Much discussion and fun was had at the July 28th installment of web analytics wednesday Toronto (#WAWTO). There was a large variety of folks who turned out – from some of the best developers in the city, some of the best strategists, and some of the best analysts and measurement scientists.
Name drop commences:
Attendees included June Li, Maciek Adwent, Jenn Fong-Adwent, Mark Dykeman, Dave Hamel, Glinski, Alex Brasil, Mike Fiorillo, Jose Davilla, Romy Klaus (in from London!), Lida and Mike Sukmanowski, Gar, and the whole Syncapse Measurement Science team – among many others. I counted some 45 attendees at the apogee. Thank you all for making it an excellent evening.
Name drop ends.
There are no stated agendas at these gatherings, only hidden ones. And indeed, I had a chance to talk extensively with Mister Glinski (@glinskiii) about our recent transformations from being such extreme analysts to becoming rounder strategists. I’ve written in this space before about the combination of evidence and product development – specifically about using analytics to make product better. Last night, we expanded on that notion of going beyond a source of proof to becoming a collaborator in the formulation of strategy – to using that skillset to make better products from the outset. That’s a subtle bit of word play – but an important one.
We’ve identified how the path from execution excellence after-the-launch and strategic collaboration before the launch is an ill-charted one. Patrick cut out his own path and is succeeding. I have too.
Such stories will be worth sharing.
This was one of possibly 500 discussions that happened over the course of the night. There was genuine overlap. Thanks all, and let’s continue to talk to one another.
Certain technologies bring about changes in customer behaviour. I’ll state that while not every behaviour-changing technology is profitable (from the beginning or ever), aiming to change a behaviour is more likely to result in a profitable technology.
It’s relatively easy for me think of such technologies. Bronze, printing press, and internet are the three that come to mind most easily.
The incremental evidence of benefits is what caused them to be adopted. That adoption, for those benefits, resulted in changes in their behaviour. We generally like to believe for the long-term good, though, for every social action there is a reaction. The environment didn’t benefit from bronze wielding humans too much. Certain factions certainly didn’t benefit from the press. And, ask the RIAA what they think of the internet.
Social internet technologies have enabled the mass expression of an already existing trait in people – the tendency for self-expression. The transference of word of mouth (WOM) from the analog world into the digital world is one of those changing customer behaviours.
As I wrote a few weeks ago on Topic Bearing WOM, a relatively small number of people are generating a large amount of content. The challenge has been to understand a relevant section of it. A very recent technology, twitter, empowers anybody to tell the world a few snippets of what they’re thinking. The result is a massive corpus of information that isn’t processable by a single human being in any meaningful amount of time.
The belief is that by enabling people to understand a large quantity of feedback, they’ll actually be enabled to respond meaningfully to the largest number of people with their limited resources. This would constitute a change in their own behaviour.
Bringing it back – Twitter is a technology which has resulted in a change in customer behaviour. It is not profitable as of yet. It could be in the future. (Lagging revenue S-curve is lagging).
It would be great for the current nest of innovators to think about which behaiours they want to change using technology upfront, and then tailor their technologies and monetization models to that end. Profit isn’t guaranteed, but at least it solves some of the ???? problem.
The next WAW Toronto will be on July 28. It’s being held on the second floor of Bar Wellington. It’s free to attend and You can sign up to attend here.
The invite:
“Developers make it possible to measure anything, statisticians and dataminers work models, IAs finesse interfaces, analysts mash and managers action. Effective Analytics takes an orchestra. Lets talk to each other and see whats possible.”
Historically, WAW’s attract a strong contingent of web analysts, social analysts (many from Syncapse), IA’s, a few dev’s, recruiters, vendors, and yes, two dataminers. And it’s a great mix. Let’s keep that mix and expand it. Additional invites to business strategists, eScientists, Marketing Scientists, and specialized developers.
“Here is a people of two distinct races, speaking different languages, with religions and social and municipal and educational institutions totally different; with sectional hostilities of such character as to render government for many years well-nigh impossible; with a constitution so unjust in the view of one section as to justify any resort to enforce a remedy. And yet, sir, here we sit, patiently and temperately discussing how these great evils and hostilities may justly and amicably be swept away forever. (Hear, Hear). We are endeavoring to adjust harmoniously greater difficulties than have plunged other countries into all the horrors of civil war. We are striving to do peacefully and satisfactorily what Holland and Belgium, after years of strife, were unable to accomplish. We are seeking by calm discussion to settle questions that Austria and Hungary, that Denmark and Germany, that Russia and Poland, could only crush by the iron heel of armed force. we are seeking to do without foreign intervention that which deluged in blood the sunny plains of Italy. We are striving to settle forever issues hardly less momentous than those that have rent the neighboring republic and are now exposing to all the horror of civil war. (Hear Hear). Have we not then, Mr. Speaker, great cause of thankfulness that we have found a better way for the solution of our troubles than that which entailed on other countries such deplorable results? And should not every one of endeavor o rise to the magnitude of the occasion and earnestly seek to deal with this question to the end in the same candid and conciliatory spirit, in which, so far, it has been discussed? (Loud cries of hear hear).”
-George Brown, Legislative Assembly, February 8, 1865
There are three major ideas on the brain as of late.
Ideas might be a dime a dozen. When I spend several hours thinking about each though, they become worth more.
I can’t and won’t talk about the first. (Nod.)
The second revolves around frustration with the difference between ‘design strategy’ and ‘business strategy’. Specifically – there being too much rigor in the one, and too little in the other. There are issues with the heuristic-based way of thinking, and with the algorithmic. I’ve finally just understood enough about the problem to be able to articulate it, and now going through that rage-phase where the more I research and the more I learn, the more I become upset about the current state of affairs. It’s not right dammit. It’s not right.
The third revolves around the decision to write a pamphlet-booklet of some sort. Somebody might say “I knew Keynes, I worked with Keynes, and you sir, are no Keynes” about that decision. I can’t bring myself to write a full on book at this time in life. So, a pamphlet, in that old Keynes style, might be the way to go. You know, sit down with the paper copy in New York and have it read by the time you’re in Vancouver sort of read. Fly-over country reading.
It’s probably around that third point that is the most agonizing. No disrespect to others who have written books – I just don’t think that I could do a very good job at writing something that long. I may be long-winded in the mouth, but frankly, I only say 16,000 per day on average (Mehl et al, Science, 317, p. 82). I couldn’t image talking for 4 consecutive days by way of a book.
That’s where I’m at.
A few major announcements in the wings and a massive weight of work – but generally feeling good.
I hope it throws some wind into the sails of people who are doing good social media marketing strategy. Absolution is frequently sought in simple numbers. The importance of activation strategy should be very clear in the charts and text of the paper.
The second is for the lack of misquotes. It would be really nice if it wasn’t misquoted.
I’ve been dividing my time between a major initiative and product development. Much of my involvement revolves around Evidence Based Marketing – and it’s literally that deadly. It’s that level of sustainable competitive advantage. It’s like a Philosoraptor armed with an RPG, riding a shark. Yeaaaaaaaaaaah.
The most interesting aspect has been the integration of measurement science with information architecture with development with creative with product development. There are continuous collisions between the desire for intuitive simplicity with utility with robust functionality with elegant design with data accuracy – all within budget and a desired launch date of yesterday.
The best business models are those which you solve a problem for a group of customers and they give you money in exchange for doing that for them. If the problems were were trying to solve were easy to solve and the integration of multiple considerations were easy – well – we’d either be doing it wrong or we wouldn’t be successful. I know that based on the quality of the discourse, the attention to detail, and a disposition towards evidence that we’re doing well. The market is voting and we’re winning.
Many of us come from a very orthodox user-centered design thinking school. Many of us come from a very orthodox product development lifecycle. Much work and time is spent doing aggressive inquiry – asking why somebody has come to a particular conclusion with a desire to understand. And when people not only come from very different professional backgrounds – but actually use different languages that within themselves have very specific meanings and biases – well – it’s all the more challenging. Much to the credit of the teams – there’s a lot more meaningful discourse aimed at solving very specific (and frequently wicked) problems.
Within socialTALK, a product that helps you manage and measure your social media presence and impact, we have an evidence based marketing experience. The initial version of that tab was designed to be very simple and laid out in an intuitive cause/effect, count and ratio, format. The initial dashboard communicated, clearly, that this is what you’re doing – and this is how people are responding. The evidence is right there. Subsequent versions of socialTALK are looking more robust – with the same attention to detail. When you put a lot of thought into it – it just naturally looks easy. (That doesn’t mean that it’s easy to actually make it that way!).
The ability to actually optimize the experience for your communities through a single interface is particularly exciting. The unification of reaction-action-reaction-action is coming together. I’m working extremely hard to make the experience of doing and learning and doing better again as elegant and clear as possible. In effect – working hard to solve your problem so you don’t have to.
The second reason why post activity has been reduced was eMetrics London. It was a whirlwind 48 hours – 18 of which were spent in airplanes and preparing for it. I went over. I listened. I said my piece. I was heard. I got some very good feedback.
In sum, I’ve been spending a lot of time feeding the shark and making sure the RPG is ready to go. Philosoraptor is always on my side.
I’m increasingly disturbed by the accuracy of Topic Bearing Word of Mouth (WOM) algorithms.
A previous study, published in this space, expressed dissatisfaction with standard sentiment analysis. My mind has since turned to the difficulty in expressing massive amounts of WOM into simple metrics that are actionable and decomposable.
So let’s just go beyond the realm of evidence based pre-optimization of marketing messages, and set the entire area of sentiment-bearing word polarity aside for awhile. It’s relevant and important. Just not the focus tonight.
Let’s turn to topic bearing WOM.
Imagine you could listen to the world, and assume that Burke’s reality is now…a reality.
If you haven’t seen the video from my ‘about’ section – here it is again. It literally is what I’m going on about:
How would you be able to make sense of the world? How would you, as a person, listen and understand all of that material? If the world is constantly changing and is what you say it is – just say.
Well indeed. So what are people saying? How do you aggregate all of that information into a format that’s understandable to mere mortals?
How could you possibly? To use a web analytics analogy – it’s akin to reading server log-files manually, one at a time, for want of a log-file reader. Or at least, a log-file reader that you don’t really trust.
The initial reaction is to do what marketing statisticians have been trained to prior to 2004: use sample statistics. I have got to ask: why use sample statistics when you have the whole data mine right there? Isn’t the only reason for sample statistics existing is for want of the database? (And nobody truly knows the overall sample size that they’re trying to project against. In the case of many topics, the n is extremely small. In others, it’s effectively undefined until semweb comes along.)
We have a massive database.
The idea of taking 1000 log files and reading them manually – and then saying that those 1000 log files are representative of the whole isn’t psychologically acceptable to most marketers. That +/- 3.1% sampling error is reinforcing your 15 to 20% interpretation error and you’re looking at a pretty dense ROE. ROE is generally not psychologically acceptable. Shows are canceled on the basis of statistical error for want of understanding to this day (and we’re 80 years into that methodology (consider radio, yup, it goes back that far)). And yet, even if you were to pitch that sampling approach and the ROE was acceptable, that really doesn’t gel because of the expectation of drillability and a broader expectation about the granularity of the data. That drillability expectation is also vital to solving the Integral Problem. If you’re a web analyst reading this, it’s just implicit within your paradigm – the way you’ve been brought up with the data – to expect that you’re able to drill into anything. It’s a bias that’s always been there.
If you’re a digital marketer or a UX strategist – you probably won’t even question that relative availability of incredibly granular data. It’s like a can opener. You just assume it. Take that away and the beans just won’t taste the same.
The big n, the overwhelming amount of data, demands a data mining approach. It demands a machine algorithm. It also demands a statistical methodology that is scalable. This heads into a domain that lies at the intersection of data mining and computability. It’s just awesome. There are many solutions, but very few solutions that will actually produce timely intelligence.
Topic Bearing WOM and the categorization of it should be, on the surface, a much easier nut to crack than sentiment-polarity, which is intensely subjective. But it’s not. If you ask 100 marketers to write a one paragraph summary of a 600 word blog, you’ll get a diversity of opinion about what the blog was actually about. Unanimity on what the topic was is extremely difficult to achieve. Not convinced? Consider the diversity of opinion about what the topic of S.11 of the Canadian Charter of Rights and Freedoms. In fact, this is a very deep problem that has been struggled against for the better part of the last decade. It’s no easier.
In the coming days, many pixels will be spent writing about the categorization of topic bearing word of mouth. There’s just a confluence of news and opinion. We might see a resurgence of opinion-mining and, in an experiment I’m doing on you – the word-of-mouth/social nexus.
So I’ll say this:
People will write. I welcome that.
Many will claim that it’s so simple. It’s not. This 892 word post has been a hike for you.
Awesome minds have been working this problem for at least 31 years, and have been really serious about it for the past six. 100% accuracy is not probable (in your lifetime). Statistical sampling is not a panacea. And even with a unified corpus even the best analysts are going to have a tough time with it. (Though, unified corpus’ are great).
Topic Bearing WOM poses a huge opportunity, and a huge challenge. It should be tackled with same amount of care that we take at Syncapse.
So enjoy.
My point stands. I’m dissatisfied with the existing algorithms to summarize topic bearing WOM. And you should be too.
Social media data. Huge amount of volume. Huge amount of complexity and simplicity in structure.
Time for a radical metaphor.
It’s like the night sky.
With the naked eye, you can see thousands individual dots of light.
And, humans being human, if you look long and hard enough, you’ll see patterns and start associating events with those patterns.
See below. I offer some evidence to back up that claim.
Of course, those relationships are one possible interpretation. (And fine. I accept where they’re coming from).
If I used something significantly more powerful, like the Hubble, and trained it at a fairly dark part of the sky – (and they did) – you’d see this:
Right there – next to the moon in that shot – is all of that complexity.
Depending on where you train that instrument – where you care to look – you’re going to get a different view.
The harder you look, frequently, the more intricacy you’ll see.
Of course, there’s the moon right next to it. Why don’t we just pay attention to the biggest and brightest thing?
Well, that’s interesting. But if you look far back enough, and wide enough, (and they did), you’d see this:
And that’s the metaphor before us.
We have instruments that enable us to look at very small bits of data, and not much else.
We have instruments that enable us to look at big, huge, massive glowing orbs. And not much else.
We have instruments that enable us to take in the whole thing. And not much else.
Social media measurement, as it’s treated by most companies these days, is entirely about listening. That is, it’s all about observation. Of course, they use a different sensory organ. But I suppose the good people at SETI listen too. (insert smirk). So, the astronomical metaphor stands. It’s an observational science. It’s not really an experimental science.
For some social media marketers – it’s an experimental science. They’re heading out to the moon and checking it out. Maybe dripping some acid into that rock to see what it’s made of. They’re some of the first ones out there.
However, even the experimenters are asked about the night sky. It’s important and has an impact. It still inspires.
So what matters? The moon? The pretty galaxies beyond it? The background radiation?
Well, they all matter. Which matters more depends on you and what you’re trying to achieve.
You only have so much focus. You have a very narrowly defined locus of attention. If you spend too much time staring at the moon, you’re not going to see faint stars. It’ll take awhile for your eyes to readjust. Different instruments in social media measurement do very different things. At least – that should be acknowledged.
That’s my point of view.
Make sense of the volume and structure that makes sense for what you’re trying to achieve.
You've found Christopher Berry. From here you can contact me, and head out to where I'm distributing content on web analytics, social analytics, website morphing,memes, and complexity analytics. You can also check out the site.
