Create a Recommendation Strategy

Follow these steps to create a recommendation strategy.

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

   

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.

   

8. If you selected Item group ID(s) in custom variable in step 7, then take the following actions.
   1. Optionally, select Pin products in custom variable to front of recommendation results if you want the products corresponding to the item_group_id value(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.

      

9. If you selected Item group ID(s) in run-time parameter (for email) in step 7, 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.

   

10. Select an option from Lookback Period if you selected a recommendation algorithm that requires a time frame from which to collect data for determining recommended products.

    

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

    

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 Recommendations.

    

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 14a through 14d to add up to four more independent Boost and Bury filters. See Using Multiple Boost and Bury Filters 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.

Using Multiple Boost and Bury Filters

While Boost and Bury filters look much like recommendation filters, they have some key differences:

• You can only add a total of five Boost and Bury filters to a recommendation strategy.
• You don't join multiple Boost and Bury filters with the AND or the OR logical operator.
• The order in which you add multiple Boost and Bury filters to a recommendation strategy doesn't impact how much a qualifying product is ultimately boosted or buried because each filter is independent and not connected in a logic sequence with the filter that might be listed before or after it.

To better understand how Boost and Bury filters impact a product's final relevancy score for a recommendations action, consider the following example.

A recommendation strategy uses the Top Selling by Purchase Count recommendation algorithm that considers 30 days of historical data and doesn't include geographic targeting. It has a recommendation filter based on the age_group attribute to exclude products for infants and children. At this stage of processing, five products identified by the recommendation algorithm and recommendation filter have these relevancy scores:

• Product A: 50%
• Product B: 30%
• Product C: 20%
• Product D: 10%
• Product E: 5%

The recommendation strategy's three Boost and Bury filters are then applied:

• Quantity < less than "1000" BURY –90%
• Product type = equals (Starts With) "Jackets" BOOST 50%
• Price ≥ greater than or equal to "59.95" BOOST 80%

Of the identified five products, only products B and C meet at least one of the Boost and Bury criteria:

• Product B
  • Product type = "Jackets"
  • Price = "$49.95"
  • Inventory = "100"
• Product C
  • Product type = "Jackets"
  • Price = "$79.95"
  • Inventory = "2000"

Two of the three Boost and Bury filters apply to product B, and the impact of those applicable filters to product B's relevancy score is calculated as follows:

30% × (1 – 90%) × (1 + 50%) = 4%

Like product B, only two of the Boost and Bury filters apply to product C, but both filters are boosting measures. The impact of those filters to product C's relevancy score is calculated as follows:

20% × (1 + 50%) × (1 + 80%) = 54%

Because of the impact of the Boost and Bury filters on just these two products, the final relevancy scores for the five products are as follows:

• Product C: 54%
• Product A: 50%
• Product D: 10%
• Product E: 5%
• Product B: 4%

The impact of the Boost and Bury filters on the final recommendations relevancy scores of products B and C wouldn't change if those filters were listed in a different order within the recommendation strategy because of how the calculations are made.