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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

Summary

In this chapter, we discussed the overall trends in marketing and learned the rising importance of data science and machine learning in the marketing industry. As the amount of data increases and as we observe the benefits of utilizing data science and machine learning for marketing, companies of all sizes are investing in building more data-driven and quantitative marketing strategies.

We also learned different types of analysis methods, especially the three types of analysis that we will be using frequently in this book—descriptive, explanatory, and predictive —and different use cases of these analyses. In this chapter, we covered different types of machine learning algorithms, as well as the typical workflow in data science. Lastly, we spent some time setting up our development environments in Python and R and testing our environment setup by building...

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