Game-changing user experience personalization and insight

This case study describes how the implementation of a customer intelligence platform for a video game company was accelerated using a reinforcement learning framework.

Problem

• Personalize in-game experience
• Reduce model development effort
• Increase long-term engagement and customer LTV

Grid Dynamics’ solution

• Reinforcement learning-based personalization platform
• MVP delivered in 8 weeks

Results

Up to 25% dollar-per-user improvement compared to baselines

Would you like to learn more about algorithmic foundations of customer intelligence software? We published a 500-page book on enterprise AI that is available for free download, and there are several chapters on customer analytics and personalization in it.

This report provides an overview of recent advances in customer intelligence by examining 10 industrial case studies. These case studies were selected from the consulting practice of Grid Dynamics and public reports to cover the most important, common, and innovative trends in data science and machine learning methods used in modern customer intelligence and marketing analytics. The report covers the following four major areas of active research and industrial adoption:

• Deep learning models that incorporate a wider range of signals and data, including textual and visual data.
• Deep learning models that process sequences of events, including User2Vec models.
• Reinforcement learning models for the dynamic and strategic optimization of marketing actions.
• Econometric and deep learning models that quantify financial and operational risks.

We have made this report publicly available to help developers of customer intelligence software navigate the latest trends in the field.