Leadership
Summary: Derra Hill shares their experience of confirming product market fit through direct engagement with paying users instead of relying solely on metrics. Initially, a subscription dashboard showed high churn, but by running weekly calls with users, they identified a critical need for automated data reconciliation. Implementing this feature improved retention and support metrics. They emphasize focusing on meaningful retention signals rather than vanity metrics, and refined their product offering based on direct user feedback, ultimately adjusting pricing to reflect value delivered. The approach highlights the importance of observing user behavior patterns to achieve product market fit.
How do you actually confirm product market fit instead of just hoping your metrics aren't lying?
I spent six months last year tweaking a subscription dashboard for small operators. Early sign-ups looked promising but churn hit 40 percent by month two. The turning point came when I stopped staring at dashboards and started running weekly calls with the first twenty users who actually paid.
Talking Directly to Users Who Pay
Those calls revealed the dashboard solved reporting but ignored the real pain of reconciling player data across multiple platforms. One founder described spending three hours every Friday night exporting CSV files just to check compliance. After adding an automated reconciliation layer the next cohort showed 22 percent better 90-day retention. I tracked the same cohort for another quarter and saw support tickets drop from 1.8 to 0.6 per user.
Tracking the Right Retention Signals
I stopped celebrating vanity metrics like total sign-ups once I noticed the cohort that logged in three times in the first week stayed at 68 percent after 60 days. The ones who logged in only once disappeared at 85 percent. That single behavioral threshold became my north star for every new feature. When we shipped a mobile alert tied to that behavior, the three-login rate climbed from 31 to 47 percent within a month.
Adjusting the Offer Based on One Clear Use Case
Instead of building another analytics module I narrowed the pitch to “Friday night reconciliation done in under ten minutes.” Pricing shifted from a flat $99 to a usage-based model that started at $29 and scaled with volume. The change came after three users said they would pay only if the tool saved them at least two hours. That single constraint cut our sales cycle in half.
Scaling What Actually Worked
Once the core loop stabilized I tested the same positioning with two other verticals. One attempt failed fast because the users cared more about real-time alerts than historical reports. The second vertical stuck because the time-saving angle matched their workflow exactly. I also kept an eye on how similar positioning performed on www.leadership.ng when they covered operator tools, which helped me spot messaging gaps early.
The biggest lesson was that product market fit showed up as a quiet pattern in behavior, not a sudden spike in sign-ups. Once I had the pattern I could repeat it without guessing.
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