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This book is published open access under a CC BY 4.0 license.
This open access book offers something for everyone working with market segmentation: practical guidance for users of market segmentation solutions; organisational guidance on implementation issues; guidance for market researchers in charge of collecting suitable data; and guidance for data analysts with respect to the technical and statistical aspects of market segmentation analysis. Even market segmentation experts will find something new, including an approach to exploring data structure and choosing a suitable number of market segments, and a vast array of useful visualisation techniques that make interpretation of market segments and selection of target segments easier.
The book talks the reader through every single step, every single potential pitfall, and every single decision that needs to be made to ensure market segmentation analysis is conducted as well as possible. All calculations are accompanied not only with a detailed explanation, but also with R code that allows readers to replicate any aspect of what is being covered in the book using R, the open-source environment for statistical computing and graphics.
List of contents
Part I. Introduction.- Chapter 1. Market segmentation.- Chapter 2. Market segmentation analysis.- Part II. Ten steps of market segmentation analysis.- Chapter 3. STEP 1: Deciding (not) to segment.- Chapter 4. STEP 2: Specifying the ideal target segment.- Chapter 5. STEP 3: Collecting data.- Chapter 6. STEP 4: Exploring data.- Chapter 7. STEP 5: Extracting segments.- Chapter 8. STEP 6: Profiling segments.- Chapter 9. STEP 7: Describing segments.- Chapter 10. STEP 8: Selecting (the) target segment(s).- Chapter 11. STEP 9: Customising the marketing mix.- Chapter 12. STEP 10: Evaluation and monitoring.
About the author
Sara Dolnicar is Professor at The University of Queensland, Brisbane, Australia. Her core research interests are the improvement of market segmentation methodology and the testing and refinement of measures used in social science research. Because her key research interests are not tied to any particular application area, she has contributed to a range of different applied research areas, including sustainable tourism and tourism marketing, environmental volunteering, foster caring, and public acceptance of water alternatives and water conservation measures.
Friedrich Leisch is Professor at the University of Natural Resources and Life Sciences, Vienna, Austria. His research interests lie in statistical computing, multivariate statistics, cluster analysis, mixture models, generalized regression, biostatistics; software development, and statistical applications in life and business sciences.
Bettina Grün is Associate Professor at Johannes Kepler University, Linz, Austria. Her research focuses on mixture models, statistical computing with R, market segmentation methods, and statistical applications in tourism and marketing.
Summary
This book is published open access under a CC BY 4.0 license.
This open access book offers something for everyone working with market segmentation: practical guidance for users of market segmentation solutions; organisational guidance on implementation issues; guidance for market researchers in charge of collecting suitable data; and guidance for data analysts with respect to the technical and statistical aspects of market segmentation analysis. Even market segmentation experts will find something new, including an approach to exploring data structure and choosing a suitable number of market segments, and a vast array of useful visualisation techniques that make interpretation of market segments and selection of target segments easier.
The book talks the reader through every single step, every single potential pitfall, and every single decision that needs to be made to ensure market segmentation analysis is conducted as well as possible. All calculations are accompanied not only with a detailed explanation, but also with R code that allows readers to replicate any aspect of what is being covered in the book using R, the open-source environment for statistical computing and graphics.
