Propensity to purchase
Propensity to Purchase uses results from a test mailing or previous campaign to generate scores. The scores indicate which contacts are most likely to respond. The Response field indicates who replied to the test mailing or previous campaign. The Propensity fields are the characteristics that you want to use to predict the probability that contacts with similar characteristics will respond.
This technique uses binary logistic regression to build a predictive model. The process of building and applying a predictive model has two basic steps:
- 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.
- Apply that model to a different dataset (for which the outcome of interest is not known) to obtain predicted outcomes.
Example. The direct marketing division of a company uses results from a test mailing to assign propensity scores to the rest of their contact database, using various demographic characteristics to identify contacts most likely to respond and make a purchase.
This procedure automatically creates a new field in the dataset that contain propensity scores for the test data and an XML model file that can be used to score other datasets. Optional diagnostic output includes an overall model quality chart and a classification table that compares predicted responses to actual responses.
Propensity to Purchase data considerations
Response Field. The response field can be string or numeric. If this field contains a value that indicates number or monetary value of purchases, you will need to create a new field in which a single value represents all positive responses. See the topic Creating a categorical response field for more information.
Positive response value. The positive response value identifies customers who responded positively (for example, made a purchase). All other non-missing response values are assumed to indicate a negative response. If there are any defined value labels for the response field, those labels are displayed in the drop-down list. See the topic Value labels for more information.
Predict Propensity with. The fields used to predict propensity can be string or numeric, and they can be nominal, ordinal, or continuous (scale) -- but it is important to assign the proper measurement level to all predictor fields.
Measurement level. Correct measurement level assignment is important because it affects the computation of the results.
- Nominal. A variable can be treated as nominal when its values represent categories with no intrinsic ranking (for example, the department of the company in which an employee works). Examples of nominal variables include region, postal code, and religious affiliation.
- Ordinal. A variable can be treated as ordinal when its values represent categories with some intrinsic ranking (for example, levels of service satisfaction from highly dissatisfied to highly satisfied). Examples of ordinal variables include attitude scores representing degree of satisfaction or confidence and preference rating scores.
- Continuous. A variable can be treated as scale (continuous) when its values represent ordered categories with a meaningful metric, so that distance comparisons between values are appropriate. Examples of scale variables include age in years and income in thousands of dollars.
An icon next to each field indicates the current measurement level.
You can change the measurement level in Variable View of the Data Editor (for more information, see Specifying Measurement Level) or you can use the Define Variable Properties dialog to suggest an appropriate measurement level for each field (for more information, see, Assigning the Measurement Level).
Fields with unknown measurement level
The Measurement Level alert is displayed when the measurement level for one or more variables (fields) in the dataset is unknown. Since measurement level affects the computation of results for this procedure, all variables must have a defined measurement level.
Scan Data. Reads the data in the active dataset and assigns default measurement level to any fields with a currently unknown measurement level. If the dataset is large, that may take some time.
Assign Manually. Opens a dialog that lists all fields with an unknown measurement level. You can use this dialog to assign measurement level to those fields. You can also assign measurement level in Variable View of the Data Editor.
Since measurement level is important for this procedure, you cannot access the dialog to run this procedure until all fields have a defined measurement level.
To obtain propensity to purchase scores
This feature is available in the Direct Marketing option.
From the menus choose:
- Select Select contacts most likely to purchase.
- Select the field that identifies which contacts responded to the offer.
- Enter the value that indicates a positive response. If any values have defined value labels, you can select the value label from the drop-down list, and the corresponding value will be displayed. See the topic Value labels for more information.
- Select the fields you want to use to predict propensity.
To save a model XML file to score other data files:
- Select (check) Export model information to XML file.
- Enter a directory path and file name or click Browse to navigate to the location where you want to save the model XML file.
- Click Run to run the procedure.
To use the model file to score other datasets:
- Open the dataset that you want to score.
- Use the Scoring Wizard to apply the model to the dataset. From
the menus choose: