Mediocre Success Is Worse Than Outright Failure

One of the wisest and most important pieces of advice I received as a startup founder was this: “The worst outcome is a mediocre success”

This is not a simplistic exhortation to go big or go home, hit a home run or strike out, be blindly ambitious, or any of those things. It's subtler. 

Startups are defined by uncertainty. As a founder, you have to discover almost everything about your business: what is the product? Who are your customers? How will you reach them? How much will they pay? Who are your competitors? How is the industry evolving? The list goes on.

A common way to answer these questions is to take the scientific method and apply it to tech startups. Frame a hypothesis, run an experiment to test the hypothesis, confirm or disprove the hypothesis, learn and iterate, and learn and iterate.

For example, your hypothesis could be that cold-calling customers will lead to sales. So you hire a couple of sales reps and tell them to cold-call 100 customers. If you get 30 new sales out of that (a terrific hit rate)—great, the hypothesis is true! You can hire more sales reps and double down on this tactic. And if you get zero new sales—that’s also great, the hypothesis is false! You can move on to other acquisition tactics, like Facebook ads or SEO or events. Either way, your experiment worked to confirm or disprove the hypothesis.

The worst outcome is to get a small but non-zero number of sales—say, one or two. Now you're in a bind. Do you double down or pull the plug? Does cold-calling work or not? Could it be that the method works but the sales reps aren’t hustling enough? Are they not following the right script or calling the right people? Maybe the method is flawed and your reps just got lucky. You just don't know.

That’s the danger of mediocre success. The point of startup experimentation isn't success or failure itself—it’s the learning. You don't really care about the sales revenue generated by your first two reps; you care whether this is a strategy that you can scale to dozens and then hundreds of reps, or whether you need to use a completely different strategy. It’s all about the learning. And mediocre successes don’t give you any learning.

Unfortunately, there’s a natural human tendency to mitigate risk and hedge our bets—to make design choices in our experiments such that mediocre success is the most likely outcome. There’s also a tendency to only do experiments that you know will work in advance—but such experiments are not useful: the delta in information is close to zero. Back to the sales experiment: as a founder, you could do the calls yourself; reach out only to the very best, most qualified prospects; create custom collateral; and offer sweetheart pricing. All of these actions will increase the chances of closing any one deal. As a result, they’re very tempting. But do they tell you if cold-calling is a viable sales strategy at scale? Nope.

This tendency is exacerbated by the fact that we’re all heavily socialized to aim for mediocre success. Schools, universities, large organizations—they don’t want big swings and big misses; they want safety and consistency. A steady seven is better than tens interspersed with zeros. This might work well in structured, predictable environments, but in startup land, it’s anathema.

So when a startup comes to me with an idea for an experiment, the one thing I tell them is to make sure that there’s a well-defined distinction between success and failure. Don't fall in the messy middle. If the hypothesis fails, make sure it fails clearly and unambiguously. If it succeeds, make sure it succeeds equally clearly and unambiguously. 

And remember that a hypothesis failing means the experiment succeeded; you learned something. That’s what it's all about. The worst outcome is mediocre success.