What’s Getting Funded

Brad Feld and Mike Brown Discuss What’s Getting Funded within the Social Data Industry and Why

@bfeld and @mikebrownjr on What's Getting Funded in the social data industry

We know the major players in the social industry, but the ways they receive funding and come into development is less established. Discussing the current state of social and where they see future funding going are Brian Feld of the Foundry Group and Mike Brown of Bowery Capital. Both lead venture capital firms that have helped some of today’s major players develop.

Discussing the status of the social space today Feld and Brown both focus on the mass of social data that is flooding the ecosystem. More and more data is being created, stemming from a growing number of places all of which is pressuring marketers and vendors alike to respond and manage. Brad expanded the definition of social data to include data from devices such as Fitbit, Nest, and other internet connected devices that generate data. Brown calls out the shift in thinking marketer’s are beginning to undertake around social, responding to the growing space and thinking beyond one or two social channels and embracing the massive spread with which they can distribute communications. Feld continues the discussion on the expanding space, explaining that his focus is not in growing the amount data, but rather on middle layers which can ingest the raw data and export it in ways that marketing organizations, hungry for new opportunities, can use.Feld explains The Foundry Group’s focus, as investors, is on finding solutions for what to do with the mass of data, how to deal with it, how to manage it across numerous systems and the accompanying privacy concerns.

VCs ability to fund and push forward technologies and vendors greatly impacts the advancement of the industry. Examining their past investments, Brown calls out one regret in passing on funding Pinterest, while Feld takes a more optimistic view claiming he has no regrets on passing any current social successes (Twitter included). He explains that the current state of a VC often has the greatest impact on investment decisions, and while Foundry Group may have passed on opportunities to partner with future successes, it meant investment in others. Looking to the future of social, Brown and Feld center on the union of social data with other sources and the technological developments this will spur. Brown predicts a shift in marketers using social data as less as an input to budget and moving towards data becoming an output from increased automation across the spectrum. He also identifies the ability of social data to open up spaces in selling products, influencing the top of the funnel for lead identification as a future impact point. Feld builds upon this, highlighting the importance of machine analysis in growing analysis opportunities and in dealing with privacy grey areas. One area of social data combination that both discuss is the current industry focus of joining CRM and social data. Evident in the acquisitions massive CRM vendors have undertaken, the emergence of the “marketing cloud” illustrates this consolidation. Feld encourages innovators to look for gaps within these systems to understand where they can address problems not being answered by current solutions.

Brown and Feld shared their tips for entrepreneurs seeking funding and ideas for development. Feld encourages those in a start up to not focus on funding but rather focus on the problem you are trying to solve, the customer you are addressing and how your product fits there. He calls out an industry trend to get carried away with “what’s hot” rather than what provides value and warns entrepreneurs against his. Feld builds on this, instructing those seeking funding to focus on why their product matters, identifying their core vision and mission and testing this with markets and buyers before going to funding. He recommends focus on identifying why it matters to people, and knowing that value before even seeking funding. All in all, the industry is changing and there’s an opportunity in providing customers unique value in the emerging gaps.

The Mind of a Marketer

An Interview with Jordan Bitterman from Mindshare

@jordanbitterman from Mindshare teaches us about the Mind of a Marketer

During an intimate chat with Jordan Bitterman, we learned how social data is being used to drive actionable insights for sales, marketing, advertising and more. Mindshare, a media buying agency has been helping brands make realtime insights to drive realtime actions. Specifically, finding the right data points that can drive business is critical in the minds of CMO’s and brands. For example, Bitterman shared how Weather FX has been used to drive decisions based on geographic targeting and finding buyers of specific hair products during humid clients. These types of insights have helped marketers sell more product to their target audience.

The Future of Social Data

While advertising has become a big component for marketers to get the word out, digital ad spend as started to ramp up and increase in ad spend because of the nature of how communication is being proliferated. Also marketers are now being to take back their own data while still relying on agencies and third parties for interpreting and analyzing it to make actionable decisions. These types of insights help create conclusions on upcoming trends and what will be emerging in the future.

However, Bitterman suggested that brands need to focus on the now heavily before moving too quickly into what’s about to happen or coming in the future. And at the moment video and mobile are still very hot vehicles marketers should be capitalizing off of for future campaigns and promotions.

Social Data for Social Good

Elena Eneva, Leysia Palen, and Patrick Meier Discuss How Organizations Are Using Social Data to Improve Social Good

Elena Eneva @palen and @patrickmeier discuss Social Data for Social Good.

What is social good? What does it mean?
The panelist demonstrated that there are a number of ways to look at social data in the context of social good—considering everything thing from the responsibility that comes with using social data to address humanitarian issues to the ways in which a technical methodology like machine learning can be applied to disaster response. In short, there wasn’t a dictionary definition because there are so many interpretations and examples of what social good is. An important point came up: the problem owners (people who are facing the problem) aren’t necessarily the people who have the skills to solve the problems and that connecting problem owners to problem solvers is an important piece of applying technical methodologies to humanitarian problems.
How can the social data ecosystem help?
Access. Access. Access. The ask for access to a variety of data sources, but particularly social, was a clear one. According to the panelists information is just as important as food, water, and shelter, during times of disaster and subsequent disaster response. In addition to access, help from the ecosystem in providing guidance in how to manage social media data during these crises—everything from understanding TOS to integrating various data sources.
In addition to data access, getting individuals in the ecosystem to devote more of their time, attracting top talent to work on these issues, prioritizing the development of open-source projects, and making social good and explicit company value are all ways to help further the work going on in the sphere of social good.
Given the examples of all work going on to leverage social data for social good, it’s clear there are infinite opportunities for individuals from all aspects of the social data industry to apply time, resources, and knowledge to assist the organizations, researchers, and universities working in this area.

Government Doing Good with Social Data

Aaron Rodericks from the Department of Foreign Affairs, Trade, and Development Canada Gives a Pecha Kucha Talk on the Use of Social Data for Social Good in Government

@arodericks from the Department of Foreign Affairs, Trade, and Development Canada Gives a Pecha Kucha Talk on the Use of Social Data for Social Good in Government

When conflicts force Canadian embassies to close and diplomats to leave, Aaron Rodericks combines the fields of data science, open source research, and social data analysis to harness insights and run campaigns for the Department of Foreign Affairs, Trade, and Development in destabilized environments.

One thing that terrifies Aaron to his core revolves around the notion that government organizations around the world are constantly make life altering decisions without referencing social data. In his eyes, the use of social data in government is imperative and can do a great deal of good.

In his job, Aaron is responsible for advocating for programs that use social data to solve demanding issues. One challenge is the ability to clear out all of the noise in order to get to the root of the real issues. Through the creation of innovative programs powered by social data, Aaron and his team have successfully been able create predictive analytics and give a voice to oppressed individuals and bring justice to those who are in need.


Andrew Patterson, Anthony Rodriguez, Daniel Jeydel, David Anderson, and Sam Farber Discuss the Use of Social Data to Run Their Operations

Andrew Patterson, Anthony Rodriguez, Daniel Jeydel, David Anderson, and Sam Farber

For sports teams, agencies, brands, and players alike, social data plays a huge part in how content is being delivered to fans. Being able to activate fans in unique ways has become a growing challenge especially with the rise of new platforms and cost in securing the valuable data. However, thanks to sites like Foursquare, Twitter and many others, major brands are now able to predict which are of their fans are most engaged at a particular sporting event or during the sports season and tailor their messaging to them.

Creating Memorable Experiences For Fans

For the NBA and MLB, having a good pulse of how their customers are engaging with their content is a big concern. For example, by being able to predict who is actually attending a live game, MLB can create customized offers, rewards and incentives in order to activate fans and “product ambassadors with reach” for further promotion and engagement.

Being able to then take the conversations happening real time during a live game and turning them into actionable insights creates lasting memories in the minds of sports fans and encourages them to continue interacting with players and sports teams. For example, during the World Series, MLB was able to analyze over 9,000 terms, nicknames and player names of athletes and map the data to create customized rewards and offers that would most appeal to these audiences. Agencies on the other hand take this data a step further. By clearly understanding and creating a brand for each player, they’re able to create customized content that would most appeal to fans of a specific player driving engagement to websites, discussions on Twitter, and more.

And for the NBA, being able to find out what content resonates the most with eager fans is critical. For example, even during the NBA finals, they’re able to make real time changes to how the game content is structured based on the number of conversations and Tweets taking place. These insights have also helped agencies like Ogilvy make media buying decisions in order to get a better ROI off social media in the eyes of CMO’s.

The Art & Science of Content Creation

Being able to craft authentic stories on behalf of the players or brands is what really helps to get fans coming back for more. And by having the right messaging and context around larger discussions with fans helps brands and players activate more fans for future promotion, rewards, content and more. The key is not letting the data mislead you but rather provide the insights to help you make better content and better decisions.

The Evolution of a Social Platform

An Interview with Jeff Glueck from Foursquare

When thinking of Foursquare, most people think of a gamified platform popular in 2010/2011 focused on badges, mayorships and check-ins. However, in the 5 years since it’s launch the platform has transformed to into a geo-data rich environment focused on seemlessly supplying users with local data. Jeff Glueck, COO, describes the Foursquare goal as being a “discovery engine for the real world” for consumers.

The goal is a lofty one, but the Foursquare team has tackled technical challenges and built the platform to quietly operate and guide consumers without the constant struggle for screen time other platforms face. With the help of a passionate user community the team has mapped over 65 million places across the globe, allowing them to start passively tracking location data of users. Gone are the days of constantly checking in, with Foursquare’s massive database on location data and today’s advancements in geo mapping through wifi, beacons and digital partners, the platform can extrapolate the aggregate habits of a population far larger than the Foursquare user base that checks-in. This mass of data and subsequent extrapolations is used to inform not only the platform consumers but also partners and customers interested in supplementing their knowledge with geo specific knowledge.

The data of Foursquare powers insights beyond their platform. The data forms the backbones of many partners insights, driving Foursquare’s programmatic business. Forty percent of Foursquare’s revenue comes from powering services outside their platform according to Glueck, with 86,000 developers and platforms utilizing the data, including Twitter and Pinterest. In the spirit of the collaborating social space, Foursquare works to enhance their and other’s data sets to get stronger insights and smarter technology. The geo-science space isn’t fail proof though, Glueck pointed out their data vetting showed 80% of publishers and platforms who claimed to have accurate geo-data failed to pass the Foursquare double check, showing the importance of Foursquare’s massive geo-mapping.

While the programmatic and data areas of the business are rapidly growing, the Foursquare team has not forgotten their consumer base. Smaller than other social platforms, the Foursquare community is uniquely passionate about contributing and sharing. The split between Swarm and Foursquare came out of analysis of the community and realization that there were two camps within the platform. The first is those who wanted a social experience, constantly sharing and community. The other, more wary of constantly posting their location, wanted local tips and insights to discover new places. The platform split allowed Foursquare to improve the experiences for both audience sets, incorporating messaging in Swarm and adapting less intrusive communications within Foursquare.

The Foursquare innovation continues beyond consumer experience enhancement. Announcing a new data and analytics business called Place Insights, Foursquare is bringing geo-data into commercial availability. Positioning themselves as the Nielsen of the real world, Place Insights takes their geo-data built in partnership with partners to build aggregate maps of foot traffic for a place over time periods. While e-commerce is a huge business, 90% of spending is still done offline, and Foursquare aims to answer a missing gap on digital tracking of what is happening in real life locations. Glueck demonstrated the platform capabilities in a heatmap of Chipotle locations in Boulder to identify an emerging hot spot where the next location could be built.

Chipotle Locations vs Checkins

In another example Glueck maps the footprint traffic of outdoor supply stores in Boulder, highlighting those retail stores an advertiser should pitch to sell advertising aimed at increase store visits.

The announcement of Place Insights signals a new era in the Foursquare story. Highlighting the huge growth in location services and the capabilities of Foursquare to add contextual details only their geo data can provide.

Jeff Glueck Announcing the Location Cloud at Big Boulder with Chris Moody

Programmatic Data-Driven Advertising

Don Springer and Evan Hanlon Discuss How Big Players Use Social Data to Drive Performance through Programmatic Ad Spend

Don Springer and @walkietalkies on using social data for Programmatic Data-Driven Advertising

 What is Programmatic advertising?

Programmatic is meant to enable an advertiser to bring as much data from the buy side and the sell side to make the best buying decision possible. This includes determining things like what amount should be paid for an impression, which channel should the ad be placed in, what type of content is delivering ROI, etc. What makes programmatic unique is that the buy and sell pieces of technology can communicate and make decisions without human judgement, allowing for the automation of media buying.
Programmatic advertising starts with data, then layers in insights, analytics, and finally targeting actions. There are a growing number of data scientists focused on getting insights to convert into action to inform audience identification. As a brand, if you know your consumer well—where they are in terms of life stages, what their interests are, etc.—the better you can target them with a relevant and compelling offer.
There are a couple the challenges in this area: in the current model of media buying, the pieces are as connected as you’d expect them to be; to date, programmatic has thrived on the idea of efficiency and tactical execution; and it is still hard to accurately measure ROI and determine exactly which data sets are valuable in which campaigns.
Brands also don’t always know what they want to do with data, but they recognize the power of data, and specifically social data. The enthusiasm to know more is there, but the ability to structure campaigns isn’t, so having tool providers to consult and educate brand customers about what is possible is an important piece.
Perhaps the most controversial challenge to programmatic is the ability for these systems to make decisions based on data that is considered personally identifiable to a user. Some view this data as private, some view it as public, and many companies (especially start-ups) are operating comfortably in the grey area. As Don said, the genie is going to be tough to put back in the bottle as an entire industry is using this data. Regardless of the stance on using this data a challenge exists right now to use this data for a unified view of a consumer.

Brands, Signal, and Pins

An Interview with Tram Nguyen from Pinterest

@alwaysbepinning from @Pinterest ends our conference with a session on Brands, Signal, and Pins

Tram Nguyen, Head of Product Marketing at Pinterest shared some of the resources available to marketers and brands alike to leverage on the platform. For Pinterest, their objective has always been provide the best user experience possible while also encouraging more brands to participate and engage with users in unique and interesting ways. For example, Pinterest now has several scalable tools geared towards analyzing user behavior in order to allow brands to target users for marketing opportunities.

What Makes Pinterest Unique?

While many brands are still heavily invested in Facebook and Twitter, Pinterest has become a goldmine for finding unique content and developing strong relationships with consumers. The way in which users express interest on Pinterest is very different from other social platforms. It’s become much more personal and intertwined in major live events from everything including weddings, to first home purchases, to planning travel. Being able to identify this purchase intent is a marketers dream for finding potential buyers based on the pins and content they find most interesting. Also because Pinterest now has over 70 million active users (40% being international and 30% male) it’s no longer a platform that can be easily ignored. These insights are making it much more attractive for brands to engage and create content that appeals to these audiences buying habits.

What’s the Vision of Pinterest?

The main vision of Pinterest has focused on 3 major areas: being able to discover thngs that are important, saving that content for the future and then being able to do act on a pin in real life. Having these three areas make it even easier for marketers to follow user behavior over a lifetime to see how they change and grow as major milestones occur (weddings, vacations, etc.). An example of how this has been effective for brands like Nordstrom is being able to find user boards or collections for in-store retail placement of clothing. Another example is a financial institution was able to identify people seeking to buy a new house and target them with content about saving money.

With over 50 billion pins across a billion boards, there’s enough content and insights to make it even easier for brands to engage users and create effective marketing.

Big Boulder 2015 Video Contest Submissions

We would like to thank everyone for their video submissions which you can watch below. Please use the hashtag provided below each video in a Tweet to vote for your favorite by 1pm MST. We will announce the winner of the Apple Watch after lunch.

To vote for Stu’s video Tweet with the hashtag #BBIStuVideo

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To vote for Dan’s video Tweet with the hashtag #BBIDanVideo

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To vote for Katherine’s video Tweet with the hashtag #BBIKatherineVideo

To vote for Naimul’s video Tweet with the hashtag #BBINailmulVideo

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To vote for Chris’ video Tweet with the hashtag #BBIChrisVideo

Data, Network, and Crowd Science

Laura Norén, Noshir Contractor, and Wei Wang Discuss How Social Data Is Changing Network and Communication Science with Online Collaborative Methods

Wei Wang @digitalflaneuse & @noshir discuss Changing Network & Communication Science w/ Online Collaborative Methods

Recommendation Systems and People

Why haven’t recommendation systems, like those used by Amazon, Netflix, etc. evolved to recommending people? Looking beyond dating sites, the panel discussed the example of connecting a customer with a relevant stylist at a department store, or connecting like-minded developers at a hackathon and why there currently isn’t a good system to do this. Nosher pointed out that there is a lot of data about individuals that isn’t captured as the key missing element. To facilitate the creation of more sophisticated recommendation systems, more comprehensive data on individuals needs to be tracked.

Combining Ethnographies and Quantitative Analysis
Laura  illustrated how ethnographies compliment quantitative analysis through an example of research on the food blogging community. Food bloggers as a community face specific challenges that impact the type of influencers with whom they engage. Understanding this context wouldn’t be possible without taking the time to actually sit down with these bloggers and influencers and understand what they’re Tweeting—when, why, and in what situations—to truly be able to capture the full context of how this community engages with influencers
How Can Academia Work with Industry?
Panelists noted that there needs to be continued exploration of ways to make research productive and beneficial for industry. With regard to what they need to further the relationship between the two, they highlighted that funding is actually not the most critical issue in working on research projects. Access to data is. Researchers and academics need the ability to mash together different kinds of data, in addition to the ability to reuse data so that it is not necessary to always start from scratch.
Overall,  major themes of the panel were: that qualitative analysis is an important aspect of this research that shouldn’t be ignored, reproducibility of the research conducted in academia is of paramount importance, and that research projects should ultimately be beneficial and relevant to the industry as a whole.