Create a Recommendation Strategy

Follow these steps to create a recommendation strategy.

Follow the steps in Create a Recommendation Strategy with a Recommendations Dataset if you want to use a Recommendations dataset instead of a recommendation algorithm in a recommendation strategy.

Follow the steps in Create a Recommendation Strategy for Market-Level Recommendations if you want to use data from markets as part of a recommendation strategy.

Follow the steps in Create a Recommendation Strategy with Offline Purchases Data if you want to use data from an Offline Purchases dataset as part of a recommendation strategy.

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

    Callout of the Recommendations Strategies tab

  2. Click CREATE RECOMMENDATION STRATEGY.

    Callout of the CREATE RECOMMENDATION STRATEGY button

  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

  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

  6. Select an option from Recommendation Algorithm to determine which algorithm the strategy uses to populate the recommendations. See Recommendation Algorithms for more information.

    Callout of the Recommendation Algorithm selector

  7. If you selected a collaborative recommendation algorithm 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 custom variable option allows you to base recommendations on item_group_id values passed at run time in custom variables.

    The Item group ID(s) in run-time parameter (for email) option is part of the Product Recommendations for Email feature. If you select this option, 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' selector

  8. If you selected a collaborative recommendation algorithm 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

  9. If you selected Item group ID(s) in custom variable in step 7, then type into Custom Variable a custom variable that your site passes to Monetate using either the setCustomVariables method call in the Monetate JavaScript 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

  10. Select an option from Lookback Period if you selected a recommendation algorithm that requires you to set how much historical data the strategy considers when calculating results.

    Callout of the 'Lookback Period' selector on the recommendation strategy configuration page, with '2 days,' '7 days,' and '30 days' options

  11. If you selected an eligible recommendation algorithm in step 6, then 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

  12. 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

  13. To further refine the items included in the strategy, 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. For more information see Filters in Recommendations.

    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

  14. 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

  15. Click SAVE.

    Callout of the SAVE button

After you save the strategy, you can preview it from the configuration page in certain situations. See Preview a Recommendation Strategy for more information.

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