Profit-Based Model Selection for Customer Retention Using Individual Customer Lifetime Values
Author(s) -
María Óskarsdóttir,
Bart Baesens,
Jan Vanthienen
Publication year - 2018
Publication title -
big data
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.774
H-Index - 27
eISSN - 2167-647X
pISSN - 2167-6461
DOI - 10.1089/big.2018.0015
Subject(s) - customer lifetime value , customer base , customer retention , customer equity , analytics , profit (economics) , measure (data warehouse) , customer profitability , business , computer science , customer to customer , marketing , customer intelligence , customer advocacy , data science , data mining , microeconomics , economics , service quality , service (business)
The goal of customer retention campaigns, by design, is to add value and enhance the operational efficiency of businesses. For organizations that strive to retain their customers in saturated, and sometimes fast moving, markets such as the telecommunication and banking industries, implementing customer churn prediction models that perform well and in accordance with the business goals is vital. The expected maximum profit (EMP) measure is tailored toward this problem by taking into account the costs and benefits of a retention campaign and estimating its worth for the organization. Unfortunately, the measure assumes fixed and equal customer lifetime value (CLV) for all customers, which has been shown to not correspond well with reality. In this article, we extend the EMP measure to take into account the variability in the lifetime values of customers, thereby basing it on individual characteristics. We demonstrate how to incorporate the heterogeneity of CLVs when CLVs are known, when their prior distribution is known, and when neither is known. By taking into account individual CLVs, our proposed approach of measuring model performance gives novel insights when deciding on a customer retention campaign. The method is dependent on the characteristics of the customer base as is compliant with modern business analytics and accommodates the data-driven culture that has manifested itself within organizations.
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