Optimizing In-game Store Offers for Every Player with GameStory
2.5%+
revenue lift
1 month
to impact
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Overview:
- GameStory builds skills-based multiplayer mobile gaming applications with cash prizes. They set out to improve price sensitivity for maximum in-game store monetization, as well as improve LTV while reducing churn.
- A classic approach to gaming monetization is to use collaborative filtering to segment customers and offerings. While this uses behavioral data, it isn’t dynamic enough to adapt based on a user's price sensitivity at a specific moment.
- Latent combined advanced large language models (LLMs) with Multi-Armed Bandit strategies to optimize in-game store configurations based on individual customers in real time. Within just a month GameStory gained a 2.5% boost in-revenue.
Collaborative filtering couldn't adapt to a player's precise price sensitivity.
At Gamestory, we set out to personalize player experience using AI. Latent built our ML engine, validated through disciplined, continuous testing, with real gains. They were rigorous with the data, and the results held up."
A classic approach to gaming monetization is to use collaborative filtering to segment customers and offerings. While this uses behavioral data, it doesn't have the dynamic ability to adapt based on a user's price sensitivity at a specific moment. After all, humans are influenced by current mood, previous experience, or general financial status.
Large language models combined with multi-armed bandit strategies to optimize offers in real time.
We combined advanced LLMs with Multi-Armed Bandit strategies to optimize four key areas of value.
Through intelligent optimization, LLMab uses AI to continuously analyze player behavior, spending patterns, and game-specific metrics, refining store offerings on clean, objective data.
For personalization at scale, the system automatically creates and evaluates multiple store configurations for different player segments, bringing more personalized experiences that lead to higher engagement and revenue.
For long-term revenue growth, and unlike traditional A/B testing, LLMab optimizes offerings in real-time rather than offering fixed variations, giving GameStory greater long-term player value that balances immediate sales with higher player retention and engagement.
Through adaptive learning, the system uses a multi-armed bandit approach that adapts to changing player behaviors over time, so GameStory can explore new configurations while maximizing the benefits from what's already driving positive results.
Smarter offers, longer player lifetime, higher revenue
LLMab now configures store offerings on its own, with recommendations that carry the nuance of human judgment instead of raw statistical optimization. Within one month, revenue rose 2.5%+. Sharper offers lift purchase rates, keep players in the game longer, and cut the churn that drags down lifetime value. The analysis it produces feeds beyond store configuration into broader game design and monetization strategy, so the value compounds upstream of any single offer. Because the model evaluates and adjusts without constant human intervention, those gains hold across the catalog. The same approach extends to other game genres and platforms, turning one monetization result into a repeatable engine for revenue and retention.
