Adobe Adds Foursquare to Adobe Social

One of the most exciting aspects of launching new social media publishers is seeing how that social data is used. Earlier this summer we announced that full coverage of Foursquare check-in data was available exclusively from Gnip. Today, Plugged In to Gnip partner Adobe announced that they’re first-to-market with a commercial Foursquare analytics platform.

While Foursquare has always provided tools for businesses to manage their own presence, the introduction of Foursquare data to Adobe Social creates entirely new opportunities. Large retailers will be able to understand check-in trends over time and compare their performance against the competition. Concert promoters will be able to analyze the impact of specific marketing campaigns. Sports teams will be able to see which games are popular with an engaged social media audience.

We’re thrilled that this data is now available to the broad range of customers who use Adobe Social and can’t wait to see how they use it to derive new insights to drive their businesses.

Kudos to Adobe for moving so quickly to adopt Foursquare into their platform!


Union Metrics for Tumblr Gets Even Smarter

Last year, Union Metrics for Tumblr was launched as the first Tumblr analytics product based on the full firehose of Tumblr data. Union Metrics, an A-List Tumblr partner, already counts Funny Or Die, Puma, Yale University, House of Radon, Digg, We Are Social, Rhode Island School of Design, and Hyatt among its clients.

Since that time period, Tumblr has continued to grow with more than 118 million blogs and 54 billion posts. Each day users create more than 75 million new posts. As Tumblr has grown, Union Metrics for Tumblr has grown alongside Tumblr, and today is announcing both awesome new analytics features and new pricing options.

We’re proud to call Union Metrics a Plugged In To Gnip partner, and we’re always excited to share the awesome products our customers are creating. What’s even better than us telling you about how cool some of these new features are? Hearing it straight from the source. We recently did an interview with Union Metrics founders, Jenn Deering Davis and Hayes Davis, about Union Metrics for Tumblr and what access to the full firehose of Tumblr social data means for bloggers, brands and marketers. What did they have to say? See for yourself!

Shiny New Features

Starting today, subscribers to Union Metrics for Tumblr can now track followers of their blogs in the Union Metrics interface, a highly sought after feature.The product now also provides integration with Google Analytics, giving users access to their blog’s traffic metrics alongside engagement performance of their content. These new features complement the existing detailed interaction and influencer analytics that Union Metrics for Tumblr already featured, coupled with competitor blog and keyword tracking. One of Gnip’s favorite features is the visualization tree of how content spreads on Tumblr, which is one of the most viral mediums on the planet.

Union Metrics Reblog Tree

Tumblr Analytics for the People

Union Metrics has also rounded out its subscription plan levels to meet the needs of professional bloggers and small business Tumblr users by adding a new lower ­cost tier, giving users the ability to monitor up to five of their Tumblr blogs for $25 per month.

Now that your interest is piqued, read more about the product updates and new features.

Building the Location Layer of the Internet With Mike Harkey of Foursquare

Mike Harkey, the Head of Platform Business Development at Foursquare, talks about how Foursquare is building the location layer of the Internet. 

Mike Harkey of Foursquare

To kick things off at Big Boulder, Gnip’s VP of Product, Rob Johnson interviewed Mike Harkey. As the Head of Platform Business Development at Foursquare, Mike talked about the evolution of Foursquare during the past four years. First introduced as the “check-in app,” Foursquare is now becoming known for its location recommendation services.

 Merchant Applications

As Mike stated, “the company is growing dramatically.” Foursquare recently received $41 million in funding in April 2013, and that is certainly shaping their growth. From a consumer application, check-ins and active uniques have grown 10% every month. However, Foursquare is really focused on providing real world applications for merchants, whose use has quadrupled in the past 6 months.

Foursquare has always offered a free solution for merchants to claim their business and run offers and specials within the app. Users can also follow merchants to keep an eye on these offers. However, at the end of the day this won’t matter if a merchant can’t see what needle Foursquare is moving for them. Enter merchant dashboards: Through the merchant API, merchants can track the value and success of their media campaigns and how Foursquare is influencing them.

 The Location Layer

Just as Facebook is the social layer of the internet, Foursquare has built the location layer. With 4 billion check-ins and 50 million places worldwide, it’s not hard to see why this data is so valuable and practical. And there’s something that’s fundamentally unique about Foursquare, in their ability to see real-time actions.

Foursquare is the first to find out when a venue opens and closes. This signal is not only beneficial for the application, but also for 3rd party platforms that rely on them. Maintaining the quality of data when it’s user-based is challenging but Foursquare has learned which levers to pull. A community of super users have the rights to edit and update data to help to “vet and validate” its quality. This further fuels the consumer application of Foursquare.

Using the Data

Foursquare check-ins show the pulse of New York City and Tokyo from Foursquare on Vimeo.

Foursquare holds itself to a higher standard with its data. They believe this data is not just theoretical, but has practical, real-world applications. For merchants, this means validating their presence on the app – according to Mike, 20% of users check-in to a place discovered by the recommendation service within 36 hours of discovery.

Since the founding of the company, people have wanted to access the data Foursquare provides. The API has always been open, but Foursquare has wanted to be careful about allowing access to the data. Gnip’s partnership with Foursquare to allow access to its firehose has tremendous possibilities for businesses. Examples include how individual users act during specific events. During Hurrican Sandy, Foursquare released visualizations around how people operated during and after a crisis.

Globally, using this data for good has been a priority for Foursquare. In Turkey, there was activity they didn’t expect during the recent riots. They had representatives on the ground of the riots and could see users posting photos and information as this was the only viable mechanism to expose this information.

The Future of Foursquare

Foursquare believes the applications for this data are virtually limitless, whether it’s making the data available for research or business applications. Foursquare is excited to see what people will build with their anonymized data from its partnership with Gnip. Foursquare has a number of products will be introduced this year. Soon, small businesses will be able to advertise through Foursquare and make the most out of this service. They will have the ability to turn on and off offers and reach long-term consumers.

Big Boulder is the world’s first social data conference. Follow along at #BigBoulder, on the blog under Big BoulderBig Boulder on Storify and on Gnip’s Facebook page.

Full Coverage of Anonymized Foursquare Check-In Data Now Available Exclusively from Gnip

We’re thrilled to announce our exclusive partnership with Foursquare to provide their full firehose of anonymized check-in data. Location is one of the most interesting ways to view data and no one understands the power of location like Foursquare. With more than 35 million registered users, nearly 4 billion total check-ins, and over 75 million API calls a day, Foursquare is the location layer for the Internet, helping to connect people with places around the world.

Foursquare has always believed in having a robust set of APIs so people can build great solutions on their data.  With today’s announcement we are offering commercial-grade access solutions that will bring a level of reliability, sustainability and completeness that has never been available before.

You may be wondering what it means to provide a full firehose of anonymized Foursquare data.  It means that we’ll be able to provide access to the realtime stream of every check-in that is taking place on Foursquare.  By providing only anonymized data with no form of user identification, Foursquare preserves users’ privacy, while unleashing countless geo-based use cases for businesses and researchers.  For example, this means we’ll be able to deliver all of the realtime activity happening at Starbucks in Portland but not who is checking in to each location.

The possibilities for what can be created with complete access to the anonymized Foursquare firehose seem endless. For example, The Wall Street Journal was able to do side by side comparisons of New York and San Francisco to find what makes each city tick. Retailers will be able to study the results of local advertising campaigns. Financial analysts will have another valuable data point to forecast Black Friday sales.  Real estate development groups will be able to better understand where they should develop new locations. As Blake Shaw, Foursquare’s data scientist, told us when we interviewed him:

“We are capturing this amazing signal about what millions of people are doing in the real world at every moment of the day in cities all around the globe. We have seen that when we aggregate check-in patterns across many individuals, we can measure features of cities at a higher resolution than was ever possible before. I think this data can act almost like a “microscope for cities.”

Foursquare will be joining our other premium publishers – Twitter, Tumblr, WordPress, Disqus, IntenseDebate, StockTwits and Estimize. We are offering both the full firehose and filtered access through our robust PowerTrack product.

We can’t wait to see what the world will build once they have access to the full Foursquare firehose!

To learn more, check out or email You can also head over to Foursquare to read their post, “Giving data nerds access to the realtime pulse of check-ins around the world.”

Data Stories: Interview with Data Scientist Blake Shaw of Foursquare

At Gnip, we believe the value of social data is unlimited. Data Stories is how we bring this belief to life by showcasing how social data is used. This week we’re interviewing data scientist Blake Shaw of Foursquare about how data science is not only shaping Foursquare and its recommendations, but how Foursquare can be a “microscope for cities.” You can follow Blake on Twitter at @metablake and check out Foursquare’s blog for more data science. 

Data Scientist Blake Shaw of Foursquare

1. Your team has found a correlation between warm days and ice cream consumption in NYC. At some point, do you envision Foursquare being able to trigger offers based on different correlations your data science has found?

Yes!  In fact, we currently trigger recommendations (which often contain deals and offers) based on a ton of different contextual signals that the team here has identified as useful.  These signals include where you are, the places you like to go, the time of the day, the preferences of your friends, and what is popular around you. Mapping all of these signals to good recommendations requires finding correlations in massive amounts of data.  Some of these correlations are simple like when it’s the morning people like to get coffee, and some correlations are more complex like when it’s cold out in New York, people are more likely to go to ramen and noodle shops.

2. One of my favorite features of the Explore feature is that Foursquare lets you know when you check into a city locations where both locals and out-of-towners like to go. How does data science and product work together to make recommendations such as these?

Tourist recommendations is definitely one of my favorite features of Explore as well. In general, there is a healthy mix of product-driven and data-driven development at Foursquare. We will often work together to brainstorm not only what would be best to build from a product perspective but also what data we should be investigating further. Tourist recommendations came from the data; we realized that it would be easy to identify places that had a statistically high proportion of tourists and surface them to Explore users who find themselves in unfamiliar areas.  The results are fantastic — it’s like having millions of people creating a travel guide, just by walking around a city and checking in.

3. Foursquare got its start in NYC. What are interesting observations you’ve seen on how people use Foursquare in smaller cities such as Boulder and Denver?

I feel like Foursquare is more of a necessity in big cities like New York, where new places are opening all the time and it’s hard to keep track of them all.  That said, we see strong usage in places like Boulder and Denver as well. As expected, users in smaller cities such as these are more interested in old favorites rather than exploring new places.

4. What signals does Foursquare use to recommend places to people?

I can’t reveal all of the signals we use to rank places, but we believe that place recommendation should be highly personalized, so we heavily weight signals about your tastes and the tastes of your friends.  We also think that from all of this data about where people are going we can discern which are the best places.  Imagine being able to ask everyone who has been to a restaurant if they would go back. We believe that by measuring signals about places such as loyalty, expertise, and sentiment we can tease out the best places. This is the idea behind our recently launched Foursquare ratings.  People are voting with their feet in the real world, not simply leaving a star or a like on a website.

5. Do you see a correlation between Foursquare sharing check-ins and badges on other social sites and increased usage of Foursquare? For example, if someone chooses to share a checkin on Twitter or Facebook, does that increase the likelihood of other people checking in?

Yes we do. Roughly a quarter of all check-ins are shared to wider audiences on Twitter and Facebook.  These in turn help spread awareness and adoption of Foursquare.

6. Foursquare recently showed a visualization of how check-ins in NYC were affected by hurricane Sandy. How else do you see check-in data being useful other than for powering your recommendation engine?

Visualization of Foursquare Checkins Before and After Hurricane Sandy

One of my favorite aspects of working at Foursquare is getting to study this data from a larger sociological perspective. We are capturing this amazing signal about what millions of people are doing in the real world at every moment of the day in cities all around the globe. We have seen that when we aggregate check-in patterns across many individuals, we can measure features of cities at a higher resolution than was ever possible before.  I think this data can act almost like a “microscope for cities.”  If you look at how the storm affected NYC, you can see how this incredibly powerful force disrupted the natural rhythm of the city. It’s striking how predictable these patterns are, and how precisely we can identify unusual events. For example, in this plot we see how check-ins at grocery stores went up more than 200% in the days before the storm.  I see this real-time pulse or “EKG” of a city being a valuable resource in the future for understanding cities, giving us a larger view of the collective movement patterns of millions of people.

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Geosocial Data: Patterns of Everyday Life

My love for checking in and thus, geolocation, began after SXSW of 2009 while I racked up points and worked hard to become the leader of Boulder, ultimately losing to Eric Wu. Since then, my views on geolocation have evolved, and I have become especially enamored with the way geosocial data allows us to leave trails of the lives we and others are living. At its best, geolocation + social connects us to friends we are close to by letting us know who is near and collectively, social data can identify common interests and patterns of behavior we couldn’t see in the past.

Since 2008, Foursquare has evolved becoming a service with 50 million users and two billion check-ins and a facelift launching tomorrow, Twitter has opened up a geolocation API, Facebook Places launched and continues to evolve, Highlight launched and Gowalla was acquired by Facebook. All of these advancements have happened in a couple of short years. Geotagging allows these new crop of social networks to add your geographic location via metadata and now you can add location to tweets, photos, videos, etc.

Patterns of My Life

Every time I check in and share my location, I start leaving a trail of my day-to-day life. This trail, at its most basic, serves as a virtual diary of where I went and with whom. Timehop emails me each day to tell me what I did a year ago, while services such as Rewind.Me allow me to search my patterns and how I stack up against others.

Tripmeter lets me see my virtual trail and the how I travel throughout the day based on Foursquare and Facebook checkins, similar to what Route does. Where Do You Go even lets you heatmap where you most often visit (hint: I hate South Boulder).

Foursquare Heat Map

Checkins Are a Moving Census

But collectively, the patterns woven by geosocial data are incredibly telling and act as a living census. Intriguingly, researchers from Carnegie Mellon have created what they call “Livehoods” which are neighborhoods defined on not only on geographic proximity, but also based on social geotagged data. Essentially, the similarities are based on where people check in. While the data only includes those using geolocation, it shows that people who check into a local restaurant and a similar bar create cultural neighborhoods. This data is more than just an intellectual curiosity. Companies can analyze customer patterns to focus marketing efforts, identify companies to partner with and determine new brick-and-mortar locations.

Example of Livehood Data

I particularly love the idea of an app using Foursquare data called “When Should I Visit?” that tells you when is a good time to visit London tourist attractions based on Foursquare checkins. Other use cases for this type of social data could tell people when to visit high-traffic destinations such as the DMV. I love knowing when not to be somewhere as much as knowing what locations and parties are trending.

HealthMaps uses geosocial data and news reports to help track epidemics as they pop up. The mapping system was created by a team of researchers, epidemiologists and software developers from Children’s Hospital Boulder to monitor real-time epidemics as they break out. Rumi Chunara, worked on this project and also helped use geosocial data to track how cholera spread in Haiti. (Rumi will be speaking at Gnip’s social data conference, Big Boulder, about social data in public service.) Geosocial data has unlimited uses in the cases of health epidemics and natural disasters.

Companies are starting to create passive geolocation checkins such as EpicMix from Vail Resorts, which enables skiers to automatically check in using the RFID tags on their ski lifts. The system tells users how much they skied, where they skied, their vertical ascents and where their friends are on the mountain. During the last Coachella, 30,000 concertgoers used RFID bands from Intellix to checkin and update their Facebook status on various portals spaced throughout concert grounds. Near field communication is another way social data provides amazing patterns.

Geosocial data allows us insight into the patterns of everyday people, and the applications for this are endless.