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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

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