Churn Predictor & Retn: 6 wks Implementation

Using a combination of Azure Databricks, data lake and Azure ML services we help you understand the traits and behaviours of customers who have churned previously and seek out those with similar attributes in the existing customer base. Our retention assessment practice will give expert recommendations to prevent slippage into at-risk states before it has already occurred, ensuring an optimised customer journey and a continuously improving prediction model. Data will be loaded, typically using Azure Data Factory into partitioned Azure Data Lake, where we build the customer churn prediction model. This works by understanding the traits and behaviours of customers who have churned previously and seeking out those with similar attributes in the existing customer base. Once identified, those consumers can be targeted with appropriate treatments to reinvigorate the relationship and encourage further purchase. Notoriously, whilst churn predictor models will drive reactivation, there can often be wastage as we target consumers when it is already too late. Our retention assessment practice will give expert recommendations to prevent slippage into at-risk states before it has already occurred, ensuring an optimised customer journey and a continuously improving prediction model.