Cloning Your Best Customers (1,173 words)
For many years, companies like Claritas, MicroVision, Trans Union and the Polk Company have offered cluster systems, which define customer populations by demographically-defined lifestyle or lifestage descriptions. Developed using statistical analysis of census data and other sources, these systems became quickly popular and widespread in marketing research circles. Terms such as "dinks," "yuppies" and "empty-nesters" became popular marketing lingo.
The problem with this method of characterizing a customer base is it ignores the fact that households are not cliches but have their own individual lifestyles. This ZIP+4 clustering, while interesting, does not allow for individual distinctions and therefore households can not be counted on to behave to specific marketing strategies with any reliability.
If finding clones of your best customer is the goal, it is essential to understand who the best customers are and what characteristics those customers share which make them unique to your database. As we have found over the years, customer databases are not homogeneous. They are made up of many unique clusters, or groups, of various sizes within the database.
For those trying to get a handle on their customer profiles for the first time, it's important to start with simple segmentation techniques. Begin by identifying what critical data elements are essential to do this fundamental segmentation and profiling. Here's what is needed.
• Clean customer addresses which have been brought up-to-date by NCOA (National Change Of Address). This is essential for the appending process.
• Transaction data that has come from the General Ledger system. This data must be associated with a specific customer record. Transaction data allows companies to segment their customer base into best, worst and average customers. Often times a customer record may not be "householded." In other words, few contacts may be associated with the transaction, leading to unclear information about who the real purchaser is. Unassociated records should be set aside and not used in your profiling.