Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting
Customer Relationship Management (CRM) enjoys increasing attention as a countermeasure to switching behaviour of customers. Because foregone profits of (partially) defected customers are significant, an increase of the retention rate can be very profitable. In this paper, we focus on the treatment of a company’s most promising customers in a non-contractual setting. We build a model in order to predict partial defection by behaviorally-loyal clients using three classification techniques: Logistic regression, ARD Neural Networks and Random Forests. Classification accuracy (PCC) and area under the receiver operating characteristic curve (AUC) are used to evaluate classifier performance. Using real-life data from an FMCG retailer we show that future partial defection can be successfully predicted. Similar to direct-marketing applications, we find that past behavioral variables, more specifically RFM variables (recency, frequency, monetary value) are the best predictors of partial customer defection.
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