Segmentation for Retailers - Data Mining Techniques in CRM: Inside Customer Segmentation - page 346

These limitations led themarketers of the organization to develop an additional
segmentation scheme that would separate customers with respect to the products
that they tend to buy. Their purchases by product category were analyzed and the
customers were grouped accordingly. This procedure is outlined in the next section.
The revealed segments also reflected the lifecycle stage of the customers and
provided valuable information for the development of tailored marketing activities.
GROUPING CUSTOMERS ACCORDING
TO THE PRODUCTS THEY BUY
The next step was to reveal the customer types with respect to the mix of products
that they tend to buy. The objective was to use the results to optimize and adapt the
offers, rewards, and incentives received by customers according to their identified
needs and preferences. The segmentation process involved the application of a
clustering model to the purchase records. Relevant data from the last six months
were aggregated at a customer (card ID) level. A high hierarchy level in the existing
product taxonomy was chosen for grouping the product codes, as follows:
• Apparel/shoes/jewelry
• Baby
• Electronics
• Computers
• Food and wine
• Health and beauty
• Pharmacy
• Sports and outdoors
• Books/press
• Music/movies/videogames
•Toys
• Home.
The segmentation fields summarized the relative spending (percentage of total
spending) of each active customer in the above product categories. Demographic
data, including age, gender, and marital status of the customers, were not included
in the model training; however, they contributed to the profiling of the clusters
generated. The revealed segments are presented in Table 8.2 along with a brief
behavioral and demographic profile.
Customers were assigned to six groups. Although the segmentation criteria
only involved purchasing preferences, the demographic profile of the clusters also