Customer Segmentation and Research Using Advance Machine Learning Algorithms

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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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