Behavioral Segmentation

TM

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Contents

1. The Importance of Segmentation in Contemporary Marketing ............................................................ 2

2. Traditional Methods of Segmentation and their Limitations ................................................................ 2

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

3. 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

4. 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.

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.

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.

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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.

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. 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

many groups will be required when using traditional methods so

getting the perfect answer can be very time consuming.

QuartileNumber 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$

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.

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.

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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.

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

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.

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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

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

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.

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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

Segmentation based on Fuzzy Logix’s proprietary algorithm

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 average spend in the other segments, however they are

distinct enough to warrant their own category.

QuartileNumber 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$

ClusterNumber 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$

The algorithms produce

near-homogenous

behavior within a segment

and heterogeneous

behavior across segments.

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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

customers who could migrate from one segment to another.

Those who could migrate to a higher value segment present up-

selling 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

3.3 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

ClusterNumber of

Customers

Minimum

Purchase

Maximum

Purchase

Average

PurchaseAverage

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$

Probability

Our algorithms calculate

the probability of

segment migration and

can be used for further

targeting and up-selling.

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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

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 between-

segment heterogeneity. On visual examination, we see that

there are three distinct clusters.

Cluster Dim 1 Dim 2 Obs

1 103 31 200

2 401 99 350

3 757 151 261

4 1018 199 189

Average

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

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

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

0

50

100

150

200

250

300

0 200 400 600 800 1000 1200

Dim

en

sio

n 2

Dimension 1

Simulated Data

Cluster Dim 1 Dim 2 Obs

1 100 31 200

2 403 103 350

3 899 169 450

Average

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.

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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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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.

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

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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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Fuzzy Logix, LLC

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Suite 130

Charlotte, NC 28262

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Contact:

sales@fuzzyl.com

704-307-4356