Overview

Product recommendations are a powerful marketing tool that you can use to increase conversions, boost revenue, and stimulate shopper engagement. Adobe Commerce product recommendations are powered by Adobe Sensei, which uses artificial intelligence and machine-learning algorithms to perform a deep analysis of aggregated visitor data. This data, when combined with your Adobe Commerce catalog, results in a highly engaging, relevant, and personalized experience.

Product recommendations are surfaced on the storefront as units with labels, such as “Customers who viewed this product also viewed”. You can create, manage, and deploy recommendations across your store views directly from the Adobe Commerce Admin.

NOTE

If your storefront is implemented using PWA Studio, refer to the PWA documentation. If you use a custom frontend technology such as React or Vue JS, learn how to integrate Product Recommendations into your headless storefront.

Privacy

Data collection for the purposes of Product recommendations does not include any personally identifiable information (PII). Also, all user identifiers like cookie IDs and IP addresses are strictly anonymized. To learn more, see the Adobe Privacy Policy.

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

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