Blendle, a small publishing startup, has a dream to create a platform that makes it easy to pay for journalism online and — as a corollary — makes it easier for journalists to get paid online.
If they aren’t calling it J-Pay, they should be.
Blendle’s secret sauce is essentially to make it easier for consumers to pay for a parcel of individual articles, instead of having to pay for a full subscription. Backed by The New York Times and Germany’s publishing powerhouse Axel Spring, Blendle has half a million subscribers in Germany and the Netherlands. Big name publications associated with the platform include The Economist, The New York Times, The Washington Post and The Wall Street Journal.
“Paywalls are getting more and more common in the United States,” Alexander Klöpping, the company’s founder, said in a statement, according to The New York Times. “Some of the best content is only available behind those paywalls, doesn’t go on the Web until days after publication in print newspapers and magazines and is still viewed through an array of intrusive ads.”
But much in the way iTunes cut through some of that clutter and expense by allowing users to buy their music a la carte, Blendle wants to make it easy for consumers to pick what they want to read instead of committing to a subscription they may not need. And like the iTunes store to which it is often compared, Blendle also curates and suggests articles chosen by the combined magic of thinking AI and human editors.
Publishing for a long time has looked into micro or incremental payments for news content, particularly as papers have been increasingly unable to sustain themselves with digital advertising revenue. Subscription models have been the go-to solution so far, but those models have had a limited track record and mostly made a generation of news readers experts at paywall hacking.
But Blendle thinks consumers can and will pay for content, provided the value is clear. So far, for users that seems to include pieces that are longer and offer analysis and background data.