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:
- on the website (through dynamic content)
- web push notification
- mobile push notifications
- mobile applications built based on Documents
Monetize customers’ data and interactions to personalize experience across multiple touchpoints in different communication channels including web, mobile application, email, and many others.
Boost conversion at any step of customer journey from home page, category or item page, to cart, to post-purchase activities.
Generate top quality real-time recommendations for both recognized, unrecognized, and first-time customers based on various types of interaction.
Configure, launch, and deploy models to run and monitor performance of recommendation with only a few clicks with a simple user interface.
Tailor recommendation results to your business needs with recommendation configuration settings, including advanced filtering, boosting, and sorting options.
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.
- 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
|Recommendation type||Minimum requirements|
|Personalized||At least 12,000 in total of:
|Similar items1||At least 12,000 in total of:
|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 item’s page visits/transactions|
|Item comparison||At least 12,000 in total of:
|Recent interactions||No requirements|
|Section||At least 12,000 in total of:
|Attributes||At least 12,000 in total of:
1Similar item recommendations can be created with only the item feed, but events are necessary for building a correct model.
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
The following are the permanent limits (cannot be changed) per a recommendation. The limits apply both for filtering and boosting options:
- Maximum number of unique segmentations: 1
- Maximum number of unique aggregates/expressions: 2
- Maximum number of unique customer attributes: 20
Introduction and requirements
Learn how your business can benefit from recommendations
Configuring item catalog for recommendations
Learn how to configure AI engine for the use of recommendations
Managing item catalogs
Learn the requirements of the item catalogs
Learn about the available types of the recommendations
After a successful training of the models, you can create a recommendation
Creating section page recommendations
Learn how to enrich a page with sections that include recommended items
Creating attribute recommendations
Learn how to create recommendations that show an item's attribute instead of the item
While creating a recommendation, use filtering to influence the results
Recommendation filters - examples of use
Check the exemplary usages and business applications of filters
You can see the preview of items shown in the recommendations
Learn how you can use recommendation in various channels