We help entertainment and video game companies to build state-of-the-art personalization platforms and improve their products using AI-powered tools.

Use cases

Engagement

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.

Engagement

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.

Operations

Prevent fraud

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.

Development

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.

Our clients

Retail
Hi-tech
Manufacturing
Finance

How our platform works

Plug-and-Play architecture
We use state-of-the-art technology to minimize engineering effort and productization complexity. Our reinforcement learning-based personalization platform can learn from app/game logs and iteratively generate action policies (decision-making models) that can be deployed directly to production. This architecture sharply reduces the initial implementation timeframe compared to traditional customer analytics and modeling techniques.
Comprehensive set of models
We have extensive experience creating AI/ML tools for app and game developers, including fraud detection models, balance tuning, price optimization, and more. For most of these use cases, we have reference implementations that help us to quickly evaluate multiple modeling approaches, determine the best option, and deploy it to production. Our tools are based on statistical models to help improve the productivity of development and operations teams, reduce risks, and improve profitability.

How to get started

We provide flexible engagement options to help you build customer intelligence solutions faster. Contact us today to start with a workshop, discovery, or proof of concept.

Learn more

A plug-and-play platform for in-app and in-game experience personalization
In this article, we discuss the design of a personalization platform that reduces implementation complexity and improves the quality of recommendations using reinforcement learning. We mainly focus on video games and entertainment use cases and discuss how a reinforcement learning approach helped to rapidly create advanced customer intelligence software.
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Customer2Vec: representation learning for customer analytics and personalization
In this article, we focus on the learning of useful semantic representations (embeddings) for products and customers using neural networks. These embedding vectors then can be used as features in downstream propensity and recommendation models as well as to build better customer intelligence platforms.
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Building a next best action model using reinforcement learning
In this tutorial, we discuss how traditional targeting and personalization models such as look-alike and collaborative filtering can be combined with reinforcement learning to solve Next Best Action problems—that is, the strategic multi-step optimization of marketing actions. We use methods that were originally developed and tested by Adobe and Alibaba, and they proved to be effective in a number of practical use cases.
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Personalizing in-game experience using reinforcement learning

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

Read more on algorithmic foundations of personalization

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.

Read more on advanced customer intelligence

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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