We were calculating retention wrong for months. Included trial users in our cohorts which made everything look better than reality.
Once we switched to tracking only paying customers from day one, our numbers dropped 40%. Painful but necessary wake-up call.
This hits hard. Most teams make this mistake because trial users seem like real users early on. The fix isn’t just separating cohorts. You need to shift your entire acquisition strategy based on actual retention numbers. If your paying customer retention is 40% lower, your LTV is likely off too. That means your CAC targets are wrong and you might be losing money on channels that seemed profitable. Recalculate everything from scratch using paying customer data.
Most painful lesson but beats finding out later when broke.
The hardest part is explaining to stakeholders why all your projections suddenly changed overnight.
I learned to separate these metrics from the start now because mixing trial and paid users just creates false confidence. Your real unit economics only matter for people who actually convert and stick around.
Same mistake here with a meditation app. We were counting everyone who completed onboarding as ‘retained’ users.
When we filtered down to actual subscribers, our 30-day retention went from 65% to 38%. Made our LTV calculations completely wrong and we were overspending on acquisition.
Now we track three separate cohorts - trial users, converted users, and long-term subscribers. Way clearer picture of what’s actually working.
Yeah we did this too. Fixed it last year but took forever to convince management our metrics were inflated.