Email Recommendations Overview

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Kickdynamic's revolutionary AI-powered recommendations for email seamlessly track on-site activity and make many recommendation scenarios immediately available for email via an API.

Our recommendations use LYNX, our AI powered prediction system that analyzes user interactions and online behavior as well as product attributes to generate high value recommendations for your customers. 
By instantly understanding your customers' profiles, LYNX delivers relevant recommendations showing products that are more likely to capture their interest, exploiting:
past onsite browsing behavior
similarities across products and users
cross-user behavioral patterns to discover similarities between users, products or both
All in real-time, on-demand, using the latest data.
Kickdynamic's recommendations require two things - a javascript tag to be added to your site and access to a regularly updated product feed (in most cases we use your Google Shopping Feed). This gives us two things - the ability to track what each individual does on your site and all the information we need about each and every product you have. 
Our  recommendation engine (LYNX) ingests this information then you have the power to set the recommendation logic you require. The recommender output is then used in Kickdynamic's Content Automation, giving you complete control to design pixel perfect product images which can easily be added to email. Here's the process:
Our recommendations are built on real-time data, with no batch processing, so content predictions are made at the moment of email open using the latest tracked behaviour and taking stock availability, latest price changes and offers, the time of the day and other conditions into consideration. Our recommendation engine gives marketers the flexibility to set their own recommendation logic and apply filters and business rules to configure the recommender to their needs. 
The following recommendation configurations are available in the UI:
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Content Automation FAQs

Implementation Documentation

Last modified 1yr ago

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