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Retention Modeling at Scholastic Travel Company (B)
Ovchinnikov, Anton S. Case QA-0865 / Published November 17, 2017 / 2 pages.
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Product Overview

This case, along with its A case (UVA-QA-0864), 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.


Learning Objectives

-Introduce students to the process and metrics for data classification -Introduce students to the main analytical machine learning techniques used for classification -Practice these concepts on an easy-to-understand problem of predicting customer retention

  • Videos List

  • Overview

    This case, along with its A case (UVA-QA-0864), 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.

  • Learning Objectives

    Learning Objectives

    -Introduce students to the process and metrics for data classification -Introduce students to the main analytical machine learning techniques used for classification -Practice these concepts on an easy-to-understand problem of predicting customer retention