Belief that Entrepreneurship is risky fosters risky ventures

You have seen my attempt to analyze data provided by a VC firm on how they decide to invest in startups. Contrary to what they thought they were doing there was just one factor that decided investment decision.

Do VCs make informed evidence based decisions by meticulously rating startups like the data we saw led us to believe? That requires a meta analysis across all VC firms and someone just did that.

These are some quotes from a  article by“>Stanford GSB Professor, Jeffrey Pfeffer, on the need for Evidence Based Management in Entrepreneurial environments:

  1.  … it has become conventional wisdom, accepted by all the parties ranging from entrepreneurs to those who provide them financing, that a high rate of failure is an inevitable consequence of doing new things, inventing new technologies, and opening up new markets—activities which are inherently risky and uncertain because they involve doing things that have not been successfully done before. Because this conventional wisdom suggests that a high failure rate is inevitable, there is often little effort expended trying to improve decision-making in new venture activity.
    (In other words, people start ventures without trying to validate customer demand and VCs invest based on all kinds of criteria but validity.)
  2. Many of the VC firms do what they do without much introspection or reflection, partly as a result of the egos and self-confidence of the VC partners. People who have survived and prospered in the venture industry have obviously done well, and those VC’s who don’t do well generally don’t last. Therefore, it is axiomatic that most fund managers (those who survived and prospered) believe they are much above average in their abilities and in their decision making.
    (Hey, smart people succeed. If not they wouldn’t have succeeded, would they?)
  3. Positive qualities get attributed to the people, groups, or companies that enjoy those good outcomes whether or not these qualities are true or causal. This means that high-performing VC’s will be perceived as having individual skill as a consequence of their performance, whether or not such skill actually exists.
    (No wonder we bow at the altar of success. This finding was first stated decades ago John Kenneth Galbraith in his Conventional Wisdom essay.)
  4. Entrepreneurs, too, mostly have strong egos, which is what is required to take on something new where the risks of failure are high. But this overconfidence among entrepreneurs and those that back them makes it difficult for people involved in creating new businesses to question things and to learn from setbacks and other experience.
    (Everyone is killing it! Disrupting status quo! I hope they would stop at that and not write seemingly erudite articles on brain science.)
  5. Most venture capitalists and entrepreneurs believe that outstanding individual people make the difference, leading them to focus on finding and recruiting stars and to eschew much attention to process, including decision making processes.
    (If you are already successful you are perceived to be outstanding and thought of as having success potential)
  6. Few of the participants in entrepreneurial activity suffer significant consequences from unsuccessful decisions, and therefore many players have less incentive than one might expect to improve their decision-making  – VCs get guaranteed principal and Entrepreneurs often, although not always, are working with other people’s money, so their financial downside, except in terms of the opportunity costs of their time, are also limited
    (Because failure is most often seen as an unavoidable risk of being an entrepreneur, there are few if any career risks for starting something that doesn’t work out!)

Wisdom of the Crowd and that of the VCs

question-1-startup-quizIn my last article I presented the results of the startup investment decision quiz. When you look at the final score it would appear very few, about 4.7%, got it 100% right. That is their investment decisions are exactly same as that of the VCs’. Let us look at it another way, say instead of you making the decision in isolation what if you are able to change your decision after seeing how the majority decided.

That is make a choice, so will many others without seeing or discussing with each other. Then you get to keep or change your choice based on the wisdom of the crowd. Since the crowd here is diverse enough with different background and experience it is reasonable to assume the crowd does not suffer from groupthink. Let us assume simple majority rule, >50% wins.

Now how does the wisdom of the crowd (of 234) compare to that of one VC firm? Here is the summary of the results with the table showing percentage of YES and NO decisions by the crowd and the VC’s decision for comparison.


Not all that bad now, comparing the wisdom of the crowd with that of the VCs. The two startups that  got the termsheet from the VCs got one from the crowd as well. The crowd made the same positive decisions. And on the rest of startups the crowd decided to give termsheet to two additional startups the VCs passed on.

While the crowd did not miss on opportunities it was little more lenient in saying yes. If these two startups really are duds (so to speak) the crowd’s decisions will be called  “false positives”. If these results scale over large number  that is twice as many startups that will get funding when they shouldn’t.

Look at it this way, when there is unlimited funding to go around (with many VC firms, angels and others with unlimited money that fancy themselves as one) and unlimited number of startups pitching their venture (everyone is a founder), there are bound to be many false positives.

And that is not all good. It is just frothy.

How well will you do as a VC?

A few months back I gave you a quiz, asking you to imagine yourself as a VC and make investment decisions.  Here are the four metrics one VC firm used to rate the startups that pitch to them for funding.


I took a random sample of 10 from their large data set and presented it as a quiz, asking you to use their metrics and make a call whether or not the startup got funding.

About 250 people took the quiz and and I finally had a chance to look at the results. Here is how the score distribution (on a 100 point scale) looks like


No surprise here to find a Normal curve. But a closer look will reveal a longer left tail. But do not despair if you scored lower score. That does not mean anything about your performance as VC but only an indication of how close do you think to a particular VC firm’s thesis. You must keep in mind that  I gave you the four metrics one VC used , gave their respective ratings and asked you to say whether or not the startup got funding.

Who says those are the four right metrics? Or the ratings are correct? Or their method is right?

Stay tuned for results on which one of the four factors most thought would be the reason to fund that startup.




Strategy, Business Model, and Product

It has been a week of arguing which sequence is right. Is it

Product > Strategy > Business Model


Strategy > Business Model > Product

For most people in the valley – running startups, working for them or mentoring them to become insanely successful – the sequence is clear. There is no argument. Anyone who says otherwise simply doesn’t get it.

Wouldn’t it help if we all understand and speak the same thing when we say  Strategy, Product or Business Model?

Here is a very simple definition for these terms. Not made up, not changed to fit present day mania. These are well established definitions for running any business. And those disrupting status quo to create frictionless something or the other are not exempt from these definitions.

Strategy – Here is a simpler and relatable definition – Strategy is about making choices under constraints (and most times under uncertainty). Choosing the only option available to you (say going for 4th and 24 with 7 points down and 20 seconds on the clock) is not strategy. Choosing all options available because you are not resource constrained is not strategy. Strategy is making hard choice, under limited resources (there are only situations with limited resources) and the outcome is far from known.

For a VC firm their strategy could be the type of ventures they even want to consider, be it the pedigree or market it plans to play in. For accelerators it would appear they could fund anything and everything from enterprise to social media startups but their choice is to limit investment choices based on the stage of the startup.

For a startup (or more generally, a business) the choices start with which customer segment and need they want to target first – a segment with compelling unaddressed need, that is not only big enough but also had big growth opportunity. You can serve all customers and all needs. The old adage about being all things to all people goes well here.

For example, choosing to serve enterprise customers with significant pain-points  (and IT budget to spend) and reach them through highly effective direct sales team vs. building out Chatter as competition for Facebook is their strategy. Another example is Netflix choosing streaming over DVD by mail as the future.

Strategy does not end with the first choice. If you have to make a hard choice among available options (and most times under uncertainty) then it is strategy. First it is the segment to target, then there are choices on routes to market, product, product features and when to deliver, pricing  and communication.

That is strategy.

Business Model –  You can do a Google search on all kinds of theoretical works on business model. In practical terms, business model is answering two questions

  1. How are you creating value to your customers? (see value equation)
  2. How are you capturing your fair share of that value created? (see Value Step function)

Together these two constitute your business model. You could be like some of the group buying sites and take a share of value you did not help create. Or you could be miss out getting your fair share. In either case your business will sooner or later will fail because it runs out of value to take or in latter case run out of cash.

You could introduce a third party (or fourth, or fifth) in the value flow – say advertisers, content producers – and decide to capture value indirectly. If your product adds compelling net new value to customers you chose to serve, charging for it remains the simplest of all business models.

And as an astute reader you noticed there are choices to make in defining the business model. It could be in how best to deliver value or how to capture value, whether to capture value upfront or align with value deliver (subscription). That is strategy does not go away when you move to business model.

Product – What is a product? Are your customers buying products? Ted Levitt said,

“Customers are not buying quarter inch drills but quarter inch holes”

Clayton Christensen said,

“customers have jobs to be done and they hire products for those jobs”

So we could say product is the value delivery medium. This is not to trivialize it. Product offers the greatest opportunity to innovate – to deliver something that does the ‘job’ better than any other alternatives available to customers, to deliver most natural way to use it, to do so in the most cost effective way for the venture that is building the product, to make it sticky, etc.

Again there are choices to make – what to build, when, how etc. More strategy in building the right product.

Given these, you decide whether one is greater than the other.  For startups, Fred Wilson argues finding the product-market fit first, deciding on strategy then business model.

For startups that begin as a personal problem the founder is trying to solve with the assumption that there are many others with the same problem it would appear

  1. start with the initial iteration
  2. keep refining it through user discovery
  3. build a large enough user base, getting early adopters to spread the word
  4. worry about monetization later

… is not only the only recipe but one that is guaranteed to deliver success (can you say Facebook, Twitter, Instagram, Pinterest?)

The argument for product first approach should not be because of what we know to be successful startups or because of one’s inability to start with strategy first.

Did you consider the possibility that when you do thousands of experiments – thousands of founders with the same personal problem, trying to address it in thousands of different ways – some experiments are going to succeed?

Product, Strategy, Business Model and Two ‘>’ Symbols

Quick! Write an inequality equation using two ‘>’ (greater than) signs and

  1. Product
  2. Strategy
  3. Business Model

Depending on where you stand and which articles you read recently there are six possible permutations.  If you had recently read what Fred Wilson, a Venture Capitalist, wrote you are  mostly likely to write down

Product > Strategy > Business Model

Is that all to it?  According to research done by four business schools, this permutation defines only one of two classes of VCs. More precisely, there are two schools of thoughts of how VCs make investing decisions. The second class of VCs believe the right permutation is,

Strategy > Business Model > Product

While Fred Wilson makes a compelling case to get product-market fit correct, then define your strategy and then worry about making money, a VC who falls in the second category will argue, equally eloquently, strategy (making choices about segmentation and needs to serve) first, finding how you add and capture value (business model) is next and what the offering (product) is last.

The two ways of reasoning are called  Effectual and Causal reasoning respectively.

Effectual – Instead of doing market research, competitive analysis, value analysis etc, go build something and keep iterating on it and building a growing customer base. Then worry about strategy and business model.

Causal: Start with customer segmentations and their unmet needs (or jobs to be done).  Make choices on the right segment you should target first and understand its value perception, alternatives and willingness to pay. Define a product version that serves that segment and offer at a price they are willing to pay.

There exists a class of VCs who apply effectual reasoning and there exists another that applies causal reasoning. You can see Fred Wilson falls in the effectual bucket.

So when you have two classes of entrepreneurs and two classes of VCs, the next obvious question is which pair would work together well. The aforementioned research suggests, cognitive similarity (“I like how you think”) was a decisive factor in how VCs decide choose to invest in startups.

Their study was conducted on 49 partners from different VC firms, by presenting them 16 different hypothetical investment opportunities and asking them to rate how likely are they to fund these ventures. From these 784 data points, the researchers employed conjoint analysis to tease out the influence of individual factors on VC’s decision. This is approach is far better than stated preference studies that ask VCs for their rating and data mining studies that succumb to data errors.



The number one deciding factor?  How similar the thought process is between the VC and the founder. The researchers call this cognitive similarity, which has nothing to do race, national, education, gender or other physical characteristics. It is how a founder thinks and how similar it is to VC’s thought process. Higher the similarity, greater the chances of getting funding.

Everything else, including the perception of the team, its experience and commitment (human capital) are influenced by VC’s reading of founder’s thought process.

“A founder who demonstrates cognitive similarity with a VC is more likely to be perceived in a positive light, and viewed as better positioned to make effective use of his or her human capital”

All other positive attributes we hear about, the product’s competitive advantage, scalability, founding team’s ability to hustle, their focus etc seem to be bestowed after the fact.

What does this mean to you as a startup founder seeking venture funding?
You are better off seeking those VCs who think like you do in terms of product, strategy and business model. If you think market demand and opportunity size first and pitch to Fred Wilson you are most likely going to come back empty. On the other hand you at least get to play if you think product-market fit first. So knowing how you reason and seeking as venture partners only those who think like yourself saves lots of wasted time and agony.

Will Fred Wilson and other VCs admit to this influence of cognitive similarity in their investment decisions?  More broadly, do VCs know and admit to the influence of cognitive similarity on their funding decisions?

No, they do not recognize this hidden factor. And I expect comments from a few stating so. In the same study that teased out this hidden factor, the researchers asked an explicit question on how much weight VCs place on cognitive similarity with founders.  VCs rated this as the the least important factor, but when they had to place a bet given a profile of venture and its founders, the hidden influence of cognitive similarity came out loud and clear.

Finally, is Fred Wilson right? Is effectual better than causal?  The proponent of this classification, Professor Saras Sarasvathy, goes one step beyond this mere classification.  She argues great entrepreneurs are ‘effectual’. They opt for doing things vs. analyzing things.  I do not subscribe to this latter part of her theory regarding what defines entrepreneurial greatness.

How do you reason?

How VCs decide to invest in your startup

Note: The statistical analysis shown here is based on data provided by one VC firm BlueRun Ventures. The ratings they did is likely post hoc and has biases. Hence the results are not as generic as the title says they are and have considerable uncertainties. This is also a long article and relies on linear regression and logistic regression.

Imagine you were asked to invest in ten startups. Given numerical ratings on the Team, Product, Market and Traction but knowing nothing about the specifics of the team, the exact product or the domain they play in, can you pick those that actually received a term sheet? Take this quiz and see how you do. Do not read ahead before you do the quiz.

What characteristics of a startup make it attractive for venture capitalists to invest in it? If you are a startup founder preparing for that pitch, wouldn’t it be nice to know the answer so you can prepare well to maximize your chances of getting that coveted term sheet? For those who are listening, there is no scarcity of advice. Everyone from VCs, startup founders who secured funding at significant valuations and others on the sideline, all have something to say.

Are any of these relevant to startup founders? What is noise and what is signal? Do any of these have hard numbers behind them?

Until now there was no hard quantitative data on startups that pitch to VCs and the outcome. Thanks to data from Jay Jamison, partner at BlueRun Ventures, I have data on 216 startups that pitched to his firm. Jamison rated them on four metrics, Team, Product, Market and Traction using a 5-point scale and also noted the outcome of their pitch. The outcome is rated as likelihood of getting term sheet on a five-point scale, with 5 meaning they got it.

startup_metricsArmed with this data we now can model if any of these traits of a startup influence its ability to get term sheet using statistical analysis. While Jamison did his initial analysis himself, it was not rigorous enough and pointed to incorrect reasons. He later shared his data with me and encouraged me to do not one but two ways of analysis this data to come up with a prediction model.

The results indeed hold surprises compared to his previous analysis. You should also note I wrote a more critical article about the data and Jamison’s previous analysis.

Stepwise Linear Regression
Let us say there is only one independent variable X and one outcome variable Y. Suppose we had several pairs of these, (x1,y1), (x2, y2) ….  based on our observations. A linear regression model tries to find a line of the form Y = mX + C that is the best possible fit, one with least error, given the set of observations.

regression_modelHow good a fit is this model in explaining changes in Y is measured as ratio of two errors and is called R2 or coefficient of determination. Khan Academy has a very nice explanation of R2  that I recommend you check out.  It is a positive ratio with maximum value of 1 and minimum of 0. Higher the value, better the fit.

What is that got to do with startups and venture funding? We will model the outcome, whether or not the startup got term sheet as a function of the four traits. We will build a model that has the best fit and also find how good it is in predicting the outcome.

In any regression model, if you try to model with maximum set of variables you will find a very good fit with very high R2. Such a model is useless. We want to find the minimal set of variables that we can control and also measure how the predictability of the model improves as we add variables one at a time. That is stepwise linear regression.

Step 1: Trying to model the term sheet outcome with each of the four variables, separately, I found that Team alone stands out as very good predictor with R2 of 34%.  That is 34% of the changes in outcome are explained by changes in Team and 66% are not explainable by Team. It however seems to fit the commonly accepted notion that VCs invest in teams and not products.

linear_modelStep 2: This step is to build yet another model that retains the Team variable from step 1 and tries to add one more from the remaining three. The second variable that has the most positive impact in improving the predictability? Market.   But it did not improve the model’s predictability much. Adding Market moved the R2 only by 10%, meaning Market characteristics have very low predictability.

Step 3: You get the picture. The third variable is Traction and it did even worse with just 5% increase in R2.

Step 4: There is no step 4. The left out variable, Product, had absolutely no role to play in predicting the outcome. If you are obsessing about the product, its features and how well it compares against the others in the market, all that have no impact whatsoever in tipping VCs’ decisions.  The product is not relevant.

So the only real startup characteristic with meaningful predictability for getting term sheet, using linear regression model, is how good a team you have assembled.

Now to yet another bigger surprise.

Logistic Regression

Jamison rated the term sheet outcome as likelihood on 1 to 5 scale But if you take a closer look at his intended meaning, it was really a binary coding – 5 means they gave term sheet to the startup and 1-4 means they said no in four different ways. The outcome is Yes or No. So we should not be running linear regression at all with such binary coding. The right analysis to do is to use logistic regression that measures the probability a startup with given characteristics will get term sheet. So I recoded the term sheet values as 0 and 1 and did just that

Even in this model the Product has no role to play. That should settle the argument with the product types obsessing over details.

The biggest surprise? The biggest predictor in the linear model, Team  and the smallest predictor, Traction have absolutely no role in predicting the outcome. The biggest predictor with close to 80% predictability (R2 McFadden used for logistic regression) is the Market rating. The model is in fact real simple. If the market rating is 5, your startup will get funding, if not it didn’t. You play in the hottest market you get funding regardless of the other factors.

This leads to unfortunate conclusions about startups and how VCs make investment decisions.

One, money flows based on the buzz and hype. The very rating of the Market attribute is questionable. Are VCs rating the market based on true value or the prevailing hype?

Two, money flows where there is already lot of money. So more startups that play in the same hot area get funded leading to too many players in a perceived hot market resulting in  many startups that are not that distinguishable from each other, fragmentation and likely too many failures.

Third, many reasonable markets with steady growth but lack the buzz, attract no funding and hence attract no startups resulting in no meaningful innovation. This  likely explains the credo of Peter Thiel’s FoundersFund, “We wanted flying cars, instead we got 140 characters”.

In conclusion

So what is relevant to the startups? It is not really black or white. Given the investment environment and the unavoidable hype in the valley, if you want to play the game just for funding then you may do well by pitching yet another social/mobile/big data or whatever the flavor of the day is.

If you have a true meaningful innovation that is lot more than 140 characters and have a team that is unmatched in its technical expertise, you will do well by waiting to find your match.