Introduction to recommendation campaigns

Recommendation campaigns 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 interations to personalize expierience accross multiple touchpoints in different communication channels includng web, mobile application, email campaigns, and many others.

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

  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 campaigns with only a few clicks with a simple user interface.

  5. Tailor recommendation results to your business needs with campaign 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)
  • Consistent item identifier in feed and events
  • Configuration of AI engine
  • Meet the minimum data requirements of interactions and events:
Recommendation type Minimum requirements
Personalized At least 10k item page visits
Similar items At least 10k item page visits
Visual similarity Packshot images defined in the item catalog
Cross-sell At least 2k transactions with basket size > 1
Cart recommendations At least 2k transactions with basket size > 1
Last seen No requirements
Top items 1 week history of itemโ€™s page visits/transactions

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