About AMA

Sangkil Moon, Wagner A. Kamakura, and Johannes Ledolter

Executive Summary
This study addresses a problem commonly encountered by marketers who attempt to assess the impact of their sales promotions: the lack of data on competitive marketing activity. In most industries, competing firms may have competitive sales data from syndicated services or trade organizations, but they seldom have access to data on competitive promotions except, if any, at a high level of aggregation. In other words, competitive promotion data are rarely available at the customer level. Promotion response models in the literature either have ignored competitive promotions, focusing instead on the focal firm's promotions and sales responses, or have considered the ideal situation in which the analyst has access to full information about each firm's sales and promotion activity.

The proposed random-coefficients hidden Markov promotion response model takes the unobserved promotion level (i.e., promotion or no promotion) by competitors in the same product category as a latent variable driven by a Markov process that is estimated simultaneously with the promotion response model. This makes it possible to estimate cross-promotion effects, even though these promotions are not directly observable, by imputing the level of competitive promotions.

The authors test proposed model on synthetic data through a Monte Carlo experiment. Then, they apply it to actual prescription and sampling data from two main competing pharmaceutical firms in the same therapeutic category. The two tests show that compared with several benchmark models, the proposed random coefficients hidden Markov model (HMM) successfully imputes competitive promotions when they are completely missing and, accordingly, reduces biases in the own- and cross-promotion parameters. Furthermore, the proposed model provides better predictive validity than the benchmark models.

From a managerial perspective, the proposed HMM model can be applied to a common situation in which a firm has access to its competitors' sales data from syndicated sources but cannot obtain information on the competitors' promotional efforts. The situation inevitably occurs to most manufacturers that sell their products through distributors or retailers (e.g., pharmaceuticals, consumer electronics). It is also a typical situation for any firm that tries to assess the effectiveness of its salespeople when it might have data on the clients' purchases in the product category but not about their exposure to the competitors' salespeople. Furthermore, there is a growing emphasis on customer relationship management, in which marketing effort is customized at the customer level. In most customer relationship management implementations, the firm has a wealth of information about its own contacts with individual customers and customers' responses to these contacts but a dearth of data on the contacts these customers might have had with the firm's competitors. The proposed HMM model provides a solution to this missing information problem by inferring such unobserved competitive promotion activities.

Biography
Sangkil Moon (PhD, University of Iowa) is Assistant Professor of Marketing in the Business Management Department, College of Management, at North Carolina State University. His research interests are empirical modeling, hidden Markov models, price perception models, customer relationship management, variety seeking, and air ticket browsing and booking. His doctoral dissertation developed a series of generalizable spatial choice models for product recommendations. He was one of two winners in the 2002 Marketing Science Institute Alden G. Clayton Dissertation Proposal Competition. He has publications in Journal of Marketing Research and Journal of Retailing. His primary teaching experiences include courses in Introduction to Marketing, Marketing Research, and Marketing Analytics. He is a recipient of two teaching awards, one from University of Iowa and the other from North Carolina State University.

Wagner A. Kamakura is Ford Motor Company Professor of Global Marketing in the Fuqua School of Business at Duke University. Before joining academia, he worked in market analysis, forecasting, and planning in Brazil. Professor Kamakura holds a PhD in Marketing from the University of Texas at Austin, an MS in Industrial Engineering from the University of Sao Paulo (Brazil), and a BS in Mechanical Engineering from the Technological Aeronautics Institute (Brazil). He is a coauthor of Market Segmentation: Conceptual and Methodological Foundations, as well as numerous articles in academic journals. He has served as editor of Journal of Marketing Research, area editor of Marketing Science, and associate editor of Journal of Consumer Research and is currently a member of the editorial boards of International Journal of Research in Marketing, Journal of Marketing Research, Journal of Retailing, and Marketing Science. His current research interests are in customer relationship management, choice modeling, market segmentation, and market structure, and database marketing.

Johannes Ledolter is C. Maxwell Stanley Professor of Management Sciences at the University of Iowa and Professor of Statistics at the Vienna University of Economics and Business Administration. He is the author of several books, including Introduction to Regression Modeling (with Bovas Abraham; published by Duxbury Press in 2006) and the forthcoming text Testing 1–2–3: Experimental Design with Applications in Marketing and Service Operations (with Arthur J. Swersey; to be published by Stanford University Press in 2007).