Predict propensity to buy an item
You can use the Predictions module to calculate the probability of customers buying a particular item. The results can be used for better targeting of your marketing efforts.
- Enable the Propensity prediction type.
itemIDattribute (the unique identifier attribute of an item) must be added to filterable attributes.
Creating the prediction
- Go to > New prediction and select Propensity as the prediction type.
- Select an audience for the prediction.
For more information, see the Predictions quick start article.
Define the item
In this section, you define the item for which you want to calculate the prediction. This is done by creating a filter that matches the item by its unique identifier in the catalog.
- In the Item feed section, click Define.
- Click Choose item feed.
- Select the catalog that contains the items you want to make the prediction for.
Result: the Item filter section appears.
- Click Define item filter.
- From the Select attribute drop-down list, select the
itemIdattribute. You can use the search field.
- From the drop-down list that appears, select the Equal operator.
- From the list of available values that appears, select an identifier.
You can use the search field.
- Click Save.
- Save the item feed configuration by clicking Apply.
Additional settings and saving
Configure the additional settings (or leave them at default) and click Save & Calculate.
The calculation takes about 1 hour per 1 million customers in the audience. After it completes, a
snr.propensity.score event is saved in the profiles of each customer in the audience. The event data includes detailed results of the prediction.
Based on the
snr.propensity.score event, you can create segmentations of customers with different propensity and use those segmentations as campaign targets:
Email, SMS, web push and mobile push can be sent manually or you can launch them by using the Automation module.
Check the use case set up on the Synerise Demo workspace
You can check the configuration of the Propensity prediction directly in Synerise Demo workspace.
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