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3 minute read / Jan 29, 2016 /

Chained Probabilities in Startup Business Models


In 1983, Lorne Whitehead, a physicist from the University of British Columbia proved he could knock down the Empire State Building with 29 dominos. A domino can knock over an other domino one-and-a-half times its size. Whitehead’s theory concretely demonstrates the power of a chain reaction.

Like a series of dominos, a startup’s success is a chain reaction. One small win leads to two other slightly bigger successes, which grow to four triumphs, then eight hits and so on until the company is a blockbuster. The first win might be witnessing a product capture the imagination of a random coffeedrinker at a local cafe. The second reaction might be closing the first paying customer. The third, hiring a top-notch executive.

At each point in the chain reaction, there is some probability the reaction continues. The probability of discovering product/market fit early, of hiring a great technical team, finding the right initial reference customers. Many different factors influence those chances: the founding team, the product itself, the pitch, the competitive environment, the press, ability to raise capital.

So that chain reaction is a series of chained probabilities. When thinking about a potential market opportunity, articulating the list of business model chained probabilities can be useful. In the case of a paid mobile game, it’s simple. The company is betting they can write a game that will differentiate itself sufficiently to entice one million people to buy it.

Other business models assume multiple hypotheses. For instance, B2B freemium businesses, like Slack andExpensify, market a two step value proposition. In other words, two hypotheses underpin the business model. First, that company can build a product appealling enough to end users to attract millions of users. And second, that activity of those users combined with specific upsell features will convince a business to pay for a team, department or company-wide license.

Marketplaces can be even more complex. In a marketplace like Beepi, the business first has to convince car owners to sell their cars with Beepi. Then they must convince buyers to trust the marketplace sufficiently to buy a car. Third, they must develop processes that scale so they can generate liquidity in the market place and a flywheel effect begins, acquiring buyers and sellers at attractive unit economics.

Yet other businesses pursue wedge go-to-markets. Chris Dixon wrote about wedge business models in 2010. Wedge businesses sell an initial product to close a customer and then cross-sell products into those accounts to juice revenue per customer. Each new product is a new hypothesis, a chained probability.

Chained probabilities aren’t independent. The first flickers of success increase the odds of the second probability, and so on. Success begets success. When startups have momentum, we’re saying the odds of their success, the odds that the conditional probabilities result in their favor is increasing. And that’s why seemingly small ideas, if they can muster momentum, can often turn out to be orders of magnitude larger than most expected.

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