Create a Recommendation Strategy with Offline Purchases Data

The Offline Purchases Data feature is part of the Monetate Personalization Enhanced product bundle and the Monetate Personalization Suite. Contact your dedicated Customer Success Manager for more information.

Follow these steps to create a recommendation strategy that uses data from the Offline Purchases dataset set as the default on the Global Settings tab of the Product Recommendations page.

If an Offline Purchases dataset is not set as the default on the Global Settings tab, then any recommendation strategy configured with offline purchases data will not work as expected. See Global Settings for more information.

  1. Click COMPONENTS in the top navigation bar, select Product Recommendations, and then click the Recommendation Strategies tab.

    Callout of the Recommendations Strategies tab on the Product Recommendations page

  2. Click CREATE RECOMMENDATION STRATEGY.

    Callout of the CREATE RECOMMENDATION STRATEGY button on the Recommendation Strategies list page

  3. Select the option on the Recommendation Permission modal to make the strategy either global or local, and then click CONTINUE. For more information see Global and Local Recommendation Strategies.

    You cannot change the strategy permission after you click CONTINUE.

    The Recommendation Permission modal of the recommendation strategy configuration page

  4. Name the strategy. Click the placeholder title, type the name into the text field, and then click the green checkmark.

    This field can contain a maximum of 64 characters.

  5. If you're creating a local strategy and if the account has multiple product catalogs, then select one from Product Catalog.

    Callout of the Product Catalog selector of the recommendation strategy configuration page

  6. Select from Recommendation Algorithm one of the algorithms that's eligible for use with offline purchases data:
    • Top Selling by Purchase Count
    • Top Selling by Gross Revenue
    • Purchased and Also Purchased
    • Trending Items by Purchase Count

    Callout of the Recommendation Algorithm selector on the recommendation strategy configuration page, with a callout of the algorithm options eligible for use with Offline Purchases data

  7. Select either Offline purchases only or Online and offline purchases from Including.

    Callout of the Including selector and of the 'Offline purchases only' option and the 'Online and offline purchases' option of the recommendation strategy configuration page

  8. If you selected Purchased and Also Purchased in step 6, then select from Base Recommendation on the type of customer behavior or other context on which to base the recommendations.

    The Item group ID(s) in run-time parameter (for email) option is part of the Product Recommendations for Email feature. If you select it, you can use up to five item_group_id values passed in a run-time parameter for a Product Recommendations for Email experience. See Preparing the Generated HTML in Run-Time Context for Recommendations Email Experiences.

    Callout of the 'Base Recommendation on Items' selector on the recommendation strategy configuration page

  9. If you selected Item(s) purchased in previous sessions or Last item purchased in any session in step 8 and if Offline Purchases Data is set as the default on the Global Settings tab, then select an option from Data Includes:
    • Online purchases only — Offline purchases aren't included in the customer purchase history
    • Online and offline purchases — Offline purchases along with online purchases are included in the customer purchase history

    Callout of the Data Includes selector on the recommendation strategy configuration page

  10. If you selected Purchased and Also Purchased in step 6, then optionally toggle Prepend context item in recommendation to YES if you want the product on which the recommendation results are based to appear at the beginning of the recommendation results.

    If you enable this option, be aware that the context product appears after any pinned products configured in a recommendations action that uses the recommendation strategy.

    Callout of the 'Prepend context item in recommendation' setting on the recommendation strategy configuration page

  11. If you selected Item group ID(s) in custom variable in step 8, then type into Custom Variable a custom variable that your site passes to Monetate using either the setCustomVariables method call in the Monetate API implementation or monetate:context:CustomVariables in the Engine API implementation.

    The custom variable value can contain a comma-separated list of up to five item_group_id values.

    Callout of the 'Custom Variable' field on the recommendation strategy configuration page

  12. If you selected any recommendation algorithm except for Trending Items by Purchase Count in step 6, then select an option from Lookback Period to set how much historical data the strategy considers when calculating results.

    Callout of the Lookback Period selector on the recommendation strategy configuration page

  13. If you selected Top Selling by Purchase Count or Top Selling by Gross Revenue in step 6, then optionally select an option from Geographic Targeting if you want the strategy to also consider the customer's location to populate the recommendations:
    • Country targeting — Only products relevant to the customer's country are recommended
    • Region targeting — Only products relevant to the customer's region, as defined in MaxMind's GeoIP2 database, are recommended

    Callout of the Geographic Targeting selector on the recommendation strategy configuration page

  14. Optionally, toggle Randomize Results to YES if you want the order in which recommended products appear in the slider to be less systematized.

    Callout of the Randomize Results toggle on the recommendation strategy configuration page

  15. To further refine the recommendations results, click ADD FILTER, select an option from SELECT ATTRIBUTE, and then complete the filter equation. Repeat this step as necessary to add as many recommendation filters as you believe the strategy needs. See Excluding Previously Purchased Products from Results in this documentation if you want to ensure results don't include items the customer has already bought.

    Callout of the ADD FILTER button on the recommendation strategy configuration page

  16. Optionally, configure up to five Boost and Bury filters to influence if recommended products that meet that filtering criteria are more likely (boost) or less likely (bury) to appear for the customer. See Boost and Bury for more information.

    Animated demonstration of a user moving the slider to adjust the boost percentage

  17. Click SAVE.

    Callout of the SAVE button on the recommendation strategy configuration page

Excluding Previously Purchased Products from Results

Follow these steps to create a recommendation filter that specifically excludes previous purchases from the recommendations results.

  1. Click ADD FILTER, and then select either Item Group ID (Product ID) or ID (SKU) from SELECT ATTRIBUTE.

    Animated demonstration of a user clicking the ADD FILTER button, typing 'product' into the search field of the SELECT ATTRIBUTE selector, and then selecting the 'Item Group ID (Product ID)' option

  2. Select ≠ does not equal for the operator.

    Callout of the 'does not equal' operator option

  3. Click USE DYNAMIC VALUE.

    Callout of the 'USE DYNAMIC VALUE' button

  4. Select [attribute] of a Previously Purchased Product.

    Callout of the 'Item Group ID of a Previously Purchased Product' option in the dynamic value selector

This filter configuration excludes from the recommendations results both products purchased online and products purchased in store that appear in the designated Offline Purchases dataset.

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