TM
Behavioral Segmentation
Contents
1.
The Importance of Segmentation in Contemporary Marketing............................................................ 2
2.
Traditional Methods of Segmentation and their Limitations................................................................ 2
3.
4.
2.1
Lack of Homogeneity..................................................................................................................... 3
2.2
Determining the Number of Groups Required .............................................................................. 3
2.3
Segmentation in Two or More Dimensions ................................................................................... 4
2.4
Logical Groups are Fixed , Subjective and Possibly Arbitrary ........................................................ 5
Segmentation Algorithms from Fuzzy Logix.......................................................................................... 5
3.1
Homogenous within Segments and Heterogeneous Between Segments .................................... 6
3.2
Cross-Selling Opportunities are Identified .................................................................................... 7
3.3
Number of Segments is Automatically Determined ..................................................................... 7
3.4
Segmentation in Multiple Dimensions.......................................................................................... 9
3.5
Useful Metrics Are Captured For Each Segment......................................................................... 10
3.6
The Benefits of Self-Organizing Segments .................................................................................. 11
3.7
Rapid Analytic Discovery ............................................................................................................. 11
3.6
Drilling-Down in a Segment ........................................................................................................ 12
3.7
Spot Trends and Improve Forecast Accuracy ............................................................................. 12
Accelerated Implementation ............................................................................................................... 13
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1. The Importance of Segmentation in Contemporary Marketing
There is a natural tendency amongst us to segment data into meaningful clusters of information. A
teacher may try to cluster his or her students into groups like high achievers, at par with grade level,
needs improvement, etc.
A cardiologist might be interested in classifying his or her patients as
having high risk, moderate risk or low risk of heart attacks in near future. Similarly, retailers may be
interested in segmenting their customers and analyzing the behavior of each segment separately.
One area that can benefit greatly from using segmentation is marketing.
The days of mass
marketing are over. No longer do companies market to as many people as possible and hope for
the best. In in mass marketing, the emphasis was on uniformity. It was not only the product that
was generic but also the packaging, price and promotion.
In today’s complex world, customers
differ vastly in their buying behavior, and therefore there is a need to customize products,
packaging, price and promotions to fit their needs.
In other words, the emphasis now is on
differentiation based on the unique needs, preferences and behavioral characteristics of each
customer segment rather than using a one-size-fits-all strategy.
Having underlined the importance of segmentation, the
questions that need to be answered are - what are the effective
segmentation methods and how can they be implemented in an
organization. In the subsequent sections, we will discuss the
traditional methods of segmentation, their limitations and how
these limitations may be overcome by using our proprietary
algorithms. Additionally, we will also discuss certain aspects of
implementation of these algorithms.
In today’s complex world,
customers differ vastly in
their buying behavior, and
therefore there is a need to
customize products,
packaging, price and
promotion to the customers’
needs.
2. Traditional Methods of Segmentation and their Limitations
Traditional methods of segmentation can be classified into two categories:

Those based on a numerical attribute of the data like average and variance, median,
quartile, etc.

Those that are based on logical groupings like discount buyers, frequent buyers, etc.
In this section, we will examine how these traditional methods of segmentation are inadequate.
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2.1
Lack of Homogeneity
Firstly, traditional methods of segmentation ignore the distribution of the underlying data which can
lead to incorrect interpretation since the degree to which items are similar is not accommodated.
In the following example, we have grouped customers from a high-end garment retailer into four
quartiles based on their purchases in a given quarter.
Quartile
Number of
Customers
Minimum
Purchase
Q1
153
$
Q2
153
Q3
153
Q4
154
Maximum
Purchase
Average
Purchase
5
$
99
$
60
$
99
$
220
$
154
$
221
$
447
$
317
$
447
$ 4,038
$
979
Now we can easily spot the discrepancy in the fourth quartile. It
is naïve to expect the same type of behavior from all customers in
this group because the range of purchases is quite large; from
$447 to $4,037. Customers, who on an average buy $2,000 worth
of clothes in a quarter, will differ quite a bit from those who
spend $500. We can state from this example that this traditional
method of segmentation by quartiles did not result in forming
groups with homogenous behavior. Regardless of the method of
segmentation, homogenous or near-homogenous behavior is of
paramount
importance
in
making
effective
management
decisions. When groups are properly segmented, the members
of each group will act in a similar way. Because their behavior is
similar, you can target groups with highly effective action.
Equal sized groups based
on sales may seem
attractive but there may
be large differences in
behavior in a given group.
Traditional approaches do
not ensure homogeneity
and are therefore quite
limiting in their
application to marketing
programs.
For
example, for each segment, the drivers of buying behavior and
churn may be different. If you target one group with the wrong
messaging, the campaign will not deliver optimal results.
2.2
Determining the Number of Groups Required
Another limitation of the traditional methods of segmentation is
their inability to determine the adequate number of groups required
to describe the data. In the previous example, if we increase the
number of groups, we see more homogeneous behavior in each
group. However, there is no way to automatically establish how
Determining the
appropriate number of
homogenous segments is
of paramount importance
in order to understand the
distinctive behavior in
different segments.
Traditional methods are
limited in their ability to
make this determination.
many groups will be required when using traditional methods so
getting the perfect answer can be very time consuming.
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An even more serious challenge is determining how many observations should be in each group. If
our goal is to preserve homogeneity in each group, the number of observations in each group will
likely be different. While the groups should exhibit dissimilar characteristics, the members within
each group should be similar.
Accomplishing this using traditional methods will require many
hours of experimentation to make sure each group is both the most intra-homogenous (members
have similar behavior) and is also most heterogeneous (each groups behavior is dissimilar from all
the other groups).
2.3
Segmentation in Two or More Dimensions
The limitations of traditional segmentation become even more
apparent if we consider two or more dimensions. For example,
if we know that retailers are interested in two different metrics
for their customers – number of visits during a quarter and
average amount of purchases per visit, then we can use
segmentation to guide us in maximizing both of these attributes
at store and customer level. Similarly, a telephone carrier may
be interested in segmenting customers using three dimensions
– how long the customer has been a subscriber, monthly fees
paid by the customer and percentage of minutes utilized.
Conventional methods
cannot handle the
complexity of
segmenting data using
two or more
dimensions. For
example number of
visits per quarter and
purchases per quarter.
Traditional segmentation is limited in capability to one dimension and doesn’t work with multiple
dimensions so we will not be able to deal with this problem unless we create a new attribute by
combining two dimensions into one. One way of combining could be to assign a certain weight of
each dimension and then adding the metrics. For example,
Combined Metric = 0.5 * Number of Visits per Quarter + 0.5 * Average Purchase per Visit
Here we have assigned a weight of 50% of each of the two dimensions. Now, traditional algorithms
can segment the underlying data but these assigned weights were completely arbitrary; even if they
are a best guess. In addition, the minimum, maximum and averages for each segment will be the
result of analyst bias because of the guesswork in weighting.
Against this backdrop of the traditional methods not being able to objectively deal with more than
one dimension, we probably have to be content with segmenting on something like total purchases
in a given quarter by all customers. However this presents another issue since two customers can
have the same amount of total purchase in a given period by different means. One of them could
be visiting the store quite often and buying low ticket items whereas another could be visiting only a
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few times and buying high priced items.
The marketing strategy for each customer needs to be
different, but a one-dimensional segmentation algorithm will classify them in the same group; which
is clearly incorrect and sub-optimal for targeted marketing.
2.4
Logical Groups are Fixed, Subjective and Possibly Arbitrary
There is one additional way to attempt traditional segmentation with multiple dimensions is by
organizing the data into logical groups. This method is fraught with analyst bias since these logical
groups are subjective in nature. In addition, data is assigned to one of these segments, even if their
behavior changes, they typically do not move to the correct segment.
A simple example will
illustrate this point. Let’s assume that we want to segment customers into two groups – those who
are discount buyers and those who are not. The first problem arises from the definition itself. What
is the threshold value that we should consider before we classify a customer as a discount buyer?
Should we state that a customer will be classified as a discount buyer if he or she were to purchase
only those items that are discounted? Obviously, this definition will run in problems because there
will be very few customers who will fit this definition. Normally, we would expect a rational
customer to buy a mix of items that are both on discount and at regular price.
This leads us to
further add bias by doing things like stating that those customers who in the past three months have
spent at least 25% of their total purchase on discounted items, are discount buyers.
The truth is that the boundary between discount buyers and
full-price buyers may be quite fuzzy and can change over time.
An effective segmentation algorithm should be able to deal with
these fuzzy criteria in an objective manner. For example, it
should be able to state that there is 60% probability that a given
customer is a discount buyer and 40% probability that he or she
is not, however even amongst the discount buyers, there may
be distinct groups. Some of them may on an average be willing
to buy an item if it is slightly discounted whereas some others
may only be willing to do for heavily discounted items
depending on the item. Again, traditional algorithms have their
Information is not
always black and
white; sometimes
there may be shades
of gray. It is difficult
for logical groups to
recognize these
shades of gray which
are very important for
businesses.
limitations in devising optimal groups.
3. Segmentation Algorithms from Fuzzy Logix
In this section we will discuss the algorithms from Fuzzy Logix which address the problem of
traditional segmentation and the distinctive advantage of using quantitative algorithms. These
algorithms have been developed over a number of years through our intensive research and
development. During that time these algorithms have been fine-tuned while working on real data
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from our clients who are in various industries like retail, media and entertainment, advertising, and
others.
3.1
Homogenous within Segments and Heterogeneous Between
Segments
Our segmentation algorithms have been developed keeping in mind the basic principle that data
within a given segment should be as homogenous as possible while making sure the different
segments are as heterogeneous as possible. Let us revisit the example of the high-end garment
retailer in the Southeastern US. We present the results of segmentation using quartiles and our
proprietary algorithm.
Segmentation based on quartiles
Quartile
Number of
Customers
Minimum
Purchase
Maximum
Purchase
Average
Purchase
Q1
153
$
5
$
99
$
60
Q2
153
$
99
$
220
$
154
Q3
153
$
221
$
447
$
317
Q4
154
$
447
$ 4,038
$
979
Segmentation based on Fuzzy Logix’s proprietary algorithm
Cluster
Number of
Customers
Minimum
Purchase
Maximum
Purchase
Average
Purchase
1
251
$
5
$
172
$
87
2
175
$
175
$
375
$
261
3
99
$
376
$
676
$
489
4
49
$
683
$ 1,149
$
854
5
28
$ 1,163
$ 1,993
$ 1,515
6
11
$ 2,097
$ 4,038
$ 2,874
It is evident that the Q4 segment in the quartile chart has a very wide range of values. When we let
the data define the segments, we fine 6 naturally occurring segments. These segments seem much
more uniform because our algorithms have derived the optimal
mix of homogeneity within a segment and heterogeneity between
the segments based on the patterns of behavior in the data.
As an example, let us consider segments 4, 5 and 6. In these
segments, the average value of customer purchases is higher than
The algorithms produce
near-homogenous
behavior within a segment
and heterogeneous
behavior across segments.
the average spend in the other segments, however they are
distinct enough to warrant their own category.
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The uniformity within segments and the differences between any two segments in this example
provides a much better opportunity to understand customers’ behavior and thereby guide targeted
marketing.
3.2
Cross-Selling Opportunities are Identified
One of the uses of segmentation is to be able to identify those
Our algorithms calculate
the probability of
segment migration and
can be used for further
targeting and up-selling.
customers who could migrate from one segment to another.
Those who could migrate to a higher value segment present upselling opportunities whereas those who have a strong
likelihood of migrating to a lower segment present threats that
need to be mitigated.
In our retail example, segment # 3 has a range of average purchases from $376 to $676. A customer
whose average purchase is close to the upper range in this segment may be a potential up-selling
opportunity, and over time, move to the next higher segment. In the following exhibit, the
probabilities of staying in segment 3 or of migrating to segment 4 have been presented for five
customers who are at the upper end of segment 3. These probabilities are calculated by our
algorithms and can be used very effectively for managing customer migration and upselling. As an
example, we can target all those customers who have a more than 20% probability of migration to
segment 4 for a targeted campaign. A somewhat similar strategy can be adopted to prevent
customers from migrating to lower segments. Therefore, we can improve customer profitability by
moving customers to higher spending segments and preventing others from moving to lower value
segments.
Segmentation statistics and crossover customers
Probability
Cluster
3.3
Number of
Customers
Minimum
Purchase
Maximum
Purchase
Average
Purchase
Average
Purchase
Segment# 3
Segment# 4
1
251
$
5
$
172
$
87
$
632
64.47%
21.97%
2
175
$
175
$
375
$
261
$
643
59.97%
26.10%
3
99
$
376
$
676
$
489
$
649
57.30%
28.68%
4
49
$
683
$ 1,149
$
854
$
673
47.09%
39.19%
5
28
$ 1,163
$ 1,993
$ 1,515
$
676
45.80%
40.59%
6
11
$ 2,097
$ 4,038
$ 2,874
Number of Segments is Automatically Determined
Our segmentation algorithms can automatically determine the number of segments that are
required to best describe the data. This is a key distinction because the behavior of the data
determines the number of segments as opposed using assumptions to try and remove the bias of
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the model builder. With our algorithms, users do not have to decide the number of segments that
he or she wants the data to be grouped by. Since the model does the grouping, the user does not
have to manually iterate through a number of options before determining the appropriate number
of segments. The algorithms that we employ are equipped with artificial intelligence to be able to
make this determination on its own.
We have tested these algorithms with large amounts of data to ensure that the number of segments
generated by these algorithms is indeed correct and coherent and we have worked with industry
experts to incorporate their input into the design of our algorithms.
In the examples below, we present a view of using traditional segmentation vs. true behavioral
segmentation and the ability of our algorithms to automatically determine the number of segments.
In the first example, using traditional segmentation, the scatter-plot shows that there are four
distinct groups, as illustrated by the dotted blue circles. Notice that segments 3 and 4 in the upper
right corner have overlap in membership and are not significantly distinct. When interpreting the
chart, also notice that the center of the circle is the average value for that segment.
Example 1
Average
Cluster
In the second example, we use our algorithms to determine the
true number of segments based on customer behavior and the
optimal mix of inter-segment homogeneity and betweensegment heterogeneity. On visual examination, we see that
there are three distinct clusters.
Dim 1
Dim 2
Obs
1
103
31
200
2
401
99
350
3
757
151
261
4
1018
199
189
Artificial intelligence
enables the algorithms
to automatically
determine the number
of segments required.
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By letting the model derive the segments our methods will produce segments that identify the true
behavior of customers, therefore your business decisions will yield results that will be more cost
effective and produce higher yields.
Example 2
Simulated Data
300
250
Dimension 2
200
150
Average
100
Cluster
50
Dim 1
Dim 2
Obs
1
100
31
200
2
403
103
350
3
899
169
450
0
0
200
400
600
800
1000
1200
Dimension 1
3.4
Segmentation in Multiple Dimensions
An example of two-dimensional segmentation for a high-end
garment retailer is presented below. The two dimensions are
number of visits by customers in a given quarter and average
sales per visit.
In the following exhibit, the sales from all
customers in each of the segments has been calculated and
displayed for current quarter as well the same quarter the
previous year. The size of the bubbles in the bubble chart is
proportional to the value of sales. The information in charts like
this helps the end-users make comparisons and draw
One classic example is
segmentation of retail
customers by number of
visits per quarter and
average purchase per visit.
Our algorithms can easily
handle a case like this and
produce actionable results.
conclusions.
As shown in segmentation statistics, there is enough variability in both these dimensions to warrant
creating four different segments. One would argue that the number of visits in segment 3 and 4 are
the same and therefore, it does not add additional value to use two segments for this data instead
of one, however not the dramatic difference in sales amounts per visit in these groups. In both
groups, the number of visits is the same; 1.9 per quarter, but the average amount spent in segment
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four is more than double the amount spent in segment three. Using this information the retailer
devised specific marketing campaigns for each segment and achieved a higher response rate that
from previous campaigns driven by recognizing the similarity of visits, but the difference in
spending.
Customer segmentation in two dimensions
It has been illustrated in the previous example that our algorithms are capable of segmenting data in
two dimensions. In reality, these algorithms are powerful enough to deal with any number of
dimensions. From our experience, we have found that in many instances, segmentation in only two
dimensions is valuable, however, there are a times when more dimensions are required. For
example in cell phone carriers are interested in grouping their customers by length of subscription,
monthly fees and percentage utilization of their assigned quota.
3.5
Useful Metrics Are Captured For Each Segment
One of the added values from segmentation is the ability to analyze
various metrics for each segment. Once the models run, you manage
the members of each segment based on their behavior.
It is
enlightening to see the differences in values for each segment. In our
example you can see that while customers have similar numbers of
Multiple metrics
captured for each
segment help us perform
comprehensive analysis
and identify critical
differences in customer
segments.
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visits, the amount they spend can vary widely. If we were to add another dimension, for example
average discount, we would gain insight into the customers who are motived by discounts and those
that normally pay full price.
3.6
The Benefits of Self-Organizing Segments
Things change. People change their behavior, inventory items that once flew off the shelf now
move slowly, a marketing mix that was effective one week isn’t the next.
Using traditional
segmentation could lead you to miss the changes and continue to market to individuals and
segments based on old behavioral patterns. By using our models, you can capture the change as it
happens. For example, when an entire population changes their behavior, such as the move from
voice to data usage with cell phones, you need models that can self-adjust not only the segment,
but also the members of the segments. One of the benefits of running our behavioral segmentation
models are that each time you run the model, the segments are recreated based on the data, so as
the behavior changes, the results reflect that change.
3.7
Rapid Analytic Discovery
Analytic discovery can take multiple iterations. To understand customers better, you may want to
view their behavior using different dimensions. For example, a retailer may want to view customers
using dimensions such as amount spent per visit, number of visits per quarter and number of
products purchased, repeat customer, new customer, etc. They might also want to include things
such as social media metrics (number of post, number of comments, number of like, time on site,
number of site visits) and then add some demographic factors (income, age, sex, marital status,
etc.). Using different combinations of these dimensions can help create a complete picture of
customer behavior, with certain questions answered by one group of dimensions and others by a
different group of dimensions.
Because our models are easy to use, can be run from existing reporting tools and also because of
the speed and scalability available, you can try many different combinations of dimensions very
quickly and without waiting for the answer to be pulled from specialized software that only a few
can use. By allowing business users to quickly work through the analytic discovery process, decision
making will be accelerated and insight increased.
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3.6
Drilling-Down in a Segment
Once the segmentation is complete, it will be important to drill-down each segment to see the members
of the segment and the related information. A good model will generate easy to understand output.
The example below shows how it’s possible to use a reporting tool to easily drill-down into each
segment. Here you can see the data related to retail customers by all, repeat and new customers and
sales by category, brand and salesperson.
In addition, the results can be sorted using any of the
columns by clicking the header. The ability to use reporting tools to run segmentation and drill-down
into the results means that you can see details to help you understand the characteristics of those in the
segment take action.
Drill-down capability for each segment
3.7
Spot Trends and Improve Forecast Accuracy
Another useful benefit of segmentation is in improving forecasting
accuracy. Since the groups are more heterogeneous and the
members of the group are more homogenous than the general
population, forecast accuracy will be improved. The reason is that
forecast models try to account for variability in an attempt to not
over
or
underestimate
the
future
values.
Segmentation reduces variability because it groups items with
similar behavior so the forecasting model will produce more
A forecast that is
obtained by taking into
account the attributes
of each segment will be
more accurate than an
aggregate forecast for
the whole enterprise.
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accurate results than if the same model is attempted on an entire population whose members may
have very different behavior.
4.
Accelerated Implementation
Even though the math in our algorithms can be quite complex and cover a wide array of
functionalities, the implementation of these algorithms is straightforward. We are able to achieve
rapid software deployment by virtue of the following:

Our models typically install in less than 30 minutes.

Over the years we have performed extensive research and development in various data mining
techniques and have tested and fine-tuned the models. We do not start from scratch in a new
enterprise, but rather leverage our previous work to speed the implementation. This
methodology leverages our existing software infrastructure and algorithms and ensures rapid
deployment with minimal re-work.

We offer proof-of-concepts to demonstrate the effectiveness of our algorithms. The period is
typically two to four weeks.

Our solution can work with almost all databases.

The underlying code used in our product suite is highly optimized to ensure speed and
throughput with your existing hardware.
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The Fuzzy Logix white paper series
Fuzzy Logix, LLC
10735 David Taylor Dr.
Suite 130
Charlotte, NC 28262
USA
Contact:
sales@fuzzyl.com
704-307-4356
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