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Unformatted text preview: ur mailing list to whom you will send solicitations.
7. Incorporating data mining in your CRM solution
In building a CRM application, data mining is often only a small, albeit critical, part of the
final product. For example, predictive patterns through data mining may be combined with
the knowledge of domain experts and incorporated in a large application used by many
different kinds of people.
The way data mining is actually built into the application is determined by the nature of the
customer interaction. There are two main ways you interact with your customers: they contact
you (inbound) or you contact them (outbound). The deployment requirements are quite
different. 8 Income > $60,000
No Yes Yes Job > 5 Years
Yes No Mustang Volvo Wagon Porsche
Outbound interactions are characterized by your company originating the contact such as in a
direct mail campaign. Thus you will be selecting the people to whom you mail by applying
the model to your customer database. Another type of outbound campaign is an advertising
campaign. In this case you would match the profiles of good prospects shown by your model
to the profile of the people your advertisement would reach.
For inbound transactions, such as a telephone order, an Internet order, or a customer service
call, the application must respond in real time. Therefore the data mining model is embedded
in the application and actively recommends an action.
In either case, one of the key issues you must deal with in applying a model to new data is the
transformations you used in building the model. Thus if the input data (whether from a
transaction or a database) contains age, income, and gender fields, but the model requires the
age-to-income ratio and gender has been changed into two binary variables, you must
transform your input data accordingly. The ease with which you can embed these
transformations becomes one of the most important productivity factors when you want to
rapidly deploy many models.
Customer relationship management is essential to compete effectively in today’s marketplace.
The more effectively you can use the information about your customers to meet their needs the
more profitable you will be. But operational CRM needs analytical CRM with predictive data
mining models at its core. The route to a successful business requires that you understand your
customers and their requirements, and data mining is the essential guide.
Appendix: Data mining technology
Decision trees are a way of representing a series of rules that lead to a class or value. For
example, you may wish to offer a prospective customer a particular product. The figure shows a
simple decision tree that solves this problem while illustrating all the basic components of a
decision tree: the decision node, branches and leaves. A simple classification tree.
The first component is the top decision node, or root node, which specifies a test to be carried out. 9 Each branch will lead either to another decision node or to the bottom of the tree, called a...
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This note was uploaded on 11/25/2010 for the course CENG ceng taught by Professor Ceng during the Spring '10 term at Universidad Europea de Madrid.
- Spring '10