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Now that we have discussed the role of the interest graph as the ultimate recommendation engine and the significant analytical power needed to process it. But how do advertisers and ad platforms use the interest graph to increase relevance and performance and make people happier?

Let’s Face It: Targeting Is Hard

Targeting is critical to good advertising. Brand marketers understand that it matters who sees your ad. While many products can have appeal to a wide variety of people, it’s often the case that only a subset of the population is likely to buy any given product.

For many brand marketers, the question is how to reach their brand’s target audience. Ideally, they want to advertise to someone who might have the problem or need that the product solves.

Imagine you’re a marketer at Clinique, selling Clinique’s new “Chubby Stick” makeup. Does Clinique expect a lot of video game enthusiasts will buy Chubby Stick? Probably not. Could Clinique interest fashion bloggers in buying Chubby Stick? Yes; that’s much more likely. Clinique probably wants to show their ads to women in the United States age 15 and over, and Clinique doesn’t want to waste budget on advertising to anyone else.

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 reach the right people. Before the Internet, media buyers relied on information such as geography (which side of which highway in which city?) and vertical (auto magazines versus fashion magazines) to decide where to place their ads. More and more, the decision is about what online venues are best for certain types of ads. Technology companies have been working this problem for over 20 years, and they haven’t nailed it yet.

TechCrunch remarks:

“Ad targeting is a difficult artificial intelligence (AI) problem, and…it does require a lot of technical heavy lifting.”

“Microsoft recently announced that it’s taking a huge $6.2 Billion writedown over the failed aQuantive acquisition. This news, and the scrutiny of Facebook’s business model following their IPO drama, show that, in online advertising, it’s all about the targeting.”

The Interest Graph Makes Ads Relevant

The Interest Graph makes ads more relevant to audience members, which increases performance, which makes buying social ads more efficient.

Benjy Weinberger at TechCrunch explains why this is important:

“Google AdWords remains phenomenally successful, generating over $36B in revenue in 2011. The key difference? targeting. Google’s sophisticated ad-targeting algorithms greatly increase the relevance to the user, and therefore the likelihood of the user clicking on an ad. This is what makes AdWords so much more effective than banner ads.”

What Is Relevance?

Think about your mailbox. We intuitively know what relevant mail is: important bills, gifts, thank you notes from friends, wedding announcements, etc. We also know what irrelevant mail is: credit card offers, coupons, and announcements that we don’t care about. There’s value in separating out the irrelevant stuff and just leaving what’s relevant. What if you had a friend who went through your mail before you got to it, throwing away all the junk and presenting you with only the good stuff? That’s what good targeting algorithms try to do with advertising.

On the face of it, people may think that no advertising is relevant to them. However, everyone buys things from time to time and everyone welcomes a great recommendation. Consider the value of having an informed sales person help you choose between expensive electronics, or the value of a music recommendation from someone you trust.

Relevant Ads Means Happier Advertisers and a Happier Audience

Increasing the relevance of advertising messages is just another form of efficient recommendations. If a brand advertiser can present its ad to people who find it relevant, the ad is no longer an ad: it is a welcome recommendation.

Our imaginary user “Andrea” can help here. Assume that, as a San Francisco resident, Andrea is used to seeing the print ads on the sides of Muni buses in the city. One day, Andrea sees a Muni ad for “The Book of Mormon,” a new musical by Trey Parker and Matt Stone, the creators of South Park. Since Andrea is a South Park fan (and she had already heard of the musical’s debut in New York), she’s thrilled to see the ad. The ad was not just a request to buy tickets to “The Book of Mormon”: it’s a message relevant to her.

The Interest Graph: a Better Predictor of Brand Affinity

Online advertisers have historically used a couple ways to target their ads:

1. Advertising on sites or apps that fit into verticals: for example, if Chevrolet wanted to advertise on news sites, they might start with Wired Auto before moving on to the Financial Times. Because the site is relevant to the ad, the ads usually perform well, but advertisers sometimes want more scale than this approach provides.

2. Showing ads to people based on browser history: for example, a shoe retailer might target people who have visited a shoe retail site in the last 60 days.

But neither of these approaches fits the new, decentralized, content-driven nature of the internet. People visit social first and branch out from there, based on what their friends and influencers recommend to them.

But a powerful, third form of targeting has emerged that’s perfect for social: Interest Graph targeting.

Interest Graph targeting transcends platform and can be applied everywhere, even on mobile, because it’s not tied to device IDs, browser history, search history, or browser cookies. It 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%, which significantly outperforms standard the banner ad CTR of 0.01%.

Key Players Are Moving to Bring the Interest Graph to Advertising

As we discussed, the interest graph is extremely useful for making effective recommendations. Companies of all kinds are capitalizing on this: for example, Highlight and Airtime are using interest graph technology to recommend new people to follow.

In the advertising world, four main players are working the problem of using the interest graph to improve advertising recommendations: Facebook, Twitter, Google+, 140 Proof, and Gravity.

Here’s how 140 Proof deploys the interest graph 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 asks 140 Proof’s interest graph algorithm for a relevant ad based on Andrea’s interests. 140 Proof assesses Andrea’s profile, confirms that the profile is marked public, and determines that she follows Ryan Lochte, Dara Torres, Patton Oswalt, and she has mentioned Comedy Central shows like South Park, qualifying her broadly for the “Sports” and “Comedy” categories. 140 Proof 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 the brand.

Now you should have a sense of what the interest graph is, why it’s important, and how it can help advertisers connect better with users. Do you have questions about the interest graph and all the ways it can be used to improve recommendations and advertising? Get in touch with us at [email protected]

Contributors: Jon Elvekrog, John Manoogian III, Vanessa Naylon & Lau Ardelean