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Effective CRM using Predictive Analytics by Antonios Chorianopoulos

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6A voluntary churn propensity model for credit card holders

6.1 The business objective

A bank was losing credit card customers to its competitors, and the marketers of the organization decided to use analytics in order to deal with this issue. Their goal was to use data mining models for identifying customers with increased propensity to churn so they could fine-tune their retention campaigns.

Each credit card customer is unique. They all have different demographic profiles and usage characteristics. They vary in terms of spending frequency and intensity, payment behavior, and limit usage. Certain customers use their cards very often, while for others, the usage is infrequent and low, maybe because their main card is with a competitor. Some customers may have increased their usage compared to the past, while others may show signs of usage decrease. The so-called transactors usually pay their full balances every month as opposed to other customers who tend to carry past balances and use their entire credit limit. “Old” customers have a long relationship with the bank, whereas others are new customers. So what makes a customer loyal and how all these characteristics are related to customer attrition. Fortunately, all this information which defines each customer’s unique “signature” could be retrieved from the bank’s mining datamart. After proper transformations, they could be used as inputs in the churn model to be trained.

Since not all customers are equally important, the ...

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