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Big data isn’t just a function of better data-tracking technology; it simply wouldn’t exist without the ever-expanding catalyst that is big content.

marketing content refers to any artifact with which a consumer interacts that relates to a brand. Content gets “big” via its amplification, iteration and dissemination across multiple channels, devices and platforms. When content “jumps the tracks” and is no longer controlled solely by the brand, but can be summoned and even manipulated on demand by consumers themselves, then it’s “big."

Based on a 2012 survey by Content Wise, marketers increased their total spending on content development by 45% from 2005 to 2012, when the percentage of marketers’ budgets allocated to content creation increased from 31% to 39%

The beauty of such Big Data applications is that they can process Web-based text, digital images, and online video. They can also glean intelligence from the exploding social media sphere, whether it consists of blogs, chat forums, Twitter trends, or Facebook commentary. Traditional market research generally involves unnatural acts, such as surveys, mall-intercept interviews, and focus groups. Big Data examines what people say about what they have done or will do. That's in addition to tracking what people are actually doing about everything from crime to weather to shopping to brands. It is only Big Data's capacity for dealing with vast quantities of real-time unstructured data that makes this possible.

Much of the data organizations are crunching is human-generated. But machine sensors — what GE people like CMO Beth Comstock called "machine whispering" when I talked with her this past summer — are creating a second tsunami of data. Digital sensors on industrial hardware like aircraft engines, electric turbines, automobiles, consumer packaged goods, and shipping crates can communicate "location, movement, vibration, temperature, humidity, and even chemical changes in the air."

the number of Google queries about housing and real estate from one quarter to the next turns out to predict more accurately what's going to happen in the housing market than any team of expert real estate forecasters. Similarly, Google search queries on flu symptoms and treatments reveal weeks in advance what flu-related volumes hospital emergency departments can expect.

Two years ago, the two greatest Jeopardy champions of all time got obliterated by a computer called Watson. It was a great victory for artificial intelligence--the system racked up more than three times the earnings of its next meat-brained competitor. For IBM’s Watson, the successor to Deep Blue, which famously defeated chess champion Gary Kasparov, becoming a Jeopardy champion was a modest proof of concept. The big challenge for Watson, and the goal for IBM, is to adapt the core question-answering technology to more significant domains, like health care. WatsonPaths, IBM’s medical-domain offshoot announced last month, is able to derive medical diagnoses from a description of symptoms. From this chain of evidence, it’s able to present an interactive visualization to doctors, who can interrogate the data, further question the evidence, and better understand the situation. It’s an essential feedback loop used by diagnosticians to help decide which information is extraneous and which is essential, thus making it possible to home in on a most-likely diagnosis. WatsonPaths scours millions of unstructured texts, like medical textbooks, dictionaries, and clinical guidelines, to develop a set of ranked hypotheses. The doctors’ feedback is added back into the brute-force information retrieval capabilities to help further train the system.

For Watson, ingesting all 2.5 million unstructured documents is the easy part. For this, it would extract references to real-world entities, like corporations and people, and start looking for relationships between them, essentially building up context around each entity. This could be connected out to open-entity databases like Freebase, to provide even more context. A journalist might orient the system’s “attention” by indicating which politicians or tax-dodging tycoons might be of most interest. Other texts, like relevant legal codes in the target jurisdiction or news reports mentioning the entities of interest, could also be ingested and parsed. Watson would then draw on its domain-adapted logic to generate evidence, like “IF corporation A is associated with offshore tax-free account B, AND the owner of corporation A is married to an executive of corporation C, THEN add a tiny bit of inference of tax evasion by corporation C.” There would be many of these types of rules, perhaps hundreds, and probably written by the journalists themselves to help the system identify meaningful and newsworthy relationships. Other rules might be garnered from common sense reasoning databases, like MIT’s ConceptNet. At the end of the day (or probably just a few seconds later), Watson would spit out 100 leads for reporters to follow. The first step would be to peer behind those leads to see the relevant evidence, rate its accuracy, and further train the algorithm. Sure, those follow-ups might still take months, but it wouldn’t be hard to beat the 15 months the ICIJ took in its investigation.

Facebook users will sit still for ads that, per current reports, will take over your desktop screen and run for 15 seconds, up to three times a day per user. What are the odds of that? 

Initially, the 15-second autoplay ads will appear on Facebook's desktop site; the social network is still working to bring them to its mobile app as well.

Facebook is expected to offer four daily "slots" for its video ads, each targeting a very large demographic: Women over 30 Women under 30 Men over 30 Men under 30

A new survey by Initiative questioned some 8,000 web users age 16-54 in Argentina, Australia, Canada, China, Germany, the Netherlands, U.S., and U.K. to find out how they were influenced in purchase decisions by social media interactions. The results are kind of amazing: A huge 99% of the "top 10%" of influencers reported that their friends quiz them before making a big purchase. This top 10% has a disproportionate influence on the opinions of others--because 72% of them access content in print, online and mobile form more than once a day, compared to just 18% of the bottom 10% of influencers. 

A different study, by Market Force, underscored the fact that brands are leveraging social media to promote themselves. Embedded in the study were stats on the power of the average user to spread brand-related messages: 81% of U.S. respondents said posts from their friends directly impacted their decision on purchasing something, and 80% or respondents said they'd tried new things based on suggestions of friends.

This is a big departure from the static print ads and traditional TV spots of the past. Initiative's study even included advice for brands to move well beyond the thinking of a traditional 30-second ad spot, and push out additional material like behind-the-scenes footage...all to drive discussion and lead to more online chatter that will lead to brand discovery. It also suggests that brands build a team of "relevant social influencers" to spread new ad campaigns and stimulate dialog.

viewers place a higher level of trust in television advertisements. Even in the second decade of the 21st century, television ads influence viewers far more than most other forms. Yes, this is also true for tech-savvy teens and young adults.

sing Tongal's platform, big brands offer "challenge rewards" not only for fully edited video advertisements, but for ideas for commercials. 

Lego, Pringles, Axe, Pepsi, Nokia, and numerous others now rely upon crowdsourcing to generate ideas and foster new talent.

anticipatory systems. 

Increasingly, rather than waiting for us to tell them what we want, in the form of a search query or command, they'll prompt us with suggestions.

Here's a simple definition of anticipatory systems. Think of them as artificially intelligent services that are aware of external context — including ambient inputs like time of day, social connections, upcoming meetings, local weather, traffic and more.

One of the most common mistakes made when designing social applications is focusing immediately on rich, heavyweight interactions rather than lightweight ones. All the best social experiences online map closely to how offline social experiences work: offline, people build relationships slowly, one lightweight interaction at a time.

Every now and again we have more heavyweight interactions, such as family dinners, big nights out with friends, birthdays, anniversaries, family vacations and so on. But if we hadn’t built the relationship through many lightweight interactions over time, we’d have no interest in the heavyweight ones. The aggregation of many lightweight interactions is very powerful. It builds deep relationships, and helps people curate parts of their identity.

It takes months and years to build relationships with people, and they all are built on many lightweight interactions over time. And people build relationships with brands in the same way as they build relationships with people: slowly, one interaction at a time. Just as we don’t suddenly become best friends with someone, we don’t suddenly fall in love with a brand – so build products that support lightweight ways for people to interact and show the aggregations of those interactions over time.

social TV lab: “Our team and Bluefin Labs (which Twitter purchased earlier this year) will come together, put mutual skin in game, and create a social TV lab that we think will give our clients unprecedented real-time data on how consumers are watching, buying and the conversations that amplify brand messages."

“the idea that the digital marketplace is moving from ad exchange and targeting and re-targeting to, while still including those things, much more toward creating real-time content and experiences.”

SMV sees Twitter not as a media silo in itself but a platform that extends across paid, owned, and earned media. "If you think the future of marketing is convergence and amplification of big moments over time, as we do, then Twitter becomes one of the essential channels you have to evaluate in any marketing plan.”

Proactive software agents will reduce the need to waste time looking for information.

Contextual search tools like Google Now, which takes into account where you are and what you are doing to provide useful information, are the first big step towards anticipatory and responsive software agents.

In the consumer world right now, Apple's Siri is the most well-known example thus far of how a software agent will interact with humans, though it has its limitations, both in speech recognition and plain common sense. As that interaction is smoothed out, though, it is not hard to imaging giving agents like Siri or Google Now's voice search more permissions to act on the information at hand, instead of just reporting it. Once that hurdle is overcome, all of that predictive and contextual information that the Internet is starting to finding for us will have a smooth, human-like interface and better able to help us manage our days.

Pinterest has beaten out Yahoo organic traffic, making Pinterest the fourth largest traffic driver worldwide

Google, Yahoo, and Bing organic traffic decreased by 15.63% on average since January, which the firm speculates may indicate more people are discovering content through social sites like Pinterest.

it could also be because Shareaholic’s data, which comes from a network of 200,000 publishers using its social sharing and content analysis tools, is more likely to reflect an engaged community where people are comfortable with using social networking sites to perform searches. In other words, it’s not a big picture study here – just a slice.

Addictive technology creates “internal triggers” which cue users without the need for marketing, messaging or any other external stimuli.  It becomes a user’s own intrinsic desire. Creating internal triggers comes from mastering the “desire engine” and its four components: trigger, action, variable reward, and commitment.

A company that forms strong user habits enjoys several benefits to its bottom line. For one, this type of company creates “internal triggers” in users. That is to say, users come to the site without any external prompting. Instead of relying on expensive marketing or worrying about differentiation, habit-forming companies get users to “self trigger” by attaching their services to the users’ daily routines and emotions. A cemented habit is when users subconsciously think, “I’m bored,” and instantly Facebook comes to mind. They think, “I wonder what’s going on in the world?” and before rationale thought occurs, Twitter is the answer. The first-to-mind solution wins.

A multi-screen world, with ad-wary consumers and a lack of ROI metrics, has rendered Don Draper’s big budget brainwashing useless to all but the biggest brands. Instead, startups manufacture desire by guiding users through a series of experiences designed to create habits

Imagine people in developing countries thinking Facebook is the gateway to the Internet. They would log into Facebook to access email, Wikipedia pages, weather information, and food prices. If they wanted additional services like the ability to stream video, they can buy it with a simple click—through Facebook. That’s Mark Zuckerberg’s vision for Internet.org. 

“Connecting the world” is Facebook’s vision—one that can’t be achieved without the support of other organizations, including the six telecom companies it partnered with for the Internet.org initiative.  Zuckerberg said the organization is looking for an additional three to five partners to bring on board, ones that will bet big that Facebook subsidies of social services will pay off by up-selling their data plans.

By using Facebook as an on-ramp to the Internet, the next one billion people will use social logins not just to control various apps, but their entire Internet usage.