From our (admittedly limited) data, we saw that self-employed folks tended to pay and get paid by other self-employed folks. This led to products like the carousel of similar “suggested accounts” that appear when you follow an account, which was a smashing success in driving increased follows.įast forward 7 years to 2021, and I’m working at a small fintech for self-employed folks. Given this data, our mandate was to design product “hooks” that helped people follow more content. When I was at Instagram in one of my earlier PM roles, our analysis pointed to the fact that the more accounts and content users followed, the more engaged they were. Having been a Growth PM for the majority of my PM career, my first bias has always been to look at data first. Oftentimes, when you drill deeper to get to the root of a user’s problem you realize that your product today isn’t actually solving for the right- or even a real- user problem. Talking to customers enables you to not just understand how customers use your product today, but what their core pain points are. When a product is pre-product/ market fit, users often engage with the product in a mishmash of ways, and you may get a number of conflicting signals if you were to only look at the data on user behavior. Typically, when a product is pre-product/ market fit, you double down on talking to customers to understand their pain points and then directly design solutions to these pain points. Vitamins (solving existing user pain points vs. qualitative inputs in product development. Here are four shortcut heuristics I’ve observed to evaluate when to lean on quantitative vs. Of course, in the span of 9 years and 4 companies I’ve gotten it right many times, but also made my fair share of mistakes along the way. qualitative inputs to inform product strategy has varied wildly across these experiences, and I’ve had to adapt the principles I use to evaluate which method to deploy in real-time as I’ve gone. My strategy for leaning on quantitative vs. In the last 9 years, I’ve had the good fortune of being able to work on mature products with massive customer bases (Facebook, Instagram, Uber), as well as new, zero-to-one products just beginning their lifecycles (at a small fintech startup, as well as here at Statsig). But how do you know when to lean on each? And what are the potential risks of using the wrong one in a given scenario? Earth image credit of Moncast Drawing.Īs a PM, data and research are two of the most powerful tools in our toolkit when it comes to product development. Four heuristics I’ve found helpful when deciding between data vs.
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