Machine Learning for Marketing in Python
From customer lifetime value, predicting churn to segmentation - learn and implement Machine Learning use cases for Marketing in Python.
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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.
- 1
Machine learning for marketing basics
FreeIn 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 cases50 xpIdentify supervised learning examples50 xpSupervised vs. unsupervised learning100 xpPreparation for modeling50 xpInvestigate the data100 xpSeparate numerical and categorical columns100 xpEncode categorical and scale numerical variables100 xpML modeling steps50 xpSplit data to training and testing100 xpFit a decision tree100 xpPredict churn with decision tree100 xp - 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 fundamentals50 xpExplore churn rate and split data100 xpSeparate features and target variable100 xpPredict churn with logistic regression50 xpFit logistic regression model100 xpFit logistic regression with L1 regularization100 xpIdentify optimal L1 penalty coefficient100 xpPredict churn with decision trees50 xpFit decision tree model100 xpIdentify optimal tree depth100 xpIdentify and interpret churn drivers50 xpExplore logistic regression coefficients100 xpBreak down decision tree rules100 xp - 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) basics50 xpBuild retention and churn tables100 xpExplore retention and churn100 xpCalculating and projecting CLV50 xpCalculate basic CLV100 xpCalculate granular CLV100 xpCalculate traditional CLV100 xpData preparation for purchase prediction50 xpBuild features100 xpDefine target variable100 xpSplit data to training and testing100 xpPredicting customer transactions50 xpPredict next month transactions100 xpMeasure model fit100 xpExplore model coefficients100 xp - 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 basics50 xpExplore customer product purchase dataset100 xpUnderstand differences in variables100 xpData preparation for segmentation50 xpUnskew the variables100 xpNormalize the variables100 xpBuild customer and product segmentation50 xpDetermine the optimal number of clusters100 xpBuild segmentation with k-means clustering100 xpAlternative segmentation with NMF100 xpVisualize and interpret segmentation solutions50 xpK-means segmentation averages100 xpNMF segmentation averages100 xpCongratulations!50 xp
In the following tracks
Marketing Analytics with PythonDatasets
Telecom DatasetCollaborators
Prerequisites
Supervised Learning with scikit-learn
Karolis Urbonas
Head of Machine Learning and Science
Karolis is currently leading a Machine Learning and Science team at Amazon Web Services. He's a data science enthusiast obsessed with machine learning, analytics, neural networks, data cleaning, feature engineering, and every engineering puzzle he can get his hands on.
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