WHAT WE DO
Development of a predictive model based on algorithms to detect the customer’s pattern behavior in their previous downward months. These data allowed the detection of customers interests and needs in the company to define shares for retention.
A predictive model based on supervised algorithms to detect the pattern of customer behaviour in their previous downward months developed. Historical data were used 18 months. Predictor’s different data sources that allowed evaluate the customer’s relationship with the company from several viewpoints were used: losses, claims, premiums, antique, ….
On customers classified with high probability of default a value clustering allowed to know the type of each of the patterns of behaviour that led the client to unsubscribe performed. From this clustering which retain customer interest was detected by value and how they should be holding the shares.
- Creating a new product for young people with conditions that made it competitive, while maintaining positive return
- Improved customer experience in pursuing claims
- Customer orientation and not a product. Encouraging customers with policies in several branches
- Premium reduction effort to high-value customers.
Claims optimization process
Premium reduction effort