UID:
kobvindex_INTEBC5471159
Umfang:
1 online resource (332 pages)
Ausgabe:
1st ed.
ISBN:
9789811088186
Serie:
Management for Professionals Series
Anmerkung:
Intro -- Preface -- References -- Contents -- List of Figures -- List of Tables -- Part I Introduction -- 1 Market Segmentation -- 1.1 Strategic and Tactical Marketing -- 1.2 Definitions of Market Segmentation -- 1.3 The Benefits of Market Segmentation -- 1.4 The Costs of Market Segmentation -- References -- 2 Market Segmentation Analysis -- 2.1 The Layers of Market Segmentation Analysis -- 2.2 Approaches to Market Segmentation Analysis -- 2.2.1 Based on Organisational Constraints -- 2.2.2 Based on the Choice of (the) SegmentationVariable(s) -- 2.3 Data Structure and Data-Driven Market Segmentation Approaches -- 2.4 Market Segmentation Analysis Step-by-Step -- References -- Part II Ten Steps of Market Segmentation Analysis -- 3 Step 1: Deciding (not) to Segment -- 3.1 Implications of Committing to Market Segmentation -- 3.2 Implementation Barriers -- 3.3 Step 1 Checklist -- References -- 4 Step 2: Specifying the Ideal Target Segment -- 4.1 Segment Evaluation Criteria -- 4.2 Knock-Out Criteria -- 4.3 Attractiveness Criteria -- 4.4 Implementing a Structured Process -- 4.5 Step 2 Checklist -- References -- 5 Step 3: Collecting Data -- 5.1 Segmentation Variables -- 5.2 Segmentation Criteria -- 5.2.1 Geographic Segmentation -- 5.2.2 Socio-Demographic Segmentation -- 5.2.3 Psychographic Segmentation -- 5.2.4 Behavioural Segmentation -- 5.3 Data from Survey Studies -- 5.3.1 Choice of Variables -- 5.3.2 Response Options -- 5.3.3 Response Styles -- 5.3.4 Sample Size -- 5.4 Data from Internal Sources -- 5.5 Data from Experimental Studies -- 5.6 Step 3 Checklist -- References -- 6 Step 4: Exploring Data -- 6.1 A First Glimpse at the Data -- 6.2 Data Cleaning -- 6.3 Descriptive Analysis -- 6.4 Pre-Processing -- 6.4.1 Categorical Variables -- 6.4.2 Numeric Variables -- 6.5 Principal Components Analysis -- 6.6 Step 4 Checklist -- References
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7 Step 5: Extracting Segments -- 7.1 Grouping Consumers -- 7.2 Distance-Based Methods -- 7.2.1 Distance Measures -- 7.2.2 Hierarchical Methods -- Example: Tourist Risk Taking -- 7.2.3 Partitioning Methods -- 7.2.3.1 k-Means and k-Centroid Clustering -- 7.2.3.2 ``Improved'' k-Means -- 7.2.3.3 Hard Competitive Learning -- 7.2.3.4 Neural Gas and Topology Representing Networks -- 7.2.3.5 Self-Organising Maps -- 7.2.3.6 Neural Networks -- 7.2.4 Hybrid Approaches -- 7.2.4.1 Two-Step Clustering -- 7.2.4.2 Bagged Clustering -- 7.3 Model-Based Methods -- 7.3.1 Finite Mixtures of Distributions -- 7.3.1.1 Normal Distributions -- 7.3.1.2 Binary Distributions -- 7.3.2 Finite Mixtures of Regressions -- Example: Australian Travel Motives -- 7.3.3 Extensions and Variations -- 7.4 Algorithms with Integrated Variable Selection -- 7.4.1 Biclustering Algorithms -- Example: Australian Vacation Activities -- 7.4.2 Variable Selection Procedure for Clustering BinaryData (VSBD) -- Example: Australian Travel Motives -- 7.4.3 Variable Reduction: Factor-Cluster Analysis -- 7.5 Data Structure Analysis -- 7.5.1 Cluster Indices -- 7.5.1.1 Internal Cluster Indices -- 7.5.1.2 External Cluster Indices -- 7.5.2 Gorge Plots -- 7.5.3 Global Stability Analysis -- Example: Tourist Risk Taking -- 7.5.4 Segment Level Stability Analysis -- 7.5.4.1 Segment Level Stability Within Solutions -- 7.5.4.2 Segment Level Stability Across Solutions -- 7.6 Step 5 Checklist -- References -- 8 Step 6: Profiling Segments -- 8.1 Identifying Key Characteristics of Market Segments -- 8.2 Traditional Approaches to Profiling Market Segments -- 8.3 Segment Profiling with Visualisations -- 8.3.1 Identifying Defining Characteristics of MarketSegments -- 8.3.2 Assessing Segment Separation -- 8.4 Step 6 Checklist -- References -- 9 Step 7: Describing Segments -- 9.1 Developing a Complete Picture of Market Segments
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9.2 Using Visualisations to Describe Market Segments -- 9.2.1 Nominal and Ordinal Descriptor Variables -- 9.2.2 Metric Descriptor Variables -- 9.3 Testing for Segment Differences in Descriptor Variables -- 9.4 Predicting Segments from Descriptor Variables -- 9.4.1 Binary Logistic Regression -- 9.4.2 Multinomial Logistic Regression -- 9.4.3 Tree-Based Methods -- 9.5 Step 7 Checklist -- References -- 10 Step 8: Selecting (the) Target Segment(s) -- 10.1 The Targeting Decision -- 10.2 Market Segment Evaluation -- 10.3 Step 8 Checklist -- References -- 11 Step 9: Customising the Marketing Mix -- 11.1 Implications for Marketing Mix Decisions -- 11.2 Product -- 11.3 Price -- 11.4 Place -- 11.5 Promotion -- 11.6 Step 9 Checklist -- References -- 12 Step 10: Evaluation and Monitoring -- 12.1 Ongoing Tasks in Market Segmentation -- 12.2 Evaluating the Success of the Segmentation Strategy -- 12.3 Stability of Segment Membership and Segment Hopping -- 12.4 Segment Evolution -- Example: Winter Vacation Activities -- 12.5 Step 10 Checklist -- References -- A Case Study: Fast Food -- A.1 Step 1: Deciding (not) to Segment -- A.2 Step 2: Specifying the Ideal Target Segment -- A.3 Step 3: Collecting Data -- A.4 Step 4: Exploring Data -- A.5 Step 5: Extracting Segments -- A.5.1 Using k-Means -- A.5.2 Using Mixtures of Distributions -- A.5.3 Using Mixtures of Regression Models -- A.6 Step 6: Profiling Segments -- A.7 Step 7: Describing Segments -- A.8 Step 8: Selecting (the) Target Segment(s) -- A.9 Step 9: Customising the Marketing Mix -- A.10 Step 10: Evaluation and Monitoring -- B R and R Packages -- B.1 What Is R? -- B.1.1 A Short History of R -- B.1.2 R Packages -- B.1.3 Quality Control -- B.1.4 User Interfaces for R -- B.2 R Packages Used in the Book -- B.2.1 MSA -- B.2.2 flexclust -- B.2.3 flexmix -- B.2.4 Other Packages -- C Data Sets Used in the Book
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C.1 Tourist Risk Taking -- C.2 Winter Vacation Activities -- C.3 Australian Vacation Activities -- C.4 Australian Travel Motives -- C.5 Fast Food -- Glossary -- References -- Index
Weitere Ausg.:
Print version Dolnicar, Sara Market Segmentation Analysis Singapore : Springer Singapore Pte. Limited,c2018 ISBN 9789811088179
Sprache:
Englisch
Schlagwort(e):
Electronic books
URL:
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