Challenge: Using simulation data​

Part1: Perform data aggregation and cluster analysis to identify meaningful customer segments for a company of choice 

Part 2: Build predictive models for customer behavior to assist managerial decisions and recommend actions

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Methods: Using IBM SPSS

• Cluster analysis:

  • Aggregated transactional data with customer data​

  • Performed multiple cluster analyses using the RFM model and quantifiable variables to identify meaningful customer segments

• Linear regression: 

  • Used customer segments identified from cluster analysis to develop a regression model to predict profit earnings​

  • ​Applied calibration/validation samples to develop and test the predictive model

 

Key Managerial Findings:​

• Customers with higher levels of education tend to spend more on Coach products

• Income is a significant factor in the revenue model

 

My Role:

Data Analyst: I analyzed the datasets using SPSS, developed audience-friendly visualizations, and generated meaningful  insights and recommendations from data findings