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A Python-based repo for modeling and predicting customer behavior. This project focuses on implementing various statistical and probabilistic models to analyze customer preferences, purchase patterns, and future actions.

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abdullahau/customer-analytics

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Models for Customer Analytics

Summarizing Buyer Behavior

Using transactions log data and marketing spend data we calculate:

  1. Monthly sales over time
  2. Total customers acquired
  3. Customer acquisition cost (CAC)
  4. Distribution of spend per purchase
  5. Initial versus repeat sales volume
  6. Initial versus repeat average order value (AOV)
  7. Sales and AOV by source
  8. First-purchase profitability
  9. Cohorted sales (the “C3”)
  10. Revenue retention curves
  11. Cumulative spend per customer
  12. Distribution of total spend by customer
  13. Customer concentration (“Pareto”) chart

What the analysis summarize:

  1. Growth
  2. Unit costs
  3. Unit profitability (unit economic performance)
  4. Retention
  5. Heterogeneity (customers, time)

Models to Implement

  • Weibull-Gamma acquisition model
  • Exponential-Gamma retention model
  • Point process transaction model
  • Simulating order flow dynamics
  • Acquisition process
  • Purchase process
  • Spend process

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A Python-based repo for modeling and predicting customer behavior. This project focuses on implementing various statistical and probabilistic models to analyze customer preferences, purchase patterns, and future actions.

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