11-03, 14:15–14:55 (America/New_York), Winter Garden (Room 5412)
The Customer Lifetime Value(CLV) model is one of the major techniques of customer analytics and it helps companies to identify who valuable customers are. A high CLV indicates customers who deserve more marketing resources. If the company overlooks CLV, it might invest more in short-term customers who buy just once.
To predict future CLV, we encounter sub-problems like forecasting the time until a customer's next purchase (“Buy Till You Die” modeling) and the probability of a customer's churn (Survival Analysis).
Recently, PyMC-Marketing was released and, it's becoming more feasible to implement these models with the Bayesian approach.
In this talk, I will show the key concepts of CLV prediction, its demonstration using Pymc-marketing, and practical tips.
Agenda:
- Introduction & Outline of the talk
- What is Customer Lifetime Value(CLV)?
- CLV Models
- Dive into the Bayesian methodology for CLV prediction
- Implementation using PyMC-marketing
- Encountered challenges and potential solutions
- Q&A
Key Takeaways:
- You will understand the key concepts and major approaches of CLV prediction.
- You will learn how to build Bayesian CLV models using PyMC-marketing.
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 retention and monetization strategies.
No previous knowledge expected
Hajime is a data professional with five years of expertise in marketing, retail, and eCommerce, working across Japan and the United States.
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.