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Customer churn

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Customer churn

A churn predictor predicts whether a customer is likely to churn. The churn predictor is built and trained from data collected from multiple customers. The data can include static configuration data and dynamic measured data. A churn predictor builder generates multiple customer instances and processes the instances based on the collected data, and based on separating the instances into one or more training subsets. Based on the processing, the builder generates and saves a churn predictor. The churn predictor can access data for a customer and generate a customer instance for evaluation against the training data. The churn predictor processes the customer instance and generates a churn likelihood score. Based on a churn type, the churn predictor system can generate preventive action for the customer.

Master data management is the processes, governance, policies, standards and tools that consistently define and manage the critical data of an organization to provide a single point of reference. One of the benefits of using MDM is that when that single point of reference is a customer profile, the master data can ensure that the treatment of customers is consistent and that preference information reaches all customer points of contact.

To ensure customer retention is front and center, broadband providers should be regularly scoring their database to understand the likelihood of a customer churning from their company (permanently disappeared and presumably to a better and more attractive offer from a rival).

Historical internal data is used to model the difference between a churned customer and one who is still engaged. There would be significant metrics in the data that identify the likelihood of churning. A parametric equation could be constructed that elicits the association and relationship between the target variable and the predictors.

This model would serve as an early warning system and also a strategic tool as to whether a customer was deemed worth retaining. The model would be run on a regular basis across the customer database to understand which customers have reached a critical value for their churn score. The theory is, these customers would then be targeted with an incentive/offer, in the process avoiding the likelihood of them churning. Alternatively, if the customer is deemed of little or no value, there would be no offer forthcoming to entice them to stay.