Propensity to purchase

Propensity to Purchase uses results from a test mailing or previous campaign to generate propensity scores. The scores indicate which contacts are most likely to respond, based on various selected characteristics.

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This technique uses binary logistic regression to build a predictive model. The process of building and applying a predictive model has two basic steps:

1. Build the model and save the model file. You build the model using a dataset for which the outcome of interest (often referred to as the target) is known. For example, if you want to build a model that will predict who is likely to respond to a direct mail campaign, you need to start with a dataset that already contains information on who responded and who did not respond. For example, this might be the results of a test mailing to a small group of customers or information on responses to a similar campaign in the past.
2. Apply that model to a different dataset (for which the outcome of interest is not known) to obtain predicted outcomes.

This example uses two data files: dmdata2.sav is used to build the model, and then that model is applied to dmdata3.sav. See the topic Sample Files for more information.

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