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Principles of marketing engineering

Author: Lilien, Gary L. Publisher: Trafford, 2007.Language: EnglishDescription: 210 p. : Graphs/Ill. ; 26 cm.ISBN: 9781425135867Type of document: BookBibliography/Index: Includes index
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Item type Current location Collection Call number Status Date due Barcode Item holds
Book Asia Campus
Main Collection
Print HF5415.125 .L55 2007
(Browse shelf)
Available 900192323
Total holds: 0

Includes index


Principles of Marketing Engineering Contents PREFACE VII High-Powered, Networked Computers vii Exploding Volumes of Data vii Reengineered Marketing Activities viii OVERVIEW VIII WHAT'S INSIDE IX ACKNOWLEDGMENTS X ABOUT THE AUTHORS XII Gary L. Lilien xii Arvind Rangaswamy xiii Arnaud De Bruyn xiii CHAPTER 1 The Marketing Engineering Approach 1 THE EMERGING MARKETING DECISION ENVIRONMENT 2 Trends that Favor Marketing Engineering 3 Examples of Marketing Engineering Success 6 TOOLS FOR MARKETING ENGINEERING 8 Market Response Models 8 Types of Response Models 9 Dynamic Effects 12 Market Share and Competition 13 Response at the Individual Customer Level 14 Objectives 14 Shared Experience and Qualitative Models 16 Choosing, Evaluating, and Benefiting from a Marketing Engineering Model 18 BUSINESS VALUE OF MARKETING ENGINEERING: FROM PROMISE TO REALITY 19 STRUCTURE OF THIS BOOK 19 SUMMARY 21 CHAPTER 2 Customer Value Assessment and Valuing Customers 22 THE CONCEPT OF CUSTOMER VALUE 24 Customer Needs and Value 26 Understanding Customer Needs 28 APPROACHES TO MEASURING CUSTOMER VALUE 30 Objective Customer Value: Should-Do Measures 3o Perceptual Customer Value: Plan-to-Do Measures 34 Behavioral Customer Value: Have-Done Measures 37 Comparison of Customer Value Measurement Approaches 40 VALUING CUSTOMERS AND CUSTOMER LIFETIME VALUE 42 SUMMARY 47 CHAPTER 3 Segmentation and Targeting 49 THE SEGMENTATION, TARGETING, AND POSITIONING APPROACH 50 SEGMENTATION ANALYSIS 52 The STP Approach 54 Segmentation Research: Designing and Collecting Data 63 TRADITIONAL SEGMENTATION 66 Reducing the Data with Factor Analysis 66 Developing Measures of Association 67 Identifying and Removing Outliers 67 Forming Segments 67 Profiling Segments and Interpreting Results 70 TARGETING INDIVIDUAL CUSTOMERS 72 SUMMARY 76 CHAPTER 4 Positioning 78 POSITIONING THROUGH BRAND LINKAGES 79 POSITIONING USING PERCEPTUAL MAPS 8o COMBINING PERCEPTUAL AND PREFERENCE MAPS 82 Attribute-Based Perceptual Maps 83 Preference Maps 87 Joint-Space Maps 88 TRANSLATING PREFERENCE TO CHOICE 91 INCORPORATING PRICE AS AN ATTRIBUTE 92 USES AND LIMITATIONS OF PERCEPTUAL AND PREFERENCE MAPS 93 SUMMARY 95 CHAPTER 5 Forecasting 97 FORECASTING METHODS 97 Judgmental Methods 98 Market and Product Analysis Methods 102 Time-Series Methods 105 Causal Methods 107 The Product Life Cycle 108 NEW PRODUCT FORECASTING MODELS in The Bass Model 112 Pretest Market Forecasting and the ASSESSOR Model 118 WHICH FORECASTING METHOD TO CHOOSE? 123 SUMMARY 125 C H A P T E R 6 New Product and Service Design 126 THE NEW PRODUCT DEVELOPMENT PROCESS 127 MODELS FOR IDEA GENERATION AND EVALUATION 130 Creativity Software 130 CONJOINT ANALYSIS FOR PRODUCT DESIGN 134 How to Conduct Conjoint Analysis 137 Strengths and Limitations of Conjoint Analysis 143 SUMMARY 143 CHAPTER 7 The Marketing Mix 145 PRICING DECISIONS 146 The Classical Economics Approach 146 Cost-Oriented Pricing 149 Demand-Oriented Pricing 149 Competition-Oriented Pricing 150 Price Discrimination 15o Pricing Product Lines 154 RESOURCE ALLOCATION AND THE MARKETING COMMUNICATIONS AND PROMOTIONS MIX 155 Advertising and Impersonal Marketing Communications 155 Advertising Decisions in Practice 157 Sales Force Decisions 159 SALES PROMOTIONS: TYPES AND EFFECTS 164 Objectives of Promotions 165 Characteristics of Promotions 166 SUMMARY 171 CHAPTER 8 Harvesting Value from Marketing Engineering 172 THE 10 LESSONS 172 Marketing Engineering Is Marketing 172 Marketing Engineering Is a Means to an End 172 Marketing Engineering Frames the Opportunity Costs Associated with Alternative Actions (or Inaction) 173 Marketing Models Require Judgment 173 Marketing Engineering as a Whole Is Greater than the Sum of its Parts 173 Data and Information Do Not Automatically Result in Value 174 Modern Software Allows for Rapid Prototyping 174 Every Model Has its Downside 174 Marketing Engineering Requires Lifelong Learning 175 Marketing Engineering Instructors Should Be Coaches Rather than Teachers 175 A LOOK AHEAD FOR MARKETING ENGINEERING 176 Online Analytical Processing (OLAP) 18o Models Offered as Web Services 181 Intelligent Marketing Systems 185 Simulations 187 Groupware for Decision Support 187 Improved Model Outputs 188 INSIGHTS FOR BETTER IMPLEMENTATION OF MARKETING. ENGINEERING 189 Be Opportunistic 189 Start Simple; Keep It Simple 190 Work Backward: Begin with an End in Mind 190 Score Inexpensive Victories 191 Develop a Program, Not Just Projects 191 SUMMARY 192 RESOURCES and READINGS 193 SUBJECT INDEX 196 COMPANY INDEX 201 NAME INDEX 2o3 REFERENCES 204

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