Analytic Foundations for Marketing Decisions

Crafted alongside top Wharton faculty contributors Stephen J. Hoch, John Wesley Hutchinson, and Jagmohan S. Raju, Analytic Foundations for Marketing Decisions is a “Marketing Math” essential. Through this simple business simulation, students will gain insight into marketing analysis by learning why and when to use common models – and how to interpret the results.

The Game

Analytic Foundations for Marketing Decisions (AFMD) provides an interactive teaching experience to help marketing students understand the key concepts behind commonly used marketing analyses. More than just an online problem set, the simple web application presents the definitions, formulae, and examples of quantitative analyses taught at Wharton.

Although statistical analysis and modeling in marketing research can be quite sophisticated, much of the day-to-day number crunching for managers is simple arithmetic and algebra. The calculations themselves are not difficult; the challenge lies in knowing which analyses to employ and what to conclude from the results. This game teaches that real day-to-day problems are messy – they do not have “answers in the back of the book” like a homework assignment. In this regard, AFMD presents problems in a “real-life” format, enabling students to not only practice the calculations, but also to reason out which calculations are appropriate in each scenario.

The Details

The analyses featured in AFMD are widely used in business and academics, but there is a seemingly endless set of variations in terminology, form, and function. The goal of this application is to create a solid foundation not only for Wharton courses, but also for the problems that students will encounter in the future.

AFMD contains both “easy” and “hard” problem sets for several hypothetical companies. Students learn when and how to calculate margin analysis, break-even analysis, chain model for segment value, customer lifetime value (CLV), and economic value to the customer (EVC). The application features extensive help notes, a glossary, and a window that allows students to display the answers to the problems once they have performed their calculations.

AFMD also features an administrative console which allows faculty to publish problem sets at various points during their course. This interface provides a publishing method that is simpler and more efficient than traditional html pages or hard copy.

Interested in using AFMD? Email the Learning Lab at learninglab@wharton.upenn.edu