Feb 02 0 comments
Analytics, Social Analytics, Social Media Analytics, Social Media Measurement

Social Media Return on Investment – A reply to Jim Novo

Jim Novo wrote in response to the last post:

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.

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Jan 21 1 comments
Social Analytics, Social Media Analytics, Social Media Measurement

Social Media Return On Investment

I’ve been fairly obsessed as of late with quantifying Social Media Return on Investment, or sROI for short.

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.

Jan 12 0 comments
Complexity Analytics, Social Analytics

Little Things that Make Big Impacts

The cleanest way I could explain the Butterfly Effect was to say:

“Let’s say my shoe is loose. So I decide to bend down and tie it really tighter, inadvertently creating a knot. Let’s say the next morning, I have a hard time getting my shoe on – for let’s say, four minutes. Then let’s say that I miss my bus by just one minute. And the bus has a frequency of thirty minutes. Well then – one seemingly unrelated decision, made 16 hours before and taking all of 2 minutes to execute, has a 30 minute tardiness impact 16 hours later. That’s pretty much like the Butterfly Effect. Writ Small. And Mundane. Without bad acting.”

The Star Trek: TNG way of saying it would be “There’s a cascade failure in the warp core”. But enough of the Laforging.

Cause and Effect dynamics are devilish. After all, my lateness could have been chalked up to not being ten minutes early as I normally am. Or it could be chalked up to the bus being on time, which is unusual. I like to think of the world as a whole bunch of cones converging on a single point. Taken from this point of view, there are as many explanations for something happening as there are people. We all have our perception and are all entitled to own opinions. Though, we’re not entitled to our own facts. (wink).

It’s just a matter of which model has the greatest predictive strength. Normally I’d head down the rabbit hole into a bias about multiple regression…but no. This isn’t going to be a statistical rant. No. I have something far funner to read. (I hope).

And of what implications for the social systems we create?

Twitter is an excellent laboratory to study for that.

And that’s where we’re going to get into a lot of trouble with each other, as social media scientists.

‘How one seemingly innocuous tweet could cause a cascade failure in the warp core?’ will be one of those great analyses someday. And it will be contested. Loudly. By very educated and sinecure analysts.

It won’t necessarily because they won’t accept that little things can make such big impacts. I’ll be referring them on back to this post at that point. And surely, every very educated analyst should be familiar, and indeed, should have experienced such dynamics in their own lives so as to be able to relate. The Butterfly is in the Sky.

Rather, the debate might be how much causality to attribute to the originating tweet, and how much causality to attribute to the reinforcing effects. And indeed, this sub-branch of analytics, of reinforcement-attribution theory, is still very young in marketing science literature. (I salute those of you who have made contributions. It’s just that I wish we had a unified language to describe it.). Someday I’d like to be able to say: “Take a look. It’s in a book.”

How do we understand cause, intervening variables, and effect – and how much we decide to respect where each other is coming from, is by and large going to paint future debates. I’m optimistic that there will exist one school of social media measurement practitioners that will rely on evidence to make assessments. And I’d like to be in that school. I’m certain that we can go twice as high.

There was a little theme running throughout the post.

That’s how little things can make big impacts. And how something little will make something big.

Jan 01 0 comments
Analytics

Roger Martin, Michael Porter, and Re-imagining the Production Possibility Frontier

Michael Porter, in “On Competition”, appears to emphasize the importance of trade-offs.

Roger Martin, in “The Opposable Mind”, appears to de-emphasize the importance trade-offs.

Porter defines strategy is the process of making choices about activities that results in sustainable competitive advantage. Both books make reference to activity diagrams – so there’s unity and acknowledgement that choice matters. At the core: Porter explains the ‘why’ of strategic decision making, and Roger Martin describes the ‘how’ of strategic decision making. The ultimate way of showing trade-offs, in my view, is though the Production Possibility Frontier.

What is very elegant about the production possibility frontier (PPF) is that it’s two dimensional and tells a very clear story. There are trade offs between quality and quantity. Luxury and Economy. Bread and Guns. Reporting and Analysis. Accuracy and Precision. Very easy to understand.

Simplicity wins when you’re communicating. And yet, we’re rarely handed a manual when it comes to seeking elegant solutions.

Instead of just plotting two trade-offs and calling it a day, and instead of heading into n-space and Riemann spheres and unicorns and stickers – there is an elegant way of increasing salience while retaining understability.

If you use GGOBI or PASW, you can select ’scatterplot matrix’ and you’ll get a n x n chart of all the activity-relationships. So, if you have 10 activities with tradeoffs and functions interrelating them, you’ll get a 100 charts, of which, 45 will be of use. (45 are just the mirror image, and 10 are straight line functions).

Through the magic of the post-it note, you can expand the number of activities under consideration quite a bit, and then start sorting them out on a very large wall.

What you might end up finding are a number of strategies that are very close any number of PPF curves (at least, the ones that matter). From there you can derive activity maps that correspond to any number of selected PPF curves. The elegant solution would be plot it against a single PPF curve, but I’m pessimistic (at this point) that such a solution would be common enough.

Instead of a single PPF, what if  it’s more of a Library of PPF’s?

Dec 21 0 comments
Marketing Science, Social Analytics, eScience

eScience

I’ve just learned of eScience as a result of a book entitled “The Fourth Paradigm”.

While I don’t have that much to say about the essence of the Fourth Paradigm yet, I have to admit that I feel immediately at home with this group within eScience. One of the best quotes in the book is:

“Need driven versus curiosity driven. Basic science is question driven; in contrast, the new applications science is guided more by societal needs than scientific curiosity. Rather than seeking answers to questions, it focuses on creating the ability to seek courses of action and determine their consequences.”

Substitute ’societal needs’ with ‘business needs’, and I have myself a nice bridge between eScience and commercial eScience. I suppose that’s been one of the fundamental misunderstandings about the Scientist-Practitioner: that they were only poking about out of curiosity. Science for the sake of science.

What if we were transparent about the intent to use science for purely commercial gain? Sounds Edisonian I suppose?

Much of the literature seems to be about very huge computing problems, like analyzing the data from the LHC. I’m not necessarily as concerned with problems of that order of magnitude. In fact, most business problems are fairly modest by comparison. What will, however, hold back commercial eScience, are the same forces that will hold back eScience. That is to say, the lack of unification among the fundamental tools.

At any rate – this field looks attractive.

Dec 10 0 comments
Analytics

WAA Research

Preliminary results from a membership survey suggest a strong level of satisfaction with the work coming out of the Web Analytics Associations’ Research Committee. And that’s heartening, since the volunteers do a lot of work.

I’ve participated in some of that research over the years, and it’s always pretty enlightening.

It’s good news.

Dec 04 0 comments
Complexity Analytics, Social Analytics

Complexity

I’ve spent a lot of time this week managing complexity.

And it’s gone well.

I think looking for simple and remembering the end goal are two key ingredients. Backcasting happens a lot. Expecting exogenous shocks instead of being all outraged when they happen is another.

That’s all that’s really on the mind.

That and how much code I have left to write. :)

Dec 03 0 comments
Analytics, Marketing Science, Social Analytics

The Seven Axioms and Predictive Validity

I published seven axioms over the past week – in a not so humble fashion. I’m taking the James Burke line to heart and just putting it out there.

The Seven Axioms are:

1. The purpose of analytics is to derive competitive advantage for the organization / firm / entity.
2. Data alone does not yield competitive advantage.
3. A sequence of progressive hypothesis testing is the most efficient and effective method to derive competitive advantage from data.
4. Predicting the future requires an understanding of cause and effect.
5. Correlation is not always Causality.
6. Accuracy over Precision.
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).

They might appear to be fairly straight-forward. And they are. In my opinion.

A statement like Accuracy over Precision was certain to cause problems. And it has.

If you look at the language around cause and effect, causality, and there being many correct right answers to the same problem: you get the point. It follows from the Axioms that, to derive competitive advantage, you need to be able to make predictions about the future, and the only way to really get there is through progressive hypothesis testing with accurate data, and understanding both complexity and causation.

Dec 02 0 comments
Analytics, Social Analytics

Champagne Dreams on a Beer Bottle Budget

I’m reading Sam Ladner’s thesis.

It’s strong work, and quite possibly one of the best reading experiences I’ve had since “Reading Virtual Minds”.

On Page 149, there’s a quote in explaining the common occurrence for ‘fires’ to occur as a result of low-ball estimation:

Curt: Why do they have the fires?

Sam: Yes

Curt: There could be a million different reasons if you think about it, I mean, clients coming in with aggressive timelines period or everybody will come in with big dreams, right?…Like you never lose the champagne dream even if you’ve got a beer bottle budget, right? You always dream big but you might not be, like, okay…”

And I’m in awe.

What a gem.

And I ask myself: how can we optimize and predict dreams? How we do rationalize the denominator here?

What a fascinating business problem.

Dec 01 0 comments
Analytics

Four Books, Simultaneously

I’ve been reading four books simultaneously these days.

Of course, I shouldn’t really say simultaneously. I can only read one at a time. More accurate language would be ‘jumping between four books’.

The first is Sam Ladner’s excellent thesis on the commodification of time in the new economy. It’s a pretty awesome read.

The second is Gladwell’s latest book. And it’s a manageable read because the chapters are well contained. It’s called “What the dog saw”, and that line is pulled from one of the Chapters on Caesar Milan. Fun!

The third is a seminal 500 page book about competition. And it’s a sobering read.

And the fourth is about mental structures in the new economy. And I haven’t decided if I’m going to admit that I even read it.

So many at the same time. Sometimes I get to a point in a book where I literally can’t stomach it. It’s either so dense or so depressing or so wrong that I need to put it down and change the channel. Instead of popping open the web browser and heading over to 4Chan, I suppose it’s easier to flip over to another book. Naturally I’m putting off the gratification of completing something. But, so be it.

But at least there’s apple sauce. Apple sauce to wash down all that awful, awful medicine.

And Sam’s thesis is not medicine. I’m actually really enjoying it.