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accept. In regression problems you’re predicting a number such as the probability that a person 1 will respond to an offer.
In CRM, data mining is frequently used to assign a score to a particular customer or prospect
indicating the likelihood that the individual will behave in the way you want. For example, a
score could measure the propensity to respond to a particular offer or to switch to a competitor’s
product. It is also frequently used to identify a set of characteristics (called a profile) that
segments customers into groups with similar behaviors, such as buying a particular product.
A special type of classification can recommend items based on similar interests held by groups of
customers. This is sometimes called collaborative filtering.
The data mining technology used for solving classification, regression and collaborative filtering
problems is briefly described in the appendix at the end of the paper.
Customer relationship management in its broadest sense simply means managing all customer
interactions. In practice, this requires using information about your customers and prospects to
more effectively interact with your customers in all stages of your relationship with them. We
refer to these stages as the customer life cycle.
The customer life cycle has three stages:
• Acquiring customers
Increasing the value of the customer
Retaining good customers Data mining can improve your profitability in each of these stages through integration with
operational CRM systems or as independent applications.
Acquiring new customers via data mining
The first step in CRM is to identify prospects and convert them to customers. Let’s look at how
data mining can help manage the costs and improve the effectiveness of a customer acquisition
Big Bank and Credit Card Company (BB&CC) annually conducts 25 direct mail campaigns each
of which offers one million people the opportunity for a credit card. The conversion rate measures
the proportion of people who become credit card customers, which for BB&CC is about 1% per
Getting people to fill out an application for the credit card is only the first step. Then BB&CC
must decide whether the applicant is a good risk and accept them as a customer. Not surprisingly,
poor credit risks are more likely to accept the offer than are good credit risks. So while 6% of the
people on the mailing list respond with an application, only about 16% of those are suitable credit
risks, for a net of about 1% of the mailing list becoming customers.
BB&CC’s experience of a 6% response rate means that within the million names are 60,000
people who will respond to the solicitation. Unless BB&CC changes the nature of the solicitation
– using different mailing lists, reaching customers in different ways, altering the terms of the 2 offer – they are not going to get more than 60,000 responses. And of those 60,000 responses, only
10,000 will be good enough risks to become customers. The ch...
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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