Why neural networks are the future of mobile gaming growth
Mobile games have traditionally turned to programmatic advertising partners for their effectiveness in achieving scale. But as gaming markets become more competitive, an acquisition-at-all-costs approach simply doesn’t cut it anymore. Mobile games now live or die by aggressive ROI targets that require users to play consistently, engage with ads, and spend on in-app purchases. They need an ad partner who can leverage data in more innovative and impactful ways to make it happen.
This is where neural network solutions like Liftoff’s Cortex play a key role. Cortex is the next evolution of Liftoff’s machine-learning platform powered by neural networks. Moving models to neural nets represents a game-changing expansion in the capacity and types of data signals models can harness. The new ML models can identify more complex patterns from larger datasets to make accurate predictions for specific outcomes. For gaming advertisers, this means connecting your game with audiences more likely to engage consistently and spend more on in-game purchases over time.
In the past five years, programmatic advertisers have seen the popularization of real-time bidding (RTB). RTB ads offer advertisers greater transparency into the ad buying process, enabling them to precisely target their ideal audience by delivering a personalized ad experience. This real-time approach has also given DSPs (demand-side platforms) that invest in robust machine learning a significant advantage.
Game developers have a wealth of first-party data that provides a holistic profile of their audience beyond their demographic, including how they play and what motivates gamers to keep coming back. Unlike traditional targeting, which relies on segmenting audiences based on characteristics like gender, region, or age, advanced machine learning solutions leverage historical data such as session time, ads watched, previous purchases, and others to model current and future user behavior.
Cortex, for example, combines
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