Category Archives: Wisdom of Crowds

A Quick Critique of Clubhouse

As you may know, I have been experimenting with Clubhouse over roughly the past six months in several capacities:  as a regular user, an occasional audience participant/questioner, and as the host of a regular room I’ve been running with Thomas Otter, the SaaS Product Power Breakfast.

I love to get involved with new social media platforms early because I’m interested in new forms of media (and the often subtle differences they bring), I enjoy watching early evolution of the products and their usage (e.g., the invention of hashtags or URL shortening on Twitter, the applause convention [1], speaking protocols [2], or the use of Instagram DMs on Clubhouse [3]), I like watching the minimum viable product (MVP) questions play out in real time, and I love to see strategy at work.

So, in that light, here is my quick critique of Clubhouse intended as both critical and constructive.

As a startup- and media-watcher, I’m of the opinion that, after raising money at a $4B valuation in April (and with maybe 50 total employees at the time), Clubhouse appears to have lost significant momentum in the past several months.  Why?

  • The pandemic is winding down.  I think Clubhouse got a significant pandemic tailwind when people were locked in, Zoomed out, and looking for new ways to connect with other humans.
  • Certain communities returned to IRL mode, notably comedians, one of several core Clubhouse communities.  Some of my favorite rooms were in Leah Lamarr’s Hot on the Mike club and it appears that many of those outstanding comedians are back working at physical clubs.  That’s great for them, but not for me — as a Clubhouse user I can’t just login when I’m free and easily find a great comedy room as I once could.
  • It’s hard to reliably find live content.  The key difference between podcasts and Clubhouse rooms is the serendipity of live content (e.g., when I stumbled into a room with John Mayer) and the potential for interactivity [4].  Without those two things, I can just listen to a recorded podcast.  If you can’t find content, what good is the app?  It becomes like cable TV — 500 channels, but nothing to watch.  Every day I am less enthusiastic about firing up the app because I think I’ll either spend half my time looking for something [5] or fail entirely.
  • The app doesn’t get the most basic thing right:  language.  While I do listen to content in two languages, the app is constantly showing rooms in my hallway with titles (and dialog) in languages that I don’t speak.
  • The app has no room-search functionality.  The single most basic, MVP-level feature is (still) missing:  search in-progress rooms by keyword (or topic) in the title or description.  Not there.  Stunning.
  • The follow paradigm is wrong.  Content discovery is based primarily on people, not topics.  Using myself as an example, I like:  enterprise software, the Grateful Dead, French language, comedy, startups, mathematics, and philosophy.  Just because you like enterprise software doesn’t mean you like the Grateful Dead or topology.  While the app notionally supports topics, they appear ignored in composing your hallway [6].
  • The app does not appear to learn.  While the app does not appear to learn what I like in formulating suggestions in the hallway, it does appear to learn some bad lessons:  e.g., if you actually stumble into a single Russian room it seems to suggest them endlessly.
  • The app breaks trust in machine learning.  In an era of sophisticated users, I’m OK to hide-room numerous times in order to teach the app my preferences.  While hide-room didn’t appear to actually do anything (yet), I was confident that at some point they’d leverage that data to improve my experience.  Then one day hide-room seems to have simply disappeared from the app, so all that teaching appears to have been wasted.  That breaks my trust.  Don’t ask me questions if you’re going to throw away the answers.
  • The app is gameable in odd ways.  It appears that long-running rooms get some advantage in hallway prioritization so there are people who run rooms for days on end (e.g., Scenes From an Airport Terminal) that pollute my hallway, and that now I can’t even hide.  If the app were focused on topics and not people and duration, they could eliminate this.
  • The community has too many hucksters and charlatans.  Everyone seems to be a millionaire, successfully running five companies, a great venture investor, and yet still somehow need $99 from you to take their masterclass.  Just reading the bios of the moderators in many rooms makes me feel vaguely ill.  Hearing the advice these people give to would-be entrepreneurs makes me feel worse.  Don’t get me wrong, some rooms are amazing and offer an experience you can find nowhere else.  But a lot of Clubhouse feels like the vapid self-help section of a bookstore.  Oh, and don’t forget your laser eyes before going into the crypto rooms.

What to do about it?

  • Strategically, Clubhouse seems to have missed the systematic expansion memo (e.g., Amazon from books to DVDs to cameras and onward, or Facebook from Harvard students to Ivy League students to College students to broader groups).  I think their decision to port the app to Android before coming even close to completing it (e.g., content discovery, search) was a big mistake.  They need to focus on completing the app first.  Get to MVP before porting the app.
  • Systematic expansion includes not only product but community.  Just as they need to prioritize their product features to complete the product in a logical order, they need to decide which communities they want to serve (and, no, “creators” is not a sufficiently focused community definition).  I think comedians may be gone for good because the time that people want to hear them is precisely the time they are out at work.  But there are lots and lots of communities on Clubhouse they can try to develop (e.g., Silicon Valley VC/startups which had an early focus but seems to have faded away, crypto, activism, real estate, investing).  Just pick some and complete the app for them.
  • Appoint community mangers.  In addition to product managers to drive functionality, appoint and empower community managers and not just to makes rules about content [7] but to help build the community in a given topic area.  Just as retailers have category managers (someone responsible for, e.g., swimwear at a business level) so should Clubhouse have community managers.
  • Play for your users, not your VCs.  Existing users definitionally were not pushing for Android.  I’m guessing the VCs were — so they could continue to show great adoption.  But what good is great adoption if, after using the app a few times, everyone drops off because they can’t find anything they want to listen to?  Without great content on the app, there is no need for the app.
  • Stay in touch and on the ground.  One of my favorite rooms was cofounder Paul Davison’s weekly introduction [8] for new members (on Thursday evenings) that I assume he’s still running.  I know he runs a weekly Town Hall as well.  Paul is a great spokesperson, communicator, and listener and I love that he stays in such direct touch with his user base.  They just need to add some more systematic strategic focus atop that and some Geoffrey Moore 101 to go with it — complete the app, use-case by use-case and don’t get stretched too far, too fast in the process.

# # #

Notes

[1] Muting and unmuting your microphone in rapid succession

[2] Examples:  Pull-to-refresh (PTR) order.  Or the “this is Dave and I am done speaking” protocol, which is seemingly for several reasons including:  to identify speakers in rooms with large numbers of moderators where you may not be able to find the speaker (e.g., if they are buried three screens down), as a basic courtesy protocol, and for accessibility reasons for people who are unable see the grey ring indicating speaker identity.

[3] A great example of not needlessly building DMs a feature, but instead supporting profiles that link to Instagram and the community quickly embracing Instagram as the default DM method on Clubhouse.

[4] If you want to raise your hand and ask a question and are so selected — itself another issue as I’d been in numerous rooms where people said they waited literally for hours

[5] And because Clubhouse can be and is often best done while multi-tasking, it needs to be fast and easy to find something, e.g., when you’re hopping on the treadmill.

[6] The app suggests if you’re not finding content you want to “follow more people” — not to like more topics.

[7] The narrow definition of community manager is about making and enforcing rules for rooms, dealing with reported speakers, etc.  While such activity is important, it’s table stakes — a community manager should be far more than a security guard, but instead a leader trying to build the community, drive membership, foster and promote rooms, etc.

[8] Even though it was notionally an “introduction” I attended for several weeks just to hear Paul talk about the app and his vision.

 

How to Train Your VP of Sales to Think About the Forecast

Imagine a board meeting.

Director:  What’s the forecast for new ARR this quarter?

Sales VP:  $4.3M, with a best case of $5.0M.

Director:  So what’s the most likely outcome?

Sales VP:  $4.3M.

Director:  What are you really going to do?  (The classic newb trap question.)

Sales VP:  I think we can come in North of that.

Director:  What’s the worst case?

Sales VP:  $3.5M.

Director:  What are the odds of coming in at or above the forecast? 

Sales VP:  I always make my forecast.

Director:   What do you mean by worst case?

Sales VP:  You know, well, if the stars align in a bad way – a lot of stuff would have to go wrong – but if that happened, then we could end up at $3.5M.

Director:  So, let’s say a 10% chance of being at/below the worst case?

Sales VP:  I’d say more like 5%.

Director:  What do you mean by best case?

Sales VP:  Well, if we really struck it rich and everything lined up just the way I wanted, that would be best case.

Director:  You mean if all the deals came in — so best case basically equals pipeline?

Sales VP:  No, that never happens, I’ve made about 10 scenarios of different deal closing combinations and in 2 of them I can get to the best case.

You see the problem?  Does it sound familiar?  Do you realize how much time we spend talking in board meetings about “forecast,” “best case,” and “worst case” without every discussing what we mean by those terms?

Do you see how this is compounded by the sales VP’s natural, intuitive view of the outcomes?  Do you see the obvious mathematical contradictions?  “I always make my forecast” says it’s a 100% number, but then the VP says it’s the “most likely” number which implies 50%.  Then the VP says there’s a 5% chance of coming in at/less than worst case (which is much lower) and then kind of implies that there’s a 20% chance of beating best case – but the 2 out of 10 is meaningless because it’s not a probability, it’s just a count of scenarios.  Nothing adds up.

The result is, if you’re not careful, the board ends up counting angels on pinheads.  What can we do to fix this?  It’s simple:  teach (and if need be, force) your sales VP to think probabilistically.  Ask him/her how often:

  • It is reasonable to miss the forecast.  A typical answer might be 10%.
  • It is likely to come in at/below the worst case? Typical answer, 5%.
  • It is likely to meet/beat the best case? Typical answer, 20%.

So, with those three questions, we’ve now established that we want the sales VP to give us:

  • A 90% number on being at/above the forecast
  • A 20% number on being at/above the best case
  • A 5% number on being at/below the worst case

Put differently, when the sales VP decides what number to forecast that they should be thinking:

  • I should come in under my forecast once every 2.5 years (10 quarters).
  • I should hit/beat the best case about once every 5 quarters (a bit less than once a year).
  • I should come in/under the worst case once every 20 quarters (once every 5 years, or for most minds, basically never).

The beauty here is that when you work at a company a long time you can get enough quarters under your belt, to start really seeing how you’re doing relative to these frequencies.  What’s more, by converting the probabilities into frequencies (e.g., once every 10 quarters) you make it more intuitive for the sales VP and the organization to think this way.

In addition, you have a basis for conversations like this one which, among other things, is about overconfidence:

CEO:  You need to work on your forecasting.

Sales VP:  You know it’s hard out there, very competitive, and we don’t have much deal flow.  Back when I was at { Salesforce | Oracle | SAP }, I was much better at forecasting because we had more volume.

CEO:  But we agreed your forecast should be a 90% number and you’ve missed it 2 out of the past 4 quarters.

Sales VP:  Yes, but as I’ve said it’s tough to forecast in this market.

CEO:  Then forecast a lower number so you can beat it 90% of the time.  I’m asking you for a 90% number and empirically you’re giving me a 50% number. 

Sales VP:  OK.

CEO:  Plus, when those two big deals slipped last quarter you didn’t drop your forecast, why?

Sales VP:  Because where I grew up, you don’t cut the forecast.  You try like crazy to hold it.  Do you know the morale problems it causes when I cut the forecast – especially if it’s below plan? So, yes, when those two deals slipped it added more risk to the forecast – and I told you and the board that — but I didn’t cut forecast, no. 

CEO:  But “adding risk” here is meaningless.  In reality, “adding risk” means it’s not a 90% number anymore.  You’ve taken what was a 90% number and it’s now more like a 60% or 70% number.  So I want you to forget what they taught you growing up in sales and always – every week – give me a number that based on all available information you are 90% sure you can beat.  If that means dropping the forecast so be it.

sales forecast

This also helps with the board and the inevitable sandbagger issue.  In my experience (and with a bit of exaggeration) you always seem to be in one of two situations:  (1) intermittently missing plan and in trouble or (2) consistently making plan and a “sandbagger” – it feels like there’s nothing in between.

Well, if you establish with the board that your company forecast is a 90% number it means you are supposed to beat it 9 times out of 10 so you can only really be labelled a sandbagger when you’re 15 for 15 or 20 for 20.  It also reminds them that you’re supposed to arrive at the forecast so that you miss once every 10 quarters so they shouldn’t freak out if once every 2.5 years if that happens — it’s supposed to happen in this system.  (Just don’t let a once-in-ten-quarter event happen twice in a row.)

I like this quantitative basis for sales forecasting and I carry it down to the salesrep and pipeline level.  I believe that each “forecast category” should have a probability associated with it.  For example, at the opportunity level, you should link probabilities to categories, such as:

  • Commit = 90%
  • Forecast = 70%
  • Upside = 30%

This, in turn, means that over time, a given salesrep should close 90% of their committed deals, 70% of their forecast deals, and 30% of their upside.  Deviations from this over time indicate that the rep is mis-categorizing the deals because the probability should be the basis for the forecast category assignment [1].

Finally, I do believe that salesreps should give quarterly forecasts [2] that reflect their sense for how things will come in given all the odd things that can happen to deals (e.g., size changes, acceleration, slippage).  I believe those forecasts should be a 70% number because the sales manager will be managing across a  portfolio of them and while there is little room for a company to miss at the VP of Sales level, there is more room for and more variance in performance across salesreps.

While I know this will not necessarily come naturally to all sales VPs — and some may push-back hard — this is a simple, practical, and rigorous way to think about the forecast.

# # #

[1] Some people do this through an independent (orthogonal) field in the CRM system called probability.  I think that’s unnecessary because in my mind forecast category should effectively equal probability and your options for picking a probability should be bucketed.  No one can say a deal is 43% vs. 52% and forecast category doesn’t indicate some probability of closing, then … what use is it and on what basis should you classify something as forecast vs. upside?

[2] Some people believe that only managers should make forecasts, but I believe both reps and managers should forecast for two reasons:  (1) provided it’s left independent and not “managed” by the managers, the aggregated salesrep-level forecast provides another, Wisdom of Crowds-y, view into the sales forecast and (2) it’s never too early to teach salesreps how to forecast which is best learned through the experience of trial and error over many quarters.

Collected Wisdom on Business and Life from the HBS Class of 1963

“The wisdom of the wise, and the experience of the ages, may be preserved by quotation.”

Isaac D’Israeli

Browsing my tweetstream I ran into this wonderful website the other day and instantly retweeted it, but also made a note to come back to have a deeper look.  On doing so, I decided it was so good that I’d do a quick post to highlight it.

The site, If I Knew Then, is actually also a book written by Artie Buerk, a member of the Harvard Business School (HBS) class of 1963 and contains collected wisdom — all in quotation form — from his classmates, gathered in preparation for their 50th reunion.

In addition to the obvious advantage of providing retrospective from an unusually successful group of people, Buerk argues their views are even more relevant because of the massive change that occurred during their lives.

It is, in fact, because these Harvard grads have lived through all these massive changes that their perspectives count for so much. They have been a part of both the “before” and the “after” pictures of a world transformed.

Consider what the world looked like in 1963:

In 1963, the average price of a new home was $12,650 — a fraction of what even the most modest home sells for today. That year, gasoline sold for 22 cents per gallon, the minimum wage was $1 per hour, [and] the average starting salary of a Harvard MBA grad was $9,500.

(Inquiring minds will be happy to know that today’s average starting salary for a Harvard MBA is around $140,000, growing at about twice the rate of inflation since 1963.)

Here are a few of the pithier quotes.

On business:

Surround yourself with the smartest, most ethical people you can find. Set clear goals, communicate them clearly, and delegate.

On careers:

Decide you like what you do, and do it better and smarter than anyone else.  If you can’t, change your career.  Don’t create an expensive lifestyle — living modestly frees you to make appropriate choices.

On leadership:

The best leaders I’ve seen have been as or more knowledgeable than anyone else about the business and the environment in which it operates. They have a clear vision they can communicate to others, and they make decisions easily.  On a personal level they are easy-going, don’t take themselves too seriously, admit their mistakes, and are quick to give others credit. They have high standards, clearly articulated, to which they hold their people.

On happiness and success:

Success is when you can spend 90 percent of your time doing the things you want to do and only 10 percent doing things you have to do.  Most people’s lives are just the opposite.

On life’s lessons:

There is no substitute for integrity. In a world where greed and taking shortcuts seem to be major themes, there is nothing that can replace one’s reputation. The ability to look back on life and say, “I did it the right way” is a treasure. There is no do-over when you lose your integrity and reputation.

Notes from James Surowiecki Talk at Mark Logic User Conference 2009

James started out by telling the story of an ox-weight guessing contest analyzed by Francis Galton. He thought the crowd, whose average guess was 1,197 pounds vs. a reality of 1,198, was a mix of a few smart people with a lot of dumb ones. In reality, the crowd was smart.

Under the right conditions, groups of people can be remarkably intelligent; even smarter than the smartest member of the group.

The jelly bean experiment usually produces:

  • 3-5% accuracy
  • Average better than 95%+ of anyone in the room

Who Wants to be a Millionaire example:

  • Phone-a-friend: “experts” get the right answer 2/3rds of the time
  • Audience poll: the crowd gets the right answer 90% of the time (and consider who the crowd is!)

Google’s entire business is built on the Wisdom of Crowds on the Internet — by using link structure as a voting mechanism.

Wikipedia a phenomenal example of collective labor (dk: despite the Maurice Jarre hoax revealed today)

Described another example of studying craters on Mars where groups did as well as geologists trained 5-7 years. Excerpt:

The result, in NASA’s words: “the automatically computed consensus of a large number of clickworkers is virtually indistinguishable from the inputs of a geologist with years of experience in identifying Mars craters.” And these people weren’t even being paid.

The racetrack is his favorite example of collective intelligence. The odds on horses are almost perfect predictors of race outcome. (In a study of seven-horse races at Belmont,) favorites predicted to win 33% and won 34%, fourth favored 12% of the time, won 12% of the time, et cetera. Almost perfect judgment. And the crowd of betters is not exclusively experts. A lot are not: cranks, rookies, those seeking a nice day at the track. “I only bet on chestnut-colored horses” — but somehow when you aggregate those bets, you get an accurate forecast.

There are companies starting to use these tools — e.g., prediction markets, attempts to use a market-based tool to predict outcomes. First one was done at a b-school at the University of Iowa. Idea: markets do a relatively good job (in general, not recently!!) of forecasting in a variety of circumstances. Can we use them to predict non-financial things? So they tried presidential elections. Since 1998 that market has done a better job than Gallup polls — election-eve forecasts off by 1.2%.

Now there are public markets for lots of things and lots of bets (e.g., will Michael Jackson be convicted, dk: will Lance win the 2009 Tour de France). See Intrade which call every sentate race correctly and 49/50 states in the recent presidential election.

HP, in the 1990s, set up an internal prediction market for printer sales. 25-30 people done on lunch hour, small financial incentives. That market was more accurate than the elaborate forecasting system 3/4ths of the time.

Eli Lilly doing this to forecast which drugs will make it through clinical trials. Microsoft used to predict when software projects will finish!

Reality is in big organizations information doesn’t often get from where it is to where it needs to be:

  • Hoarding: information is power
  • Fear, afraid to say what they think. Does boss want the truth?
  • Perverse incentives: budgeting systems get gamed

Obstacles get in the way. With right collective intelligence tools, the only incentive is to be right.

But it only works under certain circumstances:

  • Dysfunction: rioters, lynch mobs, market crazes
  • Corporate meetings: after 15 minutes we end up all now dumber than we were when we entered!

Three basic ingredients needed:

  • Aggregation: way to aggregate individual judgments into collective one. There are lots of way to do this: odds, markets, averages. Not talking about the suggestion box where a guy at the top selectively picks ideas.
  • Diversity: the collective opinion does not necessary equal “consensus.” The more diverse, the smarter and better the decisions. Diversity means different types of mistakes get made — uncorrelated errors. Diversity eliminates groupthink. The longer homogeneous groups talk, the dumber they get. Technique: Devil’s advocate, invented by the Catholic Church — appointed someone when considering canonization (i.e., Sainthood) process they would appoint (literally) an advocate for the Devil.
  • Independece. Want people relying on their own judgment and not immitating others. How many people wake up and say “I look forward to conforming today.” But we are nevertheless immitative beings. Experiment in Times Square: a guy gazes up at a window. When you put 5 people on a street corner looking up, 45% of others look up. With 8 people, 80% gaze up. Don’t go around the room asking people for a conclusion at the end of a meeting — want independence.

Actuary joke: three acturaries are hunting. First guy shoots at a duck and misses 20 feet to the right. Second shoots and misses 20 feet to the left. And the third guy shouts out “we got it!”

Alternate side of the street parking example. He lives in Brooklyn. If the other cars have not been moved to the other side, he doesn’t. And he’s never got a ticket.

Note that talkative people tend to dominate group discussions. This would be OK if talkative people were smart. However, there is no correlation between talkativeness and intelligence.

Submarine story: the USS Scorpion, lost at sea. Hopeless task to find, transmissions too infrequent. John Craven built a diverse team to try and locate it: scenario analysis. Asked the group to bet (using bottles of Scotch as the incentive) on scenarios and on variables (e.g., rate of descent). Ran the data through Bayes’ algorithm. Ship was eventually found 220 yards from where the model driven by Craven’s men predicted — and no one member of the team had actually predicted that spot.

Wow!

Great speech!

The Madness of Mobs: Twitter and Swine Flu

In talking about web 2.0, we often think about ideas like mass collaboration, a participatory web, the web as a communication platform, and generally speaking The Wisdom of Crowds in building and establishing knowledge.

I’m a big believer in the power of functional (or wise) groups to make better decisions than even the most talented individuals. I learned this first-hand years ago when I took LDP at the Center for Creative Leadership and we did a survival exercise similar to the one detailed in table 4 of this document. In our exercise, every individual — including a Brigadier General — was outperformed by the group in prioritizing a list of items necessary for wildnerness survival.

So I believe that groups guess jellybean jar counts better than individuals, that PageRank generally works for finding web pages, that feedback (used to) work on eBay (until they said sellers can only say positive things), that Diggs are useful way to identify interesting content, that Wikipedia is a great way to build an encyclopedia (particularly a technology one), and generally most of the other stuff I’m supposed to believe as good, web 2.0, Silicon Valley guy.

I believe this so much that we invited James Surowiecki, author of The Wisdom of Crowds, to keynote our user conference coming soon on May 12-14, 2009. So I’m on board with the program.

But I also wonder about the opposite, what I’ll call The Madness of Mobs. From financial bubbles to looters to Spring Breakers to a dozen other examples, we can all find examples of where everything cuts exactly the opposite way: where a wise crowd transforms to a mad mob.

So I was quite interested to find this article, Swine Flu: Twitter’s Power to Misinform, which talks precisely about how the “mass brain” of Twitter appears to be shorting out when it comes to the topic of swine flu. Excerpt (edited for brevity, and bolding mine):

Thus, Unlike basic internet search — which has been already been used by Google to track flu trends — Twitter has introduced too much noise into the process: as opposed to search requests which are generally motivated only by a desire to learn, too many Twitter conversations about swine flu seem to be motivated by desires to fit in, do what one’s friends do, or simply gain more popularity.

In such situations this, there is some pathological about people wanting to post yet another status update containing the coveted most-searched words – only for the sake of gaining more people to follow them. And yet the bottom line is that tracking the frequency of Twitter mentions of swine flu as a means of predicting anything thus becomes useless. (However, there are plenty of non-Twitter options summed up nicely on Mashable)

Hum. I should probably cop a maybe-guilty plea on blogging on swine flu. Like moths to a flame, we bloggers are drawn to hot topics.

The article continues:

If you think that my concerns about context are overblown, here are just a few status updates from random Twitter users:

I’m concerned about the swine flu outbreak in us and mexico could it be germ warfare?

In the pandemic Spanish Flu of 1918-19, my Grandfather said bodies were piled like wood in our local town….SWINE FLU = DANGER

Good grief this swine flu thing is getting serious. 8/9 specimens tested were prelim positive in NYC. so that’s Tx, Mexico and now Nyc.

Be careful of the swine flu!!!! (may lead to global epidemic) Outbreak in Mexico. 62 deaths so far!! Don’t eat pork from Mexico!!

Swine flu? Wow. All that pork infecting people….beef and chicken have always been meats of choice

Be careful…Swine Flu is not only in Mexico now. 8 cases in the States. Pig = Don’t eat

If my reading list on Twitter was only restricted to the individuals who had produced the posts above, by now I would be extremely scared … In moments like this, one is tempted to lament the death of broadcasting, for it seems that the information from expert sources should probably be prioritized over everything else.

Now, I’m pretty sure the counter-arguments to The Madness of Mobs goes like this:

  • Not all groups are wise. The Wisdom of Crowds relies of wise groups.
  • You can’t cherry-pick the scariest contributions to argue that The Wisdom of Crowds doesn’t work. Much as the abortion page on Wikipedia is the result of a rugby scrum of passionate, oppositional forces, so will be the mass brain of Twitter on swine flu. You need to look at the whole picture.

In fact, Surowiecki outlines failures of crowd intelligence and finds root causes which include groups that are too homogeneous, too emotional, too centralized, too divided, and too imitative.

Hopefully, we’ll hear more from Jim on this topic at the user conference and, in the meantime, before enslaving yourself to The Wisdom of Crowds, ponder if your crowd is a wise one, and whether you’re actually dealing with The Madness of Mobs.

Related Information / Stories

Swine Flu Tracker Map

View H1N1 Swine Flu in a larger map