ArticlePDF Available

Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression

Authors:

Abstract and Figures

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.
Content may be subject to copyright.
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) 656662
(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) 656662
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 file20%, 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 4.70.0 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) 656662
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) 656662
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) 656662
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.
References
Baier M, Ruf KM, Chakraborty G. Contemporary database marketing: concepts
and applications. Evanston: Racom Communications; 2002.
Deichmann J, Eshghi A, Haughton D, Sayek S, Teebagy N. Application of
Multiple Adaptive Regression Spines (MARS) in direct response modeling.
J Interact Market 2002;16:1527 [Autumn].
Drozdenko RG, Drake PD. Optimal database marketing: strategy, development,
and data mining. Thousand Oaks: Sage Publications; 2002.
Hughes AM. Strategic database marketing. New York: McGrawHill; 2000.
Kahan R. Using database marketing techniques to enhance your one-to-one
marketing initiatives. J Consum Mark 1998;15:4914.
Levin N, Zahavi J. Predictive modeling using segmentation. J Interact Market
2001;15:2-22 [Spring].
Linder R, Geier J, Kolliker M. Artificial neural networks, classification trees and
regression: which method for which customer base? Database Mark Cust
Strategy Manag 2004;11:34456 [July].
Magidson J. Improved statistical techniques for response modeling: progression
beyond regression. J Direct Mark 1988;2:6-17 [Autumn].
Marcus C. A practical yet meaningful approach to customer segmentation.
J Consum Mark 1998;15:494501.
Sargeant A, McKenzie J. The lifetime value of donors: gaining insight through
CHAID. Fund Rais Manag 1999;30:227 [March].
Verhoef PC, Spring PN, Hoekstra JC, Leeflang PS. December the commercial
use of segmentation and predictive modeling techniques for database
marketing in The Netherlands. Decis Support Syst 2002;34:47181.
Yang AX. How to develop new approaches to RFM segmentation. J TargetMeas
Anal Mark 2004;13:5060 [October].
Zahay D, Peltier J, Schultz DE, Griffin A. The role of transactional versus
relational data in IMC programs: bringing customer data together. J Advert
Res 2004;44:318 [March].
662 J.A. McCarty, M. Hastak / Journal of Business Research 60 (2007) 656662
... RFM was introduced by Bult and Wansbeek (1995) (Cited in Birant, 2011) and it is noted as the second most-used marketing method (McCarty and Hastak, 2007). Three attributes denoted under RFM are Recency, Frequency and Monitory values of the transactions made by the customers (Kohavi and Parekh, 2004). ...
... RFM variables that represent purchase history, frequency and total purchase value are derived using the transactional records (Chiu et al., 2009) which makes the RFM model extremely effective in customer segmentation (Cheng and Chen, 2009). RFM was discussed as most app;icable when sending promotional communications to customer databases in order to identify most probable customers that would respond to a given campaign (McCarty and Hastak, 2007). However, the RFM model was criticized as it fails to provide insights regarding new customers (McCarty and Hastak, 2007) and because it is less predictive since the three variables are isolated from the customer's geo-demographic data (Yang, 2004). ...
... RFM was discussed as most app;icable when sending promotional communications to customer databases in order to identify most probable customers that would respond to a given campaign (McCarty and Hastak, 2007). However, the RFM model was criticized as it fails to provide insights regarding new customers (McCarty and Hastak, 2007) and because it is less predictive since the three variables are isolated from the customer's geo-demographic data (Yang, 2004). Yet, RFM has dwelled over 50 years targeting customers and reducing promotional communication costs (Kohavi and Parekh, 2004). ...
... The most significant predictor (splitting) variable will be used to come up with the first-level sub-nodes, followed by the second most significant variable to further split up those first-level sub-nodes into second-level sub-nodes, and so on until the process of node formation ends when there are no more significant relations between the number of citations and the remaining predictor variables [17,18]. In this way, CHAID works in a similar fashion as regression analysis in that it uses the different predictor variables to make predictions about the number of citations, except that it allows for the detection of interaction between the variables while doing that [19], which makes it more suitable in the context of this study. ...
... Finally, Chi-square automatic interaction detection (CHAID) was also performed in SPSS v.26 to further examine how those different prediction variables (more specifically, the ones that were found to be significant) interact or work together in predicting and determining citation counts. Again, CHAID allowed for splitting or branching out our initial sample of 236 papers into different sub-nodes in terms of citation counts (which is the criterion/dependent or variable of interest in this case), based on the most significant (i.e., strongest) predictor down to the least significant one, with the branching eventually stopping when no further significant predictors could be identified [19]. In this case, the stopping criteria for the CHAID analysis in SPSS were set at 50 observations for parent nodes (before the division of any (sub)sample) and 20 for child nodes (after the division of any (sub)sample) while the alpha significance level for predictor eligibility for splitting nodes was set at 10%; these two criteria allowed us to achieve an optimal branching (i.e., the maximum number of significant nodes), given our relatively small sample size, while ensuring no node has fewer than 20 observations, as to ensure representation and actionability for each node in this case [17,18]. ...
Article
Full-text available
This paper examines how journal-, article-, and author-related factors influence citation counts in the business field using 236 journal articles collected from an AACSB medium research output business school in the Middle East between 2017 and 2021. Results from association tests demonstrated that journal rank and format, the subfield of the article, and author prestige are significantly related to the number of citations. Results from CHAID further demonstrated the presence of an interaction/joint effect among variables; in particular: (1) articles published in Q1 WoS journals that are also authored/co-authored by prestige authors resulted in the highest number of citations; (2) articles published in Q2–Q3 WoS journals that also belonged to the business and management domain resulted in an average number of citations, and (3) articles published in Q4 or unranked journals in WoS also ranked Q3–Q4 or unranked in Scimago resulted in the lowest number of citations. These results provide theoretical implications and practical recommendations for faculty and business schools interested in enhancing their scholarly impact and rankings.
... Among the many analytical tools used for marketing campaigns, RFM remains popular due to its simplicity of use. Many marketing data mining algorithms, and in particular the ones for direct marketing, are based on this concept (McCarty & Hastak, 2007). The use of such data mining techniques allow marketers to better manage their customers' databases for segmentation and generate more effective and cost efficient promotional strategies for direct marketing campaigns. ...
Article
Organizations allocate a part of their financial resources to optimize their market segmentation strategies, plan marketing campaigns, and improve customer relationships. Throughout this process, they use a vast amount of electronic records generated by online and offline purchases to design effective marketing campaigns and introduce personalized promotions for their customers by employing data analytics. The problem of selecting target customer segments, given various priorities and the budget constraint, can be modeled as a multi-objective optimization problem with flexible goals and different priorities, interdependencies and resources constraints. The main objective of this paper is to demonstrate the use of the goal programming approach to address this challenge.
... In the context of using advanced analytics to support decision-making with regard to customers, RFM has been also used to support customer value analysis and target potential customers based on identified objectives, it represents customers' behavior in a simple way. It is a scoring model which assigns scores to customers based on their prior purchasing history according to three parameters, Recency 'R', Frequency 'F', and Monetary 'M' (Mccarty & Hastak, 2007). However, there are some limitations that eliminate the current RFM from making a better contribution to the eventual advancement of the decision-making with regards to the customers. ...
Conference Paper
In today's dynamic business world, customers are considered the most valuable asset. Thus, enhancing decision-making strategies that focus on availing better services to those customers has a significant impact on gaining a competitive edge across organizations. Achieving this enhancement requires in-depth understanding of customers’ behavior by using innovative methodologies like machine learning and advanced analytics techniques to discover valuable insights. Clustering is a machine learning technique that divides customers into meaningful clusters based on their characteristics to comprehend customers’ behavior. Similarly, Recency, Frequency, Monetary and Adoption ‘RFMA’, is a scoring model that is used to simplify customers’ behavior representation. Clustering techniques can be integrated with scoring models for obtaining better results. However, there is limited research that evaluates the impact of applying clustering techniques on scoring models to ensure its effectiveness. In this paper, a new method that integrates the Expectation Maximization ‘EM’ clustering technique with RFMA model is introduced in order to assess the robustness and sensitivity of the RFMA model. This method was implemented on genuine bank data where stakeholders target prospective customers who have the ability to use digital channels. Additionally, the study conducted in this paper evaluates the impact of applying different clustering techniques such as K-means and EM on the RFMA model. The results show that the model is robust across those techniques.
... ccording toMiglautsch (2002) they may have the greatest untapped potential. Second, the limited number of selection variables that can be used by the proposed model it has been an issue of criticising. Most household characteristics have important effect on the probability of customer response so not considering them may produce inaccurate results.McCarty and Hastak (2007) argue that it is preferable to consider relational information when using RFM models. The third recognised disadvantage of RFM is the fact that it focuses only on current customers and cannot be applied to potential ones Although RFM is a useful technique for database analysis, it seems that, at least for certain situations, some other ...
Article
The current study aims to illustrate the usefulness of the utilization of customer data into the retail industry and in particular to the supermarket sector. This piece of work tries to fill the gap in the literature regarding the application of database marketing techniques to real-life examples and also to be the starting point of more research related to the Cypriot retail context. The main objective of this study is to analyse the customer database of a supermarket chain based in Cyprus in order to segment their customers into homogeneous groups and then proceed to the identification of the most valuable customers. The RFM analysis was employed in order to segment the customer database and score each customer group according their Recency, Frequency and Monetary values. The findings suggest that the most valuable group of customers was consisted from 3657 customers. These customers represent more than the 34% of the total gross sales while they comprise more than 10% of the total cardholders. Also, a number of other interesting findings were also discussed, such as other valuable segments, middle-ranked segments and the least valuable customers.
... CHAID In the construction of the CHAID model, if the algorithm used has no reasonable restriction and pruning for the growth of the decision tree, the free growth of the decision tree may cause each branch to contain only simple event data or nonevent data, which easily causes overfitting (McCarty and Hastak, 2007). In the process of optimizing the model, the following termination rule was adopted: the minimum number of records of the parent branch was 5 %, and the minimum number of records of the child branch was 3 %. ...
Article
The individual feed intake of dairy cows is an indicator of oestrus and health status and can be used as an abnormality warning in health monitoring. However, existing methods for monitoring individual feed intake are limited by the feeding environment and equipment and are difficult to apply in commercial production. In this experiment, a feed intake monitoring system for dairy cows was used to determine the daily feed intake of 10 dairy cows. The dairy cows were fed 8 diets with different concentration-to-forage ratios according to different phases or time intervals. The adaptation period of the cows to each diet was 7 days, and the trial period was 7 days. Smart collars were used to monitor the daily eating time and ruminating time of the dairy cows, which were used along with dietary components as input parameters to establish five feed intake prediction models: a linear regression model (LRM), an artificial neural network model, a support vector machine model, a K-nearest neighbour model and a chi-square automatic interaction detector model. The research results showed that R² was 0.73 for the LRM and ≥0.82 for the other four models, which indicated that the relationship between feed intake and the predictors may not be a simple linear relationship. The five models had root mean square errors (RMSEs) ≤ 1.5 kg/d and standard deviations (SDs) ≤ 0.87 kg/d, and in external verification, the five models achieved R² values ≥ 0.80, RMSEs ≤ 0.75 kg/d, and SDs ≤ 0.90 kg/d. This study shows that adding dietary ingredient data to collar-collected data can improve prediction model accuracy.
Article
Background context: Implementing machine learning techniques, such as decision trees, known as prediction models that use logical construction diagrams, are rarely used to predict clinical outcomes. Purpose: To develop a clinical prediction rule to predict clinical outcomes in patients who undergo minimally invasive lumbar decompression surgery for lumbar spinal stenosis with and without coexisting spondylolisthesis and scoliosis using a decision tree model. Study design/setting: A retrospective analysis of prospectively collected data. Patient sample: This study included 331 patients who underwent minimally invasive surgery for lumbar spinal stenosis and were followed up for ≥2 years at one institution. Outcome measures: Self-report measures: The Japanese Orthopedic Association (JOA) scores and low back pain (LBP)/leg pain/leg numbness visual analog scale (VAS) scores. Physiologic measures: Standing sagittal spinopelvic alignment, computed tomography, and magnetic resonance imaging results. Methods: Low achievement in clinical outcomes were defined as the postoperative JOA score at the 2-year follow-up <25 points. Univariate and multiple logistic regression analysis and Chi-square automatic interaction detection (CHAID) were used for analysis. Results: The CHAID model for JOA score <25 points showed spontaneous numbness/pain as the first decision node. For the presense of spontaneous numbness/pain, sagittal vertical axis ≥70 mm was selected as the second decision node. Then lateral wedging, ≥6° and pelvic incidence minus lumbar lordosis (PI-LL) ≥30° followed as the third decision node. For the absence of spontaneous numbness/pain, sex and lateral olisthesis, ≥3mm and American Society of Anesthesiologists physical status classification system score were selected as the second and third decision nodes. The sensitibity, specificity, and the positive predictive value of this CHAID model was 65.1, 69.8, and 64.7% respectively. Conclusions: The CHAID model incorporating basic information and functional and radiologic factors is useful for predicting surgical outcomes.
Conference Paper
Full-text available
We aim through this research paper to find a practical and simple way to segment customers on the basis of their profitability in order to determine the communication mix of each group of customers in proportion to their purchasing behavior, as we explain the concept of customer lifetime segmentation RFM model and marketing communication. A database for sales of an industrial organization and its integration with a clustering technique represented by K-means algorithm, we segmented customers on the basis of their profitability, and determined the communication mix for each group of customers, the results showed that using this method allows determining the profitability of customers and make optimal communication.
Chapter
Full-text available
In recent years, firms have been able to collate large customer data sets, and this has led to both challenges and opportunities when making marketing and sales-related decisions. Large customer data sets can enable an intimate understanding of customers. However, this can pose additional costs to the firm while also requiring new data analysis and management capabilities. To address this problem, we develop a new approach to market segmentation and the identification of relative segment purchase probabilities using a large point-of-sale (POS) customer data set in the Sri Lankan retail context. Stage one of our method involves supervised and unsupervised learning approaches that analyze three purchase characteristics (Recency, Frequency, and Monetary value—RFM) and product attributes to identify segments in the customer data set. Stage two of our method involves market basket analysis (MBA) to determine the probabilities of purchase behaviors for each segment. Our new approach is among the first to establish a relationship between a machine learning-based approach to market segmentation and purchase prediction.
Article
Predicting profitable customers is a strategic knowledge portfolio of retailer managers because some customers are better profitable than others in a business. The present work is an effort to demonstrate a better model of predicting profitable customers. We apply the k-means algorithm to identify customer patterns based on Recency, Frequency, and Monetary (RFM) attributes computed from a real-life dataset of UK-based and registered non-store online retail. Six data mining models have been applied to each identified pattern and overall data to predict whether each customer would purchase in the next six months or not. A comparative analysis of identified pattern characteristics and predictable performances and Type I and Type II errors have been performed to identify the target customer group in terms of better predictability and profitability. The identified patterns help to generate novel marketing strategies. Thus, the retailers may successfully target the most consistently profitable customer groups to apply diverse knowledge on marketing strategies for the specific pattern.
Data
Full-text available
The most commonly used modelling methods for targeting customers in direct marketing are artificial neural networks (ANNs), classification trees (CTs) and logistic regression (LR). These methods differ in how rules for the association between purchase behaviour and customer information are derived from the data. The authors investigated the predictive performances of the three methods in a competitive test in a simulated direct marketing scenario. The experimental design consisted of a number of situations comprising varying sample sizes and data complexities. The results show that the performance of all methods increased with the size of the customer base. This relation was less strong for ANNs than for CTs and LR, especially when data complexity was high. As a consequence ANNs outperformed the other methods when sample size was small, but CTs and LR yielded better results when sample size was large — with LR being generally superior to CTs. The combination of the prediction scores of ANNs, CTs and LR into a single model revealed synergistic effects among the three modelling approaches. The combination mostly resulted in better results than any single model. This study shows that ANNs may be especially valuable for small customer bases, but might not be used in isolation for analysing larger customer bases. Irrespective of the size of the customer base and the underlying data complexity, the combination of ANNs, CTs and LR into a single model mostly resulted in the best prediction, suggesting that model combination might be a safe way of maximising predictive performance when the degree of data complexity is unknown (as is the case for most real customer bases).
Article
Full-text available
The most commonly used modelling methods for targeting customers in direct marketing are artificial neural networks (ANNs), classification trees (CTs) and logistic regression (LR). These methods differ in how rules for the association between purchase behaviour and customer information are derived from the data. The authors investigated the predictive performances of the three methods in a competitive test in a simulated direct marketing scenario. The experimental design consisted of a number of situations comprising varying sample sizes and data complexities. The results show that the performance of all methods increased with the size of the customer base. This relation was less strong for ANNs than for CTs and LR, especially when data complexity was high. As a consequence ANNs outperformed the other methods when sample size was small, but CTs and LR yielded better results when sample size was large — with LR being generally superior to CTs. The combination of the prediction scores of ANNs, CTs and LR into a single model revealed synergistic effects among the three modelling approaches. The combination mostly resulted in better results than any single model. This study shows that ANNs may be especially valuable for small customer bases, but might not be used in isolation for analysing larger customer bases. Irrespective of the size of the customer base and the underlying data complexity, the combination of ANNs, CTs and LR into a single model mostly resulted in the best prediction, suggesting that model combination might be a safe way of maximising predictive performance when the degree of data complexity is unknown (as is the case for most real customer bases).
Article
Full-text available
We discuss the use of segmentation as a predictive model for supporting targeting decisions in database marketing. We compare the performance of judgmentally based RFM and FRAC methods to automatic tree classifiers involving the well-known CHAID algorithm, a variation of the AID algorithm, and a newly developed method based on genetic algorithm (GA). We use the logistic regression model as a benchmark for the comparative analysis. The results indicate that automatic segmentation methods may very well substitute the judgmentally based segmentation methods for response analysis, and come only short of the logistic regression results. The implications of the results for decision making are also discussed. © 2001 John Wiley & Sons, Inc. and Direct Marketing Educational Foundation, Inc.
Article
Contemporary Database Marketing: Concepts and Applications by Martin Baier, Kurtis Ruf and Goutam Chakraborty reviewed by GRAEME McCORKELL. One Market Under God by Thomas Frank reviewed by VICTOR ROSS. eMarketing eXcellence: The Heart of eBusiness by P. R. Smith and D. Chaffey reviewed by DANNY MEADOWS-KLUEInteractive Marketing (2003) 4, 305–307; doi:10.1057/palgrave.im.4340193
Article
This paper introduces the concept of the Customer Value Matrix, a customer segmentation approach that is especially well-suited for small retail and service businesses. The discussion offers insights into the reasons for the development of this practical approach, a concrete methodology for its implementation, and strategic and tactical applications of the concept. The material is supported with strong evidence from “real-world” examples featuring a variety of small retail and service businesses. The paper concludes with a discussion of the managerial implications for companies that manage chains of small retail or service businesses as to how they can take advantage of local relationship marketing.
Article
Asserts that 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”. Reviews the processes involved in database marketing. Suggests to marketers the best processes to adopt. Describes the advantages of the mathematical computation RFM (recency, frequency, and monetary value) in consumer behavioral analysis. Provides a description of how the system can be implemented by practitioners.
Article
Recency, frequency and monetary (RFM) is a simple and actionable way that has long driven direct marketing efforts. In many cases, however, this empirical segmentation is encumbered with basic shortcomings evident in two aspects: first, the advantage of simplicity often disappears in terms of statistical significance. Secondly, the three-dimensional measure is less predictive than sophisticated models, such as Chi-Square Automatic Interaction Detection (CHAID) and regression analysis. Using RFM as an entry point, this paper discusses the necessity and reality of upgrading this crude method to advanced approaches, where two options are hereby proposed:—option 1: substituting result-based statistical findings for the traditional intuition-based coding, RFM implementation becomes simple and robust. The transition also advances empirical RFM to CHAID analysis;—option 2: introducing `V=M/R' defined as a customer value, traditional RFM schemes are readily migrated to individual V-scores without in-depth statistic proceedings. The innovation is compatible to logistic regression.Journal of Targeting, Measurement and Analysis for Marketing (2004) 13, 50-60; doi:10.1057/palgrave.jt.5740131
Article
Increasing costs of direct marketing campaigns coupled with declining response rates have prompted many direct marketers to turn to more sophisticated techniques to model response behavior. The underlying premise is that even a small improvement in response rate can have significant implications for the bottom line. This article investigates the use of a recently developed technique, Multiple Adaptive Regression Splines (MARS), together with logistic regression in the context of modeling direct response. Specifically, our goal is to assess the relative effectiveness of MARS models vis-à-vis logistic regression with original predictor variables in modeling direct response behavior. Our analysis shows that the MARS models outperforms the logistic model in general, leading us to conclude that MARS offers a number of advantages over a logistic model. Direct marketing strategy implications are also discussed. © 2002 Wiley Periodicals, Inc. and Direct Marketing Educational Foundation, Inc.
Article
Although the application of segmentation and predictive modeling is an important topic in the database marketing (DBM) literature, no study has yet investigated the extent of adoption of these techniques. We present the results of a Dutch survey involving 228 database marketing companies. We find that managers tend to rely on intuition and on the long-standing methods RFM and cross-tabulation. Our results indicate that the application of segmentation and response modeling is positively related to company and database size, frequency of customer contact, and the use of a direct channel of distribution. The respondents indicate that future research should focus on models applicable for Internet marketing, long-term effects of direct marketing, irritation from direct marketing offers, and segmentation and predictive modeling techniques.