Category Archives: Behavior & Psychology

Our Brain on Reviews

There’s an interesting new study that was just published about how our brain mathematically handles online reviews that I wanted to talk about today. But before I get to that, I wanted to talk about foraging a bit.

The story of how science discovered our foraging behaviors serves as a mini lesson in how humans tick. The economists of the 1940’s and 50’s discovered the world of micro-economics, based on the foundation that humans were perfectly rational – we were homo economicus. When making personal economic choices in a world of limited resources, we maximized utility. The economists of the time assumed this was a uniquely human property, bequeathed on us by virtue of the reasoning power of our superior brains.

In the 60’s, behavior ecologists knocked our egos down a peg or two. It wasn’t just humans that could do this. Foxes could do it. Starlings could do it. Pretty much any species had the same ability to seemingly make optimal choices when faced with scarcity. It was how animals kept from starving to death. This was the birth of foraging theory. This wasn’t some homo-sapien-exclusive behavior that was directed from the heights of rationality downwards. It was an evolved behavior that was built from the ground up. It’s just that humans had learned how to apply it to our abstract notion of economic utility.

Three decades later, two researchers at Xerox’s Palo Alto Research Center found another twist. Not only had our ability to forage been evolved all the way through our extensive family tree, but we seemed to borrow this strategy and apply it to entirely new situations. Peter Pirolli and Stuart Card found that when humans navigate content in online environments, the exact same patterns could be found. We foraged for information. Those same calculations determined whether we would stay in an information “patch” or move on to more promising territory.

This seemed to indicate three surprising discoveries about our behavior:

  • Much of what we think is rational behavior is actually driven by instincts that have evolved over millions of years
  • We borrow strategies from one context and apply them in another. We use the same basic instincts to find the FAQ section of a website that we used to find sustenance on the savannah.
  • Our brains seem to use Bayesian logic to continuously calculate and update a model of the world. We rely on this model to survive in our environment, whatever and wherever that environment might be.

So that brings us to the study I mentioned at the beginning of this column. If we take the above into consideration, it should come as no surprise that our brain uses similar evolutionary strategies to process things like online reviews. But the way it does it is fascinating.

The amazing thing about the brain is how it seamlessly integrates and subconsciously synthesizes information and activity from different regions. For example, in foraging, the brain integrates information from the regions responsible for wayfinding – knowing our place in the world – with signals from the dorsal anterior cingulate cortex – an area responsible for reward monitoring and executive control. Essentially, the brain is constantly updating an algorithm about whether the effort required to travel to a new “patch” will be balanced by the reward we’ll find when we get there. You don’t consciously marshal the cognitive resources required to do this. The brain does it automatically. What’s more – the brain uses many of the same resources and algorithm whether we’re considering going to McDonald’s for a large order of fries or deciding what online destination would be the best bet for researching our upcoming trip to Portugal.

In evaluating online reviews, we have a different challenge: how reliable are the reviews? The context may be new – our ancestors didn’t have TripAdvisor or AirBNB ratings for choosing the right cave to sleep in tonight – but the problem isn’t. What criteria should we use when we decide to integrate social information into our decision making process? If Thorlak the bear hunter tells me there’s a great cave a half-day’s march to the south, should I trust him? Experience has taught us a few handy rules of thumb when evaluating sources of social information: reliability of the source and the consensus of crowds. Has Thorlak ever lied to us before? Do others in the tribe agree with him? These are hardwired social heuristics. We apply them instantly and instinctively to new sources of information that come from our social network. We’ve been doing it for thousands of years. So it should come as no surprise that we borrow these strategies when dealing with online reviews.

In a neuro-scanning study from the University College of London, researchers found that reliability plays a significant role in how our brains treat social information. Once again, a well-evolved capability of the brain is recruited to help us in a new situation. The dorsomedial prefrontal cortex is the area of the brain that keeps track of our social connections. This “social monitoring” ability of the brain worked in concert with ventromedial prefrontal cortex, an area that processes value estimates.

The researchers found that this part of our brain works like a Bayesian computer when considering incoming information. First we establish a “prior” that represents a model of what we believe to be true. Then we subject this prior to possible statistical updating based on new information – in this case, online reviews. If our confidence is high in this “prior” and the incoming information is weak, we tend to stick with our initial belief. But if our confidence is low and the incoming information is strong – i.e. a lot of positive reviews – then the brain overrides the prior and establishes a new belief, based primarily on the new information.

While this seems like common sense, the mechanisms at play are interesting. The brain effortlessly pattern matches new types of information and recruits the region that is most likely to have evolved to successfully interpret that information. In this case, the brain had decided that online reviews are most like information that comes from social sources. It combines the interpretation of this data with an algorithmic function that assigns value to the new information and calculates a new model – a new understanding of what we believe to be true. And it does all this “under the hood” – sitting just below the level of conscious thought.

Flow and the Machine

“In the future, either you’re going to be telling a machine what to do, or the machine is going to be telling you.”

Christopher Penn – VP of Marketing Technology, Shift Communications.

I often talk about the fallibility of the human brain – those irrational, cognitive biases that can cause us to miss the reality that’s right in front of our face. But there’s another side to the human brain – the intuitive, almost mystical machinations that happen when we’re on a cognitive roll, balancing gloriously on the edge between consciousness and subconciousness. Malcolm Gladwell took a glancing shot at this in his mega-bestseller: Blink. But I would recommend going right to the master of “Flow” – Mihaly Csikszentmihalyi (pronounced, if you’re interested – me-hi Chick-sent-me-hi). The Hungarian psychologist coined the term “flow” – referring to a highly engaged mental state where we’re completely absorbed with the work at hand. Csikszentmihalyi calls it the “psychology of optimal experience.”

It turns out there’s a pretty complicated neuroscience behind flow. In a blog post from gamer Adam Sinicki, he describes a state where the brain finds an ideal balance between instinctive behavior and total focus on one task. The state is called Transient Hypofrontality. It can sometimes be brought on by physical exercise. It’s why some people can think better while walking, or even jogging. The brain juggles resources required and this can force a stepping down of the prefrontal cortex, the part of the brain that causes us to question ourselves. This part of the brain is required in unfamiliar circumstances but in a situation where we’ve thoroughly rehearsed the actions required it’s actually better if it takes a break. This allows other – more intuitive – parts of the brain to come to the fore. And that may be the secret of “Flow.” It may also be the one thing that machines can’t replicate – yet.

The Rational Machine

If we were to compare the computer to a part of the brain, it would probably be the Prefrontal Cortex (PFC). When we talk about cognitive computing, what we’re really talking about is building a machine that can mimic – or exceed – the capabilities of the PFC. This is the home of our “executive function” – complex decision making, planning, rationalization and our own sense of self. It’s probably not a coincidence that the part of our brain we rely on to reason through complex challenges like designing artificial intelligence would build a machine in it’s own image. And in this instance, we’re damned close to surpassing ourselves. The PFC is an impressive chunk of neurobiology in its flexibility and power, but speedy it’s not. In fact, we’ve found that if we happen to make a mistake, the brain slows almost to a stand still. It shakes our confidence and kills any “flow” that might be happening in it’s tracks. This is what happens to athletes when they choke. With artificial intelligence, we are probably on the cusp of creating machines that can do most of what the PFC can do, only faster, more reliably and with the ability to process much more information.

But there’s a lot more to the brain than just the PFC. And it’s this ethereal intersection between ration and intuition where the essence of being human might be hiding.

The Future of Flow

What if we could harness “flow” at will? If we work in partnership with a machine that can crunch data in real time and present us with the inputs required to continue our flow-fueled exploration without the fear of making a mistake? It’s not so much a machine telling us what to do – or the reverse – as it is a partnership between human intuition and machine based rationalization. It’s analogous to driving a modern car, where the intelligent safety and navigation features backstop our ability to drive.

Of course, it may just be a matter of time before machines best us in this area as well. Perhaps machines already have mastered flow because they don’t have to worry about the consequences of making a mistake. But it seems to me that if humans have a future, it’s not going to be in our ability to crunch data and rationalize. We’ll have to find something a little more magical to stake our claim with.

 

 

How I Cleared a Room Full of Marketing Techies

Was it me?

Was it something I said?

I don’t think so. I think it was just that I was talking about B2B.

Let me explain.

Last week, I was in San Francisco talking at a marketing technology conference. My session, in which I was a co presenter, was going to be about psychographic profiling and A.I. – in B2B marketing. It was supposed to start immediately after another session on “cognitive marketing”. During this prior session, I decided to stand at the back at the room so I didn’t take up a seat.

That proved to be a mistake. During the session, which was in one of three tracks running at the time, the medium sized room filled to standing room only capacity. The presenter talked about how machine learning – delivered via IBM’s Watson, Google’s DeepMind or Amazon’s Cloud AI solution – is going to change marketing and, along with it, the job of a human marketer.

I found it interesting. The audience seemed to think so as well. The presenter wrapped up – the moderator got up to thank him and introduce me as the next presenter – and about 60% of the room stood as one and headed for the exit door, creating a solid human wall between myself and the stage. It took me – the fish – about 5 minutes of proverbially and physically swimming upstream before I could get to the stage. It wasn’t the smoothest of transitions.

I tend to take these things personally. But I honestly don’t think it was me. I think it was the fact that “B2B” was in the title of my presentation. I have found that as soon as you slap that label on anything, marketers tend to swarm in the opposite direction. If there is a B2B track at a general marketing show, you can bet your authentic Adam West Batman action figure (not that I would have any such thing) that it’s tucked away in some far-off corner of the conference center, down three flights of escalators, where you turn right and head towards the parking garage. My experience at this past show was analogous to the lot of B2B marketing in general. Whenever we start talking about it, people start heading for the door.

I don’t get it.

It’s not a question of budget. Even in terms of marketing dollars, a lot of budget gets allocated for B2B. An Outsell report for 2016 pegged the total US B2B marketing spend at about $151 billion. That compares respectfully with the total consumer Ad Spend of $192 billion, according to eMarketer.

And it’s definitely not a question of market size. It’s very difficult to size the entire B2B market, but there’s no doubt that it’s huge. A Forrester report estimates that $8 trillion was sold in the US B2B retail space in 2014. That’s almost half of the US gross domestic product that year. And a huge swath of the business is happening online. The worldwide B2B eCommerce market is projected to be $6.7 trillion by 2020. That’s twice as big as the projected online B2C market ($3.2 trillion).

So what gives? B2B is showing us the money. Why are we not showing it any love? Just digging up the background research for this column proved to be painful. Consumer spend and marketing dollar numbers come gushing off the page of even a half-assed Google search. But B2B stats? Cue the crickets.

I have come to the conclusion that it’s just lack of attention, which probably comes from a lack of sex appeal. B2B is like the debate club in high school. While everyone goes gaga during school assemblies over the cheerleading squad and the football team, the people who will one day rule the world quietly gather after class with Mr. Tilman in the biology lab to plot their debate strategy for next week’s match up against J.R. Matheson Senior High. It goes without saying that parents will be the only ones who actually show up. And even some of them will probably have to stay home to cut the grass.

Those debaters will probably all grow up to be B2B marketers.

It may also be that B2B marketing is hard. Like – juggling Rubik’s Cubes while simultaneously solving them – hard. At least, it’s hard if you dare to go past the “get a lead and hound them mercilessly until they either move to another country or give in and buy something to get you off their back” school of marketing. If you try to do something as silly as try to predict purchase behaviors you have the problem of compound complexity. We have been trying for some time, with limited success, to predict a single consumer’s behavior. In B2B, you have to predict what might happen when you assemble a team of potential buyers – each with their own agenda, emotions and varying degrees of input – and ask them to come to a consensus on an organizational buying decision.

That can make your brain hurt. It’s a wicked problem to the power of 5.4 (the average number of buyers involved in a B2B buying decision- according to CEB’s research). It’s the Inconvenient Truth of Marketing.

That, I keep telling myself, is why everyone was rushing for the door the minute I started walking to the stage. I shouldn’t take it personally.

Shopping is Dead. Long Live Shopping!

Last week, a delivery truck pulled up in my driveway. As the rear door rolled up, I saw the truck was full of Amazon parcels, including one for me. Between the four of us that live in our house, we have at least one online purchase delivered each week. When compared to the total retail spending we do, perhaps that’s not all that significant, but it’s a heck of a lot more than we used to spend.

We are a microcosm of a much bigger behavioral trend. A recent Mediapost article by Jack Loechner reported that online retail grew by 15.6 percent last year and represents 11.7 percent of total retail sales. An IRI report shows similar trends in consumer packaged goods. In 2015, ecommerce represented just 1.5% of all consumer packaged good sales, but they project that to climb to 10% in 2022. In fueling that increase, Amazon is not only leading the pack, but also dominating it to an awe-inspiring extent. Between 2010 and last year, Amazon’s sales in North America quintupled from $16 billion to $80 billion. Hence all those packages in the back of the afore-mentioned truck.

Now, maybe all this still represents “small potatoes” in the total world of retail, but I think we’re getting close to an inflection point. We are fundamentally changing how we think of shopping, and once we let that demon out of the box (or bubble wrapped envelope) there is no stuffing it back.

In the nascent days of online shopping, way back in 2001, an academic study looked at the experience of shopping online. The authors, Childers, Carr, Peck and Carson, divided the experience into two aspects: hedonic and utilitarian. I’ll deal with both in that order.

First of all, the hedonic side of shopping – the touchy, feely joy of buying stuff. It’s mainly the hedonic aspects that purportedly hold up the shaky foundations of all those bricks and mortar stores. And I wonder – is that a generational thing? People of my generation and older still seem to like a little retail therapy now and again. But for my daughters, the act of physically shopping is generally a pain in the ass. If they can get what they want online, they’ll do so in the click of an OneClick button. They’ll visit a mall only if they have to.

In an article early this year in The Atlantic, Derek Thompson detailed the decimation of traditional retail. Mall visits declined 50 percent between 2010 and 2013, according to the real-estate research firm Cushman and Wakefield, and they’ve kept falling every year since. Retailers are declaring bankruptcy at alarming rates. Thompson points the finger at online shopping, but adds a little more context. Maybe the reason bricks and mortar retail is bleeding so badly is that it represents an experience that is no longer appealing. A quote from that article raises an interesting point:

“ ‘What experience will reliably deliver the most popular Instagram post?’—really drive the behavior of people ages 13 and up. This is a big deal for malls, says Barbara Byrne Denham, a senior economist at Reis, a real-estate analytics firm”

Malls were designed to provide an experience – to the point of ludicrous overkill in mega-malls like Canada’s West Edmonton Mall or Minnesota’s Mall of America. But increasingly, those aren’t the experiences we’re looking for. We’re still hedonistic, but our hedonism has developed different tastes. Things like travel and dining out with friends are booming, especially with younger generations. As Denham points out, our social barometers are not determined so much but what we have as by what we’re doing and whom we’re doing it with. Social proof of such things is just one quick post away.

Now let’s deal with the utilitarian aspects of shopping. According to a recent Harris Poll, the three most popular categories for online shopping are:

  1. Clothing and Shoes
  2. Beauty and Personal Care Products
  3. Food Items

Personally, when I look at the things I’ve recently ordered online, they include:

  • A barbecue
  • Storage shelves
  • Water filters for my refrigerator
  • A pair of sports headphones
  • Cycling accessories

I ordered these things online because (respectively):

  • They were heavy and I didn’t want the hassle of dragging them home from the store; and/or,
  • They probably wouldn’t have what I was looking for at any stores in my area.

But even if we look beyond these two very good reasons to buy online, “etail” is just that much easier. It’s generally cheaper, faster and more convenient. We have a long, long tail of things to look for, the advantage of objective reviews to help filter our buying and an average shopping trip duration of just a few minutes – start to finish – as opposed to a few hours or half a day. Finally, we don’t have to contend with assholes in the parking lot.

Online already wins on almost every aspect and the delta of “surprise and delight” is just going to keep getting bigger. Mobile devices untether buying from the desktop, so we can do it any place, any time. Voice commands can save our tender fingertips from unnecessary typing and clicking. Storefronts continue to get better as online retailers run bushels of UX tests to continually tweak the buying journey.

But what’s that you say? “There are just some things that you have to see and touch before you buy?” Perhaps, although I personally remain unconvinced about the need for tactile feedback when shopping. People are buying cars online and if ever there was a candidate for hedonism, it’s an automobile. But let’s say you’re right. I already wrote about how Amazon is changing the bricks and mortar retail game. But Derek Thompson casts his crystal ball gazing even further in the future when he speculates on what autonomous vehicles might do for retail:

“Once autonomous vehicles are cheap, safe, and plentiful, retail and logistics companies could buy up millions, seeing that cars can be stores and streets are the ultimate real estate. In fact, self-driving cars could make shopping space nearly obsolete in some areas.”

Maybe you should buy some shares in Amazon, if you haven’t already. P.S. You can buy them online.

 

The Status Quo Bias – Why Every B2B Vendor has to Understand It

It’s probably the biggest hurdle any B2B vendor has to get over. It’s called the Status Quo bias and it’s deadly in any high-risk purchase scenario. According to Wikipedia, the bias occurs when the current baseline (or status quo) is taken as a reference point, and any change from that baseline is perceived as a loss. In other words, if it ain’t broke don’t fix it. We believe that simply because something exists, it must have merit. The burden of proof then falls on the vendor to overcome this level of complacency

The Status Quo Bias is actually a bundle of other common biases, including the Endowment Effect, the Loss Aversion Bias, The Existence Bias, Mere Exposure effect and other psychological factors that tend to continually jam the cogs of B2B commerce. Why B2B? The Status Quo Bias is common in any scenario where risk is high and reward is low, but B2B in particular is subject to it because these are group-buying decisions. And, as I’ll soon explain, groups tend to default to Status Quo bias with irritating regularity. The new book from CEB (recently acquired by Gartner) – The Challenger Customer – is all about the status quo bias.

So why is the bias particularly common with groups? Think of the dynamics at play here. Generally speaking, most people have some level of the Status Quo Bias. Some will have it more than others, depending on their level of risk tolerance. But let’s look at what happens when we lump all those people together in a group and force them to come to a consensus. Generally, you’re going to have a one or two people in the group that are driving for change. Typically, these will be the ones that have the most to gain and have a risk tolerance threshold that allows the deal to go forward. On the other end of the spectrum you have some people who have low risk tolerance levels and nothing to gain. They may even stand to lose if the deal goes forward (think IT people who have to implement a new technology). In between you have the moderates. The gain factor and their risk tolerance levels net out to close to zero. Given that those that have something to gain will say yes and those who have nothing to gain will say no, it’s this middle group that will decide whether the deal will live or die.

Without the Status Quo bias, the deal might have a 50/50 chance. But the status quo bias stacks the deck towards negative outcomes for the vendor. Even if it tips the balance just a little bit towards “no” – that’s all that’s required to stop a deal dead in its tracks. The more disruptive the deal, the greater the Status Quo Bias. Let’s remember – this is B2B. There are no emotional rewards that can introduce a counter acting bias. It’s been shown in at least one study (Baker, Laury, Williams – 2008) that groups tend to be more risk averse than the individuals that make up that group. When the groups start discussing and – inevitably – disagreeing, it’s typically easier to do nothing.

So, how do we stick handle past this bias? The common approach is to divide and conquer – identifying the players and tailoring messages to speak directly to them. The counter intuitive finding of the CEB Challenger Customer research was that dividing and conquering is absolutely the wrong thing to do. It actually lessens the possibility of making a sale. While this sounds like it’s just plain wrong, it makes sense if we shift our perspective from the selling side to the buying side.

With our vendor goggles on, we believe that if we tailor messaging to appeal to every individual’s own value proposition, that would be a way to build consensus and drive the deal forward. And that would be true, if every member of our buying committee was acting rationally. But as we soon see when we put on the buying googles, they’re not. Their irrational biases are firmly stacked up on the “do nothing” side of the ledger. And by tailoring messaging in different directions, we’re actually just giving them more things to disagree about. We’re creating dysfunction rather than eliminating it. Disagreements almost always default back to the status quo, because it’s the least risky option. The group may not agree about much, but they can agree that the incumbent solution creates the least disruption.

So what do you do? Well, I won’t steal the CEB’s thunder here, because the Challenger Customer is absolutely worth a read if you’re a B2B vendor. The authors, Brent Adamson, Matthew Dixon, Pat Spenner and Nick Toman, lay out step by step strategy to get around the Status Quo bias. The trick is to create a common psychological frame where everyone can agree that doing nothing is the riskiest alternative. But biases are notoriously sticky things. Setting up a commonly understood frame requires a deep understanding of the group dynamics at play. The one thing I really appreciate about CEB’s approach is that it’s “psychologically sound.” They make no assumptions about buyer rationality. They know that emotions ultimately drive all human behavior and B2B purchases are no exception.

We’re Becoming Intellectually “Obese”

Humans are defined by scarcity. All our evolutionary adaptations tend to be built to ensure survival in harsh environments. This can sometimes backfire on us in times of abundance.

For example, humans are great at foraging. We have built-in algorithms that tell us which patches are most promising and when we should give up on the patch we’re in and move to another patch.

We’re also good at borrowing strategies that evolution designed for one purpose and applying them for another purpose. This is called exaptation. For example, we’ve exapted our food foraging strategies and applied them to searching for information in an online environment. We use these skills when we look at a website, conduct an online search or scan our email inbox. But as we forage for information – or food – we have to remember, this same strategy assumes scarcity, not abundance.

Take food for example. Nutritionally we have been hardwired by evolution to prefer high fat, high calorie foods. That’s because this wiring took place in an environment of scarcity, where you didn’t know where your next meal was coming from. High fat, high calorie and high salt foods were all “jackpots” if food was scarce. Eating these foods could mean the difference between life and death. So our brains evolved to send us a reward signal when we ate these foods. Subsequently, we naturally started to forage for these things.

This was all good when our home was the African savannah. Not so good when it’s Redondo Beach, there’s a fast food joint on every corner and the local Wal-Mart’s shelves are filled to overflowing with highly processed pre-made meals. We have “refined” food production to continually push our evolutionary buttons, gorging ourselves to the point of obesity. Foraging isn’t a problem here. Limiting ourselves is.

So, evolution has made humans good at foraging when things are scarce, but not so good at filtering in an environment of abundance. I suspect the same thing that happened with food is today happening with information.

Just like we are predisposed to look for food that is high in fats, salt and calories, we are drawn to information that:

  1. Leads to us having sex
  2. Leads to us having more than our neighbors
  3. Leads to us improving our position in the social hierarchy

All those things make sense in an evolutionary environment where there’s not enough to go around. But, in a society of abundance, they can cause big problems.

Just like food, for most of our history information was in short supply. We had to make decisions based on too little information, rather than too much. So most of our cognitive biases were developed to allow us to function in a setting where knowledge was in short supply and decisions had to be made quickly. In such an environment, these heuristic short cuts would usually end up working in our favor, giving us a higher probability of survival.

These evolutionary biases become dangerous as our information environment becomes more abundant. We weren’t built to rationally seek out and judiciously evaluate information. We were built to make decisions based on little or no knowledge. There is an override switch we can use if we wish, but it’s important to know that just like we’re inherently drawn to crappy food, we’re also subconsciously drawn to crappy information.

Whether or not you agree with the mainstream news sources, the fact is that there was a thoughtful editorial process, which was intended to improve the quality of information we were provided. Entire teams of people were employed to spend their days rationally thinking about gathering, presenting and validating the information that would be passed along to the public. In Nobel laureate Daniel Kahneman’s terminology, they were “thinking slow” about it. And because the transactional costs of getting that information to us was so high, there was a relatively strong signal to noise ratio.

That is no longer the case. Transactional costs have dropped to the point that it costs almost nothing to get information to us. This allows information providers to completely bypass any editorial loop and get it in front of us. Foraging for that information is not the problem. Filtering it is. As we forage through potential information “patches” – whether they be on Google, Facebook or Twitter – we tend to “think fast” – clicking on the links that are most tantalizing.

I would have never dreamed that having too much information could be a bad thing. But most of the cautionary columns that I’ve written about in the last few years all seem to have the same root cause – we’re becoming intellectually “obese.” We’ve developed an insatiable appetite for fast, fried, sugar-frosted information.

 

Searching for Leaders

I was planning on writing a very erudite column on how our consumption of news has drastically changed when I decided to do a research check on Google Trends and found something interesting. It should come as no surprise to learn that Donald Trump is dominating news searches on Google. But what was surprising was that the number one audience with an appetite for “Trumpie Tidbits” is Canadians. That’s right, my fellow countrymen can’t get enough of the guy. We, as a nation, search more for news on Donald Trump than any other place on earth, even the U.S. We out search you Americans on Google by margin of almost 25% (mind you, that margin reverses for web searches for Trump, but we’re still number 2 in the world).

Why?

I could offer some psychologically plausible reasons having to do with morbid curiosity, voyeurism, schadenfreude or even the Stockholm Syndrome, but honestly I have no idea why we’re submitting ourselves to this. Maybe it’s giving us something to do during our abnormally long winters and seeing as we’re already miserable as hell, we feel we have nothing to lose?

This is somewhat ironic, given that according to several highly reputable online polls, we have the hottest leader in the world right now – one Monsieur Trudeau. But even as photogenic as Justin is, when it comes to launching a Google search, our vote still goes to Trump. When you compare searches for Trump during his election to searches for Trudeau during his election – in Canada, no less – Trump wins by a margin of 2 to 1.

But it’s not just us. Trump’s domination of the search zeitgeist is historic. Google shows relative volumes – with 100 representing the peak popularity. For Trump, this peak corresponded with his election, in November. A second peak, at 65, came with his inauguration. Never in the entire length of Barack Obama’s presidency did he ever come close to this. The nearest was during his first election in 2008, when he peaked at 55. So, in one category at least, Trump would be accurate in claiming a historic win.

I thought I’d see if this pattern holds up globally. Angela Merkel is barely a blip on Google’s search radar. Worldwide she has never peaked above 1 compared to Trump’s peak score of 100. Perhaps that’s why he refused to shake her hand. Even in Deutschland itself, she peaked at a paltry 17 in the last 5 years against the Trump standard of 100.

Poor Theresa May, the new leader of the United Kingdom, can’t catch a break either. Even on the week she assumed power Donald Trump gained more searches worldwide by a solid 3 to 1 margin.

So let’s put this to the acid test. Trump vs Putin. Worldwide over the past 5 years it was no contest. Trump: 100, Putin: 3 (scored the week of March 2 – 8, 2014, when Putin was making noises about reclaiming Crimea). And yes, even if we restrict the searches to those coming only from Russia, Trump’s best outscored Putin’s best (in June of 2013) by a margin of 2 to 1.

This probably shouldn’t surprise me. According to Google, Donald Trump outscored everyone when it came to searches in 2016. In fact, he came third on Google’s list of most popular searches of any kind, just after Pokémon Go and iPhone 7. The world is locked in a morbid fascination with all that is Trump.

I’d love to wrap up this column with something philosophical and enlightened. It would be good to pass on some tidbit of behavioral wisdom that would put all this search activity into perspective. But that’s not going to happen. All I know is that I’m as guilty as anyone. Since November 8, I search almost daily for ‘Trump” just to see what the last 24 hours hath wrought. I call it my Daily WTF Round Up.

Apparently I’m not alone.

Want to be Innovative? Immerse Yourself!

In a great post earlier this year, VC Pascal Bouvier (along with Aldo de Jong and Harry Wilson) deconstructed the idea that starts ups always equate with successful innovation. Before you jump on the Lean Start Up bandwagon, realize the success rate of a start up taking ideas to market is about 0.2%. Those slow-moving, monolithic corporations that don’t realize they’re the walking dead? Well, they’re notching a 12.5% hit rate. Sure, they’re not disrupting the universe, but they are protecting their profit margin, and that’s the whole point.

The problem, Bouvier states, is one of context. Start-ups serve a purpose. So do big corporations. But it’s important to realize the context in which they both belong. We are usually too quick to adopt something that appears to be working without understanding why. We then try to hammer it into a place it doesn’t belong.

Start-ups are agents in an ecosystem. Think of them like amino acids in a primordial soup from which we hope, given the right circumstances, life might emerge. The advantage in this market-based ecosystem is that things move freely – without friction. Agents can bump up against each other quickly and catalysts can take their shot at sparking life. It is a dynamic, emergent system. Start-ups are lean and fast-moving because they have to be. It is the blueprint for their survival. It is also why the success rate of any individual start-up is so low. The market is a Darwinian beast – red of tooth and claw. Losers are ruthlessly weeded out.

A corporation is a different beast that occupies a different niche on the evolutionary timeline. It is a hierarchy of components that has already been tested by the market and has assembled itself into a replicable, successful entity. It is a complex organism and has discovered rules that allow it to compete in its ecosystem as a self-organized, vertically integrated, hopefully sustainable entity. In this way, it bears almost no resemblance to a start up. Nor should it.

This is why it’s such a daunting proposition for a start up to transition into a successful corporation. Think of the feat of self-transformation that is required here. Not only do you have to change your way of doing things – you have to change your very DNA. You have to redefine every aspect of who you are, what you do and how you do it.

If you pull out your perspective dramatically here, you see that this is a wave. Call it Schumpeterian Gale of Creative Destruction, call it a Kontdratiev Wave, call it whatever you like – this is not simply a market adaptation – this is a phase transition. The rules on one side of the wave are completely different than on the other side – just as the rules of physics are different for liquids and gases. And that applies to everything, including how you think about innovation.

We commonly believe start-ups are more innovative than corporations. But that’s not actually true. It’s the market that is more innovative. And that innovation has a very distinct characteristic. It comes from agents who are immersed in a particular part of the market. As Bouvier points out in his post, start up CEO’s solve a problem that’s “right in front of their nose.” Think of the typical start up founder. They are ear lobe deep in whatever they are doing. From this perspective, they see something they believe to be a need. They then set out to create a new solution to that need. This is the sense making cycle I keep talking about.

For a lot of start ups, sense making is ingrained. The entrepreneur is embedded in a context where it allows them to make sense of a need that has been overlooked. The magic happens when the switch clicks and the need is matched with a solution. Entrepreneurs are the synaptic connections of the market, but this requires deep immersion in the market.

There’s something else about this immersion that’s important to consider – there is nothing quantitative about it. It’s organic and natural. It’s messy and often chaotic. It’s what I call “steeping in it.” I believe this is also important to innovation. And it’s not just me. A recent study from the University of Toronto shows that creativity thrives in environments free of too much structured knowledge. The authors note, “A hierarchical information structure, compared to a flat information structure, will reduce creativity because it reduces cognitive flexibility.”

Innovation requires insight, and insight comes from being intimately immersed in something. There is a place for data analysis and number crunching, but like most things, that’s the other side of the quant/qual wave. You need both to be innovative.

 

Damn You Technology…

Quit batting your seductive visual sensors at me. You know I can’t resist. But I often wonder what I’m giving up when I give in to your temptations. That’s why I was interested in reading Tom Goodwin’s take on the major theme at SXSW – the Battle for Humanity. He broke this down into three sub themes. I agree with them. In fact, I’ve written on all of them in the past. They were:

Data Trading – We’re creating a market for data. But when you’re the one that generated that data, who should own it?

Shift to No Screens – an increasing number of connected devices will change of concept of what it means to be online.

Content Tunnel Vision – As the content we see is increasingly filtered based on our preferences, what does that do for our perception of what is real?

But while we’re talking about our imminent surrender to the machines, I feel there are some other themes that also merit some discussion. Let’s limit it to two today.

A New Definition of Connection and Community

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Robert Sapolsky

A few weeks ago I read an article that I found fascinating by neuroendocrinologist and author Robert Sapolsky. In it, he posits that understanding Capgras Syndrome is the key to understanding the Facebook society. Capgras, first identified by French psychiatrist Joseph Capgras, is a disorder where we can recognize a face of a person but we can’t retrieve feelings of familiarity. Those afflicted can identify the face of a loved one but swear that it’s actually an identical imposter. Recognition of a person and retrieval of emotions attached to that person are handled by two different parts of the brain. When the connection is broken, Capgras Syndrome is the result.

This bifurcation of how we identify people is interesting. There is the yin and yang of cognition and emotion. The fusiform gyrus cognitively “parses” the face and then the brain retrieves the emotions and memories that are associated with it. To a normally functioning brain, it seems seamless and connected, but because two different regions (or, in the case of emotion, a network of regions) are involved, they can neurologically evolve independently of each other. And in the age of Facebook, that could mean a significant shift in the way we recognize connections and create “cognitive communities.” Sapolsky elaborates:

Through history, Capgras syndrome has been a cultural mirror of a dissociative mind, where thoughts of recognition and feelings of intimacy have been sundered. It is still that mirror. Today we think that what is false and artificial in the world around us is substantive and meaningful. It’s not that loved ones and friends are mistaken for simulations, but that simulations are mistaken for them.

As I said in a column a few months back, we are substituting surface cues for familiarity. We are rushing into intimacy without all the messy, time consuming process of understanding and shared experience that generally accompanies it.

Brains do love to take short cuts. They’re not big on heavy lifting. Here’s another example of that…

Free Will is Replaced with An Algorithm

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Yuval Harari

In a conversation with historian Yuval Harari, author of the best seller Sapiens, Derek Thompson from the Atlantic explored “The Post Human World.” One of the topics they discussed was the End of Individualism.

Humans (or, at least, most humans) have believed our decisions come from a mystical soul – a transcendental something that lives above our base biology and is in control of our will. Wrapped up in this is the concept of us as an individual and our importance in the world as free thinking agents.

In the past few decades, there is a growing realization that our notion of “free will” is just the result of a cascade of biochemical processes. There is nothing magical here; there is just a chain of synaptic switches being thrown. And that being the case – if a computer can process things faster than our brains, should we simply relegate our thinking to a machine?

In many ways, this is already happening. We trust Google Maps or our GPS device more than we trust our ability to find our own way. We trust Google Search more than our own memory. We’re on the verge of trusting our wearable fitness tracking devices more than our own body’s feedback. And in all these cases, our trust in tech is justified. These things are usually right more often than we are. But when it comes to humans vs, machines, they represent a slippery slope that we’re already well down. Harari speculates what might be at the bottom:

What really happens is that the self disintegrates. It’s not that you understand your true self better, but you come to realize there is no true self. There is just a complicated connection of biochemical connections, without a core. There is no authentic voice that lives inside you.

When I lay awake worrying about technology, these are the types of things that I think about. The big question is – is humanity an outmoded model? The fact is that we evolved to be successful in a certain environment. But here’s the irony in that: we were so successful that we changed that environment to one where it was the tools we’ve created, not the creators, which are the most successful adaptation. We may have made ourselves obsolete. And that’s why really smart humans, like Bill Gates, Elon Musk and Stephen Hawking are so worried about artificial intelligence.

“It would take off on its own, and re-design itself at an ever increasing rate,” said Hawking in a recent interview with BBC. “Humans, who are limited by slow biological evolution, couldn’t compete, and would be superseded.”

Worried about a machine taking your job? That may be the least of your worries.

 

 

The Chaos Theory of Marketing

Last week, I wrote why marketers are struggling with job security. In an effort to provide career counseling to an industry, I would offer this suggestion: start learning about the behaviors of non-linear dynamic systems. You’re going to have to get comfortable with the special conditions that accompany complexity.

Markets are always complex, but there’s a phenomenon that gives them the illusion of predictability. This phenomenon is potential. Potential, in this instance, means the gap between the current market state and a possible future state. The presence of potential creates market demand. Every time a new product is introduced, a new potential gap is created. Supply and demand are knocked out of balance. Until balance is regained, the market becomes more predictable.

Here’s an analogy that makes it a little easier to understand how this potential can impact the behaviors of a complex market. A model that’s often used to explain complexity is to imagine a pool table filled with balls. The twist is that each of these balls is self propelled and can move in any direction at random. Imagine how difficult it would be to predict where any single ball might go.

Now, imagine taking this same pool table and lifting one of the corner legs up 6 inches, introducing the force of gravity as a variable. Individual predictions are still difficult, but you’d be pretty safe in saying that the pocket that was diagonally opposite to the raised leg would eventually collect more than it’s fair share of balls. In this example, gravity plays the role of market potential. The market still behaves in a complex manner but there is a consistent force – the force of gravity – that exerts its influence on that complexity and makes it more predictable.

Marketing is built on exploiting potential – on capitalizing on (or creating) gaps between what we have and what we want. These gaps have always been around, but the nature of them has changed. While this potential was aimed further down Maslow’s hierarchy, it was pretty easy to predict purchasing behaviors. When it comes to the basics – meeting our need of food, water, shelter, safety – humans are all pretty much alike. But when it comes to purchases higher up the hierarchy – at the levels of self-esteem or self-actualization – things become tougher to predict.

Collectively, the western world has moved up Maslow’s hierarchy. A 2011 study from Heritage.org showed that even those living below the poverty line have a standard of life that exceeds those at all but the highest income levels just a few decades before. In 2005, 98.7% of homes had a TV, 84% had air conditioning, 79% has satellite or cable TV and 68% had a personal computer.

But it’s not only the diversification of consumer demand that’s increasing the complexity of markets. The more connected that markets become, the more unpredictable they become. Let’s go back to our overly simplified pool ball analogy. Let’s imagine that not only are our pool balls self-propelled, but they also tend to randomly change direction every time they collide with another ball. The more connected the market, the greater the number of collisions and subsequent direction changes. In marketing, those “collisions” could be a tweet, a review, a Facebook post, a Google search – well – you get the idea. It’s complex.

These two factors; the fragmentation of consumer demand and the complexity of a highly interconnected market, makes predicting consumer behavior a mug’s game. The challenge here is that marketing – in a laudable attempt to become more scientific – is following in science’s footsteps by taking a reductionist path. Our marketing mantra is to reduce everything down to testable variables and there’s certainly nothing wrong with that. I’ve said it myself on many occasions. But, as with science, we must realize that when we’re dealing with dynamic complexity, the whole can be much greater than the sum of its testable parts. There are patterns that can be perceived only at a macro scale. Here there be “black swans.” It’s the old issue of ignoring the global maxima or minima by focusing too closely on the local.

Reduction and testing tends to lead to a feeling of control and predictability. And, in some cases (such as a market that has a common potential) things seem to go pretty much according to plan. But sooner or later, complexity rears its head and those best laid plans blow up in your face.