Applicability of Customer Churn Forecasts in a Non-Contractual Setting
As selling a product to an existing customer is much more cost effective than acquiring new customers companies increasingly focus on retaining profitable customers rather than concentrating all marketing actions on the acquisition of new customers. For retaining customers it is very important to be able to predict whether a customer is still active. Effectless marketing expenses directed towards already inactive customers can be avoided and more intensive marketing actions can be taken in order to support active customers’ purchase intentions. Several methods exist that can be used to predict customer activity. In this paper we apply a stochastic and a data mining method to real-life B2B purchase histories and compare the usability and the quality of churn prediction of each of the methods in a non-contractual B2B environment.
KeywordsProbit Model Error Type Purchase Intention Active Customer Total Error Rate
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