What Comes After Generation Z?

We’re running out of alphabet.

The latest generation is Generation Z. They were born between 1995 and 2012 – according to one demographic primer. So, what do we call the generation born from 2013 on? Z+One? Do we go with an Excel naming scheme and call it Generation AA? Or should we just go back to all those unused letters of the alphabet. After all, we haven’t touched A to W yet. Thinking along those lines, Australian social researcher and author Mark McCrindle is lobbying for Generation Alpha. It’s a nice twist – we get to recycle the alphabet and give it a Greek flavor all at the same time.

Maybe the reason we short-sightedly started with the last three letters of the alphabet is that we’re pretty new at this. Before the twentieth century, we didn’t worry much about labeling every generation. And, to be honest, much of that labeling has happened retroactively. The Silent Generation (1925 – 1942) didn’t call themselves that right off that bat. Being Silent, they didn’t call themselves anything. The label wasn’t coined until 1951. And the G.I. Generation, who preceded them ((1901 – 1924), didn’t receive their label until demographers William Strauss and Neil Howe affixed it in 1991.

But starting around the middle of the last century, we developed the need to pigeonhole our cohorts. Maybe it’s because things started moving so quickly about that time. In the first half of the century we had the twin demographical tent poles of the two World Wars. In between we had the Great Depression. After WWII we had the mother of all generational events: the Baby Boom. Each of these eras brought a very different environment, which would naturally affect those growing up in them. Since then, we’ve been scrambling madly to keep up with appropriate labels for each generation.

The standard approach up to now has been to wait for someone to write a book about a generation, which bestows the label, and then we all jump on the bandwagon. But this seems reactive and short sighted. It also means that we get caught in our current situation, where we have a generation that remains unnamed while we’re waiting for the book to be written.

We seem hooked on these generation labels. I don’t think they’re going to go anywhere any time soon. Based on our current fascination with Millennials, we in the media are going to continue to lump every single sociological and technological trend into convenient generationally labeled behavioral buckets. So we should give this naming thing some thought.

Maybe we could take a page from the World Meteorological Organization’s book when it comes to naming hurricanes and tropical storms. They started doing this so the media would have a quick and commonly understood reference point when referring to a particular meteorological event. Don’t generations deserve the same foresight?

The World Meteorological Organization has a strict procedure: “For Atlantic hurricanes, there is a list of male and female names which are used on a six-year rotation. The only time that there is a change is if a storm is so deadly or costly that the future use of its name on a different storm would be inappropriate. In the event that more than twenty-one named tropical cyclones occur in a season, any additional storms will take names from the Greek alphabet.”

I like the idea of using male and female names. This got me thinking. Maybe we combine the WMO’s approach and that of the wisdom of crowds. Perhaps the male and female names should be the most popular baby names of that generation. In case you’re wondering, here’s how that would work out:

Silent Generation (1925 – 1942): The Robert and Mary Generation
Baby Boomers I (1946 – 1954): The James and Mary Generation
Baby Boomers II (1955 – 1965): The Michael and Lisa Generation
Generation X (1966 – 1976): The Michael and Jennifer Generation
Millennials (1977 – 1994): The Michael and Jessica Generation
Generation Z (1995 – 2012): The Jacob and Emily Generation
Generation ??? (2013 – Today) – The Emma and Noah Generation

The sharp sighted amongst you will have noticed two problems with this. First, some names are stubbornly popular (I’m talking about you Michael and Mary) and span multiple generations. Secondly, this is a very US-Centric approach. Maybe we need to mix it up globally. For instance, if we tap into the naming zeitgeist of South Korea, that would make the current generation the Seo-yeon and Min-jun Generation.

Of course, all this could be needless worrying. Perhaps those that affixed the Generation Z label knew something we didn’t.

The Rise of the Audience Marketplace

Far be it from me to let a theme go before it has been thoroughly beaten to the ground. This column has hosted a lot of speculation on the future of advertising and media buying and today, I’ll continue in that theme.

First, let’s return to a column I wrote almost a month ago about the future of advertising. This was a spin-off on a column penned by Gary Milner – The End of Advertising as We Know It. In it, Gary made a prediction: “I see the rise of a global media hub, like a stock exchange, which will become responsible for transacting all digital programmatic buys.”

Gary talked about the possible reversal of fragmentation of markets by channel and geographic area due to the potential centralization of digital media purchasing. But I see it a little differently than Gary. I don’t see the creation of a media hub – or, at least – that wouldn’t be the end goal. Media would simply be the means to the end. I do see the creation of an audience market based on available data. Actually, even an audience would only be the means to an end. Ultimately, we’re buying one thing – attention. Then it’s our job to create engagement.

The Advertising Research Foundation has been struggling with measuring engagement for a long time now. But it’s because they were trying to measure engagement on a channel-by-channel basis and that’s just not how the world works anymore. Take search, for example. Search is highly effective at advertising, but it’s not engaging. It’s a connecting medium. It enables engagement, but it doesn’t deliver it.

We talk multi-channel a lot, but we talk about it like the holy grail. The grail in this cause is an audience that is likely to give us their attention and once they do that – is likely to become engaged with our message. The multi-channel path to this audience is really inconsequential. We only talk about multi-channel now because we’re stopping short of the real goal, connecting with that audience. What advertising needs to do is give us accurate indicators of those two likelihoods: how likely are they to give us their attention and what is their potential proclivity towards our offer. The future of advertising is in assembling audiences – no matter what the channel – that are at a point where they are interested in the message we have to deliver.

This is where the digitization of media becomes interesting. It’s not because it’s aggregating into a single potential buying point – it’s because it’s allowing us to parallel a single prospect along a path of persuasion, getting important feedback data along the way. In this definition, audience isn’t a static snapshot in time. It becomes an evolving, iterative entity. We have always looked at advertising on an exposure-by-exposure basis. But if we start thinking about persuading an audience that paradigm needs to be shifted. We have to think about having the right conversation, regardless of the channel that happens to be in use at the time.

Our concept of media happens to carry a lot of baggage. In our minds, media is inextricably linked to channel. So when we think media, we are really thinking channels. And, if we believe Marshall McLuhan, the medium dictates the message. But while media has undergone intense fragmentation they’ve also become much more measurable and – thereby – more accountable. We know more than ever about who lies on the other side of a digital medium thanks to an ever increasing amount of shared data. That data is what will drive the advertising marketplace of the future. It’s not about media – it’s about audience.

In the market I envision, you would specify your audience requirements. The criteria used would not be so much our typical segmentations – demography or geography for example. These have always just been proxies for what we really care about; their beliefs about our product and predicted buying behaviors. I believe that thanks to ever increasing amounts of data we’re going to make great strides in understanding the psychology of consumerism. These  will be foundational in the audience marketplace of the future. Predictive marketing will become more and more accurate and allow for increasingly precise targeting on a number of behavioral criteria.

Individual channels will become as irrelevant as the manufacturer that supplies the shock absorbers and tie rods in your new BMW. They will simply be grist for the mill in the audience marketplace. Mar-tech and ever smarter algorithms will do the channel selection and media buying in the background. All you’ll care about is the audience you’re targeting, the recommended creative (again, based on the mar-tech running in the background) and the resulting behaviors. Once your audience has been targeted and engaged, the predicted path of persuasion is continually updated and new channels are engaged as required. You won’t care what channels they are – you’ll simply monitor the progression of persuasion.


NBC’s Grip on Olympic Gold Slipping

When it comes to benchmarking stuff, nothing holds a candle to the quadrennial sports-statzapooloza we call the Summer Olympics. After 3 years, 11 months and 13 days of not giving a crap about sports like team pursuit cycling or half heavyweight judo, we suddenly get into fist fights over 3 one hundredths of a second or an unawarded Yuko.

But it’s not just sports that are thrown into comparative focus by the Olympic games. It also provides a chance to take a snap shot of media consumption trends. The Olympics is probably the biggest show on earth. With the possible exception of the World Cup, it’s the time when the highest number of people on the planet are all watching the same thing at the same time. This makes it advertising nirvana.

Or it should.

Over the past few Olympics, the way we watch various events has been changing because of the nature of the Games themselves. There are 306 separate events in 35 recognized sports that are spread over 16 days of competition. The Olympics play to a global audience, which means that coverage has to span 24 time zones. At any given time, on any given day, there could be 6 or 7 events running simultaneously. In fact, as I’m writing this, diving, volleyball, men’s omnium cycling, Greco-Roman wresting, badminton, field hockey and boxing are all happening at the same time.

This creates a challenge for network TV coverage. The Olympics are hardly a one-size-fits-all spectacle. So, if you’re NBC and you’ve shelled out 1.6 billion dollars to provide coverage, you have a dilemma: how do you assemble the largest possible audience to show all those really expensive ads to? How do you keep all those advertisers happy?

NBC’s answer, it seems, is to repackage the Olympics as a scripted mini-series. It means throttling down real time streaming or live broadcast coverage on some of the big events so these can be assembled into packaged stories during their primetime coverage. NBC’s chief marketing officer, John Miller, was recently quoted as saying, “The people who watch the Olympics are not particularly sports fans. More women watch the games than men, and for the women, they’re less interested in the result and more interested in the journey. It’s sort of like the ultimate reality show and miniseries wrapped into one.”

So, how is this working out for NBC? Not so well, as it turns out.

Ratings are down, with NBC posting the lowest primetime numbers since 1992. The network has come under heavy fire for what is quite possibly the worst Olympic coverage in the history of the games. Let’s ignore for a moment their myopic focus on US contestants and a handful of superstars like Usain Bolt (which may not be irritating unless you’re a international viewer like myself). Their heavy-handed attempt to control and script the fragmented and emergent drama of any Olympic games has stumbled out of the blocks and fallen flat on its face.

I would categorize this as a “RTU/WTF” The first three letters stand for “Research tells us…” I think you can figure out the last three. I’m sure NBC did their research to figure out what they thought the audience really wanted in Olympics game coverage. I’m positive there was a focus group somewhere that told the network what they wanted to hear; “Screw real time results. What we really want is for you to tell us – with swelling music, extreme close ups and completely irrelevant vignettes– the human drama that lies behind the medals…” And, in the collective minds of NBC executives, they quickly added, “…with a zillion commercial breaks and sponsorship messages.”

But it appears that this isn’t what we want. It’s not even close. We want to see the sports we’re interested in, on our device of choice and at the time that best suits us.

This, in a nutshell, is the disruption that is broadsiding the advertising industry at full ramming speed. It was exactly what I was talking about in my last column. NBC may have been able to play their game when they were our only source of information and we were held captive by this scarcity. But over the past 3 Olympic games, starting in Athens in 2004, technology has essentially erased that scarcity. The reality no longer fits NBC’s strategy. Coverage of the Olympics is now a multi-channel affair. What we’re looking for is a way to filter the coverage based on what is most interesting to us, not to be spoon-fed the coverage that NBC feels has the highest revenue potential.

It’s a different world, NBC. If you’re planning to compete in Tokyo, you’d better change your game plan, because you’re still playing like it’s 1996.




Happiness as a Corporate Metric

Costa Rica is the happiest place on earth. The least happy place on earth? That would be Botswana.

At least, those are the results according to by the things measured by the Happy Planet Index. The index is a measure of three factors, life expectancy, Experienced Well Being and Ecological Footprint. Western nations tend to do very well on the first two measures, but suck at the third. The index is looking for balance – being happy without raping and pillaging the earth. Here in North America, we still have a ways to go in that department.

In another study – the 2015 UN’s World Happiness Report – a different weighting of factors treated the western world a little better. When we tip the balance towards individual happiness and away from the environment and sustainability; Denmark, Switzerland, Iceland, Norway, Finland and Canada topped the rankings. Apparently, snow is good for the soul. At the bottom of the list were Benin, Afghanistan, Togo, Syria and Burundi (it’s hard to believe anywhere scored worse than Syria – mental note: stroke Burundi off my travel bucket list).


The 4th King of Bhutan: Jigme Singye Wangchuck

In 1971, the 4th Dragon King of Bhutan, Jigme Singye Wangchuck was so enamored with the idea of happiness as a goal that he introduced a new measure of a nation’s worth: Gross National Happiness. He believed that the western world’s obsession with materialism represented by Gross National Product shouldn’t be the sole measure of progress. Things like sustainable development, care for the environment, good governance and preservation of culture deserved to be measured as well. In the 45 years since the idea of Gross National Happiness was first floated by his Royal Dragonship, it’s been slow to take, but perhaps it’s time has come. By the way, in the UN survey, Bhutan was in the middle of the pack for happiness, ranking 84th out of 157 countries.

Happiness should be important with companies as well. There’s even an investment fund that invests exclusively in companies with happy employees. But happiness can be an elusive goal, especially when we try to wrestle it to the ground in the way of a hard performance metric in a corporate environment. What exactly are we measuring when we measure happiness? And who’s happiness are we measuring? Our customers? Our shareholders? Our employees? All of the above?

Let’s single out employees. Companies like Zappos and Southwest Airlines have tried to make employee happiness a metric that matters. But what makes an employee happy? Perhaps we can find a clue in a recent survey from Ypulse that asked Millennials which companies they’d most like to work at. The top 10 answers were:

  1. Google
  2. Apple
  3. Disney
  4. Non-profit/charity
  5. School/community/university
  6. Hospital
  7. U.S. government
  8. Myself/my own company
  9. Amazon
  10. FBI/CIA

It’s an interesting list. It’s not the list you’d expect from a generation that simply wants to get rich quick. You don’t work at a hospital or the FBI if you want to make big bucks. This is a list that comes from people who want to make a difference. They want meaning. In the words of Steve Jobs, they “want to put a ding in the universe.”

I get that. I recently discovered just how hard happiness is to pin down. After selling my company, I was fortunate enough to achieve financial independence and retire at 51. I should have been deliriously happy, right? Well, I wasn’t suicidal by any means, but I would say my level of happiness actually decreased after I tried retirement. I was at the other end of my career path from Millennials, but meaning remained just as important to me.

In a study of retirement satisfaction published in the Journal of Financial Counselling and Planning, Sarah Arsebedo and Martin Seay found that psychologist Martin Seligman’s positive psychological attributes, referred to as PERMA (Positive emotions, Engagement, [Family] Relationships, Meaning and Accomplishment) – don’t go away when we retire. These things are necessary to happiness. For men in particular – and increasingly so with women – we rely on our jobs to provide many of these. This was certainly true for me.

It’s good we’re paying more attention to happiness. But it’s also important that we understand what we’re talking about when we refer to happiness. It has little to do with monetary measures of success. Whether we’re talking nations, corporations or employees, it turns out that happiness means a sense of interconnectedness, contribution and personal values. It means living beyond ourselves and leaving some footprint that won’t fade when we no longer walk this earth.

Ultimately, it means doing stuff that matters.


Ex Machina’s Script for Our Future

One of the more interesting movies I’ve watched in the past year has been Ex Machina. Unlike the abysmally disappointing Transcendence (how can you screw up Kurzweil – for God’s sake), Ex Machina is a tightly directed, frighteningly claustrophobic sci-fi thriller that peels back the moral layers of artificial intelligence one by one.

If you haven’t seen it, do so. But until you do, here’s the basic set up. Caleb Smith (Domhnall Gleeson) is a programmer at a huge Internet search company called Blue Book (think Google). He wins a contest where the prize is a week spent with the CEO, Nathan Bateman (Oscar Isaac) at his private retreat. Bateman’s character is best described as Larry Page meets Steve Jobs meets Larry Ellison meets Charlie Sheen – brilliant as hell but one messed up dude. It soon becomes apparent that the contest is a ruse and Smith is there to play the human in an elaborate Turing Test to determine if the robot Ava (Alicia Vikander) is capable of consciousness.

About half way through the movie, Bateman confesses to Smith the source of Ava’s intelligence “software.” It came from Blue Book’s own search data:

‘It was the weird thing about search engines. They were like striking oil in a world that hadn’t invented internal combustion. They gave too much raw material. No one knew what to do with it. My competitors were fixated on sucking it up, and trying to monetize via shopping and social media. They thought engines were a map of what people were thinking. But actually, they were a map of how people were thinking. Impulse, response. Fluid, imperfect. Patterned, chaotic.”

As a search behaviour guy – that sounded like more fact than fiction. I’ve always thought search data could reveal much about how we think. That’s why John Motavalli’s recent column, Google Looks Into Your Brain And Figures You Out, caught my eye. Here, it seemed, fiction was indeed becoming fact. And that fact is, when we use one source for a significant chunk of our online lives, we give that source the ability to capture a representative view of our related thinking. Google and our searching behaviors or Facebook and our social behaviors both come immediately to mind.

Motavalli’s reference to Dan Ariely’s post about micro-moments is just one example of how Google can peak under the hood of our noggins and start to suss out what’s happening in there. What makes this either interesting or scary as hell, depending on your philosophic bent, is that Ariely’s area of study is not our logical, carefully processed thoughts but our subconscious, irrational behaviors. And when we’re talking artificial intelligence, it’s that murky underbelly of cognition that is the toughest nut to crack.

I think Ex Machina’s writer/director Alex Garland may have tapped something fundamental in the little bit of dialogue quoted above. If the data we willingly give up in return for online functionality provides a blue print for understanding human thought, that’s a big deal. A very big deal. Ariely’s blog post talks about how a better understanding of micro-moments can lead to better ad targeting. To me, that’s kind of like using your new Maserati to drive across the street and visit your neighbor – it seems a total waste of horsepower. I’m sure there are higher things we can aspire to than figuring out a better way to deliver a hotels.com ad. Both Google and Facebook are full of really smart people. I’m pretty sure someone there is capable of connecting the dots between true artificial intelligence and their own brand of world domination.

At the very least, they could probably whip up a really sexy robot.













Why Marketers Love Malcolm Gladwell … and Why They Shouldn’t

Marketers love Malcolm Gladwell. They love his pithy, reductionist approach to popular science – his tendency to sacrifice verity for the sake of a good “Just-so” story. And in doing this, what is Malcolm Gladwell but a marketer at heart? No wonder our industry is ga-ga over him. We love anyone who can oversimplify complexity down to the point where it can be appropriated as yet another marketing “angle”.

Take the entire influencer advertising business, for instance. Earlier this year, I saw an article saying more and more brands are expanding their influencer marketing programs. We are desperately searching for that holy nexus where social media and those super-connected “mavens” meet. While the idea of influencer marketing has been around for a while, it really gained steam with the release of Gladwell’s “The Tipping Point.” And that head of steam seems to have been building since the release of the book in 2000.

As others have pointed out, Gladwell has made a habit of taking one narrow perspective that promises to “play well” with the masses, supporting it with just enough science to make it seem plausible and then enshrining it as a “Law.”

Take “The Law of the Few”, for instance, from The Tipping Point: “The success of any kind of social epidemic is heavily dependent on the involvement of people with a particular and rare set of social gifts.” You could literally hear the millions of ears attached to marketing heads “perk up” when they heard this. “All we have to do,” the reasoning went, “is reach these people, plant a favorable opinion of our product and give them the tools to spread the word. Then we just sit back and wait for the inevitable epidemic to sweep us to new heights of profitability.”

Certainly commercial viral cascades do happen. They happen all the time. And, in hindsight, if you look long and hard enough, you’ll probably find what appears to be a “maven” near ground-zero. From this perspective, Gladwell’s “Law of the Few” seems to hold water. But that’s exactly the type of seductive reasoning that makes “Just So” stories so misleading. You mistakenly believe that because it happened once, you can predict when it’s going to happen again. Gladwell’s indiscriminate use of the term “Law” contributes to this common deceit. A law is something that is universally applicable and constant. When a law governs something, it plays out the same way, every time. And this is certainly not the case in social epidemics.


Duncan Watts

If Malcolm Gladwell’s books have become marketing and pop-culture bibles, the same, sadly, cannot be said for Duncan Watts’ books. I’m guessing almost everyone reading this column has heard of Malcolm Gladwell. I further guess that almost none of you have heard of Duncan Watts. And that’s a shame. But it’s completely understandable.

Duncan Watts describes his work as determining the “role that network structure plays in determining or constraining system behavior, focusing on a few broad problem areas in social science such as information contagion, financial risk management, and organizational design.”

You started nodding off halfway through that sentence, didn’t you?

As Watts shows in his books, “Firms spent great effort trying to find “connectors” and “mavens” and to buy the influence of the biggest influencers, even though there was never causal evidence that this would work.” But the work required to get to this point is not trivial. While he certainly aims at a broad audience, Watts does not read like Gladwell. His answers are not self-evident. There is no pithy “bon mot” that causes our neural tumblers to satisfyingly click into place. Watts’ explanations are complex, counter-intuitive, occasionally ambiguous and often non-conclusive – just like the world around us. As he explains his book “Everything is Obvious: *Once You Know the Answer”, it’s easy to look backwards to find causality. But it’s not always right.

Marketers love simplicity. We love laws. We love predictability. That’s why we love Gladwell. But in following this path of least resistance, we’re straying further and further from the real world.

Disruption 101

We Online Spinners are talking a lot about disruption. Dave Morgan has been talking about disruption in the Advertising and Marketing Technology space. I’ve been looking at disruption in other areas, including academia. Cory Treffiletti, Kaila Colbin, Maarten Albarda have all looked at various aspects of disruption. A quick look back at the past few months’ Spin columns show that well over half of them deal with disruption in one way or another.

Maybe it’s time we did a primer on the idea of disruption.

Disruption is what happens when something stable becomes unstable. That’s kind of a “duh..obviously” statement, but there are some very important concepts lurking in there.

When an environment is stable, it allows for the development of extensive but fragile ecosystems. In a corporate sense, this allows for the development of very complicated supply chains, with several “value niches” emerging along that chain. The more complicated the chain, the higher the potential for profit. Each link adds another level of complication, allowing for someone to be squeezing a little more profit from the end consumer.

In addition to extensive ecosystems, stable environments also allow some members of those ecosystems to achieve significant scale. Things are predictable and this allows organizations to grow, embed processes and systems, thereby improving efficiency and profitability. Often, one organization can establish itself at several levels along the supply chain, maximizing its profit potential.

In our physical world, stability is generally a by-product of friction. The higher the degree of friction – or what economist Ronald Coase called “transactional costs” – the more stable the market becomes. Barriers to entry are higher. Competitive factors are dampened. Capital becomes the main predictor of success.

Then – everything changes. We get hit with instability.

In our current case, we got hit with a double whammy: The disruption we’re experiencing is caused by the removal of friction. Technology is reducing transactional costs in a huge swath of industries.

Technology is an interesting catalyst. We think that technology changes behaviors. I don’t believe so. I think technology enables behaviors to change, in that it allows its users to do something they already wanted to do, but couldn’t because of some obstacle. It allows for an attractive alternative that didn’t previously exist. That technology is usually offered to the broadest base of users available and this triggers the disruption, which starts from the ground up. Typically, technology also removes the friction that enables those delicate hierarchal supply chains to form and flourish.

When the disruption begins and the incumbent ecosystem is threatened, the first casualties are the most fragile members of that ecosystem. These are usually the smaller niche players that rely on the bigger hosts that make up the ecosystem. The bigger hosts can survive longer and often swallow up the first casualties in an attempt to shore up their defenses. They will also often make a half-hearted attempt to respond to the disruption by adopting the technology and going after the disruptors. This never works. Disruption is not in their genetic make up. Their priority is always protecting the status quo, because that’s where their profit lies.

As disruption forever alters the environment, eventually the previous ecosystem withers and dies. A new (temporary) stability emerges – along with a new ecosystem – built on the foundation of the previous disruption and the entire cycle starts again.

Innovating Along the Edges

If you want innovation, go to Switzerland. According to the Global Innovation Index, those Swiss are the most innovative people on the planet. Next is the UK, then Sweden. The Dutch are pretty damn innovative too, coming in at number four. Then you have the good old USA rounding up the top 5.

My fellow Canadians? Less innovative, apparently. We’re at #16. Those damn Luxembourgians and Icelanders even beat us (ranking 9th and 13th respectively). But hey, we beat the Japanese (19) and we’re miles ahead of China (29), Russia (48) and India (81).

Pillars of Innovation

So, what makes a country innovative? And, by extension, what lessons can we learn about encouraging innovation generally? The publishers of the index look at five pillars of innovation: Institutions, Human Capital and Research, Infrastructure, Market Sophistication and Business Sophistication.

If you look at these, it makes sense that the better off the country, the more innovative it will be. These are the countries that can invest in education and the infrastructure needed to support innovation. I would also add risk taking to the prerequisites of innovation. I suspect that may be why my fellow Canadians are less innovative on average than Americans.

But, if you talk to Sandy Pentland, there is another factor to consider: Physics. Specifically, Social Physics.

The Physics of Innovation

If we look at innovative environments, the most successful example is a city. Cities, especially some cities, are hot beds of innovation. New York, for example, or San Francisco, or Boston, continually crank out more innovative ideas per person than most places you could name. Why are cities more innovative, per capita, than rural regions? Sure, there are aspects of the five pillars there: good universities, lots of smart people, sophisticated marketers. But the main reason may come down to the nature of networks you find in a city.

Alex “Sandy” Pentland just happens to live in an innovative city – Boston. And he works at MIT, one of the most innovative institutions in the world. There he heads up perhaps the single most innovative department, the Entreneurship Program at MIT’s Media Lab. So, it’s fair to say that Pentland knows a thing or two about innovation. But what really fascinates Pentland is the way people connect and, by doing so, spread ideas. This is what he refers to as “Social Physics.”

Some cities promote innovation because they promote a certain type of network connectivity. In order for innovative ideas to spread, there needs to be two types of connection: exploration and engagement. The first offers a clue to why cities may be particularly innovative. Sparks of creativity tend to come from interface areas, or the edges of social groups, where different ideas and viewpoints come into contact with each other. If you’re surrounded by people who look, speak and think the same way you do, you get an “echo chamber.” There is no diversity in your exploration. But if you’re in an environment that lends itself to encountering diverse ideas and points of view, your exploratory connections become “mash-ups” of innovation. As Steve Jobs said, “Creativity is just connecting things. When you ask creative people how they did something, they feel a little guilty because they didn’t really do it, they just saw something. It seemed obvious to them after a while. That’s because they were able to connect experiences they’ve had and synthesize new things.” But you can only see that “something” if you’re in an environment that allows connections.

Back to those Swiss

So, the five pillars of innovation aside, perhaps the Swiss advantage in innovation comes from the fact that it’s a pretty small country that has 8 million people and 4 official languages. Also, 74% of those people live in a city. That mean’s that there are lots of social “edges” coming into contact with each other.

Once they settle on a language, I’m guessing the Swiss have some pretty interesting conversations.

How Our Brains Process Price Information

On-Off-Switch-For-Human-BrainWe have a complex psychological relationship with pricing. A new brain scanning study out of Harvard and Stanford starts to pick apart the dynamics of that relationship.

Uma R. Karmarkar, Baba Shiv, and Brian Knutson wanted to see how we evaluate a potential purchase when the price is the first piece of information we get as opposed to the last piece of information. They used both fMRI scanning and behavioral tracking to see how the study participants responded. Participants were given $40 dollars to spend and then were presented with a number of sample offers. In all cases, the price represented an attractive bargain on the product featured. But one group was given the price first, and the second group was given the price last.

There was another critical difference in the evaluation process as well. In the first phase of the study, participants were shown products that they would like to buy, and in the second phase, they were shown products that they would have to buy. The difference between the two was how they activated the reward center of our brain – the nucleus accumbens. I’ve been talking for years about the importance of understanding the balance of risk and reward in our purchase decisions. This study provides a little more understanding about how our brain processes those two factors.

In the first phase, participants were shown a variety of products that they would consider rewarding. These would fall into the first quadrant of the risk/reward matrix I introduced in my column from 5 years ago. The researchers were paying particular attention to two different parts of the brain – the nucleus accumbens and the medial prefrontal cortex. For a layman’s analogy, think of you and a five year old walking down the toy aisle in a department store. The nucleus accumbens is the five year old who starts chanting, “I want it. I want it. I want it.” The medial prefrontal cortex is the adult who decides if they’re actually going to buy it. In the study, the researchers found that the sequence in which these two parts of the brain “lit up” depended on whether or not you saw the price first. If you saw the product first, the nucleus accumbens started its chant – “I want it.” If you saw the price first, the prefrontal medial cortex kicked into action and started evaluating whether the offer represented a good bargain. In the case of the reward products, although the sequence varied, the actually purchase process didn’t. In most cases, participants still ended up making the purchase, whether price was presented first or last.

But things changed when the researchers tried a variety of products that fell into the second quadrant of the risk reward matrix – low risk and low reward. These are the everyday items we have to buy. In the study, they included things like a water filtration pitcher, a pack of AA batteries, a USB drive, and a flashlight. There was nothing here that was likely to get the nucleus accumbens starting to chant.

Now, it should be noted that this follow-up study did not include the fMRI scanning, but by tracking purchasing behaviors we can make some pretty educated guesses as to what’s happening in the respective brains of our participants. Here, presenting prices first resulted in a significant increase in actual purchases over instances when price was presented last. If price comes first, we can imagine that the prefrontal cortex is indicating that it’s a good bargain on a needed product. But if a relatively boring product is presented first for evaluation to the nucleus accumbens, there’s little to excite the reward center.

An important caveat to this part of the study comes with knowing that the prices presented represented significant savings on the products. After the simulated purchases, participants were asked to indicate a price they would be willing to pay for the product. When the price was the lead, the named prices tended to be a little lower, indicating that if you are going to lead with price, especially for quadrant two products, you’d better make sure you’re offering a true bargain.

If anything, this study provides further proof of the value of knowing a prospect’s mental landscape. What are the risk and reward factors that will be motivating them? Will the media prefrontal cortex or the nucleus accumbens be calling the shots? What priming effects might an early introduction of price introduce into the process?

When I wrote about the risk/reward matrix five years ago, one commenter said “a simple low-high risk/low-high reward graph is not very useful for driving just in time and location based offers, discounts, etc.” I respectfully disagree. While more sophisticated models are certainly possible, I think even a simple 2X2 matrix that helps map out the decision factors that are in play with purchases would be a significant step forward. And this isn’t about driving real time variations on offers. It’s about understanding the fundamentals of the buyer’s decision process. There’s nothing wrong with simplicity, especially if it drives greater usage.

An Eulogy for “Kathy” – The First Persona

My column last week on the death of the persona seemed to find a generally agreeable audience. But prior to tossing our cardboard cutouts of “Sally the Soccer Mom” in the trash bin, let’s just take a few minutes to remind ourselves why personas were created in the first place.

Alan Cooper – the father of usability personas – had no particular methodology in mind when he created “Kathy,” his first persona. Kathy was based on a real person that Cooper had talked to during his research for a new project management program. Cooper found himself with a few hours on his hands every day when his early 80’s computer chugged away, compiling the latest version of his program. He would use the time to walk around a golf course close to his office and run through the design in his head. One day, he engaged himself in an imaginary dialogue with “Kathy,” a potential customer who was requesting features based on her needs. Soon, he was deep in his internal discussion with Kathy. His first persona was a way to get away from the computer and cubicle and get into the skin of a customer.

There are a few points here that important to note. “Kathy” was based on input from a real person. The creation of “Kathy” had no particular goal, other than to give Cooper a way to imagine how a customer might use his program. It was a way to make the abstract real, and to imagine that reality through the eyes of another person. At the end we realize that the biggest goal of a persona is just that – to imagine the world through someone else’s eyes.

As we transition from personas to data modeling, it’s essential to keep that aspect alive. We have to learn how to live in someone else’s skin. We have to somehow take on the context of their world and be aware of their beliefs, biases and emotions. Until we do this, the holy grail of the “Market of One” is just more marketing hyperbole.

I think the persona started its long decline towards death when it transitioned from a usability tool to a marketing one. Personas were never intended to be a slide deck or a segmentation tool. They were just supposed to be a little mental trick to allow designers to become more empathetic – to slip out of their own reality and into that of a customer. But when marketers got their hands on personas, they do what marketers tend to do. They added the gloss and gutted the authenticity. At that moment, personas started to die.

So, for all the reasons I stated last week, I think personas should be allowed to slip away into oblivion. But if we do so, we have to find a way to understand the reality of our customers on a one to one basis. We have to find a better way to accomplish what personas were originally intended to do. We have to be more empathetic.

Because humans are humans, and not spreadsheets, I’m not sure we can get all the way there with data alone. Data analysis forces us to put on another set of lenses – ones that analyze – not empathize. Those lenses help us to see the “what” but not the “why.” It’s the view of the world that Alan Cooper would have had if he never left his cubicle to walk around the Old Del Monte golf course, waving his arms and carrying on his internal dialogue with “Kathy.” The way to empathize is to make connections with our customers – in the real world – where they live and play.  It’s using qualitative methods like ethnographic research to gain insights that can then be verified with data. Personas may be dead, but qualitative research is more important than ever.