But a much bigger gap exists, often unnoticed by companies: The amount of money contact centers and support organizations spend on social media is nominal compared to the percentage of customers’ queries that hit these emerging channels. This is the “support gap.”

Social media has become a new service and support channel that customers employ to raise concerns about products and services, describe their experiences, seek help from others, post new product insights, and consult for advice on features and functions. For companies, this is not trivial. It means that conversations about your products that would traditionally have occurred in your customer contact center are now occurring in public places. The Consortium for Service Innovation estimates that fully 90% of customer conversations about a company never touch the organization. What’s more, a mere 1% of all customer conversations are assimilated as organizational knowledge.

nGenera’s own Customer Interaction Management solution recently added social media support for its contact center product, but many companies have yet to consider this option. In most cases, if a social media strategy is being implemented by the support organization, it’s on an ad hoc basis with a few employees manually monitoring Twitter and branded Web spaces and responding to customers where appropriate. These interactions, though helpful, are not strategic in that they are not integrated with enterprise systems or contact center processes. Building processes and accountability around these activities is the first step that companies can take today—connecting to CRM systems in a robust and meaningful way will be the next horizon.

The plight of marketing and the plight of the support organization are linked and the two organizations need to work together, using shared information, on a common platform. Traditionally, marketing has been about communicating brand messaging, while the customer service department deals with problems and complaints. But a customer that you’ve engaged through social media for marketing purposes doesn’t see the separation – and customer service is becoming a key aspect of managing customer relationships online (I’ve talked before about how “customer service is the new marketing“). This is not trivial. It means matching the tremendous amount of time and energy spent on other official support channels such as e-mail and phone in order to meet customer expectations for social media and deliver a consistent and authentic customer experience. Those companies that think social media is just a cost-effective way to get the message out are in for a surprise. By marketing on social media you inadvertently open a new support channel as well—get ready!

A blues or jazz band—or any ‘jam band’ for that matter—operates using many of the design principles we’d like to see from a collaborative enterprise. Unlike an orchestra, a band is much more fluid in their interpretation of the music. They are able to improvise on the spot, blend sounds, and often play to the mood of the audience. In other words, they innovate, create mash-ups, and are responsive to users.

I’m not the first person to use the band analogy. Barry Rabkin of the Market Insight Group asks whether technology analyst firms are more like a jazz band or symphony orchestra. He alludes to the fact that the jazz band style is more agile and responsive to customer demands—another important outcome of collaboration:

“Another area where jazz musicians differ from their symphonic counterparts is that jazz musicians, sensing their audience, can and do take liberties with new selections not identified during their rehearsals. They can do this because they have a broad library of music and musical explorations in their knowledge set and, as importantly, they know how to blend their sounds together to get the best outcome possible for their audience.”

What’s more, in a symphony orchestra the conductor alone is responsible for guiding the entire team, whereas with a distributed, ad-libbing crew, anyone can start pushing with a new riff or mood and the others will follow suit. In this way, the benefit of each player’s perspective and expertise is baked into the model.

One of the factors that allows a band to operate in this manner is the existence of very well defined roles (i.e. guitarist, vocalist, drummer, base, keyboards, etc.) and somewhat open tasks (i.e. what songs to play, when to riff, what chords to use, etc.). This is another important learning for the enterprise. As Lynda Gratton and Tammy Erickson note in the HBR article Eight Ways to Build Collaborative Teams:

“Cooperation increases when the roles of individual team members are sharply defined yet the team is given latitude on how to achieve the task. [...]Assign distinct roles so team members can do their work independently. They’ll spend less time negotiating responsibilities or protecting turf. But leave the path to achieving the team’s goal somewhat ambiguous. Lacking well-defined tasks, members are more likely to invest time and energy collaborating.”

In addition to looking at how bands are structured, we might also consider how band members collect largely unstructured customer experience ‘metrics’ in real time and use the feedback to adjust their approach. These metrics provide a useful analogy for the type of approaches leading companies should take when developing customer strategies, including:

• The applause of the crowd: What kind of noise are customers and prospects making online and in social media channels? Using sentiment analysis companies can find out if it is positive (cheers) or negative (boos) and change their tune accordingly.
• Number of people dancing: How engaged is your audience? Metrics might be based on active participation on forums, comments online, rating of content, and re-broadcasting of brand messages, or more passive (i.e. head bobbing) activities such as subscribing to feeds, friending, and following.
• Song requests: What kinds of requests are coming into your contact center and support organization? In many organizations, the contact center is an untapped wealth of customer feedback, largely ignored by groups like marketing and product development. Listening to this channel and other prosumer input can lead to dramatically improved customer experience.
• Duration of stay in the bar: How long do customers hang out in your online properties? Using Web analytics, companies can now obtain this information, as well as data about how people got there, what path they take along the way, and how influential various ‘promoters’ are at bringing in prospects.
• CD and merchandise sales: How are Web interactions translating into sales? The performance is about creating an experience, but ultimately, in order to be profitable, you need people to buy your stuff.

As companies continue to seek best practices and metrics for collaboration, I firmly believe that some of the more innovative solutions will come from non-traditional fields that have deep roots in collaboration, but that have eluded formal study and analysis. (If I’ve managed to spark an interest in enterprise lessons in collaboration from other disciplines, also see my previous post on Measuring collaboration: Lessons from Shane Battier and the NBA and the related Collaboration box score.)

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.

When I originally started looking State of the Union addresses, I simply found transcripts online and did a manual count of words in text documents. This was laborious, but provided some interesting findings (note sites like Speech Wars can now automate this process). Last January I highlighted Wordle and used tag clouds to create a visualization of State of the Union addresses from notable past Presidents. This year, I’ve been spending a fair bit of time researching sentiment analysis, so I was pleasantly surprised to see that vendor Crimson Hexagon and CNN had teamed up to analyze public sentiment towards the 2010 State of the Union in real-time. Check out the video after the jump.

The impact of the new technology was not lost on the news media. The Huffington Post picked up the story and reported that, “The moment that ends up being most pivotal in changing the way the media covers big, live events may well have happened on CNN, where John King used the ‘Magic Wall’ to analyze almost 150,000 Twitter responses to President Obama’s speech.” In the article, CNN’s Senior Vice President and Washington Bureau Chief, David Bohrman is quoted as saying, “Twitter is all noise, but to be able to harness it and group it and actually intelligently cluster it and derive moods and opinions from it is very interesting.”

Whatever you might think of Twitter (Jon Stewart used the Magic Wall as an opportunity to make fun of both CNN and Twitter), this is exactly the type of technology companies are starting to think about for managing their brands, conduct market research, and pre-emptively deal with customer issues. The next level of granularity that sentiment analysis vendors are starting to offer is the ability to go beyond positive and negative sentiment to look at why sentiment is the way it is. Why are people pro-Obama? What types of issues are most often related to “Obama is too liberal?” This type of analysis is available, and I’ve seen demos from some vendors that offer fairly sophisticated drill-downs. However, some people remain sceptical about the general accuracy of this capability, as well as the limitations of most systems to crunch this type of data in real-time. Maybe we’ll see this for next year’s State of the Union—I’m hoping so.

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.

The boon for small operatives and peer-to-peer commerce is obvious. It’s a technology that will enable what we at nGenera are calling “the new localism,” or the opposing force to globalization spurred by both a struggling economy that has encouraged protectionism and “buy local” campaigns (e.g. see 3/50 Project), as well as environmental pressures to reduce transportation-related waste and third-world exploitation by supporting local production (e.g. see 100 Mile Diet).

Using devices like Square, anyone—from street artist to backyard food grower—can be a legitimate vendor accepting credit card payments. According to Om Malik (who calls it the beginning of the iEconomy):

“This is truly disruptive. The reason Square exists is because of three macro trends: the pervasiveness of the mobile Internet, the increase in the use of electronic payment systems and most importantly, the availability of low-cost, always-on computers (aka smartphones) that allow sophisticated software to conduct complex tasks on the go.

The marriage of computing and connectivity without the shackles of being tethered to a location is one of the biggest disruptive forces of modern times. It is (and will continue) to redefine business models, for decades. Square is simply riding these waves.”

But competitors are quick to point out flaws in the Square model. VeriFone CEO Douglas Bergeron (VeriFone plans to launch its own offering, PayWare Mobile in 2010) believes “that encrypting data on the iPhone itself—instead of before the data is loaded to the device—presents a security risk. Beyond that, he’s wary of Square’s decision to have a merchant account for the company itself but not requiring individual businesses to have their own. Bergeron said VeriFone’s offering will require merchants to have separate accounts. ‘It would be like sharing bank accounts with your neighbor: It just doesn’t work,’ he said.”

The URL for Square is squareup.com, as in “square-up with friends over a dinner tab,” which opens one’s imagination to the multitude of scenarios where one might need to square-up a debt or pay for a service. Dorsey and team’s target market is small business owners frustrated with credit card payments.

But what of illegitimate businesses? I’m currently reading SuperFreakonomics, which deals largely with how incentives drive human behavior. The book got me thinking about some of the more subversive uses of such a device. Specifically, the chapter, How is a street prostitute like a department store Santa? describes Chicago prostitutes that charge between $300 and $500 per hour, which may not be the kind of cash you want to be carrying around in the type of neighborhoods that support that trade. Would it be reasonable for a John to ask, “Do you take Visa?” What about cell phone-enabled drug dealers? Illegal firearm sales? Bribes? Gambling debts? Black-market goods? I wonder what the receipts would say. Much like reports of prostitution on Craigslist, social media (software and hardware) appears to provide an upside for both the legitimate and the insidious.

Of course, cash carries its own benefits as well. Most notably, it cannot be traced—a characteristic that the underground economy depends on. The cash transaction protects both the buyer and seller that require discretion. Also, for many legal, yet “under-the-table” transactions such as paying small vendors, contractors, or laborers, cash payments are preferable because they leave the door open for fudging income tax reporting.

Still, while it’s fun to muse over the possibilities, I believe the downside will be vastly outweighed by the upside. I’m excited to see how this plays out and how quickly such devices will be adopted (or how quickly they will be supplanted by software versions).

In my mind there are two approaches that companies seem to be taking to generate recommendations and help users navigate various types of information: trust networks and collective intelligence. Trust networks are based on your social graph (i.e. your online relationships) and therefore use your friends to help recommend content. Facebook Connect is a popular method of leveraging trust networks by porting your contacts into other applications (or other applications to your contacts). Collective intelligence, on the other hand, is a much more data-driven method of rating and recommendation.

The Twitter vs. Facebook debate provides another example of the relative merit of these two approaches. People often lump both social networks together because they both have status updates that detail the minutia of our everyday lives. But how these services are used is very different. Twitter is a platform for weak ties (i.e. more suited to collective intelligence) where Facebook is a platform for strong ties (i.e. by definition, a trust network). As Denis Hancock notes, “Facebook if for people I’d actually let into my house.” My Twitter contacts, on the other hand, are dominated by people I have never met and will likely never meet.

The important point is that both types of platforms are useful. While Facebook lets me keep in touch with friends and organize social events, on Twitter I find all kinds of interesting links to articles that I never would have found by polling my friends. Similarly, Twitter is a valuable platform from a market research standpoint because companies with the appropriate listening tools in place can now mine the collective opinions of millions of customers, prospects, and influencers. The argument that Twitter is a waste of time because, “why would I care what everyone is doing at every moment of the day” is valid, but the emergent trends and memes resulting from ‘what everyone is doing at every moment of the day’ can be extremely valuable.

This brings me to an interesting article I read on TechCrunch a few days ago: Location’s social paradox, which states that the main problem with location services is, “The more people you follow on them, the less useful the service is.” This is essentially recognizing the fact that for certain services—particularly those where you give up your physical location—the exclusivity of your close network of friends trumps the benefit gained from having access to many individuals. As such, most of the recent offerings (such as Foresquare and U.K.-based Rummble) rely on trust network – login and we’ll tell you what your friends are up to and what they recommend. In these cases, I would agree with MG Siegler from Tech Crunch who suggest the more friends you have on this type of network, the less useful it can be.

However, the potential to use these same services for collective intelligence applications is huge. For example, when you are considering a particular restaurant for dinner, you would be remiss to only consider the opinions of the few friends in your network that have been there. More powerful would be real-time access to the aggregated reviews of as many patrons as possible. Similarly, when driving on the highway, it may not be terribly useful to know that three of your friends are on the same highway, but it might be extremely useful to understand the traffic patterns of all the other cars on the road via the GPS signals from anonymous drivers’ cell phones. In short, social network applications that incorporate elements from both trust networks and collective intelligence (perhaps with the ability to toggle between the two or shut one off based on context) will be most valuable.

Earlier this week I spoke at a conference hosted by the Royal Flemish Society of Engineers on the topic of complexity. The keynote speaker was Prof. Geoffrey West, former President of the Santa Fe Institute that pioneered the study of complexity science using a combination of economic theory and biology/physics (the founders were an economist and a physicist – both Nobel Laureates).  The end goal of complexity research is to develop new integrated conceptual frameworks for understanding the interdependence between various complex adaptive systems that define our world, including cities, financial systems, and the environment.

West’s research suggests we may be able to use the same rubric to study both cities, and forests, and maybe even economies. Complex adaptive systems share certain characteristics. Among other things, they: have many nodes, are interconnected, are adaptive and resilient, have many participants that create bottom-up disruptive change, result in emergent phenomena, and are often subject to unintended consequences. Sounds a lot like the type of emerging business ecosystems we talk about here on the Wikinomics blog. Collaboration between large groups of disperse and diverse individuals is extremely complex; when you add in financial systems, various incentives, supply chains, and a global information network, it becomes even more so.

West also talks about different types of networks—often layered on top of each other—as a characteristic of complex adaptive systems. The better we can understand networks and their interdependence, the better equipped we will be to understand complexity. He believes that underlying all complex systems are simple rules or patterns. For example, if you look at the metabolic rate, size, and lifespan of various organisms, you can determine that every biological organism grows in the same fundamental way. Here West asks some compelling questions: Are cities and companies just very large organisms satisfying the laws of biology? If so, why do all companies eventually die, while almost all cities survive? To this end, I think there’s probably great value in studying the evolution and “biology” of collaborative networks, informal networks within enterprises, business ecosystems, information flow and knowledge networks, and the multitude of other networks that collectively define Wikinomics-enabled business practices.

So, what are the best types of structures to deal with complexity? If we base our answer on how the Santa Fe Institute is structured, we find that the solution to complexity requires a multidisciplinary approach that involves participants that can bring different perspectives and diverse expertise. It also necessitates an open, distributed, and collaborative approach, a willingness to take risks, and a relatively small executive team that is able to meet face-to-face in order to build consensus and drive decision making at the highest level. This sounds remarkably similar to what we prescribe for next generation enterprises that want to thrive in today’s dynamic business ecosystems.

Another interesting thought at the conference came from Prof. Francis Heylighen who spoke of the Internet as a global brain that may act to combat complexity at a macro level by reinforcing strong signals between parties and building “synapses.” Tied to the he global brain theory is his theory of human stigmergy—a mechanism of spontaneous, indirect coordination between agents or actions (think of the way ants and other insects develop collective intelligence that enables coordinated and fairly complicated activities).

All of this is very much related to the research we’re conducting here at nGenera regarding what we call the highly-instrumented enterprise where actions are increasingly digitized, sensors and software track and analyze new sources of data, and create new understanding of complex systems and emergent phenomena. Some examples of these types of tools in a Wikinomics context might include reality mining tools that track the behaviours of individuals; automated sentiment analysis of text, voice, and even video; platforms that generate data and offer venues for consensus-building; and enterprise monitoring tools that map the informal networks and measure productivity within organizations. In fact, I’m sure there are many more connections to be made here, and look forward to thinking more about this one and hearing thoughts from our Wikinomics readers.

Collecting and analyzing customer stories – the impact you have on specific individual – is one of three approaches that I think makes sense when calculating overall ROI in a customer experience context. The other two are mission and reach. Mission is easy – most organizations have some sort of mission statement, or if they don’t, they usually at least have a CEO or a board with a well-articulated vision. Reach is also pretty straightforward: How many people to you touch? What reach does is calculate the influence of an organization or an individual. It acts as a proxy for several influence measures including: authority, frequency (how often you create the opportunity to influence consumers), independence (lack of bias in their opinion), charisma (in the case of an individual), and persuasiveness. At the OSC, the measure of reach includes both the number of people that came through the physical location and the over five million that engage with them online.

For me, impact is the most interesting measure. When assessing impact, Kevin uses the example of Canadian astronaut Chris Hadfield. Having someone like Hadfield say that going to the OSC as a child helped inspire him to be what he is today means the OSC had a strong impact on him. Most companies have at least a couple of these types of exemplary stories. But at a more pedestrian level, any organization can listen to and analyze the stories of people that come through the doors and that write online in blogs, forums, and social networks every day. There are now several companies such as Attensity, Scout Labs, Radian6, Visible Technologies, Crimson Hexagon (and many others), that have software to help analyze unstructured information like customer stories. These companies can identify basic metrics like the percentage of positive and negative sentiment, as well as provide deeper analytics about specific product and service features that lead to customers having positive and negative experiences. The end goal for those looking for quantifiable ROI numbers around customer experience is to convert all unstructured data to these types of “numeric” representations that are consistent, tracked over time, and can be charted in a dashboard. In short, the systematic gathering and analysis of anecdotes.

However, the Machine in the story also has limitations, “the Machine did not transmit nuances of expression. It only gave a general idea of people – an idea that was good enough for all practical purposes.” The Machine is also standardized and all it domains are identical across the globe, “What was the good of going to Peking when it was just like Shrewsbury? Why return to Shrewsbury when it would all be like Peking? Men seldom moved their bodies; all unrest was concentrated in the soul.” As expected, the story eventually ends with the stopping of the Machine, and by extension the fall of mankind. The decay of the Machine is brought on by hyper-specialization of those responsible for its maintenance, and a complacent and decedent lifestyle:  

“Year by year [the Machine] was served with increased efficiency and decreased intelligence. The better a man knew his own duties upon it, the less he understood the duties of his neighbour, and in all the world there was not one who understood the monster as a whole. Those master brains had perished. They had left full directions, it is true, and their successors had each of them mastered a portion of those directions. But Humanity, in its desire for comfort, had over-reached itself. It had exploited the riches of nature too far. Quietly and complacently, it was sinking into decadence, and progress had come to mean the progress of the Machine.”

In Forster’s future, the Machine is a physical construct that acts as a replacement for reality and inhibits the movement of people and thoughts. However, the virtual layer of machinery that we are creating now has striking similarities.

In the digital world, man is no longer the measure by which progress is measured and the growth of the Web is not limited by our individual ability to create or even mental capacity to comprehend. This allows us to do tremendous things such as create simulations and models that would be impossible in the real world. However, in doing so, do we also lose a little bit of what it means to be human? For example, in the digital realm, there is no physical limit to how many “friends” we can maintain, no practical boundaries defining the amount of information we can acquire or transmit, no emotional context to our transactions and interactions, no appreciation for resources being consumed, and often a limited and sanitized semblance of our real world identities. Social networks are a perfect example.  In a recent project of ours on the topic of Pervasive Personal Identity, the original draft included the following passage:

“Humans, augmented with a rich digital self and related social graphs, experience a richer social reality and a hence a distinct advantage over humans without a rich digital self. As a consequence, organizations that embrace the rich digital self accrue significant advantages over organizations that do not.”

The language has since changed in recent versions, but it brings up many interesting questions: Would we benefit universally if all activity was mediated through social media? Do we have a choice anymore? Moreover, are we at risk of losing sight of the limitations of the machine? How are we measuring “advantage”; is it the progress of the individual, or the progress of the system? In an enterprise context, we no longer need to know everything related to our jobs, but rather simply have access to those who do know. At the same time, the big picture – strategy – is becoming increasingly complex and difficult for any one single individual to comprehend. As we suggest in a recent study on Continuous Business Strategy, technology platforms and machine-driven analytics are now an essential component of strategic decision-making.

We accept the use of technology-mediated interactions as gospel and depend on them for many routine tasks. When we interviewed members of the Net Generation and asked them to describe how they would feel if the Internet stopped. Responses ranged from annoyed to completely lost; without technology, the new generation would feel depressed, disconnected, and in all likelihood incapable of performing many day-to-day functions. Forster’s story provides an extreme example of technology-dependence to the point where the body becomes a detriment and infants born with the promise of undue strength were destroyed: “Humanitarians may protest, but it would have been no true kindness to let an athlete live; he would never have been happy in that state of life to which the Machine had called him; he would have yearned for trees to climb, rivers to bathe in, meadows and hills against which he might measure his body.”

In story, a character is reminded that man is the measure; after having to walk, he rediscovered notions of what it means to be near and far. I was reminded of this myself recently as I’ve been tackling home renovations. All things are originally measured in the context of the individual – the transportability of building materials are limited by my ability to carry them, the amount of work done on my house is measured in the time it takes for me to do it, or (usually) the amount of time for which I have to pay someone to do it. These limitations have of course been reduced thanks to the augmentation of machines and tools, and on a global scale, these limitations are harder to perceive because we have industrialized the production of most everything. But at a local level – i.e. home renovations – they are still very much real.

For me, the overall lesson is this: Technology must be used as a way to augment real world interactions, not replace them. As the antagonist in the story states, “The Machine is much, but it is not everything. I see something like you in this plate, but I do not see you. I hear something like you through this telephone, but I do not hear you.”

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.

Today people are expensive and technology is cheap so only the privileged few have EAs.  Their role has changed considerably, but at the core they still help manage information. For those without EAs, we now have more technology to help us– computer file systems, pre-programmed phone directories, Outlook, instant messaging, corporate intranets, iPhones, and wikis – and yet a large portion of my week is still spent manually organizing, finding, and sharing information.

Where am I going with all this?  I’m not convinced we’re better off.  This isn’t just because Mad Men does a great job at selling nostalgia, but rather because I don’t think our information management technology has been able to keep up with the rate at which information is growing. The point can be made that since we now have a reasonable degree of competence with the technology it makes sense to interact with information directly, rather than have it mediated by another party.  I would argue that maybe competence has become a burden.

Although it seems strange to even think what life with an EA might be like for an average 21st Century knowledge worker, I can certainly think of many modern tasks that many of my colleagues would be only too happy to divest, such as uploading and downloading content to/from shared sites, searching Google for specific tidbits of information, and generally separating signal from noise across all the various channels through which we are bombarded with information.

If mid-level knowledge workers still had EAs, would productivity improve? A lot of tasks have been automated, but a lot of new manual tasks have been created – these are the daily irritants that often prevent knowledge workers from sustained activity in core areas that create value for the enterprise. It’s real work, but it’s rarely accounted for or even acknowledged – it just becomes collaborative overhead.  I’m not suggesting we go back to the Mad Men days of “a girl” at every desk, but I think a shared resource for many collaborative and administrative tasks would be useful. Is it just me or does anyone else feel like knowledge workers could use some support resources?

I leave you with a politically incorrect clip of Peggy and Joan; a view of EAs from the past, as seen through the eyes of the Mad Men writers:

(Embed disabled – click to play video at YouTube.com)

Over the past year, we’ve been researching extensively the topic of digital identity. Not surprisingly, a lot of the theory we explore directly affects our day-to-day lives and how we interact online. As an example, I’m finding a growing “identity divide” as my various social graphs intersect (or more importantly don’t intersect) with my digital self. To put it bluntly, it’s becoming increasingly uncanny how my online persona—instead of converging—is in many ways actually diverging from the “real” me.

Why is this? One reason is that many of our most significant interactions—those with family and close friends—occur offline and are not captured as part of our digital identities. I think the bigger reason is that we’re constantly reminded that our digital identities are entities that need to be managed, so what does appear online tends to be a highly sanitized version of us. danah boyde talks extensively about the issue of digital identity. In her 2002 thesis paper “Faceted Id/entity: Managing Representation in a Digital World,” she says:

“In any given situation, an individual presents a face, which is the social presentation of one facet of their identity. I believe that an individual has a coherent sense of self, but in presenting only facets of their identity, they are perceived as fragmented. People maintain many different social facets and often associate particular facets, and therefore faces, with particular contexts.”

For knowledge workers like me, the vast majority of Internet use is work-related. In many ways, if you’re developing a professional online brand, the decision to cut out personal details is made for you. I could use Twitter and blogs to talk about how cute the baby is or to complain about how I was up all night working on a paper, but how would that contribute to my brand? Why does anyone care about what movie I saw on the weekend or who I’m cheering for in the playoffs? The general question is: What is the value of using the pubic Web as a venue to air personal issues?

I would argue not much (unless maybe if I’m a celebrity). But it does create a gap in my online identity. In a sense, it’s unfortunate that what I talk about online is of no interest to family and friends. I could develop professional distinction on the Web but few people that who are really near and dear to me would notice.

One of the models we can use to help think about this digital identity divide is the Johari Window. The image below shows a 2×2 matrix that contains different types of information about our identity – aspects that are known to us and those that known to others. Over time, more and more of the matrix comes into play as personal information is digitized and shared. Prior to social networking everyone projected a particular image within which there were characteristics of themselves that they knew about and chose to share: the Arena. There were also characteristics of which they were aware, but chose not to share: the Façade. This was changed by social networking technologies which can now uncover a whole other aspect of the self: the Blind Spot(s) – things that we don’t know about ourselves, but that are well known to others (e.g. characteristics such as habits, expressions, or even behaviors).

The true power of social networking becomes apparent in the co-operative era, during which knowledge is taken from narrow to broad base by organizations that can now link up personal information known in different contexts/realms and make it useful for their own purposes. Finally, we have the tireless machine era in which we have to imagine a world (not too far away) where machines and software will be operating 24/7/365 in the background, enabling powerful reality mining applications. Much of what is uncovered in the tireless machine era will still have been unknown to us and to others as well, but will be collected, aggregated, analyzed, monitored, and tagged by powerful, always-on systems running in the background of all Web applications. (Note: nGenera members will hear more about the Johari Window and pervasive digital identity in several forthcoming research projects).

The progression to the tireless machine era is by no means linear, and I think what we’re experiencing now are growing pains. Currently, I think the highly managed approach to most digital identities is moving more information from the Arena to the Facade, creating the divide I talk about above. The purposeful collection of personal data by companies is adding to our Blind Spots (e.g. companies can now use social network analysis and reality mining to identify behaviors and trends that we don’t perceive) but I don’t think the technology is quite good enough yet to create much that it fully Unknown.

I don’t think anyone feels comfortable with idea of always putting up a facade – most of us would like to think we “keep it real,” however I think in many ways, the Internet gives us little choice. A Net Gen take on Richard Florida’s Creative Class asks, “Why leave your personality at home? As free time blends with time on the job you want to make a statement and be comfortable at the same time.” It’s a nice thought, but it sounds unlikely for most professional environments.

Signal-to-noise ratio: I think the most visibly important metric – analogous to points on a basketball stat sheet – should be one that is focused on the value and quality of content you broadcast.  Using Twitter as an example, measure tweets versus re-tweets.  If your content is getting re-tweeted it’s safe to assume that it’s valuable (signal) and not noise. For blogs, the metric might be comments per post, indicating a compelling or timely argument worth discussion. Using online sentiment analysis tools, companies could add an additional layer of complexity to this stat by also measuring positive versus negative comments.  As a basic example of signal-to-noise, my ratio based on re-tweets/tweets is: 10/91 = 0.11.  For comments/blog posts it would be: 289/79 = 3.7.  Of course in a multi-channel world, the metrics get muddled.  If re-tweet is the new blog comment, how do you calculate the metric?

In/out ratio: How good of a curator are you of information?  We interviewed the company Cataphora a few weeks ago (recently profiled in BusinessWeek) – their software uses social network analysis to identify good content by how much it is shared and passed around and track document originators and curators to assess individual productivity.

Document “originator” stats: Building on the previous point, how much good content do you generate, where “good” is defined by the number of times your content is reused (this is similar to signal-to-noise, but attempts to quantify the strength of the signals). At a more granular level, the originator stat might also highlight certain areas of expertise – e.g. all good ideas related to robots originate from Alan.  A twist on this metric could be “conversation initiator stats,” which would track who kicks off popular conversations on ones that lead to valuable ideas that are implemented.

Responsiveness: This would be a fairly basic stat that looks at how quickly you respond to other people’s requests.  As part of this, you would also have to account for the number of requests an individual gets (which might actually be another stat around collaborative demand or reputation).

Feedback assessment: To temper responsiveness stats, you’d want to have something like the “assist” stat in basketball, where you only get credited for responses that lead to a positive outcome.  As an example, you could base it how much change occurs to documents as they flow through you.  This would have to use software that analyzes document content and tracks versioned documents (independent of filenames, which often change as they pass through different users – e.g. docs that come through me usually leave as filename_nh).

Sociometric factors: These are the intangible aspects of collaboration that don’t necessarily leave a digital trail that is easily measured.  I liken this to measuring an individual’s plus/minus in basketball – it’s based on how other people perform on their stats when they are around you.  Since there’s no “court time,” in enterprise collaboration, you could measure face time through badges, digital connections, or even video.  Using this type of reality mining, a company could also analyze things like the tone of conversations as well as emotional response in order to gauge the impact you have on the morale of those around you (without necessarily measuring content, which leads to privacy issues).  This stat might also highlight diminishing collaborative returns – if too many minuses start showing up, maybe you’re collaborating too much (or are not very good at collaborating).  A company could also develop a “starting line-up” for projects based on sociometric factors that show positive results from certain combinations of employees.

SONAR technology from Trampoline systems visually maps out the informal network within an organization and helps people connect to experts and resources on-demand. Personally, I think any company serious about collaboration should have something like this installed. Check out the video for a guided tour from Charles himself:

UnHub is a social network aggregator (inspired by the Skittles social network marketing campaign). The UnHub Personal Profile Bar is a quick easy way for individuals or small businesses to consolidate existing facets of their digital selves or establish a brand online using free social networking tools. The newer edition to UnHub is the Personal Link Shortener – a neat way to stamp your brand on a link and track the social network buzz around links that you share. Given the Twitter-generated surge in link shortening, I think this is an awesome way to add value to traditional link shortening services that typically mask provenance. Check out the preview below:

What really propelled my thinking in this area was an article written back in February in the New York Times. “The No-Stats All-Star” written by Michael Lewis, (author of “Moneyball: The Art of Winning an Unfair Game”) highlights “a basketball mystery: a player [who] is widely regarded inside the N.B.A. as, at best, a replaceable cog in a machine driven by superstars. And yet every team he has ever played on has acquired some magical ability to win.” Specifically, the article dissects the play of Shane Battier, a collaborative team player whose value is difficult to measure using traditional basketball statistics.

So why look at basketball for insight on how to measure collaboration rather than some other sport? As Lewis notes, “The difference in basketball is that it happens to be the sport that is most like life.” What the author means is that basketball is not a series of one-on-one contests between individuals, as with baseball, or a series of plays determined by a coach, as with football. Rather, basketball is a truly collaborative effort with many subtle offensive and defensive moves taking place simultaneously by a number of players. What’s more, in basketball “the player, in his play, faces choices between maximizing his own perceived self-interest and winning. The choices are sufficiently complex that there is a fair chance he doesn’t fully grasp that he is making them.” Sound familiar?

Measurement can help illuminate the tradeoffs being made and the “cost of winning,” if you measure the right things. The most common approach used to measure the team is to try and identify the individual contributions of each team member and add them together – the box score in basketball. As an example, fantasy sports are a great way to quantify the value of individual contributions because each statistically tracked player activity is weighted and given either a positive score (i.e. shots made, points, rebounds, assists, blocks, steals) or a negative score (shots taken, turnovers) which is aggregated into a composite fantasy score for each game. Battier’s overall rank on Yahoo! Fantasy Basketball for 2008/2009 was 101. Not bad, but does it represent his true value as a team collaborator?

Coaches and players know that the team impact of players is more than the sum of their parts. However, with basketball, as in traditional enterprises, managers have “measured not so much what is important as what is easy to measure — points, rebounds, assists, steals, blocked shots — and these measurements have warped perceptions of the game.” In the corporate context, this could just as easily be hours clocked, widgets produced, products sold, or deadlines met.

In basketball, one way to measure the collaborative value of a player to the team is the plus/minus or Roland rating. This is described as “the difference in how the team plays with the player on court versus performance with the player off court.” It helps quantify some of the more intangible factors such as good defence, threat of 3-point shooting, setting picks, taking charges, going after loose balls, intimidation around the rim, and the “I.Q. of where to be.”

In the 2008/2009 regular season Battier was 38th in terms of pure plus/minus. With respect to how he is rewarded for his contributions, Battier is making $6.9 million in 2009, which isn’t bad, but it’s a far cry from the $22.2 million made by Tim Duncan and $19 million made by Dirk Nowitzki (37th and 39th in pure plus/minus). As enterprise decision makers think about incenting and rewarding collaboration, it is these types of discrepancies that they will want to look for.

But there are drawbacks to the plus/minus approach as well. Some players will have overstated plus/minus statistics because they share the court with All-Stars or have weak players that substitute for them. More dynamic and representative way to approach plus/minus would be to “adjust these plus/minus ratings to account for the quality of players that a given player plays with and against.” Dan Rosenbaum, an economics professor at the University of North Carolina, demonstrated this approach for the 2003/2004 NBA season. In an enterprise, how do you account for factors like project management, the economy, competitors, support staff, and team members when evaluating an individual? If it’s all based on team performance, what incentives can you use to drive the discretionary efforts of individuals?

I’ll have to give it some more thought, but I think there are definitely some specific aspects of the statistical approach to team/individual measurement in sports that enterprises can emulate. Any thoughts on what a collaboration box score and plus/minus might look like for an enterprise?

1. At an individual level people suffer from cognitive overload. As people get busy and collaborate across a multitude of projects, the brain gets distracted, and the quality of the output suffers. In short, one person can only do so much.
2. At a project level where you run into a situation of ‘too many cooks spoiling the broth.’ In short, only so many people can do one thing.

If you put the two of these together, the worst-case scenario is that in an individual could join a project as the Nth person who ‘spoils the broth,’ while the time they dedicate towards doing so distracts them from their other work – which, continuing the cooking metaphor, leads them to burn the toast as well.

The problem is, it’s very difficult to apply a scientific approach to measure exactly how many people per project, and conversely how many projects per person is optimal. The most well-known study around this is Dunbar’s Number, which sets “a theoretical cognitive limit to the number of people with whom one can maintain stable social relationships” at 150. In terms of collaborative overhead, Dunbar speculates that “as much as 42% of the group’s time would have to be devoted to social grooming.” Now that might be acceptable for the hunter-gatherer societies described in Dunbar’s anthropological study, but I would imagine this amount of “grooming” time would be extremely unproductive in an enterprise context.

In his book Collaboration, released this month, Morten Hansen, a professor at the University of California, Berkeley and INSEAD, identifies two costs related to enterprise collaboration. The first is the opportunity cost collaborating (i.e. the opportunities individuals could have been pursuing had they not been collaborating), the second is the cost associated with fostering co-operation. In both cases, as the number of projects or the number of individuals grow, so too does the potential for diminishing returns.

At the project level, I feel as though most people have general understanding that there is a certain point at which there are simply too many stakeholders and collaboration breaks down.

However, at an individual level, I think we are less cognizant of – or less willing to admit – our own limitations. I’ve seen many cases where an enthusiastic and eager collaborator was clearly overburdened and well past the point of optimal effectiveness. Incidentally, my personal hypothesis is that this point of optimal effectiveness is a fairly small number of projects per person. My main “proof” for this is anecdotal, but I notice that the busier one is, the more likely they are to quickly skim a topic and provide feedback in short (sometimes valuable) chip-shots without contributing to a better in-depth understanding of the topic space. Worse, in some instances perceived value comes from dissenting, so instead of constructive feedback, you get wildly varying opinions with no one working towards a coherent solution.

On the subject of cognitive overload, a recent Deloitte report notes, “Even a Sunday newspaper contains more information than the average 17th century citizen encountered in a lifetime. Add to that the stress of decision-making amidst uncertainty, corporate change, and a tidal wave of tasks. Never before in history have workers been asked to absorb and make sense of so many data points.” One more sensational study even suggests that information overload is more damaging to the brain than smoking pot. I think we can certainly make an argument that where collaboration is most likely to break down is at the individual level.

This brings up another point: What about the virtues of solitude? Are we losing our capacity for individual decision-making? Moreover, who’s actually doing the deep thinking needed to solve complex problems? We talk about the multitasking Net Gen brain that is not actually doing multiple things at once, but rather switching more efficiently. Does constant switching allow for deep analytic thought?

So what is the solution? Overall, I’m wondering if there’s a Dunbar Number for the optimal number of simultaneous projects per person (small and large). How is this number affected when you take into account broader ecosystem participation and places where quick feedback from multiple participants is actually desired over in-depth participation?

As a start, I think collaborative technologies can help by streamlining different types of feedback. So, for example, a project can have 1,000 collaborators if they are providing feedback via a prediction market. Conversely, if only three people are collaborating on a document, perhaps a wiki is most effective.

One possible model for managing cognitive overload is letting individuals self govern – i.e. everyone decide where they can add the most value. Of course, this also raises many issues, including: people, especially in high-performance cultures, tend to overextend themselves; people tend to pick project that interest them, but that may not add the most value to the organization; and people tend to be social and so will gravitate towards the same projects, thus contributing to project inefficiency.

In order for this to work, you would have to architect a system that would allow people to allocate their own time in a structured way (similar to the Freiburg budget example). I’m envisioning a system where resources are finite but can dynamically allocated; where employees are guided by decisioning logic that identifies the projects that provide the most value to the organization; and where limits are set that prevent projects from being staffed by too many people and that stop people from taking on too many projects.

The idea that a company would use costly TV ad spend to promote an iPhone app seems pretty novel to me, and yet at the same time completely natural. The bigger idea of an insurance company offering a mobile application is pretty cool as well. Nationwide’s app will:

• Call emergency services
• Help you collect and exchange accident info
• Store your insurance and vehicle info for easy lookup
• Locate Nationwide agents near you
• Take and store accident photos
• Convert your iPhone into a handy flashlight

If you happen to be a Nationwide customer, the app will also:

• Help connect you with towing services
• Help you start the Nationwide claims process
• Find Nationwide Blue Ribbon Repair Facilities

Depending on how viral this goes, my only question for Nationwide would be, ‘Do you have the back-end processes in place to fulfill the promises made by this new level of customer service?’ Meaning, if I am connected to a claims agent or repair facility on the spot, will I be able to seamlessly send my accident photos and vehicle/insurance information via my iPhone? Will the response rate be equally as ‘real-time’ as entering the information into the phone, or am I going to be standing on the side of the road, on hold, wishing I had waited to get home to file the claim? Will the towing services be vetted for the same friendly and easy service I am receiving via my mobile device? Hopefully Nationwide has thought about these questions when designing the end-to-end customer experience.

My advice for all companies: New Web 2.0 channels are cool, but if you open a new channel, there had better be someone on the other end (and the processes in place) to engage the individuals coming through that new channel.

The Wikinomics team has many posts discussing the real potential of Twitter for business value. This is how I predominantly use the tool: As an efficient way to find new stories relevant to my research, as well as discover interesting or amusing content, and occasionally peek into the personal lives of interesting people. A good signal-to-noise ratio is critical for this type of usage.

I use Tweet Deck to help filter my updates, but it still becomes a challenge because filtering is by individual, not content. That means if I add someone to my “Main Feed,” I have to somewhat trust that they will act responsibly. What do I mean by responsible? The other day I opened Twitter and notice 39 tweets in a row from the same person, most of which were links. Literally 39! That’s irresponsible. It’s also not a way to get noticed on Twitter, but rather ignored.

Responsible Twitter users abide by four simple rules:

1. You learn something new everyday
2. Twitter is not chat
3. Don’t be a needy jerk
4. Ignore rules 1 to 3 if you are in marketing

RULE #1: You learn something new every day
There are two key parts of this rule: 1) “Learn” – Share a new discovery, something novel or interesting you learned today. There are a lot of people I’d like to hear from more often, but who rarely post (I’m somewhat guilty of this myself). Everyone has something they can contribute to Twitter. At the same time, it sets the bar slightly higher than posting a stream of consciousness. 2) “Something” – not everything. How much is too much? Think top-10, max. I gave Denis a hard time about this a couple of weeks ago.

In truth, Denis was actually at a conference tweeting insights from various presentations. It’s an exception to the rule that I haven’t figured out how to deal with yet, so I took him back. He hasn’t betrayed my trust since ; )

RULE #2: Twitter is NOT chat
There is a reason that the inventors of Twitter had the good sense to include a Direct Message function. No one wants their feed spammed with out-of-context snippets of conversation like, “Sorry I missed you @mybuddy, next time for sure.” This is a very simple, yet often forgotten rule.

RULE #3: Don’t be a “needy jerk”
If you missed it, Vanessa Grigoriadis has a fantastic article in New York Magazine about Facebook. In it, she talks about the declining usefulness of Facebook updates:

“This was the beginning of the end. Suddenly, Facebook began to irk me—the way friends always posted about procrastinating, being stuck in traffic, needing a nap or a vacation, or seemed to formulate their updates in declarative yet vague form, like ‘Michelle is upset’ or ‘Roya is pouting,’ thus coming off like a needy jerk and making us take time out of our day to plead with them to answer the burning question: ‘Why are you pouting?’”

I couldn’t have put it better. There is a lot of this on Twitter and it’s exceedingly unproductive. Worse; it’s noise that obfuscates the valuable signals we’re all trying to get at.

RULE #4: Ignore rules 1-3 if you are in marketing
The future of Twitter – especially in marketing – may well be bigger than my “rules.” I talked to a company Crimson Hexagon this week that makes a business out of mining user opinions online (including from Twitter) for market research. In fact, marketers love the stream of consciousness because to them it’s data, and valuable data at that: “Post all you want about what you had for lunch, just be sure to mention the brand of mustard you used and how it made you feel…” (Note; Rule #4 should have excused Denis from his misstep on Rule #1, since he is our program manager for our Marketing 2.0 program.)

Marketers also don’t want to use Direct Message. If you answer a customer question publicly, it not only makes you look good in terms of being responsive, it also potentially saves you from having to answer the same question multiple times, as well as helps boost brand mentions. For more on how to think about your brand on Twitter, see: A potential framework for how different brands are using Twitter.

As an example, a recent study by Skidmore economist Sandy Baum and the College Board, approximates the real lifetime value of a college degree at about $300,000. This estimate is based on the assumption that those with college degrees earn an average of $20,000 more per year than non-graduates, and takes into account the average cost of tuition and books, as well as annual inflation over a forty-year career. This estimate is down from previous calculations of an approximately $1 million payback. Mind you, this is for undergraduate degrees. It begs the question: What about more specialized and more expensive graduate degrees (expensive both in terms of tuition and opportunity costs)?

MBA degrees are a specific point of contention. While conventional wisdom will have people flooding into MBA schools, there is also a sense that maybe professionals should seek to upgrade through less conventional, more productive means. Indeed, the sheen associated with an MBA is tarnished by the fact that many of the financial decision makers that perpetrated the economic downturn were themselves alumni of some of the most respected business schools.

Some recent interviews I’ve done seem to corroborate these findings. Fast Company staff writer and author of Generation Debt, Anya Kamenetz says:

“I’ve never been an advocate of people going back to school and incurring large amounts of educational debt just to have a degree. [...] I’m very interested in what the long term developments are going to be because I think that higher education has been resistant to really fundamental types of innovation and change for far too long. We’ve seen information technology sweep every other industry and raise productivity and raise the potential of what you can accomplish. I think that in higher ed, they’re still working off a 14th century model. It’s lecture classes and it’s seminars and it’s educational requirements that don’t necessarily match where the jobs are these days. So, I think that you’re going to see a lot more students and families re-evaluating the other options out there; whether that be online education, vocational programming, certification programs, or programs that are run by employers. I think it’s actually going to be a fantastic area of growth for the next decade and a half or so.”

Penelope Trunk, author of Brazen Careerist is more colorful in her analysis of the value of graduate programs:

“People are going to grad school for stuff that has no bearing on the workplace. It’s not like we have more critical thinking because somebody knows the history of the little War of the Roses, right? And so, who cares? I don’t see any corporation placing a premium on any kind of graduate degree, except a top 25 business school degree. I mean most MBAs are from shitty schools so they don’t place a premium on that. Most law schools are shitty and people have to go into some other profession besides law because their degree is so bad. If you get a Masters in French and then try to get a marketing position, you’re penalized. You’re actually penalized because you look like you don’t have a clue about how to manage your life because you just spent four years learning French and you’re not using it. To me that just screams obsessive with details, scared to go out into the job market, and purposeless. I mean, I just don’t think anyone is placing a premium on graduate degrees.”

From what I’m hearing, it seems as though college age students are making important decisions about where they’re going to invest education dollars. Some of them are backing into junior colleges or community colleges; others are choosing to forgo higher education because of their financial situations. This would be especially true if their Boomer parents are now struggling with layoffs or delayed retirement. On the demand side of the equation, numbers from the U.S. Bureau of Labor Statistics suggest that overall, the United States will need 18 million new college degree holders by 2012 to cover job growth and replace retirees but at current graduation rates, the country will be six million short. Will this trend towards delaying or abstaining from higher education reinforce the impending knowledge gap among entry-level workers? Some interesting food for thought…

As part of a project I’m working on that deals with talent issues in the current recession, I recently had a chance to chat with Cali Yost, CEO of Work+Life Fit and Fast Company expert blogger. Cali talked to me about flexible alternatives to mass layoffs, whether or not there’s still a war for talent, employee engagement in recessionary climate, and the need for a new enterprise mindset around agile talent and the execution of talent strategies:

Can you give me a little bit of background on Work+Life Fit and what you guys do?

We are a business-based, work/life flexibility strategy consulting firm. We develop flexibility strategies which are a way of operating. That’s different than viewing work/life flexibility as a benefit or a policy. It is a way of being; it is a way of operating in today’s world. It’s a business strategy that allows an organization to respond rapidly to challenges but also opportunities. We help companies use flexibility to manage resources, manage costs, service clients, and manage talent in order to be competitive and agile. So, it takes work/life flexibility out of the HR sphere and puts it into the business management sphere, which is where we believe it belongs.

Why is it so important that work/life flexibility be more than an HR issue?

If it’s an HR policy, a program, a benefit, it’s not agile; it’s not part of the day-to-day decision-making process of the organization. And that’s what we do. We develop it so that it’s a strategic lever; it’s part of the management process. HR is a partner in the process, but it resides in the business. HR is there to support it. HR is there to make sure the systems align. HR is there to make sure that it’s being accounted for correctly and that the benefits are aligned correctly. But, HR is not there to be the executor of the strategy; it’s not. The execution of a flexible strategy happens in the business unit, and that’s where we need to shift our mindset on this.

How do you make this happen? What’s the process for moving to a more strategic, enterprise approach to workforce flexibility?

Essentially, what you need to do is you need to brand it from the beginning as a business strategy. I know it sounds really simple, but it actually doesn’t happen. Most flexibility strategies are branded in most organizations as a policy for women. As a result, they are not seen as a core way of operating; a core business model. It’s seen as a nice thing to do to keep moms.

What about the Gen Y, or what we call the Net Generation; aren’t they a factor as well?

I will say that’s a driver. That’s becoming more of a driver in organizations. It’s one of the reasons organizations are looking at flexibility again because it’s such an expectation of young employees. Historically, the target has been moms. The more recent driver that’s getting flexibility back on the radar screen is ‘this something we need to do now to try and get young people to work here and keep them.’

You’ve written a lot about flexible downsizing, the idea that instead of large scale layoffs, companies can look at flexible options such as shorter work weeks, salary reductions, job sharing, forced furloughs, and so on. In your column on Fast Company, you said one of the main reasons for the wholesale layoffs is that financial analysts reward companies for this type of cost cutting without realizing the true cost of layoffs. What tradeoffs are they missing in their analysis?

They’re not understanding the cost in engagement. For example, when you cut a workforce, you impact morale; you make people very afraid; you challenge their loyalty to the organization – all of those things impact engagement. Reduced engagement directly impacts the bottom line.

What are some of the measures of engagement? Well, some of the core measures are: your desire to stay with the organization; your job satisfaction; your motivation; your willingness to recommend the organization to another person; your productivity, do you feel like you’re being productive and working smarter and better. Those things are all impacted by a layoff and yet they’re not being included in the calculation of the cost of a layoff.

As an example, if you were to go out to your workforce and say, “We have to save X amount of dollars in labor cost, and there’s a couple different ways we could go about this: We could lay off 20% of our workforce, or everybody could take the 20% cut in pay in their schedule and we could add six unpaid vacation days. This is a shared sacrifice; we’re going to get through this together.” Now, all of a sudden think about how you feel about that organization. Think about how you feel about whether you’re motivated to try to make this work. I would argue that all of those aspects of engagement are maintained, if not enhanced, which again the research shows impacts your bottom line. But none of that gets quantified by these financial analysts. You just don’t hear it.

You mentioned the research shows that job satisfaction and motivation are connected to the bottom line, but it’s hard to put a dollar value on engagement, isn’t it?

It is, and I think that’s the challenge; how do we challenge the financial community to expand their understanding? You know, it’s interesting; there is so much research about the fact that layoffs really do not achieve your goals, and yet that seems to be what our business leaders are rewarded for. I really do believe it’s because the financial analysis modeling is not complex enough to account for the less-easily quantified results of downsizing. That includes increased legal liability because if you downsize and you don’t do it the right way, you’re hit with lawsuits from people who claim they were targeted unfairly. There’s also the cost of paying severance. So there are a variety of things, but my point is there’s a complexity of analysis here that needs to be taken into account, and that’s not happening. As a result, the rewards and incentives are not built into the system to encourage leaders to be more thoughtful, flexible, and strategic around how they reduce their labor cost.

On the flip side, there has to be some additional cost to the flexible alternative as well, right? I mean there’s the time to create, negotiate, manage these types of agreements, as well as the HR costs associated with the care and feeding of flexible arrangements and managing a flexible workforce.

Okay. But my response to that is; that’s why you don’t wait. You do not wait to implement a business-based flexibility strategy in your organization. A year ago we were having this conversation around the increase in energy prices and all of a sudden companies are going around trying to get people to work four days a week or work from home because energy prices went up. If you have a business-based flexibility strategy and energy costs go up; you execute your strategy toward dealing with that. Similarly, when a recession hits, you take the same strategy and you execute it toward that issue. The next challenge could be 40% of your company is purchased by an Asian organization, and now all of a sudden you’re reporting to people in another time zone. Well, you execute against that so that you can have better communications with that group in Asia and you can adjust your hours accordingly.

Again, that’s why it’s not just an HR program or policy. It’s processed based, which means there’s a process that helps figure out what flexibility is going to look like in a particular business given the resources, the people, and the realities of that business. But it is executed in the operations; it’s not executed in HR. So, there shouldn’t be this massive amount of execution overhead around flexibility if it’s already built in. And I have to tell you, downsizing ain’t no trick, it’s no pretty thing or low-cost thing to implement. It takes a lot out of HR, and it takes a huge amount of money. Not so with flexibility, if you have that strategy in place.

What’s the technology/CIO perspective on enterprise flexibility?

Do you think HR and IT talk about flexibility? No. Uh-huh. Rarely are they brought in the same conversation. And that’s why flexibility strategies need to live in the business because all these players need to come together for it to work.

I had a CIO recently tell me he loves their flexibility strategy because it allows him to rebrand what they are doing in IT; meaning it allows him to take what they’re already doing and rebrand it in the context of flexibility. Flexibility provides the context to explain how enterprise technology can provide value, and allows him to help people get more out of it. It isn’t even necessarily an additional investment. It’s just taking what’s already there and helping people apply it.

In one of your posts you interviewed Dr. Cappelli from Wharton – he mentioned one of the reasons companies might opt for layoffs instead flexible solutions is that people are easy to replace. You can fire people and then get them back if you need to.

That’s what we did in the last 20 years. What I thought was most interesting about what he said is that there is a precedent for flexible downsizing 20 years earlier, in the ‘80s. We moved away from it because we moved into a period where the employee-employer contract began to disintegrate, and people were viewed as being easily replaceable. Prior to that, companies tried to hold onto their core talent.

Is there still a war for talent?

We live in a world of rapid change, and my only fear is that a year ago you were struggling for talent, and now you think all of a sudden you have the pick of the litter; you don’t need to care about it anymore. I’m just curious where we’re going to be in a year. Are you going to be looking for people again? Does it really making sense to go into this hire-cut, hire-cut cycle? My feeling is that it doesn’t. I don’t know how you run an organization like that. I think there needs to be a lot more creativity and flexibility because I think those up and down cycles are going to happen a lot more frequently and a lot closer together.

Would you include talent marketplaces and “ideagoras” as part of your flexible work strategy? I’m thinking of things such as Elance and Guru.com for freelance work or InnoCentive and Nine Sigma for R&D solutions.

I did a blog post on my Work+Life Fit blog. I’m sort of musing about the post-recession workplace, and I go back to a model that I originally read about in business school back in the ‘90s by Charles Handy; it’s called the Shamrock Organization. Basically in that Shamrock Corporation you have your core people, and then you have the experts that come in and out of your organization on a flexible basis as you need them, and then you have this third group of part-time workers on the outside.

So, I think that the marketplaces you just described are going to become more part of this flexible management process. But, on the other hand, I also don’t know if you can staff a whole company that way. I think you’re still going to always have that core group; a group of people really committed to that organization’s success and mission, that’s the primary focus. The other expert knowledge you bring in is needed, but may not be 100% focused on that core mission. Having that core partly alleviates the risk of lost organizational memory and retaining key talent

As Charles Handy said, people will move in and out of different categories over time. Meaning, as an employee you may be a core person for part of your career, then perhaps more of an expert or a flexible consultant – a free agent, if you will – for other parts of your career. And so, I do think that’s part of it as well.

I’ve been talking to some more radical thinkers and they’re saying companies could become these miscellaneous organizational entities where you’re much more opportunistic and you can get whatever resources to pursue whatever type of initiative that you see potential in.  It’s driven more by what talent you have and what opportunities are available more than a central vision or mission for the company.

Yeah, I don’t know. Maybe there will be some companies like that. I can see that working in smaller organizations. I find that tough to imagine that in a bigger organization; I just don’t know how you would organize all of that without a core with a mission, a focus, and a vision. I think a smaller organization can sort of operate that way perhaps, but for a bigger company… I think it’ll be interesting to see how it would scale out.

Any final thoughts?

I think it’s important to frame this as another, more flexible model that will not only move us through this recession, but is a way of operating going forward. I also think there are a lot of references here to the next generation of workers, which I know is obviously a very important group of people for corporations. The only thing I would say is, just make sure that when you’re talking about younger employees that you understand that a lot of older employees feel the same way about things. Sometimes there’s a tendency to focus on the next generation, but just make sure you’re always trying to find connections to the broader employee population. It’s not just younger people fearing layoffs or flooding social networks to build leads, circulate resumes, and reconnect with possible employers.

Of course no one really knows how much accumulated wealth there is in the GI and Boomer generations, or how longer life expectancies and inheritance taxes will affect the transfer of wealth, or if the current downturn will eventually empty the Boomers coffers, leaving nothing at all. Still, there seems to be an untapped opportunity in there somewhere.

For companies with Boomer marketing strategies, it could mean it’s time to start thinking about what strategies are needed to ensure that Boomer assets and business stay with the enterprise. At the very least it’s a whole new angle on retention and relationship marketing – call it “inheritance marketing” if you will. I recently came across the term gerentocracy to describe the imbalance of political power between the young and the old. How about geriadvertising for Boomer-inspired advertising?

An interesting article in USA Today talks about how, while Boomers have enjoyed unprecedented levels of wealth; “Households headed by people in their 20s, 30s and 40s have barely kept up with inflation or have fallen behind since 1989. People 35 to 50 actually have lost wealth since 1989 after adjusting for inflation.” This would seem to suggest that targeting Boomers is the way to go.

In the book Workforce Crisis, by Ken Dychtwald, Tammy Erickson, and Robert Morison, the authors discuss the implications of the imbalance of wealth distribution (written in 2006 before the market crash):

“How should companies and governments plan for the shrinking number of young workers, young taxpayers, and young consumers? Most marketing is still youth-oriented (or “youth obsessed”) even though today’s mature adults (those over fifty) control two-thirds of the accumulated wealth in the United States. Boomers will be the most financially powerful generation of mature consumers ever. What happens to marketing and product development when 80 percent of the consumer growth comes from the fifty-plus age group? How will businesses maintain brand loyalty when customers reinvent themselves at forty, sixty, and eighty years old? Will boomers, who have been active spenders in their middle years become more frugal as they mature?”

But, Boomers are moving / have moved out of their key spending years. Tammy Erickson wrote a recent post in Harvard Business, “What Demographics Tell Us About the Economy,” where she talks about how tracking the number of people age 46 to 50 in a given economy can be used as proxy for growth in consumer spending, and how this “big spender” demographic is declining around the world. She says, “This narrower age range, 46-50 year olds, will decline in number in the United States for the next twenty five years, until about 2035, when members of Generation Y will begin to enter this age category.” Looking at this data, targeting Boomer inheritors and Gen X makes sense, but inheritance marketers may have to wait for a while to see the returns.

Regardless of how well- or poorly-informed an inheritance marketing strategy might be, there are definitely Boomer brands out there that are trying to reinvent themselves. Here’s an example from this past year that sparked my thinking on this and made me smile a bit as well. I don’t know if Canadian Club has been thinking about Boomer wealth redistribution, but I have to say the tag line for ad campaign certainly helps make my point. “Damn right your father drank it!” says CC was a cool brand for Boomers, but can still be cool for their Net Gen children. Certainly invokes images of my own dad living out his college years in California in the 60’s.

Joseph Brusuelas, economist at Moody’s Economy.com is quoted in the Wall Street Journal as saying he fears “we’ve lost a generation of investors.” His take was that people won’t invest because they won’t have the money to do so, but I think it’s safe to say that there will also be a certain lack of trust in the market. Have we gained a generation of entrepreneurs? A generation of socialists, scrutinizers, realists, and environmentalist?

Jon Stewart’s “senior black correspondent” Larry Willmore jests that the swath of white collar crime leading into the recession will change the face of criminality in America such that the Wall Street look (i.e. white men in suits and ties) will be seen as inherently untrustworthy in the future. Willmore’s analysis is satirical, but looking at our own research here at nGenera, Scrutiny and Integrity are two of the eight norms of the Net Generation that will definitely be amplified by the recession (the other norms are Freedom, Customization, Collaboration, Entertainment, Speed, and Innovation). If the Net Gen weren’t already scrutinizing corporate America before, they certainly will be now.

For some deeper thinking on the effect of the recession on the Net Gen, I turn to Fast Company staff writer and author of Generation Debt, Anya Kamenetz. As Anya rightly noted in one of her Yahoo! Finance columns, “Economic dramas shape an entire generation’s beliefs about the nature of the economy and the risks involved.” That was a year ago and already she was postulating about how the Net Gen’s psyche might be shaped. Specifically, she noted that young people:

• Won’t expect to get rich quick.
• Will “get real” about consumption.
• Will buy on the cheap.

I spoke with Anya recently so I thought I’d ask her to elaborate on her thinking.

What effect do you think this recession will have on the psyche of people entering the workforce now?

“The climate in the last 10 years has been a very unrealistic one. We have been living in this huge bubble. For young people who are entering the 20’s now, this is really all they knew: Inflated expectations, ridiculous monthly consumer debt, and the idea that you don’t need to save for the future because you can just count on the equity in your house. But, when the bubble bursts, the paradigm shifts. For young people now, they are really looking around and seeing the world as being a very different place. For older people, it can be a lot more traumatic to have this happen, but for young people, they’re more ready to maybe accept that change.

And so, if you look at ‘The Greatest Generation’ that came of age during the depression and then entered World War II, they were able to achieve at incredibly high levels and get a great education. That legacy of the depression, obviously it was very hard for a lot of people, but it also led to realistic attitude about money, and a very practical down to earth determination to try to succeed, and some strong family-oriented values. I think that I’m starting to see the beginning of that among this generation. People are saying, ‘Well, you know, this idea of endless wealth and greed, it’s not something I really want to sign up for.’ Young people are forced to consider, at a very young age, what is really important to them and what they really want to have because they know they can’t have it all.”

So, really, this is good for us?

“From a purely economic point of view, I think it could be [good for us]. It’s a mixed bag, right? Because on the one hand, the drama that you enter into upon graduation, it actually can determine your salary for years to come. So if you start in a down market or you have to take a job that’s not exactly matching your skills, it could have a long term effect on your income. But on the flip side, if you get into a habit of saving very early, you have the magic of compound interest on your side, plus you develop lifelong healthy financial habits.”

So, in the end, a generation known for being spoiled, having unrealistic expectations about work, and graduating university with an inflated sense of entitlement might actually end up being the “sensible generation.” I would add to Anya’s analysis the notion this will be a generation that will trust corporations even less than previous generations and will be more adamant in demanding integrity from their corporate and government leaders.

And while I hope all of this is true, I also know that there is a layer of insulation between the Net Gen and the economy: their parents. The extent to which many Net Geners will feel the impact of the economy is also dependent upon how well their Boomer parents are able to weather the storm and continue to provide for adult children that may still be living in their basements. Given that many Boomers are being forced to delay retirement, I would say the insulation is getting thin. In fact, if pensions shrivel up and forced retirements put a strain on the family coffers, the Net Generation may even have their own basements occupied by Boomer parents that need caring for in old age. I’m sure there is an entire school of psychology devoted to that topic, so I won’t even get started.