BEHAVIORAL ECONOMICS IN ACTION



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BEHAVIORAL ECONOMICS IN ACTION AN INTRODUCTION

THE BUSINESS OF BEHAVIOR Scientists don t need to be business leaders, but business leaders need to be scientific. There s a science to understanding how people make decisions. Many businesses have economics and marketing departments, but they don t have the one that explains human behavior: psychology. Consumers are not automatons, and yet many strategies focus only on price, and don t deliberate on the overall buying experience. Revenge and reciprocity, loss and risk aversion, the influence of peers and emotions, how and when brains get tired, are among the psychological phenomena that explain consumer behavior. But behavioral economics is not just about principles of decision-making. Equally important is the use of the scientific method of experimentation, to provide statistically-sound facts about what works and why. It s time to retire the old marketing adage that says for every dollar spent, 50% is wasted; we just don t know which half with scientifically-grounded innovation and experimentation. -Kelly Peters, CEO and Managing Partner, BEworks

YOU ARE IN THE BUSINESS OF CHANGING BEHAVIOR It s hard to believe that a single word can make a difference, but BEworks increased the number of people listening to automated calls from the collections department at a financial services company by 9% by changing the word important to urgent. Why did this work? It s a subtle psychological distinction. Urgent conveys a sense of timing in addition to importance; urgent must be handled now. Behavioral economics offers the scientific insights of psychology and the scientific method of experimentation to people in the business of changing behavior. Business leaders are already familiar with the power of psychology in marketing and sales. For example, attractive models are used in advertising to help sell cars and persuasion techniques are used to close the deal on the showroom floor. However, the strategic application of psychology as a business technique is still quite nascent. Older methods, like focus groups and surveys, don t always reflect new insights, or are exercised beyond their limits. One insight uncovered by psychology is the say-do gap: turns out that people are often unaware of the gap between their perception of their behavior and of what they actually do. In a surprising research finding, ten percent of people are willing to report how they voted in elections in which they did not even vote! Asking consumers what they want seems so obvious, and yet we have witnessed major product failures that were backed by customer opinion, like the infamous multi-million dollar fiasco of the launch of New Coke. Behavioral economics differs from other techniques. It gets into the details of engineering of actual behavior, like getting people to listen to automated voice recordings and pay their bills. The scientific method, which is the use of experimentation to measure and validate the empirical effect of an intervention, is quite new or unfamiliar to most organizations. Often business leaders rely on their experience or intuition and gut feel to make strategic choices. While there may be instances where this leads to the best course of action, unless the strategy is tested If you re in business, behavioral economics is a mandatory tool for understanding and engineering the behavior of your customers and employees. and measured against a reliable comparison, there is no way to determine if and how well it worked. The most reliable methodology of comparison and measurement is offered by science. The randomized controlled trial (RCT) provides the benefit of a clear validation of the cause and effect relationship between a strategy and the outcome. Without a randomized control, there is no way to determine if other factors, like the economy or the competitive landscape, were the drivers of the outcome or if it was the strategy. Behavioral economics is devoted to understanding and influencing how people behave. It is being employed to tackle a wide array of business challenges which include customer acquisition and long-term loyalty strategies, solving the Marketer s 4 P conundrum (Price, Product, Place, Promotion), product design, sales and service strategies, and employee productivity and retention. Essentially any problem that requires changing behavior can be best understood and solved by applying behavioral economics. 3

BEHAVIORAL ECONOMICS: HOW IT ALL STARTED. Behavioral economics was born when a trio of academics, consisting of two psychologists, Daniel Kahneman and Amos Tversky, and an economist, Richard Thaler, set out to understand the anomalies in human decision-making. They studied questions like, why are people more willing to drive through a dangerous snowstorm to attend a basketball game if they purchased the tickets themselves than if they received the tickets for free? Or, why do people demand more money to give up an object they posses than they are willing to pay to acquire the same object? Their curiosity towards such behavioral anomalies has lead to the growth of an interdisciplinary science that is being used to explain - and influence - a myriad of behaviors across many domains from banking to health care. Let s demonstrate our irrationality through two examples. 1) We don t always treat a dollar as a dollar Imagine the following scenario: You are about to purchase a lamp for $150. The salesperson informs you that the lamp you wish to buy is on sale for $100 at the other branch of the store, located a 20-minute drive away. Would you make the trip to the other store? Now imagine this variation: You are about to purchase a sofa for $1,000. The salesperson informs you that the sofa you wish to buy is on sale for $950 at the other branch of the store, located a 20-minute drive away. Would you make the trip to the other store? In studies similar to this, 68% of participants would say yes in the first scenario, but only 38% in the second scenario (Tversky & Kahneman, 1981). Yet, rationally, if you were willing to travel in order to save $50 in one scenario, then you should be equally willing to travel to save $50 in the second scenario - fifty bucks is fifty bucks. There is a psychological explanation for this phenomenon, known as mental accounting. Instead of making rational calculations based on the absolute dollar amount, we operate on a relative basis. This irrationality can be seen, for example, when we drive an extra distance for a small saving on fuel prices. 2) We are influenced by irrelevant inputs without realizing it People often need to rely on their skills of estimation to determine the value of something, like a new house or a car. We use past experience and comparisons with similar items as guides to making these estimates. These rules of thumb make us feel confident in our abilities. However, unbeknownst to us, completely irrelevant information can significantly bias the estimates we produce (Tversky & Kahneman, 1974; Ariely et al., 2003). In experiments conducted by Dan Ariely, people were asked for the last two digits of their security number and then to estimate the amount they d be willing to pay for items like a bottle of wine (Ariely et al., 2003). Fascinatingly, those with higher social security numbers were willing to pay as much as 320% more than the group with lower numbers. This anchoring-andadjustment bias is at work when a consumer sees an MSRP (manufacturer s suggested retail price) on a price tag followed by a lower price. 4

UNDERSTANDING THE WIRING People are boundedly rational. This concept, coined by Nobel Laureate Herbert Simon (1955), explains why our decisions tend to stray from what traditional economic models would predict. Constraints in time, energy, and information lead to suboptimal decision-making strategies. To be completely rational decision-makers, we would need to make decisions armed with a spreadsheet to methodically weigh the pros, cons, and economic outcomes of our decisions. Instead we either oversimplify the challenge without thinking too broadly about the number of choices we have or we satisfice, which is to pull the quickly trigger on the first option that seems satisfactory. A convenient framework known as System 1 and System 2 provides a model for understanding the influences on judgment and decisionmaking. Notably, this two-system framework is a simplified version of how our mind actually works, but it captures an important dynamic between processes that drive our automatic first reaction and those that drive more effortful, deliberative decision-making. For businesses, understanding this dynamic provides a useful lens for explaining current behaviors and influencing future behaviors. In the two-system model, System 1 is fast, effortless, automatic, and associated with emotion and intuition and System 2 is slow, effortful, controlled, and associated with logic and reason. To illustrate the interplay between these two systems in our thinking, take a moment to answer this question: A bat and a ball cost $1.10 in total. The bat costs $1 more than the ball. How much does the ball cost? Over 50% of Harvard, Princeton and MIT students who tackled this question provided an incorrect answer (see footnote on page 8 for the solution). This example raises three interesting observations about decision-making in our constrained environments: We generally have an immediate gut reaction to what the response should be, The direction of this gut reaction is predictable, and We often avoid the cognitive effort to check this gut reaction These observations can be explained using the two-system framework. Here System 1 is first to the scene (i.e. the gut reaction), leading us astray and giving the quick, seemingly obvious answer. To arrive at the correct, logical answer, we need System 2 to step in and stop, think and check the math. Of course, getting System 2 involved is slow and effortful (think how much more mental effort it takes to solve 14 x 13). In order to avoid the cognitive strain that comes from working our brain, we often let System 1 run the show and give in to quick, gut reactions that can be wrong. People often rely on heuristics to guide the decision process, for example, higher price as a signal of quality or It worked well in the past, and so it will work again. Heuristics are simply mental short cuts or rules of thumb grounded by past experience, common sense, or social norms. For the most part, heuristics do not lead to poor decision-making, such as when we assume that an elderly woman is less likely to be familiar with the latest technology than her granddaughter. However, these heuristic short-cuts are designed for efficiency rather than 100% accuracy. In some circumstances, generalizations lead to inaccurate stereotypes or decision-making errors. For example, even though higher prices do not necessarily mean better quality, many shoppers follow this heuristic (Wheatley et al., 1980). 5

Our affective states, which include emotional states (e.g., angry or happy) and motivational states (e.g., hunger or stress), have a large influence on our judgments and decision-making. For example, we tend to purchase more groceries when we are hungry than when we are full. As another example, we are more likely to impulse shop when we are feeling extremely happy or sad, compared to a neutral state (Lowenstein, 1996). Research suggests that affective states trigger immediate and automatic approach/ avoidance preferences to our environment before System 2 processes can perform a more refined evaluation (Zajonc, 1980). Consequently, we are much faster at emotionally deciding whether we like or dislike something than deliberating the rationale for why. This has two important implications: First, affective states influence the heuristics we follow. For example, Alhakami and Slovic (1994) found that risk perception and perceived benefits of an activity were influenced by our affective preference (like or dislike) for the activity. For example, activities that we like tend be perceived as low risk and high benefit and vice versa for activities we dislike. This may explain why entrepreneurs have an inflated optimism of their likelihood to succeed or why we think we ll hit it big with our stock/lottery picks. Second, affective states influence our deliberative (System 2) processing. For example, the mood you are in affects the memories that you recall, i.e. if you re in a happy state, you re more likely to remember (Bower, 1981). This may explain why things seem to get only better when we are in a happy state, but vice versa when we re in a sad state. Additionally, in certain situations, our affective urges can be so strong that they over-ride more rational System 2 responses, e.g., falling asleep while driving (Loewenstein, 1996). THE UPSIDE OF IRRATIONALITY: HOW BUSINESSES CAN USE BEHAVIORAL ECONOMICS TO CHANGE BEHAVIOR Behavioral economics allows us to understand why people think and act the way they do; it also provides a method to influence people s choices through a framework known as choice architecture. There are two prominent ways that we can architect the environment to motivate behavioral change. The first is grounded in standard economics and involves manipulating the incentive structure of different options, i.e. taking a carrot and stick approach. This approach is the typical strategy taken by most companies to change behavior, however predicting and implementing the right incentives can be costly and sometimes suffer from diminishing returns. The second is grounded in behavioral economics, and is comprised of subtle changes in the physical or social environments where decisions are made, such as the order of product presentation, how the incentives are framed, what the default option is or whether other people s choices are made salient. This approach is referred to as nudging and was popularized in the book Nudge by Thaler and Sunstein (2008). Classic examples of nudging include changing the default option from opt-in to opt-out in order to increase participation in programs such as organ donation or retirement savings. 6

NUDGES IN THE REAL-WORLD Nudging works because it leverages the psychological factors that research has shown influence our decision-making. It leads to behavioral change by either taking advantage of biases driven by System 1 processing, or by activating System 2 processing to increase more deliberative decision-making (e.g., checking the gut reaction to the bat-and-ball problem). Our work shows that nudges are broadly applicable and are not limited to any specific domain or aspect of business strategy. There are opportunities for behavioral economics to overcome business challenges wherever and whenever human behavior is involved. To illustrate this, we discuss three real-world examples of how nudges were used to solve a business problem. Increasing savings The Save More Tomorrow (SMT) program was engineered to increase employee participation in retirement savings plans (Thaler & Benartzi, 2004). The program simply asks employees to commit to allocating a portion of future salary increases toward retirement savings before they get their annual raise. Save More Tomorrow takes advantage of two heuristics. The first heuristic is called temporal discounting, which is the tendency to give less importance to events that are distant in the future than to events that are close at hand. People are more likely to agree to do something that they find unpleasant but important (such as saving money rather than spending it) when the action is in the future rather than in the present. This explains why our Future Self is such an impressive person -- in the future, we will save for retirement, eat healthy food, and exercise regularly. Our Current Self, on the other hand, tends to sacrifice long-term gains for immediate rewards, which is why in reality we often spend rather than save, eat cake rather than carrots, and watch TV rather than go for a run. Considering these behavioral tendencies, the SMT plan was offered to employees prior to a pay raise, thus having employees think about what their Future Self would prefer rather than have employees commit to a saving rate positioned in terms of what the Current Self should do. The second heuristic stems from loss aversion, which is a tendency to be more risky or exert more effort to avoid a loss (e.g., lose $100), than to reap an equal gain (e.g. gain $100). Committing a portion of one s current take-home income to retirement savings can be perceived as an immediate loss. Save More Tomorrow mitigates this by tying increases in savings to increases in future income. This way, the power of loss aversion is less likely to discourage program participation. The SMT plan had a significant impact. Not only did participation increase, but average savings rates for SMT plan participants more than tripled, rising from 3.5% to 11.6%, over the course of 28 months. Stop people from cheating just a little bit A big problem for a large insurance company was not outright fraud (e.g., arson), but smaller frauds committed by many people who cheat a little bit (e.g., people who undergo a loss of property and exaggerate their loss by 10-15%, like a 14k necklace becoming a 22k necklace). The little bit of fudging by people who view themselves as honest individuals cost the insurance company millions of dollars a year. Behavioral economics was successfully applied to solve this challenge. The psychological basis of the nudge was the recognition that we cheat more in instances where we can rationalize our 7

own selfish desires (e.g. Everyone else cheats as well ). Our ability to rationalize our dishonest behavior allows us to maintain a positive self-view while still benefitting from minor transgressions. However, drawing attention to one s own internal morals (e.g. I took an oath to not lie ) reduces one s ability to rationalize and engage in dishonest behavior. Working with BEworks Nina Mazar and Dan Ariely (2012), the insurance company found that it could reduce the number of underreported miles driven the previous year by their customers by utilizing these insights on cheating. Rather than having customers sign at the bottom to confirm that all the information provided was accurate ( I promise that the information I am providing is true ), the insurance company had customers sign at the top prior to entering their information. Signing at the top nudged the customer to think of their own self-concept as an honest individual prior to filling out the claims information. The results were impressive: those who signed at the top declared mileage rates 15% higher than the control group. The simple change in signature location reduced the extent to which customers falsified mileage information even at the risk of a potentially higher rate for the premium holder. Increasing utilization Chase designed an innovative new credit card, Blueprint, using the heuristic of mental accounting to offer borrowers both short term financing and long term financing. Blueprint allows borrowers to categorize their expenditures: regular day-today expenses like gas and groceries can be paid in full, allowing borrowers to maintain the sense of responsibility that comes with keeping up with monthly payments, while other items with a longer life like clothing or appliances can be amortized over a duration of the customer s choosing. In sum, behavioral economics can be applied to product design, positioning, process, pricing, and customer experience strategies. Wherever there are humans making decisions, behavioral economics can provide insights, and new business tactics that bring human psychology to the fore. Solution: Bat = $1.05 + Ball = $0.05 Total = $1.10 8

BEWORKS: APPLYING BEHAVIORAL ECONOMICS TO THE BUSINESS OF BEHAVIOR BEworks has developed a rigorous methodology to apply behavioral economics to business and policy challenges. Our process relies on our extensive knowledge of the academic literature as well as our own experimental research. The scope of behavioral economics is best explained through its real-world applications. SOME OF THE PROJECTS IN BEWORKS PORTFOLIO Using BE to drive Acquisition: BEworks assisted Postmedia, a major media company, in the design and development of an innovative strategy. We prompted readers to overcome the paywall barrier and become paying subscribers to Postmedia s online properties. We implemented offers and enrollment techniques that are most likely to motivate purchasing. Using BE for Product Design: BEworks conducted a comprehensive assessment and design of an innovative auto insurance product we believe will lead to safer driving among young drivers. After identifying the young drivers cognitive processes, such as their risk and hazard perception and the negative influence of hot states, we were able to engineer a product with specific nudges we may lead to safe driving behavior. Using BE to drive Engagement: BEworks helped a major loyalty program company reinvigorate itself through the science of habit formation. Our client s loyalty point collectors were overwhelmed by the information provided by the company and overloaded with multiple choices. We helped collectors overcome the challenges of the program s sophisticated breadth and complexity. Collectors are stimulated by consistent cues, rewarded with instant gratification, and have their engagement with the program reinforced on an ongoing basis to ensure they enjoy the maximum benefits of the program. Using BE to drive Retention: BEworks helped a major European insurance company address its retention objectives by developing a suite of champion and challenger letters drawing on a number of heuristics. The performance of the letters will be objectively and rigorously evaluated by a randomized control trial. Using BE to improve Collections: BEworks helped a major Canadian bank improve its accounts receivables by incorporating behavioral economics principles in its outbound automated calling scripts and letters. A subsequent project expanded the insights to the email channel. The client s primary objective was to maintain a positive relationship to the overall brand while improving repayment rates. Using BE to determine Pricing: BEworks helped a large IT company engineer a new pricing strategy. We applied behavioral concepts of fairness and risk aversion to create a new model that is expected to significantly increase the client s market share. 9

ABOUT BEWORKS There is a gap between what science tells us about how humans make decisions and what businesses seem to know. This gap is becoming more apparent to many business leaders who are turning to empirical, scientifically rigorous methods to create strategy. BEworks is passionate about understanding why people make the decisions they do. Our team of scientists and business experts offer a powerful methodology that analyzes and measurably influences the decisions consumers make. BEworks offers a systematic approach to solving complex challenges. We start by helping clients operationally define and target the behaviors they want to modify. Our solutions are grounded in science, validated by experimentation and tailored to drive specific business goals. We work collaboratively with clients throughout the project leveraging their knowledge of the business with our expertise in human behavior and decision-making. Put simply, we generate unique solutions that are proven to work through rigorous experimentation by starting with the theory, understanding the biases affecting decision making, and designing empirically-sound solutions to improve the bottom line. BEworks was founded in 2010. Our leadership team consists of Dan Ariely, renowned behavioral expert, James B. Duke, Professor of Psychology & Behavioral Economics at Duke University, and author of Predictably Irrational; Nina Mazar, Professor of Marketing at the Rotman School of Management, University of Toronto; Doug Steiner, a senior leader in financial services; Louis Ng, a chartered accountant and experienced financial turnaround expert; and CEO Kelly Peters, MBA, and experienced financial services strategy and technology innovation leader. Our scientific advisors include David Pizarro, Professor of Social Psychology at Cornell University and Supriya Syal, who is a postdoctoral fellow at the Psychology department of the University of Toronto. BEworks unique, science-driven methodology has been validated with measurable financial results. Our approach can be applied to drive acquisition, retention, product design, process optimization, pricing strategies, and any problem that involves that involves humans making judgments and decisions. www.beworks.com @BEworksInc (416) 920-1921 info@beworks.com 10

REFERENCES Ariely, D., Loewenstein, G., & Prelec, D. (2003). Coherent arbitrariness : Stable demand curves without stable preferences. The Quarterly Journal of Economics, 118(1), 73-106. Bower, G. H. (1981). Mood and memory. American psychologist, 36(2), 129. Finucane, M. L., Alhakami, A., Slovic, P., & Johnson, S. M. (2000). The affect heuristic in judgments of risks and benefits. Journal of behavioral decision making, 13(1), 1-17. Frederick, S. (2005). Cognitive reflection and decision making. The Journal of Economic Perspectives, 19(4), 25-42. Gollwitzer, P. M. (1999). Implementation intentions: strong effects of simple plans. American Psychologist, 54(7), 493. Loewenstein, G. (1996). Out of control: Visceral influences on behavior. Organizational behavior and human decision processes, 65(3), 272-292. Shu, L. L., Mazar, N., Gino, F., Ariely, D., & Bazerman, M. H. (2012). Signing at the beginning makes ethics salient and decreases dishonest self-reports in comparison to signing at the end. Proceedings of the National Academy of Sciences, 109(38), 15197-15200. Thaler, R. H., & Benartzi, S. (2004). Save more tomorrow : Using behavioral economics to increase employee saving. Journal of political Economy,112(S1), S164-S187. Tversky, A., Kahneman, D., & Choice, R. (1981). The framing of decisions. Science, 211, 453-458. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124-1131. Zajonc, R. B. (1980). Feeling and thinking: Preferences need no inferences. American psychologist, 35(2), 151. Wheatley, J. J. (1980). The effect of generic products on consumer perceptions and brand choice. Advances in Consumer Research, 8, 166-169. 11