The Challenge.

  • Often, companies only segment customers based on spending (average spend per transaction or total spend).​
  • There is a lack of understanding of the purchase behavior of different customers.​
  • All customers are targeted the same way, with no customized interactions based on customer loyalty or value.

The Solution.

  • Built a segmentation model to categorize customers by recency of purchase, purchase frequency, amount spent, and other factors.​
  • Identified different four different segments with different behavior patterns in terms of loyalty, purchase frequency, tendency to purchase on sale or at full price, etc.​
  • Output the results to a dashboard to track customer segments on a monthly basis and identify trends.

The Results.

  • Customized customer interactions and promotional campaigns by segment.​
  • Reduced promotional costs by targeting promotions only to customers more likely to buy on sale. ​​
  • Increased sales by targeting different segments in different ways.
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