The fact that you follow Snoop Lion on Twitter and "like" Starbucks on Facebook means something. It's important to a lot of people. You like nonagenarian actress Betty White, and I like hipster musician La Roux; you like Method soap, and I like PUBLIC Bikes. That's worth a few cents to brands such as NBC, Arista Records, Tide, and Specialized.
Why is that? Because knowing what you love helps us know what you might like. The fact that you might like a certain thing is called relevance. And for brands that want to find people who could possibly like what they've got, relevance is king.
Basically that means advertisers want to know what you like. And the fastest, simplest, most transparent way to know what you like is through the interest graph.
The interest graph is made of "likes," follows, and other social relationships between people and things, products, or brands. Every platform has its own interest graph. Facebook data helps us create a picture of user interests via the "like" button. You can do something similar with Twitter data by analyzing interests expressed via the follow button. Every social platform has its own version of the one-way follow relationship (on most platforms, it's called "follow").
Because all social platforms also have implemented APIs, each proprietary interest graph is available to be analyzed in aggregate. The sum of all individual interest graphs is the blended interest graph for online social platforms. Think of it as a combination of the individual interest graphs for Facebook, Twitter, Google+, Foursquare, Pinterest, Instagram, and so on.
As new users join and follow influencers, the data set for the interest graph grows by 2 billion "likes" every day -- six "likes" for every person in the United States, every day. Put another way, the current world population is estimated at 7 billion, and the interest graph is about 40 times larger -- currently sitting at around 266 billion "likes" and follows.
The interest graph has attracted attention from technologists for its potential to deliver relevance for advertisers. Dick Costolo, CEO of Twitter, said the interest graph will offer "powerful value to advertisers." And Naval Ravikant, founder of AngelList and an investor in Twitter, wrote in TechCrunch that "the interest graph lends itself brilliantly to commerce."
The interest graph holds big value for app developers, too. Airtime, for example, launched a much-touted video chat app that analyzes Facebook's interest graph to help match like-minded participants. Sean Parker, founder of Napster and an investor in Facebook, described the interest graph as a powerful tool for creating "a very nuanced view of people."
Targeting is critical to good advertising. Brand marketers understand that it matters who sees your ad. While products can appeal to a wide variety of people, for many brands it's likely that only a narrow subset of the population is most likely to buy it.
There are more than 7 billion people living in the world. It's guaranteed that not all of them will want your product. So how do you know which ones should see your ad? Ideally, you want to show your ad to someone who might have the problem or need that your product solves.
The interest graph predicts this affinity better than any other algorithm because with the interest graph, brands and publishers can understand what things -- topics, products, movies, music, etc. -- every person on the graph is interested in.
Imagine a young woman named Andrea living in San Francisco. Andrea loves swimming, but you wouldn't know it by looking at her Facebook friends, which include cousins, high school friends, college roommates, and co-workers. But if you knew that Andrea follows swim blogger @speed_endurance and Olympic swimmers @RyanLochte and @DaraTorres on Twitter and that she liked her local pool and Michael Phelps's nonprofit on Facebook, you could figure out that she was an aquaphile and use that information to target ads.
Here's an example of how the interest graph can be deployed in an advertising context. Imagine that our imaginary user, Andrea, has opened her favorite social app to check her friends' updates. While loading social data, the app taps an interest graph algorithm for a relevant ad based on Andrea's interests. The algorithm assesses Andrea's profile, confirms that the profile is marked public, and determines that she follows Ryan Lochte, Dara Torres, and Patton Oswalt and has mentioned Comedy Central shows like South Park, qualifying her broadly for the "sports" and "comedy" categories. The ad vendor then searches its current inventory for ads matching those categories, and returns a relevant ad, in this case a promotion for a major sports media brand. The app displays the ad to Andrea at the top of her social feed, and she decides whether to engage with the brand.
The good news is that interest graph-based targeting transcends platforms and can be applied everywhere -- even on mobile -- because it's not tied to device IDs, browser history, search history, or browser cookies. This means more apps get downloaded, more ads get clicked, more content gets read, etc. Interest graph-based advertising has shown higher performance than traditional display. For example, some networks report average click-through rates of 0.50 percent, which significantly outperforms the standard banner ad CTR of 0.01 percent.
Finding the right person to show ads to isn't easy, which is why hundreds of startups and companies have grown in the ad industry to help marketers serve their ads to the right people. Now, with the 266 billion "likes," follows, and other social cues available to marketers, there's never been a better opportunity to understand what's truly relevant to your target audience.
Vanessa Naylon is a creative strategist at 140 Proof.
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Couldn't agree more Vanessa; understanding the interest graph drives successful media buying on facebook (and other social platforms).
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