Who are the most in-demand audiences in social? While consumers are everywhere, marketers and advertisers value certain audiences highly for their social campaigns.
The analysts at 140 Proof Labs examined demand data for audiences in social, ranking the most requested audiences for Q1 2013. The more highly an audience is ranked, the more fervently brands were trying to reach it.
The #3 most in-demand audience was Sports Fans, and the #2 top persona was Mainstream Music Lovers. Who do you guess topped the list at #1?
Learn about the top 10 most desirable personas and where they are found in social:
If the embedded presentation above doesn’t appear in your browser, click to view Top Personas in Social Advertising on Slideshare.
We’ll update the top personas list regularly to keep you apprised of who brands are looking for in social.
September 11, 2013 - 2 months ago
Travelers have few things in common. You can safely assume most travelers have a destination in mind and the financial means to reach it. But the differences end there.
While waiting for your next flight, look around at your fellow travelers and try to guess what travel persona they fit. Are the folks in Premium Economy returning from a family trip? Is there a young couple wearing backpacks and hardy walking shoes, who might later be making a connection to an international flight? Is the last guy in the boarding line traveling solo? (That’s me — I’d rather wait at the gate than on the plane!)
Everyone you see bought a ticket for the same flight. (Congratulations, United.) But what other travel-related products did they purchase for the trip? How were those products marketed and advertised? How were those ads targeted?
Persona-based targeting creates an advantage for travel marketers. With persona-based targeting, marketers infer what people like, and what they will respond to, based on the information people provide voluntarily via social channels. This data is then used to build relevant audience personas, such as “business travelers,” or “deal seekers.” As a result, brands minimize waste in campaigns by targeting the right ads to smartly segmented subgroups — or personas — with a high degree of precision.
Here’s how to use persona-based targeting in the context of travel marketing:
When designing a campaign around a travel persona, start by compiling audiences of travel-related influencers and brands.
For example, Starwood Hotels, which operates the St. Regis in Park City, Utah, could add people who like Virgin Airlines or Deer Valley Resort on Facebook, and who follow the Twitter and Pinterest accounts of Another Something, a travel and style blog that “assumes its readers to be smart and savvy,” and was “last seen scouting Chilean designers at Milan Design Week.”
Next, look at what keywords people are seeing and sharing in order to add additional context. What’s motivating their travel? Do they like Southwest Airlines and tweet about the hassle at the rental car agency, or are they posting collections of photos or videos of exotic locations? Use the context and insight from social channels to differentiate between business warrior and world adventurer personas, and craft offers accordingly.
You can also use content shared on social channels to identify in-market travel shoppers, or use social check-in activity to gain further insight into a persona. For example, Starwood Hotels could target world adventurers who have checked into any competing luxury resort or hotel property around the globe.
Here are three travel personas and how to target them:
Business travelers aren’t usually traveling to exotic destinations. They shop for efficiency and practical comfort. You’ll find business travelers following CEOs and startup leaders on Twitter and checking in at airline lounges on Foursquare.
World travelers like to document their explorations as they conquer their bucket list. You’ll find them creating Pinterest boards ahead of their trips, posting photos and videos to Instagram and Vine while they travel, or checking into off-the-beaten-path hotels and hostels.
Family vacationers often have to spend more time in the planning phase to meet the demands of traveling in a group. You’ll find them mostly on Twitter and Facebook, following airfare and hotel deal sites before travel and posting photos and updates during their trip.
By Jon Elvekrog
This article originally appeared in MediaPost on July 29, 2013.
August 13, 2013 - 3 months ago
Through time, human beings have endeavored to create increasingly accurate representations of the human form. What started with crude cave paintings and rough sculptures eventually matured into the stylized art of ancient Egypt, and evolved over time to ultimately reach a pinnacle with Leonardo da Vinci’s Vitruvian Man.
Persona targeting in advertising has evolved in a similar way. Just as developments in art were driven by improvements in technique and increased knowledge among practitioners, targeting improvements in advertising have been driven by technology, and expertise has accumulated over time that has enabled us to better describe our fellow man.
In the beginning, brands simply targeted ads based on location… with everything from flyers posted in the town square to the out-of-home ads and billboards we still see today. Advertisers fumbled for audiences based on neighborhood demographics and nearby businesses. This type of targeting is the equivalent of Paleolithic cave paintings: rough, raw, imprecise.
And then there is print media targeting, which is more akin to ancient Egyptian art: bold but lacking dimension. Advertisers plan print media buys by selecting publications and circulation geography.
Then came television, long the gold standard for advertising. TV advertising approximates the Byzantine art of the 1300s: fairly representative and aware of perspective, but still imprecise. In addition to targeting vectors like location and audience affinity, TV also allowed advertisers to target by time of day. Enter: Saturday morning ads for Cocoa Puffs and Count Chocula.
But because many different types of people watch the same shows, there are awkward gaps in TV’s ability to target. A multi-generational family of grandparents and kids watching Dad’s favorite sitcom see the same ad for muscle cars, wasting potential impressions.
As we each spin off mountains of data while Facebooking and tweeting, a new way of looking at targeted populations has emerged that is beginning to replace the cruder ways of persona targeting: the interest graph. Interest graph is comprised of publicly available information like self-declared interests; what people share (e.g., photos from a biking trip); who people follow; and what people say online, what they retweet and what they post. It also includes “feedback loop” information from what people actually respond to, such as receptiveness to a particular campaign, which then feeds back into the database.
Since the interest graph is an actually representation of who people are and what they like, it enables marketers to better match their offers and ads to people who will actually be interested in them. This means less wasted effort, better matching… and ultimately better ROI.
Here’s what this looks like in practice for a popular audience segment: auto enthusiasts.
The challenge for automotive brands is identifying people who are currently in the market for a new or used car and people, as well as those who are generally interested in auto topics and products, regardless of whether they are currently shopping.
Traditional targeting methods include out-of-home ads near auto dealerships, print ads in auto magazines or local phone books and mass-market television — all pretty rough ways at finding this audience. But with the interest graph, advertisers can hone in on just the people who have demonstrated interest in automotive topics by following brands like Ford and auto blogs like Jalopnik. They can look to see that the top geos for auto enthusiasts include Indianapolis, Columbus, Milwaukee, and that in addition to typical peak Internet use times, this audience is also active at 11:00 p.m. Central… and adjust their strategies accordingly.
As the above example shows, brand advertisers can now paint a more accurate picture of their audience and target their brand messages more precisely than ever before. With the interest graph, we have reached an evolutionary point in a persona targeting equivalent that rivals Leonardo da Vinci’s Vitruvian Man: the most accurate representation of humanity that has ever been available to marketers.
This article originally ran in MediaPost on July 15, 2013.
July 18, 2013 - 4 months ago
Demographic profiles have long been used by marketers to segment their audience and target offers to people who are more likely to be receptive than the general population. For example, a marketer for Forever 21 might target the single, female, middle-class, age 18 to 24, college educated demographic.
And yet, critics of demographic profiling argue that broad-brush generalizations can only offer limited insight, and that their practical usefulness is debatable.
But with the advent of publicly available social data, we no longer have to guess who people are and what they like solely based on their age, gender or a few web site clicks. We can now infer who people are and what they actually like based on the information they provide voluntarily via social channels.
At 140 Proof, we use this data to build precise social “personas”, such as “eco-moms” or “auto enthusiasts.” These personas allow the brands we work with to better match their offers and ads to people who will actually be interested in them, meaning less wasted effort and ultimately better ROI.
Over the next few months, we’ll be diving deep to discuss the hottest persona-based audiences: who they are, how they’re targeted with social data, and what we’ve learned about them.
How 140 Proof Builds Personas
Unlike demographic profiles that are based on conjectures and assumptions, 140 Proof social personas are based on the Blended Interest Graph: a rich mosaic of publicly available information that spans multiple social networks. The Blended Interest Graph includes things like self-declared interests; what people share (e.g. photos from a biking trip); who people follow; and what people say online, what they retweet and what they post. It also includes “feedback loop” information from what people actually respond to, such as receptiveness to a particular campaign, which then feeds back into the database.
140 Proof has indexed over 30 billion social connections across multiple social networks, which allows us to build personas for both popular, broad audiences and very desirable niche audiences that are hard to build with demographics alone.
To build a specific persona, 140 Proof starts by targeting followers of relevant and influential Twitter handles. For example, to create a Gadget Geek persona, we’d aggregate followers of tech brands and bloggers like Engadget, Gizmodo, Daring Fireball, and Android and Me.
Then we narrow the targeting based on the context of the campaign by adding relevant keywords, additional follower relationships or even social check-in activities.
For example, to reach a Millennial TV Fans persona for an Emmys-related campaign, we would choose influencers from relevant media properties, such as the stars of Emmy-nominated shows like So You Think You Can Dance, The Voice, and Late Night with Jimmy Fallon, and then use keywords like “Oscars” or “Olympics” to identify Millennials who historically tweet about televised events.
Persona-based targeting also works great for second screen campaigns, where brands run social ads at the same time their commercials air on popular TV shows. For example, a clothing retailer advertising during Glee might supplement their usual Young Women persona targeting with additional filters to reach women following Twitter accounts like @GleeOnFox, @Gleeks, and @GossipGirl or Tumblrs like The Real Blair Waldorf.
Social check-in activities can also provide a powerful way to augment a persona; for example, a sportswear company might want to target social ads for a basketball star’s new shoe brand to Young Sports Fans who’ve checked in at stores like Foot Locker.
Finally, we layer on insights from across the Blended Interest Graph to make the campaign really sing - such as when that particular persona is most active on social, which locations have the highest concentration of people in that group, and related (and sometimes unexpected) personas that also index strongly for the campaign.
Sure beats simple age and gender, don’t you think?
In our next post, we’ll look at some of the most in-demand personas and provide some insights into how best to reach each of them.
June 21, 2013 - 5 months ago
This article is third in a three-part series about the interest graph. Need a refresher? Back up to the first post or the second post.
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 firstname.lastname@example.org.
by: 140 Proof Research Team
Contributors: Jon Elvekrog, John Manoogian III, Vanessa Naylon & Lau Ardelean
April 29, 2013 - 7 months ago
This article is the second in a three-part series about the interest graph. Read the first post or skip ahead to Inside the Interest Graph Part 3: How the Interest Graph Makes Ads Relevant.
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 - 7 months ago
This is the first part of a three-part series on the interest graph. To skip ahead, read Inside the Interest Graph Part 2: Defining the Interest Graph.
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 - 8 months 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 - 10 months ago