How Celebs grows

A/B tested removing the end-of-onboarding paywall so users reach the 'moment of maximal intrigue' (their lookalike result) as fast as possible. Works because the app already had ~10 optimized conversion placements downstream (on seeing the result, on sharing, on reopening). Advice: keep the onboarding paywall (hard or trial) unless there is a clearly better moment later in the app to ask.

Look for apps that look like short-term viral hits but tap a broad, lasting interest (e.g. celebrity lookalike curiosity). Buy them after the viral peak while revenue is falling and sellers don't know how to grow them. Judge on data: can we scale it profitably long-term and 20x revenue, making multiples of the acquisition price. Then run the studio's UA machine plus dozens of A/B tests a month per app to lift ARPU (the LTV/CAC loop).

Don't treat a losing test as permanently settled: results depend on the surrounding flow, which keeps changing. Periodically retest old assumptions, especially high-lift ones, in the current configuration. The Celebs paywall-removal test failed several times before winning.