The current business environment requires companies to design strategies that maintain customers loyal and engaged. Retention programs are popular schemes that aim to retain existing customers through incentives. Nonetheless, retention programs do not necessarily lead to returns when they target individuals who will not respond favorably. Business analytics has become in recent years a supporting field for decision-making, since it combines notions from machine learning and management science to extract helpful insights from business data. An extensive set of predictive modeling techniques and performance evaluation metrics have been developed to support targeting decisions. Conventional evaluation metrics, however, do not necessarily lead to the selection of the best model, i.e., the model that maximizes the profit of retention campaigns. Hence, the need for profit-driven evaluation approaches. This study extends the existing literature on profit-driven churn prediction evaluation by taking into account customer heterogeneity and by assessing the framework from the perspective of mutual funds. The intuition is that certain customers generate more value than others, and hence targeting on the basis of churn propensities is suboptimal. Moreover, attrition in mutual funds is highly dependent on the state of the portfolio. The multithreshold multisegment profit-driven churn prediction evaluation framework for mutual funds defines customer heterogeneity on the basis of customer lifetime value (CLV) levels. The customer base is segmented according to the different CLV levels, and the optimal fraction of customers to target is determined within each segment according to: the churn propensities, the segment-wise average CLV, and the costs and success rate of the incentive. Six classifiers are trained based on information regarding customer demographics, customer-firm interactions and financial indicators. Subsequently, current profit-driven churn prediction evaluation approaches are contrasted against our framework. The results consistently indicate that our framework leads to the largest average profit when compared with conventional profit metrics. In addition, we perform a sensitivity analysis to determine the impact of increased retention campaign costs on the average profit and the number of targeted individuals. Our approach prioritizes targeting valuable customers, i.e., segments with a large CLV, as the retention campaign becomes more expensive.
Original languageEnglish
Title of host publication34th annual conference of the Belgian Operational Research Society
Subtitle of host publicationORBEL 34
PublisherBelgian Operational Research (OR) Society
Pages146-160
Publication statusPublished - Jan 2020
Event34th annual conference of the Belgian Operational Research Society - Centrale Lille, Lille, France
Duration: 30 Jan 202031 Jan 2020
Conference number: 34
https://www.orbel.be/orbel34/index.php

Conference

Conference34th annual conference of the Belgian Operational Research Society
Abbreviated titleORBEL
CountryFrance
CityLille
Period30/01/2031/01/20
Internet address

    Research areas

  • Churn predic, Business analytics, Data Science, Profit-driven analytics

ID: 49194458