Hajime Takeda- How to Enhance Customer Targeting in Marketing - Pydata London 2024

Uplift Modeling: How to Enhance Customer Targeting in Marketing with
Causal inference has traditionally been used in the field of marketing. Uplift modeling is one of the major techniques of customer analytics and it helps companies to identify customers with the highest marketing effect when targeted.
In recent years, algorithms combining causal inference and machine learning have been a hot topic. CausalML is a good Python package developed by Uber and it provides a suite of uplift modeling and causal inference methods.
In this talk, I will show the key concepts of causal inference with machine learning, their application in marketing science (Uplift modeling), their demonstration using CausalML, and practical tips.
Agenda:
What is Causal Inference?
What is Causal Inference with Machine Learning (Causal Machine Learning)?
Common mistakes in measuring effectiveness in Marketing
Use Case #1 : Measuring Treatment Effects
Use Case #2 : Uplift Modeling
Demo using CausalML
Q&A
Key Takeaways:
- You will understand the key concepts and major approaches of Causal Inference with Machine Learning.
- You will learn how to build Uplift Modeling using CausalML.
Target Audience:
- Data analysts and data scientists who are interested in marketing science or customer analytics.
- Data analysts, data scientists, data engineers, software developers, or other IT specialists who want to collaborate with marketing teams more effectively.
- Marketers or executives who want to improve customer segmentation and targeting accuracy
Slide :
docs.google.com/presentation/...
Demo Code :
github.com/takechanman1228/Ef...
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[@KhalilMuhammad]

Lovely presentation, Jimmy