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Why I Think the Times Charging for Online Access is a Bad Idea, and How I Think They Could and Should Make Money

Every business is a subsidy business.

Ok, maybe not, but it certainly seems that way sometimes. The list of products that have been subsidized by other products is long and – mostly – honorable: OpenOffice.org, Gmail, any Microsoft product not called Office or Windows, desktop printers and, of course, newspapers. Or more accurately, the reporting contained within newspapers.

Which is why I find it surprising and a bit disappointing that as one subsidizing revenue stream – classified ads – is erased by the ever corrosive, big bad internet, the New York Times declines to pursue another subsidy in favor of direct monetization. As is their right, to be clear: my purpose is here is not to advance some idealistic “information must be free” campaign.

Nor will I attempt to claim that the economics here are simple or, as Jeff Jarvis puts it, “cockeyed.” When one of the most visible brand names in journalism is forced to borrow against its own headquarters, as the Times was forced to do little more than a year ago, it’s probably safe to conclude that its revenue stream is challenged.

That said, given the “fraught debate” chronicled by New York Magazine, it seems equally reasonable to assert that the addition of a paywall – whatever the form it ultimately takes – comes at a significant price. Nick Carr, a defender of the move, acknowledges as much, qualifying his characterization of the decision as a smart move with the following:

Saying it’s a smart move doesn’t necessarily mean it will work; it means that the risk of not trying it at all is higher than the risk of trying it and finding that it doesn’t work.

The common ground we all seem to share, then, is that the Times needs money. The kind of reporting the paper does is neither free nor cheap, regardless of how what kind of efficiencies can be driven into the operational aspects of the organization. Talent, as ever, costs real dollars.

The question, then, is where said dollars are to come from. Carr, clearly, is in favor of metered or version based pricing. Caroline McCarthy, meanwhile, suggests that such strategies can only be successful in coordinated efforts.

And perhaps they are right. But personally, I think the Times and other papers with sufficient traffic could address their revenue concerns at no impact to users by harnessing what readers already provide them: data. By engaging their primary asset – their audience – in a symbiotic revenue cycle. Readers get content for free, the Times, in return, learns more about them, analyzes the data and resells it.

In other words, turn the Visualization Lab from a cost center into a revenue source.

Consider OKCupid, the dating site. Using analytics, the folks over there can attempt to answer – using actual data, rather than opinions – difficult questions questions such as: does your race affect whether or not people write you back? What should you say in a first message? How do your looks correlate to message frequency? Should you or should you not show off your body in your profile? And so on.

But I can already here the howls of protest, “But that’s just a dating site, we’re talking about the NEW YORK TIMES.” To which I’d reply: exactly. If we can extract this kind of meaningful intelligence from a mere dating site, what kinds of questions can the Times answer for us? What if they leveraged their own user behavior in the way that they did, say, the Netflix rental data? I’d bet there are some interesting questions to be answered. And by interesting, I mean valuable. And by valuable, I mean a saleable asset.

What brands are people asking about, that end up at the Times? Are they trending up or down? How about relative to their competitors? What are the demographics (determined by profiles) – and related shifts – of the Times audience? What is the sentiment of the articles they’re reading, and what can we extrapolate about the zeitgeist locally, regionally and nationally from that? What percentage of the audience comes from the Fortune 500? When are they most actively reading? Where, geographically, are they reading from? What kind of device are they using to read?

And so on. These are all, in my view, answerable questions for the New York Times, and – if they had an analytics side to the business – data customers. Some of this data, to be sure, has historically been supplied, gratis, to advertisers as part of the sales process. But marketers – never more so than in these increasingly data driven days – need to be asking and answering deeper questions than, “how many pageviews did you the Times do this month?” Because, as Google proves, the real money isn’t just in marketing to a volume audience, but doing so meaningfully.

The audience may well extend beyond mere marketers, of course. Might not real estate developers, for example, value the New York Times readership trends on a hyperlocal basis, if you could prove with analytics a correlation between site traffic and economic growth? Retail outlets might similarly benefit. Or wired or wireless carriers. And so on. Think analytics is too distinct a business for a newspaper to get into? Fine: sell access to particular datasets to interested parties either directly or via a third party like Infochimps. It’d be a lower margin business, but low effort.

Knowing how many are reading the New York Times, who they are, where they are, what they’re reading, what they’re forwarding, what they’re cutting and pasting, and how much has value. Significant value, in my view, as I’ve argued before. Which is why I think we’re seeing Facebook and other high traffic properties move in this direction.

Is it reasonable to question how data, as an unproven but potential revenue source for the New York Times, would compare to the direct monetization scheme currently proposed? Of course. But given that one negatively impacts users, and one does not, I know which one I would try first. Not that they need be mutually exclusive, of course, but I would exhaust all of my options before embarking upon a course of action that might materially and permanently impact my relationship with my customer.

Maybe data as a product is the future of the newspaper industry, and maybe it is not. But considering that journalism is generally regarded these days as a race to the bottom, what do they have to lose by finding out?

Categories: Analytics, Business Models.