Personalize in-game and in-app experiences
We use both traditional personalization models and innovative technologies such as reinforcement learning to optimize in-game and in-app offers and user experiences. Our solutions are designed to provide a customizable balance between long-term and short-term results, customer engagement, and monetization.
Maximize customer lifetime value
Our experience personalization platforms provide unique models and algorithms for strategic optimization to maximize long-term customer engagement and value through a sequence of personalized interactions.
We create advanced models for detecting fraud related to virtual currency operations, loyalty points, and programmatic game playing. These models help to reliably detect cases of fraud, minimizing the impact on normal users.
Fine-tune the game balance
We develop reinforcement learning agents and other tools that help to fine-tune the game balance and solve other design problems. These methods provide significant improvement over the traditional methods.
How our platform works
How to get started
We offer free half-day workshops with our top experts in personalization, data science, and customer data platforms to discuss your marketing technology landscape, customer engagement strategy, and opportunities for optimization.
If you have already identified a specific use case for personalization or customer analytics, we can usually start with a 4–8 week proof-of-concept project to deliver improvements and tangible results.
If you are in the stage of requirements analysis and strategy development, we can start with a 2–3 week discovery phase to identify the right use cases for personalization, design your solution or product using industry best practices, and build a roadmap.
This case study describes how the implementation of a customer intelligence platform for a video game company was accelerated using a reinforcement learning framework.
- 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
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.
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