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By Rud O.P.

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This makes it very useful in marketing because we are often trying to predict a discrete action such as a response to an offer or a default on a loan. Technically, logistic regression can be used to predict outcomes for two or more levels. When building targeting models for marketing, however, the outcome usually has a two-level outcome. In order to use regression, the dependent variable is transformed into a continuous value that is a function of the probability of the event occurring. My goal in this section is to avoid heavy statistical jargon.

Targeting models generally fall into two categories, predictive and descriptive. Predictive models calculate some value that represents future activity. 1 Steps for successful target modeling. probability of likelihood for an action, such as response to an offer or default on a loan. A descriptive model is just as it sounds: It creates rules that are used to group subjects into descriptive categories. Companies that engage in database marketing have multiple opportunities to embrace the use of predictive and descriptive models.

Transaction Database The transaction database contains records of customer activity. It is often the richest and most predictive information, but it can be the most difficult to utilize. In most cases, each record represents a single transaction, so there may be multiple records for each customer. The transaction database can take on various forms depending on the type of business. In order to use this data for modeling, it must be summarized and aggregated to a customer level. Number of records per customer can differ.

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