Customer Segmentation and Research Using Advance Machine Learning Algorithms
November 4, 2020
British multinational tobacco company headquartered in London
Problem Statement
With access to customer demographics, user experience, product composition and user response to survey variables, the client R&D needed insight into how to collaborate the different pieces and create value out of it
Solution Approach
Data Input
Data Preprocessing
Model Dataset
Archetype Segmentation
Identify clusters within data
Bayesian Belief Networks
Identify variable change implication
Text mining using cosine-similarity
Identify key factors in user experience
Business Impact
Insights on customers segments and variable change implications for e-cigarette domain
Short, medium and long term strategies provide the roadmap to develop IT assets
Critical Success Factors
- Extracted segments based on customer buying habits
- Variable dependency graphs and their implication on sales
