For every Like that is made, Facebook is able to correspond a product affiliation to demographic information such as sex, age, geography, and education, as well as social graph data about relationships and influence within a group. With Places, Facebook can even correlate product activity to mobile location data. If mobile payments ever take off, they could get actual sales data as well.

Ad Age recently asked the very poignant question: What Happens When Facebook Trumps Your Brand Site? (alternate title for the article is: How Facebook Became the Biggest CRM Provider). The online article was accompanied by the following graphic showing the top ten brands on Facebook (in terms of total Likes):

Top brands are garnering millions of Likes, yet only driving a couple hundred thousand visitors per year to their branded sites. What this all means is that Facebook has better data about customers than most consumer products companies do. As Ad Age notes:

For many marketers, their Facebook fan bases have become their largest web presence, outstripping brand sites or e-mail programs either because a brand’s traditional web-based “owned media” is atrophying or because more consumers are migrating to social media.

While fan pages may work a lot like a marketer’s traditional “owned media,” they’re not actually owned by the marketers. Facebook hosts the pages and provides analytics for free, but growing marketer dependency on the network for CRM programs, combined with simultaneous declines in traffic for many of their own brand websites, could give Facebook a valuable revenue opportunity.

Of course, it would be difficult to sell granular individual data about users (people would object); however, Facebook could sell aggregate data (trend analysis and market research) and act as a “black box” CRM (Customer Relationship Management) solution whereby companies offer targeted promotions and messaging to individuals with select profile characteristics, mediated through Facebook. Already some companies are using basic Like data to hone their retail strategies. In one example, Urban Outfitters is arranging clothing in its online store based on Like activity and offering select promotions to all those who have liked products. Additionally, Facebook is making information about the Like activity on ads (i.e. “social context” data) available to advertisers on its site. Armed with this data, advertisers can decide to further optimize campaigns by targeting people who have expressed a Like for the ad.

With the Like button, Facebook is benefiting from the power of weak tie relationships (Facebook calls it “lightweight sharing”). Many markets point to the fact that people that Like a product aren’t real fans or brand advocates in the traditional sense. This is s feature, not a bug. By lowering the bar for Liking something, Facebook has opened a channel to—and is gathering data about—ordinary consumers of the brand who otherwise would have no formal connection to the company or its products other than isolated, anonymous purchases. This connection can be potentially valuable in terms of loyalty programs and promotions, market research, and customer support.

A number of factors suggest that the number of Likes will probably continue to grow, including: the continuing growth of the Facebook user base (see chart above, which shows no indication of plateau), expansion in global markets (70% of Facebook users are outside the U.S.), the recent proliferation of the Like button on a range of products and services (the Like button is now on over 350,000 sites), and the growing use of mobile technologies that allow users to Like physical products and experiences. With this in mind, it’s by no means hyperbolic to think that Facebook could be the largest single CRM provider in the world.

If this works as intended, there’s a lot to like. From the end-user point of view, it’s almost nothing but upside: being able to check online for spot availability, having an increased likelihood of finding free spots on every block, and saving money by parking in less-popular areas. All of this is made possible by using technology to add a market function seamlessly into something that people are already doing; just by going about their business and parking, they’re generating information that makes the system better for everyone–themselves included.

While it remains to be seen if an approach like this will be profitable for the city, some tweaks could be made to their market algorithm so that the average price of a parking spot remains what it is now, keeping revenue where it is. Even if the program doesn’t generate money hand over fist, though, it still benefits the city and community as a whole, with reduced street congestion and pollution as mentioned by the video.

I especially like solutions like this one, as they enjoy the benefits of mass collaboration without actually requiring any additional effort on the part of the mass collaborators, and ample data is generated that can be further studied to try push the parking system to be that much more efficient. Everyone wins.

Bureaucrats of Toronto, take note!

Now, Cataphora is in the business of modeling “informal networks” within the enterprise for HR and operational efficiencies, as well as to monitor compliance with internal policies and external regulations. By analyzing the relationship between e-mail data, documents that are shared, calendar information, call logs, and people, Cataphora can assess employee productivity, uncover shadow networks, and map collaborative behavior. Digital Mirror offers some of these capabilities to the public for free by analyzing your archived data from Microsoft Outlook.

I ran Digital Mirror on my own Outlook data and came up with some pretty interesting results. A caveat I would add is that you need to have a lot of archived data for this to work well—several outputs such as “Blow-Off Scorecard,” “Buck-Passing,” “Temperature Gauge,” and “Loud Talking” didn’t work for me due to lack of sufficient data. Some of the other interesting outputs that did work are shown below:

Who have you spent quality time with?

Who have you talked with, about what, and when?

Who has been stressed out, and about what?

The goal of Digital Mirror in its current incarnation is to illuminate relationships, topics of interest, tensions, and hidden processes in the workplace. I think over time, digital reflections and analytics such as these will become increasingly important and, in many cases, baked into our both our personal computing as well as corporate processes.

As more data (beyond simply data from Outlook) is incorporated, I can imagine much richer, higher-definition mirrors. Aspects of our rich digital selves that are open for analysis include things like education; employment, and resumes; health records and government documents; search history; profiles on social networks; comments and posts on forums and blogs; and location information from cell phone signals and GPS-enabled devices. They could include aspects that we actively update like registrations for groups, associations, and publications, or aspects that we are not aware of like un-tagged photos of us on other people’s Facebook or Flickr profiles and images from closed-loop IP-enabled surveillance cameras.

Today most of this information is disassociated, residing in many different databases and in many different organizations. More often than not, the information is not under the control of the individual. In the future, we can envision a composite digital picture of the individual that will augment and accompany each human from cradle to grave. As the world becomes more instrumented, multiple machines—some under our control and others not—will be slicing our data and making observations about our activities billions of times each minute, in parallel.

Beyond optimizing processes and sparking what is likely to be heated debate about privacy and data ownership, digital reflections will also help people understand how they are perceived by others. With this knowledge in hand, we can go forth in the online (and offline) world making conscious decisions about how we want to represent ourselves in different contexts. Most people don’t step out into the real world in the morning without—at least briefly—consulting a mirror. Why should the online world be any different?

Wolf’s interest in self-measurement prompted him, with colleague Kevin Kelly, to set up a website, The Quantified Self, where people can find “tools for knowing your own mind and body.” A variety of contributors, including Wolf and Kelly, write about their own experiences with self-measurement and, of course, comment on others’ postings.

I won’t re-tell individuals’ stories Wolf recounts; you can read the piece for yourself and it is absolutely worth the time to do so, probably worth re-reading.

What people learn about themselves from the data they collect – a key, of course, is to be honest and objective in the collection and reporting – is fascinating. Wolf writes: “Although they may take up tracking with a specific question in mind, they continue because they believe their numbers hold secrets that they can’t afford to ignore, including answers to questions they have not yet thought to ask.”

I found this last phrase perhaps the most memorable in the piece: “answers to questions they have not thought to ask.” The discovery of some truth in data probably makes the exercise worthwhile in and of itself; the discovery of new questions to ask generates enthusiasm for the search.

We have written quite a bit about “unbounded data” as a challenge to organizations. Although I am not sure I completely understand the phrase itself, the concept is simple: Organizations are surrounded by and suffused with data, which demands attention but creates its own set of problems. The sentence “we don’t what we don’t know” has gotten a lot of attention in organizations, in part, I think, because the idea of trying to know all of it is too daunting a task. What I take from Wolf’s piece (one of several insights and ideas it contains) is that the effort of tracking, of acquiring data, is worth it because it starts the individual (or the organization) on a path of discovery. There are few things more interesting to humans than discovery, and I’d say that the same is true for organizations.

Certainly not all discoveries are money-makers or competition-killers, but if organizations believe that discovery of new ideas and new questions is a guiding principle, it can almost ensure, by itself, that discoveries will happen.

Unbounded data is a challenge, but only overwhelming if you don’t start.

(I anticipate writing about Wolf’s article again. One post does not do it justice.)

“Open data unleashes the creative potential of citizens and private enterprise to create new services, software applications, and insights that the government cannot do by itself. The shear numbers tell the story. Millions of citizens and hundreds of thousands of companies of all sizes uniting to independently create value and enhance the common good.”

This is exactly what you are seeing below. Bachman breaks down the 2011 Federal budget in a surprisingly simple graphic, showing total spend per category, percent change, and size relative to other spending priorities (click the image for the interactive chart).

Admittedly, I’m a little late to the party on this one. Bachman has being doing this since 2004 and has been featured in numerous publications as well as on national television. Still, if you’re like me and haven’t had a chance to check it out, definitely set aside some time to do so. There’s a new graphic every year, so even if you saw the chart a couple of years ago, it’s probably worth a revisit. This will be particularly relevant for U.S. readers who can calculate where their tax dollars are going at a fairly granular level. For example, in 2011, the average American tax payer will give $3,565 to the Department of Defense, $249 to the Department of Education, $218 to Homeland Security, $93 to NASA, $53 to the Environmental Protection Agency, $35 to Nuclear Weapons Activities, $27 to financing foreign militaries in Israel and Egypt, $22 to Postal Service, $14 to National Parks, $12 to HIV/AIDS, $6 to counter-insurgency in Pakistan, and $2.21 to Obama (Executive Office of the President). As Bachman notes:

“Thousands of pages of raw data have been boiled down to one poster that provides the most open and accessible record of our nations’ spending you will ever find. If you pay taxes, then you have paid for a small part of everything in the poster. ‘Death and Taxes’ is an essential poster for any responsible citizen or information junkie.”

The next order of sophistication for something like this would be an interactive budget chart a la Freiburg model, where citizens could use the visualization to propose their own balanced budgets as a way to provide feedback about their priorities. Imagine sliders that would allow you to ratchet-up or ratchet-down the relative size of spending categories.  These individual budgets could then be aggregated into a ‘citizen budget’ that would go beyond simple ‘suggestion box’ initiatives to provide a truly useful piece of information for policy makers.  I’m guessing a collaborative citizen budget would look a lot different than the one shown above.

OKCupid gathers personal information based on community-submitted questions that users answer. This allows the company to better match couples based on the unique values of each person. Their slogan is: “We do math to get you dates,” which includes going so far as to create decision charts that visualize the formulas leading to love (or at least a date) for various individuals. With their data, OKCupid reveals information about the effectiveness of various romantic approaches, male and female attitudes and biases, insights on what behaviors result in conversations, behavioral changes based on age, and a variety of other findings.

From a research perspective, OKCupid is a fascinating subject. I’ve references them before in a post about labor incentives. Today’s post is about the potential for data-enabled business models and new markets for user data. Even more insightful than some of the racier findings from OKCupid (such as the sexual appetite of the average 40-year-old Floridian woman), user activity on this site generates a tremendous amount of data that extends beyond the realm of dating and could be useful to other groups and industries.

For example, OKCupid is able to generate detailed demographic and geographic data about political views, social issues, and public opinion on issues ranging from contraception to First Amendment rights to acceptable means of protest.

What OKCupid is doing not unique—often the collection of data can yield new insights and provide additional contexts beyond its intended purpose. As more and more customer and user processes become digitized, what we’re going to see over the next few years will be the growth of data-driven strategies that gather, interpret, and present data for new uses and new audiences. The abundance of data and relative scarcity of reliable sense-making information will create a flourishing market for data and analytics. In a recent nGenera survey we found that already over 40% of respondents say that data from external sources leads to competitive advantage.

Two years ago I wrote about how the idea that online social networks will make money selling eyeballs (advertising) or products is missing the entire value proposition of a social network. The real opportunity is in harnessing the rich data that is created by those participating in conversations and interacting with each other. Companies that have social platforms are increasingly seeing a business model around providing free services and aggregating anonymized customer and user data for sale.

OKCupid has a very open approach to data, but it’s easy to imagine a variety of groups—lobbyists, politicians, economists, sociologists, and so on—that might be interested enough in this type of information to pay for it, especially if presented in interactive charts that let the user filter based on factors such as age, race, gender, employment, and so on. If you think about the possibilities available when data extends beyond the realm of online dating, you see that companies in a variety of industries could use customer-generated interaction and polling data to gain a deep understanding of what drives purchasing behavior, brand loyalty, and even the desire for new products.

Over 90% of respondents consider data as a strategic asset and in the last 12 months, close to 60% have seen an increase in the number of data sources that they use to make decisions. I had assumed that more data meant that they were able to apply all this new information to do their jobs more effectively. I was wrong. Only 33% reported that they had the right amount of data to do their jobs! How come? Isn’t all of this data supposed to be helpful? Where is the disconnect?

My colleagues have done some great work on this topic and continue to study how organizations can flourish in this new world of unbounded data. Specifically, Naumi Haque has spent the last year studying sentiment analysis including the tools and processes that best practice organizations are using in order to better understand their customers. Denis Hancock has done some great work on how enterprises can use social media data, specifically Twitter, to work more closely with customers and partners. Jeff DeChambeau’s exciting work on online gaming gave us some great examples of what enterprise dashboards could look like. Nick Vitalari focused on open data, including the challenges and opportunities presented by public-private ecosystems. And lastly, my dear friend Tim Bevins spent time looking at the effects of the mobile channel.

I’ve linked to some of their blogs above, but urge you to check out more of the research coming from this group, including the final results from the data survey. I consider it a privilege to work with so many talented people.

When we developed our 2010 research agenda a few months ago we could not ignore the fact that now more than ever, enterprises are being forced to manage huge amounts of data from many different and often completely new channels. The term unbounded sums up the situation perfectly as it immediately suggests infinite. Enterprises generate more data, collect more data, and can consider more data than ever before. But an increasing amount of data is generated outside the enterprise, by sources that you don’t control.

Prodigious quantities of data present opportunity, complexity, and distraction. Separating signal from noise requires advanced analytics and thoughtful strategies. Many businesses can and will reap the benefits of more detailed information on every part of the ecosystem (customers, prospects, competitors, partners, and employees) if it can be harnessed. Data-driven insight will enhance intuition and expand the purview of decision makers inside and outside the enterprise in ad hoc and deliberative processes.

As we began the research into this topic we decided that it would be help set a benchmark and get a better understanding of where professionals and their companies are with regards to their readiness and ability to deal with data. Last week we launched a survey to measure how data is being used both from a functional level and across enterprises. The survey is open to all levels and functions—if you are reading this we would love to hear from you! It only takes about 15 minutes. As a thank you for your time we will provide you with a summary of the findings.

The survey is designed to gather information to help answer these questions: Are decision-makers able to get the information they need? Are enterprise processes and IT departments keeping pace with technology advancements? What new types of data are being gleaned from social media and how are these being used? What data-related issues are preventing enterprises from measuring, managing, analyzing, sharing, and collaborating more effectively?

To participate, please click on one of the links above or simply click here.

Thank you in advance for your participation, we look forward to sharing results with you.

When we study prosumers, one of the reoccurring themes is how to create incentives to get people to contribute their valuable time to an initiative. Increasingly, consumers are challenging the notion that the Internet is recruiting ground for free labor that will willingly engage with your brand, contribute ideas, and co-innovate with you—consumers want some sort of value in exchange for their time. Contrary to what is being proclaimed in popular social media echo chambers, most consumers actually don’t want to co-create with companies; the vast majority of Internet users are happy to be passive consumers and observers, with only a small fraction opting for prosumerism.

This brings be to the main point of this blog post, which is the notion of playbor. I first came across the term—a combination of ‘play’ and ‘labor’—on the Web site for a conference on digital labor hosted by The New School in New York. The Internet as Playground and Factory notes that, “Today, communication is a mode of social production facilitated by new capitalist imperatives and it has become increasingly difficult to distinguish between play, consumption and production, life and work, labor and non-labor.” The simple idea driving the playbor discussion: What happens when we collapse the often conflicting interests of work, personal ambitions, and entertainment into a single activity?

We already see examples of this happening on the Web. Consider Google’s Image Labeler, which creates a game out of the legitimate task of tagging and creating metadata for Web images. A less contentious example is Free Rice, which hosts a word game and has sponsors donate 10 grains of rice to the World Food Programme for every right answer submitted by players.

Recently, my colleague Jeff DeChambeau wrote a case study about the dating site OKCupid. Unlike eHarmony and other dating sites where the company determines the question set used for matching couples, OKCupid relies on questions submitted by its users. The notion that the users know best about what characteristics make a suitable mate makes sense. Why this is novel from a playbor perspective is that users, through actions that are apparently self-serving, are also contributing to the growth of the site and the effectiveness of its proprietary matching algorithms. What’s more, the actions of users create value for the company in the form of new data and analytic possibilities (for fun examples, see how OKCupid number crunchers use member data to determine what makes a good profile picture, or why dating an older woman might make sense).

For enterprises, these examples raise the question: How can we make work more interesting, more curious, and more playful so that users willingly play to create value? How can we align incentives in a way that lets us harness free labor? What is the appropriate division of labor across a diverse and fluid ecosystem that includes customers, prospects, partners, and competitors?

There are troubling consequences as well. How can consumers be sure of the authenticity of their experiences? Child labor is a discouraged practice, but what about video games that could be designed so that game-play elements actually contribute to the production of a commercial product like a new chip, program, or piece of software? As our environments become highly-instrumented with and capture data from our activities, how are users compensated for, or even made aware of the commercial value of their data? What does it mean for the broader economy when waged and unwaged labor collapse and are often indistinguishable? What does it mean for society when we debase the notion of pure, innocent play? The Internet as Playground and Factory has a great Vimeo page with clips from leading thinkers that are considering and debating these and other issues that arise from playbor. It’s a lot to digest, but this is a great starting point for people interested in the topic form a social studies perspective.

A recent article by Nate Anderson in Ars Technica highlights professor Mayer-Schönberger book, Delete: The Virtue of Forgetting in the Digital Age. The message: “Technology has now made ‘remembering’ the default approach to information, and in doing so, threatens to make ‘forgetfulness’ obsolete.” This is not only a profound change from 20 years ago, it can also be detrimental to our ability to think and analyze information. The article goes on to say: “Selective forgetfulness is a boon to humanity; it keeps us from drowning in our own recorded data. It allows us to sift and sort, then to think at a higher level of abstraction instead of wallowing in detail.”

But, this may all soon change.  Perhaps, computers can learn to forget too.

Researchers led by doctoral candidate Roxana Geambasu, at the University of Washington in Seattle are working on project called Vanish. The idea is to encapsulate data such as e-mails, selected text in messages, or documents that are sent over the Internet. The system would create corresponding keys for decapsulation that are widely available online, but that would deteriorate over time so that the data in readable form would only be available for a certain period of time. The overview page of the Vanish project states, “We strongly believe that realizing Vanish’s vision would represent a significant step toward achieving privacy in today’s unforgetful age.” Mayer-Schönberger suggests a similar solution that uses metadata to tag data objects with expiration dates and cites the work of Lawrence Lessig who has proposed a broader approach to combine policy and software to force privacy compliance.

nGenera’s research project Leading in an Age of Unbounded Data is looking at new sources of data available to the enterprise and how these will lead to new insights, opportunities, and challenges, as well as change enterprise processes and decision-making. One of the assumptions we make is that data will continue to grow and companies, through analytics, will develop a type of ‘sixth sense’ or situational awareness about the organization thanks to information captured from across the business ecosystem. We have already found that the growth of personal information and digital identity data will lead to rich digital profiles containing social graph information. These rich profiles present opportunities to better engage with customers and employees, improve customization, and facilitate knowledge management by anticipating user needs and connecting them to relevant people and information.

Projects like Vanish force us to think about data, not as an asset with an indefinite lifespan, but rather as something that depreciates over time, just like physical assets do. This would effectively reduce the amount of data that we need to manage and improve signal-to-noise ratio as more important facts and information would be retained while less significant information would be deleted. By eliminating the perfect memory of computers, we might also feel less pressure to maintain digital facades and manicure our online profiles. Additionally, the idea of adding expiration dates and metadata to data could accelerate the shift in power away from marketer towards consumer as it would allow individuals to dictate what personal data is used, who has access, for how long, and for what purpose.

But, self-destructing data would also diminish the value of many of the ‘big data’ opportunities that we talk about such as using large data sets to infer the truth about various situations, and using sentiment analysis to mine online customer comments and status updates for market research and product insights. It would confound companies and marketers that store petabytes of information to generate longitudinal trends and rely on usage data to drive Web analytics and build reputation and ratings, as well as improve information management through technologies such as collaborative filtering (e.g. the technology used by Amazon to recommend books to you based on the activity of people with similar behaviors). By collectively deleting our less-than-favorable digital trails, would we also be doing a disservice to future generations of anthropologists that could benefit from a complete digital history and behavior map—both good, bad, and questionable actions—of their ancestors?

The idea that all data should live on forever is a relatively new concept that many people have already taken for granted. In general, I think enterprises, governments, and individuals would benefit from more discussion on the topic instead of seeing it as a foregone conclusion. The idea of having an information lifecycle for all data is a powerful one. Personally, I would welcome more initiatives such as those by the Vanish team and professor Mayer-Schönberger that broach the topic and reintroduce a little forgetfulness into our digital lives.

Someone in the group mentioned how it would be great to be able to filter information from individuals, so that you only received from any person what you found most helpful or useful from that person. Makes sense to just use each person’s skill or knowledge strength to enhance your own. So, if you had Facebook-like connections with lots of people, you could selectively receive information about trends in mobile marketing from someone whose focus is mobile marketing. I see this as a kind of best of the best relationship approach: You get your colleagues’ best and they get yours.

But, when I thought about this approach, it struck me that it was actually anti-collaborative. A few byproducts of this approach came to mind, none of which I liked. Deciding who you want to listen to about specific topics – essentially, getting only insights and information that you think this person is best at – means you never hear from anyone else, never hear anything but what you want to hear, and you only hear one person’s views on a given topic. Taken to an extreme, I see this as exclusionary, not collaborative.

When I interviewed Randy Adkins, former Director of The Center of Excellence for Knowledge Management at the United States Department of Defense, he told me that the idea for a people search feature in the Air Force’s highly regarded knowledge management platform, Knowledge Now, originated in what he described as an unlikely place: the Air Force Audit Agency, the unit that senior leaders in the Air Force rely on to understand problems and develop solutions. The Audit Agency was having trouble locating the best people to dig deep into a specific problem and prepare solutions. With 900 auditors, knowing exactly who knew what or who was most knowledgeable about a specific weapon system or organizational process was almost impossible. Each auditor’s previous experience was invisible to the Auditor General (AG), which meant problem-solving was more complex than it needed to be. Taking advantage of the wealth of experience in the division was a high priority for the AG. “If you need to audit an F-16 supply support issue, is there anybody else out there that has had F-16 experience or supply support experience that we can put on that audit?” Adkins commented. “It benefits everybody because you have a more informed auditor, so when you are being audited, the chances of getting a more insightful person to help solve that problem is increased dramatically with People Search.”

So, yes, a collaborative platform should have a feature that exposes everyone’s strengths and skills to everyone else. But what I am getting at is this: Even with an expertise identification system, such as one based on people tagging, you never know where great ideas are going to come from. Filtering is great but if it closes off the possibilities of surprise and even creative thinking, it’s going to result in more isolation than sharing. The possibility of insights and ideas from unlikely places, it seems to me, would be minimalized if people filtered their connections to the extent that they saw individuals in a narrow way, as having useful information on just one topic. If collaboration platform software does anything well, it has to expose lots of people to lots of other people across the enterprise. It should increase the opportunity for surprise, not narrow it.

Unlike information taxonomies that catalogue data or data security protocols that either allow access or deny it, context is dynamic; it changes. Advertisers have been thinking about this for many years. An ad for Bud Light Lime might not be all that relevant in the commuter newspaper, but it makes perfect sense behind a urinal in the men’s room of the pub, or on a billboard in cottage country. But that’s still a very 1.0 view of context. What’s missing is the granularity that takes this type of generic contextualizing (e.g. if you’re at the bathroom in a pub, you’ve probably been drinking beer; maybe you’ll like our beer) to a personalized one (e.g. we know you only drink at the pub after work, not during lunch, so at lunch we’ll offer an ad for coffee; after work, we know you’re favourite drink is gin and tonic, not beer, so we’ll suggest a new premium brand of gin).

While advertisers are leading the way, for most enterprises, this type of granular ‘what you need, when you need it’ approach to information is still far from reality. The good news is that the tools to sense and record context—rich user profiles, presence awareness, geolocation data, status updates, and lifestreaming information—are exploding all around us. You might think of much of this as information exhaust—the incidental, or ambient data that is created as a by-product of simply carrying on with our daily lives.

The anthropological view (circa 2007) of ‘exhaust data’ is that it has little information content, but lots of emotional and social content that contributes to identity, intimacy between individuals, and a deeper cultural understanding. However, as tireless machines work 24/7/365 to mine this exhaust data, the information content becomes apparent as well. The data will reveal important trends about individuals and their preferences, thus enabling context-aware machines to sense our needs and respond. What this means for enterprises is greater employee productivity as users spend less time looking for and filtering information, and better customer experiences as contextual information leads to greater customer intimacy and personalization. As Edo Segal notes on TechCrunch:

“These are streams of information bubbling up in realtime, which seek us out, surround us, and inform us. They are like a fireplace bathing us in ambient infoheat. I believe that users will not go to a page and type in a search in a search box. Rather the information will appear to them in an ambient way on a range of devices and through different experiences. [...] Humanity is constructing its own synthetic sixth sense. An ambient sense that perceives the context of your activity and augments your reality with related information and experiences. Increasingly, we will be sensing the world with this sixth sense and that will change the way we collectively experience the world.”

Indeed, Gartner believes context-aware computing will provide significant competitive advantage. I agree. The firm predicts that “By 2012, the typical Global 2000 company will be managing between two and 10 business relationships with context providers.” Technologists, enterprises, and academics are beginning to understand the importance of context and we’re starting to see products and services that reflect this. Consider the following examples:

• MIThril at MIT Media Lab is working on wearable computers that gather contextual information and provide feedback; projects include the Real-Time Context Engine and the Context Aware Cell Phone Project.
• Also from MIT, SixthSense by Pranav Mistry provides the “synthetic sixth sense” alluded to by Segal. This is some of the coolest technology I’ve seen in a while. For a demo, check out the TED Talk video below:

• Cisco Context-Aware Software is a mobile solution that integrates contextual information (including location, temperature, and availability of an asset) with business process applications.
  
• Marcopolo for MAC OS is an open-source, early example of context aware computing that triggers actions based on changes in location or activity.
• Research In Motion (RIM) appears to be pursuing context-aware security for the Blackberry. The company was granted a patent in August of 2009 for a mobile device that can change security settings based on its environment.
• Earthmine uses 3-D mapping to tag the physical world. Imagine having Terminator vision, but displayed on your iPhone. Tags, such as the ones shown in the picture below, could be customized to reflect any context.

 Feel free to share any other context-aware examples you know of.

Well you sure watched that quickly! For those who are busy and those who skimmed, Suarez’s argument breaks down roughly like this:

Our world is full of robots. They’re not machine assembly line robots, nor are they wheeling around shouting “Danger Will Robinson.” Instead, they’re small bits of software that each do one thing, and do it very well. The history of computing has taught us that thinking machines are good at doing things quickly and doing them over and over, and that’s generally what these bots (short for robots) do; they perform their tasks relentlessly and single-mindedly.

At first, these bots were used to augment the abilities of humans and allow them to have greater responsibility and reach, but increasingly they are replacing people themselves, often in positions that typically made decisions that affect other people. An example that Suarez uses is bots that review credit history data to decide whether or not a given person is approved for a loan: some time ago some person made the decision that people fitting x criteria would be approved, while people meeting y criteria would not. The bot then implements this decision across all records that are sent its way, and in many cases, the repercussions of its decision  has livelihood-impacting results on the lives of the credit applicant.

Bots thrive on the Internet, as they’re not penalized for not having physical, motile bodies online. As more and more of our society has machines embedded in it, there will be more and more information generated for these bots to scour and analyze. In 2010 here at nGenera Insight, two of our main research topics–Pervasive Personal Identity and the Digital Identity Revolution, and Managing in the Age of Unbounded data–are issues that can exist only because of the efforts of software robots. It’s a big issue in our minds, but for Suarez it’s even bigger, and we need to have a serious discussion about the role that these automated, unflinching, and increasingly empowered pieces of software have in our lives and society.

It’s been a couple of days now since I first watched the video, and I’ve been rolling the argument back and forth a bit looking for problems with it: I haven’t come up with much at all. There’s great potential for good to be done with the information collected by these bots, and from the analysis and distillations they perform, but as it stands, the design of the world’s information infrastructure (that is, the internet and the devices that are constantly added to it) give end users/consumers/citizens very little control over their own information and the information collected about them. Suarez is right that we need to have an open and frank discussion about these elements, but I think first we need to really define the space and make sure we’re all on the same page.

However, this approach is far from perfect. One of the major reasons is that the increased transparency helps bring out “the truth” a little bit, but there are many things that people opt not to share openly about themselves. And it got me thinking whether the job search process could learn a little something from what’s happening with the online dating scene – because there are a lot of interesting similarities.

Sites like Match.com were / are roughly the equivalent of Monster.com. Sites like Facebook, one can argue, are to Match.com what LinkedIn is to Monster.com – lots of people can use it to find / filter potential dates through their network of acquaintances (which is often seen as a superior method). But there is also a very successful site called eHarmony – where users answer hundreds of different questions, and based on these responses are matched up with people that are most likely to be similar.

The closest thing I’ve experienced to this in job searches is various tests and psychological profiles I’ve filled out when applying for various jobs – from trying to become a cashier at a large retail chain in my youth right through to more recent times. But in each case, this was a very one-way process – and I had a general idea of which types of responses the company was looking for.

So what if you took that to the next level – and tweaked it a bit. Imagine one central site, where every worker answered a detailed questionairre about themselves. But instead of having companies be able to peruse the results, let’s say each worker also provided details on where they’d worked in the past, in which roles, and how their experience was. And maybe you throw in companies defining (in detail, but “secretly”) which attributes they believed were most important for filling a certain role. Couldn’t all that information be combined to create a reasonably accurate prediction of which person was best suited for which role?

Now (of course) I’m not the first person to think of this by any stretch of the imagination. A quick search of “eHarmony job search” points me to a 2008 mashable article about a company called realmatch, which appears to be trying to do something like this. Other posts from 2008 point towards upstarts like itzbig, climber, and jobfox attempting to do the same thing. But none of them (according to Alexa) are anywhere near Monster.com in terms of traffic rankings (though JobFox does seem to be showing steady growth).

Now I haven’t actually tried any of these, but my sense from looking at each one is that one big piece of information is missing. Each seems to follow the model of companies saying what they want, and individuals filling out surveys to indicate what they’re looking for. I imagine a lot of people fill out these surveys trying to predict exactly what the companies are saying they want – focusing on opportunity rather than accuracy.

In turn, I personally believe the critical piece of missing information is a feedback from other people that have, or are, working for the company (in the same, or similar type, roles). This would help “balance the playing field” here – and by having a more “realistic” explanation of what’s required / what type of people are suited, individual applicants would have an increased incentive to tell the truth.

Of course the nuts and bolts of making it work would be hard – particularly since incredible scale would likely be required. But my hunch is that job matching sites are going to continue to evolve, on a lagged time line, in somewhat similar fashion to the online dating platforms. And the company that figures out how to best adapt the eHarmony model may well be the one that figures out how to bring in that additional data set.

Gartner’s Hype Cycle for social media classifies Social CRM (i.e. the integration of social media into CRM systems) as a transformational technology that is two-to-five years away from mainstream adoption in customer service applications and five-to-ten years away from adoption in community marketing. While I agree that Social CRM will be transformational, I think the adoption will (and must) happen more quickly. Specifically, our research at nGenera has uncovered new data, new tools, new channels, and a new mindset that are accelerating the trend towards Social CRM.

Data: The data that is included in traditional CRM is limited to very basic identity and transactional information about customers. It does not typically include the type of rich digital profile information contained in places like Facebook and LinkedIn.  Customer feedback is collected through surveys, a method of data collection that is expensive, time-consuming, temporal, and often annoying for customers. But this is all changing. Customer data can be gathered from many sources, some old – such as the contact center – and some new. With respect to the contact center, the amount of unused customer data that is generated is astounding. One interviewee recently confided that his contact center writes the equivalent of a book every day – a book that nobody reads.  A first basic step is to generate organizational learning from contact centers. Once you’ve mastered this, you’re ready to move on to new sources of data. In this case, I’m thinking about reality mining, social networks, forums, blogs, and other digital venues where customers are engaging in behaviors that affect the company’s brand.

Tools: Listening platforms and sentiment analysis tools allow companies to capture customer preferences, complaints, feedback, and queries expressed online, while social network analysis can provide insight into the connections between individuals and identify key influencers. Companies can also track prosumer activity across branded communities and company-sponsored networks. When integrated with CRM databases, this information helps create accurate, up-to-date, and meaningful customer records. Although the CRM systems that currently offer applications to incorporate social media data only include data from a limited number of social networking sites – of which Twitter is the most common – this will likely change. Data will eventually be collected from all public online discussions as the concept of Social CRM becomes more accepted and companies develop strategies to deal with larger volumes of data. Once customer conversations have been successfully captured and incorporated into CRM databases, one can imagine a future where companies will be able to capture other forms of rich data, such as emotional data, photos, voice, and even video content (i.e. not just video metadata). According to a vendor I interviewed, companies can already correctly identify individuals online using available profile data with up to 90% accuracy.  This allows comapanies to find existing and potential customers online and gather new data about them. The contact center of the future will have a much richer digital picture of customers, allowing companies to personalizing product and service offerings, engage customers in meaningful conversations, and generate sophisticated trend data.

Channels: Many contact centers, such as those at Best Buy and Comcast now support social media channels and have dedicated teams devoted to responding to customers and prospects in public and branded digital venues. The question of whether or not to use social media as a listening platform or a contact center channel is major one for organizations as it affects the number of touchpoints that need to be managed and the complexity of customer support operations. However, as sentiment analysis tools get better, and integrate more readily with CRM, we expect this distinction to become less and less of a concern. In the future, the new sources of data (inputs) will be the same as the channels for customer interaction (output). As these channels mature, I fully expect the data and analytics to help “close the loop” with respect to customer engagement metrics – directly connecting social media investments with customer sales information. In this way, companies will be able to measure the value of customer intention and calculate the ROI of social media interactions.

Mindset: The notion of ‘relationship management’ brings with it a particular bias that data is controlled by the party that is doing the managing, rather than ownership of the data by the individual. So, in the case of CRM, it is assumed that the company is managing customer relationships by controlling the data about them and their interactions. New notions of relationship management seem to embrace the idea that ownership of both identity information and the customer-vendor relationship should reside with individuals, not companies.  Exchange of information should be based a two-way value proposition in which individuals selectively share aspects of their rich digital profiles, as well as their discretionary effort in exchange for useful and targeted messages, promotions, and reputation.  Paul Greenberg from ZDNet discusses this in more depth and notes, “Co-creation and mutually derived value, is at the core of Social CRM.” As an example, The Internet Identity Workshop and Project VRM (Vendor Relationship Management) at Harvard is exploring a highly customer-centric view of identity information where the customer controls their data and manages relationships with various vendors.

I’ve heard the argument that traditional CRM “is dead,” but this is far from the truth. In fact, as Brian notes, Social CRM does not replace transactional CRM systems, rather it augments them. What CRM is in desperate need of is new data sources and tools that help integrate and analyze this data. The future vision of CRM also requires that companies get involved in new channels and cede a certain amount of control to the customer – it’s less about management and more about engagement.

What’s equally impressive is some of the display technology being developed to display this type of data. All vendors have some form of executive dashboard, but these are highly utilitarian. From what I have seen, the bar for sentiment visualization is being set by other innovative thinkers. For example, ongoing projects like We Feel Fine from Jonathan Harris and Sep Kamvar as well as Bio Mapping from Christian Nold aim to visualize emotional data in new, interesting, and useful ways. We Feel Fine is more of an art project than a rigorous sentiment analysis tool, but it provides a useful example for how we might organize, display, and search for comments. Users on We Feel Fine can search by emotion (key word only), gender, age, location, weather, and date. It also connects emotions to associated images that are found within the document.

Bio Mapping (shown below) uses a lie detector connected to a GPS unit to measure location and physiological arousal at the same time. This is then plotted using Google Maps and other visualization software. Note, in this case the sentiment metric is intensity of emotion, not the specific emotion itself. The spikes shown on the map are locations of interest, but that is all we can determine from the data.

Although most of the complex visualization technology is still nascent you can imagine where this is type of analysis is going. Companies could segment customers based on emotional response, plot the spread of viral buzz, identify ideal test markets, and optimize local campaigns based on near-time feedback loops. Employees could gain access to a new lens on customer activity, behaviour, and satisfaction in a user-friendly display that makes analytics fun. Large retailers could use similar mapping technology powered by emotion data to optimize store layout and measure display/product appeal. The biggest challenge to wide adoption of these types of tools is the lack of valid emotional data in significant volumes. Currently, mining user comments online is the best available data source, but some early research suggests promising breakthroughs in the area of voice analysis and facial recognition as well – stay tuned.

At noon yesterday, the Sunlight Foundation announced the winners from the Apps for America contest.  The top prize (which came with a $15 000 reward) went to the makers of Filibusted.us, a web-based application that sheds light on which Senators have been filibustering legislation in the US Senate.  There were 16 prize winners in total, and I definately recommend checking out the winners for yourself (my favorite is Legistalker.org).

Next came the recently-launched INCA – the Innovative and Creative Application Contest, based out of Belgium.  This contest is open for anyone to submit an application, be it a website, widget, google mashup or mobile application, to be used by Flemish citizens to help solve “collective and social problems.”  Prizes will be awarded to the ten best submissions, with the top developer receiving a prize of 20 000 Euros (about $25 ooo USD).  Deadline for submission is April 27th. 

With INCA, Apps for America and last November’s Apps for Democracy, we’re starting to see a very exciting trend in the Gov 2.0 space:  software developers and programmers engaging in social causes and public sector development.  Can these contests help spur the creation of new services along the lines of fixmystreet or transparency tools like opencongress?  After speaking with Sunlight’s John Wonderlich and Apps for Democracy architect Peter Corbett over the past two weeks, I’m convinced that they can.

The key to improving on these contest models is to create, in the words of Peter Corbett, a “Cradle-to-Grave” strategy to promote citizen-driven innovation in the public sector.  Corbett’s Cradle-to-Grave approach seeks an 8-step process for future application contests:

1.  Problem Sourcing:  Idea-sourcing, in this case, will work best when a specific problem/social issue is being targeted (such as crime, traffic congestions, etc.)

2.  Open Data:  A key to the success of Apps for Democracy was that Vivek Kundra made DC’s government data openly available with over 240 data feeds. 

3.  Government Sponsorship:  To support the idea-sourcing contest.  This was done in DC’s Apps initiative.

4.  Establish Contest Framework: Well-constructed in all three above examples.

5.  Launch and Run the Contest:  Also well-done in the three above-cited cases.

6.  Award:  All three contests had multiple award winners (not just financial – the recognition is probably a more powerful incentive to participation).

7.  Absorption by Government:  For the best applications to live on after the contest and provide real value to the public sector, they need to be adopted, maintained, and ideally, improved upon.  This represents a murky point, and an area where the initial contests haven’t found a working model.  It’s unclear how this is best handled.  Should the government provide ongoing grants for development of applications?  Should government internalize the best applications?

8.  Commercialization:  Lastly, Corbett believes that the applications need to somehow become commercialized, although it’s unclear whether or not government should be involved.  Could iLive.at or DCHistoricTours have a business model, wherein they could be licensed to other cities to use?  Another possibility might be a sponsorship arrangement with a private company.

Overall, citizen-driven idea sourcing and app creation represents a means of maximizing web 2.0 potential in public sector development.  The room for improvement is massive – outside of DC, for example, very few governments have truly open data (Corbett’s 2nd step).  But even if we’re still years away from widespread adoption of this model, at least we have the model (or rather, a model), with forward-thinking governments seeing the merits and beginning to move in this direction. 

If you’re really into this space, there’s a major development coming over the horizon – but that’s a whole other blog post in itself.

The full report is worth reading if these issues interest you at all. It’s in beta stage for the next 10 days, so your comments could help shape the future of digital governance in the UK.

The richest of the bunch is a piece of software called Skyrails:

Check out the other six technologies here.

The Centers for Disease Control and Prevention call this type of disease monitoring Syndromic Surveillance, where they use information about things that often happen before a diagnosis to gauge the likelihood of a public health concern. I had done some work at school in 2005 looking at a project from the University of Pittsburgh called Real-time Outbreak and Disease Surveillance (RODS), which used over-the-counter medication sales as a tool for predicting community health issues. The sales data is collected through the National Retail Data Monitor Program, which uses the UPC data from each sale, and aggregates them. Any spikes determined statistically significant seem to correlate well with emergency room visits, and appear to do it faster. A study of the system’s effectiveness suggested that electrolyte sales and medications like Gravol/Dramamine spiked before children started presenting in emergency rooms for care.

Google has now weighed in with their Google Flu Trends which, instead of using OTC sales data, uses search keywords. The idea is that if you’re searching for terms like “flu” and “cough medicine” there’s a good chance you need them. Pretty neat.

I think this has the potential to be even more sensitive than the RODS program, as people only buy medicines when they’re sick enough to need them. They might Google about their symptoms before symptoms necessitate the meds, adding to the early-warning lead time.

To think even bigger, could this type of predictive use of Google search words bear fruit in other disciplines? Like, has anyone taken a look at a stock price’s volatility as a function of its search traffic (or keywords paired with a company name)? Perhaps some negative web chatter could presage rough times for the mentioned company, ideally beyond the typical rumour-mongering.

I know. The stock idea seems like a stretch. It’s just that the markets have me feeling unwell. Or is it the flu? Maybe I’ll ask Google.

Central to this development is an increased sense of autonomy from human interactions (such as self-repair, stabilizing feedback loops, and self-directed traffic management) and “smartness,”  — something that already exists in an ever-increasing form in the computational clouds of Google and Amazon, which are constantly learning about how it is that we use language, and form an understanding about how collective human behavior can be used to anticipate the actions of an individual.

These two systems are able to “manufacture intelligence” and sell it to the humans that participate in the system (by adding raw usage-information that these clouds use to refine their understanding). This money is then invested by the curators of the cloud to expand its computational power and scope, and the organism grows. While it seems like there’s an intentional blurring of the line between the hardware/software itself, and the companies that use them to deliver the services, I think it’s fair to respond that a corporation is a fairly abstract entity, and if Google were able to do it’s job of organizing the world’s information and making it universally accessible and useful with fewer employees and more computing power, its shareholders wouldn’t mind so long as the share price continued to rise. So such an organism could be viable as a company.

The final phase of the development towards something lifelike (or maybe even alive) would be self awareness, in which the Internet could map itselfs to determine whether the information it’s delivering comes form within or from without.

All of this, I suspect and hope, is still a few years away. So we don’t yet need upper-year university ethics courses on the proper and just treatment of packets and subroutines, but I find it gives a new perspective to my everyday internet-habits if I assume that each search I make, and every book I buy online, could be contributing, to an infinitessimal degree, to the creation and emergence of a new form of life; and that this process is happening in a surprisingly innocuous and organic way.

Welcome back to another edition of the Wikinomics Roundup: Week in Review, where I capture in brief, some of the thoughts, discoveries, and discussions that graced the blog throughout the past week.

This week in the roundup:

• Jeff DeChambeau discussed privacy and digital surveillance
• Dan Herman introduced us to ‘vote swapping’ and identified how this Web 2.0 technology now has the potential to influence elections
• Don Tapscott highlighted some new research findings in order address a common misconception about video games and gamers
• Denis Hancock reviewed some of the pitfalls of the traditional crowdsourcing model and introduced us to Poptent

This is Gloucester, a UK based blog, is reporting that the Government Communications Headquarters is pitching a plan that would allow it to monitor all SMS and email messages sent and recieved in the UK. The plan, slated to cost English taxpayers a potential $12bn, would be the country’s largest surveillance program, and adds another data point to the security vs. privacy debate.

As Michael Geist wrote last week, the Internet has become a system that never forgets anything, and there are more and more tools that allow people to mine information from the darkest corners of the Internet.
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So, are email and sms messages, like public discussions, simply part of a technology that is inherently tracable, or given the targeted nature of email and sms, are they granted a special class of privacy from the rest of the bits that float bout the ‘tubes?

Weigh in on the privacy debate @
Hey England, time to learn about PGP!

A few months ago University of Ottawa professor Michael Geist wrote that 27 MP’s across Canada (9% of all MPs) had won their ridings by less than 1000 votes.  The potential impact of vote swap is thus rather significant.

The second example is www.voteforenvironment.ca . Like the former example, it takes aim at the Conservative government, this time for their environmental record. Their strategy is similar to Vote Swap as it highlights closely contested swing ridings and recommends to would-be voters which of the opposition parties in those ridings would be best positioned to win the riding in the Oct. 14 election.

What do you think of vote swapping and the technology behind it? Share your thoughts @
More on voting and technology…

In my new book, Grown up Digital (a sequel to my 1997 intro to the Net Generation: Growing up Digital) I make the argument that this exposure to gaming and technology has helped enable a truly global and inter-connected generation that sees civic action as a part of their regular routine.

Amanda Lenhart, author of a report on the survey and a Senior Research Specialist with the Pew Internet & American Life Project, notes, “The stereotype that gaming is a solitary, violent, anti-social activity just doesn’t hold up. The average teen plays all different kinds of games and generally plays them with friends and family both online and offline.”

The survey certainly supports this view:

• 52% of gamers report playing games where they think about moral and ethical issues.
• 43% report playing games where they help make decisions about how a community, city or nation should be run.

Are you a videogame player, gamer alumni, or know someone who plays games?
Discuss their impact @ Kids, videogames and social activity

What I particularly like about this “modified crowdsourcing” model is that it deals with some of the inequities inherent in more traditional platforms – too much power being given to the buyers, at the expense of the sellers.
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Poptent has at least three elements that help deal with this. they are targeting the creation of a community of top-notch videographers with great skills, not the public at large. In order to participate on the site companies need to pony up $25 K in cash – which should be enough to limit “speculative requests”. Advertisers than pay something in the $5K – $7.5 K range to purchase ads they like. They are also upfront in noting (see Mark Schoneveld’s comment on October 8th at 11:19) that the contest model is not sustainable – they’ll have to evolve it over time, but you have to walk before you run.

Discuss your views, for and against, the Poptent model @
Poptent: A new community for crowdsourced advertising

Check back next week for more original Wikinomics insight. Until next week…

As Michael Geist wrote last week, the Internet has become a system that never forgets anything, and there are more and more tools that allow people to mine information from the darkest corners of the Internet. That’s become a fact of life, but it’s to be expected: people are participating in a digital medium, with full understanding that data storage is cheap and archives are plentiful, so chances are good that the things they say will be on hand, somewhere, for the foreseeable life of the Internet.

So, are email and sms messages, like public discussions, simply part of a technology that is inherently tracable, or given the targeted nature of email and sms, are they granted a special class of privacy from the rest of the bits that float bout the ‘tubes?

I’m not sure how the average citizen in the UK would feel about their own tax dollars being used to breach their privacy, but it could be a concession that people are prepared to make for their “national security.” This paricular instance aside, it seems as though the reliability and security of connections is becoming less and less trustworthy, so the honus for protection of data is being placed on end users — maybe it’s time for us all to generate some PGP private keys!

Welcome back to another edition of the Wikinomics Roundup: Week in Review, where I capture in brief, some of the thoughts, discoveries, and discussions that graced the blog throughout the past week.

This week in the roundup:

• Jeff DeChambeau revealed the new beta of the Wikinomics blog
• Ian Da Silva uncovered the power of the social web for consumers
• Patrick Harnett introduced us to Wesabe and money management on the Web
• Andrea Bettello shined the light on a different type of TV experience

In case you missed it, you can catch the last roundup HERE.

On September 15, 2008…Jeff DeChambeau revealed the new beta of the Wikinomics blog:

A pet project of mine for the last little while has been to redesign the Wikinomics blog. Given the topic of the blog, though, it seems hardly fitting to launch it in a non-collaborative way. So, I’m doing a soft-launch and would like to collect feedback from the community about what works and what doesn’t.

To showcase and test the new look, I’ve set up http://wikinomics.com/beta/.

Tell us what you think about the new site @
Presenting the Wikinomics Redesign (beta)

As it turns out, it’s a site that in its own words “provides the world’s largest and most reliable source of information on the health, environmental, and social impacts of the products in your home.”  The site was started by Berkeley professor to help address the concern that most consumers know very little about the products that are brought in to their homes everyday.  Using a team of scientific, technological and academic experts, over 60,000 household products have been rated on three separate metrics: health, environmental, and social performance.

Discuss how your products match up on GoodGuide @
Looking for safe, healthy and green alternatives? Try GoodGuide.

The neat open source aspect of Wesabe is that it has a robust API which allows Wesabe enthusiasts the flexibility to develop whatever their cost-conscious minds can dream up. Wesabe has an articulate stance on how having better information about your spending habits is the first step toward reining them in. There are widgets for Vista, OS X, and even your iPhone.

Get ideas about how Wesabe could be used in interesting ways @
Wesabe: The Frugality of Crowds

Using NanoGaming, LiveHive is essentially creating a new environment for the television viewer. The shift from traditional one-way communication is underway as this Waterloo based firm promises to take viewers (and advertisers) to a place where the viewer can now actively engage with the program, ultimately creating a stronger connection. NanoGaming is more than just a fun way for the viewer to become involved, it also introduces opportunities to create a more effective advertising campaign than traditional television commercials offer. By sponsoring NanoGaming sites, firms can access audiences from some of the highest rated TV shows (such as the Super Bowl and the Academy Awards) and because TV and Internet are combined, consumers’ levels of attention, association and recall are increased.

What else does this mean for the future of TV? Find out @
LiveHive Systems: changing the way we watch TV

Check back next week for more original Wikinomics insight. Until next week…