by Ron Kahan

Despite U.S. Constitutional reference, not all consumers are created equal. And yet, many corporate marketing initiatives treat all customers as one body, one intellect, or one segment en masse. Customer marketing communication campaigns do, in many instances, send the same message, the same offer, and use the same medium to communicate with this largely disparate customer universe.

This is contrary to the strategic goal of database marketing: to use captured information to identify customers and prospects as individuals and build a continuing relationship with them -- to the individuals greater benefit and the greater profit of the corporation.

In this regard, the catalog and retail industries were the pioneers of database marketing. Prior to the development of mass marketing, merchants had truly personal services, one-to-one relationships and recognized the customers as individuals. The local merchant knew you and your family, what you wanted, how and when you wanted it. The shop owner kept you as a loyal customer by establishing a two-way communication with you while recognizing and appreciating your business.

Information, recognition, customized services and appreciation are the customer benefits that are fundamentals of database marketing.

In today's world of mass advertising and "big box" retail store chains, it's impossible for merchants to know each customer in this individualized fashion. Only with the aid of sophisticated marketing database technology can we capture, analyze and act upon the same interpersonal marketing opportunities first identified in these earlier and simpler times.

There are two approaches to successful database marketing: cognitive and behavioral analysis. In this way, marketers can garner a clear understanding of what customers and prospects "look like" (cognitive) and how they act (behavioral).

Target marketers often go through extensive cognitive analysis of current customers by applying third party, compiled data variables to identify characteristic values. This can include both demographic (such as age, income, presence of children) and psychographic (such as lifestyle and interest) data elements. By defining characteristic parameters on current customers, these statistical models can then be focused on the non-customer universe to identify "like-kinds" of consumers for marketing solicitation. In theory, a very logical approach to refining the suspect (non-customer) market to a more likely prospect market. This is the entry-point into the practice of intelligent database marketing.

Despite the capacity of free thought, humanity as a whole is cursed with repetitive behavior and the formation of habits, making behavior therefore predictable. This is a positive human "affliction" for database marketers, presenting many opportunities on which to capitalize. It is the case that there is no greater predictor of future behavior than past behavior. This is intuitively the true premise of behavioral analysis. The most widely used behavioral characteristic variables for analysis include products or services purchased, frequency of purchases, dollar amount spent, as well as customer related preferences.

Cataloguers created behavioral analysis by accident. I believe it was Sears Roebuck & Co. who first discovered by inserting a catalog with an outgoing order that their most recent customers were most likely to order again. From this simple observation, the mathematical computation that is today referred to as RFM (recency, frequency, and monetary value) was created.

RFM is perhaps the most widely recognized behavioral analysis technique. It certainly is the easiest and fastest methodology to implement with your customer file.

This process requires that base customer information, such as name and address, have been assigned a unique key, such as an account number. Likewise, it requires that all order or sales information is stored electronically with the unique key included with each transactional record.

A summary of each customer's transactional history should be created, allowing the following sorting and segmentation:

1. Date of the last or most recent purchase
2. Total number or frequency of purchases
3. Average amount spent per order

The analysis can now begin once each account number has these three variables summarized.

1. Sort your customers by purchase dates in reverse chronological order.
2. Divide the customer list into five equal segments:

For example, if you were starting with 100,000 customers, each segment would contain 20,000 records.

3. Tag those customers who have made the most recent purchases with a "1" indicating the top segment and work your way to the least recent purchases being tagged with a "5". Segmenting into five equal groups is called quintiling.

Next sort your customers by number of orders and apply the same methodology and tagging process. And lastly, perform this sort on the average dollar amount of each order and perform the quintiling and tagging functions.

You've now created RFM scores for each of your customers, from your best customer segment (111) to your worst (555). Run some queries on the 111 segment versus the total customer population. What percentage of cumulative sales dollars is attributed to this group? You should be able to substantiate Paretto's infamous 80/20 rule, where a small percentage of your customers are attributed with the majority of revenue dollars. The major benefit of performing this analysis is the identification of your best customers. But, this is only the beginning.

The cognitive marketing characteristic segmentation can now be best utilized. Instead of simply building a model of customer characteristics, we can differentiate between our customers. Cognitive models can be built for each customer segment, from best to worst and more emphasis can be placed on acquiring "look-a-likes" of best customers.

In addition, since individuals who fall into the same customer segment do so because of their past behavior, we can now make the assumption that they'll behave in the same way in the future (or a statistically significant percentage will). When implementing a new marketing campaign, instead of targeting the entire customer file, target a percentage of each RFM segment, from 111 through 555. Test the response against break-even rates.

Then, rollout the campaign only to those RFM segments that are proven to achieve profitable response rates:

This methodology now allows marketers to test campaigns to smaller segments of customers, and direct larger campaigns only towards those customer segments that are predicted to respond profitably.

RFM is a powerful behavioral analysis technique, more powerful than any cognitive analysis. As stated earlier, it is easy and cost-effective, providing you have this customer and transactional information stored in an accessible electronic form. Through using a combination of cognitive and behavioral analysis techniques, database marketers will more effectively use electronically captured information leading to three types of benefits: (1.) increased response rates, (2.) lowered cost per order, and (3.) greater profit.

Ariss Kahan Database Marketing Group, Inc. assists clients build customer relationships through proven and innovative database marketing techniques and marketing database technologies. They specialize in customer acquisition, retention, cross-sell and up-sell initiatives and can be reached at (303) 368-9800 or via e-mail at rkahan@dbmktg.com.