After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
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
Curriculum
Course outline
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 cases50 XP
- Identify supervised learning examples50 XP
- Supervised vs. unsupervised learning100 XP
- Preparation for modeling50 XP
- Investigate the data100 XP
- Separate numerical and categorical columns100 XP
- Encode categorical and scale numerical variables100 XP
- ML modeling steps50 XP
- Split data to training and testing100 XP
- Fit a decision tree100 XP
- Predict 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.
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.
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.
Machine Learning for Marketing in Python
Course
Complete
