Product recommendations versus product relationships

Given the ever-changing complexities of online shopping, what works best for your storefront is often a combination of multiple key technologies. Using both Product Recommendations and Product Relationships gives you more flexibility when promoting products. You can leverage Product Recommendations powered by Adobe Sensei to intelligently automate your recommendations at scale. Then, you can leverage Related Product Rules when you must manually intervene and ensure that a specific recommendation is being made to a target shopper segment, or when certain business goals must be met.

Product recommendations allow you to:

  • Choose from nine distinct intelligent recommendation types based on the following areas: shopper-based, item-based, popularity-based, trending, and similarity-based
  • Use behavioral data to personalize recommendations throughout the shopper’s storefront journey
  • Measure key metrics relevant to each recommendation to help you understand the impact of your recommendations

Product Recommendations demo

Watch this video to learn about Product Recommendations:

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Catalog data retention policy

If you do not submit a query for the catalog data in your testing environment for 90 consecutive days, the catalog data is set to hibernation mode and no data is returned for any query. Catalog data in your production environment is not affected by this policy.

To re-activate the catalog data in your testing environment, submit a support request with the title: “Reactivate Product Recommendations” and include the environment IDs. The catalog data in your testing environment should be restored within couple of hours.

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