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.

The results of that study were presented at Internet Week on Friday morning and can be downloaded here.

I have hopes.

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.

The third is that I hope you’ll find it useful.

In sum, take a look, and feed on back.

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.

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.

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.

As promised:

Link to the White Paper:

syncapse-sentiment-analysis

Link to the Data Set:

The Geurilla Analytics Project _ Sentiment

The paper will speak for itself.

We can discuss it here and on Twitter.

Trying to produce something simple out of something complex is…complex.

There are seven axioms that are guiding a lot of my thought in dealing with that complexity:

1. The purpose of analytics is to derive competitive advantage for the organization / firm / entity.

It follows that the purpose of Social Media Measurement is to drive competitive advantage. If the end result isn’t competitive advantage – then it has no value. That unto itself is a value statement.

Simplicity drives competitive advantage because simple is more actionable than complex. I’m often asked questions that have very complex comprehensive answers. I have to sort out that complexity based on relevancy and action-ability. Reality is always so much more complex. And yet, people can’t act on the complexity.

They act on simplicity. And if action is the vital link between the insight/competitive advantage gap – then this mandates a simplified approach.

2. Data alone does not yield competitive advantage.

A major brand might be mentioned 2.5 million times a week on Twitter alone. Having all of that data in a database is of no value if it doesn’t result in competitive advantage.

I’ll go ahead and make a statement: very few people on Earth have the capacity to read and understand what 2.5 million tweets mean on a monthly basis.

3. A sequence of progressive hypothesis testing is the most efficient and effective method to derive competitive advantage from data.

I still hold that the scientific method is the best one we have for learning right now. Someday, somebody will figure out a better algorithm. Until then, the scientific method has this wonderful blend of flexibility, creativity, and evidence.

Progressive hypothesis testing means acting deliberately with marketing messages. The goal might be known – like ‘drive sales’, but the opportunity to message a community becomes all the more useful when, over a sequence of messages, a specific hypothesis is testing. One really basic test might be: “will the community respond more to content about special features instead of content about where our spokesperson is going to be”.

Acting deliberately isn’t always possible, especially in a reactive world, but there’s opportunity to derive learning or insight that can drive the next wave. In social media, the tempo is that much higher. This isn’t 2-year website redesign land.

4. Predicting the future requires an understanding of cause and effect.

At the core of prediction is previous cause and effect. If I touch a hot pan, it will cause my hand to burn. Therefore, I can predict, by touching a hot pan, my hand will burn. Very predictive.

Not everything, especially in marketing, is so clean. At some of the more basic roots – If I spend 500,000 dollars on commercials and run them constantly, I will get 11 GRP. If I get 11 GRP, I’ll move 25,000 toasters.

Statisticians, or Social Science Statisticians, are so incredibly jaded by such simple linear models. Sure, you might get 11 GRP’s, but not all GRP’s are made the same. Moreover, what type of commercial are you going to run? Will it resonate with those who are already looking for a toaster? Will it cause people to suddenly desire a toaster who do not have one? Will it cause people who want to judge others to go out and buy the toaster so they can have a plank to judge? Will it cause people who already have a perfectly good toaster to want to buy, and remember, that toaster – five years down the line to buy that brand?

So frequently, especially when a cause-and-effect model doesn’t jive in our own minds, will we go out and try to discredit other models by introducing other factors that we ourselves deem salient to the situation.

In the end, it comes down to R Square. The percentage of the variation our model predicts the outcome of a variable we care about. A big reason why I rattle on about the importance of goals and KPI’s is because we can anticipate a world where everybody will care about the R Square.

This is especially true in Social Media Measurement. Many people speak of things ‘going viral’. Yet, how many people have truly explored the causes of going viral? There are multiple causes of why something goes viral.

Predicting anything comes from cause and effect.

5. Correlation is not always Causality.

Even a high R Square doesn’t guarantee truth. There might be a great correlation between affinity for John Cena and a love of peanut butter – but I’d be hard pressed to derive a clean causal link between the two. (Perhaps John Cena’s fan base is concentrated in regions where peanut butter is given to young children early?). Unlikely.

Correlation is useful, but without overarching respect for your own theory and your own mental models – it’s dangerous.

This is especially true in Social Media Measurement – where correlations abound – but causality can be fleeting.

6. Accuracy over Precision.

Would you take a thermometer that is right 95% of the time and you were fairly sure that it was always off by 5 degrees, or would you take a thermometer that is right 50% of the time and you were fairly sure that it was always off by just 0.01 degree?

In Social Media Measurement you can have it both ways!

7. It is possible for there to be two optimal, equally true, answers to a problem. (And Sometimes More!) (X^2 = 4, x=-2, 2).

If there are two equally true answers to a problem, surely there could be millions of wrong ones. I’m certain that will make certain people happy to hear.

In Social Media Measurement, it is perfectly possible for two solutions to be both equally right.

A specific instance would be the sentence:

“The boy crossed the busy road carefully.”

I’ll ask you: What was that sentence about? I can see a situation where one of you says, “The boy” and another person says “The road”.

Well, in my view – they’re equally true.

There are multiple right answers. There are multiple wrong ones too.

Simplexity.

The quest for simplicity is complex.

Simplification involves obliteration. It’s possible to take a column of 300,000,000,000 numbers, a massive amount of information, and summarize them into a single figure. In fact, there several numbers that can describe the central tendency of all that information: mean, median, mode. We have a number that describes dispersion of that data: standard deviation. We have numbers that describe the peakyness: kurtosis.

What should get obliterated in the quest for simplicity?

Going back to Axiom 1, variables that do not matter to competitive advantage should be obliterated. Going to Axiom 4, you need to identify the variables that cause a desired effect, in particular, looking for reinforcing effects, all the while knowing that Axiom 5 applies (your theory of how the world works could be wrong even if mathematically it works) and Axiom 7 – it’s perfectly possible for two models to be equally right.

It all comes down to an acknowledgment that Axiom 2 is right: data alone isn’t going to yield competitive advantage, and Axiom 3 is the best way to turn that data into insights that drive competitive advantage – a sequence of progressive hypothesis testing.

I don’t believe we’ve even begun at the beginning yet: what is salient in social media measurement?

We’ll need to get all of those on the table before we can talk about causality, reinforcing effects, and come out to a resolution. I’m pessimistic that there will be a single resolution that will suit everybody: but there is probably a solution that will satisfy 90% of the situations.

What say you?

Time for a story. It’ll be fun and egregiously self-deprecating.

My first Real Time Simulation (RTS) game was Age of Empires I, back in 1998 or so. And I loved playing it online. Problem was – the online experience really sucked because most of the players were jerks. The experience sucked and the game lagged like hell.

By 2000 I had joined my first gaming community. They were referred to as gaming clans, and you could identify its members by having a telltale tag at the front of a name. MNPE_username, JCV_username…and so on. There was also this entire notion of being anonymous. It was highly prized. Back to this in a bit.

Gaming clans were never really intended to be part of the multiplayer gaming experience. At least, I don’t think Ensemble Studios ever really foresaw them. People, in a way, spontaneously formed them. The most dedicated ones would then register a domain name and host Internet forums. I don’t think we even knew the term social media at the time.

I regret not keeping a long enough list or better records – but there were many, many, many clans. The vast majority had very short half-lives. They would be founded, exist for 3 weeks, and then die a cold death. Some would persist. They’d grow and thrive. And then later, they’d fragment and explode. They died a hot death.

A very few would be self-sustaining. In effect, they’d be founded, they’d get 7 members, and from there they would be successful…continuously refreshing the membership over time and maintaining just the right size to insure familiarity and community.

Different clans had different utilities for their members. Some of them were Elite Clans. In effect, a small group of 7 to 15 people would get together and trade insider skills and knowledge. You had to have a certain rank to even apply to be part of them.

Other clans were friends only. You were invited in.

Some clans were open. Anybody could join. So long as you were fun to play with, you were pretty much guaranteed to be in.

Clans served a few purposes. There was a reason why so many people joined them back then.

For one – they provided a source of gaming quality control. The online experience sucked back in then – with people flamming and dropping, cheating and yelling. More often than not it was unpleasant. If you knew a group of people who behaved reliably well, then the experience of the game would be all the more better.

Secondly – they provided a source of quick games. If a group of people routinely logged in at the same time, chances are they’d play together and have a good game. The tag provided the boundary for friendship. Or, it guaranteed a certain level of competitive quality in team play – which was important for people’s ladders rankings and tournaments. Gaming was (and probably still is) serious business.

Thirdly – many evolved to form actual communities over time, and evolved a set of social norms and commonality. For a very long period of time, pre-Skype-Facebook-Twitter-YouTube-MySpace, they formed a type of safe-place. You could remain anonymous and still be part of a community. And it was as though somebody in Korea was just next door.

After a game had become stale – the community frequently remained.

You might ask – “Why didn’t people play with their friends?” (Like they do now – like on Xbox Live…?)

Not everybody had broadband connections at the time. It was still rare in countries other than Canada, South Korea, and Netherlands. Secondly, not many people played RTS games, either. For sure, some people knew other people ‘in real life’, but this was the exception, not the rule. Finally, Xbox live didn’t exist. Console games were consoles. Online games were social in a different way. The distinction, I suppose, in the transition from kids in front of Nintendo to young adults drinking beer in the basement playing a console game – and the notion of virtual community anytime/anywhere gaming: was a distinctive split. You could only get an online social experience through the PC. This unto itself caused a different dynamic.

You could have an authentic community experience through a clan while being anonymous. Many people back then wouldn’t imagine doing anything online unless it was through a pseudonym. (This was pre-Facebook).

Many of these communities gradually faded. For one, Ensemble Studios began incorporating clans directly into the gaming experience. While this was nice to a certain extent, they removed a few sources of quality control, and an important concept of continuity of the community in case the leader leaves. Ensemble Studios, much to their credit, actually did what we now refer to as ‘community outreach’. And they were actually quite proactive about it at the time.

Clans continue to thrive in the First Person Shooter (FPS) genre, where the quality of the game is heavily dependent on the quality of those you play with, and where dedicated servers are highly desirable. Sony, through it’s massive FPS game ‘MAG’, might still accidentally cause a renaissance of the early-era clan.

There are quite a few takeaways for social media.

People sort other people. Not always. But often. In the instance of clans, people sorted themselves into groups based on the experience they wanted but couldn’t get otherwise. I suppose if the parts of what remain of Ensemble wanted to innovate, they could introduce an jerk-sort into the experience. Those who have propensity to ruin an experience for others could be grouped together and be miserable with one another in random match-ups.

Communities are much more than joint-utility seeking entities. To see them only in that light would be too simplistic of a model. But communities do, at least, seek joint-utility. Even 4Chan has its bouts of concern about the cancer that is killing /b/.

Communities might also self-organize so as to compensate for a deficiency in a corporate offering. Perhaps some companies can understand which deficiencies exist.

Finally, communities can outlast their original purpose. For how long they can survive I’m unclear about. But they do.

Community Seeking and Online Gaming in the Early 2000’s were really interrelated. I’m curious to watch how this next wave of social gaming will change the landscape that much more.

This is an interesting line of thought Christopher, perhaps I can help with a bit of a framework. And you’re right, product is the root of Marketing decision making. I hope my attmept at a chart below makes it through the CMS without breaking…

Brand for any product is a continuum between Product-centric and Image-centric, example:

……….Product Centric………..Image Centric
Beer…….Sam Adams………………Budweiser

Image-Centric Brands tend to have commodity status, which begs the need to differentiate by creating some kind of unique Image. Product-Centric Brands differentiate on hard Features and Benefits.

If you think about the Marketing for Sam Adams, it’s all about ingredients and customization. If you think about the Marketing for Budweiser, it’s all about wanting to be like or associating yourself with the people or images in the spot – “Yea, that’s me!”.

Now, if you think about Social success stories, you find that they really gravitate towards Product stories, and not Image stories. Image stories are too easy to destroy in the social fabric; product stories bubble up *from* the social fabric.

So the success of social will largely be determined by where your Brand is on the continuum between Product-centric and Image-centric.

And here we arrive at a bit of irony.

Many of the most successful Social “Campaigns” happen when the company does absolutely nothing overt in the social space – see Apple, and many other Product-centric Brands.

And some of the lamest and most clueless Social campaigns have been from commodity Image-centric products that tried to do something overt in the social space – see various packaged goods.

Meaning, you don’t really have to *do anything* to get ROI from social if you have a successful Product-Centric Brand – the ROI is infinite because there is no spend. And the ROI for an Image-Centric Brand is likely infinitely negative – any spend will never generate enough incremental sales to pay for the spend.

As far as Marketing discplines go:

……….Product Centric………..Image Centric
……….Direct Marketing……….Mass Marketing

Direct has always been a Product-Centric approach; it has to be or the Math doesn’t work; it’s Feature / Benefit driven.

That’s not to say companies employing Direct do not have “Brands”, they most certainly do. But the Brand is very tightly tied to product, not so much with “me too” Imagery.

Make sense? Help in your quest?

This both makes sense and helps.

The nature of the product is certainly a dimension in this problem, and your model rings true.

So much of what we really consume is in our heads. Certainly, there’s often a physical aspect of most products. For certain products, it’s about how we feel about them. And to a certain extent, how we feel about a product is what we are and what our friends are like and how much our friends really influence ourselves. I can point to product diffusion studies in marketing science around artist popularity and DVD sales to back that up. The nature of the social graph is as important as the social impressionability of that social graph.

If you’ve listened to any commercial music since the advent of the Bit Torrent, you know that there isn’t really a qualitative explanation as to the variation in popularity of a given DVD. What we consume is sometimes, though not always, in our heads.

For a commoditized product that really isn’t differentiated based on the general attributes or quality of it – I can see where a company will want to compete in your head and for your friends. Of course, not everybody is as susceptible to social effects as others. Beer choice amongst friends versus beer choice amongst professional associates might be one.

I think we have to consider where a product is in terms of adoption is important too. Just because something on its own might have better features than another – it doesn’t guarantee successful diffusion.

I’ll need to think longer and harder whether or not the ROI curves asymptotic.

But, as of now, we have ourselves at least three dimensions.

At the root of the issue is a clash of belief systems.

Marketing thought is dominated by two rather large models of thinking. You have the Direct Paradigm and you have the Brand Paradigm. By Paradigm, I mean simply a way of looking at the world. Let me take one step back, and then one step forward.

People, in general, can only hold so many variables in their heads at the same time. So, we abstract. We’re supposed to derive some forms of causality that are important, throw that into some overarching architecture, and then use that framework to make decisions in a quick manner. When two people first approach a problem, and come at it from different paradigms, sometimes it can get nasty because there’s some questioning root assumptions.

The language you find in the Direct Paradigm is that the last action somebody took towards a sale is the most important. They point out, quite rightly, that repeated human behavior matters the most. A human in motion will tend to remain in motion. I wouldn’t make the accusation that all Direct people can only hold the short term in their minds at any given time. In fact, some of the best contributions and strongest predictors of campaign success are based on a very sophisticated understanding of time.

The language you find in the Brand Paradigm is that how somebody feels is the most important. They point out, quite rightly, that if somebody hates a brand, they won’t buy that brand. There’s a set of key performance indicators, invented in the early 1930’s to handle radio measurement, that attempt to quantify that. Likeability and message recall are the two big ones.

The Direct Paradigm tends to value deductive reasoning, and this is form and function as a result of having all the data. It’s inherently about data mining.

The Brand Paradigm though relies on inductive reasoning, because they’re forced to use sample statistics to perceive the world.

If you were to review what adherents of the Direct Paradigm are saying about sROI, they’re wagging their fingers and their tongues. They point out that Dell only made a fraction of their direct sales from Twitter. They point to the lack of conversion from the sales is proof positive that social media ROI is too low to justify intense spend.

If you were to review what adherents of the Brand Paradigm are saying about sROI, they’re clapping. They point out to successes like Best Buy and argue, quite rightly, that it impacts how people feel about a brand. They point to a fragmenting attention economy as being the main reason for intensifying social media spend.

So, which belief system – which paradigm – is right?

I’d argue that they’re both right.

I’ve read about an era in marketing when both the direct response and word of mouth dynamics were scientifically optimized. In my minds eye it was quite an exciting time.

I think we have to understand, fundamentally, that the Direct People have it right. They’re quite right that if you don’t have the right message to the right customer at the right time – you won’t get a sale. It’s about having the opportunity to convert being there at the right time.

I think the Brand people also have it right. How people feel about a brand is important. I’ll go so far as to say that how the friends of certain people feel about a brand, and how they consume that brand, is also a factor. Sure, you might put the right message to the right customer at the right time – but if I hate that company because they pulled their sponsorship for the Reading Rainbow…I’m not buying.

I’m optimistic that within social media measurement, through this quest for social media return on investment – that we’re going to find a satisfactory model that will be easy enough for 95% of the population to understand. That it’s going to incorporate just enough from the Direct Paradigm and just enough from the Branding Paradigm to work. In fact, what I’m seeing is a real opportunity for a Third Paradigm.

What if, under this Paradigm, we selected the most predictive elements, instead of what would be the easiest elements? What if, built into the model, we had a larger number of variables to chose from in constructing our general causal model? What if we acknowledged the dual nature social media?

I’ll add one factor that I think is especially salient: the nature of the product or service itself. There are certain products that are completely social in nature. It’s for this reason that I believe sROI is actually going to vary quite a bit depending on the sector and the competitive set.

The ultimate calculation will depend, quite heavily, on how much is borrowed from both Branding and Direct.