It’s an excellent way to present data in a very accessible, very explorable way. I found it inspiring.

The call to action is “create your chart now”. A very good, honest, call to action.

The technology adoption S-curve can be a slow beast, and expectations of growth have persistently outstripped actual adoption, at least in North America, and especially in Canada. Adoption has a few drags on it in North America and Europe. No such drags exist in Asia.

The chart below compares all the countries smartphone penetration. (Click to embiggen)

That chart masks underlining maturity in each country. The chart below compares m-commerce ‘at least one time’ usage across Germany, China, and the United States. The big three economies. (Click to embiggen).

That’s a good snapshot. M-commerce is thought of as a pretty big risk in North America.

In many ways, the melding of couponing with check-ins was the right bridge into mobile for the times. As we all watch Groupon careen into the inevitable mess, we all ask ‘what’s next’. I ask, ‘where’s the utility’, ‘how can mobile be used to salvage previously wasted parts of my day?’

I look to m-commerce as being predictable. Certain firms, like Plastic Mobile, have extensive experience with eCommerce, and understand mobile. They’re not going to replicate the pain of the nineties. There’s an inevitability to it.

Though, painfully punctuated.

This would have happened sooner if it wasn’t for the double whammy of policy plus recession.

I’ve been asking myself that, part of my series on being skeptical of root assumptions and theories, and took the opportunity to work through something in a foreign land.

I spent some time in real Mexico, and couldn’t resist a first exposure. I went to a ‘Mega’, without really knowing what it was, and took in an in-store experience. The brand was unknown to me pre-exposure. A rare opportunity. So I walked in, and the first thing I notice?

Banners hung throughout the store told me that everybody wanted to be Julio Regalado.

I have no experience with the brand or the frontman. But everywhere, literally down every isle, there he was, with that very enthusiastic face of his. He, himself, was branded and associated with a Stork logo in the background everywhere. It’s not as though the concept of a living icon is new. Example. Example.

Branding was certainly happening. I could feel it happen in my brain.

I got back to Canada and looked him up on YouTube. I found this below, and was impacted further:

Note, of course, that I’m Canadian and the target audience is Mexican, to be sure. 100% of the variance shouldn’t be explained by culture alone. So granted, there are certain aspects that are lost in translation.

No other brand I was exposed to, be it Tecate (which I believed was a form of Coca-Cola in my hotel mini-fridge and didn’t drink it for two days), or Sol, caused the same sort of branding. The Bio-Shaker, which is arguably one of the most hilarious products ever, is barely mentionable in this space. I was certainly exposed to a lot of advertising during those seven days. So there’s variance of effectiveness within the exposure set.

Just because some companies have measured branding effectiveness as being unaided recall, aided recall, preference, and likability doesn’t mean we should continue to say that these three indicators are the causes of branding. I’m not bashing the measurement scheme inasmuch as I believe that there’s so much more unanswered here on the causal side. Branding is not simply recall, preference, and likability. Indeed, the number of factors and features of brand positioning alone are many and feels very complex. It’s not insurmountable. There are probably as many heuristics as there are brand managers.

Sample size of one and kitchen table anecdote fully admitted – I’m asking the question and looking for answers.

In the meantime:

A few points you might care about.

The problem with communication flow between academia and industry is not an academic problem. They’re doing just fine, and it certainly appears as though the money is flowing. The problem is on the practitioner side, and our ability to understand, interpret, and attempt to derive some value from their efforts. It would be great if bigger and more meaningful bridges could be build between industry associations and their associations. There would be benefits on both sides for a subset of both. So long as each side understand the terms of the relationship, very, very good things would come out of it. This is an ongoing effort.

Selection bias. The purpose of sampling is to explore a very specific line of inquiry on a sample of people by way of a survey methodology. For the survey results to be accepted as valid, and for the math to really make sense, those opting in to respond ought to be representative of the whole population. Of course, the great problem is that it’s nearly impossible to be absolutely certain that your sample is reflective of the population at large. There is a check. But it’s not perfect. In the end, it’s a wicked problem that applies to social in a very specific way.

Marketing Science is challenging because the underlining black boxes is dynamic. At least in Chemistry the pH scale is the same in 1989 as it is in 2011, and will be in 2090. Same scale. Not so in the Marketing Science. You can finally get a model up and predicting, and the world shifts right from under you. It’s that shifting that I find just awesome.

There was a lot of inspiration here, and a lot of tangential thought that I’m looking forward to. Several mysteries are afoot.

I thanked Dina Mayzlin for her contributions to our community.

I thanked Iyengar for his contributions to our community.

Meeting both of them were highlights. They’re extremely friendly have excellent presentation styles.

The conference will be held in Boston next year. For me, it’s a short walk, Porter flight, and a subway ride in. I hope to see more people from industry there to shake push it along.

I took in the second half of the MSI anniversary track.

The MSI, or Marketing Science Institute, is a 50 year old institution. It’s at the nexus between business and marketing science academia. As a result, it has money and databases. Because it has both, it gets to set research priorities that are influential.

The 90 minute track I took in had to do with page 8 of their list, “Managing Brands in a Transformed Marketplace”. I can’t resist.

Branding is a problem for Marketing Scientists for many reasons. It is not transactional, it may be measured in many ways, it manifests itself in many ways, and it subject to time lags.

Direct attribution folks are able to understand many things owing to the extremely clean nature of their data. They have a lot in common with finance in that respect. Direct attribution folk tend to gravitate towards direct marketing. It’s all very clean.

Branding is not transactional. A large amount of money goes in. What comes out? What comes out is very difficult to quantify. Sure, there are brand tracking studies and instruments like the Net Promoter Score. These are based on survey methodology. And those methods are not explicitly prospective, predictive, or linked in a way that an accountant can understand. Worse, such tracking is typically deemed ‘market research’, not ‘analytics’. There’s a huge range of freedom and flexibility in how to define success. It’s extremely resistant to standardization.

The perception of brands and branding efforts vary by country and culture.

The monetary effect of branding is cumulative and heavily time lagged. The effects last well beyond the initial investment.

Problems are good, because they raise opportunities.

We need a more structured way of thinking about brands. It should exist.

The first day went very well.

Among other reasons:

• 43% said “My wall was becoming way too crowded with marketing posts and I needed to get rid of some of them”.

• 38% said “The content became repetitive or boring over time”.

• 19% said “The content wasn’t relevant to me from the start”,

• 17% “The company’s posts were too chit-chatty – not focused on real value”.

All of these reasons cited go directly to the concept of relevancy.

When does content become too much? When it ceases to be relevant.

When do you want to make some content go away? When it ceases to be relevant.

When does content become boring? When it ceases to be relevant.

This competition for relevance is one half of the defining challenge for social media marketers. To a certain extent, paying for the privileged of pushing an unwanted message into the yawning maw of consumers provided a specific degree of insurance. Relevancy was always theoretically important to marketing effectiveness. It’s just that it wasn’t a big enough factor to truly matter. Or rather, it didn’t matter to enough people.

It’s not a new problem. Rather, it’s an intensification of a latent one.

The price of that brevity is abstraction.

Jargon unites people in as much as it alienates them from each other.

I’ve experienced this first hand – visiting data miners, market researchers, marketing scientists, entrepreneurial developers, and brand managers. It becomes very easy for people to dismiss entire modes of thought purely because the jargon doesn’t resonate.

Deep within abstraction are generally understood understandings. For instance, the term ‘qualified traffic’ means something very fundamental to a search marketer. The same term, ‘traffic’, is perceived a fair bit differently among web analysts. And in terms of CRM people – well – they don’t view ‘response’ as a form of traffic.

You can see the series of commercials here.

Specific elements:

• Divergent use of a violent panda.
• Repetitious use of a song across all five ads.
• Consistent direction (ie. over the head reaction shots from Panda POV).
• Desired behavior demonstrated (“Get another one…”).
• Divergent tag line, phrased in the negative. “Never Say No To Panda”, which contradicts the affirmative bias we’ve had for years.

Brilliant – check it out.

For specific firm, I will go from being worth a stable $170/month subscriber, complete with PVR, to being worth nothing. I’m switching my Internet to a non-UBB restricted wholesaler. I will continue to spend $10/month for Netflix. I will get my live TV with the “free”, Over-The-Air broadcast signal from CN tower, which I have a clear view from. Dedicated ad impressions will take a pretty big hit, as the number of must-see, full attention shows are less than 5. I can’t anticipate myself suffering through TV without a PVR.

I can’t imagine deliberately exposing myself to an abusive medium any longer.

That attitude ought to concern broadcasters and marketers alike. I’m not alone in holding it.

The decision hasn’t been easy. But a lot of factors contributed.

The first is attention. I almost always have a second screen in front of me. My home office is positioned so I can see the TV, dead ahead. My couch is positioned in front of the TV. From my office, the TV competes with my laptop. From my couch, the TV competes with my iPad, and with journals. Finally, the content on TV just couldn’t compete.

I’ll go 2 hours with the TV on, looking up only to fast forward through the most annoying bits (what the hell is happening on CNN?). What was I paying for? That’s the second reason: the annoyance of having to fast forward using a PVR.

And that’s the third factor – choice and control. I don’t understand why the on-demand standard isn’t the ultimate standard.

TV isn’t dying. It isn’t dead. But it’s lost the right to be a constant.

But the other two screens are winning. Cutting the cable is the tipping point.

I’ll let you know how it goes.

The goal of target setting is to make a statement about a desired future state – with or without a forecast.

Targets are political artifacts. You can read all about such dynamics in public policy here.

Forecasts, ideally, are scientific artifacts.

The interplay between forecasts and targets is particularly interesting. Those who produce sophisticated forecasts should understand that the motivation of those probing models is to assess whether or not a future state is possible, or, in certain situations, just how probable a given scenario could be.

Don’t become trapped into the mindset that a trend is locked in permanently. Actively explore what needs to be true, in which circumstances, to produce a better outcome. Most importantly, and I’m learning this the very hard way, make every attempt to make affirmative recommendations.

• New information
• Executable
  
• Causes action
• Profitable

Or, more detailed, an insight is:

• A piece of information that you didn’t know before, which -
• Can feasibly executed, culturally acceptable and of a scale relevant to the firm, and -
• Causes a decision to be made that wouldn’t have been made otherwise, and -
• Results in profit or a sustainable competitive advantage

I’m finally happy with this definition. It aligns with the best innovation rhetoric very nicely and is generalizable to both design thinking and analytics communities.