Retention Modeling at Scholastic Travel Company (a)

Abstract

This case, along with its B case (UVA-QA-0865), is an effective vehicle for introducing students to the use of machine-learning techniques for classification. The specific context is predicting customer retention based on a wide range of customer attributes/features. The specific techniques could include (but are not limited to): regressions (linear and logistic), variable selection (forward/backward and stepwise), regularizations (e.g., LASSO), classification and regression trees (CART), random forests, graduate boosted trees (xgboost), neural networks, and support vector machines (SVM). The case is suitable for an advanced data analysis (data science, machine learning, and artificial intelligence) class at all levels: upper-level business undergraduate, MBA, EMBA, as well as specialized graduate or undergraduate programs in analytics (e.g., masters of science in business analytics [MSBA] and masters of management analytics [MMA]) and/or in management (e.g., masters of science in management [MScM] and masters in management [MiM, MM]).The teaching note for the case contains the pedagogy and the analyses, alongside the detailed explanations of the various techniques and their implementations in R (code provided in Exhibits and supplementary files). Python code, as well as the spreadsheet implementation in XLMiner, are available upon request. Excerpt UVA-QA-0864 Rev. Aug. 23, 2018 Retention Modeling at Scholastic Travel Company (A) On a sunny Monday afternoon in early spring 2013, David Powell entered his new office and took a deep breath. He pondered his first few days as the new data analyst for Scholastic Travel Company (STC), an educational tourism firm. Powell had filled his first week of employment meeting the firm's departmental leadership and attending a company-wide new-employee-orientation program, and he was eager to get started on his first project. Just a few hours earlier, at the weekly marketing strategy meeting, Powell's new supervisor, Stephen Blackford, stressed the urgency of a new data initiative centered on customer retention. As Blackford outlined, in less than two weeks, contract renewal opportunities would begin for customers who had gone on an STC trip in 2012. During the meeting, he presented a dataset with all of the known information about the previous year's client base (see Exhibits 1 and 2). From his past experience, Blackford was confident that models could be constructed to predict whether or not a customer would book again in 2013. With such a model, he hoped to design a more nuanced marketing strategy that would target certain subsets of the client population to save cost and improve yield. With multiple plausible methodologies in mind, Powell knew he needed to get to work immediately so he could give Blackford an accurate prediction model before the end of the week. Company Background . . .