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 interations to personalize expierience accross multiple touchpoints in different communication channels includng 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)
- 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 item’s page visits/transactions|
|Item comparison||At least 10k item page visits|
Introduction to recommendations
Learn how your business can benefit from 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
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