06-15, 15:00–15:40 (Europe/London), Minories
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
Demo Code :
https://github.com/takechanman1228/Effective-Uplift-Modeling
No previous knowledge expected
Hajime is a data professional with five years of expertise in marketing, retail, and eCommerce, working in New York.
As a Data Analyst at Procter and Gamble and MIKI HOUSE Americas, Hajime has led data-driven strategy formulation and implemented technology initiatives such as e-commerce expansion, advertising optimization, and the identification of growth opportunities.