Behavioral science in business: Nudging, debiasing, and managing the irrational mind 03-05

Behavioral science has become a hot topic in companies and organizations trying to address the biases that drive day-to-day decisions and actions.

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Although humans are known to be irrational, they are at least irrational in predictable ways. In this episode of the McKinsey Podcast, partner Julia Sperling, consultant Magdalena Smith, and consultant Anna Güntner speak with McKinsey Publishing’s Tim Dickson about how companies can use behavioral science to address unconscious bias and instincts and manage the irrational mind. Employing techniques such as “nudging” and different debiasing methods, executives can change people’s behavior—and have a positive effect on business—without restricting what people are able to do. 

Podcast transcript

Hello and welcome to this edition of the McKinsey Podcast with me, Simon London. It’s not new news that a lot of what drives human behavior is often unconscious and often irrational. We go back to the end of the 19th century and find Sigmund Freud trying to describe our unconscious and intervene on at least what he thought was more or less a scientific basis.

The good news is that our understanding of the unconscious mind has come a long way, grounded in decades of basic research into what drives ordinary, everyday human behavior. These are the biases, the heuristics, the rules of thumb that determine the great majority of our day-to-day decisions without us even being aware. So, yes, we can agree with Freud that we are often irrational, but as today’s behavioral scientists like to say, we are predictably irrational. What can be predicted can be managed, at least to some degree.

Today’s conversation is hosted by my McKinsey Publishing colleague Tim Dickson. You’ll be hearing Tim in conversation with Julia Sperling, who is a neuroscientist by training and a McKinsey partner based in Frankfurt. Tim will also be speaking with Magdalena Smith, an organization and people-analytics expert based in London, and Anna Güntner, who is a consultant based in Berlin. Without further ado, over to Tim.

Tim Dickson: Julia, Magdalena, and Anna, thanks so much for being here today.

Julia Sperling: Great pleasure.

Anna Güntner: Happy to be here.

Magdalena Smith: Thank you for having us.

Tim Dickson: The study of human behavior isn’t really new, and it’s been widely accepted since at least Sigmund Freud that a lot of what drives human behavior is in fact unconscious. So, Julia, what’s new about behavioral science, and why should executives take note?

Julia Sperling: Of course, you’re right. Human psychology has been explored and used for management purposes for the past, I’d say, over 100 years already. You’re also right that Freud gave us a very deep insight into the human mind and how it works. The issue had always been, though, that while Freud’s insights have been very useful, they have been very hard to implement because they were so deep and hard to grasp and hard to alter.

Now we have the insights that people are predictably irrational, but we also have the tools coming out of it to help alter behavior and to help guide behavior. What we use is the insight not only from behavioral sciences but also from neurosciences, most recently.

I can tell you the human brain is spectacular. At any point in time, over 11 million bits of information hit our brain, and it’s able to filter them down to about 50 only. Then seven to ten of them can be kept in short-term memory. Of course, with this enormous filtering exercise that it does, we cannot consciously make choices all the time. A lot has to happen very unconsciously. And, by the way, that’s a very different unconscious from the unconscious that Freud has been talking about.

Tim Dickson: So, Julia, what are the main applications of behavioral science for companies?

Julia Sperling: Well, number one, performance management. You can identify factors that actually hinder performance as well as those that foster it. Money, as we should already know, is not always the best motivator. The second piece is recruiting and succession planting. Here, machine learning has a much stronger ability to predict future success than those that have been, for example, choosing or selecting CVs in the past. And then last, cultures, be it for merger management, a general cultural change that you could see with bringing agility or more diversity to an institution, or something as targeted as introducing a safety culture, for example.

“With nudges—subtle interventions based on insights from psychology and economics—we can influence people’s behavior without restricting it.”

Tim Dickson: Anna, I know you’re an expert on nudging. Can you tell us exactly what nudging is and a little bit of the context for a company thinking about this?

Anna Güntner: The general idea behind nudging as well as debiasing is that people are predictably irrational. Now, with nudges—subtle interventions based on insights from psychology and economics—we can influence people’s behavior without restricting it.

With a nudge, we could get people to do whatever is best for them, without prohibiting anything or imposing fines or restricting their behaviors in any other hard way. In terms of nudging, there are different applications for companies. One certainly is marketing, and marketers have been using similar approaches for a long, long period of time.

Tim Dickson: What do you say if executives are squeamish about this and worry about nudging behaviors—changing behaviors—that may potentially be used for malignant purposes and worry that they might find sensitivities among their employees?

Julia Sperling: It highly depends on what type of nudge is used and the intent with which you use it. It is much more a function of, is the behavior that you’d like to see in your company something that is in line with your company values, that is in line with what your company stands for? That’s the decision executives have to make. Nudging is then merely a technique to make this behavior more likely, but it’s a choice of the behavior that makes the difference.

Anna Güntner: Another area of application, in particular, is safety culture. In terms of irrational thinking, this of course is absolutely something irrational—to risk your life by not sticking to the procedures.

With behavioral science, companies are able to go away from the backward-looking approach, where after something happens, you try to understand what the reasons were and take them out, to something forward looking, where you try to not attack people’s mind-sets but to change the environment in a way that becomes simpler and more intuitive for people to follow safety procedures.

One of the problems that construction companies have is that managers, once they become promoted, stop wearing the helmet, as a sign of superiority to the workers. A nudge that’s implemented by some companies is that the managers get a helmet of a different color. They use the same status bias but in a different way to help people to stick to safety procedures.

Tim Dickson: Understood. So that’s about unleashing particular behaviors. But sometimes you have to fight behaviors and biases. Magdalena, I know that’s something that you know about, and you’ve seen this in action in the workplace. Can you talk about that aspect of the situation?

Magdalena Smith: As Anna mentioned, we’re not always rational, and sometimes that rationality—or lack of rationality, rather—has a real impact on the decisions that we make. That can be extremely costly for organizations.

We have recently worked on an incredibly interesting project, where we worked with a global asset manager trying to identify the decision-making biases that their fund managers have and thereby also see what impact they have on the underlying performance of the funds.

We did that by using the data available in trading and looking at their behavior, looking at individual trades. In combination with this and analyzing the underlying decision-making process in more detail, we could identify which trades were less optimal than others.

Looking at those and looking at the potential improvement of those, if you reduced the effect, it really could show you the direct dollar impact that overcoming these biases had. They were significant. You’re talking about 100 to 200 basis points per year for a fund manager and an extra alpha on an equity fund. That is billions for a company like this over the next three to four years.

“If you want to have a diverse set of leaders in the future, you have to be aware of those little biases and fight them.”

Julia Sperling: I have a lot of clients asking—in particular with regard to their diversity efforts—how they can minimize unconscious bias. It starts with the recruiting processes, behavioral design of how to make them function in a way that doesn’t favor those—we call it a “mini me” bias—who have always been recruited to the company before and would be recruited all the time again. Because again, our human brain is biased, and we enjoy having those that remind ourselves of us around us.

If you want to replicate a homogenous leadership group again and again and again, don’t intervene. But if you want to have a diverse set of leaders in the future, you have to be aware of those little biases and fight them, as we said, right at the start of your recruiting process.

In Germany, together with about 20 other companies, we work in an initiative called Chefsache that wants to bring more women into leadership positions and create gender balance. As one of the focus topics, we looked into unconscious bias within talent processes. When you look into recruiting, for example, even with the best intentions, there was what we talked about—this mini-me bias. People make choices, make biased choices, and might miss out on talent because of those.

One of the debiasing techniques that we use, for example, is that after we’ve seen a case and we have a team speak about what they’ve seen, we now never let the most senior person in the room speak first, because there’s something called the “sunflower” bias, which is once the sun speaks, the flower follows. That means that in this group, people would more likely adopt [the senior person’s position], maybe even a different position from the one that they had before.

Another intervention is to combat the bias that occurs—in recruiting, for example—called groupthink. You make people fill out a statement on the candidate themselves before they enter the group discussions, because science has also shown that once a group starts adopting a certain opinion, it’s very hard for the individuals that haven’t spoken yet to bring in another thought or have another opinion. There we’d say, never let the most senior person in the room speak first. Make sure that everyone notes the opinion right after having seen the recruitment candidate and before sharing their opinion.

Magdalena Smith: One of the areas that is growing very fast within debiasing and within nudging is the concept of advanced analytics and machine learning. That has particularly been used, for example, when it comes to identifying talents, behaviors, and future potentials and very much used in trying to identify who the great performers are going to be in the future and where they can be found.

To follow on in your example regarding recruitment, we’ve seen a global service company that wanted to make the recruitment process more efficient. The way they did this was by acknowledging which type of candidate would automatically go through to a round of interviews.

This automatically put forward the top 5 percent of candidates. One of the very positive side effects of this, which wasn’t actually planned, but it was fantastic, was that the number of women that were put through to the first interviews increased massively.

Tim Dickson: But technology has its own biases as well. What would you say to that?

Magdalena Smith: If we look at what machine learning is, machine learning is trying to find objective insights using data through algorithms, advanced statistical algorithms. Unfortunately, somehow those algorithms have to be programmed, and they’re programmed by humans.

What you very quickly see is that assumptions come into the algorithms. You also see areas where assumptions are made in the sense that you have missing data. You have to impute numbers where you either put a value on it or an assumption that then gets amplified throughout.

Julia Sperling: That’s why you can—and have to—check very carefully whether your algorithms are working. By the way, when we use them in succession planning, for example, or when we use them in recruiting even, we always advise our clients to do a look back in the past and see whether those algorithms, if they have been used already in recruiting, would have predicted the success of those in their positions right now.

Magdalena Smith: Absolutely.

Julia Sperling: Right? So, one has to reality check very carefully every algorithm one puts in place. That’s one very practical example of how to do it.

Tim Dickson: Let’s talk about a different area of application, for example, merger management. I think you’ve seen biases at work and how to counteract them in that situation, Anna.

Anna Güntner: In merger management, the challenge that a lot of mergers—we could even say every merger—faces is that you try to bring together two different cultures and two different corporate cultures and get them to function as one. In that case, there are many biases, especially the in-group out-group bias, that are at play.

But there are also tools—debiasing techniques but also nudging techniques—that can help us prime or create a new common identity. These can be very simple interventions like, for example, if you think about how to bring together new teams. What can you do to force the exchange between people who barely know each other?

Tim Dickson: Julia, you mentioned the context of performance management. Anna, I know you have an example of a counterintuitive insight from that area.

Anna Güntner: In traditional management approaches, we tend to assume that money is the biggest motivator—that if you pay your employees more, then they will work more. Now we know that money is actually the hygienic factor. You have to pay them enough, but there are different things that motivate them, like, for example, meaningful acknowledgment of the social factor and extrinsic motivation. If it’s given for something that in the beginning was not for sale or if it’s too low, it can even reduce intrinsic motivation, like enjoyment or self-fulfillment of work. Also, we know that so-called performance-based teams, where you are paid depending on the result of your work, are actually detrimental for creative work because it makes people think narrowly in a particular direction, whereas for creativity you need to think broadly.

“One of the insights from behavioral economics that a lot of companies are now exploring is to separate developmental feedback from evaluative feedback.”

Another assumption that you would typically have is that you need to give people honest feedback. You need to tell them what they’re doing well, what they’re doing not so well, and how to improve it. But there is a lot of research that shows that people shut off and even try to avoid those from whom they have received such constructive feedback. One of the insights from behavioral economics that a lot of companies are now exploring is to separate developmental feedback from evaluative feedback.

Tim Dickson: Taking a step back and thinking about some of the broader challenges for CEOs and senior executives coming to this for the first time, what would you list as the key challenges?

Anna Güntner: One of the challenges is that you need to adopt the so-called evidence-management mind-set. You need to be ready to test the things that you promote, debiasing algorithms or nudging or anything else, based on large samples of data rather than doing it the way it is usually done—in the past or even today—when a lot of intelligent people get in the room, discuss, and then come out with a decision, which is then rolled out all across the organization.

If we take the example of nudging, it’s rather like running an A/B test. You have one group of people who don’t get exposed to a nudge and the other group of people who get exposed to the nudge. Then you can measure the difference in behavior that hopefully occurs between these two groups and also assess the profit impact.

So that’s one. Number two is that it’s still not very intuitive for many companies to think in terms of behaviors. Very often, we think in terms of KPIs [key performance indicators]—for example, customer satisfaction or sales—so it takes some conscious effort to bring it down to the kind of behavior you’re trying to change.

Julia Sperling: Very often, behaviors are being put into one box together with mind-sets, and core businesses are going to be put into a very different box. Putting those boxes together into one and showing how behaviors—and it’s nothing but behaviors that ultimately drive an outcome in an organization—can be assessed, can be influenced, can be elicited, can be fostered, etcetera, in the same stringent way as some business processes can be new for many executives.

Magdalena Smith: I’d like to add that debiasing is hard. It’s difficult. Just knowing that you have certain biases isn’t sufficient. A lot of people acknowledge that biases have a massive effect on decision making but don’t acknowledge first that they have biases themselves, which is a bias in its own way. That’s overconfidence. Even once you’ve identified a certain bias, you often need some form of external help. For example, in hospitals, they use checklists in order to make sure they don’t miss anything, they don’t make certain assumptions about things. These are props that can help them overcome some of these biases that they may have, or assumptions they make about patients, that are helpful.

There was some very interesting research coming out of the United States last year that showed the number of mistakes that were made in hospitals between the eight years of 2000 to 2009 in taking people in for accidents and emergencies. There were hundreds and thousands of mistakes being done that they specifically put down to biases, the main one being “anchoring” and assuming that they’ve seen the first kind of information that comes, and they stick to that rather than explore any other problems they could have. They estimated that this had an impact of 100,000 lives a year. Being able to save another 100,000 people a year— I think that should be motivation enough to try to use these kinds of methodologies.

Julia Sperling: This is becoming a hot topic more and more. When you look at international institutions, they’re not only starting to deploy those approaches on larger scales. They’re even building their own behavioral-insights unit. They are actively recruiting behavioral psychologists, behavioral economists to work with them. Those units are being built as we speak.

“You need to have a deep understanding of your business and the opportunity to truly understand the precise behavior that leads to the unwanted outcomes.”

Tim Dickson: Is it a question of hiring behavioral economists, or can companies generate an understanding themselves and do this themselves without the very deep academic understanding of this field?

Julia Sperling: It takes a couple of different skills. Number one, it takes a deep understanding of analytics and the ability to use data at scale; as Anna mentioned, do you compare A to B when you do nudging? You need to be able to set up these types of trials and to be able to process them properly. There is an analytical capability that you need to have and you need to build.

Number two, and this might be the even more challenging one, is you need to have a deep understanding of your business and the opportunity to truly understand the precise behavior that leads to the unwanted outcomes or the precise behavior that gives you exactly the outcome that you want. So, you need a deep understanding of your business, the way that your people are currently behaving, and the way you would need them to behave in order to fulfill the strategic and organizational goals that you have.

And then, of course number three, you need these professions that I’ve been talking about before. You need those that come up with a whole library—and McKinsey has one with over 150 different interventions that are linked to certain nudges that have proved to work in companies in the past. You deploy this database, then, to the precise behavior that you’ve identified that yields the business outcome. And you use the analytics to track the impact over time. Those are the three main capabilities that you need to build.

Tim Dickson: I’m afraid that’s all we have time for. But thanks very much to Julia Sperling, Magdalena Smith, and Anna Güntner for a fascinating discussion. Thanks to you, our listeners, for joining us. 

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The age of analytics: Competing in a data-driven world 01-06

Image credit : Shyam's Imagination Library

Big data’s potential just keeps growing. Taking full advantage means companies must incorporate
analytics into their strategic vision and use it to make better, faster decisions.
          
Is big data all hype?

To the contrary: earlier research may have given only a partial view of the ultimate impact. A new report from the McKinsey Global Institute (MGI), The age of analytics: Competing in a data-driven world, suggests that the range of applications and opportunities has grown and will continue to expand. Given rapid technological advances, the question for companies now is how to integrate new capabilities into their operations and strategies—and position themselves in a world where analytics can upend entire industries.

A 2011 MGI report highlighted the transformational potential of big data. Five years later, we remain convinced that this potential has not been oversold. In fact, the convergence of several technology trends is accelerating progress. The volume of data continues to double every three years as information pours in from digital platforms, wireless sensors, virtual-reality applications, and billions of mobile phones. Data-storage capacity has increased, while its cost has plummeted. Data scientists now have unprecedented computing power at their disposal, and they are devising algorithms that are ever more sophisticated.

Earlier, we estimated the potential for big data and analytics to create value in five specific domains. Revisiting them today shows uneven progress and a great deal of that value still on the table (exhibit). The greatest advances have occurred in location-based services and in US retail, both areas with competitors that are digital natives. In contrast, manufacturing, the EU public sector, and healthcare have captured less than 30 percent of the potential value we highlighted five years ago. And new opportunities have arisen since 2011, further widening the gap between the leaders and laggards.

Leading companies are using their capabilities not only to improve their core operations but also to launch entirely new business models. The network effects of digital platforms are creating a winner-take-most situation in some markets. The leading firms have remarkably deep analytical talent taking on various problems—and they are actively looking for ways to enter other industries. These companies can take advantage of their scale and data insights to add new business lines, and those expansions are increasingly blurring traditional sector boundaries.

Where digital natives were built for analytics, legacy companies have to do the hard work of overhauling or changing existing systems. Adapting to an era of data-driven decision making is not always a simple proposition. Some companies have invested heavily in technology but have not yet changed their organizations so they can make the most of these investments. Many are struggling to develop the talent, business processes, and organizational muscle to capture real value from analytics.
The first challenge is incorporating data and analytics into a core strategic vision. The next step is developing the right business processes and building capabilities, including both data infrastructure and talent. It is not enough simply to layer powerful technology systems on top of existing business operations. All these aspects of transformation need to come together to realize the full potential of data and analytics. The challenges incumbents face in pulling this off are precisely why much of the value we highlighted in 2011 is still unclaimed.

The urgency for incumbents is growing, since leaders are staking out large advantages, and hesitating increases the risk of being disrupted. Disruption is already happening, and it takes multiple forms. Introducing new types of data sets (“orthogonal data”) can confer a competitive advantage, for instance, while massive integration capabilities can break through organizational silos, enabling new insights and models. Hyperscale digital platforms can match buyers and sellers in real time, transforming inefficient markets. Granular data can be used to personalize products and services—including, most intriguingly, healthcare. New analytical techniques can fuel discovery and innovation. Above all, businesses no longer have to go on gut instinct; they can use data and analytics to make faster decisions and more accurate forecasts supported by a mountain of evidence. 


Original source :  Mckinsey Global Institute

Using behavioral science to improve the customer experience 10-16

By guiding the design of customer interactions, the principles of behavioral science offer a simple, low-cost route to improved customer satisfaction.

          

Service operations seem a natural setting for the ideas of behavioral science. Every year, companies have thousands, even millions, of interactions with human beings—also known as customers. Their perceptions of an interaction, behavioral scientists tell us, are influenced powerfully by considerations such as its sequence of painful and pleasurable experiences. Companies care deeply about the quality of those interactions and invest heavily in effective Web sites and in responsive, simplified call centers.

Yet the application of behavioral science to service operations seems spotty at best. Its principles have been implemented by relatively few companies, such as the telecommunications business, which found that giving customers some control over their service interactions by allowing them to schedule field service visits at specific times could make them more satisfied, even when they had to wait a week or longer. Many more companies ignore what makes people tick. Banks, for example, often disturb the customer experience by altering the menus on ATMs or the interactive-voice-response (IVR) systems in call centers. They fail to recognize the psychological discomfort customers experience when faced with unexpected changes.

Likewise, for every restaurant that surrounds a bill’s arrival with a succession of complementary desserts—thereby capitalizing on the customer’s preference for service encounters that end positively—there are a lot of call centers that ignore the importance of a strong finish. Indeed, many companies actively work against one by placing so much emphasis on average handling times that they inadvertently encourage agents to end a call once its main business is complete, leaving customers with memories of brusque treatment.

It doesn’t have to be this way. Academics such as Professor Richard Chase at the University of Southern California’s Marshall School of Business have used research on how people form opinions about their experiences to design actual services. In a 2001 Harvard Business Review article,¹ 1. For more, see Richard B. Chase and Sriram Dasu, “Want to perfect your company’s service? Use behavioral science,” Harvard Business Review, June 2001, Volume 79, Number 6, pp. 78–84. Chase and his team even laid out principles for managers to consider when designing any customer interaction. Get bad experiences over early, so that customers focus on the more positive subsequent elements of the interaction. Break up pleasure but combine pain for your customers, so that the pleasant parts of the interaction form a stronger part of their recollections. Finish strong, as the final elements of the interaction will stick in the customers’ memory. Give them choice, so they feel more in control of the interaction. And let them stick to their habits rather than force them to endure the discomfort and disorientation of unexpected change.

Here we review the experience of an insurance company that used those principles to improve its customers’ satisfaction significantly, with no incremental costs or fundamental changes in people or infrastructure. A systematic approach like this one is needed to counteract the natural tendency of service operations to focus on the needs of IT systems and work flows, not to mention the preferences of employees, managers, and service providers, largely ignoring the way customers perceive their service interactions. If companies in a broad range of service industries—including banking, telecommunications, and retailing—applied a rigorous approach, they would reap significant economic benefits, ranging from reduced churn to greater cross-selling to additional customer referrals.

Setting the stage

Executives at a leading North American health insurer sought to help patients manage their treatment programs for serious long-term illnesses, such as diabetes or congestive heart failure. Conditions like these are difficult to manage because treatment is often protracted and outcomes can depend on the patients’ willingness to make significant lifestyle changes.

Patients participating in an experimental health-management program received regular, scheduled calls from a team of nurses over a period of several months. The calls aimed to deliver additional support to patients undergoing long-term treatment, by helping them understand the available options and stick to their treatment regimes, as well as reinforcing lifestyle changes recommended by their doctors. Improved compliance helps insurers too, as better outcomes reduce the overall cost of treatment.

In the past, the clinical-treatment program for each patient had determined the content of such calls, and the company used what it considered to be a tried-and-true method for managing them. Team members had received guidelines on the objectives of the calls and used a checklist to sequence discussions with customers.

Behavioral science in action

To see if this approach could be improved, the company divided the nurses into two groups—approximately 20 in a pilot group and another 20 in a control one—and began applying a behavioral-science lens to the interactions of the former to test different versions of the call structure. Postcall surveys measured the customers’ satisfaction with each call and with the company. Key customer and operational metrics (including sign-up rates) helped estimate the financial impact. The pilot team used behavioral-science principles throughout the interactions.

1. Get bad experiences over with early

The team identified difficult issues—for example, the forthcoming lapse of certain insurance benefits or the need to transfer from one facility to another—and moved them to the start of the call. It also set up a later phase built around constructive coaching from the nurses on how to deal with the issues raised earlier. In addition, general questions that were likely to make patients uncomfortable (about current pain levels, smoking habits, eating patterns, and alcohol consumption, for instance) were moved from the end of the call to the beginning.

2. Break up pleasure and combine pain

By combining the most challenging elements of a call in its first phase, the health-management team could focus on positive aspects during the rest of it. The team found that patients responded very positively to coaching by nurses, so there was an effort to ensure that coaching on multiple topics was an explicit part of every phase of the call. A nurse might, for example, discuss the next treatment steps, how the patient could take advantage of all covered benefits, and ways of minimizing out-of-pocket expenses. There was also an effort to resolve all possible issues within a call and to transfer it to other groups only as a last resort.

3. Finish strongly

The conclusion of the health-management calls was scripted to finish on a positive note by emphasizing the tangible insurance benefits available to patients and, where medically appropriate, the likelihood of a successful outcome to the agreed-upon action plan. At the end of a program lasting several months, with calls taking place every month or so, patients received a final call from their health-management nurse. This call ended by celebrating their progress, reviewing the goals they had met, and summarizing the positive steps they had taken to achieve those goals.

4. Give customers choice

The company made an effort to give customers explicit choice on three critical elements: the type of treatment plan, which facilities to visit and which doctors to see, and the timing of future calls. In each area, the nurse was guided to tell the customer, “You have a choice; let me give you some options.” Customers explicitly had the right to make the ultimate decision, though the outcome may have been limited or strongly suggested—for example, “Hospital A is closest to your home, but B is only 15 minutes further away, and it has a specialist unit with a great track record at treating your condition.”

5. Let customers stick to their habits

In many situations, it was important for patients to change their lifestyles—say, by eating different foods, consuming less alcohol, or exercising. To encourage patients to make these changes while minimizing the discomfort they generated, nurses introduced them gradually over a series of calls. Dietary changes might be discussed initially, for instance, followed by encouragement to begin exercise. The nurses also tried to reframe the patients’ perceptions of the severity of the changes by comparing them with more unfavorable alternatives: for example, “instead of eliminating your favorite foods altogether, why not just try picking low-fat varieties next time you are in the store.”

The team also worked to ensure that the calls themselves became a positive habit for the patients. This approach gave them the option of having the same nurse on follow-up and promoted a consistent approach for every call, so that they became used to the interactions.

Results

The effect of the changes was significant. Patients in the test group reported an average satisfaction level seven percentage points higher than that of patients in the control group—for calls with the same basic content. These patients’ satisfaction levels with the company was on average eight percentage points higher than that of the control group. More important, patients in the test group were on average five percentage points more likely to say that the calls had motivated them to make positive changes in their behavior.

Notably, the program didn’t significantly affect the company’s costs or change key operational metrics, such as the length of a call or the number of calls a day. Moreover, test group nurses reported an average level of job satisfaction higher than that of the control group nurses. Finally, the impact was rapid. Most of the increase in the satisfaction levels of the test group patients happened within two weeks.

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Many other service industries could benefit from a similar approach. By breaking down frontline transactions and rebuilding them with behavioral and experiential principles, companies could systematically achieve rapid, measurable improvements in customer satisfaction.

About the author(s)

John DeVine is a principal in McKinsey’s Miami office, and Keith Gilson is a consultant in the Toronto office.

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Realizing gender equality’s $12 trillion economic opportunity 05-18

Realizing gender equality’s $12 trillion economic opportunity


Investing in access to essential services and reducing the gap in labor-force participation rates could significantly expand the global economy by 2025.


In 2015, the McKinsey Global Institute published The power of parity: How advancing women’s equality can add $12 trillion to global growth. This report, which focused on the enormous potential associated with narrowing the gender gap, found that if every country did so at the same historical rate as the fastest-improving country in its regional peer group, the world could add $12 trillion to annual gross domestic product in 2025. That’s some 11 percent higher than it would be under the business-as-usual scenario.

So what will it actually take to turn this potential into reality? Our new discussion paper, Delivering the power of parity: Toward a more gender-equal society, provides an agenda for action and investment, quantifying the progress needed on 15 gender-inequality indicators. It finds that while much of the $12 trillion opportunity comes from advancing gender equality in the world of work, progress there is closely tied to tackling gender gaps in society more broadly. In particular, improved access to services in six areas could unlock economic opportunities for women: education, family planning, maternal health, financial inclusion, digital inclusion, and assistance with unpaid care.

Addressing these areas would require incremental annual expenditures of $1.5 trillion to $2 trillion in 2025—20 to 30 percentmore than would ordinarily be spent given the current trajectories of rising population and GDP (exhibit). But the results would empower millions of women and men alike, delivering economic benefits that are six to eight times higher than the social spending estimated.

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The manager and the moron 10-04

The manager and the moron

The computer is a moron. And the stupider the tool, the brighter the master must be, says Peter Drucker. In this Quarterly archive article, he explains how “the dumbest tool we have ever had” will compel managers to think through their actions.

As all of us know, during the last 20 years the free world has had the greatest, most sustained economic advance in history. Most of us believe that this has been a time not merely of forward movement, but of vast economic change.

The facts and figures, however, do not support this impression. They show, instead, that our era has actually been a time of unprecedented non-change. It has been largely a period of linear forward movement along old trend lines, of adding new stories to an old building according to the old architectural design.

Imagine an economist in 1913, just before World War I, taking the economic trend lines of what were then already the advanced countries, and projecting each of them ahead to 1966. He would have hit it on the nose for Japan, Western Europe, and the United States, in fact for every one of the developed nations with one important exception—the Soviet Union, which is significantly below where it would have come out on our economist’s projection. 

The reason for this, as all of us know, was that the Russians imposed a political straitjacket on agriculture and froze farm technology just at the worst possible moment, when the technological revolution in farming was getting under way. Worse, they froze the agricultural population. By making it possible for anybody who stayed on the farm to be fed, no matter how poorly, they removed the economic pressure that elsewhere in the world has pushed the farmer off the farm, brought about fantastic productivity jumps in agriculture, and provided labor for the expansion of industry.

Suppose, again, that our economist, having made his projections in 1913, fell into a 50 years’ sleep. When he woke up, he would have found the industrial geography of the world virtually unchanged. Every country that is today an industrially advanced nation was well past the takeoff point in 1913. Not a single new one has joined the club, unless you count satellite economies like Canada, Australia, South Africa, and Mexico. Brazil, which has a long and distinguished history as a country of the future, may join the club tomorrow, but it isn’t quite there yet.

Compared to this linear movement, the 50 years before 1913 present the greatest imaginable contrast. During those five decades the industrial map of the world had been changing as rapidly as the physical map of the world changed in the fifteenth and the early sixteenth centuries—the Age of Discovery. Right after the Civil War, the United States and Germany emerged as economically advanced countries and rapidly overtook the old champion, Great Britain. A quarter of a century later Russia and Japan emerged, along with the western part of Austria-Hungary—the present Czechoslovakia and Austria, with Northern Italy. In short, the 50 years before 1913 were a period of very rapid shifts in economic power relationships.

Since World War I, however, such changes have been absent. This explains why economists of today are so concerned with economic development. Before 1913, it was taken for granted, but since then we’ve apparently gone sterile. And we don’t know how to start it up.

Perhaps the greatest shock to our Rip Van Winkle economist, however, would be the fact that, with the exception of the plastics industry, the main engines of growth in the past 50 years were already mature or rapidly maturing industries, based on well-known technologies, back in 1913.

The dynamos of growth

Our most rapidly advancing industry in the last 20 years of expansion has been agriculture. The productivity of farming has been increasing twice as fast as the productivity of manufacturing in all the developed countries except Russia. Yet the average farmer of today in the United States is not farming in a much more advanced way than the top farmer of 1913. Hybrid seed is about the only new development of any consequence.

And the next dynamo has been the steel industry. World steel capacity has expanded fivefold since 1913. Yet 99 percent of all steel capacity in existence today is built on a technology that was considered antiquated—and Lord knowswas antiquated—in 1913.

Our third engine of growth has been the automotive industry. Yet in 1913 Henry Ford was already producing and selling 183,000 Model T’s, and a year later the figure had climbed to 261,000—more cars than the Soviet Union has ever produced in a single year. Even the Ford Motor Company of 1913 would be a major producer in today’s free-world automotive industry.

Much the same is true of the electrical apparatus industry. Neither Westinghouse, nor GE, nor Siemens was exactly unknown in 1913. They were blue chips. And this is also true of the organic chemical industry.

Plastics is the only industry based on new technology that is economically important today in terms of contribution to gross national product, employment, and so on. As far as the economic statistician is concerned, other industries hardly exist as yet. The airplane began to have an economic impact when the jets came. But the real impact will come with the big freight jets, which will make every airstrip in the world a deep-water port. In a few years, they may make the ocean-going freighter, man’s oldest efficient transportation, look roughly the way the railroads began to look around 1950. This will be one of the greatest changes in transportation we’ve ever had. But it is still ahead of us.

Much ado, little impact

The computers, despite all the excitement they have been generating, are not yet economically important. It’s only now that IBM is shipping them out at a rate of a thousand a month that they’re even beginning to have an impact. But we haven’t begun to use the potential of the computer. So far we are using it only for clerical chores, which are unimportant by definition. To be sure, the computer has created something that had never existed in the history the world—namely, paying jobs for mathematicians. But that is hardly a major economic contribution, no matter what the graduate dean thinks.

So the economic impact of the new technologies is still in the future. If we subtracted every single one of them from the civilian economy, we would hardly notice it in the figures—perhaps a percentage point or two.

But this situation of linear movement is rapidly changing in every respect. And the greatest change is one that our Rip Van Winkle economist, looking only at the figures, wouldn’t even notice: In the past 20 years we have created a brand-new form of capital, a brand-new resource, namely knowledge.

Up until 1900, any society in the world would have done just as well as it did without men of knowledge. We may have needed lawyers to defend criminals and doctors to write death certificates, but the criminals would have done almost as well without the lawyers, and the patients without the doctors. We needed teachers to teach other ornaments of society, but this too was largely decoration. The world prided itself on men of knowledge, but it didn’t need them to keep the society running.

As late as the mid-forties, General Motors carefully concealed the fact that one of its three top men, Albert Bradley, had a PhD. It was even concealed that he had gone to college, because, quite obviously, a respectable man went to work as a water boy at age 14. A PhD was an embarrassing thing to have around.

Nowadays, companies boast about the PhDs on their payrolls. Knowledge has become our capital resource, a terribly expensive one. A man who graduates from a good business school represents some $100,000 of social investment, not counting what his parents spent on him, and not counting the opportunity costs. His grandparents and great-grandparents had to go to work at the age of 12 or 13 with the hoe in the potato patch so that he could forgo those ten years of contribution to society. And that’s a tremendous capital investment.

Besides spending all that money, we are also doing something very revolutionary. We are applying knowledge to work. Seven-odd thousand years ago, the first great human revolution took place when our ancestors first applied skill to work. They did not use skill to substitute for brawn. The most skilled work very often requires the greatest physical strength; no ditchdigger works harder than the surgeon performing a major operation. Rather, our ancestors put skills on top of physical labor. And now—a second revolution—we’ve put knowledge on top of both. Not as a substitute for skill, but as a whole new dimension. Skill alone won’t do it anymore.

Now, this has two or three important implications for management.

First, we must learn to make knowledge productive. As yet we don’t really know how. The payroll cost of knowledge workers already amounts to more than half the labor costs of practically all business I know. That represents a tremendous capital investment in human beings. But so far neither productivity trends nor profit margins show much sign of responding to it. Pretty clearly, although business is paying for knowledge workers, it isn’t getting much back. And if you look at the way we manage knowledge workers, the reason is obvious: we don’t know how.

One of the few things we do know is that for any knowledge worker, even for the file clerk, there are two laws. The first one is that knowledge evaporates unless it’s used and augmented. Skill goes to sleep, it becomes rusty, but it can be restored and refurbished very quickly. That’s not true of knowledge. If knowledge isn’t challenged to grow, it disappears fast. It’s infinitely more perishable than any other resource we have ever had. The second law is that the only motivation for knowledge is achievement. Anybody who has ever had a great success is motivated from then on. It’s a taste one never loses. So we do know a little about how to make knowledge productive.

The obsolescence of experience

Another implication flows from the creation of this new knowledge resource. The new generation of managers, those now aged 35 or under, is the first generation that thinks in terms of putting knowledge to work before one has accumulated a decade or two of experience. Mine was the last generation of managers who measured their value entirely by experience. All of us, of necessity, managed by experience—not a good process, because experience cannot be tested or be taught. Experience must be experienced; except by a very great artist, it cannot be conveyed.

This means that the new generation and my generation are going to be horribly frustrated working together. They rightly expect us, their elders and betters, to practice some of the things that we preach. We don’t dream of it. We preach knowledge and system and order, since we never had them. But we go by experience, the one thing we do have. We feel frustrated and lost because, after devoting half our lifetimes to acquiring experience, we still don’t really understand what we’re trying to do. The young are always in the right, because time is on their side. And that means we have to change.

This brings us to the third implication, a very important one. Any business that wants to stay ahead will have to put very young people into very big jobs—and fast. Older men cannot do these jobs—not because they lack the necessary intelligence, but because they have the wrong conditioned reflexes. The young ones stay in school so long they don’t have time to acquire the experience we used to consider indispensable in big jobs. And the age structure of our population is such that in the next 20 years, like it or not, we are going to have to promote people we wouldn’t have thought old enough, a few years ago, to find their way to the water cooler. Companies must learn to stop replacing the 65-year-old man with the 59-year-old. They must seek out their good 35-year-olds.

For all its importance, however, the appearance of knowledge as a new capital resource is not the most vivid change in our environment, if only because it does not yet have a visible impact on the world’s economic figures. Probably the most vivid change is in technology.

Many of the old technologies, of course, still have a lot of life in them. I think it’s quite clear that the automobile, for instance, has yet to experience its greatest growth period. In the developed countries, however, it’s in a defensive position. I don’t think we need a great deal of imagination to foresee the day when the private car will be banned in the midtown areas or the day when the internal combustion engine will be limited to over-the-road use.

Or consider steel. I think one can quite easily foretell technological changes that will cut the cost of steel by about 40 percent. But whether that’s enough to re-create momentum for the steel industry is debatable. I think that steel would probably need a greater cost advantage to make it again the universal material it used to be. Since steel, like all multipurpose materials, isn’t ideal for any one use, it has to compete on price. And, as you know, the steel industry has lost 20 percent of the markets it had before World War II. 

It’s concrete here, plastic there, and so on. Whether steel will lose the automotive-body business to one of the new composition materials in the next ten years is a moot question. Only a fool would bet on it at this point, but by the same token only a fool would bet against it. If it does happen, it’s very doubtful whether even a 40 percent reduction in cost might be enough to keep steel from joining the long parade of yesterday’s engines of economic growth.

In agriculture, the great need is for an advance in productivity—but again, not in the developed countries. By now, the agricultural population in the developed countries has shrunk to such a small percentage of the total that even tripling its productivity would make little difference in the overall economic picture.

And so on. I’m not saying that the industries based on old technologies can’t advance, but I am saying they’re unlikely to provide the impetus we need for continuing expansion. From now on, I think, the expansion will have to be powered by new industries based on new technologies, something we have not seen to any extent since before World War I.

Enter the knowledge utility

One of the most potentially earthshaking forces in our economy is the technology of information. I don’t mean simply the computer. The computer is to information what the electric power station is to electricity. The power station makes many other things possible, but it’s not where the money is. The money is in the gimmicks and gizmos, the appliances, the motors and facilities made possible and necessary by electricity, that didn’t exist before.

Information, like electricity, is energy. Just as electrical energy is energy for mechanical tasks, information is energy for mental tasks. The computer is the central power station, but there are also the electronic transmission facilities—the satellites and related devices. We have devices to translate the energy, to convert the information. We have the display capacity of the television tube, the capability to translate arithmetic into geometry, to convert from binary numbers into curves. We can go from computer core to memory display, and from either one into hard copy. 

All the pieces of the information system are here. Technically there is no reason why Sears, Roebuck could not offer tomorrow, for the price of a television set, a plug-in appliance that would put us in direct contact with all the information needed for schoolwork from kindergarten through college.

Already the time-sharing principle has begun to take hold. I don’t think it takes too much imagination to see that a typical large company is about as likely to have its own computer 20 years hence as it is to have its own steam-generating plant today. It is reasonably predictable that computers will become a common carrier, a public utility, and that only organizations with quite extraordinary needs will have their own. 

Steel mills today have their own generators because they need such an enormous amount of power. Twenty years hence, an institution that’s the equivalent of a steel mill in terms of mental work—MIT, for example—might well have its own computer. But I think most other universities, for most purposes, will simply plug into time-sharing systems.

It would be silly to try to predict in detail the effects of any development as big as this. All one can foresee for certain is a great change in the situation. One cannot predict what it will lead to, and where and when and how. A change as tremendous as this doesn’t just satisfy existing wants, or replace things we are now doing. It creates new wants and makes new things possible.

A new age of information

The impact of information, however, should be greater than that of electricity, for a very simple reason. Before electricity, we had power; we had energy. It was very expensive and rather scarce, but we had it. Before now, however, we have not had information. Information has been unbelievably expensive, almost totally unreliable, and always so late that it was of little, if any, value. Most of us who had to work with information in the past, therefore, knew we had to invent our own. One developed, if one had any sense, a reasonably good instinct for what invention was plausible and likely to fly, and what wasn’t. But real information just wasn’t to be had. Now, for the first time, it’s beginning to be available—and the overall impact on society is bound to be very great.

Without attempting to predict the precise nature and timing of this impact, I think we can safely make a few assumptions.

Assumption No. 1: Within the next ten years, information will become very much cheaper. An hour of computer time today costs several hundred dollars at a minimum; I have seen figures that put the cost at about a dollar an hour in 1973 or so. Maybe it won’t come down that steeply, but come down it will.

Assumption No. 2: The present imbalance between the capacity to compute and store information and the capacity to use it will be remedied. We will spend more and more money on producing the things that make a computer usable—the software, the programs, the terminals, and so on. The customers aren’t going to be content just to have the computer sitting there.

Assumption No. 3: The kindergarten stage is over. We’re past the time when everybody was terribly impressed by the computer’s ability to do two plus two in fractions of a nanosecond. We’re also past the stage of trying to find work for the computer by putting all the unimportant things on it—using it as a very expensive clerk. Actually, nobody has yet saved a penny that way, as far as I can tell. Clerical work—unless it’s a tremendous job, such as addressing 7 million copies of Lifemagazine every week—is not really done very cheaply on the computer. But then, kindergartens are never cheap.

Now we can begin to use the computer for the things it should be used for—information, control of manufacturing processes, control of inventory, shipments, and deliveries. I’m not saying we shouldn’t be using the computer for payrolls, but that’s beside the point. If payrolls were all it could do, we wouldn’t be interested in it.

Managing the moron

We are beginning to realize that the computer makes no decisions; it only carries out orders. It’s a total moron, and therein lies its strength. It forces us to think, to set the criteria. The stupider the tool, the brighter the master has to be—and this is the dumbest tool we have ever had. All it can do is say either zero or one, but it can do that awfully fast. It doesn’t get tired and it doesn’t charge overtime. It extends our capacity more than any tool we have had for a long time, because of all the really unskilled jobs it can do. By taking over these jobs, it allows us—in fact, it compels us—to think through what we are doing.

But though it can’t make decisions, the computer will—if we use it intelligently—increase the availability of information. And that will radically change the organization structure of business—of all institutions, in fact. Up to now we have been organizing, not according to the logic of the work to be done, but according to the absence of information. Whole organization levels have existed simply to provide standby transmission facilities for the breakdowns in information flow that one could always take for granted. Now these redundancies are no longer needed. We mustn’t allow organizational structure to be made more complicated by the computer. If the computer doesn’t enable us to simplify our organizations, it’s being abused.

Along with vastly increasing the availability of information, the computer will reduce the sheer volume of data that managers have had to cope with. At present the computer is the greatest possible obstacle to management information, because everybody has been using it to produce tons of paper. Now, psychology tells us that the one sure way to shut off all perception is to flood the senses with stimuli. That’s why the manager with reams of computer output on his desk is hopelessly uninformed. That’s why it’s so important to exploit the computer’s ability to give us only the information we want—nothing else. The question we must ask is not, “How many figures can I get?” but “What figures do I need? In what form? When and how?” We must refuse to look at anything else. We no longer have to take figures that mean nothing to us and read them the way a gypsy reads tea leaves.

Instead, we must decide on our information needs and how the computer can fill those needs. To do that, we must understand our operating processes, and the principles behind the processes. We must apply knowledge and analysis to them, and convert them to a clerk’s routine. Even a work of genius, thought through and systematized, becomes a routine. Once it has been created, a shipping clerk can do it—or a computer can do it. So, once we have achieved real understanding of what we are doing, we can define our needs and program the computer to fill them.

Beyond the numbers barrier

We must realize, however, that we cannot put on the computer what we cannot quantify. And we cannot quantify what we cannot define. Many of the important things, the subjective things, are in this category. To know something, to really understand something important, one must look at it from 16 different angles. People are perceptually slow, and there is no shortcut to understanding; it takes a great deal of time. Managers today cannot take the time to understand, because they don’t have it. They are too busy working on things they can quantify—things they could put on a computer.

This is why the manager should use the computer to control the routines of business, so that he himself can spend ten minutes a day controlling instead of five hours. Then he can use the rest of his time to think about the important things he cannot really know—people and environment. These are things he cannot define; he has to take the time to go and look. The failure to go out and look is what accounts for most of our managerial mistakes today.

Our greatest managerial failure rate comes in the step from middle to top management. Most middle managers are doing essentially the same things they did on their entrance jobs: controlling operations and fighting fires. In contrast, the top manager’s primary function is to think. The criteria for success at the top level bear little resemblance to the criteria for promotion from middle management.

The new top manager, typically, has been promoted on the basis of his ability to adapt successfully. But suddenly he’s so far away from the firing line that he doesn’t know what to adapt to—so he fails. He may be an able man, but nothing in his work experience has prepared him to think. He hasn't the foggiest notion how one goes about making entrepreneurial or policy decisions. That’s why the failure rate at the senior-management level is so high. In my experience, two out of three men promoted to top management don’t make it; they stay middle management. They aren’t necessarily fired. Instead, they get put on the Executive Committee with a bigger office, a bigger title, a bigger salary—and a higher nuisance value because they have had no exposure to thinking. This is a situation we are going to eliminate.

On the other hand, we are going to open up a new problem of development at the middle-management level. It isn’t difficult for us to get people into middle management today. But it is going to be, because we shall need thinking people in the middle, not just at the top. The point at which we teach people to think will have to be moved further and further down the line. We can already see this problem in the big commercial banks.

We will have to manage knowledge correctly in order to preserve it. And this gets us into myriad questions of teaching and learning, of developing knowledge and techniques of thinking—not only in the developed nations, but in countries that are yet unaware of the distinction between management-by-experience and management-by-thinking, countries that are unaware of management itself. But that is another subject.

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