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Making a billion Facebook users pay off October 8, 2012

Posted by David Card in Uncategorized.
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Is a billion users cool? As Facebook tries to convince the world that it is still an exciting growth business with appearances on the NBC “Today” show and a BusinessWeek cover story, CEO Mark Zuckerberg announced the social network had achieved that impressive milestone. Can Facebook do things with a billion users that it couldn’t do with half that number? Zuckerberg is dropping hints, but so far, they’re just hints.

Eighty plus percent of Facebook’s revenue comes from advertising, yet 80-plus percent of its billion users are from markets outside North America. Besides Japan and a few European countries, most regions don’t spend nearly as much on advertising per capita as the U.S. does. So, regardless of whether Facebook can solve its mobile problem, the company is “stuck” with 150-200 million users that it can monetize relatively easily via advertising.

I say “relatively easily” because Facebook is still living off advertising that, even though it is potentially highly targetable, currently commands only remnant-inventory style CPMs. That, or relatively low-priced cost-per-click prices from direct marketers. Facebook has been conservative in offering higher-value traditional formats like large units, takeovers, interstitials, or video. Instead, it has focused on incorporating users into the ads to make them truly social, and to tap into viral pass-along. That’s all very admirable, but it requires Facebook to prove the value of such marketing to conservative brand advertisers.

More clouds on the horizon

Meanwhile, some of those advertisers’ agencies are grumbling. Ignore the ageing story about General Motors cutting back its ad spending. Rather, pay attention to this post from an exec at WPP’s Team Detroit, one of Facebook defender Ford’s agencies, who appears miffed at Facebook’s news feed algorithm. Facebook regularly tinkers with its news feed to try to ensure users get relevant and engaging content. Some of its recent tweaks seem to cut back the amount of company page posts that get through. Critics interpret that as a way for Facebook to force companies to buy more ads to drive traffic to their pages, possibly resulting in an overall worse user experience.

That’s one interpretation, but it’s also likely that Facebook is more concerned with developing its relevancy targeting. That would pay off for ad-targeting – including targeting on a network off of Facebook’s own site – as well as content discovery, a key part of Facebook’s monetization strategy, if an indirect one. Content discovery and consumption is a user lock-in and habitual usage scheme that will pay off in whatever revenue model Facebook adopts.

Other options?

That kind of ranking also sounds a lot like search. Although Facebook is less prone to spam and “black hat” SEO-gaming than Google, it also keeps its EdgeRank algorithm’s sauce almost as secret as Google’s PageRank. Both companies have to work hard to balance first-screen results for clutter and user relevance that will ultimately pay off in sustainable revenues, rather than overpriced, easy scores. In a post about how it promotes apps to users, Facebook describes a ranking process approach that’s probably pretty similar to the one it uses for the news feed. Facebook’s apps recommendation engine uses demographics, friend connection data – including user ratings – and behavioral data (Likes, interactions) in selecting which apps it features to any individual. I’m still skeptical that Facebook will try to deliver general-purpose search results – that would require indexing the web and building out product information databases – but the approach will suit entertainment and content recommendations well.

Facebook has ambitions beyond advertising, apps promotion, and content discovery. Om Malik points out that Zuckerberg understands that Facebook is a core infrastructure technology provider – particularly for identity management and authentication services. Besides further user lock-in, Om speculates that those kinds of services could play out in a variety of transactions, social and commerce-oriented. And identity is relevant in any region, even where ad spending is light.

Going social: Recommendations engines need to factor in consumer reviews November 28, 2011

Posted by David Card in Uncategorized.
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Last week eBay announced it was buying Hunch, reportedly for $80 million. Even though it has its own recommendations system based on searches and popular items, eBay grabbed Hunch’s recommendations engine. The acquisition illustrates the trend of adding social media to the data-powered recommendations credited with increasing Amazon’s sales by 25 percent. But get ready, because the next stage of commerce recommendations will further mix in human-powered consumer reviews.

Pioneered by Amazon, the first wave of e-commerce recommendations engines were based primarily on collaborative filtering. That is, they compared a lot of data: “Customers who bought product x also liked product y.” As with most big data analysis, recommendations are not only about pattern matching but also about which patterns best predict future purchases. And like all pattern matching, the company with the most data usually wins. Social media, via open APIs from Facebook and Twitter, bring new data for analysis and lower the cost of entry for recommendations engines.

Game-changing social data from APIs

Hunch creates a “taste graph” of a user’s interests based on analyzing available social media data from Facebook, Twitter and others that is fine-tuned by user responses to a Q&A process on the Hunch site. That’s why it could deliver useful results without suffering from a “cold start,” or lack of enough data to be predictive.

Effective e-commerce and media recommendations will mix in big-data analysis of different information sources including product factors, observed purchase behavior, and social and interest graph data from APIs. They will create algorithm-driven engines from that data and present it alongside customer reviews that themselves can be filtered and analyzed by adding structure through categories, comparisons and ratings schemes the way Amazon and Best Buy do.

Another recommendations pioneer, Pandora creates personalized radio stations based on factor-based analysis somewhat like Amazon. Pandora programs based on mapping and matching song and artist characteristics, along with a user’s likes or dislikes. Its effectiveness makes it the leader in online radio, with 40 million active users and $75 million in third-quarter revenues. Lately, Pandora has been scrambling to build out social features to enhance its website and apps and get more inputs.

A startup with an MIT Media Lab background, The Echo Nest has begun licensing its music recommendations engine to online music programmers like KCRW and Clear Channel’s IHeartRadio. The Echo Nest automates its song-characteristic analysis by running each track through audio analysis. It also scans online blogs and music pubs for information that informs its audio analysis. This makes its approach more scalable — and cheaper — than Pandora’s engine.

A blended approach in the future

The established engine technologies still work, and social media data lets new players build them effectively. Last year, Amazon started experimenting with available social data. When connected via Facebook, Amazon remembers a user’s friends’ birthdays, suggests items popular among friends and makes recommendations based on favorites the user has listed explicitly on his Facebook profile. But so far, all of those results are on a separate, “beta” page of recommendations that Amazon links to from its personal recommendations page. They’re not mingled with Amazon’s traditional recommendations and user reviews on product pages — yet. Likewise, as it learns what social data is most predictive, I expect Amazon will incorporate the social signals it gathers from Facebook connections into its recommendations-ranking algorithms, the way Microsoft uses Twitter and Facebook in search.

Successful recommendations will take this approach of blending social and data. At GigaOM’s RoadMap conference, Wal-Mart sketched how its social commerce strategy, driven by its Kosmix acquisition, would focus on search, recommendations and local, in-store context rather than stores on Facebook. Wal-Mart is one retailer with serious data smarts. This is just one example of how, for commerce, it’s wisest to think of social media as data sources rather than shopping hubs.

Question of the week

Who do you think will build the best recommendations engine?