The Last Mile:
Using Behavioural Insights to Create Value
Leaders spend too much time on ‘first-mile’ issues like strategy, and too little time on the ‘last mile’, where consumer decisions are actually made.
— regardless of industry, mission or location EVERY ORGANIZATION — shares a common quest: they are all in the business of changing human behaviour. For-profit companies try to convince consumers who currently purchase a competitor’s product to switch to theirs; governments want to convince citizens to pay their taxes on time or renew their driver’s license online; and a welfare organization might want to encourage families to sign up to receive tuition support for their childrens’ education.
Yet if you think about what people actually do in organizations from day to day, the bulk of their efforts are spent on what I call ‘first-mile problems’. These include the efforts devoted to thinking through the competitive landscape; developing strategies to address it; designing processes of innovation; and coming up with new products and services.
Very little attention is paid to the ‘last mile’ — the part where a potential customer actually gets to your website, walks into your retail store, or talks to your sales representative, and then makes the decision to purchase your product (or not.) The last mile is also the place where an individual visits a government office to gain access to a public service — and either chooses to stay and wait, or throws up her hands in frustration and goes home.
If you take some time to think about the last mile and listen to stories from consumers who have had a bad experience there, you will quickly realize that it is not ‘big things’ that make a difference here: it is the small things that matter. Things like the manner in which a decision is presented; the ambiance of the room; the nature of the questions that are asked; or the disposition of the agent with whom the consumer interacts. These are all key
determinants of peoples’ decisions to buy a product, open an account, or consume a service.
As a society in general, I would argue that we have not spent nearly enough time thinking about this realm. In my view, this is a big mistake, and in this article I will describe how to better understand what I call ‘the last mile problem.’
A Two-part Journey
It was a beautiful summer’s day in 1997. I was on a road trip across the United States, and the last leg took me from Upstate New York to Boston. These were the days before Google Maps and GPS. I had been told that the drive would take about six-anda-half hours; so, imagine my surprise when, at the end of six-anda-half hours, I approached a sign on the highway that said, ‘Boston, 1 mile’. I was pleased that my drive time had matched the prediction, and began to feel like I had ‘arrived’: but I was wrong.
Some of you may remember that in those days, Boston was undergoing a major road renovation program known as The Big Dig: roads were being dug up and elevated highways were being reconstructed underground. When I left the expressway for my destination in downtown Boston, I got stuck in a maze of construction, one-ways and slow earth-moving equipment. It took me a full 50 minutes to get to where I wanted to be. I had become a victim of the last-mile problem.
When I look back on this journey, I am struck by the fact that it had two distinct parts: in part one, I zipped along the highway from upstate New York to Boston; in part two, I left the highway and had to inch my way to a precise downtown address. This story is not unique to Boston, of course; if you have ever zipped along the motorways in England and exited towards a destination in London, you have likely experienced the same sense of frustration. Likewise in India, where you come down the PuneBombay expressway only to find that it takes three hours to get to Bombay.
These examples are analogous to many other situations in the real world. Imagine that you own a company and you are looking to distribute your products across the U.S. Think about the difficulty of transporting goods from upstate New York to Boston, and then the cost involved with transporting those goods — once they’ve arrived in Boston — to individual stores or households. As you can imagine, the cost of this second part of the distribution system is going to be significant. It is much like my drive: the time and energy spent on the first part will pale in significance to the time, energy, and frustration experienced in the second part.
The term ‘last mile problem’ comes from the early days of the telegraph — a now obsolete technology whereby people would send messages to each other through wires that were strung across the country. Before that, the only way to communicate with others was through posted letters, which took a long time to reach the recipient. With a telegraph, the recipient at the end of the line could receive a message within a matter of hours. Of course, there was also a last-mile problem with the telegraph: while it was easy for messages to zip from the place of origin to the end of the telegraph line, someone still had to get on a horse or bicycle and physically bring that message over ‘the last mile’ to the intended recipient. The last mile of any communication network is the part that actually reaches the customer.
Last-mile problems show up across industries. I live in Toronto, and last year we had an election for a new mayor. One of the big issues in this election was public transit, which is important for ensuring connectivity and for reducing the burden on the roads of any major city. This is something that every city in the world should focus on, because we know that it cuts down on the use of gas, reduces congestion, and makes connectivity easier.
However, there is one interesting problem that designers of transit have to think more about: while it might be easy to transport people from point A to point B — say, two stations on a transit network — we also need to worry about how people get to point A from their homes, and how they get to their workplace from point B. One big barrier to using public transit is the cost — in terms of money, time and psychological angst — of these often-ignored aspects of the experience.
How can we solve this particular last mile problem? There are a number of possible solutions. In Toronto, for example, many buses are equipped with bicycle racks, so you can ride your bike to a bus station, attach it to a rack, board the bus, and hop back on it when you disembark.
In Europe, there is the Hiriko City Car. This is a two-person electric vehicle that solves the gap between getting from your home to the public transit station, and from the public transit station to your office. Other solutions include a folding electric motor scooter called a Roboscooter, and an electric-assisted bike called the Green Wheel. All of these simple solutions can help to overcome the last mile problem, thereby facilitating the use of public transit.
Issues Around Adoption
Depending on which survey you read, new product success rates are typically in the region of one-to-25 per cent. In other words, more than 75 per cent of new products fail. Why does this happen? Perhaps the product has features that are difficult to use? Maybe the marketing or advertising was problematic? Perhaps the product wasn’t physically distributed properly, or the pricing was wrong?
There is a much simpler behavioural story that might explain the bulk of this high rate of failure, and it has to do with the fact that the product developers didn’t spend any time thinking about the last mile. In short, they failed to recognize the proactive effort that would be required by consumers to adopt and use the new product.
Here’s a public-sector example. In Canada, the government introduced a new welfare initiative called the Canada Learning Bond (CLB) — a wonderful program that provides eligible low-income families with $500 to use towards their childrens’ education. While there are obviously some parameters around how the money can be spent, this is, in effect, ‘free money’ for these families.
An economist would say, ‘Wow, $500, free; who wouldn’t take the money?’ Her prediction would be that the take-up rate for the Canada Learning Bond would be close to 100 per cent. And yet, in the first year the bond was introduced, adoption rates were as low as 16 per cent, and the reason had nothing to do with the quality of the program: it was a classic last-mile problem. In order to benefit from the program, eligible families needed a bank account. But the reality was that many eligible families simply didn’t have the time to go out and open a bank account. These parents were juggling multiple jobs, and they had children to look after. Their inability to physically get to a bank proved to be the primary reason why this program failed.
Many authors have written about the fact that human decision-making is not rational — that people make choices that conflict with the standard economic model of decision making. For instance, in Nudge, Richard Thaler and Cass Sunstein make a distinction between ‘econs’ and humans. Econs are the ‘unicorns’ of the decision-making world: the mythical beasts that inhabit the pages of Economics textbooks. These forwardlooking creatures have infinite computational ability; they are unemotional; and they have an uncanny ability to assign ‘utility’ to every product or service they consume. On the other hand, real people are myopic and impulsive; emotion guides their decisionmaking, and they often make decisions quickly, without thinking too much. In short, they are irrational, as Dan Ariely described in his best-seller, Predictably Irrational.
My take on irrationality is slightly different: if people are not obeying the laws of Economics, I don’t think they’re being irrational; I just think they’re being human. I am more concerned about two other versions of irrationality: first, I believe it is irrational for an organization to believe that its stakeholders are rational and to design last-mile interfaces accordingly; and second, there is a mismatch between what individuals want to do, and what they actually do. People are often not as influenced by features of a product or program that marketers or policy makers expect them to be influenced by; and conversely, they might be influenced by factors that have been deemed irrelevant to their decision.
Here’s an example, based on research by Columbia University’s Eric Johnson and Dan Goldstein, who set out to better
understand the realm of organ donation. In their article in the journal Science, they present a chart that shows the percentages of people saying that they’re willing to donate organs in several European countries. What they found was interesting: in some countries, organ donation rates were very low. Denmark, for example, had 4.35 per cent and Germany 12 per cent. However, in others, consent rates were incredibly high: Austria had 99.98 per cent; France had 99.91 per cent; and Hungary had 99.97 per cent. What might explain these differences?
The simplest explanation turned out not to be true: it wasn’t the case that the penetration of advertising programs in one set of countries was different from the other. The only thing that was different across these countries was what is called ‘the default option’.
Think about the organ donation process in North America. In Canada, if someone wants to donate her organs, she needs to go to a provincial Service Office and obtain a form. Long and complicated, it is typically given to a customer at the end of whatever work she came in to do. For instance, after renewing your driver’s license, you might be handed the form and asked whether you would consider being an organ donor. If you do not complete and hand in the form, you will not be an organ donor.
This is what we call ‘an explicit consent’ or an ‘opt-in’ process. In such processes, anybody who wants to register needs to take some action. The default is different in places like Austria, which is an example of a ‘presumed consent’ or ‘opt-out’ process. You retain your freedom of choice about whether (or not) to donate your organs, but the assumption is that, unless you fill out and hand in a form, you do want to donate. This simple difference between an explicit consent process and a presumed consent process can push organ donation rates from, say, 4.25 per cent (in Denmark) to 99.98 per cent (in Austria.)
Defaults are tremendously powerful, for two reasons. The first is that research shows that human beings are supremely lazy, both physically and cognitively, and as a result, we stick to defaults. People are averse to undertaking any new effort, so unless a default option is something we are particularly averse to, we will stick with it. The second reason why defaults work is that they tend to signal some sort of a ‘social norm’. If the default is that ‘everybody is donating organs’, we think that we, too should donate our organs. Likewise, if the default is that ‘nobody is donating their organs’, we are likely to accept that as a suggestion.
Opting-in vs. Opting Out
Defaults shape our choices in a wide variety of domains. In one informal study a number of years ago, I was interested in seeing how to get more people to visit their doctor for an annual checkup. In this particular population, the percentage of people going for an annual checkup was low — around 16 per cent. I wanted to understand why.
When I asked people, the most common excuse I heard was, “I’m just too busy.” They claimed that they had every intention of going, but they just could not find the time. However, these same people somehow found the time to go on vacations, read books, watch TV and have relaxing barbeque get-togethers. The reality of the situation was simpler: some of them felt busier than they were. More importantly, there was some effort required to pick up the phone, make an appointment, and then set up their schedule to ensure they would be available during that time frame. While the economic costs of taking action were very low, it just seemed like a nuisance: the ‘hassle costs’ were too high.
We tried something that, in hindsight, sounds incredibly simple: we used a random date generator to assign people to a doctor’s appointment. A mailing to one of the participants might say, for example, “Sally, thank you for enrolling in a health plan. As part of your plan, you are entitled to an annual checkup with your doctor. You have been assigned to see the doctor at 10 a.m. on June 26th. Should you not be able to make it, please give us a call. Otherwise, we look forward to seeing you then.” This simple
act resulted in an increase in the number of people going for their annual checkup from 16 to 64 per cent.
The fact that simply changing a default can significantly change preferences leads to an important insight: preferences are dramatically dependent on the context in which a decision is made. In short, ‘context is everything’; it significantly influences our decision-making, and this is the first of three pillars of human decision making.
The second pillar is something that is easily explained by Sir Isaac Newton’s Laws of Motion: a body at rest will continue to be at rest, unless it is given some sort of an external push; and a body that is in motion will continue to move, unless some external force slows it down. Human decision-making is very much like that: people will continue to do whatever it is they are doing, unless they are nudged to do something different. As a result, something simple like changing defaults can often change decision-making.
The third principle that explains much of human decision making is ‘inter-temporal choice’. This is the study of the relative value people assign to two or more payoffs that occur at different points in time. Most choices — including those related to savings, work effort, education, nutrition, exercise, health care and so forth — require a decision-maker to trade-off costs and benefits at different points in time.
American author Augusten Burroughs wrote one of my favourite books, Magical Thinking, and one particular quote from it has stuck with me: “I myself am made entirely of flaws stitched together with good intentions.” In my view, this describes human behaviour nearly perfectly. Everybody intends to be good: we intend to eat healthy food, exercise regularly and save money for the future. But as indicated, there is usually a gap between our intentions and our actions. As a result, the solution to last-mile problems isn’t so much about creating awareness as it is about facilitating action.
In closing
Six years since the publication of Thaler and Sunstein’s highly influential book, Nudge, people still don’t exercise as much as they should; they still don’t take public transit often enough, and they still work too hard and don’t spend enough time with their families. As indicated herein, it isn’t that people don’t want to do these things; but given human nature, organizations must take proactive steps to help facilitate them.
Looking ahead, there is much work to be done to successfully embed the science of behavioural insights into the DNA of both government and for-profit organizations. Hopefully, all of us will embrace the challenge and use these early insights to create value — one nudge at a time.