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.