Customer Attrition Analytics in Banking

Abstract

In an era of mature markets and intensive competitive pressure, more and more companies realise that their most precious asset is their existing customer base. This realisation has resulted in a rise in emphasis on customer relationship management, in order to retain customers. This is a major area on which banks need to concentrate. Banks tend to be reactive to customer attrition, and many times it is too late to retain a customer. Customer attrition needs to be minimised, and loyal customers need to be rewarded. The objective of this study to identify the factors affecting customer attrition of trust accounts for a leading American financial services company. The company realised that its trust accounts were getting closed after a period of seven to twelve years. Initially, the company tried to identify the root cause using a small set of data, but they were unable to do so. This triggered the use of analytics to build a model to predict customer churn, and come up with strategies to retain customers. This was achieved by applying data mining techniques to the transactions history of the accounts that closed down as against those that remained active.

Keywords: Customer Attrition Analytics, Customer Relationship Management, Data Mining

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