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Segmentation approaches in data-mining: A comparison of
RFM, CHAID, and logistic regression
John A. McCarty
a,
⁎, Manoj Hastak
b,1
a
School of Business, The College of New Jersey, Ewing, New Jersey 08628, United States
b
Kogod School of Business, American University, Washington, DC 20008, United States
Received 1 November 2005; received in revised form 1 April 2006; accepted 1 June 2006
Abstract
Direct marketing has become more efficient in recent years because of the use of data-mining techniques that allow marketers to better segment
their customer databases. RFM (recency, frequency, and monetary value) has been available for many years as an analytical technique. In recent
years, more sophisticated methods have been developed; however, RFM continues to be used because of its simplicity. This study investigates
RFM, CHAID, and logistic regression as analytical methods for direct marketing segmentation, using two different datasets. It is found that
CHAID tends to be superior to RFM when the response rate to a mailing is low and the mailing would be to a relatively small portion of the
database, however, RFM is an acceptable procedure in other circumstances. The present article addresses the broader issue that RFM may focus
too much attention on transaction information and ignore individual difference information (e.g., values, motivations, lifestyles) that may help a
firm to better market to their customers.
© 2006 Elsevier Inc. All rights reserved.
Keywords: Database marketing; Data-mining; RFM; CHAID; Analytical procedures
1. Introduction
Segmentation in direct marketing has become more efficient
in recent years because of the development of database
marketing techniques. These data-mining approaches provide
direct marketers with better ways to segment their current
customers and develop marketing strategies tailored to
particular segments and/or individuals. Over the recent years,
database marketing techniques have evolved from simple RFM
models (models involving recency of customer purchases,
frequency of their purchases, and the amount of money they
have spent with the firm) to statistical techniques such as chi-
square automatic interaction detection (CHAID) and logistic
regression. More recently, neural network models are employed
in the database marketing arena (Yang, 2004).
In spite of recent statistical advances in data-mining,
marketers continue to employ RFM models. A study by
Verhoef et al. (2002) shows that RFM is the second most
common method used by direct marketers, after cross tabula-
tions, in spite of the availability of more statistically
sophisticated methods. There are a couple of related reasons
for the popularity of RFM. As Kahan (1998) notes, RFM is easy
to use and can generally be implemented very quickly.
Furthermore, it is a method that managers and decision makers
can understand (Marcus, 1998). This is an important consid-
eration in that a successful technique for a direct marketer is one
that differentiates likely responders to a particular mailing from
those who are unlikely to respond, yet does so in a way that is
easy to explain to decision makers. However, it has been argued
that the simplicity of RFM has been overemphasized, but its
ability to differentiate, relative to statistical techniques, has not
been considered to the extent that it should be (Yang, 2004).
Although the efficiency of RFM has been questioned, little
research documents its ability relative to newer statistical
techniques. This paucity of research is partly because RFM
refers to a general approach to data-mining; there are a variety
Journal of Business Research 60 (2007) 656 –662
⁎Corresponding author. Tel.: +1 609 771 3220.
E-mail addresses: mccarty@tcnj.edu (J.A. McCarty),
mhastak@american.edu (M. Hastak).
1
Tel.: +1 202 885 1973.
0148-2963/$ - see front matter © 2006 Elsevier Inc. All rights reserved.
doi:10.1016/j.jbusres.2006.06.015
of ways of applying the use of recency, frequency, and monetary
value. Research that has been conducted on the efficacy of RFM
generally focuses on proprietary or judgmental models of RFM
(e.g., Levin and Zavari, 2001; Magidson, 1988) and not on
empirically based RFM models. More recently, research has
moved away from RFM and has focused instead on newer, more
sophisticated approaches to data-mining (c.f., Deichmann et al.,
2002; Linder et al., 2004). The current study evaluates one
popular, empirically based (as opposed to judgmental) approach
to RFM. This RFM approach is compared to CHAID and
logistic regression, in an effort to understand its capabilities as a
database marketing analytical tool.
2. Analytical segmentation methods in data-mining
2.1. RFM analysis
Recency, frequency, and monetary (RFM) analysis has been
used in direct marketing for a number of decades (Baier et al.,
2002). This analytical technique grew out of an informal
recognition by catalog marketers that three variables seem
particularly related to the likelihood that customers in their
house datafiles would respond to specific offers. Customers
who recently purchased from a marketer (recency), those who
purchase many times from a marketer (frequency), and those
who spend more money with a marketer (monetary value)
typically represent the best prospects for new offerings.
As noted, RFM analysis is utilized in many ways by
practitioners, therefore, RFM analysis can mean different things
to different people. One common approach to RFM analysis is
what is known as hard coding (Drozdenko and Drake, 2002).
Hard coding RFM is a matter of assigning a weight to each of
the variables recency, frequency, and monetary value, then
creating a weighted score for each person in the database. The
assignment of weights is generally a function of the judgment of
the database marketers with a particular database; for example,
past experience may tell a marketer that recency should weigh
twice as much as frequency and monetary value. Therefore, this
application of RFM is often referred to as judgment based RFM.
The weightings could also vary as a function of the particular
mailing (Baier et al., 2002). The weights can, of course, be
empirically derived based on offerings mailed to database
members in the past, thus relying on previous data rather than
judgments.
Regardless of the way that RFM is utilized, there are two
common characteristics of RFM procedures. First, RFM is used
to segment a house file (i.e., a company's current customers)
using information related to recency, frequency, and monetary
value. RFM is not applicable to the prospecting for new
customers because a marketer would not have transaction
information for prospects. Second, RFM analysis generally
focuses on the three behavioral variables of recency, frequency,
and monetary value. Although these variables are considered
powerful predictors of future behavior, traditional RFM is
limited to these three things.
A well known, empirically based RFM method is a pro-
cedure advocated by Arthur Hughes (2000). Hughes' approach
is applicable in instances when a marketer intends to send a
mailing to customers in its database and would like to find those
in the database who are the most likely to respond to the specific
mailing. Hughes recommends a test mailing to a sample of
customers in the file; then the selection of the members of the
rest of the file is made as a function of the results of the test.
Thus, compared with hard coding RFM, Hughes' method is not
arbitrary with respect to the weighting of recency, frequency,
and monetary value. The importance of each of these is
determined by the test mailing for the particular offer.
The first step in the method is for the marketer to sort the
customer file according to how recently customers have pur-
chased from the firm. The database is then divided into equal
quintiles and these quintiles are assigned the numbers 5 to 1.
Therefore, the 20% of the customers who most recently pur-
chased from the company are assigned the number 5; the next
20% are assigned the number 4, and so on. The next step
involves sorting the customers within each recency quintile by
how frequently they purchase from the marketer. For each of
these sorts, the customers are divided into equal quintiles and
assigned a number of 5 to 1 for frequency. Each of these groups
(25 groups) is sorted according to how much money the cus-
tomers have spent with the company. These sorts are divided
into quintiles and assigned numbers 5 to 1. Therefore, the
database is divided into 125 roughly equal groups (cells)
according to recency, frequency, and monetary value.
Hughes recommends conducting a test mailing to a randomly
sampled subset of each cell (e.g., 10%). After the responses of
the test mailing are received, the proportion of respondents in
each cell can be calculated. The cells can then be ordered as a
function of response percent. The marketer can then elect to
mail to a certain portion of the remaining file (e.g., the top 20%
of the cells). Alternatively, the marketer can elect to mail to the
cells that are above a break even percent, given the cost of the
mailing and the expected revenue for each return. For example,
if a mailing costs $1.50 and the revenue received is $50.00 per
order, the break even percentage would be 3%. Thus, for the
90% of the file that is left after the test mailing, the direct
marketer would mail to the RFM cells that the test mailing
predicted a 3% or better return.
It is important to note that Hughes' method does not assume
a monotonic relationship between the dependent variable
(responded/did not respond) with the variables of recency,
frequency, and monetary value. Each cell is a discreet group that
is considered individually in terms of its performance. Thus,
if middle levels of one of the independent variables (e.g.,
frequency) are more related to response compared with higher
or lower levels of this variable, then the procedure can accom-
modate the non-monotonic nature of the relationship.
2.2. CHAID
Chi Square Automatic Interaction Detector (CHAID) (see,
for example, Sargeant and McKenzie, 1999) is a method of
database segmentation that has been used for a number of years.
Research has shown that CHAID is superior to judgment based
RFM with respect to the identification of likely responders
657J.A. McCarty, M. Hastak / Journal of Business Research 60 (2007) 656–662
(Levin and Zavari, 2001; Magidson, 1988). CHAID is similar to
the RFM approach of Hughes because it creates groupings
(nodes) of database members. The main difference is that these
groupings are not created a priori as is the case with RFM.
Rather, the file is split according to a statistical algorithm after a
test mailing is conducted. After the returns of the test mailings
are received, the procedure starts with a node that includes
everyone in the test file. The procedure then searches for the
independent variable (e.g., number of times purchased) that best
discriminates among the file members with respect to a
dichotomous variable (i.e., purchased/did not purchase on
current mailing). It splits the original node on this independent
variable into as many subgroups as are significantly different
with respect to the dichotomous variable. The procedure then
splits these new nodes according to the variables that
discriminate each of them. The procedure continues until no
other splits are significant. CHAID analysis is often called tree
analysis because a trunk (original node) is split into branches,
then more branches, etc. The terminal nodes are those that can
not be split any further.
The analysis is similar to RFM because the terminal nodes can
be evaluated according to which ones break even with respect to
expected profit and mailing costs. The direct marketer can then
use the rules that define the terminal nodes in the test mailing
(i.e., levels of the independent variables that define each
terminal node) to select the groups of people left in the file
after the test that should receive the mailing. It is also similar
to RFM in that CHAID can accommodate relationships between
the dependent variable and the predictor variables that are non-
monotonic. For example, if the number of times purchased relates
to the dependent variable, CHAID may divide the file members
into three nodes: those who purchase 1 to 3 times, those who
purchase 4 to 8 times, and a third node of those who purchase 9 or
more times. These three nodes represent discreet groupings.
An important difference between CHAID and RFM is that
CHAID can accommodate a variety of independent variables.
The independent variables could include recency, frequency,
and monetary value, but could also include other transaction
variables (e.g., used a credit card or not), as well as individual
difference variables such as demographic and psychographic
variables.
2.3. Logistic regression
Logistic regression is a modeling procedure where a set of
independent variables are used to model a dichotomous criterion
variable. Therefore, it is appropriate for direct marketers who
would like to model the dichotomous variable of respond/don't
respond to a mailing. Logistic regression is particularly useful in
these circumstances in that the actual criterion variable is
dichotomous; however, the predicted variable is the response
probability, which varies from zero to one. Therefore, the model
can provide a probability of response for everyone in the file,
given the estimated parameters for a set of predictor variables.
After a test mailing similar to CHAID, logistic regression can
be used to analyze the response variable as a function of several
independent variables (e.g., number of times purchased) and
provide an equation that can calculate the response probability
for the entire house file. The marketer can then mail to everyone
left in the file (excluding those in the test) who has a probability
higher than the break even percent. Similar to CHAID, the
independent variables are not restricted to recency, frequency,
and monetary value.
Logistic regression differs from both RFM and CHAID in
two important ways. First, logistic regression provides a
response probability for individual members of the dataset
rather than creating discreet groups of people. Therefore, in
theory, each person in the dataset may have a different response
probability. In practice, however, if few independent variables
are used to construct the logistic function and each has a small
number of different possible values, then there would be
a relatively small number of different response probabilities
across the people in the file. Second, for continuous predictor
variables, logistic regression model relationships of the
independent variables with the dichotomous dependent variable
that are monotonic; both RFM and CHAID are distribution free.
This has implications for the performance of logistic regression
in instances where the relationship between a predictor variable
and the response variable is neither continuously increasing nor
decreasing. For example, when the relationship between
recency of previous purchase and purchase on the test mailing
is curvilinear, logistic regression may not be able to capture the
relationship in ways similar to that for RFM or CHAID.
3. The studies
The viability of Hughes' approach to RFM as a method to
segment a marketer's customers is evaluated with two datasets.
The RFM method is compared with CHAID and logistic
regression. For these comparisons, CHAID and logistic
regression are limited to the same information used to create
recency, frequency, and monetary value for the RFM method.
Thus, the studies are designed to assess the discriminating
characteristics of the three segmentation approaches given the
same independent variables.
Both datasets were provided by the Direct Marketing
Educational Foundation. One of these datasets is for a multi-
division mail order company; the other is for a non-profit
organization that solicits contributions from its members.
Therefore, these two files provide somewhat different situations
for which a database marketer would apply segmentation
techniques to find likely responders to a mailing. Both datasets
include information about how recently each person purchased
(or contributed for the non-profit organization), how many
times each person purchased (contributed), and the lifetime
dollar amount of purchases (contributions). Each dataset also
has the results of a recent mailing. These results include the
percentage of the dataset that responded to the mailing. The
mail order company's dataset includes 99,200 people and has a
return rate of 27.4% for the recent mailing. Thus, the two
datasets provide for tests of the sensitivity of the segmentation
procedures at very different levels of response (i.e., less than 5%
responding versus over one quarter of the file responding to the
mailing).
658 J.A. McCarty, M. Hastak / Journal of Business Research 60 (2007) 656–662
The frequently used method of cross validation is employed
to evaluate the three segmentation methods. Each of the datasets
is randomly split in half. One half of each dataset is considered
the test group; the second half of each set is a hold out sample.
RFM (Hughes' method), CHAID, and logistic regression are
applied on the test group of each dataset using the number of
days since last activity, total number of purchases (contribu-
tions), and total dollar amount of purchases (contributions). The
parameters developed from the test groups for each of the
segmentation procedures are then applied to the hold out sample
for each dataset. Thus, one could consider the test group to be
analogous to the test mailing of a typical direct marketing
situation and the hold out sample to represent the rest of a direct
marketer's house file to which the results of the test mailing
would typically be applied.
The viability of each procedure is evaluated in two ways. For
these analyses, the percentages of all respondents who would be
reached if a mailing is sent to only a subset of the individuals in
the file (as opposed to the entire file) are calculated for the test
and hold out samples; the subsets include those file members that
each procedure assesses to be most likely to respond to the
procedure. For example, if a procedure is used to select 10%
(or 20%, or 30%, etc.) of the individuals in a file who are deemed
most likely to respond, what percentage of actual respondents
are included in the group that is selected? Note that if a procedure
performs no better than chance, one would expect a selection of
10% of names in a file to yield 10% of all respondents. Database
marketers typically refer to these proportions as gain percentages.
The researchers evaluate the three segmentation procedures at
four levels of depth in the file—20%, 30%, 40%, and 50%.
One analysis involves comparing the gain percentage for a
particular depth of the file (e.g., 10%) in the test group with the
gain percentage for the same file depth in the hold out sample.
This approach provides information about the reliability of the
model developed in the test group. If the model is reliable, it can
be expected that gain percent in the hold out sample will not
differ appreciably from gain percent in the test sample.
Alternatively, a significant difference between the proportion
in the test group and the proportion in the hold out sample
would suggest that the particular segmentation method may be
misleading at that level of file depth.
The second set of analyses involves comparing the gain
percentage for a particular depth of the file across the three
segmentation procedures. These analyses provide a head-to-
head comparison between the approaches on their ability to
discriminate between responders and non-responders.
3.1. Study 1
As noted, the first study involves data from a multi-division
catalog marketer. The marketer had made a mailing to the entire
dataset and a portion of the file responded to the offer in the
mailing. The dataset includes 96,551 members; it is randomly
split into a test sample of 48,275 people and a hold out sample
of 48,276. The overall response rate to the mailing is 2.46%.
The response rate for the test group is 2.44%; the response rate
fort the hold out sample is 2.47%.
3.1.1. Results
3.1.1.1. Reliability of the segmentation methods. Table 1
shows the proportion of respondents captured for 10%
increments of file depth from 20% to 50% of the file for each
of the segmentation methods (RFM, CHAID, and logistic
regression) for the test and hold out groups. The table also
presents the difference in proportions for each method at each
depth between the test and hold out samples. The difference
measure provides an indication of the extent to which each
segmentation method produces results in the test sample that
can be reliably replicated in the hold out sample.
As Table 1 shows, the reliability of RFM is questionable at
both the 20% and 30% levels of the file. For example, the 20%
of the test sample that RFM indicates would be the most likely
to respond captures 39.2% of actual responders. However, when
the parameters of this test are applied to the hold out sample, the
top 20% of the sample only captures 34.6% of all respondents.
Therefore, the proportion of respondents captured in the hold
out sample is significantly lower than the proportion captured in
the test sample. A significant drop in proportion (4.7%) is also
evident at the 30% of depth level. At the 40% and 50% levels of
the file, the difference in proportions of test and hold out
samples are not significantly different for the RFM segmenta-
tion method. As the table shows, the proportions of respondents
in the test and hold out samples are not significantly different for
the CHAID and logistic regression segmentation methods at all
four levels of depth. Therefore, these analyses suggest that if a
marketer elected to mail to a relatively small portion of their
house file, a test mailing using RFM may result in over
prediction of the number of respondents when the results of the
test are applied to the rest of the file.
Table 1
Percent of total responses for various levels of depth of total file
Data-mining technique
RFM (%) CHAID (%) Logistic (%)
20% depth of file
Test sample 39.2
a
38.5
a
36.5
a
Hold sample 34.6
a
37.7
b
35.8
ab
Difference 4.6 ⁎⁎ 0.8 0.7
30% depth of file
Test sample 51.3
a
49.9
ab
47.9
b
Hold out sample 46.6
a
49.9
b
47.6
ab
Difference 4.7⁎0.0 0.3
40% depth of file
Test sample 61.1
a
60.0
a
58.1
a
Hold out sample 58.0
a
58.5
a
57.1
a
Difference 3.1 1.5 1.0
50% depth of file
Test sample 71.2
a
68.8
a
67.6
a
Hold out sample 67.3
a
67.4
a
65.4
a
Difference 3.9 1.4 2.2
Note. In any row for the test or hold out sample, percentages that do not share a
common subscript are significantly different at pb.1 (two-tailed).
⁎⁎ pb.01, ⁎pb.05 (one-tailed).
659J.A. McCarty, M. Hastak / Journal of Business Research 60 (2007) 656–662
3.1.1.2. Gain percent for the segmentation methods.
Although reliability of a segmentation method is a crucial
consideration, an equally important evaluation is the relative
predictive performance of the three segmentation methods. As
Table 1 shows, the gain percentages indicate that the three
segmentation methods perform similarly within each of the four
levels of file depth in the test samples. The one significant
difference is at the 30% depth level where RFM captures a
higher proportion of respondents than logistic regression did.
When the parameters estimated in the test sample are applied
to the hold out sample, CHAID captures a significantly higher
proportion of respondents than does RFM at both the 20% and
30% levels of depth. However, the three segmentation methods
do not differ significantly with respect to performance in
gain percent at the 40% and 50% depth levels for the hold out
sample.
Overall, the results suggest that RFM underperforms, relative
to CHAID, when a marketer elects to mail to a relatively small
portion of the file. At first glance it may appear that the
differences in gain percent between CHAID and RFM are
relatively small and perhaps unimportant at a practical level even
in instances where they are statistically significant. To put these
differences in perspective in terms of potential profit, hypothet-
ical profit and cost figures can be applied to the response
information. Assume that a mailing for a direct marketer costs
$1.50 and the expected revenue per response is $100. Further
assume that the test sample represents a 10% test mailing and the
hold out sample is the rest of the house file; therefore, the results
of the hold out sample would be multiplied by a factor of nine.
Applying these assumptions to the performance of RFM and
CHAID segmentation methods with this file, the RFM method
used in the test sample would predict a profit of $284,903 in the
full file (the hold out sample multiplied by a factor of nine)
whereas CHAID would predict a profit of $277,488. The actual
mailing to the likely members of the full file as determined by the
two methods would show a very different pattern. RFM would
secure a profit of $241,469 while CHAID would provide a profit
of $274,781. Thus, CHAID would outperform RFM by $33,313.
Obviously, small differences in proportions can make a big
difference when applied to large house files.
3.2. Study 2
The second study involves data from a non-profit organiza-
tion that had made a recent solicitation for a donation from
members in its house file. The dataset includes 99,200
members; it is randomly split into a test group of 49,600 people
and a hold out sample of 49,600. The overall response rate to the
solicitation is 27.4%. The response rate for the test group is
27.3%; the response rate for the hold out sample is 27.6%.
3.2.1. Results
3.2.1.1. Reliability of the segmentation methods. Table 2
shows the proportion of respondents captured for 10%
increments of file depth from 20% to 50% of the file for each
of the three segmentation methods for the test and hold out
groups. The table also presents the difference in proportions
between test and hold outs samples for each segmentation
method at each depth.
As the table shows, the difference in proportions between
test and hold out samples for the three methods are very small
(less than a percentage point for all methods at all four levels of
depth). Moreover, none of these differences are statistically
significant. Therefore, this set of analyses suggests that for this
particular file with a rather large response rate, all three methods
are generally able to provide an accurate prediction of the
response rate when the results of a test mailing are applied to the
full file.
3.2.1.2. Gain percent for the segmentation methods. Table 2
also presents the gain percents for the three different seg-
mentation methods for the four levels of depth of the test and
hold out samples. As the table shows, there are no differences
between the performance of RFM and CHAID at all four levels
of depth for either the test or hold out samples. Therefore, for
this dataset, RFM appears to be as accurate as CHAID in
capturing likely responders when CHAID is confined to the
same independent variables as RFM.
A rather unexpected finding with respect to gain concerns the
performance of logistic regression. CHAID performed signif-
icantly better than logistic regressions at both the 20% and 30%
levels of depth for both the test and hold out samples. The RFM
method significantly outperforms logistic regression at the 30%
depth. Therefore, at least for this dataset, logistic regression may
have difficulties when a marketer would elect to mail to a small
portion of the file.
The results of study 2 suggest that RFM may perform on a
par with more sophisticated statistical techniques when the
response level is fairly high. At all four levels of file depth
Table 2
Percent of total responses for various levels of depth of total file
Data-mining technique
RFM (%) CHAID (%) Logistic (%)
20% depth of file
Test sample 36.3
ab
36.6
a
35.7
b
Hold sample 35.6
ab
36.1
a
35.2
b
Difference 0.7 0.5 0.5
30% depth of file
Test Sample 49.1
a
49.0
a
47.7
b
Hold Out Sample 48.9
a
48.8
a
47.7
b
Difference 0.2 0.2 0.0
40% depth of file
Test Sample 60.4
a
60.4
a
60.0
a
Hold Out Sample 59.9
a
60.2
a
60.4
a
Difference 0.5 0.2 0.4
50% depth of file
Test Sample 70.3
a
70.4
a
70.7
a
Hold Out Sample 70.5
a
70.2
a
70.8
a
Difference 0.2 0.2 0.1
Note. In any row for the test or hold out sample, percentages that do not share a
common subscript are significantly different at pb.1 (two-tailed).
660 J.A. McCarty, M. Hastak / Journal of Business Research 60 (2007) 656–662
tested, Hughes' RFM method is able to achieve gain levels of
likely responders as well as logistic regression at a similar level
as CHAID.
4. Discussion
The two studies present some intriguing findings with
respect to the performance of the three segmentation methods.
The two datasets present very different circumstances that may
be presented to database marketers and the results with respect
to these datasets are somewhat different. Study 2 is a non-profit
organization that solicits contributions in their house file; the
recent mailing that is modeled provided for a response rate of
roughly one quarter of the entire file. Given these features of the
dataset and mailing, RFM is as successful as CHAID and
logistic regression in capturing likely responders to the
solicitation at all tested levels of depth of the file (20% to
50%). Furthermore, the parameters of the test for RFM appear
to be as reliable as those of CHAID and logistic regression when
applied to the hold out sample. Therefore, if one were to
consider only the results of study 2, it would be concluded that
RFM is generally a robust procedure that is similar to the other
two segmentation procedures in its ability to segment likely
respondents. It appears that RFM may be successful when the
overall response rate is fairly high.
The characteristics of study 1, however, present a fairly
common scenario for database marketers. This dataset is for a
multi-division mail order company; the response rate for the
offer is under 5%. Given these relatively common character-
istics of a direct marketing situation, the results suggest that
Hughes' RFM may not perform as well as CHAID when a
marketer only mails to a small portion of the file (i.e., 30% or
less). In these instances, CHAID outperforms RFM in terms of
reliability and ability to capture likely responders. The actual
performance of CHAID in the hold out sample is quite similar to
its predicted performance in the test sample. By contrast,
the actual performance of RFM in the hold out sample is
significantly worse than its predicted performance in the test
sample. Also, CHAID captures more respondents than RFM at
both the 20% and 30% depths of the file. The superiority of
CHAID in these instances suggests that the grouping of dataset
members by a statistical algorithm, as in the case of CHAID,
may be superior to the arbitrary and a priori groupings of RFM.
In this study, CHAID creates fewer cells than the fixed and large
number used in RFM. Given the low response rate to the offer
and the large number of cells specified by RFM, there is a
greater likelihood that chance fluctuation rather than systematic
differences play a role in the outcome for RFM compared with
CHAID. Thus, the predicted level of response in the test does
not hold up when the parameters of the test are applied to the
hold out sample.
The results across the two studies allow the researchers to
consider the circumstances where RFM underperforms relative to
CHAID. The findings suggest that RFM may have difficulties
when the response rate is low (as in study 1) and the database
marketer desires to send an offering to a relatively small portion of
the entire file (30% or less). Under these circumstances, RFM may
be less reliable than CHAID. Alternatively, when the response
rate is relatively high (as in study 2) or the database marketer
desires to mail to a relatively large portion of the file, RFM may
provide results similar to CHAID and logistic regression. Overall,
the study can conclude that Hughes' approach to RFM can
perform at an acceptable level in many database marketing
situations when a direct marketer is limited to using basic
transaction variables. Given that statistical modeling can be more
costly than RFM because of the need for highly trained personnel
(Drozdenko and Drake, 2002), RFM can be considered an
inexpensive and generally reliable procedure.
Two caveats or limitations should be considered with respect
to the findings. First, the two datasets represent different sets of
circumstances, both of which are relatively common in database
marketing. The offers that are modeled in these data likely
represent fairly common mailings in direct marketing. There-
fore, the researchers assume that the characteristics of these files
are not unusual circumstances in direct marketing. Having said
this, the study must concede that these two examples may not
generalize to all other database files. House files of different
organizations may have their own peculiar characteristics and
different offers may vary on a variety of dimensions. Therefore,
the conclusions with respect to the performance of the three
segmentation methods must be tempered with the understand-
ing that they may not hold for all database marketing cir-
cumstances. For example, if there is a curvilinear relationship
between a predictor (e.g., recency) and response, this would
likely impact the performance of logistic regression, as this
method models a monotonic relationship between predictors
and response. Future research that tests these segmentation
procedures under a variety of circumstances using simulated
data would be useful. Simulating different possible relation-
ships between predictors and response will allow the research-
ers to further understand the sensitivities of the three
segmentation procedures to conditions that may arise in a
variety of direct marketing situations.
Second, the analyses compared RFM to CHAID and logistic
regression where each method is constrained to use the same
independent variables of recency, frequency, and monetary
value. This constraint is enforced to achieve a fair test of the
analytical algorithms of the three methods. Therefore, the
conclusions about the relative performance are made with the
understanding that the researchers are considering them only in
the context of these transaction variables. In practice, however,
CHAID and logistic regression are not constrained with respect
to the variables that can be used as predictors. Response to a
mailing can be modeled with a variety of variables using these
two methods. One would assume that more precise modeling
could be achieved using other variables.
This second caveat raises a broader, and perhaps more
important issue. The analysis of recency, frequency, and monetary
value, whether by an RFM model or by a statistical technique such
as CHAID, focuses entirely on the past behavior of individuals.
Although social scientists recognize the power of past behavior as
a useful predictor of future behavior, such a narrow focus likely
limits the direct marketer in their ability to understand their
customers. Zahay et al. (2004) raise this point in their discussion
661J.A. McCarty, M. Hastak / Journal of Business Research 60 (2007) 656–662
of transactional and relational data. They argue that an emphasis
on transactional information is taking a very sales oriented
approach to customers. Such an emphasis may aid sales in the
short run, however, it does not add to the long term relationship
with customers. A consideration of relational data such as
information about the motivations, attitudes, values, and lifestyles
is taking more of a marketing approach to customers. Although
these variables may be less useful than transaction information in
their ability to predict a response to an immediate marketing
activity (i.e., a mailing), they may be enormously useful in
understanding the underlying tendencies in customers. This
consideration would favor analytical techniques such as CHAID
and logistic regression that can accommodate a variety of
personality and individual difference information.
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