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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
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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

Statistical hypothesis testing

When you run A/B tests, it is important to test your hypothesis and seek for statistically significant differences among the test groups. Student's t-test, or simply the t-test, is frequently used to test whether the difference between two tests is statistically significant. The t-test compares the two averages and examines whether they are significantly different from each other.

There are two important statistics in a t-test—the t-value and p-value. The t-value measures the degree of difference relative to the variation in the data. The larger the t-value is, the more difference there is between the two groups. On the other hand, the p-value measures the probability that the results would occur by chance. The smaller the p-value is, the more statistically significant difference there will be between the two groups. The equation to...

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