Dead Reckoning: No Smooth Sailing for Startups
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random.org. The fact that we “see” patterns in randomly fluctuating data sets is a human trait and fallibility that gets us into all sorts of trouble. When you start looking for it, you’ll find dead reckoning assumptions built into just about everything we touch, from news reports about the stock market to assumptions about job markets and college majors (e.g. last year there was a tremendous shortage of police officers, so next year the shortage will surely continue).
And the startup world is certainly no exception. The adoption of a new consumer behavior, such as Flash Sales or Daily Deals, tend to be self-limiting, diffusion dynamics where at some point saturation occurs, and the number of people engaging in the behavior plateaus or even declines because the behavior wasn’t worth repeating.
The first part of such adoption curves tends to look something like this:
And of course our natural tendency is to dead reckon that graph straight on up towards infinity. I mean, why wouldn’t we? This is clearly not random—this looks positively awesome, right?
But here’s the rest of this classic S-shaped logistic curve:
Let’s say the above data represents consumer adoption of participating in Flash Sale websites. If you decided to jump into the business at year 8 based only on the seemingly promising first graph, you would have soon discovered, much to your dismay, that the market was already 75 percent saturated and the remaining 25 percent would be largely eaten up in only 2 years! To make thing worse, entrepreneurs are born optimists, so we tend to err hugely on the side of upward growth. I would bet good money that most startup types would extrapolate that first curve sharply upwards even faster than the straight-line interpolation.
It’s not that straight-line predictions are always wrong, but in the same way that naturally clumsy people should be extra careful in china shops, we should recognize our tendency to dead reckon and cast a skeptical eye on linear forecasts. There are natural physical limits to everything, including businesses, and it’s often worth ignoring historical data entirely and trying to get at least a gut feel for what the major dynamics are going to be in the future. Doing a little back of the envelope math on market sizing can go a long way to figuring out if you have some unrealistic implicit forecasting assumptions, such as assuming you will acquire more US customers than actually live in North America!
Clearly predicting the future is extremely hard, and no matter how much math, science, and theory you throw at it, there’s always going to be a huge amount of risk and guesswork. But it’s infinitely better to know when you’re guessing and when you’re not so you can constantly be on the lookout for the nasty rocks that show up exactly where it’s supposed to be smooth sailing.