Follow these steps to create a recommendation strategy that uses a Recommendations dataset.
- Click COMPONENTS in the top navigation bar, select Product Recommendations, and then click the Recommendation Strategies tab.
- Click CREATE RECOMMENDATION STRATEGY.
- 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.
- Name the strategy. Click the placeholder title, type the name into the text field, and then click the green checkmark.
- If you're creating a local strategy and if the account has multiple product catalogs, then select one from Product Catalog.
- Select Onboarded Recommendation Dataset from Recommendation Algorithm.
- Select the dataset you want to use from Recommendation Dataset.
- Select from Base Recommendation on Items the type of customer behavior or other context for the recommendations.
- If you selected Item group ID(s) in custom variable in the previous step, then take the following actions.
- 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. - 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 themonetate:context:CustomVariables
in the Engine API implementation.
- Optionally, select Pin products in custom variable to front of recommendation results if you want the products corresponding to the
- 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 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.
- Optionally, toggle Randomize Results to YES if you want the order in which recommended products appear in the slider to be less systematized.
- 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.
- 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.
- Click ADD ATTRIBUTE and then select an option from SELECT ATTRIBUTE.
- Complete the filtering equation.
- Select Boost to promote the products that meet the filtering criteria, or select Bury to suppress them.
- Adjust the slider to determine by what percentage the products that meet the filtering criteria are boosted or buried.
- 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.
- Click ADD ATTRIBUTE and then select an option from SELECT ATTRIBUTE.
- 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.