“But what will weigh on you the most is just how bad it will feel until you find product market fit. It feels like pushing a boulder uphill while wondering whether the boulder wants to move at all.” This is how Sam Gerstenzang describes the pain of finding Product Market Fit within a startup. I liked the post and it includes a few tips, from a founder’s POV, on navigating the idea maze. Still, as I read it, the sadist in me wanted to push the bar even higher and talk a bit about false PMF, which can sometimes be an even worse situation than no PMF (because you’re ramping up spend and activity, essentially running, with increasing speed, in a straight line towards a cliff.
False PMF #1: It’ll Get You 0-1 but not 1-10. A smart team in a big enough market can almost always will themselves to Series A metrics if that’s their sole goal. But that doesn’t make a company more valuable – if anything it creates a local maxima that might be mirage. The ‘let’s just raise more capital and then fix everything about our business’ is a trap – for your team, for your investors, and for yourself. So if your judge PMF merely by “numbers go up” but you know you’re not selling a specific product to a similar group of customers, who themselves are representative of a larger opportunity, it’s not PMF.
False PMF #2: You’re Giving It Away For Free. Revenue is often not the important metric early on, and pricing is usually a series of experiments. But my strong opinion is that while it’s a perfectly valid option to DELAY revenue strategically, if you’re merely avoiding the litmus test of “would someone pay for this” because you’re afraid the answer is NO, then you don’t concretely have PMF. I know there are a bunch of exceptions here and my words apply less to some consumer or open source products, but I’m a strong believer in front loading business model testing – to your benefit, not because you’re starting to manage to a revenue curve.
False PMF #3: Your Bucket is Leaky AF. I’ve seen founders convince themselves (and investors) that they have PMF because user count is going up. But when you dive in the engagement and retention just ain’t there – either you’re counting ‘customers/actives’ too liberally, or good at generating signups/pilots/whatever, but can’t get folks to retain and use. ‘Good faith’ versions of this mistake used to be more common because logging, data analysis, etc were harder to implement in young startups. Less so these days.
As Sam notes, “I’m still not sure I can help find others find product market fit – it remains the single hardest problem in startups.” And I agree. Much of the support we do with startups is helping them prioritize hypotheses/experiments, articulating what positive PMF might look like for their idea, and ultimately trying to find GREAT PMF, not just MAYBE GOOD ENOUGH.
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