Create a Recommendation Strategy with Offline Purchases Data

The Offline Purchases Data feature is part of Monetate's Premium Recommendations package. Contact your dedicated Customer Success Manager for more information.

Follow these steps to create a recommendation strategy that uses data from the Offline Purchases dataset set as the default on the Global Recommendation Settings tab.

If an Offline Purchases dataset is not set as the default on the Global Recommendation Settings tab, then any recommendation strategy configured with offline purchases data will not work as expected. See Global Recommendation Settings for more information.

  1. Click the Recommendations option from COMPONENTS in the top navigation bar, and then click Recommendation Strategies.

    Callout of the Recommendations Strategies tab



  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.

    Recommendation Strategy Permission modal

  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.

    Callout of the Product Catalog selector

  6. Select from Recommendation Algorithm one of the algorithms that's eligible for use with offline purchases data:
    • Purchased and Also Purchased
    • Top Selling by Purchase Count
    • Top Selling by Gross Revenue
    • Trending Items by Purchase Count (premium algorithm)

    Callout of the Recommendation Algorithm selector, with a callout of the algorithm options eligible for use with Offline Purchases data

  7. If the Markets feature is enabled for the account, then select an option from Data comes from:
    • This account only — The algorithm considers only data from the account in which you're building the strategy.
    • All accounts for this retailer — The algorithm considers data from all accounts within your implementation.
    • Specific Market — The algorithm considers data from multiple accounts that have been grouped together using the Markets feature. If you select this option, you must then select the specific market from Select a Market.

    Callout of the 'Data comes from' selector

  8. From Including select either Offline purchases only or Online and offline purchases.

    Callout of the Including selector and of the 'Offline purchases only' option and the 'Online and offline purchases' option

  9. If you selected Purchased and Also Purchased in step 6, then configure the customer behavior settings.
    1. Select the type of customer behavior on which to base the recommendations from Base Recommendation on Items.

      Callout of the 'Base Recommendation on Items' selector

    2. If you selected Viewed, Carted, or Purchased in the previous step, then select a customer behavior session scope option from From. If you selected Item group ID(s) in custom variable, then skip to the next step.
      • 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.

      Callout of the From selector

    3. If you selected Item group ID(s) in custom variable in step 9a, first type into Custom Variable a custom variable that your site passes to the Personalization platform using either the setCustomVariables method call in the Monetate API implementation or the monetate:context:CustomVariables in the Engine API implementation. Next, 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 after any pinned products configured in a recommendations action at the beginning of the recommendation results.

      The custom variable value can contain a comma-separated list of up to five item_group_id values.

      Callout of the 'Custom Variable' field and the 'Pin products in custom variable to front of recommendation results' option

    4. If you selected Purchased in step 9a and then selected either Past Item(s) Purchased or Last Item Purchased in step 9b, then select from Data Includes the source(s) for customer purchase history.

      If you selected All Item(s) Purchased in step 9b, you can only select Online purchases only from Data Includes.

      Callout of the Data Includes selector

  10. If you selected any recommendation algorithm except for Trending Items by Purchase Count in step 6, then select an option from Lookback Period to set how much historical data the strategy considers when calculating results.

    Callout of the Lookback Period selector

  11. If you selected Top Selling by Purchase Count or Top Selling by Gross Revenue in step 6, then optionally 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

  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

  13. To further refine the recommendations results, 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. See Excluding Previously Purchased Products from Results in this documentation if you want to ensure results don't include items the customer has already bought.

    Callout of the ADD FILTER button

  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 CSM if you want the Boost and Bury feature enabled.

    1. Click ADD ATTRIBUTE.

      Callout of the ADD ATTRIBUTE button for the Boost and Bury feature

    2. Select an option from SELECT ATTRIBUTE, and then 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 14a through 14d to add up to four more independent Boost and Bury filters. See Using Multiple Boost and Bury Filters in Create a Recommendation Strategy for more information about how multiple Boost and Bury filters can impact recommendations results.

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

  15. Click SAVE.

Excluding Previously Purchased Products from Results

Follow these steps to create a recommendation filter that specifically excludes previous purchases from the recommendations results.

  1. Click ADD FILTER.

    Callout of the ADD FILTER button

  2. Select either Item Group ID (Product ID) or ID (SKU) from SELECT ATTRIBUTE.

    Callout of the SELECT ATTRIBUTE selector and the 'ID (SKU)' option and the 'Item Group ID (Product ID)' option

  3. Select ≠ does not equal for the operator.

    Callout of the 'does not equal' operator option


    Callout of the 'USE DYNAMIC VALUE' button

  5. Select [attribute] of a Previously Purchased Product.

    Callout of the 'Item Group ID of a Previously Purchased Product' option in the dynamic value selector

This filter configuration excludes from the recommendations results both products purchased online and products purchased in store that appear in the designated Offline Purchases dataset.