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.
“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@example.com.
by: 140 Proof Research Team
Contributors: Jon Elvekrog, John Manoogian III, Vanessa Naylon & Lau Ardelean
April 29, 2013 - 2 weeks ago
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
April 15, 2013 - 1 month ago
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 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
March 25, 2013 - 1 month ago
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 ]
January 28, 2013 - 3 months ago
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 firstname.lastname@example.org.
January 28, 2013 - 3 months ago
When we got our start, there wasn’t anything really resembling a “social ad” on popular social platforms, and the Facebook Like hadn’t even been invented yet. We created 140 Proof Platform and our Relevance Engine technology with Twitter as our springboard, and our partners came to us from the verdant ecosystem of Twitter apps. Some of the first publishers in our network were the most popular apps in the Twitter ecosystem, and this was a great proving ground for us.
But we knew that focusing on a single social network for data and audience wasn’t enough. Our future would grow beyond the ecosystem regime and beyond the “tweet ad” that some of our die-hard customers still love. And we have been transforming our focus all this time in a number of ways:
First, we’ve been growing 140 Proof Platform in what, as social has matured, has become a universally integrated social landscape, greater than the sum of the many social network parts. Beyond apps and tweet ads, we’ve grown our audience and network to encompass swaths of socially plugged-in digital users wherever they go. Big premium publishers, myriad flavors of smartphone games, tablet weather apps, and desktop utilities, messaging apps, browser extensions, and of course the good old website (where digital advertising got its start) are all part of the 140 Proof Media Network now.
140 Proof Platform targeting technology, audience, and data assets have evolved too. The interest graph-powered Relevance Engine that matches audiences with relevant ads was originally powered by public Twitter API data, and now it weaves public data from many social sources — including platforms like Foursquare, Tumblr, and Pinterest. The blended interest graph is larger and richer than single platforms: we draw the richest social signals from over 2 billion produced daily. It’s the full picture of global social relationships between people and their interests as defined by what they follow and like, and the content that they see. The blended interest graph allows us a more dimensional picture than any single platform can on its own. (If you’d like to learn more about the Interest Graph, read our special report.)
And third, we’ve created a rich portfolio of creative options for our customers. Our network supports rich media ads, video ads, and mobile banners, and we still do donuts in social with 140 characters of text. Your ads can go anywhere, on any device, and sometimes they auto expand into their own apps. A cruise line can present a full microsite within our audience’s favorite iPad apps, popping tantalizing galleries into view with a tap of a banner. A finance brand can bring its latest product video to people on Scribd.com browsing for articles on investing. Or, highly social CPG brands can run short, shareable messages across all screens and devices — natively just like they always have with us.
As we’ve always felt, social is everywhere, and it’s not owned by any one social network or platform. Social doesn’t even live only on apps or the “.com’s” of Silicon Valley. Social manifests itself as more than streams and feeds and even the ecosystem; it’s a layer blanketing all of our online, and increasingly offline, activity.
Sometimes people, hearing about how our scope has grown, ask us if we’ll change our name. We plan to stick with 140 Proof, to honor our deeply social roots and the social DNA in every aspect of 140 Proof Platform and the Relevance Engine. We’re excited about the course we’ve chosen into the frontier of social, and we’re delighted to have you along for the journey.
— The 140 Proof Team
November 13, 2012 - 6 months ago
Tech industry titans are bullish on the concept of the interest graph. And CEOs, venture capitalists, and industry experts are predicting big things for the company that can capture it.
Today 140 Proof is proud to announce the first industry report on the interest graph: what it is, how technologists understand it, and why it’s important.
To learn more about what makes the interest graph the ultimate recommendation engine, click here to download the report:
Read the press release on Yahoo!: Interest Graph Growing by Over 2 Billion Data Points Per Day
August 30, 2012 - 8 months ago
You knew instinctively that the breakout popularity of social networks meant that ads were close behind. Advertisers go where the crowds are. But even those of you who have been following social for years might be surprised at how quickly social advertising has grown and flourished.
Some social networks, like MySpace, Facebook, and YouTube, had advertising built-in virtually at the time they launched. Some, like Twitter, resisted the pull to monetize and moved slowly (their progress led by other players like 140 Proof). Others, like Google+, still haven’t plugged in the advertising machine.
Social Ads: a Timeline of Events
- September 2003: MYSPACE LAUNCHES. MySpace reached the 1 million user mark within a month of its official launch. It was founded by employees from eUniverse, a marketing company. Basic display ads followed soon after launch.
- February 2004: FACEBOOK LAUNCHES. Ads were an early addition to the platform, without real targeting or quality to speak of. As Facebook’s user base grew, so did advertising demand. Facebook didn’t really turn on the revenue firehose until COO Cheryl Sandberg joined in 2007.
- August 2007: YOUTUBE LAUNCHES VIDEO OVERLAY ADS. YouTube chose to try out banners over its videos first, asserting that users wouldn’t like pre-roll media. YouTube later added pre-roll videos in 2010.
- November 2007: FACEBOOK LAUNCHES ADS POWERED BY BEACON. Facebook had integrated Microsoft’s adCenter banners in 2006, but Beacon was Facebook’s first major attempt at building social features into its advertising. Beacon aimed to bring users’ browsing data from other sites into Facebook to improve ad targeting. However, the program attracted such a volume of user backlash that Facebook ultimately revised its social ad strategy.
- February 2008: GOOGLE LAUNCHES ADSENSE for YOUTUBE. The only social platform to give content producers a cut of ad revenue, Google integrated its popular AdSense program into YouTube to incentivize video publishers to stick around.
- August 2008: FACEBOOK TESTS “ENGAGEMENT ADVERTISING.” In the great debate between the open platforms and the walled gardens, Facebook has always preferred to keep its users within Facebook. That’s why it began testing Engagement Ads, which offered advertisers anything but a click for their calls to action. Comments, virtual gifts, and likes were the first calls to action tested, with event RSVPs and other formats soon to follow.
- January 2010: 140 PROOF LAUNCHES TARGETED SOCIAL ADVERTISING 140 Proof was the first interest graph based social ad platform, with sharing built-in to every ad. It initially started with a self-serve advertising platform and then pivoted its offering to focus on the needs of big brands.
- February 2010: 140 PROOF LAUNCHES ADVERTISING A.P.I. Developers of social apps great and small began plugging into 140 Proof’s social ads API, which uses interest graph data to match social users with brand messages that are relevant for them.
- March 2010: TWITTER CEO EV WILLIAMS DELAYS AD LAUNCH @ SXSW. Most pundits had their fingers crossed before SXSW that @ev would announce Twitter’s long-awaited ad strategy in his keynote interview. But millions of watchers were disappointed when, instead of an ad platform, Ev announced Twitter’s so-called “at platform”, which sought to bring Twitter functionality to more users. (It had nothing to do with ads.)
- April 2010: 140 PROOF LAUNCHES SHAREABLE ADS. Social means connections. Why shouldn’t a social ad be social? 140 Proof’s ads launched sharing + retweet-ability in 2010 for Twitter and Facebook-powered apps. How often do people share relevant recommendations and content? A lot, it turns out.
- July 2010: FORD REVEALS THE NEW EXPLORER EXCLUSIVELY ON FACEBOOK. Ford was one of the first big brands to invest heavily in social, and it showed in 2010 when they eschewed all traditional channels and turned to Facebook as the exclusive medium for their big reveal of the new 2011 Ford Explorer.
- December 2010: TWITTER FINALLY LAUNCHES UNTARGETED ADS. After a long period of PR and limited testing, Twitter eventually coaxed a few customers into trying its ad product, roughly one year after other players in the social ecosystem had started monetizing Twitter streams with paid media.
- January 2011: FACEBOOK LAUNCHES SPONSORED STORIES. Considered by the press to be version 2 of the ill-fated Beacon, Sponsored Stories feature user photos in each ad, which Facebook claims brings a 60% boost in performance.
- May 2011: 140 PROOF LAUNCHES VIDEO ADS IN SOCIAL. Boosted by the widespread popularity of video and huge user activity around live televised events like the Super Bowl, 140 Proof began offering video ads to brand advertisers who wanted to extend their TV and pre-roll campaigns to social.
- June 2011: GOOGLE LAUNCHES GOOGLE+ Google+ is Google’s fourth attempt at a social network (after Orkut, Wave, and Buzz). Intent on getting the experience right, Google+ didn’t have ads at launch — and it still doesn’t. Though none of us really expects the world’s biggest online ad company to hold off on Google+ ads forever.
- May 2012: G.M. PULLS ITS AD SPEND FROM FACEBOOK. Just days before the Facebook IPO, the press discovered that GM was pulling its entire Facebook ad spend — about $10 million. While some opined that GM was just trying to get a better deal out of Facebook, the scars still haven’t fully healed. In spite of the GM pullout, Facebook is on track for another billion dollar year in social ads.
- July 2012: FOURSQUARE LAUNCHES PROMOTED UPDATES. Three years after launch, Foursquare brings paid placement to its user feeds in the Explore tab with Promoted Updates, brand recommendations based on user searches.
What was your favorite moment in the rise of social advertising? Did we miss any big achievements, milestones, or missteps? Let us know in the comments.
August 7, 2012 - 9 months ago