Book Image

Data Science for Marketing Analytics

By : Candas Bilgin
Book Image

Data Science for Marketing Analytics

By: Candas Bilgin

Overview of this book

Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments. The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions.
Table of Contents (9 chapters)
Chapter 7
Supervised Learning - Predicting Customer Churn
Content Locked
Section 1
Lesson Overview
Churn prediction is one of the most common use cases of machine learning.Predicting customer churn is important for an organization because acquiring new customers is easy, but retaining them is more difficult. Similarly, high employee churn can also affect a company, since it spends a huge sum of money on grooming talent. Also, organizations that have high retention rates benefit from consistent growth, which can also lead to high referrals from existing customers.