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User-specific product recommendation - based on product meta
User-specific product recommendation - based on product meta
Danielle From BayEngage avatar
Written by Danielle From BayEngage
Updated over 2 months ago

Overview:

Send product recommendations to cross-sell/up-sell to your customers based on their purchase history. This feature leverages product meta values for the beta release and requires all products in the store to be mapped with meta/custom fields. Custom fields must be configured for this feature to be enabled.

Note: Currently, this product recommendation is only available for campaigns and only applicable to BigCommerce stores

Step-by-step guide:

  1. Login to your BayEngage account with your credentials

  2. Click on the ‘email template’ at the left corner to select either a new email template or work on the existing template.

    1. Click on ‘free email templates’ to work on the new template

      b. Select ‘my template’ to edit the existing template

  3. Now select the email template that you have to work on, and drag-and-drop the ‘product widget’ from the right side

  4. Now, you will see two tabs, ‘add/edit product’ and ‘product recommendations’

    1. Add/Edit Products: This tab retains the existing functionality, allowing you to add and edit products.

    2. Product Recommendation: This feature enables product recommendations to be included in your email template.

5. Click on the ‘Product Recommendation’ tab, and a pop-up will appear with the required fields for you to fill in

6. Now, under the ‘choose recommendations algorithms’, you will find two section

a. User-Specific Recommendation: Similar Product Meta Value: This option generates product recommendations based on the product's meta values, ensuring personalized suggestions for each user.

b. Saved Config: Saved Configurations: This section displays all previously saved configurations and meta values, which can be easily reused in future email templates for consistency and efficiency.

7. After selecting the ‘Similar Product meta value.’ Now ‘choose the meta fields’ for the product recommendations

Note: You are allowed to choose only three meta-fields. The algorithm prioritizes the first meta field to generate results. If the first meta field fails to produce sufficient results, the algorithm considers the second meta field to get the remaining result.

For example, if the algorithm retrieves two results from the first meta field, it will then look into the second meta field to obtain the remaining one result.

8. Then, you can select the user interactions as ‘recent orders’ from the drop-down

Note: The product recommendations will be based on the user's recent orders

9. Now click on ‘add filter’; you will see the price range section where you can give the price range for the products to be added to the recommendation

Note: This filter is optional, which allows you to refine the results further.

10. After adding the filter, if it can’t find the product to add in the recommendation section, then it will go for a ‘fallback’

11. The fallback section has ‘recently added product’ in the drop-down. If no products meet the above conditions, recently added products will be sent as the recommendation.

12. Now you can save this configuration by check-off the box ‘save the configuration to reuse in another template’ and give the name for your future reference and save it

Note: This is an optional field, and the saved rule will be under ‘Choose Recommendation Algorithm.’ you can use it for future reference

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