PropGPT · B2C · sports betting picks app
If many start a trial but few pay, fix the product, not the marketing
Spotted 45% trial starts vs 13% paying; led to the rebuild
Workedanalytics-attributionproduct-activation
What they did
Used PostHog analytics, Superwall paywall data and user conversations to find behavior that didn't match the app's design. High install-to-trial (45%) with low trial-to-paid (13%) meant users wanted the promise but the product experience disappointed. Also: track onboarding drop-off to find screens that don't sell, and track feature clicks to find the most compelling value proposition to use in marketing.
What happened
Diagnosis led to the 4-month rebuild that lifted conversion to paid above 50%.
- Stage
- Launch (post-launch)
“For example, on Superwall, we saw that a lot of people were converting to the trial, but then nobody was staying after.”
“45% conversion to trial, but then only 13% conversion from trial to paid. What does that mean? Everybody wanted what we were selling, but the product was shit.”
“If you track all of the feature clicks on your app, you can identify your app's most compelling value proposition, and then communicate that in your marketing.”
Related topics
Get tactics like this every Monday
New tactics from the week's founder interviews, each linked to where it was said.
More from this episode
- PropGPT: Pause marketing for 4 months and rebuild the app to hand users the picksTrial-to-paid went from 13% to 50%+; MRR $1.7K to $40K peak in 2.5 months
- PropGPT: Spend a few thousand dollars on influencers at launch to see downloads per dollar~20 downloads/day and 5-10 trials/day, but stuck at $1-2K MRR
- PropGPT: Go all in on marketing right before the NBA PlayoffsConversion to paid 50%+; $40K MRR peak 2.5 months later
- PropGPT: Keep putting out influencer videos until one goes viralThe ~70th video got 600K views; revenue run-rate jumped ~$8K to $38K in 3 days