Made to Stick (19 page)

The example of Thomas Dwyer—the seventy-three-year-old former government employee—is a vivid, concrete symbol of a core organization value. It’s a symbol both to supporters and to the dancers themselves. No one wants to participate in a “dance project” and be the only balding, middle-aged guy on a stage full of Twiggys. The LLDE’s claim that diversity was a core value gained credibility from the details of Dwyer’s example, rather than from an external source.

The use of vivid details is one way to create internal credibility—to weave sources of credibility into the idea itself. Another way is to use statistics. Since grade school, we’ve been taught to support our arguments with statistical evidence. But statistics tend to be eye-glazing. How can we use them while still managing to engage our audience?

Geoff Ainscow and other leaders of the Beyond War movement in the 1980s were determined to find a way to address the following paradox: When we see a child running with scissors, we wince. We shout at her to stop. Yet when we read newspaper articles about nuclear weapons—which have the power to destroy millions of children—it provokes, at best, only a moment of dismay.

Beyond War was started by a group of citizens who were alarmed by the arms race between the United States and the Soviet Union. At this point, the combined Soviet and American nuclear arsenals were sufficient to destroy the world multiple times. The Beyond War participants went door-to-door in their neighborhoods, hoping to galvanize
a public outcry against the arms race. They struggled with the problem of how to make credible their belief that the arms race was out of control. How do you make clear to people the staggering destructive capability of the world’s nuclear stockpile? It’s so intangible, so invisible. And yet telling stories, or providing details, seems inadequate: Grappling with the nuclear arms race
requires
us to grapple with the scale of it. Scale relies on numbers.

Beyond War would arrange “house parties,” in which a host family invited a group of friends and neighbors over, along with a Beyond War representative to speak to them. Ainscow recounts a simple demonstration that the group used in its presentations. He always carried a metal bucket to the gatherings. At the appropriate point in the presentation, he’d take a BB out of his pocket and drop it into the empty bucket. The BB made a loud clatter as it ricocheted and settled. Ainscow would say, “This is the Hiroshima bomb.” He then spent a few minutes describing the devastation of the Hiroshima bomb—the miles of flattened buildings, the tens of thousands killed immediately, the larger number of people with burns or other long-term health problems.

Next, he’d drop ten BBs into the bucket. The clatter was louder and more chaotic. “This is the firepower of the missiles on
one
U.S. or Soviet nuclear submarine,” he’d say.

Finally, he asked the attendees to close their eyes. He’d say, “This is the world’s current arsenal of nuclear weapons.” Then he poured 5,000 BBs into the bucket (one for every nuclear warhead in the world). The noise was startling, even terrifying. “The roar of the BBs went on and on,” said Ainscow. “Afterward there was always dead silence.”

This approach is an ingenious way to convey a statistic. Let’s unpack it a bit. First, Beyond War had a core belief: “The public needs to wake up and do something about the arms race.” Second, the group’s members determined what was unexpected about the message: Everyone knew that the world’s nuclear arsenal had grown since World War II, but no one realized the
scale
of the growth. Third, they
had a statistic to make their belief credible—i.e., that the world had 5,000 nuclear warheads when a single one was enough to decimate a city. But the problem was that the number 5,000 means very little to people. The trick was to make this large number meaningful.

The final twist was the demonstration—the bucket and the BBs, which added a sensory dimension to an otherwise abstract concept. Furthermore, the demonstration was carefully chosen—BBs are weapons, and the sound of the BBs hitting the bucket was fittingly threatening.

Notice something that may be counterintuitive: The statistic didn’t stick. It couldn’t possibly stick. No one who saw the demonstration would remember, a week later, that there were 5,000 nuclear warheads in the world.

What did stick was the sudden, visceral awareness of a huge danger—the massive scale-up from World War II’s limited atomic weaponry to the present worldwide arsenal. It was irrelevant whether there were 4,135 nuclear warheads or 9,437. The point was to hit people in the gut with the realization that
this was a problem that was out of control
.

This is the most important thing to remember about using statistics effectively. Statistics are rarely meaningful in and of themselves. Statistics will, and should, almost always be used to illustrate a
relationship
. It’s more important for people to remember the relationship than the number.

Another way to bring statistics to life is to contextualize them in terms that are more human, more everyday. As a scientific example, contrast the following two statements:

1. Scientists recently computed an important physical constraint to an extraordinary accuracy. To put the accuracy in perspective, imagine throwing a rock from the sun to the
   earth and hitting the target within one third of a mile of dead center.
   

2. Scientists recently computed an important physical constraint to an extraordinary accuracy. To put the accuracy in perspective, imagine throwing a rock from New York to Los Angeles and hitting the target within two thirds of an inch of dead center.
   

Which statement seems more accurate?

As you may have guessed, the accuracy levels in both questions are exactly the same, but when different groups evaluated the two statements, 58 percent of respondents ranked the statistic about the sun to the earth as “very impressive.” That jumped to 83 percent for the statistic about New York to Los Angeles. We have no human experience, no intuition, about the distance between the sun and the earth. The distance from New York to Los Angeles is much more tangible. (Though, frankly, it’s still far from tangible. The problem is that if you make the distance more tangible—like a football field—then the accuracy becomes intangible. “Throwing a rock the distance of a football field to an accuracy of 3.4 microns” doesn’t help.)

Stephen Covey, in his book
The 8th Habit
, describes a poll of 23,000 employees drawn from a number of companies and industries. He reports the poll’s findings:

• Only 37 percent said they have a clear understanding of what their organization is trying to achieve and why.
  

• Only one in five was enthusiastic about their team’s and their organization’s goals.
  

• Only one in five said they had a clear “line of sight” between their tasks and their team’s and organization’s goals.
  

• Only 15 percent felt that their organization fully enables them to execute key goals.
  

• Only 20 percent fully trusted the organization they work for.
  

Pretty sobering stuff. It’s also pretty abstract. You probably walk away from these stats thinking something like “There’s a lot of dissatisfaction and confusion in most companies.”

Then Covey superimposes a very human metaphor over the statistics. He says, “If, say, a soccer team had these same scores, only 4 of the 11 players on the field would know which goal is theirs. Only 2 of the 11 would care. Only 2 of the 11 would know what position they play and know exactly what they are supposed to do. And all but 2 players would, in some way, be competing against their own team members rather than the opponent.”

The soccer analogy generates a human context for the statistics. It creates a sense of drama and a sense of movement. We can’t help but imagine the actions of the two players trying to score a goal, being opposed at every stage by the rest of their team.

Why does the analogy work? It relies on our schema of soccer teams and the fact that this schema is somehow cleaner, more well-defined, than our schemas of organizations. It’s more vivid to think of a lack of cooperation on a soccer team—where teamwork is paramount—than in a corporation. And this is exactly Covey’s point: Corporations
should
operate like teams, but they don’t. Humanizing the statistics gives the argument greater wallop.

As another example of the human-scale principle, take a mundane situation: figuring out whether a particular technological upgrade is worth the money. One example comes from Cisco, when it had to decide whether to add a wireless network for its employees. The cost of maintaining a wireless network was estimated at $500 per year per employee. That price sounds hefty—on the order of adding dental or vision insurance for all employees. But it’s not a
benefit
, it’s an
investment
. So how do you compute the value of an investment? You’ve got to decide whether you can get $501 worth of additional value from each employee each year after adding the network.

One Cisco employee figured out a better way to think about the investment: “If you believe you can increase an employee’s productivity
by one to two minutes a day, you’ve paid back the cost of wireless.” On this scale, the investment is much easier to assess. Our intuition
works
at this scale. We can easily simulate scenarios where employees can save a few minutes from wireless access—for instance, sending someone a request for a forgotten document during a critical meeting.

Statistics aren’t inherently helpful; it’s the scale and context that make them so. Not many people have an intuition about whether wireless networking can generate $500 worth of marginal value per employee per year. The right scale changes everything. We saw that Concreteness allows people to bring their knowledge to bear—remember HP’s simulation of a family at Disney World? Similarly, the human-scale principle allows us to bring our intuition to bear in assessing whether the content of a message is credible.

S
tatistics are a good source of internal credibility when they are used to illustrate relationships. In the introduction of this book, we discussed the example of the CSPI’s campaign against saturated-fat-loaded movie popcorn. The relevant statistic was that a medium-sized bag of popcorn had 37 grams of saturated fat. So what? Is that good or bad?

Art Silverman, of the CSPI, cleverly placed the popcorn’s saturated-fat content in a relevant context for comparison. He said that one bag of popcorn was equivalent to a whole day’s worth of unhealthy eating. Silverman knew that most people would be appalled by this finding.

What if Silverman had been a sleazebag? He could have picked a food item that was notoriously unhealthy but relatively low in saturated fat, such as lollipops. “One bag of popcorn has the fat equivalent of 712,000 lollipops!” (Or an infinite number of lollipops, since they’re fat-free.) This statistic is sleazy because it draws its power from
sleight of hand involving different senses of unhealthy food. A sleazy movie-theater executive, to retaliate, might have changed the domain of comparison from saturated fat to some positive attribute of corn: “A bag of popcorn has as much Vitamin J as 71 pounds of broccoli!” (We made this up.)

These possibilities are examples of why writing about statistics filled us with anxiety. Particularly in the realm of politics, tinkering with statistics provides lucrative employment for untold numbers of issue advocates. Ethically challenged people with lots of analytical smarts can, with enough contortions, make almost any case from a given set of statistics.

Of course, let’s also remember that it’s easier to lie without statistics than with them. Data enforces boundaries. Unless people are unethical enough to make up data, the reality of the data constrains them. That’s a good thing, but it still leaves a lot of wiggle room.

So what about the rest of us, who aren’t spinmeisters? What do we do? We will still be tempted to put the best possible spin on our statistics. All of us do it. “I scored sixteen points for the church basketball team tonight!” (Not mentioned: twenty-two missed shots and the loss of the game.) “I’m five feet six.” (Not mentioned: The three-inch heels.) “Revenue was up 10 percent this year, so I think I deserve a bonus.” (Not mentioned: Profits tanked.)

When it comes to statistics, our best advice is to use them as input, not output. Use them to make up your mind on an issue. Don’t make up your mind and then go looking for the numbers to support yourself—that’s asking for temptation and trouble. But if we use statistics to help us make up our minds, we’ll be in a great position to share the pivotal numbers with others, as did Geoff Ainscow and the Beyond War supporters.

CLINIC

Dealing with Shark Attack Hysteria

THE SITUATION:
Every few years the media go frothy over shark attacks. Shark attacks, however, remain extremely rare and do not vary much from one year to the next. So why do they consume so much media and public attention? The answer is that shark attacks spawn terrifying, dream-haunting stories like the following, from
The Oprah Winfrey Show:

OPRAH:
Bethany Hamilton loved to ride the waves. Surfing daily since she was 8 years old, Bethany was such a phenom, people said she had salt water running through her veins. At the young age of 13, Bethany was a rising star on the surfing circuit and had become a local celebrity, but what happened next landed Bethany in headlines around the world.

It was early morning. Bethany was in the ocean lying on her board with her arm dangling in the water. Suddenly, a deadly fifteen-foot tiger shark seized her arm. Violently, he jerked and yanked it until her arm was ripped right off of her small body. Seconds later the shark and her entire arm were gone, and Bethany was left alone on her board surrounded by bloody water.

Imagine that you are forced to combat these vivid stories. Maybe you’re the publicity director of the Save the Sharks Foundation, or maybe you’re trying to convince your junior high school daughter that it’s okay to go to the beach. How do you do it? You’ve got the truth on your side—attacks are very rare—but that’s no guarantee that people will believe you. So what source of credibility do you tap to get people to believe you?

• • •

MESSAGE 1:
We based this message on statistics published by the Florida Museum of Natural History:

You’re more likely to drown on a beach in an area protected by a lifeguard than you are to be attacked by a shark, much less killed by one. In the United States in 2000, twelve people died in lifeguard-protected areas. There were no fatalities from sharks. (In a typical year there are only 0.4 fatalities.)

COMMENTS ON MESSAGE 1:
This is okay but not great. This message taps internal credibility—the credibility of hard statistics. We have two comments: First, drowning does not seem like the right comparison to make, because many people may think drowning is a common cause of death. “Drowning is more common than shark attacks” does not feel particularly unexpected. (And maybe we’re too skeptical, but the presence of the college-student lifeguard never struck us as an ironclad guarantee of safety.) Second, the statistical comparison—12 deaths versus 0.4—is good, but it isn’t particularly vivid or meaningful on a human scale. It’s unlikely that anyone would remember these numbers a week later.

• • •

MESSAGE 2:
This message is also based on statistics published by the Florida Museum of Natural History:

Which of these animals is more likely to kill you?

A SHARK

A DEER

ANSWER:
The deer is more likely to kill you. In fact, it’s
300 times
more likely to kill you (via a collision with your car).

COMMENTS ON MESSAGE 2:
We like the unexpected idea that Bambi is more dangerous than the evil shark, followed by the doubly unexpected statistic that Bambi is
wildly more dangerous (300 times more deadly!)
. It’s absurd to the point of being funny, and humor is a nice antidote to the fear generated by shark-attack stories. In a sense, we’re fighting emotional associations with emotional associations (see the next chapter).

This message taps internal credibility with the statistic, but it also taps into the audience as a source of credibility. People in the audience know how much they fear deer when they’re driving around—i.e., not much. Few of us are afraid to go out in the evening on account of lurking deer. We know that we don’t fear deer, so why should we fear sharks? (This is more effective than comparing shark attacks with drowning—after all, most of us have at least a mild fear of drowning.)

SCORECARD
Checklist	Message 1	Message 2
Simple
Unexpected	-
Concrete
Credible
Emotional	-
Story	-	-

PUNCH LINE:
When we use statistics, the less we rely on the actual numbers the better. The numbers inform us about the underlying relationship, but there are better ways to
illustrate
the underlying relationship than the numbers themselves. Juxtaposing the deer and the shark is similar to Ainscow’s use of BBs in a bucket.