Create a Recommendation Strategy for Market-Level Recommendations

Follow these steps to create a recommendation strategy with a recommendation type that draws on data from a market.

Ensure that the account has at least one market. See Create a Market for the steps.

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

    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 about how global and local settings work within strategies, 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.

    If you select a product catalog that is not the default one and you select Specific Market or All [retailer] accounts in step 7, then you cannot duplicate into any other account a recommendations experience that uses the recommendation strategy you're creating.

    Callout of the Product Catalog selector

  6. Select one of the following options from Recommendation Algorithm:
    • Top Selling by Purchase Count — Populates with products from the catalog with the highest purchase quantity; eligible for geographic targeting
    • Top Selling by Gross Revenue — Populates with products from the catalog with the highest gross revenue; eligible for geographic targeting
    • Most Viewed (Product Detail Page) — Populates with products from the catalog with the most product detail page views
    • Purchased and Also Purchased — Populates with products from the recommendation strategy's selected product catalog that other customers most frequently purchased along with the product(s) that meet the criteria that you select from Base Recommendation on when configuring the recommendation strategy
    • Viewed and Also Viewed — Populates with products from the recommendation strategy's selected product catalog most frequently viewed after viewing the product(s) that meet the criteria that you select from Base Recommendation on when configuring the recommendation strategy
    • Trending Items by Purchase Count — Populates with products that sold the most in the last 7 days compared to the 30 days prior; premium option

    You must contact your dedicated Customer Success Manager (CSM) to request that the Purchased and Also Purchased and Viewed and Also Viewed algorithms be made available for use with market-level data in a recommendation strategy.

    Callout of the Recommendation Algorithm selector

  7. Select Specific Market from Data comes from.

    The [current account] only option limits the strategy's algorithm to considering only data from the account in which you're building the strategy. The All [retailer] accounts option allows the algorithm to consider data from all accounts within your Monetate implementation.

    Callout of the Specific Market option in the 'Data comes from' selector

  8. Choose an option from Select a Market.

    Callout of the 'Select a Market' selector

  9. If you selected Purchased and Also Purchased or Viewed and Also Viewed 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 Items selector

  10. If you selected Item group ID(s) in custom variable in step 9, 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.

      Callout of the 'Pin products in custom variable to front of recommendation results' setting

    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.

      Callout of the 'Custom Variable' field

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

    Callout of the 'Pin products in run-time parameter to front of recommendation results' setting that appears when the user selects 'Item group ID(s) in run-time parameter (for email)' option from the 'Base Recommendation on Items' selector

  12. Select an option from Lookback Period if you selected a recommendation type that requires a time frame from which to collect historical data.

    Callout of the Lookback Period selector

  13. If you selected Top Selling by Purchase Count, Top Selling by Gross Revenue, or Most Viewed (Product Detail Page) in step 6, then select an option from Geographic Targeting if you want the strategy to 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 by MaxMind's GeoIP2 database, are recommended

    Callout of the Geographic Targeting selector

  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

  15. Optionally, add one or more recommendation filters to further refine the products included in the recommendations. For more information about filtering options and logic, see Filters in Recommendations.
    1. Click ADD FILTER.
    2. Select an option from SELECT ATTRIBUTE.
    3. Complete the filter equation.
    4. Repeat this step as necessary to add as many recommendation filters as you believe the strategy needs.

    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

  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.

    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.

      Animated demonstration of a user clicking the ADD ATTRIBUTE button for the Boost and Bury feature, clicking the SELECT ATTRIBUTE selector, typing 'price' into the selector's search field, and then selecting the 'price' attribute

    2. Complete the filtering equation.

      Callout of the attribute-based filtering equation

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

      Callout of the selector to boost or bury recommended products that meet the filtering criterion

    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.

      Callout of the percentage slider

    5. Repeat steps 16a through 16d 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 of this type of filter can impact the recommendations.

      Example of three Boost and Bury filters created for a recommendation strategy

  17. 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.