SOLUTIONS POWERED BY AI
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The Challenge.

  • An e-commerce client had no visibility on how to target higher-value customers who had a higher chance of purchasing from them.​
  • This led to a lot of one-and-done customers and low-value customers.​
  • There was no targeting of customers based on predictive analytics, nor was there a data-driven understanding of which factors impact a customer's probability of purchasing more than once.
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The Solution.

  • Designed and built a propensity to purchase model to identify customers with a high probability of purchasing within the next 30 days every month.
  • Derived feature importance to identify which factors impact a customer's probability to purchase.​
  • Segmented the customer base each month into low, medium and high probability of purchasing to customize targeting.
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The Results.

  • List of customers with probability to purchase was used by the marketing team to target a subset of the customer base each month and optimize the marketing budget.
  • Targeted campaigns led to up-selling of customers with medium and high probability of purchasing.​​​
  • Understanding the factors that influence purchasing behaviour led to better profiling of customers and increased customer loyalty.
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