Product-Market Fit (PMF)

Several frameworks measure PMF. The Sean Ellis Test asks users: "How would you feel if you could no longer use this product?" — a score of 40%+ responding "very disappointed" is a strong PMF signal. Retention curves tell a clear story: if the cohort retention curve flattens significantly above 0% (a "smiling" curve), users are finding durable value. Organic growth is another signal — when word-of-mouth exceeds paid acquisition, the product is resonating. NPS, time-in-product, feature adoption breadth, and qualitative user interview themes are complementary signals. No single metric is sufficient; Product Ops triangulates across multiple data sources.

Before PMF, the primary objective is learning, not scaling. Hiring aggressively, building marketing channels, and optimizing funnels before PMF is a common — and expensive — mistake. The team should be small and focused on iterating based on customer feedback. After PMF, the objective shifts to scaling: adding sales capacity, building marketing infrastructure, professionalizing customer success, and expanding the engineering team to accelerate feature development. Product Ops signals this transition by demonstrating: stable retention curves, consistent organic referral rates, and a clear ICP whose conversion is predictable.

Yes — markets evolve, competitors emerge, and product stagnation can cause PMF to erode. Early warning signals include declining retention in new cohorts (while earlier cohorts remain healthy), rising churn reason categorized as "better alternative found," and NPS decline among power users. Product Ops monitors cohort-over-cohort retention comparisons, tracks feature adoption trends for the core value-delivering features, and conducts regular win/loss analysis with Sales. When signals suggest PMF erosion, the response is a focused discovery sprint — customer interviews, usage analytics deep dives, and competitive analysis — to identify whether product changes or messaging repositioning is required.