Banking & Insurance
How to keep your customers: anticipate cancellations with a modern data warehouse
The Challenge
In order to proactively reduce customer churn, a leading insurance company wanted to create precise cancellation forecasts. The existing data warehouse (DWH), which was getting on in years, could no longer meet the increasing demands for speed and flexibility. In addition, outdated technologies led to high operating costs and made it difficult to implement new requirements.
Our solution
To meet these challenges, the DWH was comprehensively modernized. New data sources were integrated automatically and the database objects and ETL processes were redesigned. By using the Oracle Golden Gate replication solution, near-real-time data management was implemented, which made it possible to process current data almost in real time.
The result
The modernization of the data warehouse led to a significant reduction in operating costs. The introduction of the new ETL technology improved performance and shortened the time-to-market for new requirements. Near-real-time data management enabled the company to predict customer churn based on data and implement targeted customer retention measures. In addition, the DWH was adapted to comply with the EU GDPR, ensuring adherence to current data protection guidelines. The modernization also made it possible to integrate new data and implement numerous new features: The use of real-time data enables web clip analysis, while pattern recognition allows for the analysis of typical customer behavior prior to cancellations, enabling early countermeasures to be taken. The modernized DWH also enables optimized recommendation management through the centralized storage and analysis of relevant customer data. This allows for the automated suggestion of personalized offers, such as family insurance for recently married customers.
Added value through DWH modernization:
- ▪ Early Churn Predictions – Identifying customers at risk of churning in order to implement targeted countermeasures at an early stage
- ▪ Increased Customer Loyalty – Proactive Measures Based on Real-Time Data to Reduce Customer Churn
- ▪ Faster Decision-Making – Near-Real-Time Data Management for Immediately Actionable Insights
- ▪ Cost Efficiency – Reducing Operating Costs by Replacing Outdated Technologies
- ▪ Greater flexibility —faster implementation of new requirements through optimized ETL processes
- ▪ GDPR Compliance – Ensuring Compliance with Current Data Protection Guidelines
- ▪ Advanced customer analysis and engagement —using web clip analysis, automated pattern recognition, and improved recommendation management to deliver personalized offers
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