Document Open Access Logo

Demand models for the static retail price optimization problem - A Revenue Management perspective

Authors Timo P. Kunz, Sven F. Crone



PDF
Thumbnail PDF

File

OASIcs.SCOR.2014.101.pdf
  • Filesize: 398 kB
  • 25 pages

Document Identifiers

Author Details

Timo P. Kunz
Sven F. Crone

Cite AsGet BibTex

Timo P. Kunz and Sven F. Crone. Demand models for the static retail price optimization problem - A Revenue Management perspective. In 4th Student Conference on Operational Research. Open Access Series in Informatics (OASIcs), Volume 37, pp. 101-125, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2014)
https://doi.org/10.4230/OASIcs.SCOR.2014.101

Abstract

Revenue Management (RM) has been successfully applied to many industries and to various problem settings. While this is well reflected in research, RM literature is almost entirely focused on the dynamic pricing problem where a perishable product is priced over a finite selling horizon. In retail however, the static case, in which products are continuously replenished and therefore virtually imperishable is equally relevant and features a unique set of industry-specific problem properties. Different aspects of this problem have been discussed in isolation in various fields. The relevant contributions remain therefore scattered throughout Operations Research, Econometrics, and foremost Marketing and Retailing while a holistic discussion is virtually non-existent. We argue that RM with its interdisciplinary, practical, and systemic approach would provide the ideal framework to connect relevant research across fields and to narrow the gap between theory and practice. We present a review of the static retail pricing problem from an RM perspective in which we focus on the demand model as the core of the retail RM system and highlight its links to the data and the optimization model. We then define five criteria that we consider critical for the applicability of the demand model in the retail RM context. We discuss the relevant models in the light of these criteria and review literature that has connected different aspects of the problem. We identify several avenues for future research to illustrate the vast potential of discussing the static retail pricing problem in the RM context.
Keywords
  • Revenue Management
  • Pricing
  • Retail
  • Demand Modeling

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. Makoto Abe and Kirthi Kalyanam. Store Sales and Panel Purchase Data: Are They Compatible? Mimeo, 1995. Google Scholar
  2. Greg M. Allenby and Peter E. Rossi. There is no aggregation bias: Why macro logit models work. Journal of Business &Economic Statistics, 9(1):1-14, 1991. Google Scholar
  3. Eric Anderson and Naufel J. Vilcassim. Structural demand models for retailer category pricing. London Business School Mimeo, 2001. Google Scholar
  4. Rick L. Andrews and Imran S. Currim. An experimental investigation of scanner data preparation strategies for consumer choice models. International Journal of Research in Marketing, 22(3):319-331, September 2005. Google Scholar
  5. Rick L. Andrews, Imran S. Currim, and Peter S.H. Leeflang. A Comparison of Sales Response Predictions From Demand Models Applied to Store-Level versus Panel Data. Journal of Business and Economic Statistics, 29(2):319-326, April 2011. Google Scholar
  6. Clifford L.F. Attfield. A Comparison of the Translog and Almost Ideal Demand Models. Mimeo University of Bristol, 564(4), 2004. Google Scholar
  7. George Baltas. Modelling category demand in retail chains. Journal of the Operational Research Society, 56(11):1258-1264, March 2005. Google Scholar
  8. William A. Barnett and Ousmane Seck. Rotterdam model versus almost ideal demand system: will the best specification please stand up? Journal of Applied Econometrics, 23(6):795-824, 2008. Google Scholar
  9. AP P Barten. Evidence on the Slutsky conditions for demand equations. The Review of Economics and Statistics,, 49(1):77-84, 1967. Google Scholar
  10. David E. Bell, Ralph L. Keeney, and John D.C. Little. A market share theorem. Journal of Marketing Research, 12(2):136-141, 1975. Google Scholar
  11. David Besanko, Sachin Gupta, and Dipak C. Jain. Logit Demand Estimation Under Competitive Pricing Behavior: An Equilibrium Framework. Management Science, 44(11):1533-1547, November 1998. Google Scholar
  12. Chitrabhanu Bhattacharya and Leonard M. Lodish. An advertising evaluation system for retailers. Journal of Retailing and Consumer Services, 1(2):90-100, 1994. Google Scholar
  13. Gabriel R. Bitran and Rene Caldentey. An overview of pricing models for revenue management. Manufacturing &Service Operations Management, 5(3):203-229, 2003. Google Scholar
  14. Robert C. Blattberg and Edward I. George. Shrinkage estimation of price and promotional elasticities: Seemingly unrelated equations. Journal of the American Statistical Association, 86(414):304-315, 1991. Google Scholar
  15. Robert C. Blattberg and Scott A. Neslin. Sales promotion: The long and the short of it. Marketing Letters, 1(1):81-97, 1989. Google Scholar
  16. Robert C. Blattberg and Kenneth J. Wisniewski. Price-induced patterns of competition. Marketing Science, 8(4):291-309, 1989. Google Scholar
  17. Ruth N. Bolton. The Robustness Of Retail-Level Price Elasticity Estimates. Journal of Retailing, 65(2):193-219, 1989. Google Scholar
  18. Ruth N. Bolton and Venkatesh Shankar. An empirically derived taxonomy of retailer pricing and promotion strategies. Journal of Retailing, 79(4):213-224, January 2003. Google Scholar
  19. Richard A. Briesch, William R. Dillon, and Robert C. Blattberg. Treating Zero Brand Sales Observations in Choice Model Estimation: Consequences and Potential Remedies. Journal of Marketing Research, 45(5):618-632, October 2008. Google Scholar
  20. Richard A. Briesch, Lakshman Krishnamurthi, Tridib Mazumdar, and S.P. Raj. A Comparative Analysis of Reference Price Models. Journal of Consumer Research, 24(2):202-214, 1997. Google Scholar
  21. Roderick J. Brodie and Cornelis Kluyver. Attraction Versus Linear and Multiplicative Market Share Models: An Empirical Evaluation. Journal of Marketing Research, 21(May):194-201, 1984. Google Scholar
  22. Bart J. Bronnenberg, Michael W. Kruger, and Carl F. Mela. The IRI marketing data set. Marketing Science, 27(4):745-748, 2008. Google Scholar
  23. Patrick G. Buckley. Nested multinomial logit analysis of scanner data for a hierarchical choice model. Journal of Business Research, 17(2):133-154, 1988. Google Scholar
  24. Randolph E. Bucklin and James M. Lattin. A model of product category competition among grocery retailers. Journal of Retailing, 68(3):271-294, 1992. Google Scholar
  25. Alain V. Bultez and Philippe A. Naert. Consistent sum-constrained models. Journal of the American Statistical Association, 70(351):529-535, 1975. Google Scholar
  26. Gregory S. Carpenter, Lee G. Cooper, Dominique M. Hanssens, and David F. Midgley. Modeling asymmetric competition. Marketing Science, 7(4):393-412, 1988. Google Scholar
  27. Wen-Chyuan Chiang, Jason C. H. Chen, and Xiaojing Xu. An overview of research on revenue management: current issues and future research. International Journal of Revenue Management, 1(1), 2007. Google Scholar
  28. Pradeep K. Chintagunta. Endogeneity and Heterogeneity in a Probit Demand Model: Estimation Using Aggregate Data. Marketing Science, 20(4):442-456, 2001. Google Scholar
  29. Pradeep K. Chintagunta. Investigating Category Pricing Behavior at a Retail Chain. Journal of Marketing Research, 39(2):141-154, May 2002. Google Scholar
  30. Pradeep K. Chintagunta, Jean-Pierre Dubé, and Vishal Singh. Balancing profitability and customer welfare in a supermarket chain. Quantitative Marketing and Economics, 1(1):111-147, 2003. Google Scholar
  31. Markus Christen, Sachin Gupta, John C. Porter, Richard Staelin, and Dick R. Wittink. Using Market-Level Data to Understand Promotion Effects in a Nonlinear Model. Journal of Marketing Research, 34(3):322, August 1997. Google Scholar
  32. Laurits R. Christensen and William Greene. Economies of scale in US electric power generation. The Journal of Political Economy, 84(4):655-676, 1976. Google Scholar
  33. Lee G. Cooper and Masao Nakanishi. Market-share analysis: evaluating competitive marketing effectiveness. Springer, 1988. Google Scholar
  34. Ronald W. Cotterill, William P. Putsis Jr., and Ravi Dhar. Assessing the competitive interaction between private labels and national brands. Journal of Business, 73(1):109, 2000. Google Scholar
  35. Peter J. Danaher, Andre Bonfrer, and Sanjay Dhar. The effect of competitive advertising interference on sales for packaged goods. Journal of Marketing Research, XLV(April):211-225, 2008. Google Scholar
  36. Angus Deaton and John Muellbauer. An almost ideal demand system. The American Economic Review, 70(3):312-326, 1980. Google Scholar
  37. Erwin Diewert. An application of the Shephard duality theorem: a generalized Leontief production function. The Journal of Political Economy, 79(3):481-507, 1971. Google Scholar
  38. Suresh Divakar, Brian T. Ratchford, and Venkatesh Shankar. CHAN4CAST: A Multichannel Multiregion Forecasting Model for Consumer Packaged Goods. Marketing Science, 24(3):334-350, July 2005. Google Scholar
  39. Wedad Elmaghraby and Pinar Keskinocak. Dynamic pricing in the presence of inventory considerations: research overview, current practices, and future directions. IEEE Engineering Management Review, 31(4):47-47, 2003. Google Scholar
  40. Peter S. Fader and Bruce G. S. Hardie. Modeling Consumer Choice among SKUs. Journal of Marketing Research, 33(4):442, November 1996. Google Scholar
  41. Juan-Carlos Ferrer and Diego Fuentes. A system design to bridge the gap between the theory and practice of retail revenue management. International Journal of Revenue Management, 5(2):261-275, 2011. Google Scholar
  42. Eijte W. Foekens, Peter S.H. Leeflang, and Dick R. Wittink. Hierarchical versus other market share models for markets with many items. International Journal of Research in Marketing, 14:359-378, 1997. Google Scholar
  43. Eijte W. Foekens, Peter S.H. Leeflang, and Dick R. Wittink. Varying parameter models to accommodate dynamic promotion effects. Journal of Econometrics, 89(1-2):249-268, 1998. Google Scholar
  44. Avijit Ghosh and Robert Shoemaker. A Comparison of Market Share Models and Estimation Procedures. Journal of Marketing Research, XXI(May):202-211, 1984. Google Scholar
  45. Jochen Goensch, Robert Klein, and Claudius Steinhardt. Dynamic Pricing–State-of-the-Art. Zeitschrift für Betriebswirtschaft, (3):1-40, 2009. Google Scholar
  46. Óscar González-Benito, María Pilar Martínez-Ruiz, and Alejandro Mollá-Descals. Retail pricing decisions and product category competitive structure. Decision Support Systems, 49(1):110-119, April 2010. Google Scholar
  47. Dhruv Grewal, Kusum L. Ailawadi, Dinesh Gauri, Kevin Hall, Praveen Kopalle, and Jane R. Robertson. Innovations in Retail Pricing and Promotions. Journal of Retailing, 87(1):43-52, July 2011. Google Scholar
  48. Peter M. Guadagni and John D.C. Little. A logit model of brand choice calibrated on scanner data. Marketing Science, 2(3):203-238, 1983. Google Scholar
  49. Joseph Guiltinan. Progress and Challenges in Product Line Pricing. Journal of Product Innovation Management, 28:744-756, April 2011. Google Scholar
  50. Peng Guo, Baichun Xiao, and Jun Li. Unconstraining Methods in Revenue Management Systems: Research Overview and Prospects. Advances in Operations Research, 2012:1-23, 2012. Google Scholar
  51. Sachin Gupta, Pradeep K. Chintagunta, Anil Kaul, and Dick R. Wittink. Do Household Scanner Data Provide Representative Inferences from Brand Choices: A Comparison with Store Data. Journal of Marketing Research, 33(4):383, November 1996. Google Scholar
  52. Sunil Gupta. Impact of Sales Promotions on When, What, and How Much to Buy. Journal of Marketing Research (JMR), 25(4):342-355, 1988. Google Scholar
  53. Dominique M. Hanssens, Leonard J. Parsons, and Randall L. Schultz. Market Response Models: Econometric and Time Series Analysis. Springer, 2001. Google Scholar
  54. Bruce G. S. Hardie, Leonard M. Lodish, Peter S. Fader, Alistair P. Sutcliffe, and William T. Kirk. Attribute-based Market Share Models: Methodological Development and Managerial Applications. London Business School Mimeo, 1998. Google Scholar
  55. Stephen J. Hoch, Byung-Do Kim, Alan L. Montgomery, and Peter E. Rossi. Determinants of Store-Level Price Elasticity. Journal of Marketing Research, 32(1):17, February 1995. Google Scholar
  56. Harald Hruschka. Relevance of functional flexibility for heterogeneous sales response models - A comparison of parametric and semi-nonparametric models. European Journal of Operational Research, 174(2):1009-1020, October 2006. Google Scholar
  57. Dipak C. Jain and Naufel J. Vilcassim. Testing functional forms of market share models using the Box-Cox transformation and the Lagrange multiplier approach. International Journal of Research in Marketing, 6:95-107, 1989. Google Scholar
  58. Kirthi Kalyanam. Pricing decisions under demand uncertainty: A Bayesian mixture model approach. Marketing Science, 15(3):207-221, 1996. Google Scholar
  59. Kirthi Kalyanam and Thomas S. Shively. Estimating Irregular Pricing Effects: A Stochastic Spline Regression Approach. Journal of Marketing Research, 35(1):16-29, 1998. Google Scholar
  60. Gurumurthy Kalyanaram and Russell S. Winer. Empirical generalizations from reference price research. Marketing Science, 14(3):161-169, 1995. Google Scholar
  61. University of Chicago Booth School of Business Kilts Center for Marketing. Dominick’s Data Manual. 2013. Google Scholar
  62. Byung-do Kim, Robert C. Blattberg, and Peter E Rossi. Modeling the Distribution of Price Sensitivity and Implications for Optimal Retail Pricing. Journal of Business, 13(3):291-303, 1995. Google Scholar
  63. Robert Klein. Revenue Management. Springer, Berlin, Heidelberg, 1. edition, 2008. Google Scholar
  64. Steven F. Koch. Fractional multinomial response models with an application to expenditure shares. Mimeo University of Pretoria, 2010-21(October), 2010. Google Scholar
  65. Gürhan A. Kök, Marshall L. Fisher, and Ramnath Vaidyanathan. Assortment Planning: Review of Literature and Industry Practice. Retail Supply Chain Management, 122:99-153, 2009. Google Scholar
  66. Praveen K. Kopalle. Modeling Retail Phenomena. Journal of Retailing, 86(2):117-124, June 2010. Google Scholar
  67. Praveen K. Kopalle, Carl F. Mela, and Lawrence Marsh. The dynamic effect of discounting on sales: Empirical analysis and normative pricing implications. Marketing Science, 18(3):317-332, 1999. Google Scholar
  68. V Kumar and Robert P. Leone. Measuring the effect of retail store promotions on brand and store substitution. Journal of Marketing Research, 25(2):178-185, 1988. Google Scholar
  69. V Kumar and Arun Pereira. Explaining the Variation in Short-Term Sales Response to Retail Price Promotions. Journal of the Academy of Marketing Science, 23(3):155-169, June 1995. Google Scholar
  70. Jean-Jacques Lambin. Measuring the profitability of advertising: An empirical study. Journal of Industrial Economics, 18(2):86-103, November 1969. Google Scholar
  71. Peter S.H. Leeflang, Dick R. Wittink, Michel Wedel, and Philippe A. Naert. Building models for marketing decisions. Kluwer Academic Publishers, Boston, 2000. Google Scholar
  72. Michael R. Levy, Dhruv Grewal, Praveen Kopalle, and James Hess. Emerging trends in retail pricing practice: implications for research. Journal of Retailing, 80(3):xiii-xxi, 2004. Google Scholar
  73. Warren H. Lieberman. Revenue management trends and opportunities. Journal of Revenue and Pricing Management, 3(1):91-99, 2004. Google Scholar
  74. Garry L. Lilien and Philip Kotler. Marketing Decision Making - A Model-Building Approach. 1983. Google Scholar
  75. John D.C. Little. Models and managers: The concept of a decision calculus. PhD thesis, 1970. Google Scholar
  76. John D.C. Little and Jeremy F. Shapiro. A theory for pricing nonfeatured products in supermarkets. Journal of Business, 53(3):199-209, 1980. Google Scholar
  77. Sandrine Macé and Scott A. Neslin. The determinants of pre-and postpromotion dips in sales of frequently purchased goods. Journal of Marketing Research, 41(3):339-350, 2004. Google Scholar
  78. Murali K. Mantrala, P.B. Seetharaman, Rajeeve Kaul, Srinath Gopalakrishna, and Antonie Stam. Optimal Pricing Strategies for an Automotive Aftermarket Retailer. Journal of Marketing Research, 43(4):588-604, November 2006. Google Scholar
  79. Josué Martínez-Garmendia. Application of hedonic price modeling to consumer packaged goods using store scanner data. Journal of Business Research, 63(7):690-696, July 2010. Google Scholar
  80. María Pilar Martínez-Ruiz. Evaluating temporary retail price discounts using semiparametric regression. Journal of Product and Brand Management, 15(1):73-80, 2006. Google Scholar
  81. María Pilar Martínez-Ruiz, José Luis Rojo-Álvarez, and Francisco Javier Gimeno-Blanes. Evaluation of Promotional and Cross-Promotional Effects Using Support Vector Machine Semiparametric Regression. Systems Engineering Procedia, 1:465-472, January 2011. Google Scholar
  82. Tridib Mazumdar, S.P. Raj, and Indrajit Sinha. Reference Price Research: Review and Propositions. Journal of Marketing, 69(4):84-102, October 2005. Google Scholar
  83. Jeffrey McGill and Garrett J. van Ryzin. Revenue management: Research overview and prospects. Transportation Science, 32(2):233-256, 1999. Google Scholar
  84. Kent B. Monroe and Albert J. Della Bitta. Models for Pricing Decisions. Journal of Marketing Research, 15(3):413-428, 1978. Google Scholar
  85. Alan L. Montgomery. Creating micro-marketing pricing strategies using supermarket scanner data. Marketing Science, 16(4):315-337, 1997. Google Scholar
  86. Alan L. Montgomery. The implementation challenge of pricing decision support systems for retail managers. Applied Stochastic Models in Business and Industry - Bridging the Gap between Academic Research in Marketing and Practitioners' Concerns, 1(4-5):367-378, 2005. Google Scholar
  87. Alan L. Montgomery and Peter Rossi. Estimating price elasticities with theory-based priors. Journal of Marketing Research, 36(4):413-423, 1999. Google Scholar
  88. Sridhar K. Moorthy. Using game theory to model competition. Journal of Marketing Research, 22(3):262-282, 1985. Google Scholar
  89. Sridhar K. Moorthy. Competitive marketing strategies: Game-theoretic models. In Handbooks in Operations Research and Management Science, volume 5, pages 143-190. 1993. Google Scholar
  90. Mark M. Moriarty. Retail promotional effects on intra-and interbrand sales performance. Journal of Retailing, 61(3):27-47, 1985. Google Scholar
  91. John Mullahy. Multivariate fractional regression estimation of econometric share models. University of Wisconsin-Madison Mimeo, WP2011/33, 2010. Google Scholar
  92. Masao Nakanishi and Lee G. Cooper. Simplified Estimation Procedures for MCI Models. Marketing Science, 1(3):314-322, 1982. Google Scholar
  93. Martin Natter, Thomas Reutterer, and Andreas Mild. Dynamic Pricing Support Systems for DIY Retailers - A case study from Austria. Marketing Intelligence Review, 1(1):17 -23, 2009. Google Scholar
  94. Martin Natter, Thomas Reutterer, Andreas Mild, and Alfred Taudes. Ein sortimentsübergreifendes Entscheidungsunterstützungssystem für dynamische Preis- und Werbeplanung im DIY-Handel. Marketing Zeitschrift für Forschung und Praxis, 28(4):260-268, 2006. Google Scholar
  95. Martin Natter, Thomas Reutterer, Andreas Mild, and Alfred Taudes. Practice Prize Report: An Assortmentwide Decision-Support System for Dynamic Pricing and Promotion Planning in DIY Retailing. Marketing Science, 26(4):576-583, July 2007. Google Scholar
  96. Purushottam Papatla and Lakshman Krishnamurthi. Measuring the dynamic effects of promotions on brand choice. Journal of Marketing Research, 33(1):20-35, 1996. Google Scholar
  97. Leslie E. Papke and Jeffrey M. Wooldridge. Econometric Methods for Fractional Response Variables with an Application to 401(k) Plan Participation Rates. Journal of Applied Econometrics, 11(February):619-632, 1996. Google Scholar
  98. Robert L. Phillips. Pricing and Revenue Optimization. Stanford University Press, Stanford, 1. edition, 2005. Google Scholar
  99. Vithala R. Rao. Pricing research in marketing: The state of the art. Journal of Business, 57(1), 1984. Google Scholar
  100. David J. Reibstein and Hubert Gatignon. Optimal Product Line Pricing: The Influence of Elasticities and Cross-Elasticities. Journal of Marketing Research, 21(3):259, August 1984. Google Scholar
  101. P.B. Seetharaman, Siddhartha Chib, Andrew Ainslie, Peter Boatwright, Tat Chan, Sachin Gupta, Nitin Mehta, Vithala R. Rao, and Andrei Strijnev. Models of Multi-Category Choice Behavior. Marketing Letters, 16(3-4):239-254, December 2005. Google Scholar
  102. Venkatesh Shankar and Ruth N. Bolton. An Empirical Analysis of Determinants of Retailer Pricing Strategy. Marketing Science, 23(1):28-49, January 2004. Google Scholar
  103. Venkatesh Shankar and Lakshman Krishnamurthi. Relating price sensitivity to retailer promotional variables and pricing policy: An empirical analysis. Journal of Retailing, 72(3):249-272, 1996. Google Scholar
  104. Jorge M. Silva-Risso, Randolph E. Bucklin, and Donald G. Morrison. A decision support system for planning manufacturers' sales promotion calendars. Marketing Science, 18(3):274-300, 1999. Google Scholar
  105. Jorge M. Silva-Risso and Irina Ionova. Practice Prize Winner-A Nested Logit Model of Product and Transaction-Type Choice for Planning Automakers' Pricing and Promotions. Marketing Science, 27(4):545-566, 2008. Google Scholar
  106. Aruna Sivakumar and Chandra Bhat. Fractional Split-Distribution Model for Statewide Commodity-Flow Analysis. Transportation Research Record, 1790(1):80-88, January 2002. Google Scholar
  107. Winfried J. Steiner, Andreas Brezger, and Christiane Belitz. Flexible estimation of price response functions using retail scanner data. Journal of Retailing and Consumer Services, 14(6):383-393, November 2007. Google Scholar
  108. Shivaram Subramanian and Hanif D. Sherali. A fractional programming approach for retail category price optimization. Journal of Global Optimization, 48(2):263-277, November 2009. Google Scholar
  109. Kalyan T. Talluri and Garrett J. van Ryzin. The Theory and Practice of Revenue Management. Springer US, New York, 1. edition, 2005. Google Scholar
  110. Gerard J. Tellis. The price elasticity of selective demand: A meta-analysis of econometric models of sales. Journal of Marketing Research, 25(4):331-341, 1988. Google Scholar
  111. Gerard J. Tellis. Tackling the retailer decision maze: Which brand to discount when and why? Marketing Science, 14(3):271-299, 1995. Google Scholar
  112. Henri Theil. The information approach to demand analysis. Econometrica: Journal of the Econometric Society, 33(1):67-87, 1965. Google Scholar
  113. Glen L. Urban. A mathematical modeling approach to product line decisions. Journal of Marketing Research, 6(1):40-47, 1969. Google Scholar
  114. Harald J. van Heerde, Peter S.H. Leeflang, and Dick R. Wittink. The Estimation of Pre- and Postpromotion Dips with Store-Level Scanner Data. Journal of Marketing Research, 37(3):383-395, August 2000. Google Scholar
  115. Harald J. van Heerde, Peter S.H. Leeflang, and Dick R. Wittink. How Promotions Work: SCAN*PRO-Based Evolutionary Model Building. Schmalenbach Business Review, 54(July):198-220, 2002. Google Scholar
  116. Harald J. van Heerde, Peter S.H. Leeflang, and Dick R. Wittink. Decomposing the Sales Promotion Bump with Store Data. Marketing Science, 23(3):317-334, June 2004. Google Scholar
  117. Erjen van Nierop, Dennis Fok, and Philip Hans Franses. Sales models for many items using attribute data. ERIM Report Series Research in Management, (ERS-2002-65-MKT), 2002. Google Scholar
  118. Garrett J. van Ryzin. Models of demand. Journal of Revenue &Pricing Management, 4(2):204-210, 2005. Google Scholar
  119. Naufel J. Vilcassim and Pradeep K. Chintagunta. Investigating retailer product category pricing from household scanner panel data. Journal of Retailing, 71(2):103-128, 1995. Google Scholar
  120. Gustavo Vulcano, Garrett J. van Ryzin, and Richard M. Ratliff. Estimating Primary Demand for Substitutable Products from Sales Transaction Data. Operations Research, 60(2):313-334, May 2012. Google Scholar
  121. Rockney G. Walters. Assessing the Impact of Retail Price Promotions on Product Substitution, Complementary Purchase, and Interstore Sales Displacement. Journal of Marketing, 55(2):17, April 1991. Google Scholar
  122. Lawrence R. Weatherford and Samuel E. Bodily. A taxonomy and research overview of perishable-asset revenue management: yield management, overbooking, and pricing. Operations Research, 40(5):831-844, 1992. Google Scholar
  123. Albert R. Wildt. Estimating models of seasonal market response using dummy variables. Journal of Marketing Research, 14(1):34-41, 1977. Google Scholar
  124. Dick R. Wittink, M.J. Addona, W.J. Hawkes, and J.C. Porter. SCAN* PRO: The estimation, validation and use of promotional effects based on scanner data. Internal Paper, Cornell University, 1988. Google Scholar
  125. Xin Ye and Ram M. Pendyala. A Model of Daily Time Use Allocation Using Fractional Logit Methodology. In Transportation and Traffic Theory. Flow, Dynamics and Human Interaction. 16th International Symposium on Transportation and Traffic Theory, 2005. Google Scholar
  126. Elaine L. Zanutto and Eric T. Bradlow. Data pruning in consumer choice models. Quantitative Marketing and Economics, 4(3):267-287, August 2006. Google Scholar
  127. Michael J. Zenor. The profit benefits of category management. Journal of Marketing Research, 31(2):202-213, 1994. Google Scholar
  128. Michael J. Zenor and Rajendra K. Srivastava. Inferring Market Structure With Aggregate Data: A Latent Segment Logit Approach. Journal of Marketing Research, 30(3):369-379, 1993. Google Scholar
  129. Giulio Zotteria, Matteo Kalchschmidtb, and Federico Caniatoc. The impact of aggregation level on forecasting performance. International Journal of Production Economics, 93-94:479-491, January 2005. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail