Moloco report

Inside a mobile game’s first year

How the best launches are built to last

The genre mix has reshuffled as Strategy & Casual have gained ground on RPG for IAP revenue

Share of new launch global IAP revenue for the first 365 days, global{[data-attribute="init-tooltip"][tooltip-content="<p>Source:<br><br>Moloco analysis of SensorTower data, 2021-2025. 2025 launches global IAP revenue share as of July 21, 2026.</p>"]}

APAC publishers are setting the pace, capturing the vast majority of global IAP revenue

IAP revenue for top 100 new launches by year{[data-attribute="init-tooltip"][tooltip-content="<p>Source:<br><br>Moloco analysis of SensorTower data, 2021-2025. % of first year global IAP revenue by publisher HQ as defined by SensorTower.</p>"]}

Publishers are extending the learning window as median soft launch duration nearly doubles

Moloco-supported launches, 2022-2025{[data-attribute="init-tooltip"][tooltip-content="<p>Source:<br><br>Moloco Internal Analysis, Global, 2021–2025. Covers 48 gaming apps soft launch windows between January 1st 2021 and December 31st 2025 in which the game had UA spend with Moloco prior to worldwide release date.</p>"]}

Why the first year matters more than the first month

The new launch market has never been more competitive, but the opportunity is there for those who play it right. Across the first year, monetization keeps developing: payers grow more valuable, whales take time to emerge, and many of the highest-value users arrive months after launch. At the same time, the breakout titles in our analysis show what it takes to operate against that reality: bidding for predicted value early, sustaining UA spend throughout the year, and continuing to expand creative well beyond launch.

When it comes down to it, the launch month offers some signal about whether a title is working and how publishers should adjust. But it can’t tell the whole story. It often misses the value that develops later: payers who spend more over time, whales who haven’t arrived yet, and creative or UA learnings that only compound after months of iteration.