An Engine to Find Product/Market Fit

Previously I shared Brad Feld & Neeraj Agrawal’s descriptions of how hard it is for a startup to achieve product/market fit… and the ramifications of that failure. Recently Jim Stanczak kindly shared an article identifying the solution: a numbers-based, systematic, empirical process for measuring product/market fit and iterating your product towards it. This article is pure gold. Highlights:

0) Anchor on a leading indicator.

Just ask users “how would you feel if you could no longer use the product?” and measure the percent who answer “very disappointed.”

After benchmarking nearly a hundred startups with his customer development survey, Ellis found that the magic number was 40%.

1) Segment to find your supporters and paint a picture of your high-expectation customers.

As an early-stage team, you could just narrow the market with preconceived notions of who you think the product is for, but that won’t teach you anything new. If you instead use the “very disappointed” group of survey respondents as a lens to narrow the market, the data can speak for itself — and you may even uncover different markets where your product resonates very strongly.

2) Analyze feedback to convert on-the-fence users into fanatics.

This batch of not disappointed users should not impact your product strategy in any way. They’ll request distracting features, present ill-fitting use cases and probably be very vocal, all before they churn out and leave you with a mangled, muddled roadmap. As surprising or painful as it may seem, don’t act on their feedback — it will lead you astray on your quest for product/market fit.

3) Build your roadmap by doubling down on what users love and addressing what holds others back.

To increase your product/market fit score, spend half your time doubling down on what users already love and the other half on addressing what’s holding others back.

4) Repeat the process and make the product/market fit score the most important metric.

The percent of users who answered “very disappointed” quickly became our most important number. It was our most highly visible metric, and we tracked it on a weekly, monthly and quarterly basis. To make this easier to measure over time, we built some custom tooling to constantly survey new users and update our aggregate numbers for each timeframe. We also refocused the product team, creating an OKR where the only key result was the very disappointed percentage so we could ensure that we continually increased our product/market fit.

Read the whole article and consider it an instruction manual. We certainly are at Launch413. My thanks to Rahul Vohra for taking the time to document this process and share it with the world.

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