This course will make a jump-start introduction to applications of ML and AI to create a customer-centric analysis. The course will cover practical steps of Segmentations, Churn analysis, Live Time Value models, Optimization of campaigns, Recommendation Systems, Dynamic Pricing, and Customer Network analysis combined with theoretical methodologies, which we are using on a daily basis. The course will contain six workshops where students will be working with data from Telco, Banking, and/or Retail.
The course will be based on the MS Power BI tool, however, interested students will be able to benefit from R/Python-based notebooks. The course is co-developed based on the experience and efforts of Liubomyr Bregman, Richard Bobek, and Adam Blascik.
How and why to make Customer Segmentation?
Churn Analysis and early detection
Life Time Value Forecast. Use cases and popular approaches.
Campaign Optimization and next best actions
Recommendation systems for online and offline. Review of best practices
Social Network Analysis of Customers, how to use, and how to benefit.
At the end of this course, the student should be able to:
Identify the correct use cases for her/his business model
Build and test churn, segmentation and CLV use cases
Understand the concepts which may be used for business optimization
Create a Views for Deep dive and benefit analytics with zero code
Replicate use cases discussed during the course
Price: UAH 24 000
Date: July 24 – August 2
Segmentation (3 hours)
Basics of unsupervised learning. Intuitive knowledge of k-means. Other algorithm will be covered during the course
Churn Analysis (4 hours)
Understanding of key classification algorithms like OLS and Logistic Regression.
CLV forecast (3 hours)
Understanding key concerns of Markov Chain and Decision trees.
Campaign optimizations and recommenders (2 hours)
Linear optimization basics, Matrix Factorization for Collaborative filtering
Network of customers (4 hours)
No pre requirements, basic understanding of what is graphs
Dynamic Pricing (2 hours)
Understanding the basics of demand and classification algorithms. Design and run of experiments
Final exam (2 hours)
Mini-Project submission (2 hours)
In case you have any questions – please contact us