Key terms

Deployed Model. A deployed model refers to a model that has been “trained” and then deployed to populate prediction values into your user's profiles in Intempt.

Model. In Intempt, a model represents the behavior you predict within a specific timeframe, such as a purchase, conversion, or any customer behavior tracked in your project. Models are created using an algorithm, and the results are used to explain patterns and predict future outcomes.

Model results. Refers to an interactive section of the product interface where you can view performance measurements of the model prediction quality.

Output attribute. The numeric User attribute (categorized as "Calculated attribute") stores the prediction value generated by a corresponding deployed model. The Output attribute is created by default when a new model is created.

Prediction timeframe. The timeframe, in days, weeks, or months, for which you want to predict when the action for your target attribute occurs. For example, a user’s “likelihood to return” in the next “x” days, weeks, or months.

Prediction score. The score generated by a deployed model. This score represents the likelihood that the goal event will be performed (low/medium/high) during the visitor’s next visit (assuming the next visit occurs within the prediction time window).

Training. Refers to the stage in which a model consumes and analyzes data for a predetermined period of time to be used for predictions. The size and quality of the data used during this stage is an important factor in the accuracy of results when you deploy the model.

Retraining. A model's prediction accuracy degrades over time. When you retrain a model with new data, the prediction accuracy increases and remains more accurate over a longer period. Intempt retrains the models automatically, without any manual input required.

Goal. Represents the action you want to predict with the model.