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Potential Facebook-Instagram impact April 16, 2012

Posted by David Card in Uncategorized.
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It’s been a week since Facebook announced its blockbuster $1 billion planned acquisition of Instagram, plenty of time to sketch out what it means for Facebook and the mobile social media environment. Don’t be too quick to assume that Facebook is abandoning its HTML5 mobile strategy in favor of apps. As a defensive move, the acquisition would lock down Facebook’s strong position in photo-sharing, leaving little room for would-be competitors, but it gives Facebook few new weapons and no new revenue opportunities.

What it means

It’s too easy to say Facebook will steer its mobile strategy towards apps and away from mobile access to HTML5 websites. Letting Instagram thrive as an app still fulfills the three strategic objectives I said Facebook needed from mobile: ubiquitous access and high frequency usage, a common cross-platform user experience, and distributed technologies that reinforce its platform. Facebook is still counting on HTML5 to minimize development fragmentation across mobile operating systems and the web. Especially since such a framework would better allow it to apply its Credits virtual currency and payments system broadly, without giving Apple, Google or Amazon a cut. Facebook is trying to encourage app and web integration with discovery services, test suites and streamlined payments. Instagram’s limited use of its own APIs could tie into Facebook’s own social services.

Instagram’s app is simple and elegant, two things you don’t hear about Facebook’s app or its mobile website. But Instagram also uses core social networking techniques that shouldn’t be overlooked. In his post on the announcement, Facebook CEO Mark Zuckerberg differentiates between sharing photos with friends and family versus sharing based on interest. And Instagram uses asymmetric following as Twitter does, rather than Facebook’s primarily two-way following system. Over time, those social networking technologies could add more to Facebook’s social graph than additional location data.

My initial reaction to the announcement was that Instagram might be worth a billion dollars to someone, but not to Facebook. I thought the two companies’ customer base probably had a lot of overlap – so that Facebook wouldn’t necessarily be gaining 30 million new users – and that thousands of Instagram photos were already stored on Facebook pages. Recent figures from AppData suggest that 22 percent of Instagram users connected their app to Facebook. That overlap will increase as Instagram gains more mainstream users: Contrary to potential backlash fears, Instagram received an additional growth spurt from the Facebook announcement on top of its first Android app. Already 96 percent of U.S. social network users, ages 18 to 34, uses Facebook, according to our GigaOM Pro 1Q2012 consumer survey, so there’s minimal Instagram headroom.

Instagram doesn’t have any revenue streams itself, so it won’t solve Facebook’s lack of mobile monetization. Facebook has resisted showing ads on its mobile app or mobile website, and seems more likely to show in-stream promotions than mobile display ads or interstitials between photos. What Instagram does give Facebook is a near dominant position in photo-sharing, both mobile and online. Both Om and investor and Hunch co-founder Chris Dixon saw Instagram as Facebook’s biggest competitive threat.

Whom it affects

Google missed out on a chance to gain social media customers and attack a core Facebook stronghold. But Instagram won’t add enough user data to Facebook’s interest graph to weaken Google’s.

Twitter reportedly tried to buy Instagram, and it would have welcomed the user growth and bulked up its own nascent ambitions in photo sharing and storage.

Apple doesn’t make many apps, but desktop photo manipulation and management is one of them. Instagram would have been an easy fit, offering lots of integration opportunities and bringing much-needed social DNA to Apple.

Yahoo’s Flickr is still a huge web repository for photos – including ones taken with Instagram – that could have benefited from a mobile user base.

Other smartphone photo apps like Hipstamatic and Eyeem don’t have the size or growth rates that Instagram has. There’s very little reason for any of the previous group of companies to buy any of them rather than building their own app.

Other social startups might now be in play if the bigger companies above decide they need social media users. But Pinterest is really the only one with size, growth and potential ease-of-monetization. And Facebook still has plenty of money and stock.

Key Takeaway

Although Facebook is still committed to an HTML5-based mobile web strategy, keep an eye on whether it shifts towards a series of single-function mobile apps as a medium-term bridge tactic.

Question of the week

Who is the biggest loser from a Facebook-Instagram match?
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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?