Recommendations

Recommendation allow users to present unique AI-powered item recommendations through several channels in order to promote items and encourage customers to make a purchase.

We use the AI engine to acquire information from your website and analyze large portions of data such which is mainly customers’ activity (visits to a website, purchases, historical data, and information included in the product feed. This way the Synerise application can produce a relevant recommendations that match preferences of customers and circumstances of displaying the recommendation frame.

In Synerise, a user can show recommendations within the following channels:

Business applications


  1. Monetize customers’ data and interactions to personalize experience across multiple touchpoints in different communication channels including web, mobile application, email, and many others.

  2. Boost conversion at any step of customer journey from home page, category or item page, to cart, to post-purchase activities.

  3. Generate top quality real-time recommendations for both recognized, unrecognized, and first-time customers based on various types of interaction.

  4. Configure, launch, and deploy models to run and monitor performance of recommendation with only a few clicks with a simple user interface.

  5. Tailor recommendation results to your business needs with recommendation configuration settings, including advanced filtering, boosting, and sorting options.

  6. Benefit from state-of-the-art machine learning models powered by Synerise proprietary AI engine - Cleora. No need to manually process data ingestion and cleansing processes, models parameters tuning or retraining as framework does it for you.

Requirements


  • Prepare a product feed (its upload to Synerise is described in the Configuration of AI engine procedure)
  • Use consistent item identifiers in feed and events; events must include the item identifier
  • Configure the AI engine
  • Meet the minimum data requirements of interactions and events
Note: The minimum requirements are approximate. They may vary depending on item feed size and data quality.
Recommendation type Minimum requirements
Personalized At least 12,000 in total of:
  • page.visit events from item pages
  • product.view events from item views in a mobile application
    Similar items1 At least 12,000 in total of:
    • page.visit events from item pages
    • product.view events from item views in a mobile application
      Visual similarity Packshot images defined in the item catalog
      Cross-sell At least 2,400 transactions with basket size > 1
      Cart recommendations At least 2,400 transactions with basket size > 1
      Last seen No requirements
      Top items 1 week history of itemโ€™s page visits/transactions
      Item comparison At least 12,000 in total of:
      • page.visit events from item pages
      • product.view events from item views in a mobile application
        Recent interactions No requirements
        Section At least 12,000 in total of:
        • page.visit events from item pages
        • product.view events from item views in a mobile application
          Attributes At least 12,000 in total of:
          • page.visit events from item pages
          • product.view events from item views in a mobile application

            1Similar item recommendations can be created with only the item feed, but events are necessary for building a correct model.

            Limits

            The following are the default limits, you can contact Customer Support to request changing them:

            • Maximum number of active recommendation campaigns: 1,000
            • Maximum number of active and draft recommendation campaigns: 10,000
            • Maximum number of AI recommendation models: 25
            • Maximum number of items in recommendation: 100
            • Maximum length of a filter (IQL string): 10,000 characters

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