On Monday my colleague Naumi Haque sent me a link to an interesting article by Professor Francis Heylighen titled “Why is Open Access Development is so successful? Stigmergic organization and the economics of information“. It provides a great overview of open access / self-organizing systems in relation to traditional economic theory, but the part I found most interesting was in relation to the two different types of stigmergy.

I will now admit that I didn’t actually know what that word meant before reading the article. According to Wikipedia, “Stigmergy is a form of self-organization. It produces complex, apparently intelligent structures, without need for any planning, control, or even communication between the agents. As such it supports efficient collaboration between extremely simple agents, who lack any memory, intelligence or even awareness of each other.” More simply, Heylighen says “a process is stigmergic if the work done by one agent provides a stimulus that entices other agents to continue the job.”

You can start seeing the link between stigmergy, community building, and social media from that description. But to highlight the difference between direct and indirect stigmergy, Heylighen used termite and ant examples.

Direct stigmergy: exemplified by termite hill-building, it is the “work-in-progress” itself that directs subsequent contributions.

Indirect stigmergy: exemplified by the way ants create trails of pheremones that direct other ants to food sources. The trails are left as “side effects” of the actual work being performed.

I thought about these differences, and how they might relate to companies in terms of community building and social media strategies. It occurred to me that building a community is primarily driven by direct stigmergy, while the use of social media is the indirect stigmergy that draws people to you.

The reason is simple. In the termite (direct) example, the little critters go around dropping bits of mud randomly, and where the heaps are formed stimulate other termites to add to them – causing them to grow and grow. By definition, the termite can’t add to the heap without being there. So while no centrally controlled plan is needed, the community must already be there. In an organizational context, the company /moderators start by dropping little heaps of information here and there, hopefully others do to, and where the heaps “pile up” emerge as the key focal point.

The challenge (from an organization context) is, obviously, that this approach doesn’t work if you don’t have a critical mass of termites. You can drop all the heaps you want, but if no one else is there, they’re not going to grow. This is where the indirect stigmergy / social media tie comes in. Using platforms like Facebook, Twitter, WordPress, etc., organizations (and early contributors) leave “trails” that direct people back to the (in the ant case) the “food source”.

So it’s obviously a little bit awkward trying to elegantly merge the termite and ant examples together – right now I’d end up with a bunch of ants piling in to eat the Termite hills. But if you squint a bit I think you’ll see what I’m saying. Organizations need to think about direct stigmergy principles in order to build up their own community from within, and indirect stigmergy principles in order to draw people to them. It’s a subtle, but important, distinction.

Today my favorite writer – The Sports Guy – wrote an excellent column about Bill Belichick’s “reckless” call to go for it on 4th and 2 when his team was up by 6, on their own 28 yard line, with just over 2 minutes left to play. Much of the article goes into great detail on the problems inherent with relying on statistics in such a situation (see: Insane angle #1: Statistically, it was right move). But in Insane angle #2, he can’t help but pull out his own statistics to “justify” why the Pats shouldn’t have gone for it.

Unfortunately, The Sports Guy made an all-too-common mistake while doing so – providing a reminder that however dangerous putting blind trust in statistics can be, the problem is that much worse if you don’t understand them properly.

His argument was simple. Indianapolis had already completed two long touchdown drives in the 4th quarter. By punting, New England would have forced them to do it a third time. So, to “prove” his point, he asked someone to crunch the numbers on “the number of times a team started and completed three touchdown drives in the fourth quarter to erase a double-digit deficit and win an NFL game since 2005.” The answer he found was 4 – it happens less than once per season. He then started banging his head on his desk.

It sure looks like a perfectly reasonable, statistically-based argument, but there are some major flaws. The one I’m going to focus on here (another big one is switching from %s to a raw count of a known rare situation, which is almost always an easy but meaningless thing to do) is tied to what’s called the “Gambler’s fallacy” – the belief that deviations from expected behavior in the past are likely to be evened out by opposite deviations in the future. The common example is coin flips, but I’m going to use a basketball analogy – since that’s the Sports Guy’s favorite sport.

Let’s say your playing the Cleveland Cavaliers, and for no obvious reason whatsoever they run a play to get a three-point shot for Shaquille O’Neal – who has only even attempted one such shot in the last decade or so. The defense is so confused by this that they foul him, and he steps to the line. Even though he’s only a 50% FT shooter, he hits the first two. What are the odds of him hitting the third?

The correct answer is 50%. The fact that he’s just hit his first two has absolutely no bearing on the probability he’ll hit his next one. But if you follow the underlying logic of the Sports Guy’s argument, you’d determine the answer is 12.5%. After all, that is the odds that he hits three free throws in a row. But that number goes out the window, because the first two have already been made.

That’s the Gambler’s fallacy in action, and underlies the logic of the Sports Guy’s argument. He tried to use statistics to argue down the probability of an event happening, based on that same event happening twice before.  Marching 70+ yards down the field may be difficult, but just because you’ve done it twice already in the quarter doesn’t make it more difficult the third time (and things like “how many times it’s happened to three times to win a game, etc., etc.” don’t matter much at all). If anything, it would be the opposite – and I’ve got the stats to back it up.

Just kidding – I don’t really. But since I’m writing, there is another interesting thing to point out here in terms of common statistical / analytical mistakes.

The question of the day is whether the team should have gone for it. But in his analysis, the Sports Guy brings in the particular offensive set the coach chose. Now it’s perfectly valid to question this in terms of “does the coach suck?”– but from an analytical standpoint, you really should keep the two decisions separate. Whether he made the right play call for the 4th down conversion and whether he should have gone for it on 4th down are two very different things.

Why is this distinction important? Well let’s say you’re watching a game where the coach for one team is absolutely terrible. Andy Reid in any given 4th quarter will do. On third down and six, for whatever reason, he opts not to block the defensive team’s best pass rusher, who breezes in untouched for a sack. It’s now 3rd and 12. In turn, using the above logic, you can argue that he shouldn’t have gone for it on third down, because the probability of success was so low, given the play call. But it’s not the strategy that was wrong – it was the execution. Or for a non-sports analogy, hitting on the single girl at the bar might have been a good strategy… but throwing up in her drink was poor execution. Hitting on the girl with a huge fiance standing beside her was probably a bad strategy, regardless of execution. Different things, different lessons to be learned.

The reason I’m writing about all of this is because we see variants of this situation come up all the time. Arguments for why a person, team, or company should or shouldn’t do something rage, any many people throw lots of stats and other “facts” behind them. Some have bad information. Some have information from biased samples. Some interpret it incorrectly. Some confuse strategy with execution. Some make too much from too little, others too little from too much. It’s really, really easy to do – but from my experience it usually stems from trying to over-complicate things.

In cases such as the Pats game (with so many variables in play, and a low relevant sample size to draw from), I prefer simple, back of the envelope type calculations. My “best guesses” at the percentages indicate going for it was absolutely the right call. And personally, I believe the long-term benefits to the team if they had made it (demoralizing the opposition after showing them no “respect”, increased confidence, etc) outweigh the downside here (where the coach, not the players, shoulders most of the blame). I could be wrong – particularly since you can’t really quantify the latter at all.

But what I think really doesn’t matter. To repeat the important lessons: thoughtful analysis can often be more important than the underlying data for complex circumstances such as this one (in this case, most of the Sports Guy’s best points have no numbers tied to them whatsoever). And if you want to respond to a poor use of statistics with your own statistical analysis, you should probably make sure you get it right.

As part of my ongoing research, I’ve been paying close attention to developments around Twitter’s “list” feature (launched in October) – “A great way to organize the people you follow and discover new and interesting accounts.” Yesterday I stumbled upon an interesting finding (that has me scratching my ahead a bit) in relation to follower vs. list counts as a measure of popularity.

One would think that there would be a very high correlation between the two – if a lot of people follow you, you are likely to make a lot of lists. While I haven’t actually run a regression to prove that, as I’ve looked around it generally seems like a fairly safe assumption. But there’s one interesting anomaly I’ve found recently – @Marvel vs. @Agent_M.

@Marvel is “the official Twitter for Marvel Comics, Movies, Games and More.” Agent_M is the “editor for Marvel.com. Writer, blogger, loves tacos, tattoos, comics…” I’ve been watching these two accounts with interest for some time, because the former has about 43 thousand followers, and the latter has about 1.4 million. This would seem to say something important about relative popularity, and it’s interesting when the editor is more widely followed than the content.

But the “list” count tells a slightly different story. @Marvel has been added to 1,467 lists, while @Agent_M has been added to 1,234. So even though Agent_M has 0ver 30 times more followers, his account has been added to fewer lists. Divide lists Marvel is on by total followers you get 3.4%; for Agent_M you get 0.1%. ; Why is that? And what does it mean?

I have a few ideas (that basically amount to pure speculation), but there’s one other wrinkle I’d like to throw in here from my previous research. Regular readers have heard me talk about the Twitter brand engagement matrix before (see here, here, here, here, and here). The basic idea is to differentiate between brand and personality centric accounts, and broadcasting vs. conversational strategies, by using a 2 by 2 matrix. It looks like this (with a few examples thrown in):

The general message (as each of the examples represents) is you can have success from each of the quadrants. In this case, @Agent_M is squarely in the personality / conversational quadrant (upper right). @Marvel is squarely in brand /broadcast quadrant (lower left). Two diametrically opposed approaches, with two very different results.

So in this case, the personality / conversational approach has proven far more effective at attracting followers – a sign of popularity. But the brand / broadcast approach has proven far more effective (on a relative basis) at making it onto lists – a different sign of popularity. So why? Is it a different type of person that follows the different type of accounts? Does it indicate that a higher proportion of @Marvel’s followers are actually interested – and perhaps a significant chunk of Agent_M’s aren’t paying attention? What exactly is it about the type of content @Marvel posts that makes it more likely to crack lists?

Can’t claim to have the answer (at least not yet ). But I think there are some interesting questions here that I will be paying attention to…

I was just reading about the Trouble at Twitter – how U.S. visitors were down 8% in October – on Tech Crunch. The CEO mentions that they’re hoping a “slew” (currently a couple) of new features will help revive growth on the site – such as the retweet button, lists, and geolocation features. It very well might – but it got me thinking about a peculiar competitive position they are in.

For most of the last year, Twitter has done very little innovation directly. But thanks to their open API, that doesn’t mean innovation hasn’t been happening – all kinds of new tools and applications have been constantly emerging. My question is centered around how they’re going to manage this ecosystem, as the company itself starts innovating more. There are more and more data points emerging that indicate, to me, that the line between partners and competitors is blurring.

Coming at it from one side, TechCrunch highlighted in September how StockTwits is growing up and away from Twitter, as they develop their own desktop app. To quote the article, “Yes, StockTwits is slowly breaking away from the service that inspired its name.” What should the company do if StockTwits, slowly but surely, starts to compete with them directly? On the other side, Lists are a step towards Twitter helping users sift through information directly – something TweetDeck already does (in a different, and more advanced, way). If Twitter continues to innovate on this front, what are the competitive implications?

It’s a question I constantly struggle with in our research – and it seems particularly relevant when a company is facing slowing growth (or a decline) in users, before they’ve found a business model. Could the ecosystem implode on itself? Or will they find a way to keep working with each other? What do you do if a current “partner” looks like a future competitor – and what implications does it have for prospective partners / the innovation cycle?

Every once in awhile I come across a story that, while interesting, makes absolutely no sense to me. Outvesting, which I discovered through Springwise, is just such a story. What the site has managed to do is raise $5,000 in seed capital, through 100 donations of $50 each, for a yet-unchosen Irish start-up to use. Those that have provided the capital will get to vote on who gets the money. Twitter has been a big part of how they’ve managed to accumulate the funds.

If I stop right there, it sounds like an interesting story of using prosumers to provide (an admittedly small) amount of venture funding, which is clearly targetted at people with a little Irish pride. But where I find it weird is in relation to the term outvesting. As the site clearly defines it, investing is “The act of committing money to a business while expecting income or profit in return.” Outvesting is “The act of committing money to a business while expecting to get nothing in return, other than the satisfaction of giving a leg up to Irish entrepreneurs.”

So the natural question, of course, is what exactly is the benefit of outvesting over investing? Obviously from the perspective of whoever ends up with the money, it’s pretty clear – they get money for free. But is that really a better model? Will it lead to better innovation? After all, the basic premise of investing is that the incentive to earn a positive return leads people to allocate capital to projects they deem most likely to succeed. With that incentive not in place, won’t more capital get wasted if an approach like this scaled at all?

Like I said, I find it interesting – but right now I just don’t quite get it. I’d much rather see a more traditional model here – using Twitter, micro-capital-contributions, etc. to allow people to invest in small, promising start ups. But am I just missing something here?

A couple of months ago, buried deep within a post about the Memphis Grizzlies’ prosumer experiment, I made reference to how the experience of “Snakes on a Plane” using an innovative social media marketing experiment, and then failing to put butts in the seats, was being misread. In short, people seemed to jump to the conclusion that social media marketing didn’t work for movies – people might talk about it, but they don’t pay to see it. I proposed that it’s possible the movie was just bad, and you can’t blame social media for people not wanting to see it. I then tied this back to what the Grizzlies are doing, saying “remember to distinguish between whether the approach was wrong, or the underlying product was just too messed up to use it effectively.”

I now, finally, have a good example to back up the assertion. The success of the movie “Paranormal Activity” is all over the news. It is a low budget film, that’s used a slow (and staggered) release, driven by input from potential fans, to roll it out in select theatres (160 so far). Last weekend they averaged $49,379 per theatre – about four times as much as Couples Retreat (which, in fairness, was released in 3,000 theatres). You can read all about it doing a search like this, but the basic idea is that people got behind, word of mouth did it’s job, and it was a great, low-cost marketing move.

But the key lesson to me is simple. Social media allows messages to go viral – but you can’t really control the message that is sent around. So if the movie isn’t good, social media isn’t going to help you much. If it is, a lot of good things can happen. And as this trend continues, what’s really going to matter is not how you market yourself – but whether you can indeed come up with a better mousetrap.

Back in March I wrote about a potential framework for how different brands use Twitter. It’s a simple 2 x 2 matrix. On the X-axis I separate out brand and personality centric accounts – basically whether it’s a “real” human or a company / logo. On the Y-axis I separate out broadcast and conversation centric accounts, using “% of @reply messages” as a proxy for conversational activity.

I’ve continued to use this as high-level lens for looking at how companies are using Twitter, and I find it very useful. One of the main messages that derives from repeatedly doing the analysis is that while many people argue the RIGHT way to use social media is to have people-driven conversations, I believe that there are merits to each of the four approaches. To that point, today I thought I’d cover four different accounts that are the “main” faces of different brands on Twitter – each with over 1 million Twitter followers, and each residing in a different quadrant of the matrix. These four are @WholeFoods, @padmasree, @zappos, and @woot.

@Wholefoods: I’ve talked about Whole Food’s presence on Twitter before (including all the different accounts they use, scattered all over the matrix), and it really is a remarkable story. At last count the main account has over 1.4 million followers, good for the #43 spot on the Twitterholic rankings. It’s obviously a branded account – company logo, providing “fresh organic tweets from Whole Foods Market HQ in Austin TX.” And if you scan the messages, you’ll quickly see what I mean by it being conversational – almost every single message is an @reply to someone. Great example of how you don’t necessarily need a “human face” to have a conversation. @JetBlue is another great example of this – though they do provide 5 names in the bio.

@Woot: I mentioned Woot.com recently as an example of a company using Twitter for nothing more than broadcasting sales messages – one tweet a day, highlighting the daily product that’s on offer. Many people seem to argue this is totally the wrong way to use Twitter, but at last count they had just under 1.4 million followers, good for #53 in Twitterholic. In short, broadcasting sales messages can work in certain circumstances. @amazonmp3 is another great example.

@padmasree: I stumbled upon this account when pulling together a customer Twitter presentation for Cisco a few weeks ago. For those that don’t know her, she is the CTO of Cisco – and has 1.1 million followers, far more than any other Cisco account, good for #112 on Twitterholic. Scan her account, and you can see how incredibly active – and conversational – she is. @Agent_M (comic writer for Marvel) is another great example here.

@Zappos: Zappos – rightfully – gets a lot of press about their effective use of social media across the enterprise. But there are a few things I rarely see noted. First, the main account is handled by the CEO (Tony Heish), and it has FAR more followers than all of the other Zappos accounts combined (1.4 million, good for #51 on Twitterholic) . Second, he only updates it a couple of times a day, and when he does it’s generally to broadcast a particular piece of information (a quote, a picture, what he ate – whatever). It clearly stands out from the far more conversational accounts like @padmasree, @chrisbrogan, etc. While far more active, @mashable uses it’s main account in a similar, broadcast type way.

So there you go – four different accounts that are the “main face” of different companies on Twitter, and each is extremely successful. Which approach is right for the main face of your company, and all the other accounts that might be affiliated with it, all depends on two big questions – what your customers want, and what makes sense for your value proposition.

I’ve been doing quite a few presentations on Twitter lately, and in each one I admit I’m still on the fence about the whole thing. At it’s best, I find it to be a very useful source for finding information I would otherwise have never located. But at the same time, I’m frustrated by the deluge of information. While tools like TweetDeck have helped, they’ve only taken me so far. And more and more I hear from people that want to want to use Twitter, but just can’t find a way to make it as useful as they think it could be.

So I sat down to start specing out exactly what would make it better for me – and two simple things seemed to bubble up to the top, and both are very simple. I’d like each Twitter account to be able to designate (say) 5 topics that they tend to tweet about, and then each message they send they click which ones they are relevent to. And I’d like the option, on an account by account basis, to determine whether I’ll only receive tweets with links in them.

The key word in the first one is categories – just like the ones that are enabled in many blogs. What I don’t want is a constantly evolving set of tags – I don’t want to be having to go back and check every “following” tag cloud to determine which new terms I might be interested in. I’d like each person to have about 5 (example: general, social media, investing, sports, humor would be the ones I might use). I pick which of those I want to hear about from them, and only get the messages they designate for that category when they tweet. While I’m sure many people might just click all the categories to start (maximize reach), that’s valuable information to me – I’ll probably just not follow them.

This, of course, only works with scale – everyone needs to do it. And one of the likely outcomes would be total tweet volumes would decline dramatically – which, again, I would like. I really believe that many, many more people would find using Twitter more useful if this approach was being taken. I’ve tried various things around filter large groups by key words, etc. but can’t get any of them to do exactly what I want (i.e. not every post about “social media” has the words “social media” in it).

The second one is easier – in the vast majority of cases, I’m only interested in the tweets that link to another page (blog, etc.) with more information. I really don’t want the rest – except for @sportsguy33, @shitmydadsays, and a few other accounts like that. So again, on a case-by-case basis I want to select whether I’ll see tweets that don’t have links in them.

Of course, the initial set-up for this would take a serious time investment on my part. But I really think it would be worth it. Unless someone can give me a better way to filter through the deluge of information flooding my way…

Yesterday the Globe and Mail had a story about how the wealth of Canadian households had rebounded last quarter, after declines for most of the last year. While this is seen as positive news in regards to our economic health, I believe there’s an important element the report leaves out – wealth of Canadian households by age-group. My hunch is that older Canadians got a fair bit wealthier during this time period, but younger Canadians – on aggregate – treaded water while lurching into ever-greater debt. And it is this group of Canadians that I’m concerned about.

The reason I started thinking about this was simple. The article presents two key facts:

1. The resulting increase in the value of household financial assets (including shares, mutual funds, and pension assets) was the principal factor behind the rise in household net worth.

2. The use of credit also rose more quickly in the quarter, with notable borrowing for mortgages as resale housing markets picked up.

Now the argument is that, on aggregate, the increase in #1 more than offset the increase in #2. But think about it in terms of age-groups. The increase in the value of household financial assets (shares, mutual funds, etc.) flows disproportionately to older Canadians. After all, on average they have far more invested than younger Canadians. In contrast, the increased use of credit (i.e. getting mortgages) is likely driven by younger Canadians – more likely to be buying their first home, upgrading, etc.

So while it’s all fine and good to say that, overall, household debt and a percentage of net worth decreased in the quarter (good news!), what I’d really like to see is this broken out by age group. My hunch is that household debt as a percentage of net worth for older Canadians declined by a fair bit, while the household debt as a percentage of net worth for younger Canadians might have even increased.

Why this matters is because debt as a percentage of net worth as a good indicator of the risk our economy is facing – particularly if interest rates start to rise (which Garth Turner, among others, believes is absolutely going to happen) again someday. If- and again I say if because it’s just a hunch – we’re in a situation where the younger Canadians are racking up huge piles of debt to buy houses and things, and in general they have very few other financial assets, this “rebound in wealth” might be a bit of an illusion. And if it is an illusion created by nothing other than the dramatic decline in interest rates, we have more than a few data points from recent history indicating that it will not end well.

Yesterday I talked about how broadcasting – contrary to popular belief – can be a very effective strategy on Twitter. I gave two examples – Amazonmp3 and Whole Recipes – and decided I should provide a few more over the coming weeks. So today I thought I’d highlight another in woot.com, which is a pretty cool company anyway you look at it.

Woot.com, in their own words, is “an online store and community that focuses on selling cool stuff cheap.” What’s different about it is that the company sells one new item every day, and it stays up for 24 hours or until it sells out. For example, today’s offer is a Gateway computer – if you want one better get it before they’re all gone. And before I get to the Twitter account, you know how people argue all companies need to be nice, provide good customer service and all that? Check out there “what is woot?” page. Hilarious. Among other things, no you cannot talk to a live person, you can post a question on the board but there is no guarantee they will respond, there is no customer support, you can’t return anything, and if you think there’s a problem the product it’s probably your fault – so just Google it (though if you dig far enough defective products can be returned).

So basically they do everything most people recommend against, while attracting tons of traffic and customers – and tied to my underlying argument, their twitter account follows the same “against the grain” model. What they do each day is broadcast the deal -period. No @replies, no RTs, no conversations, just the deal. And they have 1,268,731 followers – good for #51 on the twitterholic rankings, right after the Zappos CEO (who as I’ve noted before, is a broadcaster himself). And what’s interesting is that even though they’re not “really” engaged, a lot of their customers are – you can check out some of the comments and stats here. Wikipedia highlights a variety of things that customers have contributed over time – status checkers, widgets, bots, etc. And if you dig through an @woot search on Twitter, there’s lots of people  promoting their various sales for them.

Now Woot might be a bit of an extreme case – but I would argue a lot of the examples from the “other side” of Twitter (extremely active, conversational, “always on all the time”) are extreme cases as well. For woot, broadcasting seems to work – and it’s an approach that makes sense given what the company does. And I’d also argue many companies could learn a lot from them…

(side note on Woot – check out this interesting “treatise” on whether or not they’re an indicator of financial stability).