---
schema_version: "1"
id: a462f9b6f0c54a8d1539872daf7dcd0a562fd209374fd1dd911f42a3a5f05b82
title: Configure a Propensity to Buy Template (Beta)
collection: admin
version: current
locale: en-us
portal_url: https://docs.salesforce.com/admin/current/en-us/data/264-0-0/c360_a_ai_predictive_model_configure_propensity_to_buy.md
canonical_url: https://help.salesforce.com/s/articleView?id=data.c360_a_ai_predictive_model_configure_propensity_to_buy.htm&release=264.0.0&type=5
source_site: help.salesforce.com
content_type: documentation
content_hash: 2c193f3731a2b8727200ebf36d8eef4fcdccf3c04bba8befc6005bb7cff3ca84
description: Configure the Propensity to Buy (beta) template to automatically create the data transforms, output data model objects, and predictive model required to generate propensity scores. Query the scores to assess a customer's likelihood of making a purchase within your specified prediction window.
products:
  - Data 360
release: "264"
---
# Configure a Propensity to Buy Template (Beta)

Configure the Propensity to Buy (beta) template to automatically create the data transforms, output data model objects, and predictive model required to generate propensity scores. Query the scores to assess a customer's likelihood of making a purchase within your specified prediction window.

### Required Editions

<table class="slds-table slds-table_bordered slds-max-medium-table_stacked slds-m-bottom_small edition"><colgroup><col></colgroup><tbody><tr class=""><td class="slds-cell-wrap" style="vertical-align:top;">Available in: <strong>All Editions</strong> supported by Data 360. See <a href="https://help.salesforce.com/s/articleView?id=data.c360_a_dc_editions.htm&amp;release=264.0.0&amp;type=5" title="Data 360 is available in several supported Salesforce editions. Some Data 360 features also require additional licenses.">Data 360 edition availability</a>.</td></tr></tbody></table>

![Note](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-data-data_cloud-264-0-0-production-enus/f3569962-3c9b-4f83-9d0c-b6a574e785da/images/icon_note.png)

Note This template is unavailable in the Developer edition.

<table class="slds-table slds-table_bordered slds-max-medium-table_stacked slds-m-bottom_small permission">
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            <th scope="colgroup" class="slds-cell-wrap" colspan="2" style="text-align:center;vertical-align:top;" id="d119407e72">USER PERMISSIONS
              NEEDED</th>
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        </thead>
        <tbody>
          <tr class="">
            <td data-label="USER PERMISSIONS NEEDED" class="slds-cell-wrap" style="vertical-align:top;" headers="d119407e72 ">Allow users to manage models in AI Models</td>

            <td class="slds-cell-wrap" style="vertical-align:top;" headers="d119407e72 ">Enables you to create, update, and delete models in AI Models.</td>

          </tr>
        </tbody>
      </table> <table class="slds-table slds-table_bordered slds-max-medium-table_stacked slds-m-bottom_small permission">
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            <td data-label="PERMISSION SETS" class="slds-cell-wrap" style="vertical-align:top;" headers="d119407e103 ">Data Cloud Architect</td>

            <td class="slds-cell-wrap" style="vertical-align:top;" headers="d119407e103 ">Admin-level access to all AI Models features, including the ability to create,
              update, delete, and activate models.</td>

          </tr>
          <tr class="">
            <td data-label="PERMISSION SETS" class="slds-cell-wrap" style="vertical-align:top;" headers="d119407e103 ">Data Cloud User</td>

            <td class="slds-cell-wrap" style="vertical-align:top;" headers="d119407e103 ">Restricted access to use a model, including getting predictions and
              improvements derived from a model.</td>

          </tr>
        </tbody>
      </table>

## Set Up and Query the Template

1. Configure and run the template.
    1. Click the App Launcher, search for Data Cloud, and then select it.
    2. Click the **AI Models** tab.
    3. From the Predictive section, click **Create from Template**.
    4. Select Propensity to Buy, and then click **Next**.
    5. Specify a name and description for your template configuration.
        
        The system names all generated data transforms and model assets based on the configuration name that you provide.
    6. Select the target data space, the product that you want to evaluate, and the time horizon (prediction window). Optionally specify a product category.
    7. To run the template configuration, click **Train**.
2. Monitor the template installation.
    
    To monitor the template installation, from Setup, go to **App Hub** > **Scoring**, and **Templates**.
    
    https://<org-domain-url>/lightning/setup/AppHub/home (Org Setup → App Hub)
    
    https://<org-domain-url>/lightning/setup/ScoringFramework/home (Org Setup → Scoring)
    
    https://<org-domain-url>/lightning/setup/TemplateStudio/home (Org Setup → Templates)
    
    - The template installations create these seven assets: 3 transforms, 3 output DMOs, and 1 model. To verify that all assets are successfully created and executed, go to these Data 360 tabs: DataTransforms, Data Model Object, and AI Models.
    - Propensity Training Transform
    - Propensity Inference Transform
    - Propensity Scoring Transform
    - Propensity Scoring Model
    - PropensityTraining\_\_dlm(output DMO)
    - PropensityInference\_\_dlm (output DMO)
    - PropensityScoring\_\_dlm (output DMO)
    
    Asset names vary depending on the configuration name that you specify. For example, if you name your configuration "Shirt", the template automatically creates these assets.
    
    Shirt Scoring Transform Definition
    
    ShirtPropensityScoringData1\_\_dlm
    
    Shirt - Propensity Scoring Model
3. Query propensity scores.
    
    To validate the generated scores that predict purchase likelihood within your specified prediction window, run a query on the
    
    PropensityScoring\_\_dlm
    
    data lake object (DLO) in the Query Editor. For example, run this query on your data space to return the first 1,000 records.
    
    ```
    
          SELECT Id__c, ProductPropensityScore__c FROM PropensityScoringData__dlm 
           ORDER BY Id__c DESC LIMIT 1000
    ```
    
    Replace **PropensityScoringData\_\_dlm** with the name of your output DMO with predictions. For example, ShirtPropensityScoringData1\_\_dlm.
    
    To verify that your template assets were successfully created and executed based on your configuration name, first go to the Data Transforms tab and verify that these assets exist. Then go to the AI Models tab and verify that your model is active. Check that your template created the seven assets based on your configuration name.

## Considerations

**Data Requirements**

Use at least 6 months of purchase history and behavioral engagement data, such as browse and cart events, for the individuals in your data space. Model Builder requires at least 400 records to build a model. Limited transaction history or sparse engagement data can result in scores that don't reflect purchase intent.

Individuals with no purchase history or minimal engagement data receive low-confidence scores. Treat these scores as insufficient data rather than low purchase intent. Products need historical sales data to produce meaningful propensity scores.

**Score Interpretation**

The ProductPropensityScore\_\_c value represents the relative likelihood that an individual purchases the specified product or product category within the configured prediction window. Most individuals receive low scores because the base purchase rate for a single product is typically below 5%.

Segment scores by decile or percentile for activation. For example, target the top 10–20% of scored individuals instead of applying a fixed numeric threshold. Score distributions vary by product and data volume.

**Time Horizon and Freshness**

The prediction window affects the model's predictions. Shorter windows favor recent behavioral signals, such as active cart events, while longer windows weigh historical purchase patterns more heavily.

Scores are point-in-time predictions and become unreliable after the prediction window passes. To keep scores current, rerun the inference transform at a cadence equal to or shorter than the configured prediction window.

**Scheduling and Execution**

The template creates Training, Inference, and Scoring batch data transforms that run in sequence. The transforms aren't automatically scheduled after initial creation. Manually rerun or schedule the transforms and model retraining to keep scores current. Execution time increases with data volume.

**Scale**

The template generates scores for each individual-product or individual-product category combination. The output DMO (PropensityScoring\_\_dlm) can grow significantly when a data space contains many individuals and products. Plan your storage and query strategy accordingly.

Each template configuration targets one product or product category. To score multiple products, create a separate configuration for each product or select a product category during configuration.

**Credit Consumption**

The template consumes credits when it creates transforms for training and scoring data. Because this template is deterministic, it doesn't consume agentic credits.

**Other**

The template supports only the standard Data Cloud objects and fields listed in [Propensity to Buy Template (Beta)](https://help.salesforce.com/s/articleView?id=data.c360_a_ai_predictive_model_template_propensity_to_buy.htm&release=264.0.0&type=5 "The Propensity to Buy template establishes a starting point for predicting which customer will buy a product or product category within a particular time frame. The template preconfigures model details, prediction inputs, and selects an algorithm. To align the model with your data, customize default variables and set a prediction window—the number of days used to calculate the purchase likelihood. After model training, Model Builder generates purchase likelihood scores on your records so you can identify high-value customer segments and drive targeted marketing campaigns."). You can't add custom DMOs or custom fields to the model configuration. Scoring is batch only. Real-time, event-triggered scoring isn't supported.