Preface |
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xvii | |
About the Authors |
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xxiii | |
PART I The Basics |
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1 | (60) |
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1 | (28) |
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Marketing Engineering: From Mental Models to Decision Models |
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1 | (5) |
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Marketing and marketing management |
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1 | (1) |
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2 | (3) |
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Why marketing engineering? |
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5 | (1) |
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Marketing Decision Models |
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6 | (7) |
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6 | (1) |
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Characteristics of decision models |
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7 | (1) |
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Verbal, graphical, and mathematical models |
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8 | (3) |
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Descriptive and normative decision models |
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11 | (2) |
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Benefits of Using Decision Models |
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13 | (6) |
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Philosophy and Structure of the Book |
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19 | (4) |
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19 | (2) |
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Objectives and structure of the book |
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21 | (1) |
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Design criteria for the software |
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22 | (1) |
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23 | (2) |
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23 | (1) |
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Running marketing engineering |
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24 | (1) |
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25 | (3) |
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How Many Draft Commercials Exercise |
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28 | (1) |
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Tools for Marketing Engineering: Market Response Models |
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29 | (32) |
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29 | (2) |
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31 | (2) |
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Some Simple Market Response Models |
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33 | (4) |
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37 | (2) |
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39 | (3) |
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Multiple Marketing-Mix Elements: Interactions |
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42 | (1) |
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42 | (2) |
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Market-Share Models and Competitive Effects |
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44 | (2) |
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Response at the Individual Customer Level |
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46 | (4) |
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Shared Experience and Qualitative Models |
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50 | (2) |
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Choosing, Evaluating, and Benefiting From a Marketing Response Model |
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52 | (1) |
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53 | (1) |
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Appendix: About Excel's Solver |
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54 | (2) |
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56 | (2) |
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Conglomerate, Inc. Promotional Analysis |
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58 | (2) |
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Conglomerate, Inc. Response Model Exercise |
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60 | (1) |
PART II Developing Market Strategies |
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61 | (172) |
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Segmentation and Targeting |
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61 | (56) |
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61 | (14) |
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61 | (1) |
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Segmentation theory and practice |
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62 | (2) |
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64 | (2) |
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Segmenting markets (Phase 1) |
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66 | (2) |
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Describing market segments (Phase 2) |
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68 | (1) |
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Evaluating segment attractiveness (Phase 3) |
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69 | (1) |
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Selecting target segments and allocating resources to segments (Phase 4) |
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70 | (3) |
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Finding targeted customers (Phase 5) |
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73 | (2) |
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75 | (3) |
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Segmentation Research: Designing and Collecting Data |
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78 | (5) |
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Developing the measurement instrument |
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78 | (1) |
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79 | (1) |
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Selecting and aggregating respondents |
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79 | (4) |
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83 | (13) |
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Using factor analysis to reduce the data |
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84 | (1) |
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Forming segments by cluster analysis: Measures of association |
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84 | (4) |
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88 | (4) |
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Interpreting segmentation study results |
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92 | (4) |
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Behavior-Based Segmentation: Cross-Classification, Regression, and Choice Models |
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96 | (5) |
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Cross-classification analysis |
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96 | (1) |
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96 | (1) |
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Choice-based segmentation |
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97 | (4) |
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Customer Heterogeneity in Choice Models |
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101 | (1) |
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Implementing the STP Process |
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102 | (1) |
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103 | (1) |
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Conglomerate Inc.'s New PDA (2001) |
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104 | (1) |
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Introducting the Connector |
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104 | (1) |
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105 | (1) |
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105 | (1) |
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106 | (1) |
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106 | (1) |
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Facts About the PDA Market |
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106 | (1) |
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107 | (1) |
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107 | (3) |
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Questions for determining segmentation-basis or needs variables |
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107 | (1) |
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Questions for determining variables for discriminant analysis |
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108 | (2) |
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Appendix: PDA Features Guide |
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110 | (3) |
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110 | (1) |
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110 | (1) |
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110 | (1) |
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111 | (1) |
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111 | (1) |
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111 | (1) |
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111 | (1) |
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111 | (1) |
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111 | (1) |
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111 | (1) |
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112 | (1) |
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112 | (1) |
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112 | (1) |
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ABB Electric Segmentation Case |
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113 | (1) |
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113 | (1) |
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113 | (1) |
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New Strategy at ABB Electric |
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113 | (1) |
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Establishing the MKIS Program |
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114 | (1) |
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115 | (1) |
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Postscript: Situation in 1988 |
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116 | (1) |
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117 | (38) |
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Differentiation and Positioning |
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117 | (2) |
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117 | (1) |
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118 | (1) |
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Positioning Using Perceptual Maps |
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119 | (3) |
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Applications of Perceptual Maps |
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122 | (6) |
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Perceptual Mapping Techniques |
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128 | (11) |
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128 | (8) |
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Similarity-based methods for perceptual mapping |
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136 | (3) |
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139 | (6) |
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139 | (1) |
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139 | (2) |
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External analysis using PREFMAP3 |
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141 | (4) |
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Incorporating Price in Perceptual Maps |
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145 | (1) |
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146 | (1) |
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Appendix: Factor Analysis for Preprocessing Segmentation Data |
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147 | (1) |
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Positioning the Infiniti G20 Case |
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148 | (1) |
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148 | (1) |
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148 | (1) |
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148 | (7) |
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Strategic Market Analysis: Conceptual Framework and Tools |
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155 | (33) |
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Strategic Marketing Decision Making |
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155 | (4) |
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Market Demand and Trend Analysis |
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159 | (16) |
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160 | (1) |
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Market and product analysis |
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161 | (1) |
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162 | (5) |
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167 | (7) |
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174 | (1) |
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175 | (5) |
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Cost Dynamics: Scale and Experience Effects |
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180 | (3) |
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183 | (2) |
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Bookbinders Book Club Case |
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185 | (1) |
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The Bookbinders Book Club |
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185 | (3) |
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Models for Strategic Marketing Decision Making |
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188 | (45) |
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Market Entry and Exit Decisions |
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188 | (10) |
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Shared Experience Models: The PIMS Approach |
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198 | (3) |
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201 | (7) |
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The Boston Consulting Group (BCG) approach |
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201 | (2) |
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203 | (1) |
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204 | (1) |
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Analytic Hierarchy Process |
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205 | (3) |
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208 | (4) |
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212 | (1) |
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ICI Americas R&D Project Selection Case |
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213 | (3) |
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Product Planning Using the GE/McKinsey Approach at Addison Wesley Longman Case |
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216 | (1) |
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216 | (4) |
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217 | (1) |
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The new marketing book promotional challenge |
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217 | (1) |
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217 | (3) |
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Appendix: Details of the Three Books from AWL Promotional Material |
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220 | (3) |
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Portfolio Analysis Exercise |
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223 | (3) |
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226 | (5) |
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ACME Liquid Cleanser Exercise |
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231 | (1) |
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231 | (1) |
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231 | (2) |
PART III Developing Marketing Programs |
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233 | (229) |
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233 | (69) |
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233 | (3) |
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New Product Decision Models |
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236 | (3) |
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Models for identifying opportunities |
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236 | (2) |
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Models for product design |
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238 | (1) |
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Models for new product forecasting and testing |
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239 | (1) |
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Conjoint Analysis for Product Design |
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239 | (14) |
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239 | (3) |
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Conjoint analysis procedure |
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242 | (8) |
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Other enhancements to the basic conjoint model |
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250 | (1) |
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Contexts best suited for conjoint analysis |
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251 | (2) |
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Forecasting the Sales of New Products |
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253 | (10) |
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Overview of the Bass model |
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253 | (2) |
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Technical description of the Bass model |
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255 | (6) |
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Extensions of the basic Bass model |
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261 | (2) |
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Pretest Market Forecasting |
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263 | (8) |
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Overview of the ASSESSOR model |
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264 | (2) |
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266 | (2) |
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268 | (3) |
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The validity and value of the ASSESSOR model |
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271 | (1) |
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271 | (1) |
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Forte Hotel Design Exercise |
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272 | (1) |
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272 | (1) |
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272 | (1) |
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272 | (5) |
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Conjoint Analysis (Matching hotel attributes to customer preferences) |
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274 | (3) |
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Zenith High Definition Television (HDTV) Case |
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277 | (1) |
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277 | (2) |
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Zenith HDTV Efforts to Date |
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279 | (1) |
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279 | (2) |
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281 | (2) |
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Johnson Wax: Enhance Case (A) |
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283 | (1) |
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283 | (1) |
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283 | (1) |
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New-Product Development at Johnson Wax |
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284 | (1) |
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The Hair Conditioning Market |
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284 | (1) |
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285 | (1) |
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Enhance Product Development |
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286 | (1) |
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The ASSESSOR Pretest Market |
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286 | (2) |
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288 | (4) |
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290 | (1) |
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Preference model estimates of share |
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291 | (1) |
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292 | (10) |
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Advertising and Communications Decisions |
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302 | (52) |
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The Bewildering Nature of Advertising |
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303 | (1) |
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Advertising Effects: Response, Media, and Copy |
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304 | (6) |
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Advertising response phenomena |
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304 | (4) |
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308 | (1) |
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309 | (1) |
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Advertising Budget Decisions |
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310 | (9) |
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319 | (5) |
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Advertising Copy Development and Decisions |
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324 | (11) |
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324 | (3) |
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Estimating the creative quality of ads |
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327 | (1) |
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328 | (7) |
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335 | (1) |
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Blue Mountain Coffee Company Case |
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336 | (1) |
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Blue Mountain's Market Position |
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336 | (1) |
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337 | (3) |
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Planning for Fiscal Year 1995 |
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340 | (3) |
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The market planning model |
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340 | (1) |
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Recent developments: The U.S. coffee market in transition |
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341 | (2) |
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Convection Corporation Case |
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343 | (1) |
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Using a Communication Planning Model to Aid Industrial Marketing Budget Decisions |
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343 | (1) |
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343 | (4) |
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344 | (1) |
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344 | (1) |
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345 | (1) |
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346 | (1) |
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ADVISOR: An Approach to Marketing Budget Planning |
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347 | (6) |
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Budget task force meeting |
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348 | (5) |
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Johnson Wax Ad Copy Design Exercise |
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353 | (1) |
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Salesforce and Channel Decisions |
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354 | (60) |
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Introduction to Salesforce Models |
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354 | (3) |
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Sales-response models for representing the effects of sales activities |
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354 | (2) |
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Salesforce management decisions |
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356 | (1) |
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Salesforce Sizing and Allocation |
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357 | (8) |
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357 | (2) |
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Market-response methods (the Syntex model) |
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359 | (6) |
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Extending the Syntex Model: Reallocator |
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365 | (1) |
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366 | (3) |
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The GEOLINE model for territory design |
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367 | (2) |
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369 | (4) |
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Using conjoint analysis to design a bonus plan (the MSZ model) |
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370 | (3) |
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Improving the Efficiency and Effectiveness of Sales Calls |
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373 | (6) |
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373 | (6) |
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Marketing Channel Decisions |
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379 | (5) |
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379 | (5) |
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384 | (2) |
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Syntex Laboratories (A) Case |
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386 | (1) |
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386 | (1) |
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386 | (1) |
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Syntex Labs' Product Line |
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387 | (1) |
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387 | (1) |
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387 | (1) |
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387 | (1) |
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388 | (1) |
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388 | (1) |
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388 | (1) |
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Sales Management at Syntex Labs |
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389 | (2) |
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389 | (1) |
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390 | (1) |
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Allocation of Sales Efforts Across Products and Physician Specialties |
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390 | (1) |
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Geographic Allocation of Sales Force |
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390 | (1) |
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Sales Force Strategy Model |
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391 | (18) |
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Model Development Process |
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392 | (1) |
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Defining the model inputs |
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392 | (1) |
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393 | (2) |
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Results of the SSM Analysis |
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395 | (1) |
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395 | (14) |
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The John French Exercise: Sales Call Planning for UBC (CALLPLAN) |
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409 | (2) |
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411 | (3) |
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Price and Sales Promotion Decisions |
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414 | (48) |
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Pricing Decisions: The Classical Economics Approach |
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414 | (4) |
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Pricing in Practice: Orientation to Cost, Demand, or Competition |
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418 | (6) |
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418 | (1) |
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419 | (3) |
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Competition-oriented pricing |
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422 | (2) |
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Interactive Pricing: Reference Prices and Price Negotiations |
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424 | (2) |
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426 | (8) |
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Understanding price discrimination |
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426 | (2) |
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Geographic price discrimination |
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428 | (1) |
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Temporal price discrimination |
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429 | (4) |
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Nonlinear pricing or quantity discounts |
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433 | (1) |
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Other forms of price discrimination |
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434 | (1) |
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434 | (1) |
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Sales Promotions: Types and Effects |
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435 | (5) |
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436 | (1) |
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Characteristics of promotions |
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436 | (4) |
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Aggregate Models to Analyze Promotional Effects |
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440 | (4) |
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Analyzing Individuals' Responses to Promotions |
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444 | (4) |
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448 | (1) |
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Account Pricing for the ABCOR2000 Exercise |
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449 | (1) |
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449 | (2) |
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450 | (1) |
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Price Planning for the ABCOR2000 Exercise |
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451 | (1) |
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452 | (1) |
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453 | (2) |
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Part 1: Training Exercise |
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453 | (1) |
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Part 2: The bid-competition simulation |
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454 | (1) |
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Forte Hotel Revenue Management Exercise |
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455 | (1) |
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How the Generalized Revenue Model Works |
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456 | (2) |
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458 | (1) |
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458 | (1) |
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459 | (1) |
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460 | (2) |
PART IV Conclusions |
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462 | (19) |
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Marketing Engineering: A Look Back and a Look Ahead |
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462 | (19) |
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Marketing Engineering: A Look Back |
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462 | (3) |
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Using Marketing Engineering Within Firms |
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465 | (2) |
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Marketing Engineering: A Look Ahead |
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467 | (13) |
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480 | (1) |
References |
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481 | (18) |
Subject Index |
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499 | (12) |
Company Index |
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511 | (4) |
Name Index |
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515 | |