DataCamp
FreeMachine Learning for Marketing in Python
4.8(173)14k studentsINTERMEDIATEEnglish
Korshub's take
The Korshub Take
This intermediate course bridges the gap between data science and marketing strategy using Python. It focuses on applying statistical techniques and machine learning algorithms to optimize customer journeys, reduce churn, and maximize customer lifetime value (CLV).
Who It's For
Data analysts, marketers, and data scientists looking to leverage machine learning to make data-driven marketing decisions and segment customer behavior.
Key Takeaways
- Churn Prediction: Use telecom customer data to build churn prediction models and interpret key underlying churn drivers.
- Customer Lifetime Value (CLV): Construct RFM (Recency, Frequency, Monetary) datasets from online retail data to measure and forecast CLV.
- Customer Segmentation: Build actionable customer segments using product purchase data from a grocery store.
About this course
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.
Price
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