Shan Wang of Nieman Journalism Lab writes about how The Wall Street Journal paywall has been tweaked to bend to each individual reader.
Wang writes, “For the past couple of years, the Journal — home to one of journalism’s oldest paywalls — has been testing different ways to allow non-subscribers to sample its stories — refining a subscription prediction model that allows it to show different visitors, who have different likelihoods of subscribing, different levels of access to its site.
“Non-subscribed visitors to WSJ.com now each receive a propensity score based on more than 60 signals, such as whether the reader is visiting for the first time, the operating system they’re using, the device they’re reading on, what they chose to click on, and their location (plus a whole host of other demographic info it infers from that location). Using machine learning to inform a more flexible paywall takes away guesswork around how many stories, or what kinds of stories, to let readers read for free, and whether readers will respond to hitting paywall by paying for access or simply leaving. (The Journal didn’t share additional details about the score, such as the exact range of numbers it could be. I asked what my personal score was; no luck there.)
“‘I think back to maybe eight months ago, when we were looking at all these charts with a lot of different data points. Now we’ve got a model that’s learned to a point where, if I get a person’s score, I pretty much know how likely they will be to subscribe,’ Karl Wells, the Journal’s general manager for membership, told me when we spoke last week, with a Journal spokesperson on the call. ‘What we’ve found is that if we open up the paywall — we call it sampling — to those who have a low propensity to subscribe, then their likelihood to subscribe goes up.’ (The Journal’s model looks at a window of two to three weeks.)”
Read more here.