AuDaScience Retention BRAINs™

Customer Retention Optimization – big data analytics for churn prediction and reward optimization

Retention BRAINs provides a unique and innovative approach to customer churn prediction modeling.

Some unhappy customers can get very loud. Many others just silently churn. Within customers' big data hides the priceless information about who is more susceptible to soon migrate to your competition. Within only hours [instead of months], Retention BRAINs turns this data into valuable retention recommendations, enabling you to keep paying customers happy and even win back old customers.

Retention BRAINs is based on the BRAINs [Big-data Recommendations for Actionable Insights] analytical platform, designed and built on many years of statistical and data analysis experience and more specifically, churn prediction modeling and retention optimization modeling for large consumer based organizations.

By using powerful automated machine learning, retention managers with no statistical know-how can easily produce lists of high probability customer churn and win-back which justify retention efforts.

Retention BRAINs enables consumer oriented service providers in highly competitive markets such as banks, insurance companies, telecom operators, media channels, utilities, healthcare organizations, etc., to build and deploy a larger number of churn prediction and customer retention models, by different customer segments, business lines and churn definitions (full churn, partial churn, churn by products).

Retention BRAINs requires fewer resources while providing prediction with more than 50% better accuracy than classic manual modeling practices, thereby preventing potential revenue loss of millions of dollars each year.

Retention BRAINs easily integrates into the company's Customer Service platforms, Retention operations and IT environment (DWH, Campaign Management), providing clear, measurable ROI in months.

Act before they churn

Retention BRAINs makes the design of prediction models for customers of various segments a simple operation. The patent-pending AuDaScience Multi Segment Modeling technology generates dozens of machine learning models in hours, a process that normally requires many months of combined analytics and statistics expertise.

Using a modern Drag/Drop interface, the application enables retention managers to automatically define, develop and deploy many sophisticated statistical customer retention models. The results recommend and prioritize different retention strategies according to the organization’s dynamic service, marketing and sales policies.

Core differentiators