Analyze textual, audio, and video data
We build customer analytics solutions that are able to extract useful signals from virtually any source — call transcripts, customer reviews, camera footage, and many others. These signals can be automatically converted into meaningful attributes and tags and then used in downstream models and analytical processes.
Improve Conversion Rates for New Visitors
We build models that personalize the experience of unregistered visitors based on their in-session behavior and device and location data.
These models also identify customers with a high propensity for cart abandonment and prescribe the best mitigating action.
Analyze the dynamics of churn risk
We develop models that not only identify at-risk customers but also estimate how the risk level is likely to evolve in the long run. This helps to determine optimal treatment and intervention time.
Automatically optimize next best action
We extensively use prescriptive models to determine optimal marketing actions for each customer. These models incorporate a wide range of signals and optimize both customer engagement and value.
How our customer analytics platform works
How to get started
We offer free half-day workshops with our top experts in personalization and data science to discuss your marketing technology landscape, customer experience strategy, and opportunities for optimization.
If you have already identified a specific use case for personalization or customer analytics, we usually can 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.
Would you like to learn more about algorithmic foundations of personalization and customer analytics? We published a 500-page book on enterprise data science that is available for free download, and there are several chapters on 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 4 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.
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