Attributes as inputs for predictive metrics

Attributes can be used as inputs for predictive metrics. Data mining often analyzes non-numeric, demographic, and psychographic information about customers, looking for attributes that are strong predictors.

For example, your MicroStrategy project contains a Customer attribute with related attributes for age, gender, and income. You can include an attribute, such as the Customer attribute, directly in a training metric.

By including an attribute directly in a training metric, a predictive metric is then created that includes the attribute as one of its inputs. When using attributes directly in training metrics to create predictive metrics, be aware of the following:

• The ID attribute form for the attribute is used by the training metric to include the attribute information in a predictive metric. If attributes include additional attribute forms other than the ID form that are to be used as inputs for predictive metrics, you can create metrics based on these attribute forms. Once these metrics are created, they can then be used as inputs for predictive metrics. This scenario for creating attribute-based predictive metrics is described in Creating metrics to use additional attribute forms as inputs for predictive metrics.

• Attribute forms must use a text or numeric data type. If the attribute form uses a date data type, the data cannot be correctly represented when creating the predictive metric. If an attribute form uses date values, you must convert the date values into a numeric format to use the attribute form to create predictive metrics.