Market Segmentation Analysis [electronic resource] : Understanding It, Doing It, and Making It Useful / by Sara Dolnicar, Bettina Grün, Friedrich Leisch.
Вид матеріалу:
Текст Серія: Management for ProfessionalsПублікація: Singapore : Springer Nature Singapore : Imprint: Springer, 2018Видання: 1st ed. 2018Опис: XXI, 324 p. 123 illus., 51 illus. in color. online resourceТип вмісту: - text
- computer
- online resource
- 9789811088186
- 658.83 23
- HF5415.2-.34
- HD38.7
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. .
Open Access
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 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 readersto replicate any aspect of what is being covered in the book using R, the open-source environment for statistical computing and graphics.
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