Dynamic Paywall: Optimizing conversion with a machine learning model (Round 3)

Luxembourg Project type: medium Saint-Paul Luxembourg


In recent years publishers established paywalls and are now seeing promising results. The technology and models applied by all publishers are most of the time rule-based. Rule-based models however are too rigid to tap the full potential. With this project Saint Paul will overcome the limitations and establish a paywall 2.0: A dynamic paywall that automatically optimizes the settings based on machine learning algorithms in order to find each user’s individual tipping point to convert. 

The solution

Today's e-commerce is highly personalized and automated. What is true for e-commerce isn’t true for publishers. Paywalls are still rigid and settings are configured manually. This does not allow for much personalization and means a lot of manual effort for the publishers. Saint Paul will set up a project team to build and optimize a dynamic paywall. A machine learning model shall help to determine the individual tipping point where a user accepts to take the registration and payment hurdle, both to achieve maximum conversions and reduce manual configuration work for the publisher.