Key Metrics for Purchase-Based Segmentation

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To effectively segment customers based on their purchasing behavior, businesses should focus on several key metrics:

Recency (R): How recently did a customer make a purchase? Recent purchasers are often more engaged and responsive to marketing efforts.
Frequency (F): How often does a customer make purchases? High-frequency buyers are often loyal and valuable customers.
Monetary (M): How much money does a customer spend? High-monetary value customers contribute significantly to revenue.
Average Order Value (AOV): The average amount spent per transaction. This metric helps identify customers who make larger purchases.

Product Categories Purchased: Which specific product country email list lines or categories do customers gravitate towards? This can reveal product preferences and potential for cross-selling.
Purchase Channel: Do customers prefer online, in-store, or a mix of channels? Understanding channel preference aids in optimizing marketing and sales efforts.
Time Between Purchases: The duration between a customer's consecutive purchases. This helps predict future buying cycles.
Use of Promotions/Discounts: Do customers primarily purchase during sales or with discount codes? This indicates price sensitivity.
Product Returns/Refunds: While seemingly negative, analyzing return behavior can highlight product issues or customers with specific purchasing habits (e.g., buying multiple sizes to try on).
Methodologies for Purchase-Based Segmentation
Several methodologies can be employed to segment customers based on these purchasing metrics:

1. RFM (Recency, Frequency, Monetary) Analysis
RFM analysis is a widely used and highly effective technique. Customers are scored on each of the three dimensions (Recency, Frequency, Monetary), typically on a scale of 1 to 5 (or 1 to 3). These scores are then combined to create segments. For example.