Approaching Start-Ups As Experiments

Niraj DawarJuly 14, 20224 min

As anyone managing or advising startups knows, it’s not easy to step back from the daily fires that need to be put out to focus on the longer-term goals of building the business and going to market. Yet stepping back is often exactly what the doctor orders. So let’s step way back and see what we see.

I have advised startups for more than twenty years, and simultaneously have designed and run behavioral experiments for my academic research for over thirty-five. It took a while for the light bulb to go off, but eventually I began to think of start-ups as experiments. There are valuable lessons to learn from this analogy. These may sound abstract, even theoretical, but I have found that they are helpful for founders and start-up teams, including VCs. In exchange for the level of abstraction, I’ll keep it simple: let’s look at three lessons.

By definition, a start-up environment and its expected outcomes are uncertain. The idea/product may or may not work, and there may or may not be a market for it. It is uncertain how big the market is, or could be, and there is uncertainty about whether this particular venture is the means of realizing the value that is posited. In Silicon Valley, startup ideas are sometimes described as “hypotheses” that need to be tested. From there, it is not a large leap to think of startups as experiments designed to test those hypotheses. This way of conceptualizing startups carries implications.

First, to be viable, to get funded, and to attract a competent team, a start-up idea must be an interesting hypothesis – one worth putting together a start-up to test it. In practice, this often means an idea to serve a large, growing, and hopefully unserved market. But it also means that the hypothesis must fit into a current narrative about the state of technology in its domain, and the narrative of markets, such as the evolution of consumer behavior or ecosystem within which it plays. For example, social media may have been just as technically feasible as search in the early days of the internet, but as a hypothesis it made sense to test search first and social media later because the market, including consumer behavior and the advertising ecosystem, provided a better test later. In general terms, there is a natural trajectory or progression for the evolution of ideas, and the hypotheses are best tested in a certain order.

The implications for start-ups are clear: know the narrative, know where your idea/hypothesis fits, know when is the right time to test it.

Which brings us to a second implication of treating startups as tests of hypotheses: the test must be strong. In practice, what this means is that you must take the best shot possible: the startup idea should not fail because of weak management, weak funding, weak products, or a weak economy. If it fails, it should fail because the hypothesis was not true: it should fail because there was no market there. Of course, in practice there are many more moving parts in a startup than are measured, let alone managed, so a pristine attribution for success or failure is not possible. But there’s no denying that everything about a startup should be designed to test whether there is a market there. A tight design is one that rules out extraneous explanations or possible attributions for the success or failure of a startup: if there is a market, it will be found and addressed, and if there isn’t a market, that disappointing truth will be revealed for all to see. The team’s efforts are designed to deliver a conclusive answer: yes or no. What this means is that every startup must have success metrics that provide a test of the central hypothesis.

Which brings us to the third implication of treating startups as hypotheses:

Everything the startup team does is about reducing the uncertainty that you begin with. Specifically, you are out to reduce two types of uncertainty: (1) Is there a market there? And (2) How quickly can you provide a definitive answer to this question?

Conceptualizing a startup as an experiment means that that definitively demonstrating that there isn’t a market there can be almost as much a success as proving that there is a market. It is not surprising that in experienced start-up environments such as in Silicon Valley, the failure of any single startup carries little stigma for the founders or team: it is understood that the team accomplished its mission of definitively testing the hypothesis by demonstrating that there was no market there.

For the funders of the startups, the value of looking at them as experiments is evident. Not every experiment will succeed, but each experiment needs to be tight enough to validate or rule out the central hypotheses that was funded. Knowing that the startup provided a disconfirmation of the hypothesis also helps make another critical decision: knowing when to stop funding it.

A startup designed as a strong experiment, with clear hypotheses, measurable success hurdles, and a well-defined timeline (milestones) is far more likely to succeed than one that is set up to try and sell a product.

Contributed to Branding Strategy Insider by: Niraj Dawar, Professor Emeritus of Marketing and Author of TILT: Shifting Your Strategy From Products To Customers

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