How to keep your customers: Anticipate cancellations with a modern data warehouse

_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 In addition, outdated technologies led to high operating costs and made it difficult to implement new requirements.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 approach

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 solution

The modernization of the DWH 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 cancellations based on data and initiate targeted customer retention measures. In addition, the DWH was adapted to the EU GDPR, ensuring compliance with 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 analyses, while pattern recognition allows typical customer behavior to be analyzed before cancellations and countermeasures to be taken at an early stage. The modernized DWH also enables optimized recommendation management through the central storage and evaluation of relevant customer data. This means that individually tailored offers can be automatically suggested, such as family insurance for recently married customers.

Added value through DWH modernization:

Early termination forecasts - identification of customers at risk of churning in order to implement targeted countermeasures at an early stage
Increased customer loyalty - proactive measures based on current data to reduce customer churn
Faster decision-making - near-real-time data management for immediately usable insights
Cost efficiency - reducing operating costs by replacing outdated technologies
Greater flexibility - faster implementation of new requirements thanks to optimized ETL processes
GDPR compliance - ensuring compliance with current data protection guidelines
Advanced customer analysis and targeting - use of web clip analysis, automated pattern recognition and improved recommendation management for individually tailored offers