Database Reference
In-Depth Information
Behavioral segmentation is also based on transactional data and separates
customers according to attributes that summarize their shopping habits, such as:
• Frequency and recency of purchases
• Total spending amount
• Relative spending amount per product group/subgroup
• Size of basket (spending amount and number of items per visit or transaction)
• Preferred payment method
• Preferred period/day/time of purchases
• Preferred store and channel, and so on.
The derived segments can be used for the ''personalized'' handling of seg-
mented customers through the development of differentiated sales and marketing
strategies, tailored to their recognized consuming habits.
Transactional data are logged at the point of sale and typically record the
detailed information of every transaction, including the universal product code
(UPC) of each purchased item, which allows detailed monitoring of the groups
and subgroups of products that each customer tends to buy. A prerequisite of
behavioral segmentation is that every transaction is identified with a customer.
This issue is usually tackled by introducing a loyalty program which assigns an
identification field (card ID) to each transaction and permits the tracking of the
purchase history of each customer and aggregation of the transactional information
at a customer level.
In this chapter we focus on the efforts of a retailer to segment its cus-
tomers according to their consuming habits and more specifically according to
the product mix they buy. A high-level grouping of products was selected for this
first segmentation attempt. The relevant data were readily available within the
organization's mining data mart and MCIF which stored all the processed trans-
actional information. In addition, the marketers of the organization also decided
to employ a recency, frequency, monetary (RFM) analysis to examine and group
their customers according to their purchase frequency, recency, and value. These
applications are described in the following sections.
THE RFM ANALYSIS
RFM analysis is a common approach for understanding customer purchase behav-
ior. It is quite popular, especially in the retail industry. As its name implies, it
involves the calculation and the examination of three KPIs - recency, frequency,
and monetary - that summarize the corresponding dimensions of the customer
relationship with the organization. The recency measurement indicates the time
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