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I was interested to read today that Esquire is currently experimenting with a per-article paywall. For $1.99, you can read a 10,000-word piece about a neurosurgeon who claims to have visited heaven. Esquire’s EIC on the experiment: “…great journalism—and the months that go into creating it—isn’t free. So, besides providing the story to readers of our print and digital-tablet versions of the August issue, we are offering it to online readers as a stand-alone purchase.”
I predicted that payment systems and paid services/content were going to take off this year (see here), but this isn’t what I had in mind. But it did get me thinking. What if you added social and elastic elements to the price? For example, the article would initially cost, say, $1.99, but if enough people decided to buy it, the price goes down for everyone. The more people who buy, the cheaper the price gets. It’d never go to zero, of course, but there’d be some kind of a demand/price curve that satisfies the two most important things publishers care about: readership (the more, the better) and revenue (ideally, enough to cover the costs of creation and make a fair profit).
The tools to do this already exist. There are plenty of sites that crowdsource demand to create pricing leverage, and sites like Kickstarter have gotten all of us used to the idea of hitting funding goals. And the social sharing behaviors already exist as well: Nearly all content has social sharing widgets attached these days. Why not combine the two? Those who initially paid the highest price – $1.99 say – would be motivated to share a summary of the article with friends and encourage them to buy it as well. They are economically incented to do so – the more friends who buy, the greater the chance that their initial $1.99 charge will decrease. And they’re socially incented to do so – perhaps they could get credit for being one of the early advocates or tastemakers who recognized and surfaced a great piece of content before anyone else did.
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Yahoo has always been proud of the algorithms that drive its choice of personalized content, but it’s hard to grok exactly what they do behind the scenes to make the magic happen. Today the company released a visualization of its “C.O.R.E.” (Content Optimization and Relevance Engine) technology, and the result is pretty cool. From a release sent to me by Yahoo:
- C.O.R.E. (Content Optimization and Relevance Engine) is a suite of technologies developed by Yahoo! Labs to surface the stories most interesting to you, based on your reading behavior over time.
- Every hour C.O.R.E. processes 1.2 terrabytes of data in order to learn how a user’s behaviors and interests influence the likelihood of clicking on a specific article. And, every day, C.O.R.E. personalizes 2.2 billion pieces of content for Yahoo! users.
- Since optimizing with C.O.R.E., Yahoo!’s Homepage click-through rate has increased 300%.
- Yahoo!’s personalization approach is a clever mix of scientific algorithms and human judgment, as editors have control to override C.O.R.E. at any time, to ensure certain stories are seen.
- Initially developed within Yahoo! Labs, C.O.R.E. has become a vital tool used throughout the day by editors across the company to bring our users personalized news, first.
The visualization lets you see stories through filters of gender, age, and interest. The image above, for example, shows a male in may age range interested in business and finance. Well worth playing around with, and a very good example of what I call “dependent web” content.
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