In the search for product/market fit, marketers are often falling into several traps: some of them can be lethal from day 1.
Today, I will focus on one of them, that is, the scope, which I believe, is the very first common trap marketers get caught in. Being meticulous and paying attentions to facts — even very small — is key to success. However, we are still human: we make mistakes and our resources are always limited — even more for SME. To reduce the risk, you must narrow down your scope of experimentation.
To make an (exaggerated) analogy with the scientific community, when a “theoretical scientist” comes up with a revolutionary theory, which looks totally cool on paper, a bunch of other scientists called “experimental scientists” come along to prove if the theory is right or not. So, what do they do? They slice up the theory into hypotheses which lead to many experiments. The goal is to have, for each experiment, a controlled environment to test one hypothesis.
The search for product/market fit is no different. Once you have determined the idea for a new product, you lay down hypotheses and start experimenting each one of them. Like an experimental scientist, the more you narrow them down, the more you will be able to interpret collected facts. The best experiment would start with a simple and clearly defined hypothesis, and, if possible, linked to only one variable. (E.g. do the potential customers prefer the product in black or yellow?)
From my experience, it’s very easy to lie to oneself in this process. I have been there and I have often seen marketers make that mistake: the more elements your hypothesis encompasses, the more likely you will mix the facts up.
“But how far should I narrow down the scope?” This is a question I get every time during a Value Proposition Design workshop, and I always reply with the following question: “The only thing that matters is getting to product/market fit, but at which costs?”. Failing fast is essential but companies also want to reduce their risk, and use minimum amounts of their precious resources. My suggestion is then to first list the hypotheses and prioritize them according to their contribution to the core idea. Then resources can be allocated based on these priorities, since you may not have enough time, people or just money to perform all experiments. I don’t have a specific number of hypotheses but, empirically, it seems that around 5 to 10 in an early phase would be well enough.
I certainly leave you with more questions than answers; especially how to measure product/market fit. That will be for another time….