It is widely believed that a flourishing democracy requires an independent, diverse, and financially solvent press. With print newspapers set to disappear in the next few years, the future of quality journalism is highly uncertain. This year, the online version of the New York Times will generate about $200M in revenue, a number that will need to approximately triple to support the current Times newsroom.
Most people who understand Internet economics believe that the best hope for online journalism is online advertising. Luckily, online advertising has significant room for improvement. Most of the revenue of the Times’ online business is generated through display ads. The main metric used to price display ads is derived from the rate at which users click on the ads, a rate which today is dismally low. Thus the Times could continue to support its current newsroom staff if display ads became even moderately effective.
Lots of smart people are working on improving the efficacy of display advertising. Large companies like Google and Microsoft are investing billions in the problem. As usual, though, the best ideas are coming from startups. Companies like Blue Kai and Magnetic are bringing search intent (particularly purchasing intent – the core of Google’s profits) to display ads. Companies like Media6Degrees are using social relations to target ads based on the principle that “birds of a feather flock together” (Facebook will likely start doing this soon as well). Solve Media turns the hassle of registration into an engaging marketing event. Convertro is working on properly attributing online purchases “up the funnel” from sites that harvest intent (search, coupon sites) to sites that generate intent (media, commerce guides). All told, there are a few hundred well-funded ad tech startups developing clever methods to improve display advertising.
Many of these targeting technologies rely on gathering information about users, something that inevitably raises concerns about privacy. Until recently, online privacy depended mostly on anonymity. There is a big difference between advertisers knowing, say, users’ sexual preferences and knowing users’ sexual preferences plus personally identifiable information like their names. Like most people, I don’t mind if it’s easy to find my real name along with my job history, but I do mind if it’s easy to discover other personal details about me. When I’m not anonymous (e.g. on Facebook) I want to control what is disclosed – to have some privacy – but when I’m anonymous I’m far less concerned about information gathered for marketing purposes.
Before the rise of social networks, online ad targeting services (mostly) tracked people anonymously, through cookies that weren’t linked to personally identifiable information. Social networks have provided the means to de-anonymize information that was previously anonymous. Apparently, the wall has been breached between 1) my real identity plus my self-moderated public information, and 2) my anonymous, non-self-moderated private information.
The good news is that the things users want to keep secret are almost always the least important things to online advertisers. It turns out that knowing people are trying to buy new washing machines or plane tickets to Hawaii is vastly more monetizeable than their names, who they were dating, or the dumb things they did in college. Thus, there are probably a set of policies that allow ad targeting to succeed while also letting users control what is associated with their real identities. Hopefully, we can have an informed and nuanced debate about what these policies might be. The stakes are high.
Note: As with almost everything I write on this blog, I have a ton of conflicts of interest. Among them: I’m an investor, directly or indirectly, in a bunch of technology startups. Some of these – including some companies mentioned above – are trying to create new advertising technologies. I am currently the CEO & Cofounder of Hunch, which among other things is trying to personalize the internet through an explicit user opt-in mechanism.
