Michael E. Raynor, Mumtaz Ahmed and Andrew D. Henderson, Harvard Business Review April 2009, Are “Great” Companies Just Lucky?:

Studies that examine high-performing companies to uncover the reasons for their success are both popular and influential. … But there’s a problem: The “great” companies from which these studies draw their conclusion are mostly just lucky.

The issue is about…

… the folly of attributing outcomes arising from systemic variation (the random nature of coin tosses) to the supposedly unique attributes to a few individuals, who are really just the luckiest coin flippers. Similarly, we can credibly claim that a firm is remarkable only when its performance is so unlikely that systemic variation alone cannot account for its results. Most success studies don’t address this fact, relying instead on the “self-evident” nature of exceptional performance.

Meaning: simply out-lasting one’s competitors is not a sufficient justification for claiming a firm is “great”, and that any attempt to figure out why those firms have succeeded (by “staying in the game”) will misinterpret the true causes of success.

Case in point: have you looked at the original list of “Good to Great” companies? How are they doing now?

It’s simply too much fun to skewer convention, especially a convention that might be more ephemeral than we think.

Matthew Stewart in The Atlantic Online, The Management Myth:

The best business schools will tell you that management education is mainly about building skills—one of the most important of which is the ability to think (or what the M.B.A.s call “problem solving”). But do they manage to teach such skills?

What they don’t seem to teach you in business school is that “the five forces” and “the seven Cs” and every other generic framework for problem solving are heuristics: they can lead you to solutions, but they cannot make you think. Case studies may provide an effective way to think business problems through, but the point is rather lost if students come away imagining that you can go home once you’ve put all of your eggs into a two-by-two growth-share matrix.

Next to analysis, communication skills must count among the most important for future masters of the universe. To their credit, business schools do stress these skills, and force their students to engage in make-believe presentations to one another. On the whole, however, management education has been less than a boon for those who value free and meaningful speech. M.B.A.s have taken obfuscatory jargon—otherwise known as bulls***—to a level that would have made even the Scholastics blanch. As students of philosophy know, Descartes dismantled the edifice of medieval thought by writing clearly and showing that knowledge, by its nature, is intelligible, not obscure.

Spot on.

There are, however, at least two crucial differences between philosophers and their wayward cousins. The first and most important is that philosophers are much better at knowing what they don’t know. The second is money. In a sense, management theory is what happens to philosophers when you pay them too much.

Before you protest, having completed an MBA gives me the right to make fun of MBAs.

(Thanks to Will Dearman (@wtd) for the link.)

Pardon me for a brief three-part rant, starting and ending with why Facebook and Twitter are ultimately mere signals of broader cultural and technological shifts; signposts without directions, stopovers instead of destinations.

Lost in the woods? Blaze a trail.
Facebook was designed to be closed, created its terms of service around the promise of privacy, developed a user interface and a set of “privacy constraints” that created a false expectation of privacy with its users, neglected the fact that the technological reality didn’t meet the promise, fought through numerous user interface (e.g. Newsfeed) and business model changes (e.g. Beacon, App developer platform) that challenged this misunderstood notion of privacy, implemented technological changes to bring openness to a closed platform, and now faces the unrealized realization that their path will be their own. Time to stop following and start blazing.

Will we still light fires once we’ve all been burned?
Deep within the conversations about Facebook and Twitter is a reminder: privacy is a cultural interpretation, a philosophical question rather than a technological answer, government mandate or legal certainty.

Privacy is a cultural expectation codified into law; technology creates new possibilities for culture to exploit, frame new realities, refresh our governments, rewrite the rules of law.

(A relevant digression: do you think copyright law will remain unchanged by a remix culture?)

What will happen once we’ve all been burned by a private foible becoming unexpectedly public?

Will we still rake our public figures through the coals? Or will be put our hot irons away and as a collective society merely shrug our shoulders, an unceremonious acknowledgment that humans make mistakes, a recognition that we are all public figures in our own spheres?

It’s not about what it does but how it’s used.
Why does this matter? Facebook, Twitter, email, macro-messages, micro-messages, data, the web, the Internet: it’s not about what it does but how it’s used.

Focusing on Facebook and Twitter on their own is a nauseating endeavor; Twitter and Facebook are the latest case studies to be misunderstood and misapplied, the latest incarnates of the broader technological and cultural shifts framing our lives:

• The shifting roles (and power) of individuals and companies, the clash between the economic returns available to individuals, non-structured groups and hierarchical organizations;
• The fundamental economics of scale butting up against its new technological realities, creating new strategies to capture the shifting returns to scale and scope;
• The mixing of the online and the offline, a rationalization of what a “real life” truly means;
• The increasing importance of ideals, the shift of returns from hard assets to soft assets, the decreasing returns from controlling differential access to an asset and the increasing returns from understanding what to *do* with access to an asset;
• Shifting transaction costs reframing the value created by communication, demonstrated by the shift of the time and attention spent between work, entertainment and communication, human nature’s hardwired quest for stimulation reaching the next plateau;
• Some things getting easier to create (content), some things getting harder (making sense of content), reframing our notion of experts, leaders, people, networks and connections.

Yet all trend lines break once we stress systems to their breaking points; as humans, we’re exceedingly good at tearing down our own systems.

Will economic returns always flow towards openness? No. Will we always care about privacy? Not in the same way. Will being connected always be important? Not in the same way. Will everything we believe to be true ultimately prove to be true? No.

Depression, recession, expansion, growth, decline; these are mere manifestations of greater subtexts, proofs of the continued existence of humanity. Facebook will not be the last social network, Twitter will not be the last communication platform; we have needs we have to realize, there are moguls and captains of industry we haven’t met yet, markets yet to be created, bubbles and bursts yet to be experienced, shocks to systems we have yet to create.

More importantly, why and how are people changing? Where is the value flowing? And where are you headed?

—

Updated 5/5: Yes, I changed the title; the initial title “A brief rant: Three notes on privacy, communication, technology and culture” simply wasn’t that good.

Skype and Twitter should merge; we should have one single platform to communicate with people using text and voice publicly and privately using any device. I know it won’t happen, but it’s still a good idea.

Twitter and Skype should merge. Why?

• 1. A combination would simplify the mess of communication use cases and create one single platform for people and companies to exchange information using voice and text, publicly and privately, using any device.

  Each new communication tool, network and platform launches by focusing on one use case (between people, public, private, over mobile network, etc.) and then quickly tries to figure out how to integrate with other communication methods, devices and platforms.

  This splintering and re-aggregation is noisy and wasteful; not only are we are forced to use and participate in a range of tools and networks (i.e. social network fatigue), but as we choose our preferred method of contact (email, phone, SMS, private Twitter, public Twitter, comment, etc.) and our preferred provider (i.e. Twitter, any IM provider, Facebook, Bebo, et. al.) we create enormous inefficiencies and missed communications (i.e. “oh, I don’t check Twitter often.”, “I can’t direct message you through Friendfeed because you don’t use Friendfeed? what gives?”).

  Both Twitter and Skype are really just platforms that transmit information over dumb pipes; the key differences are how information is delivered (voice v. text) and displayed (private v. public); but there is no need for these use cases to be split into separate companies.

• 2. Skype is already pursuing the strategy of powering private communications using any mix of client devices over any communications pipe. Voice and SMS; fixed-line, mobile and VOIP; iPhone, computer, WIFI phone; Skype is reducing the need for use cases to align for communication to happen; people don’t need to think about how the other person is using Skype in order to make a connection (granted, differences in voice quality across devices and networks dictate best options, so that’s still kind of a pipe dream, but it’s not far off).

  Skype has a bright post-eBay future and now has the potential to take on the mobile and fixed-line telecom operators in a way previously impossible; adding a public communication service to their private communication service would allow Skype to take advantage of the broader trend of public communication via micro-messaging.

  Perhaps instead of merging with Twitter they should just create their own public micro-messaging service…

• 3. Each could (Twitter) and do (Skype) earn revenue from facilitating the exchange of information and from providing value-added features. * Combining the two companies would create very interesting opportunities, to say the least.
• 4. Looking at this slightly differently: what do you think Google’s end-game is with Google Voice?

I’m probably wrong, so now it’s your turn; let’s talk about the obvious and non-obvious reasons why they won’t, can’t and shouldn’t merge.

—

* Please don’t turn this into a discussion about Twitter’s lack of / future / non-existent / yet-to-be-turned-on revenue model. Thinking about Twitter as a stand-alone business just isn’t that much fun anymore.

Yes, a “black swan” is an intellectual construct as overused as a “tipping point” and a “long tail”, but they each remain valuable bits of language to express and transmit dense theories and bodies of thought; but using the idea of a “black swan” helps us frame an important question: are our current structures in academia and scientific research suppressing the incentives to pursue revolutionary ideas?

Honestly, I don’t think so; I think the ability for revolutionary ideas to accrue massive gains (public and private) are becoming even larger due to the power laws of the interactions facilitated by the Internet. But I’m open to thoughts.

Mark Buchanan at PhysicsWorld.com, In search of the black swans:

The publish-or-perish ethic too often favours a narrow and conservative approach to scientific innovation. Mark Buchanan asks whether we are pushing revolutionary ideas to the margins.

… The price to pay for not moving to re-establish such independence will lie in a failure to realize the huge and unpredictable discoveries that move science forward most in the long term — discoveries made possible only when individuals leap out of what is comfortable and accepted, and wander out into spaces unknown. It is the true enormity of the potential gains that makes this goal of reaching the “efficient frontier” so important.

If today we seem to have a dearth of new Einsteins, Smolin suggests, this may just reflect that we have become a little too risk averse. New Einsteins, he points out, will not be working in areas that have been well established for decades. … New Einsteins may be slipping out of view and out of science altogether just because our scientific culture currently simply has no way of encouraging them.

To beat an idea to death, it all comes down to incentives:

Creating tactics without building a strategy is the same as building a boat without a blueprint; creating a strategy without understanding the underlying incentives of the players is the same as creating a blueprint without thinking about the basic physics behind how boats work; in short, not a good idea.

What happens if lose sight of incentive structures?

• Compensation tied to short-term time horizons leads to decisions to maximise short-term return (until we figure out how to tie compensation to value creation present and future);
• “Win and I win, lose and you lose” structures redistribute private risks to the public;
• “Attempts at local maximization create global minimization.”

Need I even explain how we’re seeing this today?

How do we start re-framing incentives?
With conscious attention to our lives, thoughts, actions and attention, today and tomorrow; by capturing richer data about the full impact of our lives and the externalities we impose on our worlds; by structuring data about our lives to change our behavior.

You’ve probably already read and listened to these, but check out…

• Umair Haque on behavioral innovation at BRITE ’09 Conference (video)
• Umair Haque on Constructive Capitalism
• Umair Haque, Why Ideals are the New Business Models

Selected highlights from an Edge Roundtable talk; click through to the Edge site to check out the video for a richer experience and read the rest of the transcript to dig in deeper, including bits about Mirror Worlds, asynchronous communication, The New Tork Times, McLuhan and more.

John Markoff and Clay Shirky talk to David Gelernter, An Edge Roundtable: Lord of the Cloud:

On Cloud Computing

GELERNTER: But the [cloud computer] field during the 1980′s said … “This is a very pretty idea. This is a beautiful elegant idea. It’s stupid because it’s impossible. It will never work. It is grossly inefficient. There is no way that you can take information, just float it out there, and expect people to search this whole vast collection, or somehow or other find what they want. And, you know, how are you going to find out what computer to put it on? How am I going to know what computer to look for it on?”

We said from the very beginning that those questions are in a larger sense irrelevant because software develops the algorithms it needs to provide the service users want. In the final analysis, the question is not, what can software engineers build? It’s the question, What do users need? If we identify our user need, the software technology will come along — in combination with hardware, obviously, and interconnect technology.

SHIRKY: The other thing I think is so striking about it … is that you [Gelernter] were flying in the face of the dominant view of the computing industry. What you said, which is simple economics but radical to the industry is, computers will become abundant because people care about them. And when they become abundant enough, we’ll stop caring about them. … Because then we can take them for granted.

In ’91, no one was ready to take computers for granted yet. They were the fetish object themselves. What I got … was the sense of, “oh right, this is going to fade into the background and the computer stops being the object you care about.” That movement from object to fabric was absolutely prescient.

On Lifestreams

GELERNTER: Well, Lifestreams was already my idea that instead of keeping my information in separate pieces of digital Tupperware with some of it in this app, and some of it in that app, and some of it in the file system, and some of it in my Web brower, and some of it on my laptop, and some in my palm, and some in my cell … — I didn’t want to do that. I wanted every information object I owned arranged in an electronic diary or journal or narrative. Or ‘Lifestream’ is what I call it.

So, in principle, the first thing would be your electronic birth certificate. Then every piece of electronic stuff you either generated or received, including all of your e-mail and every draft of every paper you wrote and every photograph and snapshot and thing you scanned in and your receipts when you travel and your tax returns … it would all be on this one stream. So any computer and device that I used, whether it was portable or large screen, or whatever, I would tune in this Lifestream.

… There was a thesis in the late 90′s and Eric Freeman [who had written this PhD thesis] had a really hard time. He almost got thrown out of there because people said, “Well, are you talking about saving everything? Indexing everything? You’ve got to throw things away and you can’t index everything. And you need a file hierarchy — you’ve got to put things in files. …” Nobody believed it then.

“Too early is wrong.”

GELERNTER: You know, in a sense, there is a window of opportunity and being too early is as bad as being too late.

SHIRKY: “Too early is wrong.”

GELERNTER: Exactly. I mean Babbage had brilliant ideas about computing in the nineteenth century. It didn’t do him a hell of a lot of good at the time. …

SHIRKY: We all make our living, in a way, on positive externalities [of] ideas that are 50 years old. And the funding climate now seems, both on the commercial and on the industrial and on the government side, all to be about three to five year paths. A seven-year timeframe for research — that’s long-term thinking right there. And I wonder, is the university the last place that’s got a long enough time horizon to hold what we used to get from a lot of different sources.

On the persistence of data and lifestreams

GELERNTER: This is not just a small software thing. This is going to be your life. I would love to be able to inherit a Lifestream from my grandparents and see what their lives were really like and there is no reason I shouldn’t leave my Lifestream to my children and my descendants. There’s no reason why these shouldn’t go for centuries or millennia. Or let you immerse yourself in the lives of your forbears, the lives of an earlier time. In such a radically a-historical world as ours, it’s important. But on the other hand, there are cases where I want to suppress the past and it’s important for me that I be able to do that. Certainly it is conceivable. It is doable.

MARKOFF: Except that there are multiplicities of Lifestreams and you may delete a portion, but your life intersected with those of others, which will not reflect that, given search. It gets very tricky.

GELERNTER: It gets very tricky very fast. …

SHIRKY: In the digital world, privacy is the right to insist on incomplete information.

GELERNTER: We live in an environment in which the industry — and certainly computer science or the research field — is always thinking that people care more about privacy than they do. I remember, it wasn’t all that long ago in the mid late 90′s, when people were saying, “Well, what will be the killer app on the Web? You know, mainly it’s going to be for pornography. Right? People are never going to send their credit cards over the Web.”

… They don’t care. The point is that the convenience obviously trumps a very marginal risk in their minds. And in the larger context of privacy, I think people have been aware subliminally, or at some level, for a long time.

A temporary digression…

A little known fact: despite my range of geeky interests, my deepest area of knowledge is actually baseball. Always has been, always will be. In another life I would have been a General Manager (GM) of an major league baseball (MLB) team in the United States.

I grew up reading and re-reading box scores, digging into baseball box scores to pick out interesting lines, figuring out the impact of each day’s lines on overall season statistics, looking for signals in the daily randomness. I used to love the USA Today’s expanded box scores (as opposed to the streamlined lines published by other papers in the 80s and 90s); give me a USA Today Sports section during baseball season back then and I could spend an entire day just digging into box scores. I used to pick out individual lines and re-write starting lineups, bullpens, rotations and 25-man rosters using various sets of days’ box scores.

Bill James changed my life.
The first time I read one of Bill James‘ Bill James Baseball Abstracts it opened my eyes to an entirely new way to look at baseball. Bill James was an outsider who meshed his love for baseball, a critical eye towards baseball “truisms’ and statistical genius to create the real study of baseball statistics; Bill James appealed to my naturally contratrian, fact-based mind and I read and re-read every bit of analysis of his I could find.

Bill James is why I played baseball in high school (and given that I played baseball like a statistician, it showed).

Despite their forward thinking Bill James and SABR (Society for American Baseball Research) used to be fairly unrecognized voices, but the Internet changed that; an explosion new voices and content opened baseball fans and analysts to a deeper world and gave hard-core Sabermetrics enthusiasts access to broader sets of data and the ability to publish their own research and engage far broader audiences (early examples: sites like the Baseball Prospectus, the Hardball Times and many other Sabermetrics-inspired websites).

Baseball is a dream for statisticians; each action on the field has a variety of potential results and an innumerable number of concurrent states, creating an enormous mix of situational data to be mined by careful eyes.

Yet few observers were open to new ways to analyze the game; traditional print and TV baseball “analysts” remained stuck in their traditional ways, hidebound to baseball “truths”, dismissive of new metrics and methods to analyze performance.

Structured data changed sports management.
Up until the 1990s teams were run exclusively by consummate insiders, guys who had grown up in and played the game. Sandy Alderson of the Oakland Athletics was one of the few progressive minds; Billy Beane, his successor as GM of the Oakland Athletics, was the first upper-level baseball executive to really blend new analysis tools with traditional scouting methods. Beane’s creative and non-traditional methods for player analysis and team construction were the subject of Michael Lewis’s book Moneyball. Moneyball was divisive and controversial within baseball because it was grossly misunderstood by traditional analysts; as I wrote last year, “Moneyball was about strategy, not tactics: constantly measuring and re-evaluating tactics and alternatives, not about determining and defining the “winning tactic”. But few understood that at the time.

It’s a vastly different world today.

Teams across sports leagues have realized the potential of creating and analyzing structured data to find an edge, to find better ways to structure teams, measure the “marginal revenue product” of players, predict future player performance, analyze trades and decide on salaries, compensation structures and contracts. Baseball teams were the first; along with Beane, Mark Shapiro and their hires of non-traditional Assistant GMs (and future GMs) like Paul DePodesta, J. P. Ricciardi, Dan O’Dowd, Josh Byrnes and Jon Daniels, progressive thinkers have continued to propagate through the baseball executive ranks. For example, following his work at Baseball Prospectus Keith Law worked as Assistant GM for the Toronto Blue Jays for a couple of years (still the only MBA * that has worked in an upper-level management role in a MLB front-office); Theo Epstein of the Boston Red Sox later hired Bill James as a consultant; former investment banker and private equity analyst Andrew Friedman now runs the Tampa Bay Rays (and was name Baseball Executive of the Year last year as the Rays made it to the World Series).

But NFL and NBA teams have started to catch up; examples include the Houston Rockets’ Daryl Morey (the only MBA running a major-league professional sports franchise in the US), entrepreneur and Dallas Mavericks’ owner Mark Cuban and the magnificent baseketball statistics site 82games.

The future is in creating strategies, not copying tactics.
This past baseball off-season was fascinating; the economic recession brought new constraints to teams and forced all participants (teams, players, agents) to adapt to the shifting environment and create new strategies for constructing teams. Free agents faced a limited number of teams willing to sign players, and beyond the marquee free agents signing for well-capitalized teams, players were faced with vastly fewer options, lower salaries and contracts with fewer guaranteed years.

Granted, some teams vastly misjudged the market, but the constraints of the market forced many teams to search for new ways to gain an edge. For example, defense has traditionally been one of the hardest aspects of player and team performance to measure and translate into impact on wins and losses, but in this past off-season defense became one of the focuses for many baseball scouts and executives searching for new ways to construct teams.

But teams have started to copy tactics much faster than since the days when Moneyball was first released; much like the broader web and business arena, creating enduring strategies by structuring data and capitalizing on fundamental trends is becoming even more important. Playing the game by copying the newest tactics is waste of time, a recipe for activity without accomplishments.

There’s a big difference between copying and creating. Which do you want to do?

It’s a fun world we live in…

—

* And not just any MBA, but from my alma mater…

Linking together a couple thoughts; since “we form ourselves more by the routes we take than the destinations we reach”, the realtime web will force us to redevelop our broken systems and change our routes throughout the web…

Michael Lewkowitz, The real-time web. Game on!:

The result [of public micro-messaging] is streams of succinct, interest-driven messages that create relevant, real-time context around every account, topic, and object they reference… which effectively means anything and everything. With that comes an increasing expectation that the web will orient itself around each person, topic, or object based on it’s history and real-time context.

… So now what? For me, there are 2 things. First, for the real-time web to reach its full potential we need a neutral, platform-independent application infrastructure and public dataset. Second, this will be the fastest evolution we’ve ever encountered and with that comes an unprecedented opportunity to seed a whole new wave game-changing ventures.

Continuing the conversation, my comment:

Creating a solution that cuts across platforms is critical.
The current slate of micro-messaging applications and platforms is just the start; many more websites and social networks will create micro-messaging applications. Combining information and conversations across silos will be critical to creating real context / relevance; creating ways to combine #hashtags from across the web is a tremendous opportunity.

The realtime web will force us to change how to consume information.
We’re stretching 1) the technology behind the communication systems we created and 2) the human potential for processing and understanding information. Breaking points are powerful stimuli for innovation

Our ability to understand and experience has yet to catch up with our ability to create. I’m not kidding; developing better ways to experience and use the realtime web is key to developing “personal APIs”.

But that’s just theory and questions; looking forward to solutions and answers…

Continue the conversation over at Lewkowitz’s original post on the opportunity of the realtime web…

Collaborating or Following? | Austin, Texas | Mar 2009

John Hagel, John Seely Brown and Lang Davison, Introducing the Collaboration Curve:

… the more participants–and interactions between those participants–you add to a carefully designed and nurtured environment, the more the rate of performance improvement goes up.

Think about this for a minute. If what we’re seeing … is indicative of something broader, then we’re seeing the emergence of a new kind of learning curve as we scale connectivity and learning through pull, rather than scaling efficiency through push. We call it the “collaboration curve.”

Collaboration curves hold the potential to mobilize larger and more diverse groups of participants to innovate and create new value. In so doing they may also reverse the diminishing returns dynamics of the experience curve and deliver increasing returns to performance instead.

The evidence for the collaboration curve is, as yet, mostly anecdotal. But these curves may explain the rise of network-centric efforts ranging from open source software development to “crowdsourcing” to “networks of creation.” In nearly all of these group efforts, rapid leaps in performance improvement arise as participants get better faster by working with others. These leaps in performance describe the shape and power of the collaboration curve, a new force in our professional and personal lives that turns the experience curve on its side, and explains why the whole of us, working, playing, and, learning together, can often be greater than the sum of our parts.

Continuing the conversation, my comment:

The most important qualifier behind the emergence of collaboration curves: they require a “carefully designed and nurtured environment”.

While we are seeing increasing collaboration throughout the economy (increasing information sharing, increased communication, and increased packaging of economic value creation into individual actors structured in fluid work environments), much of the collaboration still hits the walls of our ability to process and structure information. We’re lost in a sea of information and knowledge created by increased “collaboration”; true collaboration is based on understanding and solving others’ problems, but much of today’s sharing collaboration is really publishing instead of a true “exchange” of value. Is this what you are seeing?

Are we stretching the boundaries of our systems, forcing us to create better environments and structures for collaboration? Where are the “best practice” collaboration environments emerging?

How do experience and collaboration curves differ for individuals, groups, companies and industries? Each have different incentives and structures for building, storing, using and sharing information and acquired knowledge: how do the curves “work” differently within these groups, and where are the biggest opportunities for change and progress?

How can we develop “personal APIs” into our lives to scale collaboration?

Returning to an old post, I wish I could copy me:

As Valeria [Maltoni] pointed out in the comments, the value is in “the experience of the execution”, which is created by one’s background, experience, knowledge, ideas and insights. The ability to apply those experiences to changing environments is what creates unique content, a style, a brand, all examples of sustainable competitive advantages.

However, packaging those experiences and acumen to deliver value requires me. It requires my time, research, understanding, indexing of other experiences, judgment, insights, and ability to structure, communicate and deliver valuable content and solutions.

Which means it requires me. And until it’s possible to copy and replicate me, it’s going to be pretty hard for me to scale.

The implication: how can I scale “me” from a one-to-one services model to a product-based model that still captures and delivers “me” and my insights?

Sharing, publishing and broadcasting information is easy, but listening, thinking and using our skills to develop customized solutions for people is hard. It’s easy to read anything I’ve written, it’s easy to download generic financial models I’ve created, but how will you make sense of it and use it?

“Scaling listening” * would be a start; how can we “scale doing”?

–

* Ethan Bauley, of course…

The recession will not have a lasting impact on the way people shop; the current “post-consumer era” is a phenomenon; we are living through a temporary forced respite rather than a permanent desired change in the nature of consumption.

The recent sharp decline in retail consumer sales is a signal of a change in behavior, not a change in fundamental human nature. Human nature simply doesn’t change as fast as behavior.

The idea of the “post-consumer” era is a fad.

Not everyone agrees.

The Economist, From buy, buy to bye-bye:

Asked whether they want more stuff, consumers in rich countries have responded with an emphatic “No”. The breathtaking speed with which retail sales have plummeted in both America and Europe (see chart) has caught retailers and manufacturers by surprise. In response, companies have tried desperately to prop up revenues using a variety of promotions, advertising and other marketing ploys, often to no avail.

But as they battle with these immediate problems, marketers are also pondering what longer-term changes in consumer behaviour have been triggered by the recession.

Read the rest of the article for the details, but the primary changes highlighted are a “trend to thrift”, an “anti-business” sentiment, a decline in acceptance to paying a green premium and accelerated use of social media by consumers for researching companies and products.

I don’t buy it.

“Anti-business” does not mean anti-consumption.

A “move from passion to compassion in marketing” will not lead to reduced spending; it’s just a shift from one expensive outlet of personal expression and fulfillment to another.

If I was a marketer placing bets on how consumer behavior will change…

• Bad bet: People will return to buying the same expensive items that were popular before the recession.
• Good bet: People will continue to buy expensive goods and services to improve their lives and promote themselves.

As I noted to Nicolas Gabard,

A shift towards “community” and “relationships” doesn’t lead to reduced focus on consumption, it merely shifts the destination of dollars, attention and competition.

I think our level of consumption and focus on good will remain the same; what will change is a break from buying mass-produced corporate goods to buying goods produced by individuals, tied to causes, etc. But frugality, thriftiness and “aspirational luxury shopping” will return as soon as people feel more secure and as soon as “downsizing” and thriftiness is no longer in style.

While the nature of what we do and buy to establish status may change, “conspicuous consumption” is an ingrained need. Yesterday we bought expensive gas-guzzling SUVs to “keep up with the Joneses”, tomorrow it will be expensive, gas-efficient cars using electricity or alternative fuels.

The quest to establish and promote one’s status is fundamental human nature; while our methods for creating and displaying status may have changed, the need has not disappeared. Why can’t paying the “green premium” be the new badge of conspicuous consumption? (if it isn’t already..)

Lastly, social media will definitely have an impact on how consumers buy and how marketers promote and sell (example: Direct Marketing is Dead), it might change what we buy (I’m hopeful it does), but it will not drastically change why or how much we buy.

Most observers have been surprised by the sudden decline in retail spending, consumer confidence and retreat from conspicuous consumption; why do we expect to be able to predict the future now?

Thoughts?

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Related:

• Jonathan Birchall, FT.com, ‘New’ US shopper to emerge from crisis
• David Armano, Marketing in a Post-Consumer Era