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Marketing analytics : a practical guide to improving consumer insights using data techniques / Mike Grigsby.

By: Material type: TextTextPublisher: London ; New York, NY : Kogan Page Limited, 2018Edition: Second editionDescription: xiv, 217 pages : illustrations ; 24 cm. ; pbkContent type:
Media type:
Carrier type:
ISBN:
  • 9780749482169
  • 0749482168
  • 9780749482176
  • 0749482176
Subject(s): DDC classification:
  • 658.83 23
LOC classification:
  • .G754 2018
Contents:
[Part 1: Overview-how can marketing analytics help you?] A brief statistics review -- Brief principles of consumer behaviour and marketing strategy -- What is an insight?
[Part 2: Dependent variable techniques] What drives demand? Modelling dependent variable techniques -- Who is most likely to buy and how do I target them? -- When are my customers most likely to buy? -- Panel regression-how to use a cross-sectional time series -- Systems of equations for modeling dependent varialbe techniques.
[Part 3: Inter-relationship techniques] What does my (customer) market look like? Modelling inter-relationship techniques -- Segmentation-tools and techniques.
[Part 4: More important topics for everyday marketing] Statistical testing-howdo I know what works? -- Implementing big data and big data analytics.
[Part 5: Conclusion] The finale-what should you take away from this?

28.02
CW848, CWB07

Includes bibliographical references and index.

[Part 1: Overview-how can marketing analytics help you?] A brief statistics review -- Brief principles of consumer behaviour and marketing strategy -- What is an insight?

[Part 2: Dependent variable techniques] What drives demand? Modelling dependent variable techniques -- Who is most likely to buy and how do I target them? -- When are my customers most likely to buy? -- Panel regression-how to use a cross-sectional time series -- Systems of equations for modeling dependent varialbe techniques.

[Part 3: Inter-relationship techniques] What does my (customer) market look like? Modelling inter-relationship techniques -- Segmentation-tools and techniques.

[Part 4: More important topics for everyday marketing] Statistical testing-howdo I know what works? -- Implementing big data and big data analytics.

[Part 5: Conclusion] The finale-what should you take away from this?

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