In this chapter, we have discussed product recommender systems. We have learned how personalized product recommendations improve conversion and customer retention rates, according to a study conducted by Salesforce. We have discussed the two approaches, collaborative filtering and content-based filtering, to building product recommendation systems; how they differ from one another; and what their assumptions are. Then, we dove deeper into how we can build collaborative filtering-based recommender systems. As you might recall, the first step to building a collaborative filtering-based recommender system is to build a user-to-item matrix, and then the next step is to use cosine similarity to compute the similarities between the users. We have also discussed the two different approaches to utilizing a collaborative filtering algorithm for product recommendations—a...
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.
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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