Category Archives: Consumer Psychology

The Psychology of Usefulness: The Acceptance of Technology – Part One

oldpeopletech7_317161In the last post, I talked about what it takes to break a habit built around an online tool, website or application. In today’s post, I want to talk about what happens when we decide to replace that functional aid, whatever it might be.

So, as I said last time, the biggest factor contributing to the breakdown of habit is the resetting of our expectation of what is an acceptable outcome. If our current tools no longer meet this expectation, then we start shopping for a new alternative. In marketing terms, this would be the triggering of need.

Now, this breakdown of expectation can play out in one of two ways. First, if we’re not aware of an alternative solution, we may just feel an accumulation of frustration and dissatisfaction with our current tools. This build up of frustration can create a foundation for further “usefulness foraging” but generally isn’t enough by itself to trigger action. This lends support to my hypothesis that we’re borrowing the evolved Marginal Value algorithm to help us judge the usefulness of our current tools. To put it in biological terms we’re more familiar with, “A bird in the hand is worth two in the bush.” You don’t leave a food patch unless: A) you are reasonably sure there’s another, more promising, patch that can be reached with acceptable effort or B) you have completely exhausted the food available in the patch you’re in. I believe the same is true for usefulness. We don’t throw out what we have until we either know there’s an acceptable alternative that promises a worthwhile increase in usefulness or our current tool is completely useless. Until then, we put up with the frustration.

The Technology Acceptance Model

Let’s say that we have decided that it’s worth the effort to find an alternative. What are the mechanisms we use to find the best alternative? Fred Davis and Richard Bagozzi tackled that question in 1989 and came up with the first version of their Technology Acceptance Model. It took the Theory of Reasoned Action, developed by Martin Fishbein and Icek Ajzen, put forward a decade earlier (1975, 1980) and tried to apply it to the adoption of a new technology. They also relied on the work Everett Rogers did in the diffusion of technology.

First of all, like all models, the TAM had to make some assumptions to simplify real world decisions down to a theoretical model. And, in doing so, it has required a number of revisions to try to bring it closer to what technology adoption decisions look like in the real world.

Let’s start with the foundation of the Theory of Reasoned Action. In it’s simplest form, the TRA says that voluntary behavior is predicted by an individual’s attitude towards that behavior and how they think others would think of them if they performed that behavior.

TRA

So, let’s take the theory for a test drive – if you believe that exercising will increase your health and you also believe that others in your social circle will applaud you for exercising, you’ll exercise. With this example, I think you begin to see where the original TRA may run into problems. Even with the best of intentions, we may not actually make it to the gym. Fishbein and Ajzen’s goal was to create an elegant, parsimonious model that would reliably predict both behaviors and intentions, creating a distinction between the two. Were they successful?

In a meta-analysis of TRA, Sheppard et al (1988) found that attitude was a fairly accurate predictor of intention. If you believe going to the gym is a good thing, you will probably intend to go to the gym. The model didn’t do quite as good a job in predicting behavior. Even if you did intend to go to the gym, would you actually go?

The successful progression from intention to behavior seemed to be reliant on several real world factors, including the time between intention and action (the longer the time interval, the more the degree of erosion of intention) and also lack of control. For example, in the gym example, what if your gym suddenly increased it’s membership fees, or a sudden snowstorm made it difficult to drive there.

Also, if you were choosing from a set of clear alternatives and had to choose one, TRA did a pretty good job of predicting behaviors. But if alternatives were undetermined, or there were other variables to consider, then the predictive accuracy of TRA dropped significantly.

Let me offer an example of how TRA might not work very well in a real world setting. In my book, The BuyerSphere Project, I spent a lot of time looking at the decision process in B2B buying scenarios. If we used the TRA model, we could say that if a buyer had to choose between 4 different software programs for their company, we could use their attitudes towards each of the respective programs as well as the aggregated (and weighted  – because not every opinion should carry the same weight) attitudes towards these programs of the buyer’s co-workers, peers and bosses to determine their intention. And once we have their intention, that should lead to behavior.

But in this scenario, let’s look at some of the simplifying assumptions we’ve had to make to try to cram a real world scenario into the Fishbein Azjen model:

  • We assume a purchase will have to be made from one of the four alternatives. In a real world situation, the company may well decide to stick with what they have
  • We assume the four choices will remain static and we won’t get a new candidate out of left field
  • We assume that attitudes towards each of the alternatives will remain static through the behavioral interval and won’t change. This almost never happens in B2B buying scenarios
  • We assume the buyer – or rational agent – will be in full control of their behaviors and the ultimate decision. Again, this is rarely the case in B2B buying decisions.
  • We assume that there won’t be some mitigating factor that arises in between intention and behavior – for example a spending freeze or a change in requirements.

As you can see, in trying to create a parsimonious model, Fishbein and Azjen ran into a common trap – they had to simplify to the point where it failed to work consistently in the real world.

But, in this review by Alice Darnell, she pointed out Sheppard’s main criticism of the TRA model:

Sheppard et al. (1988) also addressed the model’s main limitation, which is that it fails to account for behavioural outcomes which are only partly under the individual’s volitional control.

I’ve added bolding to the word volitional on purpose. I’ve highlighted many external factors that may lie beyond the volitional control of the individual, but I think the biggest limitation of the TRA lies in its name: Theory of Reasoned Action. It assumes that reason drives our intentions and behaviors. It doesn’t account for emotion.

Applying Reasoned Action to Technology Acceptance

Now, let’s see how Rogers and Bagozzi took Fishbein and Azien’s foundational work and applied it to the acceptance of new technologies.

In their first model (1989) they took attitudes and subjective norms (the attitudes of others) and adapted them for a more applied activity, the use of a new technological tool. They came up with two attitude drivers: Perceived Usefulness and Perceived Ease of Use. If you think back to Charnov’s Marginal Value Theorem, this is exactly the same risk/reward mechanism at work here.  In foraging, it would be yield of food over perceived required effort. In Technology Acceptance, Perceived Usefulness is the reward and Perceived Ease of Use is the risk to be calculated. In the mental calculation, Rogers and Bagozzi assume the user would do a quick mental calculation, using their own knowledge and the knowledge of others to come up with a Usefulness/Ease value that would create their attitude towards using.  This then becomes their Behavioral Intention to Use – which should lead to Actual System Use.

tam

The TAM model was clean and parsimonious. There was just one problem. It didn’t do a very good job of predicting usage in real world situations. There seemed to be much more at work here in actual decisions to accept technologies. In the next post, we’ll look at how the TAM model was modified to bring it closer to real behaviors.

Never Underestimate the Human Ability to Ignore Data

First published January 30, 2014 in Mediapost’s Search Insider

ignore_factsIt’s one thing to have data. It’s another to pay attention to it.

We marketers are stumbling over ourselves to move to data-driven marketing. No one would say that’s a bad thing. But here’s the catch in that. Data driven marketing is all well and good when it’s a small stakes game – optimizing spend, targeting, conversion rates, etc. If we gain a point or two on the topside, so much the better. And if we screw up and lose a point or two – well – mistakes happen and as long as we fix it quickly, no permanent harm done.

But what if the data is telling us something we don’t want to know? I mean – something we really don’t want to know. For instance, our brand messaging is complete BS in the eyes of our target market, or they feel our products suck, or our primary revenue source appears to be drying up or our entire strategic direction looks to be heading over a cliff? What then?

This reminds me of a certain CMO of my acquaintance who was a “Numbers Guy.” In actual fact, he was a numbers guy only if the numbers said what he wanted them to say. If not, then he’d ask for a different set of numbers that confirmed his view of the world. This data hypocrisy generated a tremendous amount of bogus activity in his team, as they ran around grabbing numbers out of the air and massaging them to keep their boss happy. I call this quantifiable bullshit.

I think this is why data tends to be used to optimize tactics, but why it’s much more difficult to use data to inform strategy. The stakes are much higher and even if the data is providing clear predictive signals, it may be predicting a future we’d rather not accept. Then we fall back on our default human defense: ignore, ignore, ignore.

Let me give you an example. Any human who functions even slightly above the level of brain dead has to accept the data that says our climate is changing. The signals couldn’t be clearer. And if we choose to pay attention to the data, the future looks pretty damn scary. Best-case scenario – we’re probably screwing up the planet for our children and grand children. Worst-case scenario – we’re definitely screwing up the planet and it will happen in our lifetime. And we’re not talking about an increased risk of sunburn. We’re talking about the potential end of our species. So what do we do? We ignore it. Even when flooding, drought and ice storms without historic precedent are happening in our back yards. Even when Atlanta is paralyzed by a freak winter storm. Nothing about what is happening is good news, and it’s going to get worse. So, damn the data, let’s just look the other way.

In a recent poll by the Wall Street Journal, out of a list of 15 things that Americans believed should be top priorities for President Obama and Congress, climate change came out dead last – behind pension reform, Iran’s nuclear program and immigration legislation. Yet, if we look at the data that the UN and the World Economic Forum collects, quantifying the biggest threats to our existence, climate change is consistently near the top, both in terms of likelihood and impact. But, it’s really hard to do something about it. It’s a story we don’t want to hear, so we just ignore the data, like the afore-said CMO.

As we get access to more and more data, it will be harder and harder to remain uninformed, but I suspect it will have little impact on our ability to be ignorant. If we don’t know something, we don’t know it. But if we can know something, and we choose not to, that’s a completely different matter. That’s embracing ignorance. And that’s dangerous. In fact, it could be deadly.

Who Owns Your Data (and Who Should?)

First published January 23, 2104 in Mediapost’s Search Insider

Lock backgroundLast week, I talked about a backlash to wearable technology. Simon Jones, in his comment, pointed to a recent post where he raised the very pertinent point – your personal data has value. Today, I’d like to explore this further.

I think we’re all on the same page when we say there is a tidal wave of data that will be created in the coming decade. We use apps – which create data. We use/wear various connected personal devices – which create data. We go to online destinations – which create data. We interact with an ever-increasing number of wired “things” – which create data. We interact socially through digital channels – which create data.  We entertain ourselves with online content – which creates data. We visit a doctor and have some tests done – which creates data. We buy things, both online and off, and these actions also create data. Pretty much anything we do now, wherever we do it, leaves a data trail. And some of that data, indeed, much of it, can be intensely personal.

As I said some weeks ago, all this data is creating a eco-system that is rapidly multiplying and, in its current state, is incredibly fractured and chaotic. But, as Simon Jones rightly points out, there is significant value in that data. Marketers will pay handsomely to have access to it.

But what, or whom, will bring order to this chaotic and emerging market? The value of the data compounds quickly when it’s aggregated, filtered, cross-tabulated for correlations and then analyzed. As I said before, the captured data is its fragmented state is akin to a natural resource. To get to a more usable end state, you need to add a value layer on top of it. This value layer will provide the required additional steps to extract the full worth of that data.

So, to retrace my logic, data has value, even in it’s raw state. Data also has significant privacy implications. And right now, it’s not really clear who owns what data. To move forward into a data market that we can live with, I think we need to set some basic ground rules.

First of all, most of us who are generating data have implicitly agreed to a quid pro quo arrangement – we’ll let you collect data from us if we get an acceptable exchange of something we value. This could be functionality, monetary compensation (usually in the form of discounts and rewards), social connections or entertainment. But here’s the thing about that arrangement – up to now, we really haven’t quantified the value of our personal data. And I think it’s time we did that. We may be trading away too much for much too little.

To this point we haven’t worried much about what we traded off and to whom because any data trails we left have been so fragmented and specific to one context, But, as that data gains more depth and, more importantly, as it combines with other fragments to provide much more information about who we are, what we do, where we go, who we connect with, what we value and how we think, it becomes more and more valuable. It represents an asset for those marketers who want to persuade us, but more critically, that data -our digital DNA – becomes vitally important to us. In it lays the quantifiable footprint of our lives and, like all data, it can yield insights we may never gain elsewhere. In the right hands, it could pinpoint critical weaknesses in our behavioral patterns, red flags in our lifestyle that could develop into future health crises, financial opportunities and traps and ways to allocate time and resources more efficiently. As the digitally connected world becomes denser, deeper and more functional, that data profile will act as our key to it. All the potential of a new fully wired world will rely on our data.

There are millions of corporations that are more than happy to warehouse their respective data profiles of you and sell it back to you on demand as you need it to access their services or tools.  They will also be happy to sell it to anyone else who may need it for their own purposes. Privacy issues aside (at this point, data is commonly aggregated and anonymized) a more fundamental question remains – whose data is this? Whose data should it be? Is this the reward they reap for harvesting the data? Or because this represents you, should it remain your property, with you deciding who uses it and for what?

This represents a slippery slope we may already be starting down.  And, if you believe this is your data and should remain so, it also marks a significant change from what’s currently happening. Remember, the value is not really in the fragments. It’s in bringing it together to create a picture of who you are. And we should be asking the question – who should have the right to create that picture of you – you – or a corporate data marketplace that exists beyond your control ?

Revisiting Entertainment vs Usefulness

brain-cogsSome time ago, I did an extensive series of posts on the psychology of entertainment. My original goal, however, was to compare entertainment and usefulness in how effective they were in engendering long-term loyalty. How do our brains process both? And, to return to my original intent, in that first post almost 4 years ago, how does this impact digital trends and their staying power?

My goal is to find out why some types of entertainment have more staying power than other types. And then, once we discover the psychological underpinnings of entertainment, lets look at how that applies to some of the digital trends I disparaged: things like social networks, micro-blogging, mobile apps and online video. What role does entertainment play in online loyalty? How does it overlap with usefulness? How can digital entertainment fads survive the novelty curse and jump the chasm to a mainstream trends with legs?

In the previous set of posts, I explored the psychology of entertainment extensively, ending up with a discussion of the evolutionary purpose of entertainment. My conclusion was that entertainment lived more in the phenotype than the genotype. To save you going back to that post, I’ll quickly summarize here: the genotype refers to traits actually encoded in our genes through evolution – the hardwired blueprint of our DNA. The phenotype is the “shadow” of these genes – behaviors caused by our genetic blueprints. Genotypes are directly honed by evolution for adaptability and gene survival. Phenotypes are by-products of this process and may confer no evolutionary advantage. Our taste for high-fat foods lives in the genotype – the explosion of obesity in our society lives in the phenotype.

This brings us to the difference between entertainment and usefulness – usefulness relies on mechanisms that predominately live in the genotype.  In the most general terms, it’s the stuff we have to do to get through the day. And to understand how we approach these things on our to-do list, it’s important to understand the difference between autotelic and exotelic activities.

Autotelic activities are the things we do for the sheer pleasure of it. The activity is it’s own reward. The word autotelic is Greek for “self + goal” – or “having a purpose in and not apart from itself.” We look forward to doing autotelic things. All things that we find entertaining are autotelic by nature.

Exotelic activities are simply a necessary means to an end. They have no value in and of themselves.  They’re simply tasks – stuff on our to do list.

The brain, when approaching these two types of activities, treats them very differently. Autotelic activities fire our reward center – the nucleus accumbens. They come with a corresponding hit of dopamine, building repetitive patterns. We look forward to them because of the anticipation of the reward. They typically also engage the prefrontal medial cortex, orchestrating complex cognitive behaviors and helping define our sense of self. When we engage in an autotelic activity, there’s a lot happening in our skulls.

Exotelic activities tend to flip the brain onto its energy saving mode. Because there is little or no neurological reward in these types of activities (other than a sense of relief once they’re done) they tend to rely on the brain’s ability to store and retrieve procedures. With enough repetition, they often become habits, skipping the brain’s rational loop altogether.

In the next post, we’ll look at how the brain tends to process exotelic activities, as it provides some clues about the loyalty building abilities of useful sites or tools. We’ll also look at what happens when something is both exotelic and autotelic.

360 Degrees of Seperation

First published December 5, 2013 in Mediapost’s Search Insider

IMT_iconsIn the past two decades or so, a lot of marketers talked about gaining a 360-degree view of their customers.  I’m not exactly sure what this means, so I looked it up.  Apparently, for most marketers, it means having a comprehensive record of every touch point a customer has had with a company. Originally, it was the promise of CRM vendors, where anyone in an organization, at any time, can pull up a complete customer history.

So far, so good.

But like many phrases, it’s been appropriated by marketers and its meaning has become blurred. Today, it’s bandied about in marketing meetings, where everyone nods knowingly, confident in the fact that they are firmly ensconced in the customer’s cranium and have all things completely under control. “We have a 360-degree view of our customers,” the marketing manager beams, and woe to anyone that dares question it.

But there are no standard criteria that you have to meet before you use the term. There is no rubber-meets-the-road threshold you have to climb over. No one knows exactly what the hell it means. It sure sounds good, though!

If a company is truly striving to build as complete a picture of their customers as possible, they probably define 360 degrees as the total scope of a customer’s interaction with their company. This would follow the original CRM definition. In marketing terms, it would mean every marketing touch point and would hopefully extend through the customer’s entire relationship with that company. This would be 360-degrees as defined by Big Data.

But is it actually 360 degrees? If we envision this as a Venn diagram, we have one 360-degree sphere representing the mental model of customers, including all the things they care about. We have another 360-degree sphere representing the footprint of the company and all the things they do. What we’re actually looking at then, even in an ideal world, is where those two spheres intersect. At best, we’re looking at a relatively small chunk of each sphere.

So let’s flip this idea on its head. What if we redefine 360 degrees as understanding the customer’s decision space? I call this the Buyersphere. The traditional view of 360 degrees is from the inside looking out, from the company’s perspective. The Buyersphere moves the perspective to that of the customer, looking from the outside in. It expands the scope to include the events that lead to consideration, the competitive comparisons, the balancing of buying factors, interactions with all potential candidates and the branches of the buying path itself.  What if you decide to become the best at mapping that mental space?  I still wouldn’t call it a 360-degree view, but it would be a view that very few of your competitors would have.

One of the things that I believe is holding Big Data back is that we don’t have a frame within which to use Big Data. Peter Norvig, chief researcher for Google, outlined 17 warning signs in experimental design and interpretation. One was lack of a specific hypothesis, and the other was a lack of a theory. You need a conceptual frame from which to construct a theory, and then, from that theory, you can decide on a specific hypothesis for validation. It’s this construct that helps you separate signal from noise. Without the construct, you’re relying on serendipity to identify meaningful patterns, and we humans have a nasty tendency to mistake noise for patterns.

If we look at opportunities for establishing a competitive advantage, redefining what we mean by understanding our customers is a pretty compelling one. This is a construct that can provide a robust and testable space within which to use Big Data and other, more qualitative, approaches. It’s relatively doable for any organization to consolidate its data to provide a fairly comprehensive “inside-out” view of customer’s touch points. Essentially, it’s a logistical exercise. I won’t say it’s easy, but it is doable.  But if we set our goal a little differently, working to achieve a true “outside-in” view of our company, that sets the bar substantially higher.

360 degrees? Maybe not. But it’s a much broader view than most marketers have.

Whom Would You Trust: A Human or an Algorithm?

First published October 31, 2013 in Mediapost’s Search Insider

I’vmindrobote been struggling with a dilemma.

Almost a year ago, I wrote a column asking if Big Data would replace strategy. That started a several-month journey for me, when I’ve been looking for a more informed answer to that query. It’s a massively important question that’s playing out in many arenas today, including medicine, education, government and, of course, finance.

In marketing, we’re well into the era of big data. Of course, it’s not just data we’re talking about. We’re talking about algorithms that use that data to make automated decisions and take action. Some time ago, MediaPost’s Steve Smith introduced us to a company called Persado, that takes an algorithmic approach to copy testing and optimization. As an ex-copywriter turned performance marketer I wasn’t sure how I felt about that. I understand the science of continuous testing but I have an emotional stake in the art of crafting an effective message. And therein lies the dilemma. Our comfort with algorithms seems to depend on the context in which we’re encountering them and the degree of automation involved.

Let me give you an example, from Ian Ayre’s book “Super Crunchers.” There’s a company called Epagogix that uses an algorithm to predict the box-office appeal of unproduced movie scripts. Producers can retain the service to help them decide which projects to fund. Epagogix will also help producers optimize their chosen scripts to improve box-office performance. The question here is, do we want an algorithm controlling the creative output of the movie industry? Would we be comfortable take humans out of the loop completely and see where the algorithm eventually takes us?

Now, you may counter that we could include feedback from audience responses. We could use social signals to continually improve the algorithm, a collaborative filtering approach that uses the power of Big Data to guide the film industry’s creative process. Humans are still in the loop in this approach, but only as an aggregated sounding board. We have removed the essentially human elements of creativity, emotion and intuition. Even with the most robust system imaginable, are you comfortable with us humans taking our hands off the wheel?

Here’s another example from Ayre’s book. There is substantial empirical evidence that shows algorithms are better at diagnosing medical conditions than clinical practitioners. In a 1989 study by Dawes, Faust and Meehl, a diagnosis algorithmic rule set was consistently more reliable than actual clinical doctors. They then tried a combination, where doctors were made aware of the outcomes of the algorithm but were the final judges. Again, doctors would have been better off going with the results of the algorithm. Their second-guessing increased their margin of error significantly.

But, even knowing this, would you be willing to rely completely on an automated algorithm the next time you need medical attention? What if there was no doctor involved at all, and you were diagnosed and treated by an algo-driven robot?

There is also mounting (albeit highly controversial) evidence showing that direct instruction produces better learning outcomes that traditional exploratory teaching methods. In direct instruction, scripted automatons could easily replace the teacher’s role. Test scores could provide self-optimizing feedback loops. Learning could be driven by algorithms and delivered at a distance. Classrooms, along with teachers, could disappear completely. Is this a school you’d sign your kid up for?

Let’s stoke the fires of this dilemma a little. In a frightening TED talk, Kevin Slavin talks about how algorithms rule the world and offers a few examples of how algorithms have gotten it wrong in the past. The pricing algorithms of Amazon priced an out-of-print book called “The Making of a Fly” at a whopping $23.6 million dollars. Surprisingly, there were no sales. And in financial markets, where we’ve largely abdicated control to algorithms, those same algorithms spun out of control in 2012 no fewer than 18,000 times. So far, these instances have been identified and corrected in milliseconds, but there’s always a Black Swan chance that one time, they’ll crash the economy just for the hell of it.

But should we humans feel too smug, let’s remember this sobering fact: 20% of all fatal diseases were misdiagnosed. In fact, misdiagnosis accounts for about one-third of all medical error. And we humans have no one but ourselves to blame but for that.

As I said – it’s a dilemma.

What Does Being “Online” Mean?

plugged-inFirst published October 24, 2013 in Mediapost’s Search Insider

If readers’ responses to my few columns about Google’s Glass can be considered a representative sample (which, for many reasons, it can’t, but let’s put that aside for the moment), it appears we’re circling the concept warily. There’s good reason for this. Privacy concerns aside, we’re breaking virgin territory here that may shift what it means to be online.

Up until now, the concept of online had a lot in common with our understanding of physical travel and acquisition. As Peter Pirolli and Stuart Card discovered, our virtual travels tapped into our evolved strategies for hunting and gathering. The analogy, which holds up in most instances, is that we traveled to a destination. We “went” online, to “go” to a website, where we “got” information. It was, in our minds, much like a virtual shopping trip. Our vehicle just happened to be whatever piece of technology we were using to navigate the virtual landscape of “online.”

As long as we framed our online experiences in this way, we had the comfort of knowing we were somewhat separate from whatever “online” was. Yes, it was morphing faster than we could keep up with, but it was under our control, subject to our intent. We chose when we stepped from our real lives into our virtual ones, and the boundaries between the two were fairly distinct.

There’s a certain peace of mind in this. We don’t mind the idea of online as long as it’s a resource subject to our whims. Ultimately, it’s been our choice whether we “go” online or not, just as it’s our choice to “go” to the grocery store, or the library, or our cousin’s wedding. The sphere of our lives, as defined by our consciousness, and the sphere of “online” only intersected when we decided to open the door.

As I said last week, even the act of “going” online required a number of deliberate steps on our part. We had to choose a connected device, frame our intent and set a navigation path (often through a search engine). Each of these steps reinforced our sense that we were at the wheel in this particular journey. Consider it our security blanket against a technological loss of control.

But, as our technology becomes more intimate, whether it’s Google Glass, wearable devices or implanted chips, being “online” will cease to be about “going” and will become more about “being.”  As our interface with the virtual world becomes less deliberate, the paradigm becomes less about navigating a space that’s under our control and more about being an activated node in a vast network.

Being “online” will mean being “plugged in.” The lines between “online” and “ourselves” will become blurred, perhaps invisible, as technology moves at the speed of unconscious thought. We won’t be rationally choosing destinations, applications or devices. We won’t be keying in commands or queries. We won’t even be clicking on links. All the comforting steps that currently reinforce our sense of movement through a virtual space at our pace and according to our intent will fade away. Just as a light bulb doesn’t “go” to electricity, we won’t “go” online.  We will just be plugged in.

Now, I’m not suggesting a Matrix-like loss of control. I really don’t believe we’ll become feed sacs plugged into the mother of all networks. What I am suggesting is a switch from a rather slow, deliberate interface that operates at the speed of conscious thought to a much faster interface that taps into the speed of our subconscious cognitive processing. The impulses that will control the gateway of information, communication and functionality will still come from us, but it will be operating below the threshold of our conscious awareness. The Internet will be constantly reading our minds and serving up stuff before we even “know” we want it.

That may seem like neurological semantics, but it’s a vital point to consider. Humans have been struggling for centuries with the idea that we may not be as rational as we think we are. Unless you’re a neuroscientist, psychologist or philosopher, you may not have spent a lot of time pondering the nature of consciousness, but whether we actively think about it or not, it does provide a mental underpinning to our concept of who we are.  We need to believe that we’re in constant control of our circumstances.

The newly emerging definition of what it means to be “online” may force us to explore the nature of our control at a level many of us may not be comfortable with.

Bounded Rationality in a World of Information

First published October 11, 2013 in Mediapost’s Search Insider.  

Humans are not good data crunchers. In fact, we pretty much suck at it. There are variations to this rule, of course. We all fall somewhere on a bell curve when it comes to our sheer rational processing power. But, in general, we would all fall to the far left of even an underpowered laptop.

Herbert Simon

Herbert Simon

Herbert Simon recognized this more than a half century ago, when he coined the term “bounded rationality.”  In a nutshell, we can only process so much information before we become overloaded, when we fall back on much more human approaches, typically known as emotion and gut instinct.

Even when we think we’re being rational, logic-driven beings, our decision frameworks are built on the foundations of emotion and intuition. This is not bad. Intuition tends to be a masterful way to synthesize inputs quickly and efficiently, allowing us generally to make remarkably good decisions with a minimum of deliberation. Emotion acts to amplify this process, inserting caution where required and accelerating when necessary. Add to this the finely honed pattern recognition instincts we humans have, and it turns out the cogs of our evolutionary machinery work pretty well, allowing us to adequately function in very demanding, often overwhelming environments.

We’re pretty efficient; we’re just not that rational. There is a limit to how much information we can “crunch.”

So when information explodes around us, it raises a question – if we’re not very good at processing data, what happen when we’re inundated with the stuff? Yes, Google is doing its part by helpfully “organizing the world’s information,” allowing us to narrow down our search to the most relevant sources, but still, how much time are we willing to devote to wading through mounds of data? It’s as if we were all born to be dancers, and now we’re stuck being insurance actuaries. Unlike Heisenberg (sorry, couldn’t resist the “Breaking Bad” reference) – we don’t like it, we’re not very good at it, and it doesn’t make us feel alive.

To make things worse, we feel guilty if we don’t use the data. Now, thanks to the Web, we know it’s there. It used to be much easier to feign ignorance and trust our guts. There are few excuses now. For every decision we have to make, we know that there is information which, carefully analyzed, should lead us to a rational, logical conclusion. Or, we could just throw a dart and then go grab a beer. Life is too short as it is.

When Simon coined the term “bounded rationality,” he knew that the “bounds” were not just the limits on the information available but also the limits of our own cognitive processing power and the limits on our available time. Even if you removed the boundaries on the information available (as is now happening) those limits to cognition and time would remain.

I suspect we humans are developing the ability to fool ourselves that we are highly rational. For the decisions that count, we do the research, but often we filter that information through a very irrational web of biases, beliefs and emotions. We cherry-pick information that confirms our views, ignore contradictory data and blunder our way to what we believe is an informed decision.

But, even if we are stuck with the same brain and the same limitations, I have to admit that the explosion of available information has moved us all a couple of notches to the right on Simon’s “satisficing” curve. We may not crunch all the information available, but we are crunching more than we used to, simply because it’s available.  I guess this is a good thing, even if we’re a little delusional about our own logical abilities.

Google Glass and the Sixth Dimension of Diffusion

First published August 29, 2013 in Mediapost’s Search Insider

Tech stock analyst and blogger Henry Blodget has declared Google Glass dead on arrival. I’m not going to spend any time talking about whether or not I agree with Mr. Blodget (for the record, I do – Google Glass isn’t an adoptable product as it sits – and I don’t – wearable technology is the next great paradigm shifter) but rather dig into the reason that he feels Google Glasses are stillborn.

They make you look stupid.

The input for Google Glass is your voice, which means you have to walk around saying things like, “Glass, take a video” or “Glass, what is the temperature?” The fact is, to use Google Glass, you either have to accept the fact that you’ll look like a moron or the biggest jerk in the world. Either way, the vast majority of us aren’t ready to step into that particular spotlight.

Last week, I talked about Everett Rogers’ Diffusion of Technology and shared five variables that determine the rate of adoption. There is actually an additional factor that Rogers also mentioned: “the status-conferring aspects of innovations emerged as the sixth dimension predicting rate of adoption.”

If you look at Roger’s Diffusion curve, you’ll find the segmentation of the adoption population is as follows: Innovators (2.5% of the population), Early Adopters (13.5%), Early Majority (34%), Late Majority (34%)  and Laggards (16%).  But there’s another breed that probably hides out somewhere between Innovators and Early Adopters. I call them the PAs (for Pompous Asses). They love gadgets, they love spending way too much for gadgets, and they love being seen in public sporting gadgets that scream “PA.” Previously, they were the ones seen guffawing loudly into Bluetooth headsets while sitting next to you on an airplane, carrying on their conversation long after the flight attendant told them to wrap it up. Today, they’d be the ones wearing Google Glass.

 

This sixth dimension is critical to consider when the balance between the other five is still a little out of whack. Essentially, the first dimension, Relative Advantage, has to overcome the friction of #2, Compatibility, and #3, Complexity (#4, Trialability, and #5, Observability, are more factors of the actual mechanics of diffusion, rather then individual decision criteria). If the advantage of an innovation does not outweigh its complexity or compatibility, it will probably die somewhere on the far left slopes of Rogers’ bell curve. The deciding factor will be the Sixth Dimension.

This is the territory that Google Glass currently finds itself in. While I have no doubt that the advantages of wearable technology (as determined by the user) will eventually far outweigh the corresponding “friction” of adoption, we’re not there yet. And so Google Glass depends on the Sixth Dimension. Does adoption make you look innovative, securely balanced on the leading edge? Or does it make you look like a dork? Does it confer social status or strip it away? After the initial buzz about Glass, social opinion seems to be falling into the second camp.

This brings us to another important factor to consider when trying to cash in on a social adoption wave: timing. Google is falling into the classic Microsoft trap of playing its hand too soon through beta release. New is cool among the early adopter set, which makes timing critical. If you can get strategic distribution and build up required critical mass fast enough, you can lessen the “pariah” factor. It’s one thing to be among a select clique of technological PAs, but you don’t want to be the only idiot in the room. Right now, with only 8,000 pairs distributed, if you’re wearing a pair, you’re probably the one that everyone else is whispering about.

Of course, you might not be able to hear them over the sound of your own voice, as you stand in front of the mirror and ask Google Glass to “take a picture.”

 

The Open and Shut Mind

First published June 13, 2013 in Mediapost’s Search Insider

A few years ago I was invited to a conference on advertising at a major university. The attendees were a fairly illustrious group of advertising professionals, including several senior executives from major agencies. There was also a healthy sprinkling of academics with impeccable credentials. I was in privileged company.

The organizer of the conference asked me to come up with a “dinner topic.” She explained that she wanted to generate a lively discussion at the various tables as we dug in and broke bread. It was okay if it was a “little” controversial. I must have ignored the qualifier, because my suggestion was, “Is advertising evil?” I have never been one for half measures.

As the ad illuminati settled at their tables, I set the stage by providing two opposing points of view:

First, the positive side of advertising. It can be a way to touch the very core of what makes us human, sometimes moving us to greatness. It can unify communities, create bonds and motivate us en masse. Not only can it be a social “lubricant” but, at its best, advertising can be a powerful change agent as well.

Now, the “evil” side: Does advertising take all this power and fritter it away to drive pure avarice?  Does it short-circuit our Darwinian behavioral wiring, chaining us to a hedonistic treadmill where we constantly want something we don’t have? Regular readers will detect a theme here.

It wasn’t difficult to read the mood of the room as I was wrapping up. My dad has a saying that, despite its off-color nature, sums up the atmosphere of this particular gathering better than anything else I can think of: “It went over like a fart in the house of worship.” I cautiously headed back to my table to take part in the planned “lively discussion.”

My tablemates didn’t know where to start. It seemed that it had never crossed their mind that advertising could be anything but the highest of callings. To have a debate, you need to at least have an abstract understanding of the opposing viewpoint, even if you don’t agree with it. At my table the most common question was, “What do you mean, ‘Is advertising evil?’” I had apparently introduced an entirely foreign concept.

I swallowed and forged ahead, sketching out the basis of my hypothesis. I tried to stay in the abstract, hoping to generate a philosophical debate and avoid getting caught in an emotional catfight. It seemed, though, that I had not only hit a hot button, but had taken a sledgehammer and smashed it to smithereens. Advertisers, at least based on this particular sample, seemed unwilling to discuss the philosophical pros and cons (or at least the cons) of their profession. I just wanted the whole evening to end as soon as possible.

My purpose here is not to reopen the debate. I use this story to illustrate an unfortunate human tendency. We live in a world of grays, but we like to think in black and white. I doubt that advertising is totally evil, but I also doubt that advertising is totally good. The truth lies between the two extremes; advertising is most likely a rather dirty gray.  If we’re willing to consider alternatives to our beliefs, perhaps it will move us a little closer to reality. I think advertising would do nothing but benefit from a deeper evaluation of its moral standing.

But we often forego a search for the truth, content to stick with our beliefs, which often bear little resemblance to reality. If those beliefs are attacked, we defend them vociferously, turning a deaf ear to counter-arguments. We don’t listen, because open minds require the burning of a lot of energy.

In a simpler evolutionary environment, beliefs were a heuristic shortcut for survival.  But today, they often polarize us at either end of a moral spectrum, with no middle ground left for discussion. Case in point, the current American political landscape.

I have spent most of my adult life trying to fight this natural tendency. I have tried to keep an open mind and not let my beliefs blind me to an opposing viewpoint — at least, not when it comes to those things I believe to be truly important. Morality, religion and politics are just three arenas where open minds are much harder to find than staunchly held beliefs.

And, apparently, you can add advertising to that list as well.