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How can we make love statistics? Interactive graphs?

Think you’re good at guessing stats? Guess again. Whether we consider ourselves math people or not, our ability to understand and work with numbers is terribly limited, says data visualization expert Alan Smith. Smith explores the mismatch between what we know and what we think we know.

Alan Smith. Data visualisation editor
Alan Smith uses interactive graphics and statistics to breathe new life into how data is presented. Full bio
Filmed April 2016

Back in 2003, the UK government carried out a survey. And it was a survey that measured levels of numeracy in the population.

And they were shocked to find out that for every 100 working age adults in the country, 47 of them lacked Level 1 numeracy skills. Now, Level 1 numeracy skills — that’s low-end GCSE score. It’s the ability to deal with fractions, percentages and decimals.

this figure prompted a lot of hand-wringing in Whitehall. Policies were changed, investments were made, and then they ran the survey again in 2011. So can you guess what happened to this number? It went up to 49.

0:57 And in fact, when I reported this figure in the FT, one of our readers joked and said, This figure is only shocking to 51 percent of the population.”

But I preferred the reaction of a schoolchild when I presented at a school this information, who raised their hand and said, “How do we know that the person who made that number isn’t one of the 49 percent either?”

1:20 (Laughter)

So clearly, there’s a numeracy issue, because these are important skills for life, and a lot of the changes that we want to introduce in this century involve us becoming more comfortable with numbers. (Can’t learn numeracy without using a pen and pencil?)

it’s not just an English problem. OECD this year released some figures looking at numeracy in young people, and leading the way, the USA — nearly 40 percent of young people in the US have low numeracy. Now, England is there too, but there are seven OECD countries with figures above 20 percent. That is a problem, because it doesn’t have to be that way. If you look at the far end of this graph, you can see the Netherlands and Korea are in single figures. So there’s definitely a numeracy problem that we want to address. (It is the method used to learning numeracy)

 as useful as studies like these are, I think we risk herding people inadvertently into one of two categories; that there are two kinds of people: those people that are comfortable with numbers, that can do numbers, and the people who can’t.

And what I’m trying to talk about here today is to say that I believe that is a false dichotomy. It’s not an immutable pairing. I think you don’t have to have tremendously high levels of numeracy to be inspired by numbers, and that should be the starting point to the journey ahead.

one of the ways in which we can begin that journey, for me, is looking at statistics. Now, I am the first to acknowledge that statistics has got somewhat of an image problem.

2:52 (Laughter)

It’s the part of mathematics that even mathematicians don’t particularly like, because whereas the rest of maths is all about precision and certainty, statistics is almost the reverse of that.

But actually, I was a late convert to the world of statistics myself. If you’d asked my undergraduate professors what two subjects would I be least likely to excel in after university, they’d have told you statistics and computer programming, and yet here I am, about to show you some statistical graphics that I programmed. (You think you comprehended probability and statistics, but you forget them if Not practiced)

 what inspired that change in me? What made me think that statistics was actually an interesting thing? It’s really because statistics are about us.

If you look at the etymology of the word statistics, it’s the science of dealing with data about the state or the community that we live in. So statistics are about us as a group, not us as individuals. And I think as social animals, we share this fascination about how we, as individuals, relate to our groups, to our peers. And statistics in this way are at their most powerful when they surprise us.

there’s been some really wonderful surveys carried out recently by Ipsos MORI in the last few years. They did a survey of over 1,000 adults in the UK, and said, for every 100 people in England and Wales, how many of them are Muslim? Now the average answer from this survey, which was supposed to be representative of the total population, was 24. That’s what people thought. British people think 24 out of every 100 people in the country are Muslim. Now, official figures reveal that figure to be about five. So there’s this big variation between what we think, our perception, and the reality as given by statistics. And I think that’s interesting. What could possibly be causing that misperception?

I was so thrilled with this study, I started to take questions out in presentations. I was referring to it. Now, I did a presentation at St. Paul’s School for Girls in Hammersmith, and I had an audience rather like this, except it was comprised entirely of sixth-form girls.

And I said, “Girls, how many teenage girls do you think the British public think get pregnant every year?” And the girls were apoplectic when I said the British public think that 15 out of every 100 teenage girls get pregnant in the year. And they had every right to be angry, because in fact, I’d have to have closer to 200 dots before I could color one in, in terms of what the official figures tell us.

And rather like numeracy, this is not just an English problem. Ipsos MORI expanded the survey in recent years to go across the world. And so, they asked Saudi Arabians, for every 100 adults in your country, how many of them are overweight or obese? And the average answer from the Saudis was just over a quarter. That’s what they thought. Just over a quarter of adults are overweight or obese. The official figures show, actually, it’s nearer to three-quarters.

5:56 (Laughter)

5:57 So again, a big variation.

I love this one: they asked the Japanese, for every 100 Japanese people, how many of them live in rural areas? The average was about a 50-50 split, just over halfway. They thought 56 out of every 100 Japanese people lived in rural areas. The official figure is seven.

So extraordinary variations, and surprising to some, but not surprising to people who have read the work of Daniel Kahneman, for example, the Nobel-winning economist. He and his colleague, Amos Tversky, spent years researching this disjoint between what people perceive and the reality, the fact that people are actually pretty poor intuitive statisticians. (I read many of their research papers in the late 80’s)

And there are many reasons for this. Individual experiences, certainly, can influence our perceptions, but so, too, can things like the media reporting things by exception, rather than what’s normal. Kahneman had a nice way of referring to that. He said, “We can be blind to the obvious” — so we’ve got the numbers wrong — “but we can be blind to our blindness about it.” And that has enormous repercussions for decision making.

at the statistics office while this was all going on, I thought this was really interesting. I said, this is clearly a global problem, but maybe geography is the issue here.

These were questions that were all about, how well do you know your country? So in this case, it’s how well do you know 64 million people? Not very well, it turns out. I can’t do that. So I had an idea, which was to think about this same sort of approach but to think about it in a very local sense. Is this a local? If we reframe the questions and say, how well do you know your local area, would your answers be any more accurate?

I devised a quiz: How well do you know your area? It’s a simple Web app. You put in a post code and then it will ask you questions based on census data for your local area. And I was very conscious in designing this. I wanted to make it open to the widest possible range of people, not just the 49 percent who can get the numbers.

I wanted everyone to engage with it. So for the design of the quiz, I was inspired by the isotypes of Otto Neurath from the 1920s and ’30s. Now, these are methods for representing numbers using repeating icons. And the numbers are there, but they sit in the background. So it’s a great way of representing quantity without resorting to using terms like “percentage,” “fractions” and “ratios.”

So here’s the quiz. The layout of the quiz is, you have your repeating icons on the left-hand side there, and a map showing you the area we’re asking you questions about on the right-hand side. There are 7 questions. Each question, there’s a possible answer between zero and a hundred, and at the end of the quiz, you get an overall score between zero and a hundred.

And so because this is TEDxExeter, I thought we would have a quick look at the quiz for the first few questions of Exeter. And so the first question is: For every 100 people, how many are aged under 16? Now, I don’t know Exeter very well at all, so I had a guess at this, but it gives you an idea of how this quiz works. You drag the slider to highlight your icons, and then just click “Submit” to answer, and we animate away the difference between your answer and reality. And it turns out, I was a pretty terrible guess: five.

How about the next question? This is asking about what the average age is, so the age at which half the population are younger and half the population are older. (This is the definition of the median) And I thought 35 — that sounds middle-aged to me.

9:35 (Laughter)

9:39 Actually, in Exeter, it’s incredibly young, and I had underestimated the impact of the university in this area. The questions get harder as you go through. So this one’s now asking about homeownership: For every 100 households, how many are owned with a mortgage or loan? And I hedged my bets here, because I didn’t want to be more than 50 out on the answer.

 these get harder, these questions, because when you’re in an area, when you’re in a community, things like age — there are clues to whether a population is old or young. Just by looking around the area, you can see it. Something like homeownership is much more difficult to see, so we revert to our own heuristics, our own biases about how many people we think own their own homes.

the truth is, when we published this quiz, the census data that it’s based on was already a few years old. We’ve had online applications that allow you to put in a post code and get statistics back for years. So in some senses, this was all a little bit old and not necessarily new. But I was interested to see what reaction we might get by gamifying the data in the way that we have, by using animation and playing on the fact that people have their own preconceptions.

It turns out, the reaction was more than I could have hoped for. It was a long-held ambition of mine to bring down a statistics website due to public demand.

11:06 (Laughter)

This URL contains the words “statistics,” “gov” and “UK,” which are three of people’s least favorite words in a URL. And the amazing thing about this was that the website came down at quarter to 10 at night, because people were actually engaging with this data of their own free will, using their own personal time.

I was very interested to see that we got something like a quarter of a million people playing the quiz within the space of 48 hours of launching it. And it sparked an enormous discussion online, on social media, which was largely dominated by people having fun with their misconceptions, which is something that I couldn’t have hoped for any better, in some respects. I also liked the fact that people started sending it to politicians. How well do you know the area you claim to represent? (All candidates to public office must go through such quizzes in their locality and the nation)

 then just to finish, going back to the two kinds of people, I thought it would be really interesting to see how people who are good with numbers would do on this quiz. The national statistician of England and Wales, John Pullinger, you would expect he would be pretty good. He got 44 for his own area.

12:16 (Laughter)

Jeremy Paxman — admittedly, after a glass of wine — 36. Even worse. It just shows you that the numbers can inspire us all. They can surprise us all.

12:31 So very often, we talk about statistics as being the science of uncertainty. My parting thought for today is: actually, statistics is the science of us. And that’s why we should be fascinated by numbers. 

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“Whether we consider ourselves math people or not, our ability to understand and work with numbers is terribly limited.”

A talk from TEDxExeter.
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Alan Smith explores the mismatch between what we know and what we th…
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The Psychology of Defeating Fear: Low Self-Esteem and Hate Live in the Mind

Emotional Agility: Get Unstuck, Embrace Change, and Thrive in Work and Life.

No correlation between facts of lower crime rates and our feeling of increased fear?

August 18, 2016

Fear has always had a hold on us, but never with such fervor. Welcome to the end of times.

We cannot sink lower. ISIS is at our door, our elected leaders are malevolent man-children, amber alerts are lighting up our phones, immigrants are bringing a plague of violence, someone was murdered while playing Pokemon GO, climate change is flooding our homes and starving our crops. How can we go on?

But, breathe deep and let the clouds of panic part; it turns out there’s very little correlation between the above mindset and reality.

Terrorism, despite it reported epidemic, is less prevalent in the Western world now than it was in the 1970s and ’80s. Crime is decreasing.

Immigrants actually lower crime in gateway cities, and don’t affect crime rates elsewhere.

Rates of rape and sexual assault have been declining for decades, and are now a quarter or less of their peaks in the past.

Despite Zika and Ebola hype, infectious diseases are down. The list continues and is wonderfully documented at length in Steven Pinker’s book, The Better Angels of Our Nature: Why Violence Has Declined.

However, that’s not what we like to hear, because we don’t feel safe. This good news feels inaccurate. Why?

we have the non-stop news cycle to thank for that, and social platforms that turn every smartphone user into an independent correspondent capturing every horror from the grocery aisle to the protest march.

We are experiencing an oversaturation of fearful messages.

“What is really fascinating when we look at the brain research around fear is that our brains proxy anything that feels unfamiliar, incoherent or inaccessible as being unsafe,” says Harvard psychologist Susan David, author of Emotional Agility.

We like familiarity. We like it so much that hearing that terrorism is likely to strike us personally at any moment is somehow more comforting than the message that it’s not, because the fear is more familiar to us at this stage.

We’ve come to trust it. If we hear something often enough, we associate familiarity with truth.

It even works on a personal level, where people are drawn to those who hurt them and belittle them purely because the message is familiar. It feels cozy and you’ve been there before. You know how this works. It’s scarier to try something new.

And of course fear is heavily embedded in politics.

We have politicians who are effectively demagogues, who aim to inspire fear and cement our bond to them by hyperbolizing a threat to our mortality.

So how can we repel deceptive messaging and see clearly?

Psychologist Daniel Kahneman identified two kinds of thinking systems:

“System 1 thinking is the intuitive response, the emotional visceral ‘us’ and ‘them’ that can sometimes arise out of fear. System 2 is the deliberate thoughtful examination of: what is this person saying? Is it in line with how I really want to be? Is it connected with how I really want to raise my children? Is this a world that I want to support?’

David says that if we can step back from our fear and see it for what it is – manipulated panic rather than data – we can protect ourselves from the demagoguery message and re-align with our true values.

It is difficult to do, and the repetition makes it harder to see straight.

Here David draws on the 2016 US election as an example. “… We used to hear things that the politicians would say and we would be like, ‘Oh my goodness how can the person possibly say that thing?’

But what happens over time is the more familiar something sounds… even if the story is inaccurate, even if the story doesn’t serve us, the more we are likely to become immured to it and immune to it.”

Things that were said in the election six months ago that horrified people are now being met with a light-hearted ‘Oh, there we go again’.

David questions the media ethics in pushing out stories that overexpose inaccurate messages of fear that could incite violence and hatred. It familiarizes us to an incorrect message, leaving our values open to corruption.

Susan David’s most recent book is Emotional Agility: Get Unstuck, Embrace Change, and Thrive in Work and Life.

Law of small numbers that looms large

Suppose you own 100 stores in cities and in rural areas.

You hire a consultant to study the problem of shoplifting in your stores. The rates of theft compared to volume of sales show that shoplifting is highest in rural areas.

It turned out that the lowest rates are also located in the stores in rural areas. What gives?

In small stores, even a single theft looms large and the rates vary greatly.

If you order the rates according to size of stores, most of the shoplifting are in the vast middle, while the small stores occupy the tails of the curve.

It is not the location of the stores that is the deciding factor but their size.

If you read this caption “Start-ups employ smarter employees” be on your guard.

Start ups have small number of employees and their average IQ fluctuate far more than those employed by large companies.

Daniel Kahneman reveals that even experienced scientists succumb to the Law of small numbers: They tend to forget the random distribution law in their initial assessments.

Read: The Art of Thinking Clear

 

‘Meaning Quotient’ MQ of work? And how to increase it?
 
Musicians talk about being “in the groove,” sportsmen about being “in the zone.
Can employees in the workplace experience similar performance peaks and, if so, what can top management do to encourage the mental state that brings them about?

Through a few simple techniques, executives can boost workplace “MQ” and inspire employees to perform at their peak.

Susie Cranston and Scott Keller published in McKinsey Quarterly this Jan 2013:

Increasing the ‘meaning quotient’ of work

We’ve long been interested in work environments that inspire exceptional levels of energy, increase self-confidence, and boost individual productivity.

When we ask leaders about the ingredient they think is most often missing for them and for their colleagues—and by implication is most difficult to provide—they almost invariably signal the same thing: a strong sense of meaning.

By “meaning,” we and they imply a feeling that what’s happening really matters, that what’s being done has not been done before or that it will make a difference to others.

The idea of meaning at work is not new.

Two contributions to McKinsey Quarterly1 over the past year have highlighted this theme.

In one, the authors demonstrate how misguided leaders often kill meaning in avoidable ways. The author of the other suggests that “meaning maker” is a critical role for corporate strategists.

In this article, we will show from our research how meaning drives higher workplace productivity and explain what business leaders can do to create meaning.

Meaning and performance

The mental state that gives rise to great performance—in sports, business, or the arts—has been described in different ways.

The psychologist Mihàly Csìkszentmihàlyi studied thousands of subjects, from sculptors to factory workers, and asked them to record their feelings at intervals throughout the working day. Csìkszentmihàlyi came up with a concept we consider helpful.

He observed that people fully employing their core capabilities to meet a goal or challenge created what he called “flow.” More important, he found that individuals who frequently experienced it were more productive and derived greater satisfaction from their work than those who didn’t.

The subjects set goals for themselves to increase their capabilities, thereby tapping into a seemingly limitless well of energy. And they expressed a willingness to repeat those activities in which they achieved flow even if they were not being paid to do so.

Athletes describe the same feeling as being in the zone.

Bill Russell, a key player for the Boston Celtics during the period when they won 11 professional-basketball championships in 13 years, put it thus:

“When it happened, I could feel my play rise to a new level. . . . It would surround not only me and the other Celtics, but also the players on the other team. . . . At that special level, all sorts of odd things happened. The game would be in the white heat of competition, and yet somehow I wouldn’t feel competitive. . . . I’d be putting out the maximum effort . . . and yet I never felt the pain.”2

Flow sounds great in theory, but few business leaders have mastered the skill of generating it reliably in the workplace.

An easy first step is to consider what creates flow in your own work situation—a question we have put directly to more than 5,000 executives during workshops we’ve conducted over the last decade.

In this exercise, individuals initially think about their own personal peak performance with a team, when, in other words, they have come closest to the feelings Csìkszentmihàlyi and Russell describe. Then they pinpoint the conditions that made this level of performance possible: what in the team environment was there more or less of than usual?

The remarkably consistent answers we’ve received fall into three categories.

1. The first set includes elements such as role clarity, a clear understanding of objectives, and access to the knowledge and resources needed to get the job done. These are what one might term rational elements of a flow experience or, to use a convenient shorthand, its intellectual quotient (IQ) coined by Daniel Goleman . When the IQ of a work environment is low, the energy employees bring to the workplace is misdirected and often conflicting.

2. Another set of answers includes factors related to the quality of the interactions among those involved.  Respondents often mention a baseline of trust and respect, constructive conflict, a sense of humor, a general feeling that “we’re in this together,” and the corresponding ability to collaborate effectively. These create an emotionally safe environment to pursue challenging goals, an environment with a high emotional quotient (EQ). When the EQ of a workplace is lacking, employee energy dissipates in the form of office politics, ego management, and passive-aggressive avoidance of tough issues.

While IQ and EQ are absolutely necessary to create the conditions for peak performance, they are far from sufficient.

3. The longest list of words we have compiled from executives’ answers to our peak-performance question over the last ten years has little to do with either of these categories. This third one describes the peak-performance experience as involving high stakes; excitement; a challenge; and something that the individual feels matters, will make a difference, and hasn’t been done before.

We describe this third category as the meaning quotient (MQ) of work. When a business environment’s MQ is low, employees put less energy into their work and see it as “just a job” that gives them little more than a paycheck.

The opportunity cost of the missing meaning is enormous.

When we ask executives during the peak-performance exercise how much more productive they were at their peak than they were on average, for example, we get a range of answers, but the most common at senior levels is an increase of five times.

Most report that they and their employees are in the zone at work less than 10 percent of the time, though some claim to experience these feelings as much as 50 percent of it.

If employees working in a high-IQ, high-EQ, and high-MQ environment are five times more productive at their peak than they are on average, consider what even a relatively modest 20%-point increase in peak time would yield in overall workplace productivity—it would almost double.

When we ask executives to locate the bottlenecks to peak performance in their organizations, more than 90% choose MQ-related issues.

Executives point out that much of the IQ tool kit is readily observable and central to what’s taught in business schools. The EQ tool kit, while “softer,” is now relatively well understood following Goleman’s popularization of the concept in the mid-1990s. The MQ tool kit is different.

What to do differently?

Business leaders, we know from other sources, are striving hard to find the missing MQ ingredients so they can improve motivation and workforce productivity.

Late last year, for example, a survey (conducted by The Conference Board and McKinsey) of more than 500 US-based HR executives identified employee engagement as one of the top five critical human-capital priorities facing organizations.3

Management thinkers are also on the case.

Gary Hamel urges modern managers to see themselves as “entrepreneurs of meaning.” In The Progress Principle, Harvard Business School professor Teresa Amabile and her coauthor Steve Kramer present rigorous field research highlighting the enormous benefits that a sense of forward momentum can have for employees’ “inner work life.”4 Csìkszentmihàlyi writes extensively about “the making of meaning” in his book Good Business.5

In our experience, though, there’s often a disconnect between the desire of practitioners to create meaning in the workplace, the good ideas emerging from cutting-edge research, and the number of specific, practical, and reliable tools that leaders know how to use.

Often, platitudes about communication, quality feedback, job flexibility, and empowerment are used as substitutes for such tools. Much of this amounts to little more than advice about how to be a good manager.

Inspirational visions, along the lines of Walt Disney’s “make people happy” or Google’s “organize the world’s information,” have little relevance if you produce ball bearings or garage doors.

In McKinsey’s research, we’ve uncovered a set of specific, actionable techniques underpinned both by experience and a significant body of social-science work.

The full tool kit can be found in Beyond Performance: How Great Organizations Build Ultimate Competitive Advantage.6 The three examples described here are not only among the most counterintuitive (and therefore the most often overlooked) but also the most powerful.

Strategy #1: Tell five stories at once

We typically see organizational leaders tell two types of stories to inspire their teams.

The first turnaround story runs along the lines of “We’re performing below industry standard and must change dramatically to survive—incremental change is not sufficient to attract investors to our under-performing company.

The second good-to-great story goes something like this: “We are capable of far more, given our assets, market position, skills, and loyal staff, and can become the undisputed leader in our industry for the foreseeable future.

The problem with both approaches is that the story centers on the company, which will inspire some, but by no means all employees. Our research shows that four other sources give individuals a sense of meaning, including their ability to have an impact on

  • society—for example, making a better society, building the community, or stewarding resources
  • the customer—for instance, making life easier and providing a superior service or product
  • the working team—for instance, a sense of belonging, a caring environment, or working together efficiently and effectively
  • themselves—examples include personal development, a higher paycheck or bonus, and a sense of empowerment

Surveys of hundreds of thousands of employees show that the split in most companies—regardless of management level, industry sector, or geography (developed or developing economies)—is roughly equal. It appears that these 5 sources are a universal human phenomenon.

The implication for leaders seeking to create high-MQ environments is that a turnaround or a good-to-great story will strike a motivational chord with only 20 percent of the workforce. The same goes for a “change the world” vision like those of Disney and Google or appeals to individuals on a personal level.

The way to unleash MQ-related organizational energy is to tell all five stories at once.

A recent cost-reduction program at a large US financial-services company began with a rational-change story focused on the facts: expenses were growing faster than revenues. Three months into the program, it was clear that employee resistance was stymieing progress.

The management team therefore worked together to recast the story to include elements related to society (more affordable housing), customers (increased simplicity and flexibility, fewer errors, more competitive prices), working teams (less duplication, more delegation, increased accountability, a faster pace), and individuals (bigger and more attractive jobs, a once-in-a-career opportunity to build turnaround skills, a great opportunity to “make your own” institution). The program was still what it was—a cost-reduction program—but the reasons it mattered were cast in far more meaningful terms.

Within a month, the share of employees reporting that they were motivated to drive the change program forward jumped to 57 percent, from 35 percent, according to the company’s employee-morale pulse surveys. The program went on to exceed initial expectations, raising efficiency by 10 percent in the first year.

Strategy #2: Let employees ‘write their own lottery ticket’

The first strategy gives specific and practical guidance about how to tell the story. Yet the best meaning makers spend more time asking than telling.

In one of Daniel Kahneman’s famous experiments, researchers ran a lottery with a twist. Half of the participants were randomly assigned a lottery ticket. The remaining half were given a blank piece of paper and asked to write down any number they pleased. Just before drawing the winning number, the researchers offered to buy back the tickets from their holders. The question they wanted to answer was how much more would you have to pay people who “wrote their own number” than people who received a number randomly.

The rational answer should be no difference at all, since a lottery is pure chance, and therefore every ticket number, chosen or assigned, has the same odds of winning. A completely rational actor might even want to pay less for a freely chosen number, given the possibility of duplicate ones. The actual answer? Regardless of geography or demographics, researchers found they had to pay at least five times more to those who chose their own number.

This result reveals a truth about human nature: when we choose for ourselves, we are far more committed to the outcome—by a factor of at least five to one.

In business, of course, leaders can’t just let everyone decide their own direction. But they can still apply the lessons of the lottery-ticket experiment. The head of financial services at one global bank we know first wrote down his change story, shared it with his team for feedback, and then in effect asked all individual team members to write their own lottery ticket: what change story, in each of the businesses, supported the wider message?

His team members in turn wrote change stories, shared them with their teams, and the process continued all the way to the front line. Although this method took far longer than the traditional road-show approach, the return on commitment to the program was considered well worth the investment and an important reason the bank achieved roughly two times its revenue-per-banker-improvement targets.

Likewise, when Neville Isdell took charge at Coca-Cola, in 2004, he co-created a turnaround strategy by bringing together his top 150 employees for three multi-day “real work” sessions. The process was then cascaded further down into the organization, at small working meetings where participants could in effect write their own lottery ticket about the implications for their particular parts of the business.

With hindsight, this process of creating and interactively cascading what became known as The Manifesto for Growth is seen as a pivotal intervention in a two-year turnaround in which the group stopped destroying shareholder value and generated returns of 20 percent, driven by volume increases equivalent to selling an extra 105 million bottles of Coke a day. In this period, staff turnover fell by 25%, and the company reported what external researchers called unprecedented increases in employee engagement for an organization of this size.

Leaders who need to give their employees more of a sense of direction can still leverage the lottery-ticket insight by augmenting their telling of the story with asking about the story. David Farr, chairman and CEO of Emerson Electric, for example, is known for asking virtually everyone he encounters in the organization 4 questions:

(1) how do you make a difference? (testing for alignment with the company’s direction);

(2) what improvement idea are you working on? (emphasizing continuous improvement);

(3) when did you last get coaching from your boss? (emphasizing the importance of people development); and

(4) who is the enemy? (emphasizing the importance of “One Emerson” and no silos, as well as directing the staff’s energy toward the external threat). The motivational effect of this approach has been widely noted by Emerson employees.

Strategy #3: Use small, unexpected rewards to motivate

US author Upton Sinclair once wrote, “It is difficult to get a man to understand something when his salary depends upon his not understanding it.” The flip side, however, isn’t true. When business objectives are linked to compensation, the motivation to drive for results is rarely enhanced meaningfully.

The reason is as practical as psychological. Most annual-compensation plans of executives are so full of key performance indicators that the weighting of any one objective becomes largely meaningless in the grand scheme of things. Furthermore, most compensation plans typically emphasize financial metrics whose results depend on myriad variables, many beyond individual control. On top of that, most companies don’t have deep enough pockets to make compensation a significant driver of MQ in the workplace.

Leaders of organizations that successfully instill meaning understand the power of other methods. Terry Burnham and Jay Phelan’s book, Mean Genes,7 describes an experiment in which 50 percent of a group of people using a photocopier found a dime in the coin-return slot. When all were asked to rate their satisfaction level, those who got the dime scored an average of 6.5 on a scale of 1 to 7, while those who didn’t scored just 5.6.

The lesson here is that when we aren’t expecting a reward, even a small one can have a disproportionate effect on our state of mind. And that’s also true of employees in the workplace.

At ANZ Bank, John McFarlane gave all employees a bottle of champagne for Christmas, with a card thanking them for their work on a major change program.

The CEO of Wells Fargo, John Stumpf, marked the first anniversary of its change program by sending out personal thank-you notes to all the employees who had been involved, with specific messages related to the impact of their individual work.

Indra Nooyi, CEO of PepsiCo, sends the spouses of her top team handwritten thank-you letters. After seeing the impact of her own success on her mother during a visit to India, she began sending letters to the parents of her top team, too.

Some managers might dismiss these as token gestures—but employees often tell us that the resulting boost in motivation and in connection to the leader and the company can last for months if not years. As Sam Walton, founder of Wal-Mart Stores, put it, “Nothing else can quite substitute for a few well-chosen, well-timed, sincere words of praise. They’re absolutely free—and worth a fortune.”

 

Of the three Qs that characterize a workplace likely to generate flow and inspire peak performance, we frequently hear from business leaders that MQ is the hardest to get right. Given the size of the prize for injecting meaning into people’s work lives, taking the time to implement strategies of the kind described here is surely among the most important investments a leader can make.

About the authors

Susie Cranston is a senior expert in McKinsey’s San Francisco office, and Scott Keller is a director in the Southern California office.


adonis49

adonis49

adonis49

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