JOURNAL OF CONSUMER MARKETING, VOL. 15 NO. 5 1998, pp. 491-493 © MCB UNIVERSITY PRESS, 0736-3761 491

Despite US Constitutional reference, all consumers are not created equally.And yet, many corporate marketing initiatives treat all customers as onebody, one intellect, or one segment en masse. Customer marketingcommunication campaigns do, in many instances, send the same message,the same offer, and use the same medium to communicate with this largelydisparate customer universe.

This is contrary to the strategic goal of database marketing: to use capturedinformation to identify customers and prospects as individuals and build acontinuing relationship with them — to the individuals greater benefit andthe greater profit of the corporation.

In this regard, the catalog and retail industries were the pioneers of databasemarketing. Prior to the development of mass marketing, merchants had trulypersonal services, one-to-one relationships and recognized the customers asindividuals. The local merchant knew you and your family, what youwanted, how and when you wanted it. The shop owner kept you as a loyalcustomer by establishing a two-way communication with you whilerecognizing and appreciating your business.

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

In today’s world of mass advertising and “big box” retail store chains, it isimpossible for merchants to know each customer in this individualizedfashion. Only with the aid of sophisticated marketing database technologycan we capture, analyze and act on the same interpersonal marketingopportunities first identified in these earlier and simpler times.

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

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

Despite the capacity of free thought, humanity as a whole is cursed withrepetitive behavior and the formation of habits, making behavior thereforepredictable. This is a positive human “affliction” for database marketers,

Using database marketingtechniques to enhance your one-to-one marketing initiativesRon KahanPartner, Ariss Kahan Database Marketing Group, Inc., Denver, Colorado,USA

Using capturedinformation

Defining characteristicparameters

presenting many opportunities on which to capitalize. It is the case that thereis no greater predictor of future behavior than past behavior. This isintuitively the true premise of behavioral analysis. The most widely usedbehavioral characteristic variables for analysis include products or servicespurchased, frequency of purchases, dollar amount spent, as well ascustomer-related preferences.

Catalogers created behavioral analysis by accident. I believe it was SearsRoebuck & Co. who first discovered by inserting a catalog with an outgoingorder that their most recent customers were most likely to order again. Fromthis simple observation, the mathematical computation that is today referredto as RFM (recency, frequency, and monetary value) was created.

RFM is perhaps the most widely recognized behavioral analysis technique. Itcertainly is the easiest and fastest methodology to implement with yourcustomer file.

This process requires that base customer information, such as name andaddress, have been assigned a unique key, such as an account number.Likewise, it requires that all order or sales information is storedelectronically 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 threevariables summarized:

(1) Sort your customers by purchase dates in reverse chronological order.

(2) Divide the customer list into five equal segments (see Table I). Forexample, if you were starting with 100,000 customers, each segmentwould 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 recentpurchases being tagged with a “5”. Segmenting into five equal groups iscalled quintiling.

Next, sort your customers by number of orders and apply the samemethodology and tagging process. And finally, perform this sort on the averagedollar amount of each order and perform the quintiling and tagging functions.

492 JOURNAL OF CONSUMER MARKETING, VOL. 15 NO. 5 1998

Table I. RFM analysis

MonetaryScore* Recency Score* Frequency Score* ($)

1 4/97-6/97 1 13+ 1 1,200.00

2 11/96-3/97 2 8-12 2 741.33-1,199.99

3 2/96-10/96 3 5-7 3 416.76-741.32

4 12/94-1/96 4 2-4 4 128.47-416.75

5 9/93-11/94 5 1 5 1.00-128.46

* 1 = most recent, frequent or largest $ and 5 = least recent, frequent or smallest $

The creation of RFM

You have now created RFM scores for each of your customers, from yourbest customer segment (111) to your worst (555). Run some queries on the111 segment versus the total customer population. What percentage ofcumulative sales dollars is attributed to this group? You should be able tosubstantiate Paretto’s infamous 80/20 rule, where a small percentage of yourcustomers are attributed with the majority of revenue dollars. The majorbenefit of performing this analysis is the identification of your bestcustomers. But, this is only the beginning.

The cognitive marketing characteristic segmentation can now be bestutilized. Instead of simply building a model of customer characteristics, wecan differentiate between our customers. Cognitive models can be built foreach customer segment, from best to worst and more emphasis can be placedon acquiring “ look-a-likes” of best customers.

In addition, since individuals who fall into the same customer segment do sobecause of their past behavior, we can now make the assumption that theywill behave in the same way in the future (or a statistically significantpercentage will). When implementing a new marketing campaign, instead oftargeting 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 achieveprofitable response rates (see Figure 1). This methodology now allowsmarketers to test campaigns to smaller segments of customers, and directlarger campaigns only towards those customer segments that are predicted torespond profitably.

RFM is a powerful behavioral analysis technique, more powerful than anycognitive analysis. As stated earlier, it is easy and cost-effective, providingyou have this customer and transactional information stored in an accessibleelectronic form. Through using a combination of cognitive and behavioralanalysis techniques, database marketers will more effectively useelectronically captured information leading to three types of benefits:

(1) increased response rates;

(2) lowered cost per order; and

(3) greater profit.

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JOURNAL OF CONSUMER MARKETING, VOL. 15 NO. 5 1998 493

Thousands of Dollars

BreakEven

$15

Mail only to profitable cells

Many RFM cells are unprofitable

RFM Cells

$10

$5

$0

($5)

($10)111 155 215 245 315 355 423 445 525 555

Figure 1. Profit and loss from RFM cells

Differentiate customers