The Effectiveness of Customized Promotions in Online and Offline Stores

This study investigates the effectiveness of customized promotions at three levels of granularity (mass market, segment specific, and individual specific) in online and offline stores. The authors conduct an empirical examination of the profit potential of these customized promotion programs with a joint model of purchase incidence, choice, and quantity and through optimization procedures for approximately 300 conditions. They find that (1) optimization procedures lead to substantial profit improvements over the current practice for all types of promotions (customized and undifferentiated); (2) loyalty promotions are more profitable in online stores than in offline stores, while the opposite holds for competitive promotions; (3) the incremental payoff of individual-level over segment- and mass market–level customized promotions is small in general, especially in offline stores; (4) for categories that are promotion sensitive, individual-level customized promotions can lead to a meaningful profit increase over segment- and mass market–level customized promotions in online stores; and (5) low redemption rates are a major impediment to the success of customized promotions in offline stores. Optimal undifferentiated promotions should be the primary promotion program in this channel, and firms can benefit from offering a combination of optimal undifferentiated and customized promotions for suitable categories in offline stores.

[1]  Jie Zhang,et al.  Customizing Promotions in Online Stores , 2004 .

[2]  Avi Goldfarb,et al.  Advertising Bans and the Substitutability of Online and Offline Advertising , 2010 .

[3]  J. Chiang.  A Simultaneous Approach to the Whether, What and How Much to Buy Questions , 1991 .

[4]  Stephen J. Hoch,et al.  Price Discrimination using in-Store Merchandising , 1996 .

[5]  Rick L. Andrews,et al.  An Empirical Comparison of Logit Choice Models with Discrete versus Continuous Representations of Heterogeneity , 2002 .

[6]  Naufel J. Vilcassim,et al.  Investigating Household Purchase Timing Decisions: A Conditional Hazard Function Approach , 1991 .

[7]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[8]  Dominique M. Hanssens,et al.  Competitive Reactions to Advertising and Promotion Attacks , 2005 .

[9]  James D. Hess,et al.  Customized Products: A Competitive Analysis , 2005 .

[10]  Peter E. Rossi,et al.  The Value of Purchase History Data in Target Marketing , 1996 .

[11]  Sofia B Villas-Boas,et al.  Vertical relationships between Manufacturers and Retailers: Inference with Limited Data , 2007 .

[12]  P. Danaher,et al.  A Comparison of Online and Offline Consumer Brand Loyalty , 2003 .

[13]  A. Rangaswamy,et al.  Consumer Choice Behavior in Online and Traditional Supermarkets: The Effects of Brand Name, Price, , 2000 .

[14]  Sunil Gupta,et al.  Consumers' Price Sensitivities Across Complementary Categories , 2007, Manag. Sci..

[15]  Z. John Zhang,et al.  Competitive Coupon Targeting , 1995 .

[16]  Edward J. Fox,et al.  Consumer Shopping and Spending across Retail Formats , 2004 .

[17]  The Role of Retail , 2009 .

[18]  Fred M. Feinberg,et al.  Do we care what others Get? A Behaviorist Approach to Targeted Promotions , 2002 .

[19]  Carl F. Mela,et al.  Customer Channel Migration , 2008 .

[20]  D. Wittink,et al.  Competitive Reaction versus Consumer response: Do Managers Overreact? , 1996 .

[21]  Sunil Gupta,et al.  Brand Choice, Purchase Incidence, and Segmentation: An Integrated Modeling Approach , 1992 .

[22]  Pradeep K. Chintagunta,et al.  Investigating Purchase Timing Behavior in Two Related Product Categories , 1998 .

[23]  V. Padmanabhan,et al.  The Decomposition of Promotional Response: An Empirical Generalization , 1999 .

[24]  John G. Lynch,et al.  Interactive Home Shopping: Consumer, Retailer, and Manufacturer Incentives to Participate in Electronic Marketplaces , 1997 .

[25]  S. Villas-Boas.  Vertical Contracts between Manufacturers and Retailers: Inference with Limited Data , 2004 .

[26]  J. Louviere,et al.  The Role of the Scale Parameter in the Estimation and Comparison of Multinomial Logit Models , 1993 .

[27]  Richard Staelin,et al.  Manufacturer Allowances and Retailer Pass-Through Rates in a Competitive Environment , 1999 .

[28]  K. Pauwels.  How Dynamic Consumer Response, Competitor Response, Company Support, and Company Inertia Shape Long-Term Marketing Effectiveness , 2004 .

[29]  Scott A. Neslin,et al.  The role of retail promotion in determining future brand loyalty: its effect on purchase event feedback , 1999 .

[30]  W. Hanemann.  Discrete-Continuous Models of Consumer Demand , 1984 .

[31]  Dominique M. Hanssens,et al.  The Category-Demand Effects of Price Promotions , 2000 .

[32]  Gary J. Russell,et al.  A Probabilistic Choice Model for Market Segmentation and Elasticity Structure , 1989 .

[33]  Michel Wedel,et al.  An Empirical Bayes Procedure for Improving Individual-Level Estimates and Predictions From Finite Mixtures of Multinomial Logit Models , 2004 .

[34]  Sean R Eddy,et al.  What is dynamic programming? , 2004, Nature Biotechnology.