Use cases
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.
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.
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.
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.
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.
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
Implementation highlights
Our customer data platform can be deployed both on-premise and in the cloud (AWS, Google Cloud, or Microsoft Azure). In the latter case, we extensively use cloud-native services for scalable big data processing, business logic execution, and model management.
Our customer data platform can be deployed both on-premise and in the cloud (AWS, Google Cloud, or Microsoft Azure). In the latter case, we extensively use cloud-native services for scalable big data processing, business logic execution, and model management.
Data collection can be challenging for financial companies that receive data from a large network of clients or partners such as merchants or banks. We develop integration SDKs, use scalable components to process both data feeds and real-time streams, and ensure exactly-once processing semantics.
Data collection can be challenging for financial companies that receive data from a large network of clients or partners such as merchants or banks. We develop integration SDKs, use scalable components to process both data feeds and real-time streams, and ensure exactly-once processing semantics.
The platform includes data privacy management services to handle PII and PCI data, manage the access levels for different categories of consumers, and perform operations required by GDPR and other standards.
The platform includes data privacy management services to handle PII and PCI data, manage the access levels for different categories of consumers, and perform operations required by GDPR and other standards.
The platform provides finance companies with customer data enrichment and reconciliation capabilities through integration with external providers and the ability to plug in custom business logic. This includes identity resolution, household grouping, deduplication, and probabilistic scoring capabilities.
The platform provides finance companies with customer data enrichment and reconciliation capabilities through integration with external providers and the ability to plug in custom business logic. This includes identity resolution, household grouping, deduplication, and probabilistic scoring capabilities.
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.
Learn more
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.
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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