MARKET-SHARE ANALYSIS - UCLA Anderson School of Management

290 Pages·2010·2.62 MB·English

MARKET-SHARE ANALYSIS
International Series in Quantitative Marketing
Editor:
Jehoshua Eliashberg
The Wharton School
University of Pennsylvania
Philadelphia, Pennsylvania, U.S.A.
Market-Share Analysis
Evaluating Competitive Marketing Effectiveness
Lee G. Cooper
Anderson Graduate School of Management
University of California, Los Angeles
Masao Nakanishi
School of Business Administration
Kwansei Gakuin University
Nishinomiya-shi, JAPAN
Kluwer Academic Publishers
Boston Dordrecht London
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Kluwer Academic Publishers
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Library of Congress Cataloging-in-PublicationData
Cooper, Lee G.
Market-share analysis: evaluating competitive marketing effective-
ness / Lee G. Cooper, Masao Nakanishi.
p. cm. – – (International series in quantitative marketing)
Bibliography: p.
Includes index.
ISBN 0–89838–278–5
1. Marketing – – Decision making – – Mathematical models.
I. Nakanishi, Masao, 1936– . II. Title. III. Series.
HF5415. 135.C66 1988
658.8‘02 – – dc 19 88–12092
CIP
Original Copyright (cid:13)c 1988 by Kluwer Academic Publishers
Copyright (cid:13)c 2010 by Lee G. Cooper
All rights reserved. Printed in the United States of America
Contents
List of Tables x
List of Figures xii
Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
1 Scope and Objectives 1
1.1 Interest in Market-Share Analysis . . . . . . . . . . . . . . 1
1.2 Need for a Analytical Framework . . . . . . . . . . . . . . 5
1.3 The Process of Market-Share Analysis . . . . . . . . . . . 10
1.3.1 Stage 1: Specification of Models . . . . . . . . . . 11
1.3.2 Stage 2: Data Collection and Review . . . . . . . . 13
1.3.3 Stage 3: Analysis . . . . . . . . . . . . . . . . . . . 14
1.3.4 Stage 4: Strategy and Planning . . . . . . . . . . . 14
1.3.5 Stage 5: Follow-Up . . . . . . . . . . . . . . . . . . 15
2 Understanding Market Shares 17
2.1 Market Shares: Definitions. . . . . . . . . . . . . . . . . . 17
2.2 Defining Industry Sales . . . . . . . . . . . . . . . . . . . . 19
2.3 Kotler’s Fundamental Theorem . . . . . . . . . . . . . . . 21
2.3.1 A Numerical Example . . . . . . . . . . . . . . . . 23
2.4 *Market-Share Theorem . . . . . . . . . . . . . . . . . . . 24
2.5 Alternative Models of Market Share . . . . . . . . . . . . 26
2.6 Market-Share Elasticities . . . . . . . . . . . . . . . . . . 31
2.7 Sales-Volume Elasticities . . . . . . . . . . . . . . . . . . . 36
2.8 *Market Shares and Choice Probabilities . . . . . . . . . . 38
2.9 Appendices for Chapter 2 . . . . . . . . . . . . . . . . . . 44
2.9.1 *Calculus of Market-Share Elasticities . . . . . . . 44
2.9.2 *Properties of Market-Share Elasticities . . . . . . 45
2.9.3 *Individual Choice Probabilities . . . . . . . . . . 46
2.9.4 *Multivariate Independent Gamma Function . . . 52
v
vi CONTENTS
3 Describing Markets and Competition 55
3.1 Market and Competitive Structure . . . . . . . . . . . . . 55
3.2 Asymmetries in Market and Competition . . . . . . . . . 56
3.3 Differential Effectiveness . . . . . . . . . . . . . . . . . . . 57
3.4 Differential Cross Elasticities . . . . . . . . . . . . . . . . 59
3.5 Properties of Fully Extended Models . . . . . . . . . . . . 62
3.6 Determining Competitive Structures . . . . . . . . . . . . 65
3.7 Hierarchies of Market Segments . . . . . . . . . . . . . . . 68
3.8 Distinctiveness of Marketing Activities . . . . . . . . . . . 69
3.9 Time-Series Issues . . . . . . . . . . . . . . . . . . . . . . 78
3.10 Appendix for Chapter 3 . . . . . . . . . . . . . . . . . . . 84
3.10.1 *Log-Linear Time-Series Model . . . . . . . . . . . 84
4 Data Collection 87
4.1 The Accuracy of Scanner Data . . . . . . . . . . . . . . . 87
4.2 Issues in Aggregation. . . . . . . . . . . . . . . . . . . . . 89
4.3 National Tracking Data . . . . . . . . . . . . . . . . . . . 93
4.3.1 Store-Level Scanner Data . . . . . . . . . . . . . . 93
4.3.2 Store Audits . . . . . . . . . . . . . . . . . . . . . 95
4.3.3 Household Scanner Panels . . . . . . . . . . . . . . 96
4.3.4 Other Data Sources . . . . . . . . . . . . . . . . . 97
4.4 Market Information Systems. . . . . . . . . . . . . . . . . 98
5 Parameter Estimation 103
5.1 Calibrating Attraction Models. . . . . . . . . . . . . . . . 103
5.1.1 Maximum-Likelihood Estimation . . . . . . . . . . 104
5.1.2 Log-Linear Estimation . . . . . . . . . . . . . . . . 106
5.2 Log-Linear Regression Techniques . . . . . . . . . . . . . 108
5.2.1 Organization of Data for Estimation . . . . . . . . 110
5.2.2 Reading Regression-Analysis Outputs . . . . . . . 114
5.2.3 The Analysis-of-Covariance Representation . . . . 118
5.3 Properties of the Error Term . . . . . . . . . . . . . . . . 119
5.3.1 Assumptions on the Specification-Error Term . . . 120
5.3.2 Survey Data . . . . . . . . . . . . . . . . . . . . . 120
5.3.3 POS Data . . . . . . . . . . . . . . . . . . . . . . . 123
5.4 *Generalized Least-Squares Estimation. . . . . . . . . . . 125
5.4.1 Application of GLS to the Margarine Data . . . . 126
5.5 Estimation of Differential-EffectsModels . . . . . . . . . . 128
5.6 Collinearityin Differential-Effects Models . . . . . . . . . 134
5.6.1 Three Differential-EffectsModels . . . . . . . . . . 137
CONTENTS vii
5.6.2 Within-Brand Effects . . . . . . . . . . . . . . . . 139
5.6.3 Remedies . . . . . . . . . . . . . . . . . . . . . . . 141
5.7 Estimation of Cross-Effects Models . . . . . . . . . . . . . 143
5.8 A Multivariate MCI Regression Model . . . . . . . . . . . 148
5.9 Estimation of Category-Volume Models . . . . . . . . . . 149
5.10 Estimation of Share-Elasticities . . . . . . . . . . . . . . . 152
5.11 Problems with Zero Market Shares . . . . . . . . . . . . . 153
5.12 The Coffee-Market Example . . . . . . . . . . . . . . . . . 156
5.12.1 The Market-Share Model . . . . . . . . . . . . . . 156
5.12.2 The Category-Volume Model . . . . . . . . . . . . 165
5.12.3 Combining Share and Category Volume . . . . . . 168
5.13 Large-Scale Competitive Analysis . . . . . . . . . . . . . . 168
5.13.1 How Large Is Too Large? . . . . . . . . . . . . . . 170
5.13.2 Is BLUE Always Best? . . . . . . . . . . . . . . . . 172
5.14 Appendix for Chapter 5 . . . . . . . . . . . . . . . . . . . 175
5.14.1 Generalized Least Squares Estimation . . . . . . . 175
6 Competitive Maps 177
6.1 *Asymmetric Three-Mode Factor Analysis . . . . . . . . . 182
6.2 Portraying the Coffee Market . . . . . . . . . . . . . . . . 185
6.2.1 Signalling Competitive Change . . . . . . . . . . . 187
6.2.2 Competitive Maps: The Structure Over Brands . . 193
6.3 *Elasticitiesand Market Structure . . . . . . . . . . . . . 201
6.4 *Interpretive Aids for Competitive Maps . . . . . . . . . . 204
6.5 *Appendix for Chapter 6 . . . . . . . . . . . . . . . . . . 211
7 Decision-Support Systems 219
7.1 CASPER . . . . . . . . . . . . . . . . . . . . . . . . . . . 222
7.2 Using HISTORY . . . . . . . . . . . . . . . . . . . . . . . 223
7.3 Simulating Static Occasions . . . . . . . . . . . . . . . . . 231
7.4 The Assumptions Underlying Planning . . . . . . . . . . . 249
7.5 What If There Were No Experts? . . . . . . . . . . . . . . 252
7.6 Dynamic Simulations . . . . . . . . . . . . . . . . . . . . . 253
7.7 Management Decision Making . . . . . . . . . . . . . . . . 256
8 A Research Agenda 259
8.1 Estimation Problems . . . . . . . . . . . . . . . . . . . . . 259
8.1.1 Missing Data . . . . . . . . . . . . . . . . . . . . . 259
8.1.2 Constrained Parameter Estimation . . . . . . . . . 260
8.1.3 Long-Run Effects . . . . . . . . . . . . . . . . . . . 260
viii CONTENTS
8.2 Issues in Decision Support . . . . . . . . . . . . . . . . . . 261
8.2.1 Game Theory . . . . . . . . . . . . . . . . . . . . . 261
8.2.2 Expert Systems . . . . . . . . . . . . . . . . . . . . 262
8.3 The Integration of Panel Data. . . . . . . . . . . . . . . . 262
8.4 Market-Basket Models . . . . . . . . . . . . . . . . . . . . 264
Index 272
List of Tables
2.1 Numerical Example of Kotler’s Fundamental Theorem . . 24
2.2 Numerical Example — The Effect of Reducing Price . . . 24
2.3 Effect of Correlation Between Purchase Frequencies and
Choice Probabilities . . . . . . . . . . . . . . . . . . . . . 41
2.4 Relations Between Market Shares and Choice Probabilities 43
3.1 Numerical Example of Cross Elasticities for MCI Model . 60
3.2 Direct and Cross Elasticities for Seven Brands . . . . . . . 66
3.3 Interlocking and Nested Brand Groups . . . . . . . . . . . 67
4.1 Aggregating Market Shares and Causal Conditions . . . . 90
5.1 POS Data Example (Margarine) . . . . . . . . . . . . . . 112
5.2 Data Set for Estimation . . . . . . . . . . . . . . . . . . . 113
5.3 Regression Results for MCI Equation (5.8). . . . . . . . . 115
5.4 Regression Results for MNL Equation (5.9) . . . . . . . . 117
5.5 GLS Estimates for Table 5.3. . . . . . . . . . . . . . . . . 127
5.6 Data Set for Differential-EffectsModel . . . . . . . . . . . 131
5.7 Regression Results for Differential-EffectsModel (MCI) . 132
5.8 Log-Centered Differential-Effects Data . . . . . . . . . . . 133
5.9 Hypothetical Data for Differential-EffectsModel . . . . . 134
5.10 Condition Indices Australian Household-Products Example143
5.11 Regression Results for Cross-Effects Model (MCI) . . . . 146
5.12 Coffee Data — Average Prices and Market Shares . . . . 157
5.13 Regression Results for Cross-Effects Model (MCI) . . . . 159
5.14 Regression Results for Category-Volume Model . . . . . . 167
5.15 Computer Resources for Two Applications . . . . . . . . . 171
5.16 Summary of BLUE Parameters — IRI Data . . . . . . . . 173
5.17 Summary of BLUE Parameters — Nielsen Data . . . . . . 174
ix
x LIST OF TABLES
6.1 Average Market-Share Elasticities of Price . . . . . . . . . 186
6.2 Coordinates of the Idealized Competitive Conditions . . . 193
6.3 Common Scaling Space . . . . . . . . . . . . . . . . . . . 213
6.4 Dimensionalityof Common Scaling Space . . . . . . . . . 214
6.5 The Core Matrix for the Coffee-Market Example . . . . . 216
6.6 Joint-Space Coefficients for Chain-Week Factors. . . . . . 217
6.7 Elasticities for Idealized Competitive Conditions . . . . . 219
7.1 Default Price and Promotion Table . . . . . . . . . . . . . 232
7.2 Default Costs . . . . . . . . . . . . . . . . . . . . . . . . . 232
7.3 Default Discounts Offered to Retailer by Manufacturer . . 234