VixT: From Vanity Installs
to Paying Users
Measurement infrastructure for a mobile app launch - in-app event taxonomy, server-side event forwarding, and conversion restructuring that shifted optimisation from cheap installs to actual revenue.
The brief
VixT was a video meme creator app entering an overcrowded category. The founding team had a product, a small seed budget, and no measurement infrastructure. Their launch strategy relied on paid user acquisition across Meta and Google App Campaigns - but they had no way to distinguish a user who downloaded the app from one who actually created content, shared a meme, or upgraded to premium. They were about to spend money acquiring users they couldn't measure.
What we found
Google and Meta optimised for installs - the only event they received. Cost per install looked reasonable at £0.83. But 60%+ never opened the app again. Actual cost per activated user was £3.40, cost per premium subscriber was £14.20. None of this was visible in ad platform reports.
Key in-app actions - first meme created, first share, third-day retention, premium trial, premium conversion - weren't flowing back to Meta or Google. The algorithms had no signal beyond the install.
Both platforms claimed credit for the same installs. Total reported installs exceeded store actuals by roughly 22%.
What we built
8 post-install events: app_open, first_meme_created, first_share, day3_retention, premium_trial_start, premium_converted, meme_exported_with_watermark, meme_exported_clean. Each with parameters for content type, template, and share destination.
Key conversion events forwarded server-to-server via Conversions API with hashed user identifiers. Meta received post-install signals enabling optimisation beyond installs.
Replaced the single "install" conversion with tiered structure: primary = premium_trial_start, secondary = first_meme_created and first_share. Smart Bidding shifted from cheap installs to engaged users.
Firebase and Meta SDK deduplication. Self-referral exclusions. Install attribution aligned between platforms and store actuals within 3% variance.
Full funnel dashboard: impressions → clicks → installs → activations → retention → trials → conversions. By platform, campaign, creative, geography. Cost metrics at every stage.
Results
Over 90 days post-launch
The cost per install went up - and that was the point. Google and Meta stopped finding the cheapest possible installs and started finding users who actually used the app. Premium conversion rate nearly doubled because the algorithms had real engagement signals to optimise against.
Tech stack
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