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ES-Working Paper No. 12 FACULTEIT ECONOMISCHE EN SOCIALE WETENSCHAPPEN & SOLVAY BUSINESS SCHOOL ES-Working Paper no. 12 THE CASE FOR PRESCRIPTIVE ANALYTICS: A NOVEL MAXIMUM PROFIT MEASURE FOR EVALUATING AND COMPARING CUSTOMER CHURN PREDICTION AND UPLIFT MODELS Floris Devriendt and Wouter Verbeke April 30th, 2018 Vrije Universiteit Brussel – Pleinlaan 2, 1050 Brussel – www.vub.be – [email protected] © Vrije Universiteit Brussel This text may be downloaded for personal research purposes only. Any additional reproduction for other purposes, whether in hard copy or electronically, requires the consent of the author(s), editor(s). If cited or quoted, reference should be made to the full name of the author(s), editor(s), title, the working paper or other series, the year and the publisher. Printed in Belgium Vrije Universiteit Brussel Faculty of Economics, Social Sciences and Solvay Business School B-1050 Brussel Belgium www.vub.be The case for prescriptive analytics: a novel maximum profit measure for evaluating and comparing customer churn prediction and uplift models a, a Floris Devriendt ⇤, Wouter Verbeke aData Analytics Laboratory, Faculty of Economic and Social Sciences and Solvay Business School, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium Abstract Prescriptive analytics and uplift modeling are receiving more attention from the business analyt- ics research community and from industry as an alternative and improved paradigm of predictive analytics that supports data-driven decision making. Although it has been shown in theory that prescriptive analytics improves decision-making more than predictive analytics, no empirical evi- dence has been presented in the literature on an elaborated application of both approaches that allows for a fair comparison of predictive and uplift modeling. Such a comparison is in fact prohib- ited by a lack of available evaluation measures that can be applied to predictive and uplift models. Therefore, in this paper, we introduce a novel evaluation metric called the maximum profit uplift measure that allows one to assess the performance of an uplift model in terms of the maximum potential profit that can be achieved by adopting an uplift model. The measure is developed for evaluating customer churn uplift models and for extending the existing maximum profit measure for evaluating customer churn prediction models. Both measures are subsequently applied to a case study to assess and compare the performance of customer churn prediction and uplift models. We find that uplift modeling outperforms predictive modeling and allows one to enhance the profitabil- ity of retention campaigns. The empirical results indicate that prescriptive analytics are superior to predictive analytics in the development of customer retention campaigns. Keywords: Analytics, Business applications, Prescriptive analytics, Uplift modeling, Customer churn prediction, Customer retention ⇤Corresponding author Email addresses: [email protected] (Floris Devriendt), [email protected] (Wouter Verbeke) Preprint submitted to European Journal of Information Sciences April 9, 2018 1. Introduction The term business analytics is used as a catch-all term covering a wide variety of what essentially are data-processing techniques. In its broadest sense, business analytics strongly overlaps with data science, statistics, and related fields such as artificial intelligence (AI) and machine learning [1]. Analytics is used as a toolbox containing a variety of instruments and methodologies allowing one to analyze data in support of evidence-based decision-making with the aim of enhancing efficiency, efficacy, and, thus ultimately, profitability. Types of analytical tools, in increasing order, are descriptive, predictive, and prescriptive analytics. While descriptive analytics o↵er insight into current situations, predictive analytics allow one to explain complex relations between variables and to predict future trends. As such, predictive analytics o↵er more uses than descriptive analytics. Currently, prescriptive analytics are receiving more attention from practitioners and scientists in that they add further value by allowing one to simulate the future as a function of control variables to prescribe optimal settings for control variables. At the core of prescriptive analytics is uplift modeling, which is introduced below. In the experiments reported in this article, the use and performance of predictive and prescriptive analytics is thoroughly compared. Business analytics is being applied to an increasingly diverse range of well-specified tasks across a broad variety of industries. Popular examples include tasks related to credit scoring [2, 3], fraud detection [4], and customer churn prediction [5, 6], the latter being the application of interest in this article. Customer churn prediction models are designed to predict which customers are about to churn and to accurately segment a customer base. This allows a company to target customers that are most likely to churn during a retention marketing campaign, thus improving the efficient use of limited resources for such a campaign, i.e., the return on marketing investment (ROMI), while reducing costs associated with churning [7]. Generally speaking, customer retention is profitable to a company because (1) attracting new clients costs five to six times more than retaining exist- ing customers [8–11]; (2) long-term customers generate more profits, tend to be less sensitive to competitive marketing activities, tend to be less costly to serve, and may generate new referrals through positive word-of-mouth processes, whereas dissatisfied customers might spread negative word-of-mouth messages [12–17]; and (3) losing customers incurs opportunity costs due to a reduc- tion in sales [18]. Therefore, a small improvement in customer retention can lead to a significant increase in profits [19]. However, it has been reported that marketing actions undertaken to retain customers may actually provoke the opposite behavior and may cause or motivate a customer to churn. As noted in Radcli↵e and Simpson [20], churn risk is highly correlated with customer dissatisfaction, and the goal in turn becomes to prevent a dissatisfied customer from actually leaving. Any attempt made to contact a dissatisfied customer with the goal of retaining him or her can actually hasten 2 the process and provoke the customer to leave earlier than expected [20]. Therefore, it is necessary to evaluate the e↵ectiveness of a retention campaign at the individual customer level. Predictive models fail to di↵erentiate between customers who respond favorably (i.e., who do not churn) to a campaign and customers who respond favorably on their own accord regardless of a campaign (i.e., who would not have churned in any case and who were not targeted by a campaign). To address this shortcoming of predictive models, uplift modeling has recently been proposed as an alternative means of identifying customers who are likely to be persuaded by a promotional marketing campaign, rather than predicting whether customers are likely to respond to a promo- tional marketing campaign (which may or may not be the result of the campaign). Uplift modeling can be applied to identify customers who are likely to be retained through a retention campaign as an alternative to predicting whether customers are likely to churn [21]. More precisely, uplift modeling aims at establishing the net di↵erence in customer behavior resulting from a specific treat- ment a↵orded to customers, e.g., a reduction in the likelihood to churn with retention campaign targeting. In this paper we aim to contrast customer churn prediction (CCP) and customer churn uplift (CCU) modeling for customer retention by comparing their performance when applied to an ex- perimental case study of the financial industry. To compare the performance of these approaches, a common evaluation procedure is applied. However, given the di↵erent forms of output that these models produce, to evaluate prediction and uplift models, di↵erent performance measures are used. In evaluating classification models and, more specifically, CCP models, the receiver operating char- acteristic (ROC) curve or lift curve are typically used. Performance can be expressed as the area under the ROC curve, as the top decile lift or as the (expected) maximum profit. In evaluating uplift models, the Qini curve and uplift per decile plots are typically used. Performance is typically reported in terms of the Qini index or top decile uplift. As the goal of customer churn modeling is to maximize ROMI, in Verbeke et al. [22], the authors introduce the maximum profit (MP) measure for evaluating CCP models. The MP measure calculates profit generated when considering the op- timal fraction of top-ranked customers according to the CCP model of a retention campaign. The MP measure allows one to determine the optimal model and fraction of customers to include, yield- ing a significant increase in profitability relative to that achieved when using statistical measures [22–24]. In this article, we extend the MP measure to evaluate the performance of CCU models, and we introduce the maximum profit for uplift (MPU) measure. Both the MP and MPU measure are then used to compare the performance of CCP and CCU logistic regression and random forest models through an experimental case study. Our main contributions are threefold: 1. We introduce an application of uplift modeling for customer retention. 2. We extend the maximum profit measure for evaluating uplift models. 3 3. We apply and
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