Case Article—The RealPro Customer Benefits Program: Rekindling Shopper Loyalty Through a Subscription Service

In this case study, students combine data-based insights with strategic considerations to make fundamental business decisions at the German grocery retail chain Real. In response to dwindling numbers of customers and reduced revenues, Real developed the RealPro customer benefits program to achieve a quick turnaround. For a fixed annual fee, RealPro members receive substantial and permanent discounts of 20% on nonpromoted items from a broad range of food categories. Students employ data analytics methods to extract insights from the provided data set, which contains point-of-sale information from the actual market test of RealPro. Based on these insights, decisions concerning the rollout and design of the RealPro program must be made. We provide data analysis solutions in both Excel and R to analyze 75 thousand customer transactions. In the case extension, students can apply the difference-in-differences method and two covariate balancing algorithms for in-depth statistical analyses. For this purpose, we provide an additional unbalanced data set with 83 thousand transactions, on which the students can test and analyze propensity score matching and entropy balancing models.

Case Article—Louisiana Branch Lines

Louisiana Branch Lines is a struggling Southeast U.S. railroad in just four cities and 12 markets. Their marketing, operations, and finance performance is poor and their departments disjointed. In this customizable, nine-part case, instructors can choose to focus on basic problem structuring and descriptive and predictive statistics, optimization model building, simulation of solutions, or the integration of all of the above. It is based on a real-world case.

Logistic Regression via Excel Spreadsheets: Mechanics, Model Selection, and Relative Predictor Importance

Logistic regression is one of the most fundamental tools in predictive analytics. Graduate business analytics students are often familiarized with implementation of logistic regression using Python, R, SPSS, or other software packages. However, an understanding of the underlying maximum likelihood model and the mechanics of estimation are often lacking. This paper describes two Excel workbooks that can be used to enhance conceptual understanding of logistic regression in several respects: (i) by providing a clear formulation and solution of the maximum likelihood estimation problem; (ii) by showing the process for testing the significance of logistic regression coefficients; (iii) by demonstrating different methods for model selection to avoid overfitting, specifically, all possible subsets ordinary least squares regression and l1-regularized logistic regression (lasso); and (iv) by illustrating the measurement of relative predictor importance using all possible subsets.

Case Article—Bayer New Drug Development Decision Making

The interactive case study requires student teams to engage with the instructor using a structured decision analysis process in deciding whether to develop a new drug to treat blood clots in legs. There is role-playing in the interactive case study where student teams are decision consultants and the instructor serves as the decision maker, subject matter expert (SME), and coach. Student teams are responsible for managing the analytical process, framing the decision, collecting data from the SME (instructor), constructing the Excel model, assessing probabilities for the most-sensitive uncertainties from the SME, evaluating the Excel-based decision-tree model, and presenting evaluation results and recommendations to the decision maker (instructor). The goal of the case is to improve the analytical, modeling, and consulting skills of the students. The interactive case study is the culmination of a semester-long elective MBA course, entitled Decision Making Under Uncertainty. Since 2010, I have taught this course 31 times to 870 graduate students.

Teaching Systems Thinking in Higher Education

Systems thinking is recognized as an essential skill for understanding complex problem solving and decision making associated with many of the contemporary issues faced by individuals and communities. In this article, our goal is to contribute to the knowledge of curriculum and pedagogy of formal systems thinking teaching in higher education. We believe that accumulating this knowledge can provide a better foundation for including systems thinking in higher-educational programs. To achieve this goal, the purpose of this study is to examine whether the use of a set of systems thinking concepts and methods can effectively promote systems thinking in higher-education settings. The study shows that systems thinking skills can be promoted effectively through the delivery of a combination of systems thinking methods and concepts.