A Framework to Determine the Value of Consumer Consideration Set Information for Firm Pricing Strategies

Managers allocating budgets using scarce economic resources increasingly invest in consumer information to set prices. This highlights the importance of developing methods to efficiently use consumer information in firm pricing strategies so as to increase profits. In this article we utilize such a method which includes information about consumer consideration sets. Extant pricing strategies assume that all brands in the market are included in this competitive set while setting prices. Through an empirical illustration in the ketchup market we demonstrate that firms can improve profits substantially by including consumer consideration sets in arriving at their optimal pricing strategies. We find that the ability of a brand to increase prices seems to depend on both the number of competitors it faces as well as the proportion of households for whom there are competitors in their consideration sets. Failure to incorporate consideration sets leads to biased equilibrium margin inferences and a substantial reduction in net profits.

[1]  J. Bettman.  An information processing theory of consumer choice , 1979 .

[2]  Steven M. Shugan.  The Cost Of Thinking , 1980 .

[3]  Michel Wedel,et al.  Retrieving Unobserved Consideration Sets from Household Panel Data , 2005 .

[4]  G. Stigler.  The Economics of Information , 1961, Journal of Political Economy.

[5]  K. Sudhir,et al.  Optimal Marketing Strategies for a Customer Data Intermediary , 2007 .

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

[7]  John Roberts,et al.  Development and Testing of a Model of Consideration Set Composition , 1991 .

[8]  Donald G. Morrison,et al.  Making the Cut: Modeling and Analyzing Choice Set Restriction in Scanner Panel Data , 1995 .

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

[10]  John R. Hauser,et al.  Testing the Accuracy , 1977 .

[11]  K. Sudhir,et al.  Structural Analysis of Manufacturer Pricing in the Presence of a Strategic Retailer , 2001 .

[12]  Raj Sethuraman,et al.  A meta-analysis of national brand and store brand cross-promotional price elasticities , 1995 .

[13]  Raj Sethuraman,et al.  Assessing the External Validity of Analytical Results from National Brand and Store Brand Competition Models , 2009, Mark. Sci..

[14]  K. Srinivasan,et al.  Price Uncertainty and Consumer Search: A Structural Model of Consideration Set Formation , 2003 .

[15]  John D. C. Little,et al.  A Logit Model of Brand Choice Calibrated on Scanner Data , 2011, Mark. Sci..

[16]  Brian T. Ratchford,et al.  The Value of Information for Selected Appliances , 1980 .

[17]  Pradeep K. Chintagunta,et al.  Store Brands: Who Buys Them and What Happens to Retail Prices When They Are Introduced? , 2004 .

[18]  Jagmohan S. Raju,et al.  Positioning of Store Brands , 2002 .

[19]  D. G. Morrison,et al.  A Decision Support System for Planning Manufacturers' Sales Promotion Calendars , 1999 .

[20]  Jean-Pierre Dubé,et al.  Appendix for : Competitive Price Discrimination Strategies in a Vertical Channel Using Aggregate Retail Data , 2002 .

[21]  A. Rangaswamy,et al.  A Fuzzy Set Model of Search and Consideration with an Application to an Online Market , 2003 .

[22]  Siddhartha Chib,et al.  Markov chain Monte Carlo and models of consideration set and parameter heterogeneity , 1998 .

[23]  Sunil Gupta.  Impact of Sales Promotions on when, what, and how Much to Buy , 1988 .

[24]  G. Tellis,et al.  Tackling the Retailer Decision Maze: Which Brands to Discount, How Much, When and Why? , 1995 .

[25]  John R. Hauser,et al.  The Competitive Implications of Relevant-Set/Response Analysis , 1989 .

[26]  Pradeep K. Chintagunta,et al.  Investigating Category Pricing Behavior at a Retail Chain , 2002 .

[27]  Rick L. Andrews,et al.  Studying Consideration Effects in Empirical Choice Models Using Scanner Panel Data , 1995 .

[28]  G. Haines,et al.  The Theory of Buyer Behavior. , 1970 .

[29]  D. Gensch.  A Two-Stage Disaggregate Attribute Choice Model , 1987 .

[30]  Jack M. Feldman,et al.  Self-generated validity and other effects of measurement on belief, attitude, intention, and behavior. , 1988 .

[31]  John R. Hauser,et al.  Testing the Accuracy, Usefulness, and Significance of Probabilistic Choice Models: An Information-Theoretic Approach , 1978, Oper. Res..

[32]  Bart J. Bronnenberg,et al.  Limited Choice Sets, Local Price Response, and Implied Measures of Price Competition , 1996 .

[33]  M. Ben-Akiva,et al.  EMPIRICAL TEST OF A CONSTRAINED CHOICE DISCRETE MODEL : MODE CHOICE IN SAO PAULO, BRAZIL , 1987 .

[34]  Robert C. Blattberg,et al.  Price-Induced Patterns of Competition , 1989 .

[35]  Kenneth E. Train,et al.  Discrete Choice Methods with Simulation , 2016 .

[36]  J. M. Villas-Boas,et al.  Endogeneity in Brand Choice Models , 1999 .

[37]  K. Train.  Discrete Choice Methods with Simulation , 2003 .

[38]  Kenneth Train,et al.  Customer-Specific Taste Parameters and Mixed Logit: Households' Choice of Electricity Supplier , 2000 .

[39]  David Besanko,et al.  Logit Demand Estimation Under Competitive Pricing Behavior: An Equilibrium Framework , 1998 .

[40]  Peter E. Rossi,et al.  Similarities in Choice Behavior Across Product Categories , 1998 .

[41]  D. McFadden,et al.  MIXED MNL MODELS FOR DISCRETE RESPONSE , 2000 .

[42]  D. Fudenberg,et al.  Behavior-Based Price Discrimination and Customer Recognition , 2007 .

[43]  B. Wernerfelt,et al.  An Evaluation Cost Model of Consideration Sets , 1990 .

[44]  Greg M. Allenby,et al.  A Choice Model with Conjunctive, Disjunctive, and Compensatory Screening Rules , 2004 .

[45]  S. Chib,et al.  Model of Brand Choice with a No-Purchase Option Calibrated to Scanner-Panel Data , 2004 .