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Course

Machine Learning for Marketing in Python

IntermediateSkill Level

4.8+

Updated 06/2022

From customer lifetime value, predicting churn to segmentation - learn and implement Machine Learning use cases for Marketing in Python.

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PythonMachine Learning4 hr16 videos53 Exercises4,450 XP14,114Statement of Accomplishment

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Course Description

The rise of machine learning (almost sounds like "rise of the machines"?) and applications of statistical methods to marketing have changed the field forever. Machine learning is being used to optimize customer journeys which maximize their satisfaction and lifetime value. This course will give you the foundational tools which you can immediately apply to improve your company’s marketing strategy. You will learn how to use different techniques to predict customer churn and interpret its drivers, measure, and forecast customer lifetime value, and finally, build customer segments based on their product purchase patterns. You will use customer data from a telecom company to predict churn, construct a recency-frequency-monetary dataset from an online retailer for customer lifetime value prediction, and build customer segments from product purchase data from a grocery shop.

Prerequisites

Supervised Learning with scikit-learn

1

Machine learning for marketing basics

In this chapter, you will explore the basics of machine learning methods used in marketing. You will learn about different types of machine learning, data preparation steps, and will run several end to end models to understand their power.

Why use ML for marketing? Strategies and use cases

Identify supervised learning examples

Supervised vs. unsupervised learning

Preparation for modeling

Investigate the data

Separate numerical and categorical columns

Encode categorical and scale numerical variables

ML modeling steps

Split data to training and testing

Fit a decision tree

Predict churn with decision tree

2

Churn prediction and drivers

In this chapter you will learn churn prediction fundamentals, then fit logistic regression and decision tree models to predict churn. Finally, you will explore the results and extract insights on what are the drivers of the churn.

Churn prediction fundamentals

Explore churn rate and split data

Separate features and target variable

Predict churn with logistic regression

Fit logistic regression model

Fit logistic regression with L1 regularization

Identify optimal L1 penalty coefficient

Predict churn with decision trees

Fit decision tree model

Identify optimal tree depth

Identify and interpret churn drivers

Explore logistic regression coefficients

Break down decision tree rules

3

Customer Lifetime Value (CLV) prediction

In this chapter, you will learn the basics of Customer Lifetime Value (CLV) and its different calculation methodologies. You will harness this knowledge to build customer level purchase features to predict next month's transactions using linear regression.

Customer Lifetime Value (CLV) basics

Build retention and churn tables

Explore retention and churn

Calculating and projecting CLV

Calculate basic CLV

Calculate granular CLV

Calculate traditional CLV

Data preparation for purchase prediction

Build features

Define target variable

Split data to training and testing

Predicting customer transactions

Predict next month transactions

Measure model fit

Explore model coefficients

4

Customer segmentation

This final chapter dives into customer segmentation based on product purchase history. You will explore two different models that provide insights into purchasing patterns of customers and group them into well separated and interpretable customer segments.

Customer and product segmentation basics

Explore customer product purchase dataset

Understand differences in variables

Data preparation for segmentation

Unskew the variables

Normalize the variables

Build customer and product segmentation

Determine the optimal number of clusters

Build segmentation with k-means clustering

Alternative segmentation with NMF

Visualize and interpret segmentation solutions

K-means segmentation averages

NMF segmentation averages

Congratulations!

Machine Learning for Marketing in Python

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FAQs

What marketing use cases does Machine Learning for Marketing in Python cover?

The course covers three core use cases: predicting customer churn, forecasting customer lifetime value, and segmenting customers based on purchase patterns.

Which machine learning models are used in this course?

You will use logistic regression and decision trees for churn prediction, linear regression for CLV forecasting, and clustering models for customer segmentation.

What datasets are used for the hands-on exercises?

You will work with telecom customer data for churn prediction, online retailer transaction data for CLV analysis, and grocery store purchase data for customer segmentation.

Do I need marketing experience to take this course?

No marketing background is needed, but you should know pandas, basic statistics, and supervised learning with scikit-learn. Marketing concepts are explained as they arise.

What is RFM analysis and is it covered here?

RFM stands for recency, frequency, and monetary value. You will construct an RFM dataset from raw transaction data and use it as input for customer lifetime value prediction.

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