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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.

With the development of inclusive financial business in China in recent years, this case describes the credit risk control of “mobile credit”, a smart online credit platform launched by Shanghai Mobanker Co. Ltd. (referred to as “Mobanker”, previously named as “Shanghai Mobanker Financial Information Service Co., Ltd.”) which provides technical services for inclusive finance industry.

This case exposes students to predictive analytics as applied to discrete events with logistic regression. The VP of customer services for a successful start-up wants to proactively identify customers most likely to cancel services or “churn.” He assigns the task to one of his associates and provides him with data on customer behavior and his intuition about what drives churn. The associate must generate a list of the customers most likely to churn and the top three reasons for that likelihood.

Data used to develop the case included primary data from employees and supervisors of a commercial floorcovering manufacturing plant in Northwest Georgia. The case company is not disguised.

The survey was developed using existing instruments from the Organizational Behavior and Human Resources Literature. Instruments were listed in Exhibits 2 through 7. The survey administration had the support of the Vice President for Resources and Facilities, and employees and their supervisors were given time to complete the surveys. The data gathered was analyzed by the researcher using SPSS statistical software.

Established in 1957, J&J started as a family-owned business but had grown and diversified its product offerings by focusing on commercial flooring. It survived several economic downturns and remained competitive in a market dominated by more prominent flooring manufacturers. J&J Industries strived to empower its 800 employees with various incentive programs. Employees remained loyal to J&J; many had worked for the company for over 15 years. However, management wanted to measure the impact of empowering and initiatives on employee performance and satisfaction to determine the real power of employee incentive programs. The Resources and Facilities Vice President employed Professor Lopez, a Management Professor, to develop a survey to measure these constructs and analyze the data to guide future incentive programs. Data from the employee and supervisor survey was provided along with the statistical analysis results for interpretation and recommendations for VP Fordham.

The target audience for this case is primarily students in a research methodology course and students studying quantitative regression analysis and interpretation. The focus is predominantly on graduate-level students in Master of Business Administration or Master of Accounting programs in business. Graduate students should have completed courses in management or organizational behavior, business statistics or quantitative methods or data visualization and cleaning as background knowledge for this case. Specifically, students should understand regression analysis and know when and how the tool is used for managerial decision-making.

This case study intends to add knowledge and understanding of supply chain management particularly with respect to international logistics.

The case study can be used in both undergraduate and postgraduate levels. Students pursuing Master of Science in Logistics, Supply Chain Management and those doing bachelor degrees in the same areas can have a better insight and special interest of the case. Professional boards may also use the case to empirically make students understand this area.

The railway sub-sector in East Africa – Tanzania in particular – is an important transport mode but has a declining performance. The market share is estimated at only 4 percent of the freight market. Still knowledge about traffic, particularly for freight, is scant. The main dilemma is whether traffic of the central corridor is more intra- or inter-Tanzania. The case studies techniques appropriate for meaningful traffic forecasting and through a simple regression model it resolves the freight conflicts between Kenya rail and the Central Corridor. It provides students with applied traffic forecasting tools.

The case focuses on techniques of traffic forecasting, development of traffic scenarios and on issues related to intermodal transport especially between road, rail and ocean. At the end of using this Case students should be able to: explain the methods, techniques and models used in traffic forecasting; understand intermodal linkages in international Logistics; use different approaches to make logistics market assessment; and forecast traffic in all modes using different scenarios.

Teaching notes are available for educators only. Please contact your library to gain login details or e-mail support@emeraldinsight.com to request teaching notes.

The decision maker is in charge of procurement auctions at the department of transportation of Orangia (a fictitious U.S. state). Students are asked to assist him in estimating the winning bids in various auctions concerning highway repair jobs using data on past auctions. The decision maker is faced with various professional, statistical, and ethical dilemmas.

To analyze highway procurement auctions from the buyer-auctioneer perspective, establish basic facts regarding the project price-to-estimated cost ratio, set up and estimate a structural regression model to predict the winning bid, and compute the probability the winning price will be below estimated cost. Difficulties include heteroskedasticity, logarithmic specification, and omitted variable bias. Also to estimate a Logit regression and predict bidder collusion probability.

Supplements the (A) case.

This case describes an innovative response-modeling project at INTUIT. The case can help students understand the basics of (and the issues surrounding) response modeling, an important tactic in data-base marketing.

Uber had pioneered the growth and delivery of modern ridesharing services by leveraging the explosive growth of technology, GPS navigation, and smartphones. Ridesharing services had expanded across the world, growing rapidly in the United States, China, India, Europe, and Southeast Asia. Even as these services expanded and gained popularity, however, the pickup experience for drivers and riders did not always meet the expectations of either party. Pickups were complicated by traffic congestion, faulty GPS signals, and crowded pickup venues. Flawed pickups resulted in rider dissatisfaction and in lost revenues for drivers. Uber had identified the pickup experience as a top strategic priority, and a team at Uber, led by group product manager Birju Shah, was tasked with designing an automated solution to improve the pickup experience. This involved three steps. First, the team needed to analyze the pickup experience for various rider personas to identify problems at different stages in the pickup process. Next, it needed to create a model for predicting the best rider location for a pickup. The team also needed to develop a quantitative metric that would determine the quality of the pickup experience. These models and metrics would be used as inputs for a machine learning.

Narayana, the head of Market Dynamic's (MD) Telecom vertical was working on the data analysis plan for the research on the telecom project that they had done for CWP. CWP was a well known consultant and had conducted a research with MD to generate consumer insights in the telecom space. These would help bring credibility for CWP and help in business development. CWP had requested for an early delivery and Narayana was planning to work on the analysis plan himself as his chief analyst was on leave. This case highlights the importance of an analysis plan in research. Specifically, it illustrate the role of different tools in data analysis and familiarizes participants with various tools and their applications. This case would be useful for students in Business Research and Market Research courses.