The Client, a Media house owning 4 local newspapers, needs to increase customer loyalty of their digital users.
Conversion from paper to digital is key to the success of the business.
Digital users expect relevance and will churn if the perceived value of their subscription is not satisfying.
The challenge for the clients is to ensure that digital users are exposed to the most relevant articles for them individually.
One Prediction is continuously ranking all articles for each individual client using the core features of Cognitive Match and Machine Learning.
One Prediction models differentiate paying customers, former customers and unknown leads.
They are based on the digital users behaviour captured in Google Analytics 4, and which is made available in One Prediction through a standard connector.
The Client exposes the most relevant articles on the website and emails.
In addition, One Prediction is able to automatically predict a “relevance period” for each article: How many hours / days before an article will no longer be relevant.
One Prediction demonstrated that digital users were effectively reading the articles that One Prediction had scored highest.
Most digital users would read the #1 ranked article, then #2, #3 etc.
Very few would read only articles with a low rank in One Prediction.
One Prediction was able to expand the lifetime of articles from the “back catalogue”.