What SeatGeek does is gather information from hundreds of different secondary market sites for event tickets, and apply an algorithm to it in order to predict where prices for individual events will go. The company asserts that it is directionally correct about 80% of the time (currently limited to MLB games and music concerts). The value for customers is rather obvious here – if prices are going to go down, you wait; if they’re going to go up, you buy.

How SeatGeek itself will perform is unclear – there are a number of business model challenges and contradictions I discuss in my research paper. But there’s one particularly important long-term implication, which spans industries, that’s worthy of more attention – the role of data in regards to the customer relationships.

In many traditionally B to B to C marketplaces, the Internet (among other things) is allowing the “middle B” to solidify their hold on the customer relationship, and leverage data to become ever-more popular. The challenge for the “first B” in many industries is to gain access to that data for their own competitive advantage. There are a variety of different ways to do this that my research team is currently exploring.

In SeatGeek’s case, professional sports teams, various musicians and others are the event originators – the “first B”. Companies like TicketMaster are the “middle B”. In the long-term, it may well be that SeatGeek (or a company like them) empowers the event originators at the current intermediaries expense. I’d hypothesized we might see the same process / battle play out in, say, the book industry as the Kindle (and other such devices) grow in popularity. This morning I was reading the Economist, and I found the following quote from “a Hulu for Print” quite interesting…

Publishers are irked at the prospect of formatting content for multiple devices with slightly different requirements—a problem that will worsen. They are even more irked at the current market leader, Amazon, which returns as little as 30% of the sale price of a digital magazine to publishers and provides less detail about customers’ reading habits than they would like.

For further reading on SeatGeek on the web, I recommend this, this, and this. An interesting overview of the secondary ticket market is here.

The idea behind The Hipstery is simple. Instead of offering a bunch of t-shirts for customers to pick from, the company invites customers to fill in a survey. They then take these results, apply an “innovative style algorithm technology” to them, and select a t-shirt that seems to suit the customer. Think of it as a way to surprise yourself with a gift.

We’re cautious not to overstate what The Hipstery is actually doing here – the humorous tone of the site makes it appear that the algorithm might be part of a joke. But there’s no reason it need to be. We think the idea behind it points towards an interesting opportunity popping up all over the web.

Without getting into the details of the framework (which is where the Johari Window, among other things, comes in), the idea is to create “blind spot” – things you can no about your customers, but they don’t know about themselves – and capitalize on them. Doing so requires data, and analytical capability. We’ve already seen this happen in online dating – while sites like Facebook offered certain advantages over services like Match.com, it’s eHarmony that captured the blind spot opportunity (and found a business model). If you squint a bit, you can make the argument that The Hipstery model is to Threadless in the clothing industry what eHarmony is to Facebook in online dating.

There’s of course much more to the story than that. But the overall message is that The Hipstery is a very interesting company to check out, and many organizations should consider looking for their own “blind spot” opportunities to exploit.

I was reminded of this when I stumbled upon StickK today. I’m always fascinated by online business models where making people pay for something creates a better experience for them than offering it for free. I’ve seen these properties in dating sites, job searches, and a variety of others. Then I got to thinking about how many people have goals they want to acheive, but need an incentive to actually follow through. In turn, what if you set-up a service where people voluntarily offered to cough up some of their money if they failed to achieve a goal they set for themselves? And what if you had most of that money going to charity, so they’d actually not feel that bad about it.

That seems to be exactly what StickK is. I know they didn’t steal it from me – notably because the site started before I’d ever thought of it, and I can’t remember if I ever wrote about the idea anywhere anyway. But even if I had wrote about it, and the company emerged after that, AND I could make a compelling case that my ideas drove the whole thing (again, that’s clearly not the case), I wouldn’t be upset about it anyway. I would have thought “hey, I shared my thought – good for them putting it into action.” And I’d realize that there were probably many, many other people that had thought about it before.

Of course, then when I went to the site, I realized I wouldn’t ACTUALLY pull the trigger on taking part. Anyone have any thoughts on whether this idea is a good one – and how the site is doing?

For example, one easy technique for locating new “lighthouse case study” ideas is searching through various contests that are floating around out there. Two of the more well-publicized ones recently are the TechCrunch 50, and the Mashable Open Web Awards. The first provides a nice list of start-ups to consider, and the various stories swirling around them point to many more. Great resource. I also had high hopes for the Mashable Open Web awards as a pointer towards great case studies. But thus far have been mostly disappointed.

The reason is that I suspect the system is being gamed, and I don’t know what to make of many of the results I’ve seen. For example, I was really interested in “Twitter user of the year”, particularly since there are many, many interesting Twitter users out there. I watched the updating list for a few hours last week. @ivetesangelo proved quite popular. In fact, my rough count indicated @ivetesangelo was getting about 60% of the votes. @sookiebontemps and @tommcfly were also quite popular. I greatly suspect that the system was being gamed. I scanned through a variety of other lists, saw voting patterns that defied all logic (at least in relation to what the contest intended), and moved on.

In some ways it’s not really fair to compare the two directly – but since both are seeking to identify the “best” in a relatively similar space, I think the difference between the two experiences is important. By applying some structure, expertise, and filtering, TechCrunch provides a lot of value to me; by being almost entirely open, the Mashable Web Awards are left open to gaming, and various different interpretations of what’s going on. I have a hunch of some expert panel had narrowed down a list of the best things going on out there (perhaps guided by voter input) and let people work from there, the output would be more interesting – and useful.

I see this kind of thing pop up a lot. In general, I find open to be good – to a point. But there’s a reason things like hierarchies emerged out of self-organizing systems.

With that in mind, think about most online job sites. The way most operate is to (in relation to the above example)  make the very attractive female pay to post her ad, while allowing any suitor to send her “applications” for free. It sounds rather absurd when you think about it that way, but that’s exactly how most work.

LocalBacon is a new job-search site (profiled at the TechCrunch 50 conference) that’s trying to do it the other way around. Employers can post jobs for free, and applicants must pay something to apply. Now there are a lot of wrinkles to iron out around pricing, and various other interesting features that might be worthy of discussion, but I just want to focus on the simplicity of the general idea. Stop making one customer pay, make the other customer pay, and increase the value for both parties… while perhaps creating a sustainable business model in the process.Nice.

Would love to hear examples of other businesses / industries such an approach could work in…

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.

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.

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…

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?