LTV) class with Professor Tom Eisenmann exposed me to the approaches entrepreneurs use to erect attractive and scalable business models from scratch.

Despite the clear interrelatedness of the two topics, the content of the courses could not have been more different. CTBM defines a business model as the logic of a firm, the way it operates to create and capture value for its stakeholders. Expressed as a set of policy, asset, and governance choices (and the consequences derived from those choices), business models are most effective when they support virtuous loops of value creation aligned with a company’s mission. The diagram below captures a simplified representation of the business model for Microsoft’s operating systems and productivity applications business. Here, management decides to set low prices for its operating system, set high prices for its applications, and invest heavily in R&D for next generation operating systems, putting in motion a series of consequences that are self‐reinforcing and defensible, creating a sustainable business model.

Source: CTBM Introductory Note, Jan. 2011

In the world of CTBM, analytical techniques such as game theory and classic optimization are the tools of choice for decision‐making. Changing the pricing scheme is about finding the Nash Equilibrium. Augmenting the product portfolio is about evaluating and optimizing the payoff matrix. The CTBM toolset must be familiar to any entrepreneur building a new business model in practice, right?

Absolutely not. Instead, we see in LTV that the Lean Start Up Methodology is the approach preferred by many experienced entrepreneurs and venture capitalists. According to this methodology, entrepreneurs should not try to abstractly design the perfect business model in a top down fashion. Rather, entrepreneurs should launch stripped‐down versions of key business model elements piece‐by‐piece, building up gradually. And entrepreneurs should not seek to predict what will work based on purely analytical models. They should leverage “hunch” to develop working hypotheses, then rapidly iterate through cycles of trial and error. Successful business models are more often discovered or nurtured rather than designed in their entirety and set in motion with a few tactical decisions.

So why the fundamental disconnect? The answer seems to lie in the fact that business model design from scratch is a messy undertaking. The number of decisions that must be made is often astounding. The interactions between various sets of choices are often complex. Competitive dynamics are constantly altering the viability of certain approaches. In situations of such uncertainty, designing a business model is a highly strategic exercise that does not lend itself to white‐boarding in a vacuum or deriving purely analytical solutions. Instead, one increases the likelihood of success through a balance of other factors. Experience accumulated over time provides the intuition to develop accurate hypotheses about what works and what does not. Experimentation provides a constant stream of market feedback to update and refine initial assumptions.

The Lean Start Up Methodology works because it prescribes how the entrepreneur should apply these factors in a manner that best conserves cost and risk. The CTBM toolkit, in contrast, is best applied in situations where the uncertainty is more manageable—e.g., to identify incremental improvements to business models that are already in operation. Here, the interactions between choices are better understood, and the range of outcomes more limited. In such a case, decisionmaking takes place at the tactical (not the strategic) level, making analytical approaches more tractable.

The Lean Start Up Methodology is less appropriate in such a situation; while experience and experimentation are still fundamental elements of the business model design process, analysis rises in importance as the scope of uncertainty becomes more manageable. LTV and CTBM each provide frameworks for addressing the challenge of designing business models for technology companies. Contrasting the two clarifies the context in which each toolkit is appropriate.

Inbound Marketing Lessons Learned

2011-05-05T09:02:00.001-07:00

by Chris Tam & Kathy Wang

Our blog was by far our most effective tool of inbound marketing and during the initial phase of growth was the top landing page for our site, versus the actual website itself.

What was great about blogging as an inbound marketing tool:

Blog posts are easily shared. All we needed was a few “influencers” on the web to find our content interesting, pass it along, and we got huge spikes in traffic. Though our traffic figures eventually stabilized after these “spikes,” each time our content was shared, some of the new readers to our site stayed, explored, and checked out our main landing page.

You can also use blogs to help “exchange” traffic. It was difficult for us to convince a successful blogger or content producer to plug heroiQ or include a link to us on their site - after all, our main landing page isn’t going to be a muddled list of a bunch of links. But a blog is perfect for that - we would link our blog posts to other blogs we liked, and some of them in turn did the same for us - “sharing traffic.”

Blogs are continually updated. If someone were to just go to www .theheroiq .com every day, they would see the same content, over and over again. There’s nothing dynamic about a landing page like that. Conversely, our blog was updated on average five times a week, so readers knew that they could come back and always see new content (and then perhaps wander back over to our main landing page). It’s also a great way to get yourself bookmarked - we currently have 450 subscribers on Google Reader alone.

What wasn’t so great about blogging as an inbound marketing tool:

Content, especially original content, is hard. It isn’t an easy task to continually entertain your audience with unique and original content, and anonymous readers on the internet are some of the most picky. They aren’t like your friends, who may stick around out of politeness - bore them a few times, and you’ll find yourself un-bookmarked.

It can be difficult to commercialize your main product: Successful inbound marketing blogs - such as OK Cupid’s for example - offer interesting content that’s easily shared. But it can be difficult to lead readers from your blog directly back to your site. Blogs can and will increase general traffic to your site. But to do anything above and beyond that through your blog is difficult. Readers hate “gimmicks” or feeling that they’re being used or pushed too hard to try out your product. OK Cupid usually never includes links to sign up for its dating site on its enormously successful blog posts for this reason. We encountered the same difficulty.

But isn’t blogging dead? (see here, here, and here)

Blogging is far from dead. In our experience, Twitter and Facebook were also useful tools - we utilized both and generated a following on both platforms. But the end goal was to lead these users back to the heroiQ site - and our draw ended up being our blog posts! It’s hard to keep putting up the same link (www.theheroiq.com) and expect your Twitter/Facebook followers to keep clicking. You need to continuous, new, fresh content - blogs are perfect for that.

See all these spikes? They were from blog posts!

Five Tips to Boost User Conversion Through Valuable Content

2011-05-05T08:07:00.000-07:00

by Douglas Melchior

What kind of content will drive clicks and convert visitors into users or customers? I posed this question to marketing directors at five different consumer Internet companies, including Brett Below at Rent the Runway, Andrew Sinkov of Evernote, Liz Wald of Etsy, and Shawna Strayhorn of Refinery29. I overlaid these practical findings with research from Brian Halligan at Hubspot.

Content can be anywhere on the website, and is viewed by search engines and humans alike. Content on the homepage gets the most views, but it has to be useful to people and peak interest immediately. Here are 5 strategic tips to maximize the value of content on your site and outbound communication:

1) Write Content for All Sides of the Platform. At Etsy, the marketplace for handmade goods, the marketing team creates content for both buyers and sellers. Use these two types of content to engage a small but active buyer community, and to attract sellers with the benefits and best practices of your platform. Writing for sellers will help frame your business as an opportunity to the seller rather than a margin muncher or competitive threat.

2) Integrate User-Generated Content into Your Site. The most effective Web 2.0 sites engage users in a framed environment. Get users to write about their success stories or uses of your product and include that content on your site. For Rent the Runway, that content might include wedding coverage detailing the bridesmaids’ dresses or crowdsourcing a look for this year’s prom.

3) A Picture is Worth a Thousand Words. Graphics-heavy emails, websites, and conversion pages are substantially more likely to get user conversions. If you’re website is about handmade crafts, for example, include plenty of pictures of those crafts to get higher conversion. In the world of instant gratification and massive media consumption, pictures peak interest a lot more than craftily assembled words.

4) Tell, Don’t Sell. Endless selling of the website’s merchandise won’t drive traffic. For email communication, focus on industry tips and picks rather than on individual merchandise. Click-through rates can double when the messaging is about advice rather than specific items. For email communication, focus on items that everyone buys often. This will keep interest high and allow for future upselling.

5) Find a Partner and Exchange Mailing Lists. Partnerships with well-known bloggers and industry experts are an excellent way to boost your credibility and mailing list. Find an industry blogger, and pay him/her to write an article on your behalf and send to both of your mailing lists. Ask for a reciprocal link, and chances are you will get a boost to your subscriber base, while mutually benefiting both parties’ brand images.

The Science of Business and the Business of Science

2011-05-05T07:34:00.000-07:00

by Douglas Romanoff

Eric Ries champions the Lean Start Up Methodology as a means for entrepreneurs to more efficiently manage financial and human resources while increasing their odds of success. At the root of his approach is the idea that entrepreneurs should measure progress in units of validated learning, rapidly iterating through build‐measure‐learn cycles designed to test hypotheses and refine business model elements. By avoiding excessive subjectivity, and instead gathering empirical and measurable evidence, the entrepreneur operates with less risk and more cost effectiveness.

Although Toyota’s management philosophy is credited as the inspiration for lean concepts, the true origins of the approach lie in a discipline outside the realm of business. The Scientific Method has served as the gold standard of systematic inquiry for chemists, biologists, and physicists over many centuries. According to its tenets, researchers should gradually refine elements of a scientific model through a series of structured experiments, obtaining measurable data to validate clearly articulated hypotheses. Sound familiar? The researcher’s scientific model is the entrepreneur’s business model; the researcher’s test tube, the entrepreneur’s Minimum Viable Product (MVP). So Eric Reis’s breakthrough is not in the creation of the Lean Start Up Methodology itself, but rather in borrowing the method from science and applying it to entrepreneurship, reframing its underlying concepts in the nomenclature of business. His insight is that both cutting edge entrepreneurship and cutting edge science are experimental endeavors, and can share techniques to investigate poorly understood phenomena, acquire new knowledge, and integrate that new knowledge to refine previously held beliefs.

If we agree that Lean Start Up Methodology originates in the techniques of science, there is irony in the notion that it does not apply to science‐driven businesses. In his blog, Union Square Ventures partner Fred Wilson argues that there is a growing cleavage in the venture capital industry between businesses such as software and businesses such as clean technology. Many students in Launching Tech Ventures would seem to agree, arguing in class that lean approaches do not apply to startups involving substantial technical risks and manufacturing assets. Fast product development cycles are not practical and customer feedback is less relevant for such business, they might add. In brief, the science of business does not appear to apply to the business of science.

I argue that this view is by and large misguided, though not altogether without its merits. The first fault in such a line of thinking is definitional. Cleantech is not a monolithic and uniform industry of its own, but rather an umbrella term that encompasses energy generation, transportation, telecommunications, and other verticals. Each of these verticals supports an array of business models with very different economics. In fact, there are subsectors and segments of the value chain in cleantech that are more similar to information technology, services, or electronics than not—consider OPower, SunRun, and Enphase. All lean concepts that apply in these more familiar industries apply to related spheres of cleantech. Comments about the applicability of the Lean Start Up Methodology to cleantech businesses therefore err by generalizing to broadly.

In keeping with the underlying spirit of such arguments, however, let’s focus on a subset of cleantech that is driven by science: energy storage. Battery manufacturing involves the technical risks and physical assets described earlier, and as a result does not lend itself to rapid iteration to the same extent as, say, developing brokerage software for real estate agents. Upon closer inspection, however, this line of thinking confuses cheapness with leanness by using an inappropriate benchmark for cleantech. The Lean Start Up Methodology does not promise to level the playing field between Aquion and RentJuice with regards to funding requirements. A new software application is in most cases cheaper (e.g., requires less capital) to develop than a new battery chemistry. Game over.

But being lean is not about being cheap—it’s about being efficient with resources. By applying the concepts of the Lean Start Up Methodology, Aquion has more efficiently used its resources while increasing its odds of success relative to other battery start ups operating under the status quo. Founders Jay Whitacre and Ted Wiley stage technology development through a series of MVPs (e.g., bare bones R&D solutions that are “good enough”) designed to validate key hypotheses (e.g., potential product performance and manufacturability) with the smallest set of product features (e.g., precision only to the degree that is necessary, and no more). The result is rapid iteration by battery development standards and greater capital efficiency relative to peers in the same industry.

Clean technology and leanness to are two concepts that seem to invite misunderstanding and misuse. By applying them both with greater precision, I believe we reveal more clearly where the one relates to the other.

Creating Culture Through Prototype Development

2011-05-05T07:18:00.000-07:00

by Aunim Hossain

Baller. You’ve spent months running around, talking to everyone you know (or don’t know), attending conferences, scouring the online job resources…all with the goal of building your dream team that will take this from a great idea to a company that will change the world. And you rocked it. You have the team you need: one product manager (you), three developers with front-end and back-end skills, and one artist/UI guru.

You feel like you’ve arrived. You say to yourself, “now, it’s all about getting it done, but that should be pretty simple with the team of ninjas I assembled, right?” But this is where many promising founders stumble. Even with the right team in place, success is not guaranteed. While there are many variables that determine success, one of the most important variables that founders can control (and often don’t) is building the right culture.

Culture is created through the processes that founders and the team members put in place to complete tasks. As those tasks are completed, the team figures out which processes work and which should be revised. When a particular process becomes second nature for the team to complete a certain task, it has effectively become culture. As founders, it is our job to guide the creation of the best processes and make sure that the processes that become culture are the right ones for the team.

In this post, I hope to start a list of the best processes that have come to define the culture of our team. In this first try, I’ll talk about our experiences with communication and time management.

So tell me what you want, what you really really want…

Oh those Spice Girls were sages. And it makes sense that they knew what they were talking about when it comes to communication: they were a startup had to manage a team of five divas. In our startup, with my own team of seven divas, we thought that putting everyone in a room together would ensure strong levels of communication. But several major problems from spec misunderstandings, to code deployment issues, to artist delays continued to creep into our work hours. In the end, we figured out that the fundamental issue was a lack of communication. Although we’re still working a lot of these issues out, here are a few processes that we’ve put in place that will hopefully create a culture of over-communication within our team:

Create a Backlog of Detailed Specs: As the product person, my ultimate goal is to take the idea that is trapped in my head and make it real. But because I need help from other people to make this happen, my first task is to take the idea that is in my head and put it in the heads of everyone on the team. Now before we figure out how to really do mind-melds, we have to rely on specs. So make your specs as detailed as possible. Don’t assume that your team will fill in the details, because where there is uncertainty, there is room for miscommunication. Also, if there is a feature that is important in the future, don’t wait to write the spec. It is critical to build a long backlog of specs so that the team knows where the product is going and they can build functionality to be flexible and accommodate what’s coming next.

Daily Scrums: Although we started this practice on our first day and most teams use this process, I know that it has been instrumental to our team’s cohesiveness. Functionally, a Scrum for us is a daily team meeting that lasts only 30 minutes (everyone is standing up so that it’s clear that the meeting will be short). One by one, each person has the floor and talks about what they worked on yesterday, what they are working on today, and any questions that they have for other people. After setting it up, our team started using it for functions that I had not thought about before I saw it happening. For example, we use it as our daily deliverable when I am able to see where we are at on the code and art. Other team members use it as a way to make sure they are on the right track when solving a problem. Other team members use it as a way to look at the big picture of what we’re doing and get pumped up about our progress.

Other Practices: We set up skype chat for each of the team members so that communication lasts all day. Often, there is a tremendous amount of collaboration that happens in a seemingly silent office. We also set up a wiki that documents, in a central place, the components of each of our Sprints, and what everyone is responsible for. It is a constantly evolving document that is primarily updated by me, but it is very helpful in understanding what people are working on in the long term and where there may be gaps.

In the end, as we set up all of these processes, the most important lesson that I have learned is that great communication starts at the top. Unless you demonstrate that you are communicating yourself, your team members will likely not communicate either. For example, if you say that everyone on the team should email whenever they have questions, it will probably take several emails from you before the team buys in and takes it from there.

Time is on my side, yes it is…

In this section, we learn from the Rolling Stones. It’s an ageless, optimistic message that runs in contrast to the typical entrepreneur’s mindset, that stresses impatience (and the cornerstone of lean methodology). Now, I definitely agree with this mindset: we can’t just sit on our ideas while someone is out there hustling with a similar one; we can’t just keep working on prototypes without getting them out to users who can teach us; we can’t go back and spend days cleaning up code when there is so much to be done with our existing code base. I agree with all of that. What I don’t agree with, however, is the mentality that time is the enemy. As a founder, it is important to frame time as our friend.

This is an important lesson for me because I was always under the impression that, by setting stretch deadlines, I could get more out of me and more out of the team. By framing our work as a race against time, we could encourage hard work and higher performance. However, this view has only served to make us look at the short-term goals and lose track of the long-term needs of the company. With time as the enemy, we introduced all-nighters, hardcoding, a lack of flexibility, low morale, and stress into our processes. By doing so, we ended up being more behind in reaching our goals. Thus, these processes below ensure that we all look at time as our friend in product development:

Balance Speed vs. Scalability of the Code: When your team is “costing out” features (determining how much development time, cost, and art is needed for a feature), it is important to tease out the real tradeoffs between getting done on time and the corners that will need to be cut. For the product person, it makes sense to include the entire wish list in the spec, but you should be aware of what tasks are vital and which can be pushed. As an engineer, it is vital that you understand which shortcuts can be used now to meet this deadline, and which shortcuts don’t make sense because it’ll take more time to revisit three deadlines down the line. It’s very hard for everyone to understand these tradeoffs, especially early on in the development cycle.

Therefore, I believe that it is important to set easy deadlines with several days of cushion for the first few sprints. This will give the team the flexibility to focus on building scalable code early and establish understandings of the tradeoffs that they will have to make later. Technical debt is inevitable, but it should be avoided early because it compounds. As the team learns more about how they work together and about where the product is going, the deadlines can become tighter. But early on, focus on the scalability vs. speed.

Establish Best Practices…before you need them: When you believe that time is on your side, you can spend some time thinking about what processes you will likely need in the future and build to them now. By doing so, the team can learn how to use them before they are really in a jam. On our team, we set up bug tracking through Bugzilla, subversion hosting and deployment through Beanstalk, and other systems before we had bugs and multiple developers in the code. By doing so, we were able to create a plan for scaling so that when we hired people, they could get ramped up quickly. Indeed, if you can take the time to look ahead and plan for the future, you can save a lot of time today.

Now, there are only a few lessons that I have learned as we tried to create a culture through the prototype development process. In your comments, please feel free to comment on these processes, but also feel free add your own best practices. I’m looking forward to reading them.