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Probability Models for Customer Analytics
Peter S. Fader, Wharton School, University of Pennsylvania
Bruce Hardie, London Business School
Friday, April 26, 2013
8:30 am – 4:30 pm
The Feinberg School of Medicine, Northwestern University
680 North Lake Shore Drive, Suite 1400, Chicago, IL 60611
Sponsored by the Chicago Chapter of the American Statistical Association
Central to a complete understanding of today’s leading-edge customer analytics techniques is a sound intuitive appreciation of the basic behavioral and methodological foundations upon which these sophisticated tools are built. For example, emerging “hot topics” such as hierarchical Bayes models and hidden Markov processes are often built on simple probability modeling concepts (e.g., Poisson counts, Bernoulli “coin flips,” and exponential interpurchase times) — yet how many researchers are comfortable at precisely defining these concepts or explaining the motivation for using them?
This workshop aims to fill in these gaps by bringing practitioners fully up to speed on the basic methods that may underlie many of their current or future research activities. Our two broad objectives are: (1) to review the essential terminology and logic associated with the area of probability models as applied to customer analytics, and (2) to develop participants’ skills through a set of data-oriented case studies that demonstrate the model-building process in detail. We will illustrate all of the steps required to develop a probability model, estimate its parameters, interpret the results, and draw appropriate managerial conclusions from it. Careful and extensive use is made of the Solver tool in Microsoft Excel, which makes it possible to construct all of these models within a familiar spreadsheet environment. By the end of the tutorial, participants should be quite comfortable with all of the aforementioned principles and models and the managerial issues that surround them.
This program will benefit all analytics professionals – as well as more senior managers who want to gain a firmer grip on these concepts and methods. The material is somewhat technical, so some basic aptitude with probability/statistics would be beneficial for participants. For instance, it helps (but is by no means required) to have a little familiarity with basic probability distributions (such as the Poisson and the binomial), even if the details are largely forgotten. Similarly, participants should be comfortable with Microsoft Excel, although there is no need for any advanced capabilities (we will rely exclusively on ordinary “built-in” Excel functions). Finally, participants may wish to bring a laptop to follow along with the model-building exercises, but it is not required. All we ask from each participant is to bring an open mind, a sharp pencil and a high level of interest in customer analytics. All materials presented (including the detailed spreadsheets) will be made available to all participants immediately after the seminar.
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Peter S. Fader is the Frances and Pei-Yuan Chia Professor of Marketing at the Wharton School of the University of Pennsylvania and Co-Director of the Wharton Customer Analytics Initiative. Peter’s research interests include: customer lifetime value; sales forecasting for new products; and using behavioral data to understand and forecast purchase activities across a wide range of industries. Peter has received many awards and honors, including the David Hardin Award for the best paper published in Marketing Research magazine, the Paul E. Green Award for the best article published in the Journal of Marketing Research, and the best paper award at the American Marketing Association’s Advanced Research Techniques Forum.
Bruce G.S. Hardie is Professor of Marketing at the London Business School. Bruce’s primary research interest is in the development of data-based models to support marketing analysts and decision makers, with a particular interest in models that are easy to implement. Bruce has published papers in the areas of applied probability models, customer-based analysis, and customer analytics.