We create customer data platforms and advanced analytics solutions tailored to the needs of payment providers and insurance and financial companies.

Use cases

Data platform

Manage customer data efficiently

Our customer data management solutions include powerful capabilities for inbound and outbound data integrations at any scale. We help financial companies to collect data in batch and stream modes from their internal sources, partner systems, mobile apps, and many others. The consolidated data and insights can be streamed back to partners, internal teams, ad networks, and more.

ML platform

Simplify AI/ML productization

Productization of AI/ML prototypes can be a big challenge without a solid ML platform. Our customer intelligence platform provides capabilities for efficient experiment tracking, model versioning, feature management, and production model deployment to overcome these challenges. This helps to rapidly deliver projects around personalization, lifetime value estimation, churn prevention, default risk scoring, and more.

Data platform

Monetize your data

We help financial companies to monetize their data by providing advanced insights, marketing services, and risk analytics to their partners. We make this possible using our extensive expertise in data engineering, MLOps, marketing sciences, and machine learning.

Data platform

Be confident in your data

Our customer intelligence solutions come with a comprehensive set of data quality, privacy, and integrity checks. This helps to prevent major data issues and provide quality guarantees to business users and partners.

Advanced analytics

Provide advanced insight

We are instrumental in developing advanced personalization, risk scoring, and fraud detection models and algorithms. This helps to create customer intelligence platforms that provide advanced insights to internal teams, improve and personalize customer experience across multiple channels, and provide analytics and marketing services to external partners.

Advanced analytics

Improve customer engagement

We develop state-of-the-art personalization and targeting models that help financial companies to strategically improve customer engagement. These models are focused on determining the optimal action sequences that maximize customer lifetime value, optimize product usage, and prevent churn and complaints.

Our clients

Finance & Insurance

Implementation highlights

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

Cross-channel marketing spend optimization using deep learning
We explore how deep learning methods can be used to analyze sequences of customer interactions and how the insights gained from such analyses can be used for spend optimization. We gradually build a solution that can be applied to several common scenarios, including campaign optimization, channel optimization, and channel parameters optimization.
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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 to improve customer analytics and segmentation as well as features in the downstream customer intelligence software and components such as propensity and recommendation models.
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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 optimize multi-step marketing action policies (aka Next Best Action policies). 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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Read more on customer analytics and personalization

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

Read more on customer intelligence solutions

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 areas of advanced customer analytics.

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