Propensity predictions let you evaluate how likely customers are to buy products with specific features, such as brand, category, color, and much more. You can create a propensity prediction from scratch or by using one of the predefined scenarios: - Find customers who will buy a specific item, brand, or category; - Find customers who will buy any item; - Find customers who will buy through a specific communication channel; - Find customers who will buy offline Predefined scenarios are configured using a user-friendly wizard that allows you to adjust scenario settings according to your preferences. This is done through a step-by-step configuration form, which provides hints on the interface to guide you. The option of creating a prediction based on a predefined scenario is available in the initial part of creating the prediction ([Select the model type](#select-the-model-type)). ## Prerequisites - [Configure AI Engine for Propensity and Best Fit](/docs/settings/configuration/ai-engine-configuration/engine-configuration-for-propensity). It usually takes several hours to initialize the model and perform necessary calculations. - The attributes that you want to use for propensity predictions must be configured as [filterable attributes](/docs/settings/configuration/ai-engine-configuration/engine-configuration-for-propensity#selecting-filters). ## Configure a prediction ### Select the model type 1. Go to Image presents the AI Hub icon **AI Hub > (AI Predictions) Models**. 2. Click **New prediction**. 1. On the pop-up select one of the following options: - **Use predefined prediction** - This option allows you to use the ready-made prediction scenarios and adjust its settings to your preferences in a user-friendly, step-by-step configuration form by following hints shown on the interface. If you select this option, you can skip reading the rest of the article. - **Create from scratch** - This option allows you to create your own configuration of a propensity prediction to achieve your custom business objective. If you use this option, select **Propensity**. ### Select customers to be analyzed Select the audience for whom you want to prepare a prediction.
Selecting an audience to be analyzed
Selecting a group of customers
1. In the **Audience** section, click **Define**. 2. Click **Choose segmentation** and perform one of the following actions: - From the list, select an existing segmentation. You can use the search field. - To define a new group segmentation, at the bottom of the dropdown list, click the **Create new** button. 1. In the **Segmentation name** field, enter the name of the group of customers and click **Next step**. 2. Build the segmentation. See [this article](/docs/analytics/segmentations/creating-segmentations) for instructions. 3. Click **Create segmentation**. 3. Confirm by clicking **Apply**. ### Select items 1. In the **Item feed** section, click **Define**. 2. Click **Choose item feed**. 3. From the list of available catalogs, select the item feed you want to analyze. This can be the same catalog as the one you use for recommendations. **Result**: The **Items filter** sub-section appears. 1. Click **Define item filter**. 2. Define the filters that describe the item or items that you want to calculate the prediction for. - The filters are created using the same [visual builder](/docs/ai-hub/recommendations-v2/recommendation-filters#visual-builder) as recommendations filters, but some operators are not available for Predictions. - You can use [the `created` attribute](/docs/ai-hub/recommendations-v2/recommendation-filters#the-created-attribute) which lets you filter the items based on the date of adding an item to the feed. - Only attributes [selected as filterable when configuring the AI engine](/docs/settings/configuration/ai-engine-configuration/engine-configuration-for-propensity#selecting-filters) are available for use.
The Item selection view after configuring the filters
The Item selection view after configuring the filters
3. Click **Apply**. ### Additional settings By default, the calculation is performed once and the results are presented on a 5-point scale. 1. If you want to leave the settings as default, the predictions is ready to calculate. Continue to [saving the prediction](#save-the-prediction). 2. If you want to change the settings, in the **Settings** section, click **Change**. 3. To schedule a recurring calculation: 1. Select the **Set up recurring prediction calculation** checkbox. 2. In the input field, enter the number of days between calculations. 4. To change the scale from 5-point to 2-point, select the **2-point scale** radio button. 5. Click **Apply**. ### Save the prediction To save the prediction: - as a draft, click **Save**. - and calculate, click **Save & Calculate**.
You can check the logs of the activated prediction. [Learn more](/docs/ai-hub/predictions/predictions-functionalities#previewing-logs).
## Results The maximum calculation time is 24 hours. ### Understanding propensity prediction results | Attribute name | Description | |-------------|-------------------------------------------------------------------------------------------------| | action | An event type for the prediction - `snr.propensity.score` | | added by | An entity that generated this event. In the case of prediction events, it is always Synerise. | | modelId | A unique identifier of the prediction | | modelName | The name of the prediction | | score_label | Prediction output: two- or five-point scale label. You can select the scale in the [settings of the prediction](#additional-settings). | | score | The result of the prediction | | percentile | [Prediction percentile](/docs/glossary#percentiles) | | clientId | Customer's identifier | | time | Time when a prediction was generated, as a Unix timestamp | | configurationVersion | Allows to verify whether two consecutive calculations ran with the settings (*only for debug*) | | modelVersion | Model version allows to verify whether two consecutive calculations ran on the same model parameters (*only for debug*) |