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Hands-On Data Science for Marketing

You're reading from   Hands-On Data Science for Marketing Improve your marketing strategies with machine learning using Python and R

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789346343
Length 464 pages
Edition 1st Edition
Languages
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Author (1):
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Yoon Hyup Hwang Yoon Hyup Hwang
Author Profile Icon Yoon Hyup Hwang
Yoon Hyup Hwang
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Table of Contents (20) Chapters Close

Preface 1. Section 1: Introduction and Environment Setup FREE CHAPTER
2. Data Science and Marketing 3. Section 2: Descriptive Versus Explanatory Analysis
4. Key Performance Indicators and Visualizations 5. Drivers behind Marketing Engagement 6. From Engagement to Conversion 7. Section 3: Product Visibility and Marketing
8. Product Analytics 9. Recommending the Right Products 10. Section 4: Personalized Marketing
11. Exploratory Analysis for Customer Behavior 12. Predicting the Likelihood of Marketing Engagement 13. Customer Lifetime Value 14. Data-Driven Customer Segmentation 15. Retaining Customers 16. Section 5: Better Decision Making
17. A/B Testing for Better Marketing Strategy 18. What's Next? 19. Other Books You May Enjoy

Using regression analysis for explanatory analysis

In Chapter 2, Key Performance Indicators and Visualizations, we discussed what descriptive analysis is and how it is used to better understand a dataset. We experimented using various visualization techniques and building different types of plots in Python and R.

In this chapter, we are going to expand our knowledge and start to discuss why, when, and how to use explanatory analysis for marketing.

Explanatory analysis and regression analysis

As we briefly discussed in Chapter 1, Data Science and Marketing, the purpose of explanatory analysis is to answer why we are using the data, whereas the purpose of descriptive analysis is to answer what we are using the data for, and...

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