In a recent post at the paidContent blog, Tom Weber praises Amazon’s Kindle because it lets him return to ‘unitasking’ –  doing one thing at a time, in this case reading books, newspapers, and magazines. He writes that “Scrolling through an online newspaper or magazine can be like strolling down a state-fair midway, with dozens of options bleating for attention,” and he’s willing to pay for fewer options: “…I’ve been routinely purchasing some of these publications [on the Kindle] when I could have grabbed my laptop and read them for free on the web. In effect, I’m paying for the lack of distraction.”

Weber is highlighting how much value he gets from from using a technology that offers him fewer choices, not more. As I’ve written before, a lot of my favorite technologies have this characteristic: they present simplicity to me instead of complexity, and a small set of features instead of a large one. The iPod, Gmail, Delicious (or, as it was then known, del.icio.us), the Kindle and Twitter each seemed pretty straightforward to me when I started using them, and I think I kept using them in large part because of that simplicity.

Most technology developers don’t want to build swiss army knives –  products that do everything for everyone. Instead, they want to satisfy fairly tightly defined needs; they want to offer a music player, or webmail, or a book reader. So it can seem strange that we don’t come across more obviously simple products. Yet complexity, in the form of large feature sets and jam-packed user interfaces, still reigns in devices, applications, and sites.

I can think of at least three reasons why complexity in technology products is the status quo. First, technologies are not developed by normal people; they’re developed by technologists. And at the risk of oversimplifying and engaging in occupational profiling, geeks like complexity. They like the challenge of fitting a lot into a little, and they also like using the end products they and their peers at other companies come up with. It’s fun for them to learn about bells and whistles, or to master an opaque user interface (in their saddest delusion, they hope that others will find sexy their advanced tool-use abilities).

Relatively few geeks can put themselves in the shoes of a neophyte, and we really shouldn’t expect them to be able to. Very few people are able to step outside their own frames of reference and see with other eyes (in this BusinessWeek article, Google CEO Eric Schmidt talks about how important this ability is). This difficulty helps explain why tech companies have for a while now been hiring anthropologists, who are trained to observe the activities and interactions of culturally distant others and draw inferences from them.

“High tech anthropology” really started proving its value around 1981, when Xerox was trying to figure out why one of its new copiers was perceived as overly complex. Company managers blamed unsophisticated users (for neither the first nor the last time) and proposed adding more complexity to the machine in the form of a video display terminal. Berkeley graduate student Lucy Suchman managed to get one of the copiers installed at her university, and videotaped some people as they walked up to it and had a hard time figuring out how to run off a few copies. Legend has it that when they saw the tape, Xerox engineers dismissed these folk as too dumb to bother with; Suchman then identified them as a couple of the world’s foremost computer scientists. Legend further has it that this incident led to the single green ‘copy’ button that’s now standard on all Xerox copiers.

The second reason that technology products are typically so complex is that the minimum required functionality –  the smallest possible set of things that the product must do in order to be effective and popular — still seems quite large. The distinction between frivolous bells and whistles and absolutely required capabilities is not always clear up front, especially because of a very understandable tendency to conflate ‘requirements’ with ‘expectations’ and with ‘what other similar products already have.’ Email clients need folders, portable digital music players need song search capability, and PDAs need menus, don’t they? Sure they do, I thought, until the original versions of Gmail, the iPod, and the iPhone showed me that they don’t. It seems that a large part of the art of being a good technology designer is looking with fresh eyes at the idea of required functionality and being willing to leave some ‘required’ stuff out.

The third reason I can identify for tech product complexity is the hardest to address, because it seems to be rooted deep in our wiring as humans. It’s the fact that we think we like choice more than we actually do. Many people have commented on what social scientist Barry Schwartz has termed “The Paradox of Choice:” the mutually incompatible truths that we like having lots of alternatives, and yet we don’t. We crave choice while finding it paralyzing, and even disheartening.

The most powerful demonstration of this I’ve come across is a series of experiments conducted by the psychologists Sheena Iyengar and Mark Lepper and written up in their paper “When Choice is Demotivating: Can One Desire Too Much of a Good Thing?” I encourage you to read it (pdf here); it’s clearly written, and describes some fantastic work. Their most amazing experiment was one in which they offered one set of subjects a choice among 30 Godiva chocolates and a second set a choice among 6 of the same treats. Before choosing, after choosing, and after tasting they asked all subjects a bunch of questions.

People facing 30 chocolates reported that the process of choosing was more difficult and frustrating than did the people facing only 6. However, the people with 30 possibilities also reported that they enjoyed the decision-making process more. This finding confirms the widespread belief that we like having lots of options, even though we can find them a bit overwhelming. The punchline of the study, though, is that the people who were given fewer choices were more satisfied after they got to eat the chocolate they picked (Iyengar and Lepper took care to eliminate the possibility that this difference could have been due to the fact that all the really nasty chocolates were in the 30-choice set, but not in the 6-choice one.).

I find this study to have profound implications and a clear prescription: If we can somehow convince our target users and customers to turn away from their fondness for a proliferation of choices, we’ll leave them better off and more satisfied. But this convincing is far from easy because it runs counter to our hardwired preference for more.

Word of mouth seems particularly powerful for getting us to embrace simplicity (”You’re not using del.icio.us? You gotta try it!” “I LOVE MY iPOD!”), as does trust in a technology provider’s brand / reputation (”I trust Google for search, so I’ll give Gmail a try.”). What else works? How have you seen technology companies (or, indeed, any companies) convince people to walk away from the complexity they desire and pick up a more limited alterntative?” Leave a comment, please, and let us know what you’ve observed.

A future post will expand on this topic and discuss how smart technologists start simple, then expand into greater complexity over time.

My friend Don Sull and I met in HBS’s doctoral program, which we both slogged through in the mid 1990s. He’s now cranking out mounds of good work at London Business School, and also blogging for the Financial Times.

His current work concentrates on helping companies navigate their increasingly turbulent competitive environments, and his most recent blog posts discuss how IT can and should help with this task. I’ve written about this topic in an Harvard Business Review article last summer and a couple blog posts.

I’m thrilled to see a general management scholar of Sull’s caliber pay serious attention to technology issues. He’s encouraging his executive readers to take IT seriously, and offering excellent them advice. Please keep it up, Don.

In his recent posts on IT for execution, Sull concentrates on IT’s ability to provide needed information to business decision makers. As he writes:

Chief Information Officers, Finance Directors, CEOs and outside directors should be asking, and answering, a… fundamental set of questions: What type of supporting data do we need to make sense of a rapidly changing market? What other information is required to support execution? What organizational, behavioral, and cultural changes will we need to capture the benefits of improved information?

He also kindly cites my work on the incredibly agile (and hence wildly successful) Spanish retailer Inditex, parent company of the worldwide clothing chain Zara. Sull writes:

When companies start with the question of what data do we need to execute effectively, they can achieve a great deal without massive investments in IT. Consider Zara. The Spanish retailer surpassed the Gap in 2008 as the world’s largest fashion retailer. Zara leads the world in “fast fashion” a retail category pioneered by European companies including Sweden’s H&M and Britain’s Topshop. These companies track fashion globally, spot emerging trends, and translate them into new products. Zara can move a product from design table to store rack in three weeks. Zara, like other fast fashion retailers, succeeds or fails based on the quality of their market data.

Sull stresses that “Zara’s business model demands good information,” which is certainly true. But my work with the company (see this Sloan Management Review article and this case study) revealed something I found fascinating: Zara succeeds in large part because the company makes comparatively light use of market data and sales information, at least as these terms are commonly understood in the retailing industry.

The decisions about which clothes should to go which stores at what time(s) are probably the most important decisions made by any large apparel retailer. Most chains make them by collecting large amounts of daily sales data from stores, combining it with other hopefully relevant information, then applying a variety of statistical techniques to generate a forecast –  a quantitative prediction about what will sell. This forecast is used to push the ‘right’ items –  the ones predicted to sell — over time to each store.

Each retailer forecasts differently, of course, but I find their techniques broadly similar: they all gather lots of data, analyze it centrally, then use the resulting predictions to determine shipments to stores. In this model, the stores themselves have fairly limited roles: they are expected to record data accurately and send it promptly, then do their best to sell whatever headquarters decides to send them.

This seems sensible enough, and it also seems logical that as the business world gets more and more turbulent more and more supporting data will be required. This data will need to be acquired, analyzed, shared, and interpreted with ever-greater velocity, requiring ever-bigger computers, ever-faster networks, and ever-more-quantitative decision makers.

But Zara, operating in an intensely turbulent environment, does something totally different. The company doesn’t really generate a store-level sales forecast at all. Instead, it relies on its store managers to tell headquarters what they think they could sell immediately at their locations. Headquarters then gets as many of these clothes as possible to the stores as quickly as possible.

What’s more, the store managers are given very few quantitative or analytical tools to help them make their short-term predictions. They rely largely on intuition and experience, on walking the floor and talking to customers and employees.

Information technology is still critically important at Zara. The company uses technology to present store managers with a multimedia digital order form, and to transmit completed forms back to headquarters. IT is used heavily to support execution, in short, but not at all to assist with data-based analysis or decision making about getting the right clothes into stores at the right time.

Zara is obsessed with making good decisions about what clothes to stock, but has configured itself so that people making these decisions operate in what looks like a ‘data vacuum’ – a lack of aggregated, filtered, and massaged information from throughout the corporation. This is because the good information that Zara’s business model requires is not the kind that’s easy to digitally encode, transmit, aggregate, and analyze. Instead, it’s information that comes from watching, talking, and listening, then using the computer between our ears to pattern match, draw conclusions, and peer just a little bit into the future.

As I wrote here, Zara believes that the relevant knowledge for fast fashion forecasting isn’t general knowledge (the kind that can be digitized), it’s specific knowledge (the kind that can’t). Three critical business design considerations flow from this belief. First, Zara spends almost no time on store-level sales forecasting and other similar kinds of data analysis. Second, it has moved decision making down very low in the organization, because this is where the relevant knowledge is. And third, it gives these decision makers very little market data or other forms of general knowledge.

So in addition to the IT-related questions Sull lists in the quote at the top of this post, I’d add one more:

For this decision, what’s the relevant knowledge?  What’s the mix of specific knowledge and general knowledge required to make this decision well?

If this question is not asked, the danger is that executives will assume that all or most of the relevant knowledge will be general knowledge, and will therefore get to work digitizing, analyzing, and sharing it. Zara’s success shows how beneficial it can be to question this assumption.

I want to be clear: I believe that in many if not most situations business decision makers are well-served by the kinds of information served up by computers. But in at least some cases they’re not. Companies that do a good job of figuring out which cases these are will have an edge over those making the blanket assumption that more turbulent times call for greater reliance on data.

Have you seen other circumstances where classic market data is just not that useful?  Leave a comment, please, and tell us about them.

Matthew Fraser recently wrote a blog post on the state and pace of the publishing industry, using as an example my book Enterprise 2.0: New Collaborative Tools for Your Organization’s Toughest Challenges, which will be published this fall by Harvard Business Press. Fraser mentions the Facebook group “The Urgency of NOW. Move it up, HBP,’” which was started by Susan Scrupski in an effort to speed up the book’s publication date. Susan and Michael Krigsman have also blogged about this issue.

I deeply appreciate the enthusiasm for the book, and the work done to accelerate its publication. I know how busy we all are, and the fact that some people took the time to investigate the publication timeline for my book and then report on it is amazing and gratifying to me. As is the fact that more than 150 people would take the trouble to join a Facebook group devoted solely to getting Enterprise 2.0 onto bookshelves quicker. So thanks, all — you’ve brightened my year.

I’d love to see it published tomorrow (heck, yesterday). But I’m also hugely ignorant about the best ways to publicize and otherwise support a mainstream business book (and even though Enterprise 2.0 has a technology focus, both HBP and I consider it to be a mainstream business book). I understand that it’s important to get it reviewed in newspapers and magazines, to line up interviews, to not flood bookstores with too many books at once from the same publisher, and so on. HBP knows how to do all these things much better than I do, so I have to give them a good deal of deference when they counsel patience and tell me the book will do better if it’s released at the right time instead of at the earliest possible opportunity.

HBP has been a great partner on this project. I’ve received excellent advice from my editors Brian Surette and Monica Jainschigg, and thanks to them the manuscript is much better than was the draft I submitted last September. People at the Press are aware that this is a timely book and topic, and have heard the voices encouraging them to publish it sooner rather than later. I am confident that they will publish it as soon as they feel they can give it the support it deserves.

Between now and the publication date the first chapter of the book, which describes its genesis, goals, and structure, is available for download. I’m also going to write an article about Enterprise 2.0 in Harvard Business Review this fall. While I’ve got you here, let me ask a question: what would you like to have covered in the article?  Which topics related to Enterprise 2.0 should it discuss? Leave a comment, please, and let us know — I’d like to crowdsource the article a bit. And if you have any questions or comments about the book, I’d love to hear them.

A June 25 post to the New York Times Bits blog starts with “To get the best predictions about when your company’s latest product will ship or how it will sell, you might try asking your employees — anonymously.” A skeptical but fair response to this is something like “Sure, I might, but why should I? Our company has professionals who make these kinds of forecasts for a living. Why on earth should I place more trust, or any trust, in the predictions that come from an anonymous crowd?”

A convincing answer to this question is “You should place more trust in the crowd if it gives you better answers than your current methods do.” And the post in the NYT presents evidence that corporate prediction markets do just that.

Prediction markets are a technology that harnesses collective intelligence / the wisdom of crowds to gaze into the future and generate a consensus forecast (in other words, the crowd’s aggregate best guess) about what will happen. I’ve written about them here, and here’s a nice overview paper about them from Justin Wolfers and Eric Zitzewitz.

On the Internet they’ve been shown to deliver more accurate predictions about political elections and movie revenues than other techniques like polls and statistical forecasting methods. Pioneering efforts to use them within companies show that they’re also highly accurate when deployed behind the firewall (see, for example, the case I wrote with Karim Lakhani and Peter Coles about Google’s internal prediction market and this paper written by Google’s Bo Cowgill and his colleagues).

But are they more accurate than the other techniques companies use to forecast future events of interest? The NYT post presents data from the prediction market startup Crowdcast (disclosure: I am on Crowdcast’s advisory board, and have a small amount of stock in the company), which has deployed its technology within multiple large companies.  As Claire Cain Miller writes in the Times:

At a media company with a new product to ship, 1,200 employees predicted a ship date and sales figures that resulted in 61 percent less error than executives’ previous prediction, according to Crowdcast. A pharmaceutical company asked a panel of scientists and doctors to predict regulatory decisions and new drug sales using Crowdcast, and they were more accurate than the company’s original prediction 86 percent of the time.

Crowdcast chief scientist Leslie Fine, a deceptively normal looking person with what I’m pretty sure is a 4-digit IQ (she has a doctorate in experimental economics from CalTech, and they don’t just hand those out), gave me some more information about the setting in which accuracy vs. the ‘official’ forecast was improved by 61%:

For a major media and gaming company, we predicted total hardware and software sales, as well as metacritic scores for games.  Metacritic is an aggregate review score, and is by far the #1 predictor of sell-through.  Our client there is the Senior Director for Publishing Intelligence.  He starts the markets off at the company’s ‘official’ best guesses for future sales and scores, as soon as the publishing intelligence guys come up with them. For metacritic scores, they do this by ranking the game on a number of axes (out of 10): playability, graphics, etc..  They add them up, which yields the score.  For hardware and software sales, the official prediction comes from applying growth percentages to historical data.

There are also some great stats coming out of a survey we did at this company.  They’re a real turn on (ok, if you’re a total nerd):
- 80.6% say they participate because it’s fun.
- 35.5% say they do so because it “Gets me thinking about stuff that helps me do my job”
- 44.1% say “I believe my input is valuable”

- 52.1% say they usually research the forecast before betting; another 38% say at least sometimes
- 54.7% believe that that business leaders value the knowledge

Crowdcast has had two important insights; the first is common to all prediction market vendors, while the second is unique to them (as far as I know). The common one is captured well in a couple paragraphs from the Times post:

“It’s a huge problem in corporations — by the time information gets to the top, it’s meaningless or too late,” said Mat Fogarty, Crowdcast’s founder and chief executive. Mr. Fogarty spent a decade doing corporate forecasting at Electronic Arts and Unilever, where he saw how inaccurate official corporate forecasts could be. “There is a lot of information not getting to decision-makers, and that’s expensive,” he said.

At Electronic Arts, Mr. Fogarty said, the most junior people often had the most insight into the quality of new video games, and he picked up the best information about the latest version of Madden while playing soccer games on the company lawn.

Fogarty saw the same thing that the great Austrian economist Friedrich von Hayek noted in his seminal 1945 article “The Use of Knowledge in Society:”

The… problem of a rational economic order is determined precisely by the fact that the knowledge of the circumstances of which we must make use never exists in concentrated or integrated form but solely as the dispersed bits of incomplete and frequently contradictory knowledge which all the separate individuals possess. The economic problem of society is… a problem of the utilization of knowledge which is not given to anyone in its totality.

Hayek also saw how beautifully prices established by markets solved this vexing problem:

We must look at the price system as such a mechanism for communicating information if we want to understand its real function… The most significant fact about this system is the economy of knowledge with which it operates, or how little the individual participants need to know in order to be able to take the right action. In abbreviated form, by a kind of symbol, only the most essential information is passed on and passed on only to those concerned. It is more than a metaphor to describe the price system as a kind of machinery for registering change…

The marvel is that in a case like that of a scarcity of one raw material, without an order being issued, without more than perhaps a handful of people knowing the cause, tens of thousands of people whose identity could not be ascertained by months of investigation, are made to use the material or its products more sparingly; i.e., they move in the right direction…

I have deliberately used the word “marvel” to shock the reader out of the complacency with which we often take the working of this mechanism for granted. I am convinced that if it were the result of deliberate human design, and if the people guided by the price changes understood that their decisions have significance far beyond their immediate aim, this mechanism would have been acclaimed as one of the greatest triumphs of the human mind.”

(sorry for the long quote, but I really love this paper)

Fogarty started Crowdcast because he believed, as did Hayek, that markets were marvelous devices for aggregating and sharing important knowledge. Fogarty further believed that they could be used to address the problems of poor intra-company information flows he had witnessed firsthand. In the era of high bandwidth, powerful browsers, and software as a service, he saw an opportuity to bring Hayek’s marvel inside the firewall, and to apply it to a range of tasks.

The company’s second insight was that most people are not stock traders by nature, and the typical trader’s interface of current prices, bid-ask spreads, and order books is off-putting and alienating to most (it certainly is to me). So the Crowdcast team replaced it with a much simpler interface –  one that lets participants make their trades graphically, by moving sliding bars across a bell curve. They make a prediction by selecting a range of values; a tight range indicates a confident prediction (and a high payout if it turns out to be correct), while a large range corresponds to a less confident prediction. This demo from Crowdcast shows how the system works. I find it simple and highly intuitive, and Fine has worked to make sure all the underlying math is solid.

The evidence is mounting that corporate prediction markets work as advertised, delivering quick, accurate, and decisive predictions in areas of great interest. Furthermore, the evidence so far also suggests that they work better than current corporate forecasting techniques, at least in some circumstances. So are there any good reasons left for not using them, or at least experimenting with them? I ask seriously: why would any enlightened company not avail itself of this technology? Can you come up with any legitimate reasons not to jump in with prediction markets?  Leave a comment, please, and let us know.