When you’re in Atlanta, as I was during a recent trip, the obvious place to start talking about orange juice and data is with Coca-Cola. Foreman can tell you all about how the beverage giant — whose headquarters tower over the city just a just a mile away from MailChimp’s office — uses advanced algorithms and giant vats of different juices to ensure the proper flavor of its Simply Orange line of orange juice. However, it’s something else Coca-Cola is doing that inspired the way Foreman thinks about data and that’s helping MailChimp re-imagine what it means to engage with fans, readers and customer through their inboxes.

Anyone familiar with how large web companies came to pioneer the practice of what we now call “big data” should appreciate the analogy. Coca-Cola, which also owns Minute Maid, produces a lot of excess pulp when it makes orange juice. For decades, presumably, it had just been throwing that pulp away, but in 2006 it decided to make use of it by launching a new product called Minute Maid Pulpy. Sold primarily in Asian countries, Pulpy has become a billion-dollar business for Coca-Cola.

Once MailChimp is done with its primary business of sending emails, it has a lot of pulp of its own in the form of data. And rather than just ignoring it or writing up some cute blog posts (which he also does), Foreman and his bosses want to turn that data into revenue.

First things first: Making better orange juice

Neil Bainton

Actually, though, MailChimp first brought in Foreman in 2011 to help the company improve its core business of letting users build and send their emails. MailChimp’s culture was built around many things, COO Neil Bainton told me, but data wasn’t one of them. It had “various fits and starts” through the years trying to work data into its business model, and each step just added more complexity.

The challenges were technological as well as cultural, but Foreman had a plan, of which focus was a key aspect. Keeping a tight focus meant Foreman and his lone-developer sidekick could build what they needed to in a short timeframe. It also meant the company didn’t have to worry about some massive overnight transformation into a data-obsessed company like Google.

John Foreman

“[They] don’t need to be afraid the entire culture is gonna fall down if we bring in this weird math guy,” he joked.

Foreman’s first project — deploying artificial intelligence models that would automatically detect spammy email lists from MailChimp’s users – is actually critical to the way MailChimp operates, though. It was up and running in production within a year, after a technologically challenging effort of merging separate database instances for each customer into a single environment that would let MailChimp run complex analyses across its customer base.

It’s such an important project, Foreman explained, because internet service and email providers keep reputation scores on the IP addresses that send email through their systems. Because MailChimp serves as the email engine for its millions of users, sending too many messages that get flagged as spam and lower MailChimp’s reputation will have a negative impact on everyone. The company used to deal with spam manually, and only after recipients began complaining about the messages they received.

“It used to be before we had that AI model in place that everyone had a crappier experience,” Foreman said.

Say goodbye to those ’90s fans, Pearl Jam

Source: MailChimp

Now, however, MailChimp knows some of the telltale signs of spam for which it should be on the lookout. If too high a percentage of email addresses on a given list are also available via publicly available lists or those you can buy on sketchy corners of the internet, it’s probably spam. Too many old and far-more-likely-to-be-dead Earthlink or Compuserve addresses, or letters within one keystroke of each other as if someone just mashed the keyboard? Probably spam.

Thankfully, though, about 98 percent of the spam that MailChimp identifies is what Foreman calls “ignorant” — that is, people or companies that just don’t know the laws or best practices around sending emails. But ignorance doesn’t mean MailChimp relaxes its rules. Recently, it even flagged Pearl Jam for spammy practices because the band was trying to reconnect with old fans whose email addresses read like a who’s who list of 1990s email providers.

Having such a high percentage of ignorant spam actually has a positive effect on the company’s overall goal of monetizing its vast data repositories. Because the AI model automates what used to be a manual process, and because most innocent spammers will fall in line quickly once they’re notified (as opposed to nefarious spammers who constantly try to outsmart the system), MailChimp can pretty much set the model loose, forget about it and get to work on new efforts, Foreman said.

Now, about that pulp

Spam under control, MailChimp can focus its efforts on actually building new products with data, just like Coca-Cola did with that extra pulp. One of its first orders of business is figuring out how to help customers get to know better the people to whom they’re sending their newsletters.

With this in mind, the company built a service called Wavelength that shows customers other newsletters that are similar to theirs. But the system that powers Wavelength also stores pretty much every interaction that every email address in the company’s database has with the newsletters they’re sent. That means what emails they open and when they open them, what links they click and when they click them, and what other newsletters they’re subscribed to. MailChimp also has a feature called Ecommerce360 that lets customers track clicks right through to conversions (marketing speak for someone actually buying something).

The company has been playing around with this data to identify clusters of users based on their behaviors and their interests — some of which Foreman has detailed on the company’s blog — and now it wants to roll it out to customers via a product MailChimp is calling ChimpQuery. Built atop Google’s BigQuery analytics service, ChimpQuery will let customers start doing this type of clustering and segmentation on their own, while saving MailChimp the troubles of hosting that infrastructure itself. (You can play with a monstrous, interactive graph of the entire MailChimp subscriber list here.)

If you sell knitting supplies and you find out there’s a big cluster of people on your mailing list who also are interested in wedding planning and custom jewelry, there might be an opportunity to create your content with these interests in mind or even to partner with companies in those spaces.

A sample cluster of subscribers.

Another topic that has been on Foreman’s mind lately is what he calls “frequency elasticity of engagement.” He’s done research suggesting that blasting the heck out of your email list might actually have detrimental effects in the long term (regardless of how the Obama campaign successfully exploited this strategy) but noted that engagement also has a lot to do with content and a particular company’s given user list. MailChimp’s data could help customers figure out the ideal schedule for emailing their subscribers.

For example, Birchbox has really high engagement because people love the service and have to open their emails to find out what goodies they’re receiving. Emails from a company like Papa John’s, on the other hand, might sit in someone’s inbox essentially as spam until they want to order a pizza and go searching for a coupon. Everyone has to figure out what pace and engagement metrics work for them.

Reining expectations back in

However, now that management is fully sold on the power of data, Foreman sometimes finds himself managing expectations rather than just pitching his ideas. COO Bainton, for example, is adamant that MailChimp start aiding its publishing-industry customers by using techniques such as natural-language processing and semantic analysis to help them personalize emails based on readers stated and unstated interests (that is, what boxes they check when they sign up and what stuff they actually click on).

Foreman, well, he’s pretty sure that’s too big a challenge for MailChimp to tackle considering how many publishing customers it has. MailChimp would have to understand all those customers’ industries to some degree (open source tools tend to highlight technically but not situationally relevant relationships, he said, and don’t always understand things like sarcasm) and probably the different languages they publish in, as well. Rather than understand content, he’d rather focus personalization efforts around how users are connected.

The company also needs to balance its ambitions with what’s legally and socially acceptable. The creep factor might be more important than what’s legal when it comes to email marketing. MailChimp determines the legality of everything it does before rolling it out, Foreman explained, but in era of “post-modern spam” where legitimacy is in the eye of the recipient and where some people use their “spam” button as a proxy for unsubscribing, companies must be careful not to offend.

“The more we can tell you about that list without getting creepy is really useful,” Bainton said. However, he added, ”I think expectation is more important than law.”

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• How big data analytics drives competitive advantage
• Sector RoadMap: Social customer service in 2013
• Why the next front in big data might be psychological

As individuals, doctors probably don’t see enough of any given adverse reaction to notice patterns emerging. But as a collection, their notes on patients’ medical records can provide valuable insights, as a group of Stanford researchers recently discovered. Using “18 years of patient data from 1.8 million patients [consisting of] 19 million encounters, 35 million coded ICD-9 diagnoses, and >11 million unstructured clinical notes,” the team was able to accurately identify interactions by analyzing the free-form text that doctors had entered about patients’ symptoms, conditions and prescription regimens.

A key aspect to being able to predict adverse interactions is understanding the relationships among the different sets of terminologies used in different medical fields. It’s a lot easier to spot patterns across hospitals or even an individual patients’ records when you know that a radiologist writing X is the same, or related to, an oncologist writing Y. We covered an earlier collaboration between the study’s leader, Nigam Shah, and medical-data startup Apixio around this very topic in 2011.

How Shah’s team developed its patient-feature matrix

Shah and his team hope their work can complement the current process for tracking drug reactions, the FDA’s Adverse Event Reporting System. Whereas that system requires doctors and patients to manually alert the FDA of potential adverse side effects, their method could highlight potential problems that no one noticed or took the time to report. I’d consider this similar to some early research by social medical sites such as PatientsLikeMe, whose users are producing lots of data about their conditions, drugs, dosages and side effects that could produce correlations ripe for controlled experiments.

A press release announcing the study’s publication highlights some of its future promise and current limitations:

“[T]he research team is working on refinements that will cull even more useful information from clinical notes, such as reports of reactions caused by drug combinations, the use of medications typically prescribed for one condition but found effective for treatment of a different health problem, or finding medical profiles of patients that fit a certain scenario. …

One downside is that most electronic health record systems are set up for patient care, not patient research, Goodman noted. In this study, the researchers mined a data system created for this kind of research, which isn’t widely available. The researchers used the Stanford Translational Research Integrated Database Environment, known as STRIDE.”

This is just one of many ways in which researchers are experimenting with big data concepts to help medical professionals make sense of more data than they could possibly analyze on their own. Other examples we’ve covered recently include an artificial intelligence model for prescribing safe, cost-effective treatments, the application of Google PageRank-like algorithms to map the spread of cancer cells throughout the body, and the use of graph data structures to organize highly complex sequencing data.

Feature image courtesy of Shutterstock user Maksym Dykha.

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• A near-term outlook for big data
• Sector RoadMap: Social customer service in 2013
• Why the next front in big data might be psychological

And although there’s a semantic element to it, this is beyond the realm of semantic search. We’re talking about doing a video chat, sending an email or just surfing the web, and seeing relevant content appear before your eyes. Why? Because the web and, heck, even our laptops are so full of information we don’t always know what to look for or have the extra attention to devote to looking for it.

Most recently, I spoke with Christopher Eakins, CEO of a company called Grapple Data that wants to revolutionize desktop search. Presently, the company’s flagship product, called Aikin, is doing something similar to semantic search on the surface. It’s responding to searches with a list of files, emails, contacts or other content — ranked by relevance — that a standard keyword search wouldn’t detect.

He says the product addresses the problem of information workers “being force-fed more than we can chew,” often across applications that don’t interact with each other at all. One might think of Aikin, he said, as a device that records, indexes and keeps track of everything you do on your machine, so you don’t have to remember specific file names, people or even keywords later on. If you have an idea what you’re looking for, it will find that content and then some.

Going forward, though, Eakins hopes Grapple can do away with desktop search altogether, or at least make it less necessary. That’s where the real innovation comes in. He wants to enable what he calls “information gravitation,” where relevant content would start to surface based on the subject of an email someone is typing, for example. It’s like those targeted ads in Gmail, only in real-time and, presumably valuable to users.

I first came across the concept in April 2012 while covering a company called PureDiscovery. Historically dedicated to semantic search and indexing within large corporate datasets, PureDiscovery CEO Dave Copps explained the company’s plans for going much, much bigger. Essentially — first within corporate networks and then across the entire web — it wants to teach is BrainSpace software to learn how people and pieces of content are related and then surface both automatically based on who you follow, what your interests are or even what text you highlight on a web page.

The plan appears to be coming along. The web part, which is definitely a bigger-picture undertaking, seems to have materialized in the form of a beta-mode application called Grokkit. (I’m still waiting for my invite.)

There’s also the work that Expect Labs is doing around its MindMeld application, which my colleague Om Malik lauded as “herald[ing] the era of anticipatory computing.” MindMeld is a video-chat application that also uses voice recognition and some serious data analysis to figure out what a conversation is about and surface relevant information related to that from the web or users’ social graphs. It also tries to predict where a conversation is going and queue up content that it thinks will be relevant in the future.

The point of all of this stuff — and even some of what we’re seeing in the enterprise IT world with startups like Ayasdi and BeyondCore — is that people don’t always know what they’re looking for or the right queries to enter in order to find it. If more information (or at least more relevant information) really is better, this should be a welcome trend.

Feature image courtesy of Shutterstock user photobank.kiev.ua.

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• Sector RoadMap: Social customer service in 2013
• Connected consumer first-quarter 2013: Analysis and outlook
• Readers weigh in: future prospects for Twitter

As I explained in March when, Gravity has built a semantic-analysis engine that tries to gauge a site visitor’s interest by looking at more than the articles that person reads. Thanks to an expansive database of topics and a hybrid man-machine machine learning system that takes into account behavior as well as content, Gravity can determine other topics that might be of interest even if those connections aren’t visible to the naked eye. The result of this analysis is called an interest graph, which is like a social graph only that’s concerned with interests rather than people.

Currently, Gravity claims its total body of graph data exceeds 18 million megabytes, or 18 terabytes. The company says the new money will help it expand operations in the United States and even deploy its own content-marketing platform.

Of course, interest graphs are useful for more than just automatically presenting visitors with the news content they’re interested in. Gravity also has a product for advertisers to better target potential customers, and an analytics service so publishers can get in-depth visualizations of who’s reading their content and what content works better than other content.

However, the obvious elephant in the room when talking about interest graphs is privacy and how to collect and analyze user data without crossing any ethical guidelines. This will become even more of an issue as web platforms try to share data across services in order to create a more unified browsing experience in which interest graphs follow users around the web to inform personalization algorithms at every step. And as we’ll discuss later this month at our Structure: Europe conference in Amsterdam, some governments take user privacy much more seriously than others, which can make businesses based on that data a little trickier to operate.

Here’s Gravity Co-Founder and CTO Jim Benedetto, along with privacy attorney Ashlie Beringer, discussing the issue with me at our Structure: Data conference last March.

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• Sector RoadMap: Content personalization in 2013
• NewNet Q4: Platform mania and social commerce shakeout
• NewNet Q4: Platform mania and social commerce shakeout

Admittedly, I don’t spend too much time searching academic or professional databases anymore, but this is a novel approach from what I’ve seen. Powered by Atigeo’s software product called xPatterns, the new interface for exploring PubMed presents a hub-and-spoke-like diagram (which it calls “bubbles and sticks”) that viewers can manipulate by adding and subtracting search terms or by searching for related terms. With every click, a users drills down further into the results, although the original map of terms remains.

Atigeo markets xPatterns to a number of industries, from the public sector to advertising, to help them draw better connections between their data, but this use case is particularly cool. That’s because while semantic analysis is already used rather extensively to produce more-relevant search results (or just to proactively present users with content), it’s not every day someone rethinks the process of how we actually navigate search results. Given a little time for experimentation and acclimation, perhaps the xPatterns approach will catch on.

I don’t see why it has to be limited to scholarly databases either. I can see everyday web users wanting to parse through search results on their favorite content sites using a similar approach. Sure, you can trust a site is delivering you exactly what you want to see, but sometimes it might be nice to dig down, see a little of what the system sees to find that needle in the haystack.

Feature image courtesy of Shutterstock user Michelangelus.

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• Cloud computing infrastructure: 2012 and beyond
• The importance of putting the U and I in visualization
• A near-term outlook for big data

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• Infrastructure Q1: IaaS Comes Down to Earth; Big Data Takes Flight
• Infrastructure Q2: Big data and PaaS gain more momentum
• Infrastructure Overview, Q2 2010

Essentially, the DataPop service works like this: DataPop gathers information on products, services, promotions and other relevant  data from customer web sites; it then automatically presents ads for those products, etc., when someone searches for something related on Google or Microsoft; the ads appear as if a human wrote them, not just a collection of keywords.

In some ways, DataPop’s service is similar to that offered by BloomReach, only DataPop focuses on search ads rather than on web site content. Regardless, though, DataPop is part of a large collection of startups and established companies trying to leverage big data to revolutionize the accuracy with which marketers can put their messages in front of consumers.

According to the press release announcing the funding, DataPop plans to use the money double its headcount this year, including by adding more experts in “big data, semantics and natural language processing.” That’s a smart move, as the competition is all doing the same thing, even going so far as to buy struggling big data startups and acquire their tech and talent. Although it’s a huge market with room for lots of players, it appears as if the marketing service providers that will prosper going forward are those that can make the best use of big data techniques.

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• Cloud computing infrastructure: 2012 and beyond
• Sector RoadMap: Social customer service in 2013
• NewNet Q4: Platform mania and social commerce shakeout

The patent system is broken — on that almost everyone agrees. There’s a backlog of applications that results in exorbitant wait times to get a patent issued, and the merit of patents that do get granted is often questionable. If you’re forced to litigate a patent-infringement suit — an increasingly likely scenario – the costs can be crippling.

When it comes to software patents, the situation is particularly dire, which leads many critics to argue that software patents should be abolished altogether. Patent trolls are a widely cited nuisance, but there’s a more fundamental problem. Litigaton is expensive, but litigation is all too common because there are so many software patents out there, and it can be very difficult — and very expensive — to find out whether a new invention possibly infringes on even one of them.

As we’ll discuss in depth at our Structure:Data conference in New York later this month, techniques such as machine learning and natural-language processing are already having transformative effects in a number of fields. Why not the patent system, too?

Software patents don’t scale …

Timothy B. Lee, a Cato Institute fellow (and frequent Ars Technica contributor), and Christina Mulligan of Yale’s Information Society Project explore one big software-patent problem in a new research paper titled “Scaling the Patent System.” The gist of Lee and Mulligan’s argument is simple: software is such a wide-ranging and nebulous topic that it’s nearly impossible to index software patents in a manner that would make it easier to search for them. The system just doesn’t scale.

Current USPTO search engine

Property records are easily searchable because county recorders organize them in a logical manner based on geography. Even chemical patents, the authors point out, are relatively easy to search by chemical formula. With software patents, however, there’s no such luck:

[I]n the absence of a precise, standardized scheme for classifying software inventions, patent applicants are free to use any terms they like — or even make up new ones — to describe their software inventions. The scope of a patent’s claims will not always be obvious from a patent’s title or abstract. And a single software patent can claim multiple applications that are only loosely connected to each other.

Lee and Mulligan’s paper doesn’t even touch on the problems that arise with prior art, generally defined as “all information that has been disclosed to the public in any form about an invention before a given date.” It only compounds the issue of searching the USPTO database when attorneys or patent examiners are forced to search articles, presentations and anything else that might negate the novelty of a proposed invention.

Unfortunately, the authors conclude, “Only dramatic reforms — such as excluding industries with high discovery costs from patent protection, establishing an independent invention defense, or eliminating injunctions — can return the patent system to its proper role of promoting innovation.”

… but big data does

Looking outside the law, though, and into the world of big data analytics, one needn’t look too hard to find some methods for making it easier to search for patents. The answer lies in semantics. If the problem is that keyword searches aren’t effective, then build a search engine that addresses a wide variety of sources and that takes into account related terms based on how frequently they’re linked, or based on the ontologies present in different industries.

• A startup called Apixio is already doing something similar in the field of medical records. It uses natural-language processing, machine learning and sematic association to make its Medical Information Navigation Engine (MINE) as easy to use as possible. Describing the service last April, I wrote that “when a doctor types a patient’s name and ‘chest pain’ into the search box, MINE is able to find ontological references to chest pain that bear little resemblance to the actual term.”
• 

  Factually accurate, but irrelevant connections for Vanessa Laine

  Another method for doing this comes from Gravity, a startup that uses a hybrid man-machine process to personalize content for readers of sites such as the Wall Street Journal. Gravity’s system is complex to say the least (here’s a video tutorial that explains part of it), but the gist is that humans first serve as guides for machine-learning algorithms by determining connections between terms within large data sets, then the algorithms take over to complete the job faster than humans ever could. When they’re done, the humans step in one more time to kill any bad connections between terms. The result is a system that can determine with high accuracy that a person tweeting about Vanessa Laine (Los Angeles Laker Kobe Bryant’s ex-wife), for example, is probably more interested in basketball than about Laine’s date of birth or other accurate but irrelevant information.

• Even IBM’s now-famous Watson question-answering machine could prove beneficial if the USPTO were to leverage its capabilities. The system has actually been suggested as an aid to help judges better interpret statutes against the Constitution, but loaded with patent data, it could help identify potential infringements and even answer with some certainty which ones might be the most relevant to any given application.

Indeed, a startup company called IP Street is already attempting to bring the benefits of semantic technology to bear on the patent field. By analyzing the entire library of patents issued by the USPTO, Founder and CEO Lewis Lee told me IP Street is able to extract meaning from patents using information from the patent claims. A succinct explanation on the company’s website explains that “[the core] technology, known as LSI or latent semantic indexing, uses complicated mathematics and matrix decomposition (SVD) to identify similarities among documents. This allows you to enter an entire document (such as a product description, idea for a patent, etc.) and compare it to the universe of patents and patent applications—comparing across just the claims or the entire document.”

Big data won’t solve all the complaints people have about patents, but it could make life a lot easier for the inventors, attorneys and examiners tasked with determining whether a patent infringes a previous patent, or is even patent-worthy in the first place. The question now is whether the USPTO wants to leave simplification of the process in the hands of private parties like IP Street, or if the agency wants to bring a few big data experts on board to improve what it’s able to offer those who rely on it.

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• How big data analytics drives competitive advantage
• Cloud and data first-quarter 2013: analysis and outlook
• How to use big data to make better business decisions

ADmantX came out of beta in March, officially making its technology available to advertisers that want to target ads based on the content and meaning of web pages.

While many advertising technology firms are focused on targeting ads based on user behavior, using tracking cookies and the like, ADmantX is based on the semantic web, natural language processing and social collaboration. The technology is designed not only to recognize the words on a page but also to recognize the emotional appeal of its content and the reader emotions and behaviors likely to be surfaced by the content.

It works not only to target ads but also to protect advertising from appearing against unsavory content, ensuring brand safety. As a result, the ads that it does serve can command higher CPMs. Since it doesn’t rely on user tracking, it also sidesteps concerns about behavioral targeting and do-not-track regulation.

The tech firm spun off late last year from Expert System, which provides semantic technology to discover and interpret text. With the outside round of financing, Atlante Ventures Mezzogiorno will take a minority stake in the company, with Expert System maintaining a majority.

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• Survey: How apps can solve photo management
• Social networks will displace business processes, not socialize them
• Sector RoadMap: Social customer service in 2013

Viewers can, for example, find which shows have mentioned San Francisco in recent weeks and how many programs mentioned Barack Obama since data gathering began in the fall of 2009 (1423 times). Channelography also allows users to browse through various shows, making it possible to quickly learn which persons or places were mentioned on a specific episode of BBC Newsnight or the children’s show Arthur.

Channelography is based on semantic analysis of closed captions, which is performed by cross-referencing the data with Wikipedia, Musicbrainz and various other openly available data collections. This type of analysis is performed by Muddy, a semantic indexing and categorization tool developed by Rattle Labs in cooperation with the BBC’s now-defunct Backstage R&D initiative.

Granted, Channelography may not be the most convenient EPG for everyday use. But one of the things that’s really fascinating about it is the amount of additional aggregate information that can be gathered from it. For example, who would have guessed that Afghanistan gets more mentions these days on British TV than Northern Ireland?

The makers of Channelography clearly recognized this potential for data analysis as well, which is why they also created a companion dashboard to reveal trends across the BBC’s network. The Channelography dashboard not only reveals how much of the BBC’s programming consists of repeats, but also how often companies like Facebook and Microsoft have been mentioned on the programs, and even which clichés are the most common amongst BBC journalists.

Channelography was developed by Rattle and commissioned by the BBC. The project was only available internally until this week’s official unveiling, and Rattle actually produced a paper guide to make sense of the BBC’s 2010 programming for the broadcaster’s staff as well. (Check it out here; it contains some beautiful visualizations). Rattle’s Director James Boardwell wrote on his blog this week that the company wants to build a similar semantic guide for radio next.

He also said that using captions for semantic analysis of TV content could help broadcasters to add SEO to online platforms, and even offer a new kind of cultural analysis. From his blog post:

“How perhaps different people appear together or cluster and how over time the data could become a proxy for British culture more generally and the things that pre-occupy us, for example how Victorian drama is replaced by Edwardian or how Shakespeare’s influence ebbs and flows, all hugely interesting and only do-able when you have data available on this scale by a media organisation as central to the culture of a nation as the BBC.”

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• The Ultimate Guide To TV Everywhere
• Frenemy mine: The pros and cons of social partnerships for online media companies
• Smart TV forecast: gigabit Wi-Fi in the living room