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:
- on the website (through dynamic content)
- emails
- web push notification
- mobile push notifications
- mobile applications built based on Documents
Business applications
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Monetize customers’ data and interactions to personalize experience across multiple touchpoints in different communication channels including web, mobile application, email, and many others.
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Boost conversion at any step of customer journey from home page, category or item page, to cart, to post-purchase activities.
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Generate top quality real-time recommendations for both recognized, unrecognized, and first-time customers based on various types of interaction.
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Configure, launch, and deploy models to run and monitor performance of recommendation with only a few clicks with a simple user interface.
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Tailor recommendation results to your business needs with recommendation configuration settings, including advanced filtering, boosting, and sorting options.
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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
Recommendation type | Minimum requirements |
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Personalized | - At least 1,500 unique profiles who visited a product page more than once. - At least 12,000 in total of:
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Similar items1 | At least 12,000 in total of:
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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 itemโs page visits/transactions |
Item comparison | At least 12,000 in total of:
|
Recent interactions | No requirements |
Section | - At least 1,500 unique profiles who visited a product page more than once. - At least 12,000 in total of:
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Attributes | - At least 1,500 unique profiles who visited a product page more than once. - At least 12,000 in total of:
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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
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
Contents
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Introduction and requirements
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Configuring item catalog for recommendations
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Managing item catalogs
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Recommendation types
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Creating recommendations
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Creating section page recommendations
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Creating attribute recommendations
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Recommendation filters
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Recommendation filters - examples of use
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Previewing recommendations
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Recommendation statistics
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Distributing recommendations