Create a Recommendation Strategy with a Recommendations Dataset

Follow these steps to create a recommendation strategy that uses a Recommendations dataset.

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

   

2. Click CREATE RECOMMENDATION STRATEGY.

   

3. Select the option on the Recommendation Strategy 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.

   

4. Name the strategy. Click the placeholder title, type the name into the text field, and then click the green checkmark.
5. If you're creating a local strategy and if the account has multiple product catalogs, then select one from Product Catalog.

   

6. Select Onboarded Recommendation Dataset from Recommendation Algorithm.

   

7. Select the dataset you want to use from Recommendation Dataset.

   

8. Select from Base Recommendation on Items the type of customer behavior or other context for the recommendations.

   If you're creating the recommendation strategy to use in a Product Recommendations for Email experience, then you can select only No Lookup Key — Show All Items in Dataset or Item group ID(s) in run-time parameter (for email).

   If you select Item group ID(s) in run-time parameter (for email), you can use up to five item group ID values in the Product Recommendations for Email experience. See Preparing the Generated HTML in Run-Time Context for Recommendations Email Experiences.

   

9. If you selected Item group ID(s) in custom variable in the previous step, then take the following actions.

   The option to base recommendations on item_group_id values passed at run time in custom variables is part of the Premium Product Recommendations package. Contact your dedicated Customer Success Manager (CSM) for more information.

   1. Optionally, select Pin products in custom variable to front of recommendation results if you want the products corresponding to the item_group_idvalue(s) derived from the custom variable to appear at the beginning of the recommendation results.

      If you select this option, be aware that pinned products configured in the recommendation strategy appear after any pinned products configured in a recommendations action that uses the recommendation strategy.

      

   2. 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 the 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.

      

10. If you selected Item group ID(s) in run-time parameter (for email) in step 8, then optionally select Pin products in run-time parameter to front of recommendation results.

    If you select this option, be aware that pinned products configured in the recommendation strategy appear after any pinned products configured in a recommendations action that uses the recommendation strategy.

    

11. If you selected Viewed, Carted, or Purchased in step 8, then select the session scope option from From on which you want to target the customer behavior. The options available in From are determined by the recommendation basis you selected in the previous step.

    The Last Carted Item(s) option only considers the very last product put into the cart. It does not consider multiple products.

    The page referenced by the Item(s) on the Page and First Item on the Page options is the page on which the recommendation action exists. All other options reference the behavior of the customer currently exposed to the recommendation action.

    

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

    

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 Recommendation Strategies.

    

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.

    Contact your dedicated Customer Success Manager (CSM) if you want the Boost and Bury feature enabled.

    1. Click ADD ATTRIBUTE and then select an option from SELECT ATTRIBUTE.

       

    2. Complete the filtering equation.

       

    3. Select Boost to promote the products that meet the filtering criteria, or select Bury to suppress them.

       

    4. Adjust the slider to determine by what percentage the products that meet the filtering criteria are boosted or buried.

       You can only set the percentage using the slider and cannot type a number into the text field to the left it. Furthermore, you can only adjust the percentage in increments of 10.

       

    5. Repeat steps 15a through 15d to add up to four more independent Boost and Bury filters. See Using Multiple Boost and Bury Filters in Create a Recommendation Strategy to better understand how having more than one Boost and Bury filter can impact the recommendations.

       

15. Click SAVE.

    

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