Project Description



The Client, a publication house, wants to leverage their digital content effectively to drive engagement on digital channels to drive sales and loyalty in 4 countries.
By determining the relevance of every article to every individual reader, the Client would be able to show the right content to every individual to increase their perception of relevance and subsequently increase their engagement.


There are thousands of articles to choose from. Traditional methods such as tagging articles to identify similarities, and simple algorithms such as “others have also read this article” are not sufficient to match the readers’ interests at scale. There is a need for an “engine” that inspires readers to engage with more content and build a habit of using the Client’s digital services and subscribing.


Determine an individual reader's preference to the Clients content across brands and feed that into an automated decision process of who should see what content, in what order, using One Prediction.
One Prediction’s “Cognitive Match” feature builds a unique profile per reader, using AI and ML. The “Cognitive match” assesses the relevance of a specific piece of content for each individual profile.
The most relevant content items based on One Prediction’s scores are automatically merged into emails, using the Client’s existing Marketing Automation platform.


+11% conversion to paying clients after the free 30 day-period when the content is personalized using One Prediction, compared to a selection of popular articles. The Cognitive Match features account for 70% of the models’ outcome.