Product-Based Segmentation

[taniyabithi]

"Champions" (High R, High F, High M): Your most valuable and loyal customers. They bought recently, buy often, and spend the most.
"Loyal Customers" (High F, High M): Frequent and high-spending customers who might not have purchased very recently.
"Potential Loyalists" (High R, Medium F, Medium M): Recent customers who have bought a few times and spent a moderate amount. They have the potential to become loyal.
"At Risk" (Low R, Low F, Medium M): Customers who used to buy frequently and spend well but haven't purchased recently. They are at risk of churning.
"Lost Customers" (Very Low R, Low F, Low M): Customers country email list who haven't purchased in a long time and have low frequency and monetary value.
2. Behavioral Clustering
More advanced techniques like K-means clustering or hierarchical clustering can be used to identify natural groupings within your customer data based on a broader range of purchasing behaviors. These algorithms can uncover hidden patterns and create segments that might not be immediately apparent through manual analysis. This approach often requires more sophisticated data analysis tools and expertise.


Lifecycle-Based Segmentation
This method categorizes customers based on where they are in their journey with your business. While not purely purchase-based, purchasing behavior heavily influences these stages:

New Customers: First-time buyers.
Active Customers: Regular purchasers.
Lapsed Customers: Customers who haven't purchased in a defined period.
Churned Customers: Customers who have stopped purchasing entirely.
Grouping customers by the specific products or categories they purchase can reveal niche markets or cross-selling opportunities. For example, customers who buy athletic wear versus those who buy formal attire.