Identification of customers who are highly likely to leave the company or stop using a service should be taken seriously and prevented by creating relevant offers. It should also be tackled in every customer segment separately: predicting churn probability for postpaid customers, for example, requires a completely different approach then predicting it for business or prepaid customers. Utilizing all available data sources requires data integration, as well as building high scale data lake architecture.
Reducing and keeping churn rate under control results in a more stable business revenue. Retention strategy is optimized, while customer targeting strategies are in line with the most appropriate offers.
Developed and fully deployed in the customer environment, the classification algorithm predicts the probability that the target risk event will happen. All available data sources are included in the calculation, using the latest integration tools. Different customer segments are targeted with appropriate campaigns at the right moment, as we assume the reason for customer dissatisfaction and combine it with profitability estimation.