Can Mining and Filtering Monetize NewNet? December 20, 2010Posted by David Card in Uncategorized.
Tags: Nielsen Media Research, real time, real-time feeds, Social, social graphs, social media monetization
One of the keys to monetizing NewNet technologies like real-time feeds and social media will be harnessing the massive amounts of data they create. In recent weeks, there have been a handful of announcements illustrating creative ways of using this data to enhance products, often via recommendations. But most of them have not shown clear revenue strategies.
What the initiatives have in common is their use of information from feeds or social graphs. Foursquare posted a job listing for a data scientist to assist in mining its own data to enhance product features, but there may be more opportunities — and competitive differentiation — in combining data sources. The recent initiatives display at least two ways of tapping those veins:
- Mining happens behind the scenes. Companies license and/or utilize APIs to extract information and apply it to applications and services to aid in targeted marketing, aid personalization, or create entirely new products.
- Filtering is more visible to the end customer. Like mining, filtering adds relevance, but is generally controlled by the user.
Who’s Doing It, and How
Mining NewNet data from multiple sources may require the resources of a company with a big, established business —rather than a startup — for deployment if not development. Social media buzz-monitoring companies like Cymfony (part of ad agency giant WPP) and Buzzmetrics (part of Nielsen) sold themselves to ad agencies and market research firms. Because changing an established user interface is a tricky thing, innovations in filtering multiple data sources will likely originate at startups.
Examples of each include:
- Wowd filters Facebook’s feed. It applies its own algorithms to Facebook APIs to automatically create natural groups of a user’s friends by analyzing users relationships to each other and posted info. Wowd allows the user to filter by time, topic, and trends.
- Clicker, that makes an Internet video guide, is one of the few companies that pulls in Facebook data via “Instant Personalization.” It maps a user’s self-professed Likes into genres and topics to produce recommendations it shows alongside editorial suggestions, friends’ viewing, and popularity.
- Google mined its own traffic and embedded content for YouTube Trends, and tweaked its social search presentation. Microsoft appears to be using Facebook data in its basic Bing results, as well as offering an alternative social view. MTV Networks created a new music discovery space by mining social data.
But Payoff Remains a Challenge
A simple ad revenue model for a site or app that filters a Twitter or Facebook feed produces pretty small dollars. I used traffic data from Compete, “visits” as a proxy for page views, and assumed a low-cost ad (CPM of fifty cents to a dollar). If a filter company showed a single, relatively untargeted ad per page, and siphoned of 10 percent of Twitter’s site traffic, it could generate yearly ad sales that would be measured in the tens of thousands of dollars to perhaps half a million. If the company managed to appeal to one percent of Facebook’s US users, the figures are in the same ballpark.
My model is very simple, and very conservative. If Facebook is really approaching $2 billion in revenues, it generates roughly $2 to $3 per user per year. Google is more efficient: it gets $25 per user/year. To get to multi-million dollar yearly ad sales, a filtering company would have to attract a million users, preferably of a distinct demographic, job description or sphere of interest. That would enable it to offer a better-targeted audience and a richer palette of ads and marketing opportunities to advertisers, and charge a CPM in the $3-plus range.
Active personalization — convincing a user to set up a customized experience — is tough. Yahoo never got more than 15 to 20 percent of its users to build out a My Yahoo page. Those who did were its most valuable users, the ones that used multiple Yahoo products and converted to paid services. The passive personalization enabled by mining could indirectly contribute to customer monetization via retention and increased usage frequency.