In this chapter, we discussed the concepts and importance of product analytics. We briefly discussed how product analytics starts from tracking events and customer actions, such as website or app visits, page views, and purchases. Then, we discussed some of the common goals of product analytics and how it should be used to generate actionable insights and reports. With these discussions on product analytics, we explored how we can utilize product analytics for customer and product retention in our programming exercises, using e-commerce business data. First, we analyzed the time series trends in the revenue and the numbers of purchase orders. Then, we drilled down to identify the patterns of monthly repeat customers. We have seen from the data that even though monthly repeat customers represent a relatively small portion of the overall customer base, they drive roughly...
Hands-On Data Science for Marketing
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Hands-On Data Science for Marketing
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Overview of this book
Regardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry. With this book, you will learn to implement data science techniques to understand the drivers behind the successes and failures of marketing campaigns. This book is a comprehensive guide to help you understand and predict customer behaviors and create more effectively targeted and personalized marketing strategies.
This is a practical guide to performing simple-to-advanced tasks, to extract hidden insights from the data and use them to make smart business decisions. You will understand what drives sales and increases customer engagements for your products. You will learn to implement machine learning to forecast which customers are more likely to engage with the products and have high lifetime value. This book will also show you how to use machine learning techniques to understand different customer segments and recommend the right products for each customer. Apart from learning to gain insights into consumer behavior using exploratory analysis, you will also learn the concept of A/B testing and implement it using Python and R.
By the end of this book, you will be experienced enough with various data science and machine learning techniques to run and manage successful marketing campaigns for your business.
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Current Title:
Hands-On Data Science for Marketing
Table of Contents (20 chapters)
Preface
Free Chapter
Section 1: Introduction and Environment Setup
Data Science and Marketing
Section 2: Descriptive Versus Explanatory Analysis
Key Performance Indicators and Visualizations
Drivers behind Marketing Engagement
From Engagement to Conversion
Section 3: Product Visibility and Marketing
Product Analytics
Recommending the Right Products
Section 4: Personalized Marketing
Exploratory Analysis for Customer Behavior
Predicting the Likelihood of Marketing Engagement
Customer Lifetime Value
Data-Driven Customer Segmentation
Retaining Customers
Section 5: Better Decision Making
A/B Testing for Better Marketing Strategy
What's Next?
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Customer Reviews