The
media update team look at how
The Journal is moving the goal posts through their use of a smart paywall.
Tailoring access according to specific readers
Subscriptions are
back in fashion as a way for news organisations to generate revenue. The likes of
The New York Times internationally, and
BusinessLive and
Netwerk24 in South Africa, are examples of large publishers who support the principle of putting quality content behind a paywall. They each have multiple options for subscribers to choose from and readers select the option that suits them most.
However, American publication,
The Wall Street Journal, are taking it one step further. They are using non-subscriber reader data to generate a profile of each of those readers, and based on that information, the paywall adapts accordingly.
According to a
report by Shan Wang, a staff writer at
NiemanLab,
The Journal “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”.
Each user who visits
WSJ.com receives a score based on over 60 metrics, such as the operating system they are using or the device they are reading on. Machine learning is then applied to dictate the nature of the paywall, if any, that is implemented upon that reader’s experience.
As Karl Wells, the publisher’s general manager for membership, tells Wang, “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 subscribe.”
“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.”
A paywall that adapts
The Journal calss non-subscribers into three groups: hot, warm, and cold. Those with a high chance of subscribing will hit a paywall, but those who score lower might have the opportunity to browse stories during a web session for free, and then hit a paywall later.
Alternatively, they may be offered a guest pass, which offers access for varying lengths of time, in exchange for providing an email address, which, as Wang states, gives
The Journal more information and signals to analyse.
The passes play a role, says Wells, in making their subscription model more predictive. A person willing to pass on their email address is more likely to become a subscriber. Also, possessing that email address means
The Journal could email readers identified as potential subscribers more often than those with lower scores.
The Journal is not the first organisation to use propensity modelling techniques, which Want says are “common in the app world for trying to convert users into paying users" to increase subscriptions”.
The Financial Times has been using reader data for years to target readers more effectively with offers they are likely to respond to. Scandinavian media company, Schibstead, has also developed a prediction model that picks readers who are
three or five times more likely than the average to buy a subscription.
For more information, visit
www.niemanlab.org.
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Times Select, the digital successor to The Times newspaper, takes a digital snapshot daily of what is happening in the South African news environment. Read more in our article, How Times Select aims to combine the best of online and newspapers.