Recommendations are old and boring. My mom consistently suggested that I clean my room and eat my veggies as a kid. We are constantly being blasted with branded messaging, which always strongly recommends that you buy something, even if you don’t really need it. Recommendations are nothing new.
Lucky for us, social media has given recommendations a face lift. The Facebook “Like” button has pioneered digitized recommendations based on the taste graphs of people you know. We have seen this with cool executions such as Urban Outfitter’s storefront, organized based on the amount of “Likes” each product received.
Interestingly enough, however, most of us probably never realize we are making a recommendation with the simple click of a button. It’s based on a chance that someone comes across a Facebook page and decides to show preference for a brand or product. They may even like a competing brand’s product just as much, if not more.
But we are moving forward. There are a number of new ways that people are sending and receiving recommendations to one another in more deliberate ways, creating more meaningful exchanges. These platforms are also becoming more industry-specific, allowing brands to more easily participate.
Players in the Recommendation Space
Tagged is one of the top five social networks in the U.S. to date. Its site boasts huge numbers: 5 million new connections every day, over 100 million members, 25 million monthly unique visitors. What makes the platform so popular? Its promise to connect people with – not their existing friends, but – people they haven’t met yet. It’s the eHarmony of friendship, showing people who they should be getting to know.
Bloodhound is a recently-released app that also incorporates recommendations. The app specifically aggregates conferences being held and then uses “social collaborative filtering” based on user activity to give real-time suggestions of what panels or talks attendees should check out.
Marketers can get help via recommendations, too. iQU (pronounced “eye-cue”) is a gamer-profiling company that aims to help online game publishers target the right audience to ultimately increase profits. iQU is creating a social graph on an international scale to determine how gamers behave on digital platforms and forecast what kinds of games they will like in the future.
As recommendations become more sophisticated, there are some points marketers should keep in mind. In his TED talk, Eli Pariser discusses “filter bubbles,” or overly-personalized online experiences based on recommendations that algorithms make. For example, Google uses 57 signals to personalize the content people see within search results, even when they are logged out. The same happens in Facebook news feeds, based on what people click.
This limits the amount and variety of content people are exposed to – which can be a very bad thing for consumers and brands.
Marketers should look to create platforms that offer personalized recommendations while still allowing users to engage in the experience through an neutral lens. This will help participants to be exposed to a larger range of options, potentially opening them up to more purchase opportunities.
As we move forward, marketers should look to construct social strategies that encompass the current and future states of recommendations. Brands should feature their products on Facebook to allow for recommendations based on “Likes” at a more granular level. This will allow consumers to recommend your brand and its products through services like Springpad that base recommendations from Facebook.
Additionally, creating proprietary platforms that incorporate recommendations based on algorithms allows your target audience to have a tailored experience when engaging with your brand.
The capability to recommend will become integrated into more and more services as we move forward, so get your brands involved at the start to position them as leaders moving forward.
Emily Knab, 8.25.2011
Friday, 26 August 2011
Posted by Jon Barnard at 11:49