Category Archives: Corporate Strategy

Why Cognitive Computing is a Big Deal When it comes to Big Data

IBM-Watson

Watson beating it’s human opponents at Jeopardy

When IBM’s Watson won against humans playing Jeopardy, most of the world considered it just another man against machine novelty act – going back to Deep Blue’s defeat of chess champion Garry Kasporov in 1997. But it’s much more than that. As Josh Dreller reminded us a few Search Insider Summits ago, when Watson trounced Ken Jennings and Brad Rutter in 2011, it ushered in the era of cognitive computing. Unlike chess, where solutions can be determined solely with massive amounts of number crunching, winning Jeopardy requires a very nuanced understanding of the English language as well as an encyclopedic span of knowledge. Computers are naturally suited to chess. They’re also very good at storing knowledge. In both cases, it’s not surprising that they would eventually best humans. But parsing language is another matter. For a machine to best a man here requires something quite extraordinary. It requires a machine that can learn.

The most remarkable thing about Watson is that no human programmer wrote the program that made it a Jeopardy champion. Watson learned as it went. It evolved the winning strategy. And this marks a watershed development in the history of artificial intelligence. Now, computers have mastered some of the key rudiments of human cognition. Cognition is the ability to gather information, judge it, make decisions and problem solve. These are all things that Watson can do.

 

Peter Pirolli - PARC

Peter Pirolli – PARC

Peter Pirolli, one of the senior researchers at Xerox’s PARC campus in Palo Alto, has been doing a lot of work in this area. One of the things that has been difficult for machines has been to “make sense” of situations and adapt accordingly. Remember, a few columns ago where I talked about narratives and Big Data, this is where Monitor360 uses a combination of humans and computers – computers to do the data crunching and humans to make sense of the results. But as Watson showed us, computers do have to potential to make sense as well. True, computers have not yet matched humans in the ability to sense make in an unlimited variety of environmental contexts. We humans excel at quick and dirty sense making no matter what the situation. We’re not always correct in our conclusions but we’re far more flexible than machines. But computers are constantly narrowing the gap and as Watson showed, when a computer can grasp a cognitive context, it will usually outperform a human.

Part of the problem machines face when making sense of a new context is that the contextual information needs to be in a format that can be parsed by the computer. Again, this is an area where humans have a natural advantage. We’ve evolved to be very flexible in parsing environmental information to act as inputs for our sense making. But this flexibility has required a trade-off. We humans can go broad with our environmental parsing, but we can’t go very deep. We do a surface scan of our environment to pick up cues and then quickly pattern match against past experiences to make sense of our options. We don’t have the bandwidth to either gather more information or to compute this information. This is Herbert Simon’s Bounded Rationality.

But this is where Big Data comes in. Data is already native to computers, so parsing is not an issue. That handles the breadth issue. But the nature of data is also changing. The Internet of Things will generate a mind-numbing amount of environmental data. This “ambient” data has no schema or context to aid in sense making, especially when several different data sources are combined. It requires an evolutionary cognitive approach to separate potential signal from noise. Given the sheer volume of data involved, humans won’t be a match for this task. We can’t go deep into the data. And traditional computing lacks the flexibility required. But cognitive computing may be able to both handle the volume of environmental Big Data and make sense of it.

If artificial intelligence can crack the code on going both broad and deep into the coming storm of data, amazing things will certainly result from it.

The Human Stories that Lie Within Big Data

storytelling-boardIf I wanted to impress upon you the fact that texting and driving is dangerous, I could tell you this:

In 2011, at least 23% of auto collisions involved cell phones. That’s 1.3 million crashes, in which 3331 people were killed. Texting while driving makes it 23 times more likely that you’ll be in a car accident.

Or, I could tell you this:

In 2009, Ashley Zumbrunnen wanted to send her husband a message telling him “I love you, have a good day.” She was driving to work and as she was texting the message, she veered across the centerline into oncoming traffic. She overcorrected and lost control of her vehicle. The car flipped and Ashley broke her neck. She is now completely paralyzed.

After the accident, Zumbrunnen couldn’t sit up, dress herself or bath. She was completely helpless. Now a divorced single mom, she struggles to look after her young daughter, who recently said to her “I like to go play with your friends, because they have legs and can do things.”

The first example gave you a lot more information. But the second example probably had more impact. That’s because it’s a story.

We humans are built to respond to stories. Our brains can better grasp messages that are in a narrative arc. We do much less well with numbers. Numbers are an abstraction and so our brains struggle with numbers, especially big numbers.

One company, Monitor360, is bringing the power of narratives to the world of big data. I chatted with CEO Doug Randall recently about Monitor360’s use of narratives to make sense of Big Data.

“We all have filters through which we see the world. And those filters are formed by our experiences, by our values, by our viewpoints. Those are really narratives. Those are really stories that we tell ourselves.”

For example, I suspect the things that resonated with you with Ashley’s story were the reason for the text – telling her husband she loved him – the irony that the marriage eventually failed after her accident and the pain she undoubtedly felt when her daughter said she likes playing with other moms who can still walk. All of those things, while they don’t really add anything to our knowledge about the incidence rate of texting and driving accidents, are all things that strike us at a deeply emotional level because we can picture ourselves in Ashley’s situation. We empathize with her. And that’s what a story is, a vehicle to help us understand the experiences of another.

Monitor360 uses narratives to tap into these empathetic hooks that lie in the mountain of information being generating by things like social media. It goes beyond abstract data to try to identify our beliefs and values. And then it uses narratives to help us make sense of our market. Monitor360 does this with a unique combination of humans and machines.

“A computer can collect huge amounts of data and the compute can even sort that data. But “sense making” is still very, very difficult for computers to do. So human beings go through that information, synthesize that information and pull out what the underlying narrative is.”

Monitor360 detects common stories in the noisy buzz of Big Data. In the stories we tell, we indicate what we care about.

“This is what’s so wonderful about Big Data. The Data actually tells us, by volume, what’s interesting. We’re taking what are the most often talked about subjects…the data is actually telling us what those subjects are. We then go in and determine what the underlying belief system in that is.”

Monitor360’s realization that it’s the narratives that we care about is an interesting approach to Big Data. It’s also encouraging to know that they’re not trying to eliminate human judgment from the equation. Empathy is still something we can trump computers at.

At least for now.

Want to Be More Strategic? Stand Up!

article-1388357-0050C69D00000258-771_472x345One of the things that always frustrated me in my professional experience was my difficulty in switching from tactical to strategic thinking. For many years, I served on a board that was responsible for the strategic direction of an organization. A friend of mine, Andy Freed, served as an advisor to the board. He constantly lectured us on the difference between strategy and tactics:

“Strategy is your job. Tactics are mine. Stick to your job and I’ll stick to mine.”

Despite this constant reminder, our discussions always seemed to quickly spiral down to the tactical level. We all caught ourselves doing it. It seemed that as soon as we started thinking about what needed to be done and why, we automatically shifted gears and thought about how it should be done.

A recent study may have found the problem. We were sitting down. We should have stood up. Better yet, we should have taken the elevator to the top of the building (we actually did do this at one board retreat in Scottsdale, Arizona). Two researchers at the University of Toronto (home, I should point out, of what was the tallest free standing structure in the world for many years – the CN Tower), Pankaj Aggarwal and Min Zhao, found that a subject’s physical situation impacted how strategic they were. When subjects were physically higher up, say standing on a tall stool, they were more likely to look at the “big picture.”

Our physical context has more than a little impact on how we think. It’s a phenomenon called Mental Construal. And it’s not just restricted to how strategic our thinking is. It can impact thinks like social judgment as well. In a 2006 paper, University of Michigan professor Norbert Schwartz gave some examples that fall under the category called “situated concepts.” For example, the mental images you retrieve when I say “chair” might be different if we’re standing in a living room rather than an airplane or movie theatre. Another example, which unfortunately speaks to a darker side of human nature, is how you would respond to the face of a young African American when shown in the context of a church scene versus the context of a street corner scene.

Schwartz also talks about levels of construal. We’re more successful staying at strategic levels when our planning is trouble free. The minute we hit a problem, we tend to revert to finer grained tactical thinking. Again, in my board experience, the minute we started hitting problems we immediately tried to solve them, which effectively derailed any strategic discussion.

In his book, Creativity: Flow and the Psychology of Discovery and Invention, Mihaly Csikszentmihalyi found that physical contexts can also impact creativity. Physicist Freeman Dyson found that walking was essential to drive the creative process,

“Again, I never went to a class that (Richard) Feynman taught. I never had any official connection with him at all, in fact. But we went for walks. Most of the time that I spent with him was actually walking, like the old style of philosophers who used to walk around under the cloisters.”

In a study where subjects were given pagers and were signaled at random times of the day, they were asked to rate how creative they felt. It turned out the highest level of creativity came while they were walking, driving or swimming. Perhaps it was the physical stimulation, but it may have also been mental construal at work. Perhaps physical movement primed the brain for mental movement.

So, if you need to be strategic, find the highest vantage point possible, with room to walk around, preferably with the smartest person you know.

When Are Crowds Not So Wise?

the-wisdom-of-crowdsSince James Surowiecki published his book “The Wisdom of Crowds”, the common wisdom is – well – that we are commonly wise. In other words, if we average the knowledge of many people, we’ll be smarter than any of us would be individually. And that is true – to an extent. But new research suggests that there are group decision dynamics at play where bigger (crowds) may not always be better.

A recent study by Iain Couzin and Albert Kao at Princeton suggests that in real world situations, where information is more complex and spotty, the benefits of crowd wisdom peaks in groups of 5 to 20 participants and then decreases after that. The difference comes in how the group processes the information available to them.

In Surowiecki’s book, he uses the famous example of Sir Francis Galton’s 1907 observation of a contest where villagers were asked to guess the weight of an ox. While no individual correctly guessed the weight, the average of all the guesses came in just one pound short of the correct number. But this example has one unique characteristic that would be rare in the real world – every guesser had access to the same information. They could all see the ox and make their guess. Unless you’re guessing the number of jellybeans in a jar, this is almost never the case in actual decision scenarios.

Couzin and Kao say this information “patchiness” is the reason why accuracy tends to diminish as the crowd gets bigger. In most situations, there is commonly understood and known information, which the researchers refer to as “correlated information.” But there is also information that only some of the members of the group have, which is “uncorrelated information.” To make matters even more complex, the nature of uncorrelated information will be unique to each individual member. In real life, this would be our own experience, expertise and beliefs.  To use a technical term, the correlated information would be the “signal” and the uncorrelated information would be the “noise.” The irony here is that this noise is actually beneficial to the decision process.

In big groups, the collected “noise” gets so noisy it becomes difficult to manage and so it tends to get ignored. It drowns itself out. The collective focuses instead on the correlated information. In engineering terms this higher signal-to-noise ratio would seem to be ideal, but in decision-making, it turns out a certain amount of noise is a good thing. By focusing just on the commonly known information, the bigger crowd over-simplifies the situation.

Smaller groups, in contrast, tend to be more random in their make up. The differences in experiences, knowledge, beliefs and attitudes, even if not directly correlated to the question at hand, have a better chance of being preserved. They don’t get “averaged” out like they would in a bigger group. And this “noise” leads to better decisions if the situation involves imperfect information. Call it the averaging of intuition, or hunches. In a big group, the power of human intuition gets sacrificed in favor of the commonly knowable. But in a small group, it’s preserved.

In the world of corporate strategy, this has some interesting implications. Business decisions are almost always complex and involve imperfectly distributed information. This research seems to indicate that we should carefully consider our decision-making units. There is a wisdom of crowds benefit as long as the crowd doesn’t get too big. We need to find a balance where we have the advantage of different viewpoints and experiences, but this aggregate “noise” doesn’t become unmanageable.

Have the Odds Caught Up with Apple?

google-vs-appleGoogle has just surpassed Apple as the most valuable brand in the world. In diving deeper on this, there are several angles one could take. If you live in the intersection of brand and technology marketing, as I have for the last several years, this is noteworthy on many levels. One, for instance, are the dramatic shifts in Millward Brown’s assigned brand value for the two companies – with Google soaring 40 percent, and Apple plunging 20 percent. According to Millward Brown’s Brandz™ Study, Google’s brand is worth $158 billion, up from $113 billion last year. And the post-Jobs Apple is down to $147 billion from last years $185 billion number one spot. Combined, that’s an $83 billion swap in valuations. Apple was one of the few brands to actually loose ground in this year’s report.

I personally find this interesting because of some recent research I’ve been doing on corporate strategy for an upcoming book. It comes as a surprise to no one reading this column that I’m a big believer in corporate strategy. But in my research, I’ve been forced to admit that strategy is a little understood and over-hyped concept. Actually, let me clarify that – strategy as it’s taught in most MBA programs is little understood and over-hyped. Executives and consultants pull matrices and strategic frameworks out of thin air, and injudiciously apply them to any and all situations. With all due deference to the Michael Porters, Peter Druckers, Jim Collins and Tom Peters of the world, I suspect the world of corporate strategy is more complex than 5 universal steps, a four box matrix or simple models illustrated with a few circles and arrows. The mistaken assumption with all this is that all strategic wisdom must flow from top to bottom.

Let’s go back to Apple and Google. Apple, under Jobs, was a traditional hierarchy. More than this, it was a hierarchy ensconced in an ivory tower. Due, no doubt, to the considerable hubris of Mr. Jobs, Apple believed that all good things had to be laboriously squeezed out of their own design process and mercilessly tweaked to perfection.

Google, on the other hand, fully embraces the concept of a market to drive innovation. Notice I say “a market”, not “the market.” Here, I refer to markets as a tool, not an entity. The distinction is important. Markets are built to facilitate exchanges. They use valuation mechanisms (such as pricing) to protect fairness and introduce equilibrium in the market. It their most ideal form, markets allow any member of the marketplace to contribute and be judged on the value of their contribution, not their status. In Google’s case, the 20% free time rule, Google Labs and their experimentation with prediction markets all use market dynamics to drive both innovation and corporate strategy. Markets allow for a Darwinian approach to strategy, pulling it up from the bottom rather than driving it down from the top. And, as evolutionary biologist Leslie Orgel liked to say, “Evolution is cleverer than you are.”

But there are trade-offs. Bottom up approaches to strategy need some mechanism to pick winners and losers. There needs to be the corporate equivalent of natural selection. This, again, is where markets can help. Without robust and definitive selection tools, the bottoms-up organization can vacillate endlessly, never making any headway. Also, management of execution in bottom-up organizations can be a much more challenging balancing act. Dictatorships might not be a lot of fun for the “dictatees” but you can definitely get the trains running on time.

Here’s one last thing to keep in mind. Every time we trot out Apple in the era of Steve Jobs as an example of anything to do with corporations, we tend to forget that in the normal distribution of visionary talent, Jobs was an extreme outlier. He was a once in a generation anomaly. You can’t build a corporate strategy around the hope that you have a Steve Jobs on the executive payroll. Sooner or later, the odds will catch up to you.

Will Apple’s brand value bounce back in 2015? Perhaps. But in the dynamically complex market that is today’s reality, I’d be placing my bets on organizations that have learned to adapt and evolve in complexity.

Today, Spend Some Time in Quadrant Two

First published April 17, 2014 in Mediapost’s Search Insider

Last week, I ranted, and it was therapeutic — for me, at least. Some of you agreed that the social media landscape was littered with meaningless crap. Others urged me to “loosen up and take a chill pill,” intimating that I had slipped across the threshold of “grumpy old man-itis.” Guilty, I guess, but there was a point to my rant. We need to spend more time with important stuff, and less time with content that may be popular but trivial.

Hey, I’m the first to admit that I can be tempted into wasting gobs of time with a tweet like: “Prom season sizzles with KFC chicken corsages.” This is courtesy of Guy Kawasaki. Guy’s Twitter feed is a fire hose of enticing trivia. And the man (with the team that supports him) does have a knack of writing tweets with irresistible hooks. Come on. Who could resist checking out a fried chicken corsage?

But here’s the problem. Online is littered with fried chicken corsages. No matter where we turn, we’re bombarded by these tasty little tidbits of brain candy. Publishers have grown quite adept at stringing these together, leading us from trivial link to trivial link. Personally, I’m a sucker for Top Ten lists. But after succumbing to the temptation for “just a second” I find myself, 20 minutes later, having accomplished nothing other than learning what the 10 Biggest Reality Show Blunders were, or where the 10 Most Extravagant Homes in the U.S. happen to be.

Entertaining? Absolutely.

Useful? Doubtful.

Important?  Not a chance.

merrillcoveymatrixWe need to set aside time for important stuff. A few decades ago, I happened to read Stephen Covey’s “First Things First,” which introduced a concept I still try to live by to this day. Covey called it the Urgent/Important matrix. It’s a simple two-by-two matrix with four quadrants:

1 – Urgent and Important – for example, a fire in your kitchen.

2 – Not Urgent but Important – long-term planning.

3 – Urgent but Not Important – interruptions.

4 – Not Important and Not Urgent – time-wasters.

Covey’s Advice? Better balance your time in these quadrants. Quadrant One takes care of itself. We can’t ignore these types of crises. But we should try to minimize the distractions that fall into Quadrant Three and cut down the time we spend in Quadrant Four. Then, we should move as much of this freed-up time as possible into Quadrant Two.

Covey’s Quadrants are more applicable than ever to the online world.  I suspect most of us spend the majority of time in the online equivalents of Quadrant Three (responding to emails or other instant forms of messaging that aren’t really important) or Quadrant Four (online time wasters). We probably don’t spend much time in Quadrant Two (which I’ll abbreviate it to Q2). In fact, in writing this column, I tried to find a quick guide to finding important stuff online. I have a few places I like to go, which I’ll share in a moment, but despite the vast potential of online as a Q2 resource, it doesn’t seem that anyone is it making it easy to filter for “importance.” As I said in my last column, we have filters for popularity and recency, but I couldn’t find anything helping me track down Q2 candidates.

So, here is my contribution to helping you set aside more quality Q2 time:

Amazon Kindle and DevonThink: Reading thought-provoking books is my favorite Q2 activity.  I try to set aside at least an hour a day to read. Anytime someone suggests a book or I find one referenced, I download immediately it from Kindle and add it to the queue. Then, as I read, I use Kindle’s highlight feature to create a summary of the important ideas. After, I copy my highlighted notes into DevonThink, a tool that helps track and archive notes and resources for future reference.

Scientific American & Science Daily: I’m a science geek. I love learning about the latest advances — in particular, new discoveries in the areas of psychology and neuroscience. When I find an interesting article, I again save it to DevonThink.

Google Scholar and Questia: Every so often, I dive into the world of academia to find research done in a particular area, usually related to a blog post or column idea. Google Scholar usually unearths a number of publicly available papers on most topics. And, if you share my predilection for academic research, a subscription to Questia is worth considering.

Big Think, weforum.org and TED: Looking for big ideas — world-changing stuff? These three sites are the place to find them.

HBR, Wired, The Atlantic and The Economist: Another favorite topic of mine is corporate strategy — particularly how organizations have to adapt to a rapidly evolving environment. I find sites like these great for giving me a sense of what’s happening in the world of business.

Hey, it may not be a fried chicken corsage, but these aren’t bad ways to spend an hour or two a day.

 

The Bug in Google’s Flu Trend Data

First published March 20, 2014 in Mediapost’s Search Insider

Last year, Google Flu Trends blew it. Even Google admitted it. It over predicted the occurrence of flu by a factor of almost 2:1.  Which is a good thing for the health care system, because if Google’s predictions had have been right, we would have had the worst flu season in 10 years.

Here’s how Google Flu Trends works. It monitors a set of approximately 50 million flu related terms for query volume. It then compares this against data collected from health care providers where Influenza-like Illnesses (ILI) are mentioned during a doctor’s visit. Since the tracking service was first introduced there has been a remarkably close correlation between the two, with Google’s predictions typically coming within 1 to 2 percent of the number of doctor’s visits where the flu bug is actually mentioned. The advantage of Google Flu Trends is that it is available about 2 weeks prior to the ILI data, giving a much needed head start for responsiveness during the height of flu season.

FluBut last year, Google’s estimates overshot actual ILI data by a substantial margin, effectively doubling the size of the predicted flu season.

Correlation is not Causation

This highlights a typical trap with big data – we tend to start following the numbers without remembering what is generating the numbers. Google measures what’s on people’s minds. ILI data measures what people are actually going to the doctor about. The two are highly correlated, but one doesn’t not necessarily cause the other. In 2013, for instance, Google speculated that increased media coverage might be the cause for the overinflated predictions. More news coverage would have spiked interest, but not actual occurrences of the flu.

Allowing for the Human Variable

In the case of Google Flu Trends, because it’s using a human behavior as a signal – in this case online searching for information – it’s particularly susceptible to network effects and information cascades. The problem with this is that these social signals are difficult to rope into an algorithm. Once they reach a tipping point, they can break out on their own with no sign of a rational foundation. Because Google tracks the human generated network effect data and not the underlying foundational data, it is vulnerable to these weird variables in human behavior.

Predicting the Unexpected

A recent article in Scientific American pointed out another issue with an over reliance on data models –  Google Flu Trends completely missed the non-seasonal H1N1 pandemic in 2009. Why? Algorithmically, Google wasn’t expecting it. In trying to eliminate noise from the model, they actually eliminated signal coming during an unexpected time. Models don’t do very well at predicting the unexpected.

Big Data Hubris

The author of the Scientific American piece, associate editor Larry Greenemeier, nailed another common symptom of our emerging crush on data analytics – big data hubris. We somehow think the quantitative black box will eliminate the need for more mundane data collection – say – actually tracking doctor’s visits for the flu. As I mentioned before, the biggest problem with this is that the more we rely on data, which often takes the form of arm’s length correlated data, the further we get from exploring causality. We start focusing on “what” and forget to ask “why.”

We should absolutely use all the data we have available. The fact is, Google Flu Trends is a very valuable tool for health care management. It provides a lot of answers to very pertinent questions. We just have to remember that it’s not the only answer.

Will Women Make More Empathetic Marketers?

First published Feb 27, 2014 in Mediapost’s Search Insider

empathyAt the risk of sounding sexist, I wonder if women might make better marketers then men?

If you’ll remember, I proposed a new way of defining the job description of a marketer in last week’s column: to understand the customer’s reality, focusing on those areas where we can solve their problems and improve that reality.

If we’re painting with incredibly broad strokes here – which we are – and we had to attach that description to one gender, which gender would you pick?

I know I’m dancing on shaky ground here – or, in my case – thin ice, but I think we all agree that while equal, men and women are different. Men are better at some things. Women are better at others. Yes, there’s a normal distribution curve in both cases, but for some things, the female curve is going to be further to the right. When I look at the qualities that might make an awesome marketer in the new world order, I have to say it seems better suited to the natural strengths of women. That’s why I don’t believe it was coincidence that more women showed a positive response to my column last week then men.

Let me give you an example of a sex based difference we found in our own research that will help explain my reasoning. We looked at how men and women navigate websites using an eye tracking station. When we looked at aggregate heat maps, which showed all activity, there was little difference. But when we sliced the activity into half-second by half-second increments, there was a significantly different scan pattern between men and women. Men went right to the navigation bar and starting mapping out the architecture of the site. They made a mental wireframe to help them get around. Their first priority was how they were going to get things done. Women, however, first looked at images, especially people and the main content on the homepage. Their first priority was whom they were dealing with and what the site was about.

That, in a nutshell, sums up a crucial difference between men and women. Men are driven by tasks – they work to get stuff done. Women are empathizers – they work with people.  In the end, both often get to the same place. But they may take very different paths to get there.

The new world of marketing I’m proposing is all about nurturing relationships – true one-to-one relationships. It’s much more about “who” and “why”, and less about “what”.  It’s about sensing what the world looks like from the prospect’s perspective and moving an organization’s internal strategy closer to that perspective. I’m not saying men can’t do that, but I am saying that women can do it at least as well as men. And perhaps that can help bring more balance to the world of marketing. While total head counts of men and women in marketing are roughly equal (with some reports giving women a slight edge) the same cannot be said of pay scales. According to the latest Marketing Rewards Survey, published by the Chartered Institute of Marketing, the gap between men’s and women’s salaries has widened by 10% since 2012. This gap shows up most noticeably at the highest levels of the industry, where twice as many men (18%) reach director level as women (7%). This also holds true for marketing heads, with men almost doubling women again – 22% vs 12%. These numbers are out of the UK, but the Bureau of Labor Statistics has similar numbers for the US.  They’ve lumped in Marketing and Sales Managers, but the stats show that women earn about 67.7% of what men earn.

There are going to be some massive shifts in marketing in the coming decades. One of them might be between the genders in who holds the top marketing roles.

 

Now, That’s a Job Description I Could Get Behind!

First published February 20, 2014 in Mediapost’s Search Insider

I couldn’t help but notice that last week’s column, where I railed against the marketer’s obsession with tricks, loopholes and pat sound bites got a fair number of retweets. The irony? At least a third of those retweets twisted my whole point – that six seconds (or any arbitrary length of message) isn’t the secret to getting a prospect engaged. The secret is giving them something they want to engage with.

tweet ss

As anyone who has been unfortunate to spend some time with me when I’m in particularly cynical mood about marketing can attest to, I go a little nuts with this “Top Ten Tricks” or “The Secret to…” mentality that seems pervasive in marketing. I’m pretty sure that anyone who retweeted last week’s column with a preface like “Does your advertising engage your consumer in 6 seconds or less? If not, you’re likely losing customers” didn’t bother to actually read past the first paragraph. Maybe not even the first line.

And that’s the whole problem. How can we expect marketers to build empathy, usefulness and relevance into their strategy when many of them have the attention span of a small gnat? As my friend Scott Brinker likes to say when it comes to marketer’s misbehaving, “This is why we can’t have nice things.”

Marketing – good marketing – is not easy but it’s also not a black box. It’s not about secrets or tricks or one-off tactics. It’s about really understanding your customers at an incredibly deep level and then working your ass off to create a meaningful engagement with them. Trying to reduce marketing to anything less than that is like trying to breeze your way through 50 years of marriage by following the Top 3 Tricks to get lucky this Friday night.

Again, this is about meaningful engagements. And when I say meaningful, it’s the customer that gets to decide what’s meaningful. That’s what’s potentially so exciting about breakthroughs like the Oreo Super Bowl campaign. It’s the opportunity to learn what’s meaningful to prospects and then to shift and tailor our responses in real time. Until now, marketing has been “Plan, Push and Pray.” We plan our attack, we push out our message and we pray it finds it’s target and that they respond by buying stuff. If they don’t buy stuff, something went wrong, probably in the planning stage. But that is an awfully long feedback loop.

You’ll notice something about this approach to marketing. The only role for the prospect is as a consumer. If they don’t buy, they don’t participate.  This comes as a direct result of the current job description of a marketer: Someone who gets someone else to buy stuff. But what if we rethink that description? Technology that enables real time feedback is allowing us to create an entirely new relationship with customers. What would happen if we redefined marketing along these lines: To understand the customer’s reality, focusing on those areas where we can solve their problems and improve that reality?

And as much as that sounds like a pat sound bite, if you really dig into it, it’s far from a quick fix. This is a way to make a radically different organization. And it moves marketing into a fundamentally different role. Previously, marketing got its marching orders from the CEO and CFO. Essentially, they were responsible for moving the top line ever northward. It was an internally generated mandate – to increase sales.

But what if we rethink this? What if the entire organization’s role is to constantly adapt to a dynamic environment, looking for advantageous opportunities to improve that environment? And, in this redefined vision, what if marketing’s role was to become the sense-making interface of the company? What if it was the CMO’s job was to consistently monitor the environment, create hypotheses about how to best create adaptive opportunities and then test those hypotheses in a scientific manner?

In this redefinition of the job, Big Data and Real Time Marketing take on significantly new qualities, first as a rich vein of timely information about the marketplace and secondly as a never ending series of instant field experiments to provide empirical backing to strategy.

Now, marketing’s job isn’t to sell stuff, it’s to make sense of the market and, in doing so, help define the overall strategic direction of the company. There are no short cuts, no top ten tricks, but isn’t that one hell of a job description?

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

In Part Two of this series, I looked at Davis and Bagozzi’s Technology Acceptance Model, first proposed in 1989.

Technology_Acceptance_Model

As I said, while the model was elegant and parsimonious, it seems to simplify the realities of technology acceptance decisions too much. In 2000, Venkatesh and Davis tried to deal with this in TAM 2 – the second version of the Technology Acceptance Model.

TAM2

In this version, they added several determinants of Perceived Usefulness and demoted Perceived Ease of Use to being just one of the factors that impacted Perceived Usefulness.  Impacting this mental calculation were two mediating factors: Experience and Voluntariness. This rebalancing of factors provides some interesting insights into the mental process we go through when making a decision whether we’ll accept a new technology or not.

Let’s begin with the determinants of Perceived Usefulness in the order they appear in Venkatesh and Davis’s model:

Subjective Norm: TAM 2 resurrects one of the key components of the original Theory of Reasoned Action model – the opinions of others in your social environment.

Image: Venkatesh and Davis also included another social factor in their list of determinants – how would the acceptance of this technology impact your status in your social network? Notice that our calculation of the image enhancement potential has the Subjective Norm as an input. It’s a Bayesian prediction – we start with our perceived social image status (the prior) and adjust it based on new information, in this case the acceptance of a new technology.

Job Relevance: How applicable is the technology to the job you have to do?

Output Quality: How will this technology impact your ability to perform your job well?

Result Demonstrability: How easy is it to show the benefits of accepting the technology?

It’s interesting to note how these factors split: the first two (subjective norm and image) being related to social networks, the next two (Job Relevance and Output Quality) being part of a mental calculation of benefit and the last one, Demonstrability, bridging the two categories: How easy will it be to show others that I made the right decision?

According to the TAM 2 model, we use these factors, which combine practical task performance considerations and social status aspirations, into a rough calculation of the perceived usefulness of a technology. After this is done, we start balancing that with how easy we perceive the new technology to be to use. Venkatesh and Davis commented on this and felt that Perceived Ease of Use has a variable influence in two areas, the forming of an attitude towards the technology and a behavioral intention to use the technology. The first is pretty straight forward. Our attitude is our mental frame regarding the technology. Again, to use a Bayesian term, it’s our prior. If the attitude is positive, it’s very probably that we’ll form a behavioral intention to use the technology. But there are a few mediating factors at this point, so let’s take a closer look at the creation of Behavioral Intention..

In forming our intention, Perceived Ease of Use is just one of the determinants we use in our “Usefulness” calculation, according to the model. And it depends on a few things. It depends on efficacy – how comfortable we judge ourselves to be with the technology in question. It also depends on what resources we feel we will have access to to help us up the learning curve. But, in the forming of our attitude (and thereby our intention), Venkatesh and Davis felt that Perceived Usefulness will typically be more important than Perceived Ease of Use. If we feel a technology will bring a big enough reward, we will be willing to put up with a significant degree of pain. At least, we will in what we intend to do. It’s like making a New Year’s Resolution to lose weight. At the time we form the intention, the pain involved is sometime in the future, so we go forward with the best of intentions.

As we move forward from Attitude to Intention, this transition if further mediated in the model by our subjective norm – the cognitive context we place the decision in. Into this subjective norm falls our experience (our own evaluation of our efficacy), the attitudes of others towards the technology and also the “Voluntariness” of the acceptance. Obviously, our intention to use will be stronger if it’s a non-negotiable corporate mandate, as opposed to a low priority choice we have the latitude to make.

What is missing from the TAM 2 model is the link between Perceived Ease of Use and actual Usage. Just like a New Year’s Resolution, intentions don’t always become actions. Venkatesh and Davis said Perceived Ease of Use is a moving, iteratively updated calculation. As we gain hands-on experience, we update our original estimate of Ease of Use, either positively or negatively. If it’s positive, it’s more likely that Intention will become Usage. If negatively, the technology may fail to become accepted. In fact, I would say this feedback loop is an ongoing process that may repeat several times in the space between Intention and Usage. The model, with a single arrow going in one direction from Intention to Usage, belies the complexity of what is happening here.

Venkatesh and Davis wanted to create a more realistic model, expanding the front end of the model to account for determinants going into the creation of Intention. They also wanted to provide a model of the decision process that better represented how we balance Perceived Usefulness and Perceived Ease of Use. I think they made some significant gains here. But the model is still a linear one – going in one direction only. What they missed is the iterative nature of acceptance decisions, especially in the gap between Intention and Behavior.

In Part Four, we’ll look at TAM 3 and see how Venkatesh further modified his model to bring it closer to the real world.