Why customer segmentation? People with similar attributes tend to display
similar patterns in various ways.
This fact is particularly important in customer relationship management, marketing,
and risk management. For example, people with certain life-styles tend to buy
certain-types of products. Promoting products particularly targeted towards
the demographic group can lead to successful marketing.
In credit and insurance industry, good customer segmentation can lead to minimum
exposure to risk involved in credits and insurances.
Similarly, in catalog sales, customers can be selectively targeted
to reduce marketing cost. Customer segmentation can be used in
Note that customer segmentation is a very important tool
for customer lifecycle management - CLM.
Technically speaking, customer segmentation is a process that divides
customers into smaller groups called segments.
Segments are to be homogeneous within and desirably heterogeneous in between.
In another words, customers of the same segments possess the same or similar
set of attributes. But customers of different segments have differing
sets of attributes.
Segmentation process can be very complicated. Therefore, it's best to use advanced
What information is used in customer segmentation?
Segmentation is normally performed along with the following demographic, geographic,
psychographic, and behavioral variables;
- Demographic segmentation variables describe characteristics of customers and
include age, gender, race, education, occupation, income, religion,
marital status, family size, children, home ownership, socioeconomic status, and so on.
Note that demographic segmentation normally refers to segmentation with these demographic
- Geographic variables include various classification of geographic
areas, for example, zip code, state, country, region, climate, population,
geographical census data. Note that this information can come from
national census data. For more, see geographic segmentation.
- Psychographic segmentation variables describe life style, personality, values, attitudes,
and so on.
Note that psychographic segmentation normally refers to segmentation with these psychographic
- Behavioral segmentation variables include product usage rate and end, brand royalty,
benefit sought, decision making units, ready-to-buy stage, and so on.
- Past business history, Customers' past business track records
can be extremely useful for segmentation. This may include total amounts purchased,
purchasing frequency, (credit) default records, (insurance) claims, responsiveness
for marketing campaigns, and so on.
What is your motivation for Customer Segmentation?
This is very important since there are many ways you can segment customers.
Without clearly defined motivation, no clear segmentation objectives.
Segmentation is meaningless. You need to have clearly defined motivation
and objectives to achieve. For example, to optimize profits for campaign marketing,
or to monitor customer or market trends, or to manage customer loyalty programs,
or to use for customer lifecycle management,
and so on.
Depending on your motivation, different segmentation techniques are employed.
Note that CMSR Data Mining Software provides several
segmentation and segment monitoring tools such as decision tree, neural clustering, etc.
Customer Segmentation for Trend Analysis and Forecasting
Timely identification of newly emerging trends is very important
to businesses. For example, sales patterns of various customer segments
indicate market trends. Upward and downward trends in sales signify
new market trends. The same can be applied to loans, mortgages, credits,
and so on.
Trend analysis and forecasting over well-designed customer segmentation
is a powerful tool for monitoring and detecting newly emerging trends.
For more, please read Trend Analysis and
Customer Segmentation using Neural Clustering (SOM)
Neural clustering segments customers MxN cells in such a way that customers with similar
attributes are clustered together in a cell or nearby cells. In the left figure, colored circles are pie charts representing distribution
for combination of gender and race. Notice that all cells contain objects
of the same single type. Furthermore, nearby cells of the same type objects
are clustered together. An example of perfect clustering! The middle figure
shows histograms for a numerical variable. You will notice that nearby
cells have similar distributions.
The right is all-in-one distribution charts for a specific cell segment.
The following figure shows an example
of 6x6 neural clustering. Customers are clustered using 6x6 cells. Each cell contains
customers with similar attributes. Cells show pie charts where green pies represent customer
proportions who bought products before (or customers churned if it's churn management, etc.).
There are two cells containing green proportions.
Now customer care can be focused on those customers of the greeny cells, ignoring customers in other cells.
Customer segmentation using Decision Tree
divides populations into smaller segments repeatedly.
At a node, it selects a single variable in such a way that segmentation
boosts proportions of the largest categorical value in each resulting segment.
There are many applications that this type of hierarchical segmentation is useful,
especially for direct marketing and customer targeting.
The following figure shows CMSR decision tree;
Marketing Response Analysis with Gains & Profit Charts
Breakdown of populations into smaller segments induces segments having
concentration of certain values. This provides means for cost-effective
customer selection methods.
For example, for catalog marketing,
segments can be visualized with a special response and profit gains chart
as shown at the right figure. The blue curve indicates response capture ratios.
The green curve
shows quantity (or volume) capture ratios. Similarly, the red curve describes
profit and loss amounts. Steep rising curves at the left end are a good
indication of good segmentation, since selecting a small number of left-end
customer segments can capture most potential responses.
This will lead to a small number of catalog mailings, and therefore result in
efficient marketing campaigns.
For more, read Direct Mail Catalog Marketing.
For information about software, please read Data Mining Software.
Software download is available from the page.