Social recommendations are tricky and all involve an element of trying to get your brand or product to go viral. Advertisers have to invest upfront in apps or games (such as the Whopper Sacrifice) or create compelling profile pages and then hope that they take off by word of mouth. It is much harder to pull off and target than simply buying AdWords on Google. However, a combination of search and social recommendations, that advertisers can measure in a CPCs has a chance of giving Google a run for its money. This is the premise behind social search.
To date, Google's dominance in search has not prevented new search engines from springing up. New entrants have differentiated themselves by sector (e.g. job search or housing), region (Yandex, Baidu), presentation (Kosmix) or ability to answer specific types of questions (WolframAlpha) amongst others. Those that have tried to compete head on (such as Cuill) have not fared too well. All had one thing in common - they have access to the same data as Google, which makes it that much more difficult for them to compete. How does social search differ?
Most importantly, social search has the potential to be highly targeted. Facebook, MySpace and others collect data such as their users’ comments, likes and dislikes and limit (or allows users to limit) what is publicly accessible. This additional data, should in theory makes search results more accurate – even Google agrees with this. Due to this, the blogosphere is awash with articles guessing how social search is going to affect Google's dominance. A recent article in GigaOM compared Google's search to looking for things in the library, and Facebook/Twitter driven search and recommendations as exchanging ideas in a coffee shop. While Google is great at indexing all published articles and can use your past results (if you let it) to make intelligent guesses to your current needs, it is never going to be as good as your friends' recommendations. To put it another way, if Google is the Database of Intentions (to quote John Battelle), then Facebook/Twitter could very likely form the Database of Suggestions.
The flip side of the coin is that determining the relevance of social recommendations may be quite difficult. Visitors to Google are signaling their intentions by making the effort to search for something specific. Users of Facebook may be driven by curiosity after finding a link on their friends' wall. Unless it is possible to filter this traffic, advertisers will have difficulty getting consistent ROI. Presumably, Google with all its PhDs should be able to come up with a model that works? Google has had a fair crack at this through its partnership with Fox, powering MySpace search amongst other things. However, Google executives have from time to time let it be known that this deal has not met expectations. A recent TechCrunch article provides actual numbers.
Besides relevance, there are also concerns about personal privacy and the damage to brands as dynamic user generated content (UGC) impacts the ability of advertisers to filter ads tied to objectionable content. However, the benefit of doubt has to be with the social networks. With an audience of 100s of millions and ranking in the top 5 sites in most countries, they clearly have share of eyeballs. Advertising dollars have to follow and this will stimulate innovation. They are also the victims of high expectations - after all, how many years did it take for Search 1.0 to evolve to produce an AltaVista or Google?
While social search is still on the horizon, social recommendations are already having an impact on SEO practioners and agencies. Agencies are beginning to talk about multichannel internet marketing, increasingly using not just SEO and SEM but also blogs, social networks, Twitter and others as a source of traffic. It will be interesting to contrast these different methods against SEO and to explore the effect they will have on the digital agency business model.
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