Because multiple options are better than just one – Product Management Series

In my last article on defining and evaluating Influence Skills of product managers  (reminder – Influence Skills was rated as the most important quality in a survey) I mentioned the book Influence by Robert Cialdini. The book, in my opinion, is about influence tactics and not about building a longer lasting working relation based on trust and mutual value in a multi-encounter environment.

The book does present many tactics you can put to use when you are trying to break in or get what you want in some zero-sum games. In my opinion it does not help build an end to end process for win-win in outcomes in situations where you meet the same people over and over.

For instance using asking for a small act and then relying on escalation of commitment to get more and more of what you want does not sound to me like a mutual value-creation and fair value-share arrangement. As I wrote before, Influence is based on trust, mutual value-add and effective communication.

But that is just that, my opinion.

There are two invaluable tactics from the book that I recommend you use without compromising on mutual value and trust.

Because, Because, Because

The Influence book tells us about the effect of using the word ‘because’ in asking for an action from anyone. When asking for a favor/task  from others, a Harvard study found, you will have greater success if you explain the reason for your ask,

People simply like to have reasons for what they do.

For example,

“would you help me get the SKUs created in two weeks because of product launch”

In fact the study went a step further and tested just the use of the word ‘because’ even with illogical reasons and found that it had better effect than giving reason without using ‘because’.

Like saying

“would you help me get the SKUs created in two weeks because I am in a hurry”

I am not going to make a recommendation that you use ‘because’ with illogical reasons but stop with their primary finding about people like to have reasons for what they do and give a valid reason after ‘because’.  In fact this fits perfectly with my recommendation about showing mutual value and effective communication. Using ‘because’  helps us get the value message across effectively.

Options over Ultimatum

The second tactic that helps is giving your peers/customers/bosses multiple options and asking them to pick one over presenting them a single option and making it a ultimatum. Presenting multiple options changes the decision from saying yes or not to a single option to picking the best among the multiple options you present.

Here is a real life case study from the world of politics,

The WSJ article on  President Obama won the Health Care vote describes how he changed the conversation:

Mr. Obama’s most effective move may have been calling for a bipartisan summit on health care, shifting the conversation away from Democratic paralysis. Aides knew there was little chance they would reach a bipartisan agreement, but it forced Republicans to put ideas on the table, framing the choice as between two sets of ideas, rather than simply a referendum on one.

 It is easier for the people you work with to compare the merits of different options vs. deciding merits of picking or not picking the only available path you present.

I recommend you go one step further and present three options and invariably you will get the middle option.

Present multiple options because it turns a yes or no decision into informed choice among multiple options based on relative value.

Sure the market seems big but what can you address?

Here is a tried and tested triangulation framework to size your opportunity

Top-down: You can get an estimate of this from several secondary market research reports and analyst reports. Remember reading certain analyst firms predicting cloud spending reaching $xyz billions by 2016? That is  top down estimate. Even if your product is entirely new you can get an estimate of the market spend from related categories (like hand dryer market sizing)

Bottom-up: If top-down is about what others are making in revenues you need to find what exactly are the customers spending. Say if you see analyst report that fitness apparel revenues will reach $70 billion by 2016 you need check who is spending, how much and from what budget to generate that kind of revenue projection. Many sources are available including census.gov. You need to reconcile what you see as revenue with what you see from customer spend checking if the revenue projections are matched with customer spend.

Your Reach: Sure you can stop with the two but can you reach the identified market with your product, channels and limited resources? This is the hard part and requires you to make strategic decisions to define the segment you can successfully serve better than any other competition. For instance, there are indeed 30 million public toilets in USA but can you serve all of them with your $1500 designer hand dryer?

If you need help sizing your startup’s opportunity, let us talk (I have square device don’t worry).

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.

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 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.

How 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.

Step 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.

 

Should your startup be business model driven?

Zipcar CEO Scott Griffith described Zipcar as business model company. He was likely alluding to the shift  from buying and owning for expected future usage to on-demand rent culture. You likely are thinking isn’t that what other car rental companies do, but I will give benefit of doubt to  Zipcar and interpret this fine granular renting. The essence here is how he saw his venture – as an entity that is business model driven and if I may take it to extreme, a business whose sole purpose is about business model disruption and business model innovation.

There is considerable obsession around business model. When you read advice columns from startup accelerators and their ilk., you will see the chatter on business model canvas, what is the right canvas, business model innovation, recurring revenue, subscription model etc.

In the words of Netflix CEO Reed Hastings I would like to say here, forget the focus on business model and business model driven venture strategy. All this obsession about business model is just plain wrong.

Now that I made by bottom line, let me start over from the beginning by asking

What is a business model?

Most treat business model as just monetization models – Ad revenue, affiliate income, subscription model, pay per use etc. That is only part of the equation. Scamming is also a monetization model but is that a viable business model?  Does the money you take as your revenue represent your fair share?

The correct and complete definition of business model should include first the total value created and then how you get your fair share.

Business model is how you create value for your target customers and how you get your fair share of that value.

If you did not help create value you cannot get your share – well you can but you should not and it isn’t sustainable. Value creation is the prerequisite.

If you look at value creation, the obvious step before that is customers with their unmet needs. You create value through your product or service innovations that fulfill those unmet needs better than any other alternatives available.  If you see customers and their unmet needs as the invariable here then it is not too hard to see that there is no such thing as a business model company. There are only customer driven companies. And if you choose to be business model driven company at the expense of customers and their needs you will find yourself getting disrupted.

Once you established clear value the simplest way to get your fair share is what Instapaper’s Marco Arment said – charge for it. Does this mean there is no need or room for innovations on the second part of equation – the monetization model (what most incorrectly call as the business model innovation)?

Absolutely not. Such innovations should be second order after you have clearly established your customer segment and your value creation innovation. Examples of such monetization model innovations include (but not limited to)

1.  In the value chain with just you and your customers you can introduce a third (or a fourth) player with each indirect value flows. You create content that is of value to customers, who create value to third parties and you get your share from these third parties.
2. If customer value realization occurs only in spurts then you can design a subscription model that is aligned with value realization.

But if the job customer hired your product for gets better alternatives and your product gets fired because of that reason then it does not matter how you choose to get paid for the job.

The questions you ask are not

1. Does by business produce recurring revenue?
2. Does my product create switching costs?

but

1. Do customers get recurring value from my product (and hence am I getting a fair share of that recurring value add)?
2. Does my product continue to improve to stay relevant such that it creates better value than any other alternatives?

Not the shift from you to customers in the second set of questions.

There are no business model companies, only customer centric companies.

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?

 

The two flows – Established Enterprises vs. Entrepreneurial Ventures

See the full research here.

In finding the first customer within their immediate vicinity, whether within their
geographic vicinity, within their social network, or within their area of professional expertise, entrepreneurs do not tie themselves to any theorized or pre-conceived “market” or strategic universe for their idea. Instead, they open themselves to surprises as to which market or markets they will eventually end up building their business in or even which new markets  they will end up creating.

While a traditional (read established) business start with well defined markets, segments, targeting and product positioning to reach end customers entrepreneurial ventures start with single customers they have and move up to market definitions and sometimes creating new markets in that process. Of course, once they reach market definition the ventures now become established enterprises and revert to the first flow for their decision making.

The problem is the risk involved and how many of the startups actually move past each stage. The fact that some have made it does not mean any startup can succeed by starting with few available customers, identify more and move to define an entire market. What it really means is, as many startups try their hypotheses, testing different customers, a few will eventually traverse the path to define the market.