100 Things Every Designer Should Know About People

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This is the most helpful guide to user-interface design I've seen. It conveys what science knows about human behavior and how that should influence your design of a website or app. Why fight instincts? Here are 100 useable tips, explained, on taking advantage of the natural tendencies in the way our eyes, brains, and emotions work. Some of the 100 tips are common sense, and some are revelatory. Each is revealed with a principle, some examples, and a takeaway. I use this set as a kind of informal check-list of possibilities. As more of our life migrates to the web, it is ever more important to remember that design is about function, and not just good looks. This overlooked gem of a book encapsulates a lot of wisdom on how to make the functional work for people.

-- KK  

100 Things Every Designer Needs to Know About People
Susan M. Weinschenk
2011, 256 pages
$18

Available from Amazon

Sample Excerpts:

People believe that things shown close together belong together
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People process information better in bite-sized chunks

Applying the concept of progressive disclosure:
Progressive disclosure means providing only the information people need at the moment.
Progressive disclosure requires multiple clicks. You may have heard it said that Web sites should minimize the number of times that people have to click to get detailed information. The number of clicks is not important. People are very willing to click multiple times. In fact, they won't even notice they're clicking if they're getting the right amount of information at each click to keep them going down the path. Think progressive disclosure; don't count clicks.
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Steven Palmer (1981) traveled around the world and asked people to draw a coffee cup. Figure 5.2 shows examples of what they drew.

What most people drew when asked to draw a coffee cup

What's interesting about these drawings is the angle and perspective. A few of the cups are sketched straight on, but most are drawn from a perspective slightly above the cup looking down, and offset a little to the right or left. This has been dubbed the canonical perspective. Very few people would draw a coffee cup as in Figure 5.3, which is what you'd see if you were looking at a coffee cup from above.


Most people don't draw a coffee cup like this.
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People read faster with a longer line strength, but they prefer a shorter line length
Have you ever had to decide what column width to use on a screen? Should it be a wide column with 100 characters per line? Or a short column with 50 characters per line? Or something in between?

The answer depends on whether you want people to read faster or to like the page.

Mary Dyson (2004) conducted research on line length, and combed other studies to determine what line length people prefer. Her work showed that 100 characters per line is the optimal length for on-screen reading speed; but we prefer a short or medium line length (45 to 72 characters per line).
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Takeaways

Use concrete terms and icons. They will be easier to remember.
Let people rest (and even sleep) if you want them to remember information.
Try not to interrupt people if they are learning or encoding information.
Information in the middle of a presentation will be the least likely to be remembered.

Multi-Channel Attribution: Definitions, Models and a Reality Check

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April 2, 2012 

A wise person said: "To guarantee success, spend 95% of your time defining the problem and 5% of the time solving it."

I believe deeply in that quote. In my life I spend an extraordinary amount of time understanding the problem and attempting to define it clearly. As if by magic, I find that it is then much easier to find the optimal solution (or realize none exists!).

Multi-Channel Attribution is a red hot topic in our industry, and yet it is so poorly understood. I'm convinced that the resulting problems (confusion, FUD, angst, daily prayers, and wasted budget) are due to the lack of a clear framework that can help clearly define the problem.

In this post my hope is share a framework that will help define the problem clearly. Included in the post are recommendations for measurement and data analysis. And as if that was not enough, :), I'll close the post with my thoughts on digital marketing attribution models.

This is going to be a lot of fun. Roll up your sleeves, put a smile on your face, grab a pinch of common sense, a heavy dose of reality and let's go…

Three Types of Multi-Channel Attribution Problems.
A huge amount of confusion and disagreement on this topic exists simply because there is no general consensus about those three words. Multi-Channel Attribution.

So let's try and fix that problem.

There are three types of attribution problems in our non-line world.

Multi-Channel Attribution, Online to Store:
This is the attempt by Marketers and Analysts to try and understand the offline impact (revenue/brand value/butts in seats/phone calls/etc) driven by online marketing and advertising. We'll refer to this quest for doing effective attribution as MCA-O2S.

While I'm using the term Store here, it encompasses sales (or leads or catalog requests) driven to a retail store or company call center, people driven to donate blood via online campaigns, or essentially any offline outcome driven by the online channel.

An example of MCA-O2S is Verizon wanting to know how many in-store offline phone activations are driven by online search advertising, for every online activation that the same search advertising drives.

[In case you were curious... It's 5 new accounts activated offline for every 1 activated online! If you are not calculating the offline impact, and you are not giving your online channel due credit. In this case, it would be 5x less credit! You can see why MCA-O2S is supremely critical for every company on the planet.]

Here's the Post-It on which I'd sketched MCA-O2S in planning this post. The red dots represent activity we would like to ensure we are measuring to 1) ensure we understand behavior, and 2) deliver insights that will influence our marketing and advertising…

I spend a lot of time with CEOs and CMOs and when they talk about multi-channel attribution, they're invariably talking about MCA-O2S. Yet when most of my digital peers talk about this topic, they're not talking about MCA-O2S. You can imagine why things might get a little confusing.
So when you meet a CEO and they use say "Help me solve the amazing multi-channel attribution problem", you say: "which type of MCA are you interested in?" Clarity will help foster a valuable conversation.

Almost all current, hot and heavy, literature on the topic of attribution modeling does not cover MCA-O2S. That's because when it comes to MCA-O2S your only bffs are a set of 16 strategies I've outlined over two posts (links immediately below) or the fantastic world of controlled experiments (as in the Verizon case above). So less automated algorithms "distributing credit" and more thoughtful deliberative discreet measurement strategies that inform strategic decisions.

Two helpful blog posts on multi-channel analytics: 1. Tracking online impact of offline advertising. 2. Tracking offline impact of online advertising.

MCA-O2S. It's mandatory. Attribution is driven by experiments. And when you win, you win huge!

Multi-Channel Attribution, Across Multiple Screens:
Senior leaders, especially in larger companies, have started to refer to this when they use the magical words multi-channel attribution.

With the massive adoption of mobile phones and tablets we are all increasingly "four screen" people (TV, desktop, tablets, smart phones). That has directly translated into a more complex fragmented influence landscape (drives the "old timers" bananas). That in turn has translated into many senior leaders deeply desiring, as they put it, "multi-channel attribution." What they really mean is MCA-AMS.

What they really really want is to understand how individuals experience a company or government's digital existence across multiple devices, what media (advertising and marketing) they are being exposed to, and what outcomes (conversions!) are happening as a result.

An example of MCA-AMS is the ability to understand that a search I did on my tablet computer while watching a television commercial resulted in a click on a paid search ad to a camera site which logged into my memory which later caused me to read reviews of the camera on my Nexus S while stuck in traffic and that finally caused a sale for Sony when I got home and happened to be on my laptop.

Attribution in this case is the quest to apportion credit across the TV commercial, tablet paid search ad, reviews read on on the mobile phone for a "direct" conversion on the PC. Amazing, right?
Here's my sketch on MCA-AMS and the raw complexity of the customer experience that we are trying to understand… the red dots indicate what we're trying to measure and understand the impact of…

The primary challenge is that as we switch devices it is increasingly difficult to keep track of the same person as they interface with our digital existence (and are exposed to online and offline marketing and advertising). Actually, I should not say increasingly difficult, I should say almost impossible (cookies, uuids, privacy, government, et al).

Perhaps the only exceptions to the "its almost impossible" scenario would be companies that service customers who are mostly logged in (think Amazon, NY Times) across all four screens all the time. Such companies usually also own massive data warehouses where they have an ability to periodically do cannonballs into the data and identify correlations in consumption and purchase patterns. Often, though not always, they can also tease out causations between devices used during outcomes (five-second segmentation in say Google Analytics) and their media plans while focusing on customer analysis (not visitors, not cookies, not uuids, customers).

Even then it is hard, very hard. And for the rest of us this will remain a complex, and I'm sorry to be so real, unsolvable challenge. At least for now.

Some ideas from the two multi-channel blog posts above can help with MCA-AMS. I've leveraged controlled experiments to get very good "kinda sorta understanding" of reality.

I believe that real solutions will come from the evolution of cookies, updating privacy policies, government decisions and evolving user habits. All that first, then our ability to have nonline data.
Because of all of the above you can see why attribution models don't even enter the picture with MCA-AMS. But when you meet executives and they say "help us with our multi-channel attribution problem", most definitely ask the clarifying question: "do you mean MCA-O2S or MCA-AMS?"
MCA-AMS. Complex, hard challenge. Not a huge problem yet for most, but heading in that direction.

Multi-Channel Attribution, Across Digital Channels:
Almost all of the time when people in our ecosystem (unlike CEOs, CMOs) talk about Multi-Channel Attribution, this is the one they are referring to.

MCA-ADC is the effort to understand which digital marketing channels (Social, Display, YouTube, Referral, Email, Search, others) contributed to a particular conversion (or multiple conversions).
At the moment all web analytics tools, like SiteCatalyst, WebTrends, Google Analytics, CoreMetrics, and others, by default attribute a conversion to the channel immediately prior to the conversion. This is also known as last click attribution.

With MCA-ADC you are trying to go beyond the last click and get this, complete, picture of all marketing activity prior to the conversion (in this case from Google Analytics):

For this website, 767 conversions came from people who visited the site in the above precise order starting with social then a direct visit then an organic search then a referral click-through and finally one last direct visit which lead to the conversions.

The attribution bit here is the burning desire inside all digital marketers to figure out how to dole out credit for the above conversions. Should Direct get 50%? How about Social? 100%? What about Organic? 2%? But let's put that delightful thought on the back burner for just a minute while we understand a critical, often hidden, nuance. [Analysis Ninjas are magnificent at understanding nuance!]

When people talk about MCA-ADC they are still just talking about one device. Because in very close to 0% of the cases do any of these analytics tool have an idea about the behavior of one homo sapien across multiple screens (AMS).

So what you are seeing above are all the conversions that can be tied to multiple visits by a unique browser (notice I did not say person) to your website/digital existence. BTW it is fantastic that GA does this because most other tools don't even show you this.

Say, the Organic Search above had happened on a mobile phone… regardless of the digital analytics tool used, to most websites today that visit would be invisible in the above chain (cookies!). #omg
Hence it is important to separate out MCA-AMS (across multiple screens) from MCA-ADC (across digital channels) – at least for now, until the cookies, ids, privacy policies, government guidance and user habits problem is solved.

When it comes to measuring MCA-AMS you'll use the guidance provided in the above section. For MCA-ADC you'll use a different set of reports (multi-channel funnels ) and attribution models.
I'm sure you are already familiar with nuance number two when it comes to MCA-ADC. A blind-spot if you will.

The above picture does not capture what the impact of this behavior was on your offline existence (O2S). Web Analytics tools are not awesome at that. Ok, they stink at it.

So it is possible that an additional 3,835 people went and made purchases in your stores or via your phone channel (taking the Verizon numbers from above). That would also be invisible from the above report. None of the channels above, whether glorious social, beloved direct, magnificent search, sweet referral, would ever get "credit." Unless you are willing to use the methodologies outlined in the MCA-O2S section above.

When you talk about MCA-ADC, ensure that you are aware and communicate to your leadership, that you are not reporting on MCA-O2S (online to store) and it is extremely unlikely to be reporting the impact of MCA-AMS.

Here's one last Post-It sketch. The red dots are what you are likely measuring when you attempt MCA-ADC…

And if I wanted to be pedantic I would say it is really MCA-ADCFOD. Multi-channel attribution across digital channels for one device.

Now it is true that with sufficient analytical skills, time, patience, and God's direct blessing to you, it might be possible to do complete multi-channel attribution analysis where the multi-channel includes multiple online ad channels, behavior of the person across devices and the impact online and offline.

Sadly, that is incredibly hard to do as a whole. And when I say incredibly hard, I mean almost impossible. And when I say almost impossible, I mean only attempt that after you know you've fixed all other problems with your advertising, your online and offline existence and your people. All three.
I know that sounds like a bummer, but a dose of reality is particularly needed in this discussion. There are simply too many fake promises being made by vendors, consultants, tweeters, gurus and fairies. That is unhelpful to the entire ecosystem.

To close this section…

Next time you hear someone utter the words multi-channel attribution, the single greatest gift you can give yourself is to ask in your sweetest possible voice: "Are you referring to MCA-O2S, MCA-AMS or MCA-ADC?"

You'll earn their respect for knowing that there are three types, and you'll be able to put into context what they are asking for and proceed to have a career and business-enhancing discussion.

Multi-Channel Attribution Models.
For MCA-O2S and MCA-AMS, it is a complex undertaking to identify "which advertising/marketing vehicle deserves how much credit." It requires patience and skills. And it requires your execution of multiple of the 16 strategies I've outlined for tracking online impact of offline and offline impact of online. Even more, it requires an ability (people + skills + desire) to execute controlled experiments.
So the question "who deserves how much credit" is tertiary at best.

With MCA-ADC that quest is a little bit easier. We have the multi-channel funnel reports at our disposal. Additionally in some tools we also have an ability to apply attribution models to the behavior you see in the two pictures above in the MCA-ADC section. #sweetness
The most common attribution models bundled into even the simplest web analytics tools are: Last click, first click, and even distribution.

If you are lucky, you have access to a more sophisticated tool which would include: Adjustable, based on mathematical algorithms, time decay model.

If you are among the chosen few, you'll likely have access to a digital analytics tool that allows you to create a customized attribution model.

Each of these models are applied to MCA-ADC (still without benefit of O2C or AMS) and provide you with incrementally better understanding of your digital media spend.

Each of these models comes with its own pros and cons. [If you have my book Web Analytics 2.0 please jump to page 358.] Some of them have more cons and barely any pros. Those should be avoided like the plague.

A couple of them pass the common sense test, and hence will put you in a better place than staying with last click attribution.

But most of what you'll get out of playing with these models is a deep and profound appreciation for how they'll, even in their most shining moment, give you directional guidance how to adjust your media spend (shift dollars/euros/pesos from Search to Display or from Display to Email or… other combinations).

You'll realize (even if you use the greatest customized model created by your most magnificent consultant at a equally magnificent cost to you) that success then will come not from that rough output, but rather from your ability to take that rough output, make changes, observe the impact (over weeks, or months if you are small sized), identify insights and be less wrong over time.

If you happen to be in a larger company, say you spend more than $10 million on digital marketing per year, you'll quickly see, having learned to be less wrong over time, that the question you want to answer with multi-channel attribution modeling is not "who gets how much credit" but rather "how can I optimally balance my digital marketing portfolio."

That will then drive you to seek solace in the arms of the only solution that actually works. The solution that is hard. The solution that requires unique people skills and an undying desire to scale un-imagined heights of glory. Media Mix Models. Executed via persistent controlled experiments.
When you reach that point, fame, fortune and happiness will be yours.

Multi-Channel Attribution: Closing Thoughts
This is a tough challenge. Simply because reality is complicated.

Customer experiences are ever more complex, influence channels intersect a lot more, content consumption is fragmented, the three-step "attract, acquire, retain" model is now broken into 37 different pieces.

So, you don't have a choice. You are going to have to deal with the multi-channel attribution problems, all three of them, if you want your company to have an effective advertising and marketing strategy.

Here's the good news: You don't have to try to boil the ocean in one go. In fact, that might be hazardous to your health if you attempt to do that. Take gradual steps. Increase your sophistication over time.

Here's what I recommend:

1. First clarify what problem you are solving for your management team. O2S or AMS or ADC. 2. Use the appropriate set of solution (see sections above). If MCA-ADC…
3. Get really, really good at understanding your multi-channel funnel reports. They are free. They are awesome. Use the Venn diagram in the Overview report to display reality to your management team. They'll love you, and stop wasting money.
4. Start to experiment with the simple models. You are moving away from last click, you'll abandon first and even very quickly. Spend some love and attention on the time decay attribution model (ideally with several mathematical options to apply).
5. Experiment with changes in your digital portfolio based on your time decay results.
6. Measure outcomes. Go back. Analyze the data. Change some more.
7. As you master that, shift slowly to playing with media mix modeling type controlled experiments.

If at any step you notice diminishing margins of return, go back to the previous step and optimize that one some more until it is truly worth the incremental company investment to take the next step.
If you understand the frameworks, if you internalize the challenges, if you define your company's immediate unique problem clearly, and follow a step wise approach outline above you'll not just do fine. You'll be fantastic.
Good luck!

Marketing To Women: 30 Stats To Know

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by Ekaterina Walter, Mar 28, 2012, 7:48 AM

Women are earning, spending, and influencing spending at a greater rate than ever before -- and they account for $7 trillion in consumer and business spending in the United States, and over the next decade they will control two-thirds of consumer wealth. Women make or influence 85 percent of all purchasing decisions, and purchase over 50 percent of traditional male products, including automobiles, home improvement products and consumer electronics.

But 91% of women say that advertisers don't understand them.

Recognizing the power and influence of women must be a top priority for marketers if they are going to tap into the market's full potential. Here are 30 surprising stats to help marketers get a handle on this misunderstood demographic.

Earning Power
1. The average American woman is expected to earn more than the average American male by 2028
2. Fifty-one percent of U.S. private wealth is controlled by women
3. Women account for over 50% of all stock ownership in the U.S.
4. Women control more than 60% of all personal wealth in the U.S.

Spending Power
1. Women account for 85% of all consumer purchases, including everything from autos to health care
2. Women make 80% of healthcare decisions and 68 percent of new car purchase decisions
3. Seventy-five percent of women identified themselves as the primary shoppers for their households
4. Women influenced $90 billion of consumer electronic purchases in 2007
5. Nearly 50% of women say they want more green choices, with 37% are more likely to pay attention to brands that are committed to environmental causes

Women and Cars
1. Women buy more than half of the new cars in the U.S., and influence up to 80% of all car purchases
2. Women request 65% of the service work done at dealerships
3. Women spend over $200 billion on new cars and mechanical servicing of vehicles each year
4. Forty-five percent of all light trucks and SUVs are purchased by women

Mom Power
1. Moms represent a $2.4 trillion market
2. Fifty-five percent of active (daily) social media moms said they made their purchase because of a recommendation from a personal review blog
3. 18.3 million Internet users who are moms read blogs at least once a month
4. In 2014, 63% (nearly 21 million) of all online moms will read blogs
5. Moms mention brands an average of 73 times per week compared with just 57 times per week among males
6. Seventy-seven percent of mom bloggers will only write about products or brands whose reputations they approve of, and another 14% will write about brands or products they boycott
7. Ninety percent of moms are online vs. just 76% of women in general
8. Sixty-four percent of moms ask other mothers for advice before they purchase a new product and 63% of all mothers surveyed consider other moms the most credible experts when they have questions

Women Online
1. As early as 2000, women were found to have surpassed men in Internet usage
2. Seventy-eight percent of women in the U.S. use the Internet for product information before making a purchase
3. Thirty-three percent research products and services online before buying offline
4. Women account for 58% of all total online spending
5. Twenty-two percent shop online at least once a day
6. Ninety-two percent pass along information about deals or finds to others
7. The average number of contacts in their e-mail or mobile lists is 171
8. Seventy-six percent want to be part of a special or select panel
9. Fifty-eight percent would toss a TV if they had to get rid of one digital device (only 11% would ditch their laptops)

The sources for the statistics cited in this article are as follows: She-Economy, Ms Smith Marketing, StartUpNation, Clickz, Inc.com, Girl Power Marketing, Catalyst, Forbes.

Read more: http://www.mediapost.com/publications/article/170413/marketing-to-women-30-stats-to-know.html#ixzz1qtuiLJY2

Why Big Retail Is Running Scared Of The Millennial Generation

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(Image credit: Getty Images via @daylife)

Spending a good portion of your early twenties in pseudo poverty was once a rite of passage, but for those Millennials whose pockets are currently empty, it seems less like a temporary college era phenomenon and more like a new financial reality. And this lack of buying power has retailers running scared.

As William Hawk writes in the 2011 Consumer Expenditure Survey Anthology, “compared with average U.S. consumer units, those headed by persons 25 years or younger earn lower incomes, are less likely to own a home or a car, and spend less on food, gifts, health care, and retirement plans. They are also more likely to rent a home and spend more on education and alcohol.” And that spending on education is a major contributor to the debt burden faced by many Millennials.

According to recent survey data from a PNC Financial Services, the average member of Gen Y carries $45 000 in debt, with student debt being the most common form. Add to that an employment rate for 18 – 24 year-olds that hovers only slightly above 50% and a majority of the Millennial population (or at least the female half) claiming that they’ve delayed major purchases and life changes because of the recession and you have the making of one toxic target market.


And retailers have taken notice. Or at least those who analyze retailers have, with Bloomberg recently quoting from a report by WSL Strategic Retail that documents how the fortunes of retailers such as Gap, Urban Outfitters and Aeropostale have been suffering in light of decreased consumer spending by twentysomethings and cautions these retailers and others in their shoes about aiming their marketing campaigns at a cohort that currently lacks spending power. The report’s authors also warn that a generation of potential consumers who are unwilling or unable to spend will eventually hurt higher-end retailers – those who can’t afford Gap prices today are unlikely to be able to trade up to Neiman Marcus (or even Banana Republic) anytime soon.

Indeed, according to research from Yale cited in The Atlantic, those who graduate and land their first jobs during a recession tend to earn less than their peers who graduate during better economic times – a wage gap that still persists almost two decades after college graduation. As well, with Gen Y’s tendency toward brand loyalty – 70% of those surveyed by Edelman Digital claimed that they stick with brands they’ve become accustomed to – weaning them from entrenched lower-end options over the long-term may be an uphill battle for those offering luxury products and services.

And even if Millennials did have cash to flash, there is no guarantee it would translate to bigger bottom lines for retailers. Not only do 37% of Millennials claim to distrust big business according to survey data, but the nature of the shopping experience sought by twentysomethings – sensory, shareable and less about finding a deal than participating in a social event – favors browsing over buying and those niche retailers that are able to create unique, immersive buying environments. That’s hardly the forte of the chain store. As well, 40% of Gen Y participants in the aforementioned Edelman Digital study claimed a preference for buying local, even if these goods or services were more expensive than mass-market alternatives. Add into the mix a youthful penchant for purchase history as a form of personal expression and you have an outlook that favors putting precious disposable income toward a one-of-a-kind dress from the boutique on the corner vs. khakis from the mall.

In addition to attitudes about the buying experience, there’s also the distinct possibility that having had to practice fiscal austerity during their formative buying years will have a long-lasting effect on Gen Y’s psyche. Even now, they’re more likely than older generations to both support an expansion of government and its programs – a New Deal mentality perhaps? – and to believe that you ”can’t be too careful” when dealing with people, according to the Pew Research Center. These findings are echoed in a National Bureau of Economic Research paper by Giuliano and Spilimbergo that looked at 18- 25 year-olds who came of age during economic downturns over the last 30 years. “We find that individuals experiencing recessions during the formative years believe that luck rather than effort is the most important driver of individual success, support more government redistribution, and have less confidence in institutions,” they write.

Lack of cash, skepticism about big business, a preference for customized products and heuristic purchasing experiences and possibly a lifelong tendency toward scrimping? Retailers have a right to be nervous about the Millennial market.

Is there a bias against creativity?

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By Amanda Enayati, Special to CNN

updated 8:25 AM EDT, Wed March 28, 2012

Amanda Enayati

Editor's note: CNN contributor Amanda Enayati ponders the theme of seeking serenity: the quest for well-being and life balance in stressful times.

(CNN) -- Creativity has taken center stage in recent years, with a slew of books, articles and TED talks extolling the virtues of imagination and exhorting young and old to go out and exercise their creative muscle.

In a 2010 IBM poll of CEOs worldwide, creativity was identified as the single most important leadership trait for success, enabling businesses to rise above an increasingly complex environment.

The future belongs to "creators and empathizers, pattern recognizers and meaning makers," declared author Daniel Pink in the introduction to his best-selling book "A Whole New Mind: Why Right-Brainers Will Rule the Future."

Creativity also matters to our emotional well-being as we find our way in an uncertain, rapidly shifting world. Imagination underpins our ability to remain resilient during difficult and stressful times since creative people tend to be more tolerant of ambiguity and better able to come back from defeat.

And yet, despite its growing importance, creativity suffers from an odd sort of paradox. According to psychologist and Wharton management professor Jennifer Mueller, research shows that even as people explicitly aspire to creativity and strongly endorse it as a fundamental driving force of positive change, they routinely reject creative ideas and show an implicit bias against them under conditions of uncertainty. Subjects in Mueller's study also exhibited a failure to see or acknowledge creativity, even when directly presented with it.

It would appear that we suffer from a bias against creativity. But we are in denial about it, possibly because of what it may say about us.

"Because there is such a strong social norm to endorse creativity, and people also feel authentic positive attitudes toward creativity, people may be reluctant to admit that they do not want creativity; hence, the bias against creativity may be particularly slippery to diagnose," Mueller and her colleagues suggest.

Why the bias?

"Creativity is doing something differently than you've done before," says Beau Lotto, a neuroscientist and founder of Lottolab, a hybrid art studio and science lab. From an evolutionary standpoint, uncertainty was a bad thing. "If you weren't sure that there was a tiger in front of you, by the time you were sure it was too late," Lotto observes. "Our brains thus evolved to take uncertainty and make it certain."

Mueller says, "We are intolerant of uncertainty in general. The more creative something is, the more novel it is. And the more novel it is, the greater the uncertainty we are likely to have about its feasibility."

These negative associations tend to be unacknowledged, and there is evidence that they are unconscious, as in the case of executives who demand creativity but continue to reject creative ideas.

Herein, however, lies the dilemma: Creativity is what we need to help us get through times of greatest uncertainty and difficulty. And it's exactly during those times, perhaps when we need it most, that we are least likely to embrace creativity.

Imagination scares us because it demands a foray into the unknown. "But only by going into a space of uncertainty can we do anything new," Lotto says. "That is a tremendous challenge, isn't it?"

Another reason for the bias against creativity may be the perception that something can either be creative or practical, but that much more rarely can it be both. Many (and perhaps even most) people hold the belief that for every success story such as Steve Jobs, the "patron saint of the creative class," there are thousands (or more) chronically unemployed and underemployed "artists."

This belief gives rise to a duality, where practical and creative endeavors lead largely separate existences -- one slogged at during the workweek and the other indulged on nights and weekends, or dismissed as a luxury.

The creativity versus practicality dissonance also manifested in aspects of Mueller's research. She refers to the two separate mind-sets of the "why" people and the "how" people.

People focused on "why" tend to frame the world in more abstract ways. In general, they don't tend to have feasibility concerns. Those who are in a "how" mind-set, however, are so focused on feasibility that they are likely to overlook or dismiss creative ideas.

"Most boardrooms are all 'how,' and the 'why' is crushed," Mueller says. "This is why Steve Jobs was so remarkable. He had a solid grasp of the 'why' and was also able to overcome objections to the 'how.' He was able to overcome the reality distortion field."

The folly of seeking certainty

There are a number of problems with our obsession with creating certainty, and the most important is that certainty does not exist.

"Certainty is an illusion! A delusion!" Lotto says.

Or, as Clint Eastwood once said: "If you want a guarantee, buy a toaster."

The second problem is that the more we seek to create tools to make life predictable -- from packaged foods to Starbucks, GPS devices to smartphones, Yelp to Trip Advisor -- the more we diminish aspects of our brains capable of dealing with the unexpected.

"Technology is an amazing empowerment and a huge disablement," says Laura Richardson, principal designer at frog design. "We are losing our capacity for resilience."

Richardson is a big believer in the "MacGyver" manifesto. (MacGyver, of course, was the ever-resourceful television character who was able to solve complex problems with duct tape, paper clips and any other material he found handy.)

"I remember being locked out of the house once when I was growing up," Richardson says. "I found an old ruler, somehow prodded up the window latch and got in. It was an amazing sense of accomplishment."

She says some of our best stories are about those times when we were forced into something and had to use ingenuity to find our way out.

Richardson says she believes that the future favors the flexible. She quotes MAKE magazine founding editor Dale Dougherty, honored by the White House as a Champion of Change, who wrote: "Our future security lies in knowing what we are capable of creating and how we can adapt to change by being resourceful."

Becoming comfortable with uncertainty

Is it possible to overcome our inclination toward the predictable?

Mueller notes an important exception to our avoidance of the unknown. Research shows that the framing of uncertainty changes the way people react.

"We don't mind uncertainty when it's associated with something positive, like hope," she says. "Frame something positively, and people will behave differently."

David Kelley, founder of the design firm IDEO, observes, "Creativity is being comfortable with having ideas and not fearing being judged when you put your ideas out there."

Kelley, recognized as one of America's leading design innovators, is passionate about democratizing creativity by helping people develop creative confidence.

He's not, however, teaching people how to be creative. "They are inherently creative," Kelley says. "All we are doing is taking away the blocks."

Those blocks form early, according to Kelley. Though young children are naturally and unabashedly creative, they either opt out of thinking of themselves as creative or have it hammered out of them in elementary school. "A kind of atrophy sets in, when they start to trust their analytical minds but not their intuitive minds."

Removing those blocks has a great deal to do with fear. He suggests approaching the fear of creativity the same way you would approach any other kind of phobia, such as the fear of heights or snakes.

Kelley finds that creative confidence carries over into other aspects of people's lives -- in the way they solve problems, sing karaoke, throw dinner parties. "Once you have done something dangerous and succeeded, you try it in other places. And you begin to learn how to synthesize your experience and intuition to make complex and important decisions."

With the challenges we are facing, we need to rethink what creativity means, Richardson says. We need to expand what we mean by creative. Creativity is not just about painting or drawing or art. It is about problem-solving. It's the flexibility of your mind, the ability to see things that no one can see and envision something entirely different. We are creating the future, bringing about change. And there is something incredibly empowering about that.