Subject: MRN MKTG Quantitative WPS Vol. 1, No. 6, 09/27/2002 Date: Fri, 27 Sep 2002 19:47:39 -0500 From: Anne Coughlan & Alvin Silk Reply-To: admin@SSRN.COM To: QUANTITATIVE-MARKETING-WPS@PUBLISHER.SSRN.COM _________________________________________________________________ Q U A N T I T A T I V E M A R K E T I N G A B S T R A C T S Working Paper Series Vol. 1, No. 6: September 27, 2002 _________________________________________________________________ Publisher: MKTG Subject Matter Journals a division of Social Science Electronic Publishing, Inc. (SSEP) and Social Science Research Network (SSRN) Editors: ANNE T. COUGHLAN Associate Professor of Marketing, Northwestern University Mailto:a-coughlan@kellogg.northwestern.edu ALVIN J. SILK Lincoln Filene Professor of Business Administration Emeritus, Harvard Business School Mailto:asilk@hbs.edu Copyright: SSEP, Inc. 2002. All rights reserved. Leading Social Science Research Delivered To Your Desktop http://www.SSRN.Com/ SEARCHING THE SSRN ELECTRONIC LIBRARY To search the entire SSRN Electronic Library by author, title, JEL code, or full text of the abstracts in our database, please visit http://papers.ssrn.com/ To browse all abstracts published in this journal, please visit http://www.ssrn.com/link/Quantitative-Marketing.html REDISTRIBUTION Individual and professional subscriptions to the journal are for single users. It is a violation of copyright to redistribute this document electronically or otherwise without the explicit permission of Social Science Electronic Publishing, Inc. Site licenses for organizations are available by contacting Mailto:Site@SSRN.Com SIGN OFF To stop delivery of one or more of the SSRN journals, write to Mailto:Remove@SSRN.Com Include the JOURNAL name or the NETWORK name or ALL in the subject line. If your address has changed, let us know by writing to Mailto:AddressChg@SSRN.Com ALIGNMENT If this document is misaligned, please set type face to a non-proportional font such as Courier 10. PAPER DOWNLOADS If you need assistance downloading papers from our web site, please contact Mailto:Support@SSRN.Com T A B L E of C O N T E N T S _________________________________________________________________ "Why Don't Prices Rise During Periods of Peak Demand? Evidence from Scanner Data" PETER E. ROSSI University of Chicago Econometrics and Statistics JUDITH A. CHEVALIER Yale University School of Management National Bureau of Economic Research (NBER) ANIL K. KASHYAP University of Chicago Graduate School of Business "Three Principles of Competitive Nonlinear Pricing" FRANK H. PAGE University of Alabama PAULO KLINGER MONTEIRO EPGE/FGV "A Product-Market-Based Measure of Brand Equity" KUSUM L. AILAWADI Tuck School of Business at Dartmouth DONALD R. LEHMANN Columbia University Columbia Business School SCOTT NESLIN Tuck School of Business at Dartmouth "A Probabilistic One-Step Approach to the Optimal Product Line Design Problem Using Conjoint and Cost Data" WINFRIED J. STEINER University of Regensburg-Graduate School of Economics, Department of Marketing HARALD HRUSCHKA University of Regensburg Faculty of Economics S S R N I N F O R M A T I O N _________________________________________________________________ * Administrative Information - Missing issues & change of address - Solicitation of abstracts * Directors * Advisory Board * Subscription to SSRN Journals _________________________________________________________________ ACQUIRING PAPERS Download papers directly from the included web address or contact the author or other contact person directly. Provide an address to which the author or other contact person can send a paper copy and mention that you saw the abstract in SSRN. Some of SSRN's Partners in Publishing require a subscription or charge a fee for electronic downloads. EDITORIAL POLICIES To provide the broadest coverage of research in Quantitative Marketing we do not referee working papers. We accept abstracts of working papers in Quantitative Marketing whose topics suit the coverage of the journal and which are part of the worldwide scholarly discourse. W O R K I N G P A P E R A B S T R A C T S _________________________________________________________________ "Why Don't Prices Rise During Periods of Peak Demand? Evidence from Scanner Data" BY: PETER E. ROSSI University of Chicago Econometrics and Statistics JUDITH A. CHEVALIER Yale University School of Management National Bureau of Economic Research (NBER) ANIL K. KASHYAP University of Chicago Graduate School of Business Document: Available from the SSRN Electronic Paper Collection: http://papers.ssrn.com/paper.taf?abstract_id=319966 Paper ID: Yale SOM Working Paper No. MK-12 Date: July 2002 Contact: PETER E. ROSSI Email: Mailto:peter.rossi@gsb.uchicago.edu Postal: University of Chicago Econometrics and Statistics Walker 403 Chicago, IL 60637 UNITED STATES Phone: 773-702-7513 Fax: 773-702-2857 Co-Auth: JUDITH A. CHEVALIER Email: Mailto:JUDITH.CHEVALIER@YALE.EDU Postal: Yale University School of Management Box 208200 135 Prospect Street New Haven, CT 06520-8200 UNITED STATES Co-Auth: ANIL K. KASHYAP Email: Mailto:ANIL.KASHYAP@GSB.UCHICAGO.EDU Postal: University of Chicago Graduate School of Business 1101 East 58th Street Chicago, IL 60637 UNITED STATES Paper Requests: Contact Michael Iannazzi, Postal: P.O. Box 208200, New Haven, CT 06520-8200. Phone: 203-432-6027. Fax: 203-432-9992. Fee $20. ABSTRACT: We examine retail and wholesale prices for a large supermarket chain over seven and one-half years. We find that prices fall on average during seasonal demand peaks for a product, largely due to changes in retail margins. Retail margins for specific goods fall during peak demand periods for that good, even if these periods do not coincide with aggregate demand peaks for the retailer. This is consistent with "loss leader" models of retailer competition. Models stressing cyclical demand elasticities or cyclical firm conduct are less consistent with our findings. Manufacturer behavior plays a limited role in the counter-cyclicality of prices. Keywords: Pricing, Seasonality, Retail, Competition JEL Classification: L13, E32, L81 ______________________________ "Three Principles of Competitive Nonlinear Pricing" BY: FRANK H. PAGE University of Alabama PAULO KLINGER MONTEIRO EPGE/FGV Document: Available from the SSRN Electronic Paper Collection: http://papers.ssrn.com/paper.taf?abstract_id=311619 Paper ID: Warwick Economics Research Paper No. 592 Date: June 2002 Contact: FRANK H. PAGE Email: Mailto:FPage@cba.ua.edu Postal: University of Alabama P.O. Box 870244 Tuscaloosa, AL 35487 UNITED STATES Phone: 205-348-6097 Co-Auth: PAULO KLINGER MONTEIRO Email: Mailto:PKLM@FGV.BR Postal: EPGE/FGV sala 1125 Rio de Janeiro RJ 22253-900, BRAZIL ABSTRACT: We make three contributions to the theory of contracting under asymmetric information. First, we establish a competitive analog to the revelation principle which we call the implementation principle. This principle provides a complete characterization of all incentive compatible, indirect contracting mechanisms in terms of contract catalogs (or menus), and allows us to conclude that in competitive contracting situations, firms in choosing their contracting strategies can restrict attention, without loss of generality, to contract catalogs. Second, we establish a competitive taxation principle. This principle, a refinement of the implementation principle, provides a complete characterization of all implementable nonlinear pricing schedules in terms of product-price catalogs and allows us to reduce any game played over nonlinear pricing schedules to a strategically equivalent game played over product-price catalogs. Third, applying the notion of payoff security (Reny (1999)) and the competitive taxation principle, we demonstrate the existence of a Nash equilibrium for the mixed extension of the nonlinear pricing game. Moreover, we identify a large class of competitive nonlinear pricing games whose mixed extensions satisfy payoff security. Keywords: competitive nonlinear pricing, delegation principle, implementation principle, competitive taxation principle, Nash equilibria for discontinuous games JEL Classification: D82, L51 ______________________________ "A Product-Market-Based Measure of Brand Equity" BY: KUSUM L. AILAWADI Tuck School of Business at Dartmouth DONALD R. LEHMANN Columbia University Columbia Business School SCOTT NESLIN Tuck School of Business at Dartmouth Paper ID: Marketing Science Institute Working Paper, Report No. 02-102 Date: May 2002 Contact: KUSUM L. AILAWADI Email: Mailto:KUSUM.L.AILAWADI@DARTMOUTH.EDU Postal: Tuck School of Business at Dartmouth 100 Tuck Hall Hanover, NH 03755 UNITED STATES Phone: 603-646-2845 Fax: 603-646-1308 Co-Auth: DONALD R. LEHMANN Email: Mailto:R.R.MACDONALD@STRATH.AC.UK Postal: Columbia University Columbia Business School 3022 Broadway New York, NY 10027 UNITED STATES Co-Auth: SCOTT NESLIN Email: Mailto:scott.neslin@dartmouth.edu Postal: Tuck School of Business at Dartmouth Hanover, NH 03755 UNITED STATES Paper Requests: Contact Rachel Carr at Mailto:rcarr@msi.org or postal: Marketing Science Institute, 1000 Massachusetts Ave., Cambridge, MA 02138 USA. Phone: 617-491-2060, Fax: 617-492-1065 Fees: $18 general, $12.60 academic (plus shipping) per paper. ABSTRACT: Conceptually, brand equity can be thought of as the incremental revenue that a product earns as a brand over the revenue it would earn if it were sold without the brand name. However, the brand can have an impact on revenue through a variety of mechanisms, e.g., reduced price sensitivity, higher advertising effectiveness, greater distribution etc. Developing a structural model of all these relationships is difficult, likely to contain specification error, and not very practical for managers. Instead, we propose a simple and easily quantifiable measure of brand equity. We base our measure on the implicit assumption that outcomes in the market involve optimal decisions by firms who select a price for their brands in order to maximize net revenue. Their decisions depend on the demand curves and costs faced by branded and unbranded goods. In essence, these revenues result from a reduced form of the complex relations among brand, marketing mix elements, and customers. Instead of trying to estimate the hypothetical revenue that a branded product would earn if it did not have the brand name, we use the revenue of the private label product as a reasonable benchmark. Hence, the difference in revenue between a branded good and the corresponding private label represents the equity of the particular brand. We calculate this measure for brands in twenty-three different packaged goods categories and validate it by examining its behavior over time and its association with other variables with which brand equity should be related in specific ways. The measure is reliable in that it correlates strongly with its lagged value. It exhibits a trend over time that is consistent with conventional wisdom on the erosion of brand equity during the past decade. As expected, it is positively associated with advertising but not with promotion. Further, it is positively associated with stockpileability and hedonic categories and negatively associated with private label quality. Finally, the "up" price elasticity, i.e., the effect on sales when price is increased, is significantly lower than the "down" price elasticity for high equity brands. Overall, the measure is simple, intuitive, and easy to calculate from public sources, and has fairly strong face and construct validity. ______________________________ "A Probabilistic One-Step Approach to the Optimal Product Line Design Problem Using Conjoint and Cost Data" BY: WINFRIED J. STEINER University of Regensburg-Graduate School of Economics, Department of Marketing HARALD HRUSCHKA University of Regensburg Faculty of Economics Document: Available from the SSRN Electronic Paper Collection: http://papers.ssrn.com/paper.taf?abstract_id=319463 Paper ID: Review of Marketing Science Working Paper 441 Date: March 2002 Contact: WINFRIED J. STEINER Email: Mailto:winfried.steiner@wiwi.uni-regensburg.de Postal: University of Regensburg-Graduate School of Economics, Department of Marketing Universitstrasse 31 Regensberg D-93053, GERMANY Co-Auth: HARALD HRUSCHKA Email: Mailto:harald.hruschka@wiwi.uni-regensburg.de Postal: University of Regensburg Faculty of Economics Universitstrasse 31 Regensberg D-93053, GERMANY ABSTRACT: Designing and pricing new products is one of the most critical activities for a firm, and it is well-known that taking into account consumer preferences for design decisions is essential for products later to be successful in a competitive environment (e.g., Urban and Hauser 1993). Consequently, measuring consumer preferences among multiattribute alternatives has been a primary concern in marketing research as well, and among many methodologies developed, conjoint analysis (Green and Rao 1971) has turned out to be one of the most widely used preference-based techniques for identifying and evaluating new product concepts. Moreover, a number of conjoint-based models with special focus on mathematical programming techniques for optimal product (line) design have been proposed (e.g., Zufryden 1977, 1982, Green and Krieger 1985, 1987b, 1992, Kohli and Krishnamurti 1987, Kohli and Sukumar 1990, Dobson and Kalish 1988, 1993, Balakrishnan and Jacob 1996, Chen and Hausman 2000). These models are directed at determining optimal product concepts using consumers' idiosyncratic or segment level part-worth preference functions estimated previously within a conjoint framework. Recently, Balakrishnan and Jacob (1996) have proposed the use of Genetic Algorithms (GA) to solve the problem of identifying a share maximizing single product design using conjoint data. In this paper, we follow Balakrishnan and Jacob's idea and employ and evaluate the GA approach with regard to the problem of optimal product line design. Similar to the approaches of Kohli and Sukumar (1990) and Nair et al. (1995), product lines are constructed directly from part-worths data obtained by conjoint analysis, which can be characterized as a one-step approach to product line design. In contrast, a two-step approach would start by first reducing the total set of feasible product profiles to a smaller set of promising items (reference set of candidate items) from which the products that constitute a product line are selected in a second step. Two-step approaches or partial models for either the first or second stage in this context have been proposed by Green and Krieger (1985, 1987a, 1987b, 1989), McBride and Zufryden (1988), Dobson and Kalish (1988, 1993) and, more recently, by Chen and Hausman (2000). Heretofore, with the only exception of Chen and Hausman's (2000) probabilistic model, all contributors to the literature on conjoint-based product line design have employed a deterministic, first-choice model of idiosyncratic preferences. Accordingly, a consumer is assumed to choose from her/his choice set the product with maximum perceived utility with certainty. However, the first choice rule seems to be an assumption too rigid for many product categories and individual choice situations, as the analyst often won't be in a position to control for all relevant variables influencing consumer behavior (e.g., situational factors). Therefore, in agreement with Chen and Hausman (2000), we incorporate a probabilistic choice rule to provide a more flexible representation of the consumer decision making process and start from segment-specific conjoint models of the conditional multinomial logit type. Favoring the multinomial logit model doesn't imply rejection of the widespread max-utility rule, as the MNL includes the option of mimicking this first choice rule. We further consider profit as a firm's economic criterion to evaluate decisions and introduce fixed and variable costs for each product profile. However, the proposed methodology is flexible enough to accomodate for other goals like market share (as well as for any other probabilistic choice rule). This model flexibility is provided by the implemented Genetic Algorithm as the underlying solver for the resulting nonlinear integer programming problem. Genetic Algorithms merely use objective function information (in the present context on expected profits of feasible product line solutions) and are easily adjustable to different objectives without the need for major algorithmic modifications. To assess the performance of the GA methodology for the product line design problem, we employ sensitivity analysis and Monte Carlo simulation. Sensitivity analysis is carried out to study the performance of the Genetic Algorithm w.r.t. varying GA parameter values (population size, crossover probability, mutation rate) and to finetune these values in order to provide near optimal solutions. Based on more than 1500 sensitivity runs applied to different problem sizes ranging from 12.650 to 10.586.800 feasible product line candidate solutions, we can recommend: (a) as expected, that a larger problem size be accompanied by a larger population size, with a minimum popsize of 130 for small problems and a minimum popsize of 250 for large problems, (b) a crossover probability of at least 0.9 and (c) an unexpectedly high mutation rate of 0.05 for small/medium-sized problems and a mutation rate in the order of 0.01 for large problem sizes. Following the results of the sensitivity analysis, we evaluated the GA performance for a large set of systematically varying market scenarios and associated problem sizes. We generated problems using a 4-factorial experimental design which varied by the number of attributes, number of levels in each attribute, number of items to be introduced by a new seller and number of competing firms except the new seller. The results of the Monte Carlo study with a total of 276 data sets that were analyzed show that the GA works efficiently in both providing near optimal product line solutions and CPU time. Particularly, (a) the worst-case performance ratio of the GA observed in a single run was 96.66%, indicating that the profit of the best product line solution found by the GA was never less than 96.66% of the profit of the optimal product line, (b) the hit ratio of identifying the optimal solution was 84.78% (234 out of 276 cases) and (c) it tooks at most 30 seconds for the GA to converge. Considering the option of Genetic Algorithms for repeated runs with (slightly) changed parameter settings and/or different initial populations (as opposed to many other heuristics) further improves the chances of finding the optimal solution. Keywords: Conjoint Analysis, Product Line Design, Probabilistic Choice Modeling, Genetic Algorithms P A R T N E R S in P U B L I S H I N G _________________________________________________________________ Editor and Subscription Information for Journals Carrying Accepted or Recently Published Papers Abstracted in this Issue Please mention SSRN when subscribing to these journals. YALE SCHOOL OF MANAGEMENT RESEARCH PAPERS Contact: Michael Iannazzi Email: Mailto:michael.iannazzi@yale.edu Postal: Yale School of Management 135 Prospect Street P.O. Box 208200 New Haven, CT 06520-8200 Phone: 203-432-6027 Fax: 203-432-9992 URL: http://mba.yale.edu For hard copies of the Yale School of Management Research papers, or for more information, please contact Michael Iannazzi at the above address. There is a charge of $20.00 for papers. _________________________________________________________________ REVIEW OF MARKETING SCIENCE (ROMS) Contact: Ram C. 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D I R E C T O R _________________________________________________________________ MKTG SUBJECT MATTER JOURNALS ALVIN J. SILK Lincoln Filene Professor of Business Administration Emeritus, Harvard Business School Mailto:asilk@hbs.edu Please contact us at the above addresses with your comments, questions or suggestions for MKTG-Sub. A D V I S O R Y B O A R D _________________________________________________________________ QUANTITATIVE MARKETING FRANK M. BASS University of Texas System Eugene C. McDermott Professor of Management, School of Management, University of Texas at Dallas HUBERT GATIGNON The Claude Janssen Chaired Professor of Business Administration and Professor of Marketing, Dean of Ph.D. Program, Research Director - The INSEAD-Wharton Alliance, Director - The Alliance Center for Global Research and Development DIPAK C. JAIN Dean, Kellogg School of Management, Sandy and Morton Goldman Professor of Entrepreneur Studies, Professor of Marketing WAGNER A. KAMAKURA Ford Motor Company Professor of Global Marketing and Editor of Journal of Marketing Research, Fuqua School of Business, Duke University GILLES LAURENT Professor of Marketing and Associate Dean for Research at Groupe HEC, France GARY L. LILIEN Distinguished Research Professor of Management Science, Research Director - Institute for the Study of Business Markets JOHN D.C. LITTLE Institute Professor, Massachusetts Institute of Technology, Professor of Management Science, MIT Sloan School JAGMOHAN S. RAJU Professor of Marketing, The Wharton School, University of Pennsylvania RAM C. RAO Professor of Marketing and Founders Professor in the School of Management, The University of Texas at Dallas, Editor of the Internet based journal Review of Marketing Science (ROMs) BRIAN T. RATCHFORD Pepsico Chair in Consumer Research, Robert H. Smith School of Business, University of Maryland JOHN ROBERTS National Australia Bank Professor of Marketing, Australian Graduate School of Management, Australia PETER E. ROSSI Joseph T. Lewis Professor of Marketing and Statistics, Graduate School of Business, University of Chicago SUBRATA K. SEN Joseph F. Cullman Professor of Marketing, Past Editor of Marketing Science, Yale School of Management, Yale University STEVEN M. SHUGAN Russ Berrie Foundation Eminent Scholar and Professor of Marketing and Editor-in-Chief, Marketing Science, Warrington College of Business Administration, The University of Florida RICHARD STAELIN Edward and Rose Donnell Professor of Business Administration and Associate Dean for Executive Education, Fuqua School of Business, Duke University WILFRIED R.R. 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