Page 169 - DCAP208_Management Support Systems

Management Support Systems

Notes It would also be pretty easy to use this model if you actually had to target those customers
that are likely to churn with a targeted marketing offer.
You may also build some intuitions about your customer base. E.g. “customers who have been
with you for a couple of years and have up to date cellular phones are pretty loyal”.

Viewing Decision Trees as Segmentation with a Purpose

From a business perspective decision trees can be viewed as creating a segmentation of the
original dataset (each segment would be one of the leaves of the tree). Segmentation of customers,
products, and sales regions is something that marketing managers have been doing for many
years. In the past this segmentation has been performed in order to get a high level view of a
large amount of data - with no particular reason for creating the segmentation except that the
records within each segmentation were somewhat similar to each other.

In this case the segmentation is done for a particular reason – namely for the prediction of some
important piece of information. The records that fall within each segment fall there because they
have similarity with respect to the information being predicted – not just that they are similar –
without similarity being well defined. These predictive segments that are derived from the
decision tree also come with a description of the characteristics that define the predictive segment.
Thus the decision trees and the algorithms that create them may be complex, the results can be
presented in an easy to understand way that can be quite useful to the business user.

Applying Decision Trees to Business

Because of their tree structure and ability to easily generate rules decision trees are the favored
technique for building understandable models. Because of this clarity they also allow for more
complex profit and ROI models to be added easily in on top of the predictive model. For instance
once a customer population is found with high predicted likelihood to attract a variety of cost
models can be used to see if an expensive marketing intervention should be used because the
customers are highly valuable or a less expensive intervention should be used because the
revenue from this sub-population of customers is marginal.
Because of their high level of automation and the ease of translating decision tree models into
SQL for deployment in relational databases the technology has also proven to be easy to integrate
with existing IT processes, requiring little preprocessing and cleansing of the data, or extraction
of a special purpose file specifically for data mining.

Use of Decision Trees

Decision trees are data mining technology that has been around in a form very similar to the
technology of today for almost twenty years now and early versions of the algorithms date back
in the 1960s. Often times these techniques were originally developed for statisticians to automate
the process of determining which fields in their database were actually useful or correlated with
the particular problem that they were trying to understand. Partially because of this history,
decision tree algorithms tend to automate the entire process of hypothesis generation and then
validation much more completely and in a much more integrated way than any other data
mining techniques. They are also particularly adept at handling raw data with little or no
pre-processing. Perhaps also because they were originally developed to mimic the way an
analyst interactively performs data mining they provide a simple to understand predictive
model based on rules (such as “90% of the time credit card customers of less than three months
who max out their credit limit are going to default on their credit card loan.”).
Because decision trees score so highly on so many of the critical features of data mining they can
be used in a wide variety of business problems for both exploration and for prediction. They

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