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 hello@140proof.com.
by: 140 Proof Research Team
Contributors: Jon Elvekrog, John Manoogian III, Vanessa Naylon & Lau Ardelean
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April 29, 2013 - 2 weeks ago
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Now that we have introduced the concept of the interest graph and why it’s the ultimate recommendation engine for brands and technologists, we’ll explain the basics of what the interest graph is and how it’s mapped and analyzed to offer relevance to businesses.
What Is the Interest Graph?
The interest graph is made of Likes, Follows, and other social relationships between people and things, products, or brands. As Naval Ravikant, founder of AngelList, described in TechCrunch, the interest graph is asymmetrical, organized around interests, public by default, and aspirational.
Relationships in the interest graph are asymmetrical, meaning they’re one-way follow relationships (not two-way friendships). Users can follow or like @Rihanna without Rihanna being required to follow or like the users in return. This means that they’re organized around interests, not friendships. In fact, friendships are described by the social graph, a picture of who knows whom. The social graph has been explored for various purposes, including advertising, but its best use tends to be limited to making recommendations for more friendships. (You may have seen the “People You May Know” sections on LinkedIn that encourage you to connect with more people.) It’s important to make the distinction that knowing each other in real life isn’t required by the interest graph. You can like Tom’s Shoes, Converse, and Puma without having ever visited their retail stores.
And because mutual following is not required, people can follow based on what they want versus who they already know. This means following is aspirational. You can follow @BillGates on Twitter because you admire his philanthropic work and want to learn more about it, without Bill Gates needing to decide you’re worth following in return. Asymmetrical following emphasizes interests because it helps people reach for their wishes and hopes.
Due to the nature of social platforms, interests (and the interest graph) are public by default. Information about who and what people follow is standard public information in user profiles, which means that it’s not only revealing, it’s noninvasive too. (However, users can make their profiles private, and some ad platforms use this as an automatic opt-out feature.)
The Blended Interest Graph (BIG)
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 and/or data streams, each proprietary interest graph is available to be analyzed in aggregate. For the purposes of this article, we’re talking about all of these secondary interest graphs taken as one — the Interest Graph. Think of it as:
- The Facebook Individual Interest Graph
- and Twitter’s Individual Interest Graph
- and Google+’s Individual Interest Graph
- and Foursquare’s Individual Interest Graph
- and Pinterest’s Individual Interest Graph
- and Instagram’s Individual Interest Graph
…and on and on. The sum of all individual interest graphs is the Blended Interest Graph (BIG) for online social platforms.
BIG = Big Data
The interest graph is made up of basic social building blocks like Facebook Likes and Twitter Follows. 140 Proof CEO Jon</h>s Elvekrog likes Domino’s Pizza on Facebook. That’s one data point for the interest graph. For example, 140 Proof CTO John Manoogian III (@jm3) follows creative agency @Mekanism and Forbes journalist @a_greenberg on Twitter. That’s two data points for the interest graph.
These individual data points add up to a huge set of information. And the interest graph is growing at an accelerated pace. Every relationship in social is represented by the interest graph.
As new users join and follow influencers, the data set 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 about 266 billion Likes and Follows.
The interest graph is a picture of the present moment. To analyze the interest graph is to understand what’s happening right now. Unfollows are discarded and new follows are included and folded into the analysis.
How Do Social Companies Analyze the Interest Graph?
Understanding and mapping the interest graph requires a dedicated team steeped in information theory, big data architecture, and lightning fast calculation. The biggest challenge in harnessing the power of the interest graph is making millions of decisions in real time. With over 2 billion new interest signals every day, any delay in processing means relevance could be compromised. At 140 Proof, an elastic architecture composed of hundreds of cloud servers grinds public social data, collects interest signals from social platforms, analyzes the data, and makes rapid decisions about people and personas. Dedicated data scientists, engineers, and statisticians ensure that computation happens not just instantly but accurately.
by: 140 Proof Research Team
Contributors: Jon Elvekrog, John Manoogian III, Vanessa Naylon & Lau Ardelean
Read Part Three: How the Interest Graph Makes Ads Relevant
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April 15, 2013 - 1 month ago
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Why are tech industry titans so bullish on the concept of the interest graph? And why are CEOs, venture capitalists, and industry experts predicting big things for the company that can capture it?
The Basics
The fact that you follow Snoop Lion on Twitter and like Starbucks on Facebook means something. It’s important to a lot of people. One person likes nonagenarian actress Betty White and another likes hipster musician La Roux; one person likes Method soap and another likes PUBLIC Bikes; that’s worth a few cents to NBC, Arista Records, Tide, and Specialized.
Why is that? Because knowing what you love helps businesses understand what new things you might like. Relevance is the magic ingredient. Brands, bands, games, and teams you like are highly relevant to you. And for businesses who want to find people who might like what they’ve got, relevance is king. Advertisers have always been interested in what you like, and the fastest, simplest, most transparent tool for knowing what people like is something called the interest graph.
The Interest Graph Is the Ultimate Recommendation Engine
The interest graph could ultimately prove to be the best indicator of brand affinity. In this report, we’ll explain how that’s possible by walking through what the interest graph is and how it’s analyzed.
“Interest Graph” = “Virtual Diagram of Connections to What We Love”
The terminology is easier than it looks: A graph is a picture of the connections between objects. The picture can be real or imagined; what we care about is the data, not necessarily producing a physical graph. Objects, in this case, are people and things. In the interest graph, we understand what things 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 who her Facebook friends are. Her Facebook friends 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 I liked my local pool and Michael Phelps’s nonprofit for kids on Facebook, you could figure out she was an aquaphile.
The Interest Graph is growing in importance for social companies as they build out their future business plans. Because advertising is the main source of revenue for online businesses (see: Google), knowing which ads to show to whom is key to sustained happiness for both users and advertisers.
Technology Leaders Put Their Weight Behind the Interest Graph
The interest graph has attracted attention from technologists for its potential to deliver relevance for advertisers. Dick Costolo, CEO of Twitter, has said that the interest graph will offer “powerful value to advertisers.” And Naval Ravikant, founder of AngelList and 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 investor in Facebook, describes the interest graph as a powerful tool for creating “a very nuanced view of people.”
And Benchmark venture capitalist Bill Gurley, in conversation with Goldman Sachs, explains why he values the interest graph, especially over a concept like the social graph (a concept we’ll explain more later):
Social graph signals have not been helpful in optimizing advertising. It seems intuitive to everyone that your friends’ recommendations would be powerful motivators…but when you look a little deeper, you hang out with people who have very different tastes than you. And you may have a special affinity through a hobby or something that they don’t share. One of the mythical high grounds that everyone’s thinking about…is this notion of an interest graph. Facebook connects you with people you know. But what connects you, if you’re into road biking, with the top 15 road bikers that are within 15 miles of where I live?
[For a platform to] capture the interest graph, they’d be closer to the Google search paradigm, because they’d be right in line with demand generation, and with discovery that relates to product purchases. Context, for the history of the Internet, has been a big deal. The websites that do verticals, while they may not have abundant traffic, have always had huge CPMs, relative to the “Yahoo! Mail”s of the world. That may be this middle ground, between search and the social graph, to bring together people with like interests.
by: 140 Proof Research Team
Contributors: Jon Elvekrog, John Manoogian III, Vanessa Naylon & Lau Ardelean
Read Part Two: Defining the Interest Graph
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March 25, 2013 - 1 month ago
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[Twitter] said in a blog post that it will allow advertisers to use an application programming interface, or API, which makes it simpler for marketers to automate purchases of ads on Twitter.
Until now, marketers for the most part have to manually set ad details, such as the amount of money allocated for Twitter ads and the type of users the marketers wanted to reach. The manual method clashes with how the world’s biggest marketing agencies and companies typically buy digital ads in huge volumes.
Facebook made its own [API] broadly available less than a year before its 2012 IPO, and research firm eMarketer Inc. estimates roughly 60% of Facebook’s advertising revenue comes from ads bought through its API. The move also amps up pressure on Twitter to prove its ads are effective.
An API also carries some risk. Right now Twitter works directly with most of the big companies that buy ads on the company’s service, and it is ceding some control in allowing marketers to buy ads on their own or through marketing intermediaries.
If Twitter ads don’t perform as well as others, it will be more clear to advertisers and they can move their ad dollars accordingly.
Company officials described the API announced Wednesday as a first step, and Twitter’s ad offering for now is more limited than those on other digital services. For example, Facebook’s advertising system allows marketers to use data gleaned from other websites to target ads on Facebook.
“This new ads management API release is really just a small incremental step forward,” said John Manoogian III, co-founder of social advertising firm 140 Proof. “Blended interest graph data from many social sites and services is what drives real relevancy in social ads, and we’re a little disappointed to see that Twitter isn’t yet adopting those approaches that customers have been asking for.”
The San Francisco company said in a blog post that it will allow advertisers to use what is called an application programming interface, or API, which makes it simpler for marketers to automate purchases of ads on Twitter.
Until now, marketers for the most part have to manually set ad details, such as the amount of money allocated for Twitter ads and the type of users the marketers wanted to reach. The manual method clashes with how the world’s biggest marketing agencies and companies typically buy digital ads in huge volumes.
“We think it will give you a fuller set of options to manage advertising on Twitter,” April Underwood, a Twitter product manager for revenue, wrote in the blog post. Twitter said it has been testing the API since January.
An API, already in place at bigger companies such as Facebook Inc. and Google Inc., has the potential to significantly boost Twitter’s advertising revenue, but it also amps up pressure on Twitter to prove its ads are effective.
Twitter also has to tread carefully to avoid annoying its users with ads, and to maintain the company’s nascent relationships with advertisers.
Read the full story on wsj.com
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February 25, 2013 - 2 months ago
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Conquer globally, target locally. That’s the offer we’re extending to all our brand advertising customers. Over 200 million Facebook users include a location on their posts and updates. Foursquare users alone create over 5 million check-ins a day — that’s over 50 check-ins per second.
Brands who advertise in social are adding a new arrow to their quiver: Location Check-in Targeting (LCT). Brands can use LCT to make their messages even more relevant to their audience. (And since social users value sharing and real-time conversation, they’re more likely to respond to and pass along the brand message than other audiences.)
One of our customers recently took advantage of location check-in targeting to bring local flavor to a national campaign. As a sponsor of the US Open, this brand decided not only to publicize its sponsorship to nationwide US Open viewers but also to the lucky fans who were posting photos and tweeting from the event itself, via their favorite social apps and sites.
Case Study: Tech + Tennis
Objective: Drive National Brand Awareness
Increase brand awareness of a major technology company sponsoring the US Open through Blended Interest Graph targeting.
Goal: Reach Tech + Tennis Fans
The campaign used two strategies to reach tech enthusiasts and tennis fans to amplify US Open sponsorship tie-ins:
First, reach a national audience with the Blended Interest Graph, by targeting tech followers and tennis fans following IBM, Wired, Cloudera, Roger Federer, #USOpen, NBC Tennis, and the Williams sisters.
The brand also maximized reach among US Open attendees by targeting people based on their location-based Foursquare check-ins.
Targeted Check-In Locations: US Open Sites
- USTA Billie Jean King National Tennis Center
- Billie Jean King National Tennis Center
- US Open 2012
- Arthur Ashe Stadium
Brand messages ran just before and during the US Open across 140 Proof’s network of social apps and sites, driving online and foot traffic to customer sites both online and real.
Results: Social Performance at Scale
- Highly targeted campaign achieved 15x CTR versus standard display
- Campaign performance stayed high in the post-event period, capitalizing on buzz and sharing
- Local check-in targeting drove event attendees to sponsor hospitality
- Reached: Highly engaged target audience — and their friends
- The campaign achieved a 5% earned reach bonus via sharing, and the average follower count for sharers was over twice the national average.
What would you like to know about location check-in targeting? Let us know in the comments. If you’d like to add location check-in targeting to a specific brand campaign, write to us at hello@140proof.com.
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February 18, 2013 - 2 months ago
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140 Proof is proud to sponsor this week’s cover of Ad Age’s print magazine as part of our effort to get out the message about Blended Interest Graph targeting technology for brand advertising.
The sponsorship kicks off 140 Proof’s campaign in 2013 to help marketers and media planners everywhere understand what the Blended Interest Graph can do for brands.
B.I.G. = Blended Interest Graph
The Blended Interest Graph unites audience data from social platforms like Facebook, Pinterest, Tumblr, Twitter, and the next generation of digital communities.
Our customers call it B.I.G.
Because it maps over 20 billion connections between people and the things they love, the Blended Interest Graph is the perfect targeting technology for brands. And it’s exclusively available from 140 Proof.
Learn more about the Blended Interest Graph by downloading our Special Report: Inside the Interest Graph
“Think B.I.G.”: About the Cover Image
For the cover, 140 Proof Creative Director Lau Ardelean (@lauardelean) built a virtual city using Cinema 4D and Illustrator to help show the scale of data included in the Blended Interest Graph. Much like the crowded borough of Manhattan, the Blended Interest Graph is bursting with interest data from social platforms, and 2 billion new data points are created daily. Ardelean collaborated with Creative Strategist Vanessa Naylon (@vnaylon) to explain how 140 Proof helps ambitious brands create winning social advertising campaigns. Moral support and backseat design-driving by John Manoogian III (@jm3).
Think B.I.G. Wallpapers for Desktop and iPhone
Think B.I.G. even when on a small screen. Download the cover image as a wallpaper:
[ B.I.G. City Desktop ] [ Just plain B.I.G. Desktop ] [ iPhone ]
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January 28, 2013 - 3 months ago
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How would you navigate a city of 20 billion?
Tokyo, the world’s largest metropolitan area, hosts almost 36 million people. Its rail system, also the largest in the world, transports 40 million passengers daily. But even Tokyo’s efficient, modern infrastructure wouldn’t be prepared to deal with a population of 20 billion.
That’s the engineering challenge that 140 Proof Labs developers faced when they set out to create the Blended Interest Graph. The B.I.G. unites audience data from social platforms like Facebook, Pinterest, Tumblr, and Twitter. This data includes Likes, follows, pins, check-ins, and other public social signals like hashtags and keywords.
It’s the biggest map of audience interest data in social, bigger than any one platform. It’s a massive virtual city built from the connections between people and the things they love. And with over two billion new public data points created every day, the Blended Interest Graph is not just big but constantly growing.
140 Proof architected the Blended Interest Graph to help brands navigate the sprawling world of social. Brand advertisers and media planners use 140 Proof’s B.I.G. targeting technology to power their social advertising campaigns. B.I.G. targeting helps ad campaigns reach target audiences at scale based on who people follow, what they like, and other social signals that indicate interests.
140 Proof builds custom audience segments that include fans and followers of influencers, related brands, check-ins, pins, trends, and more. For example, to promote their recent sponsorship of the U.S. Open, IBM reached people publicly checking in at the event with Foursquare with custom creative designed for event attendees. And family-friendly brands can reach family decision makers by targeting people who pin baby photos, clothing, and furniture on Pinterest.
What’s the point of building this virtual city of interests? Relevance is the key to why we do what we do. When messages between people (or between brands and people) are more relevant, life gets better. Decisions are more efficient, and people are happier. The trains run on time, and the city keeps on moving.
To talk to us about using Blended Interest Graph targeting technology for your brand campaign, contact us at hello@140proof.com.
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January 28, 2013 - 3 months ago
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Ten campaigns of 2012 raised the bar for social stream initiatives, by virtue of their unique adaptation to the social stream, specialized targeting, or creative approach.
Let’s jump in:
10. PBS: Downton Abbey
For PBS, Downton Abbey was a big hit with social users. People were always talking about it. Even Patton Oswalt couldn’t help live-tweeting it. Maybe that’s why the Downton Abbey Season 2 social ad campaign won such a big response with 140 Proof audiences.
Aided by interest graph targeting and exclusively focusing on native social ad units, this campaign had the highest performance (by CTR) of any campaign that ran in 2012. We doff our caps to you.
9. Alaska Airlines: Disney Dream Suite
Alaska Airlines, in a West Coast initiative in partnership with Disney, aimed to drive people in social to enter the Disney Dream Suite Sweepstakes. Disney fans could enter for a chance to win an evening’s stay in one of the most special places in the entire Disneyland Resort, the Disneyland Dream Suite, built to honor one of the dreams of Walt Disney.
Since 2011, moms and other family decision makers have taken to social in droves, so 140 Proof could offer Alaska Airlines a large audience to reach with the opportunity. The grand prize: a trip to Disneyland Resort, including a once-in-a-lifetime stay in the Disneyland Dream Suite.
8. 5 Gum: #TheSwitch
To celebrate the launch of new RPM™ flavors, 5 Gum integrated a launch campaign across multiple social platforms, most notably YouTube and Twitter. 5 Gum’s flavors emphasize duality and changeability, so they decided to help people choose music according to their tastes, too. 5 Gum sponsored #TheSwitch, a live online music event where viewers chose an artist to suit their mood. Viewers could stimulate their senses by switching between bands in real time according to their mood.
The first live session launched on March 28, and viewers were invited to select an upbeat, energizing performance from Givers, or a relaxing, soulful set from Theophilus London. Archived performances are available on YouTube.
7. Goldman Sachs: 10,000 Small Businesses
Goldman Sachs, as part of a nationwide economic recovery effort, designed its “10,000 Small Businesses” program for business owners poised for growth in selected cities across the country. 140 Proof helped amplify awareness of special content promoted by Goldman Sachs, highlighting stories of economic progress.
Participants applied for the 10,000 Small Businesses program and were selected based on involvement in their own communities and current business revenue. They were rewarded with educational seminars and peer review from investment luminaries such as Warren Buffett.
6. Nicki Minaj: Pink Friday
Rap artist Nicki Minaj is shooting for the celebrity fragrance hall of fame. Differentiating yourself in the celebrity fragrance market these days ain’t easy: stars must contend with other big names such as Katy Perry’s Purr and Lady Gaga’s Fame.
To announce the availability of Pink Friday, Minaj chose social as a primary awareness vector. 140 Proof helped Minaj reach her fans through the Blended Interest Graph, targeting fashion, celebrity, music, beauty, and urban audiences. The campaign drove to the website of partner retailer Macy’s, where fans could pick up the fragrance in its trademark pink-and-gold bottle.
5. Chevrolet: Super Bowl
The new way to run a successful Super Bowl campaign now requires more than TV. To make it in the top ten advertisers for the Super Bowl, a brand must have a social strategy.
For Chevrolet, that meant taking over the mobile, desktop, and tablet screens of automotive and sports audiences on Super Bowl Sunday to drive awareness of their TV spot. A classic Continuity strategy. It worked! Chevrolet survived the Super Bowl, just like its trucks survived the Apocalypse.
4. ESPN: Monday Night Football
The cool thing that ESPN did with Monday Night Football was turning a standard Second Screen strategy inside out: instead of running the flight during the show in order to catch people tuning into the show, they conducted time-locked, 6-hour flights encouraging people to tune into Monday Night Football.
Many media brands haven’t yet discovered that they can build up to events in social much like they do in TV media. ESPN has one of the best, most diverse media strategies in social right now, and social is a perfect space for them, too. Sports fans are highly active on social platforms and there are a lot of influencers for them to choose from when building a targetable audience.
3. Oscar Mayer: Bacon Barter
Josh Sankey had one task: cross the United States. But he also had one challenge: no money. To make his way from New York to Los Angeles, he needed to trade the only thing he had in abundance: bacon.
Oscar Mayer, seeing the potential their Butcher Thick Cut Bacon had for people nationwide, assigned Sankey the Bacon Barter assignment and promoted the journey across social, where it was most likely to be taken up by social audiences and go viral.
2. Skittles
One thing that ordinary people do to set themselves apart in social platforms is they write great copy. That’s what Skittles set about doing here. Sample creative from the campaign: “Remember to go through life with open hands. No one can pour Skittles into a fist.”
Skittle dares to use social the way it was meant to be used. Great social creative never lasts long and isn’t a perfect brand message, but it does fit perfectly in a social context. Congratulations, Skittles.
1. IBM: US Open
To carry off the greatest paid social campaign of 2012, IBM took advantage of the Blended Interest Graph to promote its sponsorship of the US Open. IBM took 140 Proof’s powerful, standard interest graph targeting and added a twist. To reach their chosen audience of tech followers and tennis fans, they targeted the followers of influencers like IBM, Wired, Cloudera, Roger Federer, #USOpen, NBC Tennis, and the Williams sisters. Then, in an innovative expansion, they also targeted all people checking in locally at the US Open.
By using newly developed location check-in targeting, IBM reached 5,000 US Open attendees with locally-tuned messaging while conducting a larger campaign nationwide reaching fans following the event on TV.
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December 14, 2012 - 5 months ago
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