MARKET-SHARE ANALYSIS - UCLA Anderson …

1 MARKET-SHARE ANALYSISI nternational Series in Quantitative MarketingEditor:Jehoshua EliashbergThe Wharton SchoolUniversity of PennsylvaniaPhiladelphia, Pennsylvania, AnalysisEvaluating Competitive Marketing EffectivenessLee G. CooperAnderson Graduate School of ManagementUniversity of California, Los AngelesMasao NakanishiSchool of Business AdministrationKwansei Gakuin UniversityNishinomiya-shi, JAPANK luwer Academic PublishersBoston Dordrecht LondonDistributors for North America:Kluwer Academic Publishers101 Philip Drive, Assinippi ParkNorwell, Massachusetts 02061 USAD istributors for the UK and Ireland:Kluwer Academic PublishersFalcon House, Queen SquareLancaster LA1 1RN, UNITED KINGDOMD istributors for all other countries:Kluwer Academic Publishers GroupDistribution CentrePost Office Box 3223300 AH Dordrecht, THE NETHERLANDSL ibrary of Congress Cataloging-in-Publication DataCooper, Lee ANALYSIS : evaluating competitive marketingeffective-ness / Lee G.

2 Cooper, Masao cm. (International series in quantitative marketing)Bibliography: 0 89838 278 51. Marketing Decision making Mathematical Nakanishi, Masao, 1936 . II. Title. III. 02 dc 1988 12092 CIPO riginal Copyrightc 1988 by Kluwer Academic PublishersCopyrightc 2010 by Lee G. CooperAll rights reserved. Printed in the United States of AmericaContentsList of TablesxList of FiguresxiiForeword .. xiii1 Scope and Interest in MARKET-SHARE ANALYSIS .. Need for a Analytical Framework .. The Process of MARKET-SHARE ANALYSIS .. Stage 1: Specification of Models .. Stage 2: Data Collection and Review .. Stage 3: ANALYSIS .. Stage 4: Strategy and Planning .. Stage 5: Follow-Up .. 152 Understanding market market shares : Definitions .. DefiningIndustrySales .. Kotler sFundamental Theorem.. A Numerical Example .. * MARKET-SHARE Theorem .. Alternative Models of market Share.

3 MARKET-SHARE Elasticities .. Sales-Volume Elasticities .. * market shares and Choice Probabilities .. Appendices for Chapter 2 .. *Calculus of MARKET-SHARE Elasticities .. *Properties of MARKET-SHARE Elasticities .. *Individual Choice Probabilities .. *Multivariate Independent Gamma Function .. 52vviCONTENTS3 Describing Markets and market and Competitive Structure .. Asymmetries in market and Competition .. Differential Effectiveness .. Differential Cross Elasticities .. Properties of Fully Extended Models .. Determining Competitive Structures .. Hierarchies of market Segments .. Distinctiveness of Marketing Activities .. Time-Series Issues .. Appendix for Chapter 3 .. *Log-Linear Time-Series Model .. 844 Data The Accuracy of Scanner Data .. Issues in Aggregation .. National Tracking Data .. Store-Level Scanner Data .. Store Audits.

4 Household Scanner Panels .. Other Data Sources .. market Information Systems .. 985 Parameter Calibrating Attraction Models .. Maximum-Likelihood Estimation .. Log-Linear Estimation .. Log-Linear Regression Techniques .. Organization of Data for Estimation .. Reading Regression- ANALYSIS Outputs .. The ANALYSIS -of-Covariance Representation .. Properties of the Error Term .. Assumptions on the Specification-Error Term .. Survey Data .. POS Data .. *Generalized Least-Squares Estimation .. Application of GLS to the Margarine Data .. Estimation of Differential-Effects Models .. Collinearity in Differential-Effects Models .. Three Differential-Effects Models .. Within-Brand Effects .. Remedies .. Estimation of Cross-Effects Models .. A Multivariate MCI Regression Model .. Estimation of Category-Volume Models .. Estimation of Share-Elasticities .. Problems with Zero market shares .

5 The Coffee- market Example .. The MARKET-SHARE Model .. The Category-Volume Model .. Combining Share and Category Volume .. Large-Scale Competitive ANALYSIS .. How Large Is Too Large? .. Is BLUE Always Best? .. Appendix for Chapter 5 .. Generalized Least Squares Estimation .. 1756 Competitive *Asymmetric Three-Mode Factor ANALYSIS .. Portraying the Coffee market .. Signalling Competitive Change .. Competitive Maps: The Structure Over Brands .. *Elasticities and market Structure .. *Interpretive Aids for Competitive Maps .. *Appendix for Chapter 6 .. 2117 Decision-Support CASPER .. Using HISTORY .. Simulating Static Occasions .. The Assumptions Underlying Planning .. What If There Were No Experts? .. Dynamic Simulations .. management Decision Making .. 2568 A Research Estimation Problems .. Missing Data .. Constrained Parameter Estimation .. Long-Run Effects.

6 Issues in Decision Support .. Game Theory .. Expert Systems .. The Integration of Panel Data .. market -Basket Models .. 264 Index272 List of Numerical Example of Kotler s Fundamental Theorem .. Numerical Example The Effect of Reducing Price .. Effect of Correlation Between Purchase Frequencies andChoice Probabilities .. Relations Between market shares and Choice Probabilities Numerical Example of Cross Elasticities for MCI Model . Direct and Cross Elasticities for Seven Brands .. Interlocking and Nested Brand Groups .. Aggregating market shares and Causal Conditions .. POS Data Example (Margarine) .. Data Set for Estimation .. Regression Results for MCI Equation ( ) .. Regression Results for MNL Equation ( ) .. GLS Estimates for Table .. Data Set for Differential-Effects Model .. Regression Results for Differential-Effects Model (MCI). Log-Centered Differential-Effects Data.

7 Hypothetical Data for Differential-Effects Model .. Condition Indices Australian Household-Products Regression Results for Cross-Effects Model (MCI) .. Coffee Data Average Prices and market shares .. Regression Results for Cross-Effects Model (MCI) .. Regression Results for Category-Volume Model .. Computer Resources for Two Applications .. Summary of BLUE Parameters IRI Data .. Summary of BLUE Parameters Nielsen Data .. 174ixxLIST OF Average MARKET-SHARE Elasticities of Price .. Coordinates of the Idealized Competitive Conditions .. Common Scaling Space .. Dimensionality of Common Scaling Space .. The Core Matrix for the Coffee- market Example .. Joint-Space Coefficients for Chain-Week Factors .. Elasticities for Idealized Competitive Conditions .. Default Price and Promotion Table .. Default Costs .. Default Discounts Offered to Retailer by Manufacturer ..234 List of The Process of MARKET-SHARE ANALYSIS .

8 Share Elasticities for MCI and MNL Models .. Cross Elasticities in the Fully Extended Model .. Comparison of Zeta-Score and Exp(z-Score) Transforms . Comparison of Zeta-Score and Exp(z-Score) Elasticities . A System of Models for Competitive ANALYSIS .. Three-Mode Array of Elasticities .. The Number of Factors Over Chains and Weeks .. Competitive Structure Over Stores Factors 1 & 2 .. Competitive Structure Over Stores Factors 3 & 4 .. The Competitive Map Shelf Prices .. The Competitive Map Folgers On Sale .. The Competitive Map Maxwell House On Sale .. The Competitive Map Chock Full O Nuts On Sale .. Preference and the Ideal Point .. Preference and the Anti-Ideal Point .. Preference and the Saddle Point .. Folgers market Share Chain 1 .. market shares Week 10 Chain 2 .. market shares Week 13 Chain 2 .. market shares Week 30 Chain 2 .. Maxwell House Actual market shares Chain 2.

9 Maxwell House Estimated market shares Chain 2 .. CFON s market shares Simulation Results .. Total Category Volume Simulation Results .. 238xixiiLIST OF CFON s Sales Simulation Results .. CFON s Contribution to Store Profits Simulation Total CFON Profits Simulation Results .. Folgers market shares Simulation Results .. Folgers Sales Simulation Results .. Folgers s Contribution to Store Profits Simulation Maxwell House s market shares Simulation Results .. Maxwell House s Sales Simulation Results .. Maxwell House s Contribution to Store Profits Simula-tion Results .. 248 FOREWORD xiiiForewordIn April 1971, Los Angeles and its satellite cities were treatedto one of its least interesting and least publicized elections inyears. Nothing seemed to be hotly contested. A few LosAngeles city councilmen were up for reelection as were somemembers of the Board of Education and the Board of Trusteesof the Community Colleges.

10 Nakanishi, Cooper and Kassarjian [1974]Our colleague, Professor Harold H. Kassarjian, ran for one of theseats on the Board of Trustees and received 17,286 votes. While helost the election, he had collected the data which he felt characterizedvoting in suchlow-involvementcases. He asked us to join him in writinga follow-up to a study of a similar election which had been publishedthe previous fall inPublic Opinion Quarterly. Neither of us was contentwith the methods and models used in the prior study. shares are differentthan other criteria, be they vote shares , market shares or retail stores shares of customers. Different methods are needed to reflect their specialnature. And thus began a research collaboration, running 17years, our combined research efforts have covered diverse areas ofconsumer choice behavior, in recent years we came to the realization thatour models and analytical methods might be very profitably employedin the ANALYSIS of MARKET-SHARE figures for consumer products.