This is interesting: Non-subscribers visiting the Wall Street Journal‘s site now get a score, based on signals, that indicates how likely they’ll be to subscribe. This is all done with machine learning. The idea is to make paywalls “porous” and variable in their “leakiness.” Karl Wells, the Journal’s general manager for membership, says we’ve always had only three kinds of paywalls so far — metered, freemium, and hard:
Metered considers people who will want to read more than, say, five stories. Freemium assumes, this and not that is the type of content people will pay for. This is what we’ve tried to move on from…. Our model now is to flip that and start with the reader. The content you see is the output of the paywall, rather than an input.
