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

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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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The infrastructure conundrum: Improving productivity 07-07

The infrastructure conundrum: Improving productivity. 

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Infrastructure productivity can and should be much better. Here’s how to start improving.

From now through 2030, the world will need to spend at least $57 trillion to build the ports, power plants, rails, roads, telecommunications, water systems, and other infrastructure that the global economy needs. For advanced economies, the priority is to renew aging and dilapidated infrastructure; for emerging ones, it is to build the structures required to support growth—this is the larger part of the total bill.

Exhibit 1.

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Our research, based on 400 global case studies, suggests that governments could boost infrastructure productivity by $1 trillion a year in three ways: improving project selection, streamlining delivery, and making the most of existing investments. None of these actions requires radical change, and successful examples exist.

1. Project selection. Beyond palpable abuses of spending power, the more common problem is that decisions about whether or not to build are sometimes made without considering the larger socioeconomic objectives of the country. This happens when officials look at projects one by one rather than considering how each particular project fits into the entire portfolio.

Or they do not evaluate whether other projects might have better returns. This matters: research shows that countries that take the time to get the planning right are able to eliminate noneconomic projects and reduce project overruns in the projects they do launch. The key is to create a rigorous, transparent, and fact-based process to decide what needs to be done, and in what order (see sidebar, “Infrastructure diagnostic”). 

Exhibit 1.

Infrastructure diagnostic.

Getting policy right requires putting together the right information, and then drawing the right conclusions. But when it comes to infrastructure there’s a problem: the information doesn’t exist.
Competitiveness rankings from the Word Economic Forum and the International Institute for Management Development business school measure the availability of infrastructure.

The Construction Sector Transparency Initiative and country and sector-specific benchmarks, such as the UK Cost Review, measure costs. The International Monetary Fund’s proposed Index of Public Investment Effectiveness compiles data on transparency, audit standards, and internal controls to evaluate governance. To complement these metrics, we have developed a three-part infrastructure diagnostic. It provides a comprehensive assessment of infrastructure delivery and offers a database of more than 500 examples of good and best practices.

Part 1. Establish a starting point. What’s the state of the infrastructure? Does planned funding match future needs? Where are the biggest improvement opportunities?

Part 2. Measure effectiveness and productivity. Five areas are evaluated: project selection, funding and finance, delivery, asset utilization and maintenance, and governance. These five areas can be broken down into 30 categories and 78 subcategories, each representing a global best practice. For each category, we also codify average and low performances against a set of clear criteria, providing a basis for scoring each government’s performance.

Part 3. Define outcomes. What’s the cost of delivering a road in Country X compared to next-door Country Y? Do projects come in on time and on budget? Do they meet quality requirements? How many changes are required after first sign-off? The diagnostic considers quantitative indicators on availability, cost, and time to come up with an aggregate outcome, and also creates a basis for benchmarking.

The diagnostic compares participants not only against global best practices but also across regions, asset classes, and time. For each of the 78 categories, there are clear descriptions of good, average, and bad performance by international standards. Each category is scored from one (worst) to five (best). The final score, based on 400 criteria, reflects all participant responses. The goal is to help governments compare their performance, and also to learn from one another—something that doesn’t happen nearly often enough.

Using the diagnostic, infrastructure providers can figure out where they are compared with their peers. Bad? Average? World class? This assessment can be done over the course of a day, a week, or a month.

So far, we have done roughly a dozen case studies, and some interesting patterns are emerging. One is that even the best countries score an average 3.7 out of 5.0, so there is room for improvement everywhere. Another is that in almost every single case, there are issues with capabilities and data, such as lack of an effective program manager and standard international benchmarks.

The diagnostic is and should be a moving target. Over time, many of the 500 best practices will only be good practices, as countries learn and improve. The assessment can work at different geographical levels—country, regional, or city—and with different asset classes. The strength of the diagnostic is that it provides a fact base in which to ground discussion. It marks the beginning of a systematic effort to analyze global infrastructure performance.

Getting policy right requires putting together the right information, and then drawing the right conclusions. But when it comes to infrastructure there’s a problem: the information doesn’t exist.
Competitiveness rankings from the Word Economic Forum and the International Institute for Management Development business school measure the availability of infrastructure.

The Construction Sector Transparency Initiative and country and sector-specific benchmarks, such as the UK Cost Review, measure costs. The International Monetary Fund’s proposed Index of Public Investment Effectiveness compiles data on transparency, audit standards, and internal controls to evaluate governance. To complement these metrics, we have developed a three-part infrastructure diagnostic. It provides a comprehensive assessment of infrastructure delivery and offers a database of more than 500 examples of good and best practices.

Part 1. Establish a starting point. What’s the state of the infrastructure? Does planned funding match future needs? Where are the biggest improvement opportunities?

Part 2. Measure effectiveness and productivity. Five areas are evaluated: project selection, funding and finance, delivery, asset utilization and maintenance, and governance. These five areas can be broken down into 30 categories and 78 subcategories, each representing a global best practice. For each category, we also codify average and low performances against a set of clear criteria, providing a basis for scoring each government’s performance.

Part 3. Define outcomes. What’s the cost of delivering a road in Country X compared to next-door Country Y? Do projects come in on time and on budget? Do they meet quality requirements? How many changes are required after first sign-off? The diagnostic considers quantitative indicators on availability, cost, and time to come up with an aggregate outcome, and also creates a basis for benchmarking.

The diagnostic compares participants not only against global best practices but also across regions, asset classes, and time. For each of the 78 categories, there are clear descriptions of good, average, and bad performance by international standards. Each category is scored from one (worst) to five (best). The final score, based on 400 criteria, reflects all participant responses. The goal is to help governments compare their performance, and also to learn from one another—something that doesn’t happen nearly often enough.

Using the diagnostic, infrastructure providers can figure out where they are compared with their peers. Bad? Average? World class? This assessment can be done over the course of a day, a week, or a month.

So far, we have done roughly a dozen case studies, and some interesting patterns are emerging. One is that even the best countries score an average 3.7 out of 5.0, so there is room for improvement everywhere. Another is that in almost every single case, there are issues with capabilities and data, such as lack of an effective program manager and standard international benchmarks.

The diagnostic is and should be a moving target. Over time, many of the 500 best practices will only be good practices, as countries learn and improve. The assessment can work at different geographical levels—country, regional, or city—and with different asset classes. The strength of the diagnostic is that it provides a fact base in which to ground discussion. It marks the beginning of a systematic effort to analyze global infrastructure performance.

None of this is easy; in fact, it is almost bewilderingly complex. Take trying to calculate the socioeconomic benefits of a project. The relatively simple part is to calculate the direct benefits. This proposed road, if built, will shorten travel time by X minutes, and there are Y thousand people traveling every day, adding up to Z time saved. But that is only the beginning. With a better road, companies can recruit in a wider region, finding higher-skilled labor. How can that be calculated?

None of this is easy; in fact, it is almost bewilderingly complex. Take trying to calculate the socioeconomic benefits of a project. The relatively simple part is to calculate the direct benefits. This proposed road, if built, will shorten travel time by X minutes, and there are Y thousand people traveling every day, adding up to Z time saved. But that is only the beginning. With a better road, companies can recruit in a wider region, finding higher-skilled labor. How can that be calculated?
Additionally, decisions are sometimes made on a political basis rather an economic one. There can be a lot of horse trading: “I agree on this project if you agree on that one.” Even more common is a simple lack of knowledge. McKinsey has found cases where the cost of infrastructure in one country was up to 50 percent higher than in a neighboring country with similar characteristics—a discrepancy driven by different approaches to design, engineering, management, procurement, and sourcing.

Despite these challenges, there are ways that project delivery can be improved. One example is Infrastructure Ontario (IO), a corporation owned by the province of Ontario that provides a wide range of services to support the government’s initiatives to modernize and maximize the value of public infrastructure and real estate. Over the past decade, IO has implemented a long-term investment plan and essentially rebuilt the province’s hospital infrastructure, building more than two dozen new structures. IO has organizational independence, clear responsibilities, and a close partnership with the private sector. South Korea’s Public and Private Infrastructure Investment Management Center is a similar organization; it has saved 35 percent of the nation’s infrastructure budget by rejecting 46 percent of projects that it reviews, compared with 3 percent before it was established. The United Kingdom set up a cost-review program that identified 40 major projects for prioritization, reformed overall planning processes, and then created a cabinet subcommittee to oversee delivery. These measures reduced spending by as much as 15 percent.

2. Streamlining project delivery. In simple terms, “delivery” refers to getting the job done. Both the supplier and the client bear responsibility for this, and both parties can often fall short.

In the construction sector, labor productivity, when measured in real value added per hour worked, has been flat or worse in many developed economies for decades. In the United States, productivity in the construction sector has fallen about 20 percent since 1989; in the rest of the country’s economy, it has risen almost 40 percent. Germany has seen the same trend, to a slightly lesser degree, since 1991.
One reason for stagnating productivity is the construction industry’s structure. For smaller projects, the sector is fragmented; the ten largest companies account for only 3 to 4 percent of global market share. Therefore, there are limited scale efficiencies, investments, and innovation. At the top end, for bigger projects, there are sometimes not enough capable bidders to compete. What’s more, incentives are usually structured such that neither the agents representing the public nor the contractors are rewarded for innovating and taking risk.

It is a core responsibility of governments to provide infrastructure. However, this area typically accounts for less than 5 percent of the budget. As a result, infrastructure often receives less attention than it should. There are ways for government to increase the focus on infrastructure while also saving money. Convoluted permit and land-acquisition processes are major causes for cost overruns. By accelerating these processes, governments can cut costs. They can also improve management of contractors, by rigorously tracking their performance. These are important tasks, but ones that tend not to receive a lot of political credit.

In addition, infrastructure is a long-term investment, which can lead to complications when it intersects with the political cycle, which is often much shorter. It’s not uncommon for a government to plan a project, and then run into numerous problems. The next government will go through a lot of pain to actually build the planned project and face the bad press for any overruns. Then the third government cuts the ribbon and takes the praise. None of the three is accountable from end to end. There is little incentive to invest and plan well right from the start.

Then, there is the human element. In most projects, the skill set and capabilities of the project manager makes the difference. McKinsey analysis has found that only about 20 percent or so of project managers routinely deliver projects under budget and on time. A small minority are clearly unfit for the work. The bulk of people in the middle sometimes do well and sometimes not so well. Building their capabilities could lift productivity significantly.

An investment in early-stage planning, typically spending 3 to 5 percent of the total projected cost, is critical to improving project delivery. This involves making the commercial case as well as completing the technical drawings, specifications, risk assessments, and environmental and social-impact analyses. Eager to break ground, clients often rush this phase, later landing in trouble. Banks and donors often do not want to fund early-stage development but should insist that it occur; not investing in planning often leads to disaster. Preliminary McKinsey research has found that countries that consistently invested 1 percent or less up front experienced much larger overruns in time and costs that reached 50 percent or more. In one drastic example, the owner made 42,000 change requests in the course of a single project.

3. Underutilization. The cheapest, least intrusive infrastructure is that which doesn’t have to be built. “Intelligent” transportation systems, which use advanced signaling to squeeze more capacity out of existing roads and rail lines, can sometimes double asset utilization at a relatively low cost. Active traffic management on England’s M42 roadway, for example, directs and controls the flow of traffic; this has reduced journey times by 25 percent, accidents by 50 percent, pollution by 10 percent, and fuel consumption by 4 percent—at only 20 percent of the cost of widening the road.

Pricing mechanisms and improved maintenance are other ways to increase existing capacity. But such simple fixes are underused, often for political reasons. Take congestion charges. If there is no charge to use the road at 6 a.m. and a $5 fee an hour later, some people will move their commuting time to save money, smoothing but demand. That is the theory, and it has worked in Riga, Singapore, and even central London, where the red-and-white “C” has become a familiar urban icon. The Panama Canal also uses congestion pricing, and so do many airports and railways, charging more to the boats, planes, and trains that want to use the facilities at more popular times of day. In each case, the result is that more traffic moves along, with fewer jams.

Although effective, congestion charges provoke opposition. There are ways, however, to make a case for their implementation. One is to demonstrate success. In Stockholm, residents were clearly ready to vote “no” on a referendum on the subject, so city authorities decided to test the idea by running a pilot plan for six months. When people saw how the system worked—traffic at peak hours fell by 20 percent—their opinions changed, and they voted to approve the program in 2006.

Another way to lower the cost of infrastructure is through better maintenance. Maintenance is not glamorous; in fact, it is time-consuming and sometimes tedious, and not nearly as exciting as cutting the ribbon on a new project. However, if assets are allowed to deteriorate, the costs of both operation and reconstruction increase markedly. And when countries do not make the most of what they have, they need to build new structures, which is much more expensive.

Leading countries avoid this in part through good timing. They schedule maintenance often enough to avoid dilapidation and breakdowns. But they also seek to do so at the right times, to keep disruption to a minimum. The World Bank Group has estimated, for example, that if African nations had spent $12 billion on road maintenance in the 1990s, this would have led to savings of $45 billion in reconstruction costs.

The role of money

Money plays a part in all three issues. If countries learned from one another with respect to best practices in productivity, cost cutting, and other practical measures, we estimate that total infrastructure spending could be reduced by almost 40 percent. It would be ideal—but unlikely—to recover that figure. Still, it gives an idea of the scale of the opportunity.

One interesting development: the B-20, a business group that offers policy recommendations to the G-20 group of industrialized nations, has proposed setting up a “global infrastructure hub” to exchange best practices and develop benchmarks. In most countries, the annual spending needed to bring infrastructure up to the level required far exceeds what they have spent historically (Exhibit 2).
In North America and Western Europe, the gap ranges between 0.5 and 1.1 percentage points of GDP per year and rises to 2.0 to 3.0 percentage points in Brazil, India, and Indonesia. Fiscal concerns have only made the infrastructure gap wider. This is such an enormous investment, especially given the fiscal constraints that many nations face, that the all-too-common response has been paralysis.

Exhibit 2

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Institutional investors and others have sufficient funds available to finance all the world’s infrastructure needs—as long as the projects are attractive. Even in poorer countries, the lack of access to money is not the problem. A vibrant bond market in Malaysia has contributed more than half of the private-sector infrastructure investments since the early 1990s. The challenge is to make investors feel confident that they will get their money back by capitalizing sensible projects that will be completed and then run well.

Even so, there is often a sizable gap between the resources that are needed and the resources that are available. Public–private partnerships (PPPs) can help narrow that gap. But project specific financing represented only around 20 percent of total infrastructure investment in the boom year of 2008 and collapsed to around half that level a year later. PPPs with private financing remain small in comparison to traditional public or corporate financing by utilities and other private-infrastructure owners.

Still, there is a larger benefit to PPPs: they bring the discipline of the private sector to risk assessment, evaluation, and construction. Many PPPs also entail a 20- to 30-year concession that includes operations and maintenance. That long-term responsibility encourages the partnership to optimize the total cost of ownership, so there is no cutting back on maintenance. In this sense, PPPs have enormous potential. But they need to be managed carefully, with recognition of their limits.
Governments acknowledge the importance of infrastructure productivity, but most initiatives seem to assume that private-sector involvement will guarantee high productivity without improvements in planning, delivery, and governance. This attitude fails to recognize the efforts required to deliver complex PPP projects successfully and results in missing opportunities to raise infrastructure productivity. Moreover, just because capital is private does not guarantee it will be deployed perfectly; both the conditions of the contract and the capabilities of the provider need to be scrutinized.

Public financing is going to continue to be dominant. Particularly in the developed world, this money is cheap—well below 3 percent for ten-year government bonds in the United States and the United Kingdom. Public financing is especially important as a way to provide lower cost of capital in cases where risk is difficult to measure.

In large, high-risk greenfield developments such as high-speed-rail networks, it may be the only option. To work most effectively, governments should provide certainty on infrastructure budgets beyond annual budgeting or electoral cycles, such as Sweden’s ten-year plans for national transport. Capital recycling can free up funds for major projects; the Australian state of New South Wales, for example, has announced plans to sell off some publicly owned assets, such as ports and regional airports, to finance new investment.

Money is necessary to build the infrastructure the global economy needs, but it is not enough. Governance, commitment, and more than a little imagination are also required. As it is, the world spends much more money than it should for the results it gets. Even small improvements would bring huge benefits.

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