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?

Conversation with Small Business Owners – We didn’t want to be seen as greedy

This is my conversation with a small business owner who isn’t ready to disclose their business’ identity for this article. This is meant to give you a fly in the wall view into the minds of how a business consultant thinks vs. how an entrepreneur thinks and operates. Any lessons you take away are your own, framed by your mind and context, and not the intention of this article.

Q: So how did you decide to open a hair salon when there are so many out there? (I know from my previous research this market is so crowded and fragmented with not even chains with >10% share of market)

A: That is what I trained for and I really enjoy beauty works. After my training I worked for others first but I guess I got tired of working for others and my husband supported me to start my own.

Q:  … but how did you know people would come and the investment was going to pay off?

A: I don’t understand what you are asking. People always need haircut.

Q:  How did you know this was the right thing to do?

A: You just know. I had the skills.  I am very good at it.  So I knew I can do this too.

Q: So about your price, how did you set it?

A: I saw how high priced the place I worked before charged. People are afraid of such high prices, most of them, mostly men, come once and they are gone.
There are too many $8-$10 places around here that give bad haircuts. So I set a price that will not look too cheap or too high.

Q: I see you have several packages, simple haircut to extras added … how did you arrive at what packages to offer and their prices? (I bet it is not based on ay conjoint analysis)

A: That is easy. Most places charge similar price for extras, you simply add for each extra and set a price.

Q: And do you know how your different packages are doing … are customers taking up these over simple haircut?

A: Mostly people come just for haircut.

Q: You have 3 other people working for you but I see them mostly idle, why?

A: People ask for me all the time for appointment. I am working non-stop. People call and book appointments weeks in advance for me. Others, they will get busy soon, if not they move on.

Q: If you are in such high demand why do you charge the same price for all 4? Shouldn’t you charge a higher price than the other 3?

A: This is crazy. How can I tell customers it is $18 for one and $23 for another. They will be confused and will not like it. They will see me as greedy.

Q: Why would they? Don’t they see more value with your skills and seek after you? You are simply charging for that value.

A: It is too complex. What if I lose business because I am too expensive?

Q: You might lose a few. But don’t you cover that from higher profit from others, from sales to your employees, more free time for you to run the business or enjoy with family?

A: I don’t know.

Q: Do you keep track of how occupied 4 of you are? Say for 50 hour week, how many hours is each one busy and what is the sales per person?

A: I think it is in the computer when we ring up. But what will I do with it?

Q: Finally, you recently raised your prices. Why and how did you know to do it?

A: I didn’t want to raise. My rents went up. I had to manage the increase. We didn’t want to be seen as greedy. I still wanted my price to be below $20 because that is the magic number. After $20 people think it is expensive. So we decided to increase to just below $20. and still cover our rent increase.

How entrepreneurs estimate the probability of picking a red ball from a urn?

Stay with me on this till the end of this article.

Suppose you asked me the probability of picking a red ball from a urn that has 10 red balls and 10 green balls, I would say the answer is 1/2. I cannot say with certainty what the next pick will be but if you picked a ball enough times I can say 50% of those instances will be red balls (yes you return the ball after each pick).

But what if you brought a mystery urn (of unknown size) and didn’t reveal how many red and green balls are in the urn? Heck, what if there were lot more colors than just red and green? I would have no idea. My best approach would be to guess a number (using my gut feel or intuition) and say something like  1/1000.

But as someone too analytically bent I would find it hard to play this game. You didn’t tell me how many total balls, the different colors and whether the urn had a red ball or not. I cannot even apply Bayesian reasoning to refine my answer.

Entrepreneurial outcomes,  says Saras Sarasvathy, is like estimating chances of picking red balls from such a mystery urn. According to Sarasvathy entrepreneurial thinking is

Whatever the initial distribution of balls in the urn, I will continue to acquire red balls and put them in the urn. I will look for other people who own red balls and induce them to become partners and add to the red balls in the urn. As time goes by, there will be so many red balls in the urn that almost every draw will obtain one. On the other hand, if I and my acquaintances have only green balls, we will put them in the urn, and when there are enough, will create a new game where green balls win

It is hard not to imagine someone you know doing exactly this – hustling to find those with red balls to add to their urn (discover a few early adopters and build on them) or pivoting to redefine the game as picking green balls not red balls.

That is the clear distinction between the mind of an entrepreneur with irrational optimism and that of rational person. And I do not use rational and irrational to say one is better than other rather  use irrationality as equally positive trait as rationality (or rationality as equally negative trait as irrationality), like the way Dan Ariely did.

Rational people, there are many of us, refuse to play the guessing game of picking red balls from unknown mystery urns. But if picking red balls is important to significantly improve our lives, someone has to do it. However no single person can keep on trying with a single urn. And most run out of cash and time before they can add enough red balls to their urn.

That is why we need many many entrepreneurs with irrational optimism to keep picking balls from their own mystery urns. Most end up picking all kinds of different balls but a few will find urns and change it in such a way to always draw red balls.

Where do you fall?


It’s not the size of payoff but the size of worst case that drives some entrepreneurs

In the past I wrote about the difference between the mind of an analyst and that of an entrepreneur,

It is impossible for someone who rationally estimates net present value of all options, stress tests their assumptions, meticulously conduct sensitivity analysis, determines market sizes and customer demands to start a new venture. (Dan Ariely calls this Optimism Bias, Gavin Cassar attributes this to selection bias)

Corollary: To start a venture one needs to be risk seeking and at the very least be willing to suspend their  rational mind to make the leap.

A research conducted by Saras Sarasvathy, Assoc Prof at University of Virginia points to evidence on how entrepreneurs evaluate outcomes, that it turns out it is indeed different from the methods of a rational decision maker,

Sarasvathy interviewed 45 successful entrepreneurs, all of whom had taken at least one business public (see caveat at the end of this post).  It was not the planned outcome, carefully constructed from comprehensive business plan and understanding of customer needs that drove these founders. They started with resources they had and imagined the possibilities.  Instead of estimating the size of rewards from a venture from successful outcomes or the likelihood of such outcomes, they asked, “what is the worst that can happen if they failed”.

If the failure was not all that bad they went right ahead.

In fact the failure is indeed not bad. Another research conducted by Pfeffer of Standard GSB, found that

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

Since entrepreneurship is already viewed and accepted by all as a high risk activity, failure is not only accepted but glorified as example of risk taking.

It’s not the size of payoff that drives them but imagining what is the worst that can happen. And it appears the worst case is not worst at all.

Caveat: Sarasvathy interviewed 45 successful entrepreneurs whose venture went public and more importantly agreed to talk to her for her study. Clearly there is survivor, selection and availability biases here. The results are also based on the conversations with those entrepreneurs which is prone to hindsight and narrative biases.

Take this for what it is worth.

How do VCs decide to invest in a startup – Regression Analysis -Part 1

This is a multi-part article. I decided to make it lot more technical (as in statistical analysis) so this would not only serve as a prediction model for startup funding but also serve as a model for you when you see similar such predictions. You will not see the results of the regression analysis in this article but you can read it all here if you can answer a statistics question.

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.

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.

Armed 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 that enabled me to do not one but two ways of analysis of this data to come up with a prediction model.

The results indeed hold surprises compared to his previousanalysis. In the next part I will go into details of regression analysis, its metrics and pitfalls.

Again, see if you can predict which startups got funding, take this quiz.


If you are asking entrepreneurs to be rational

What is the biggest resource an entrepreneur can waste? According to Kevin Ready, author of  “Startup: An Insider’s Guide to Launching and Running a Business, it is not money. Ready says it is time spent trying to keep a start-up live long after its viability has been discredited.

Kevin Ready says,

I call this creature a “zombie start-up.”
… many intrepid entrepreneurs hold on and continue the vision — sometimes for years. Herein lies the true cost and risk of start-ups: Time.

When you hold on to a dead idea at the expense of other possibilities, even though you are not burning cash to keep it alive you are keeping yourself away from what you could be doing elsewhere.

Time is the one resource that we can never recover. The opportunity cost for chasing the wrong idea is immeasurable. What is the cost of a lost year? How about two years? A decade?

Kevin Ready makes a very good point. (Although he says we can’t put a price tag on time lost. We can.)

I would also add a close relative of opportunity cost,  sunk cost. Many are not able to walk away because they have already sunk so much of their time and money into the venture. Doing so may seem like they are wasting their “investment”.

But recognizing sunk cost bias, walking away from what is sunk and taking into account opportunity cost before making choices are rational behaviors.  If entrepreneurs are wired to do scenario analysis, calculate expected value over all possible options, consider opportunity cost of leaving their current job, etc.,  they would not be entrepreneurs at all. (See The Mind of an Entrepreneur and that of an Analyst.)

It  takes an irrational sense of optimism to believe their venture will be a big success when the base rate says less than 3% of the ventures live past their third year.

It is the irrational sense of optimism that makes an entrepreneur.

Don’t ask those irrational optimists to look at opportunity cost.