How Donald Trump Won The Election: A Behavioral Economics Explanation

November 14, 2016



How Donald Trump Won The Election: A Behavioral Economics Explanation

November 14, 2016

by Tim Gohmann, Ph.D., Chief Science Officer

While the media focused on Donald Trump’s denigration of women, war heroes, Latinos and Muslims, Trump was building not just support but commitment from his core target — working-class, non-college–educated white males — to get out and vote. What was juvenile and embarrassing to the intellectual was the “silver bullet” that gave Trump believability, causing his core target to identify with him.

That believability was used to offer the core target the ultimate payoff — a demand for skilled manual labor, the real resource needed, according to Trump, to “Make America Great Again” (starting with its infrastructure). Note that Trump never suggested that technological leadership was needed to make America great again, for that was the domain of the educated elites, those who had shown that brain power, not manual labor, had made them millions without ever having to break a sweat.

Trump’s core target felt that what they had to offer was no longer valued by America, and what jobs were available were being siphoned off by immigrants. This made the core target believe that they no longer “counted,” i.e., they felt “discounted.” This is the classic behavioral economic paradigm of Reciprocity, in which one group (Trump’s core target) perceived themselves to have been treated “unfairly” (because the intellectual elites had been more successful and immigrants were taking their jobs). This “unfairness” was, perhaps, best defined by Trump’s claim that trade treaties were unfair to the American worker. Finally, Trump claimed that only he could make America great again, thereby eliminating the possibility of someone else at a later date accomplishing the same task and implying that this was the core target’s “last chance.”

Trump’s campaign execution was a simple yet elegant display of behavioral economics in practice as follows: 1. IDENTIFICATION — make such disparaging remarks about minorities that the core target “see themselves” in the candidate; 2. UTILITY — communicate the most motivating expected campaign result to the core target — a restoration of the value of their labor (and financial status), the cornerstone to making America great again; and 3. LOSS AVERSION — motivate the core target by suggesting that this was their only chance to recover their social and financial status, thereby empowering them to turn out in such record numbers that the opposition was overwhelmed. Trump’s cost-efficient strategy, focusing on the only voter audience with both sufficient numbers and a motivation that could be converted into turnout, resulted in a decisive victory, a disappointed opposition and bewildered pollsters.

What kept the pollsters from being more accurate? Just as with Brexit, most researchers had no mechanism to accurately assess the strength of voter preference, and, thus, the likelihood of turning out to vote. In behavioral economics terms, an accurate measurement of the magnitude of the Expected Utility associated with a Trump or Clinton vote was never calculated and, thus, never entered into the voter-turnout models. In short, the pollsters were in error because they stuck to outmoded techniques rather those used by Trump to construct and execute a winning campaign.

Picture by www.theplaidzebra.com

Picture via Charity Charge

Turning A 16 Billion Dollar Problem Into Doing Good

June 21, 2016


Picture via Charity Charge

Turning A 16 Billion Dollar Problem Into Doing Good

June 21, 2016

by Christian Goy


How Behavioral Science Lab helped create a new kind of charitable credit card

You want to change the world. You want to build a company that does good. You want to take something that is broken and fix it. That is exactly what Stephen Garten did by creating Charity Charge. This is his story how behavioral economics helped him fulfill his vision.

Every year, on average, an American household donates $1,296[1] to five charities. This does not include schools or religious organizations. Also on average, Americans carry 3.6 credit cards[2] and accumulate approximately $56 billion in loyalty and credit card points alone.[3]

What do these two facts have in common? Nothing. That is until Garten realized they were related and could be used to change the world.

Although an average American household donates $1,296 annually, it also wastes $205 per year in unredeemed credit card points. The value of those points has increased since 2011, and $16 billion dollars in free flights, hotel nights, gas or simply cash back go unredeemed.3

“What if we could transform those $16 billion in unredeemed points into positive, real-world change?” What if there was a credit card that would turn those precious, hard-earned rewards into useful points?

The key here is useful. But how do you create a useful credit card? A credit card that people would choose over a bank? A credit card unlike any other charity-giving card?

With those questions and the social good in mind, Garten consulted experts in credit cards, branding and the Behavioral Science Lab.

Garten wanted these questions answered to be reassured that his vision for a better credit card wouldn’t be dismissed as just a quixotic idea.

Since human beings are notoriously bad explainers of their own behavior, the Behavioral Science Lab has developed tools like MINDGUIDE® and BrandEmbrace® to help clients understand their audience — their biases, expectations and decision models.

The decision to choose a credit card or give to a charity is not made in a vacuum. They don’t just happen online or offline. Each brand, product or service is surrounded by a set of elements that play a vital role in one’s decision to adopt that card or give to that charity.

This understanding is behavioral economics. The interaction of multiple elements — economic and psychological — directs consumers to decide whether a product is fulfilling their expectation and delivers utility.

We told Garten and team that the secret to success was to make it easy for people to give. If they would create a credit card that allowed people to give to their specific charity and deliver on the inherent drivers of their decision, Charity Charge would create utility for their consumers.

Our charitable giving study showed that the majority of donors gave to a charity because of a Personal Connection to a Cause. Additionally, if a charity wanted to deliver utility, it had to satisfy the secondary driver of Personal Connection to Cause for four additional Decision Types.[4] (Figure 1)


Figure 1: Charitable Giving Decision Types

When making a decision or defining the expected utility of a product, humans use a unique set of elements in a special order, creating individual decision templates. These templates are the evaluation mechanisms establishing a Utility Expectation that drives preference, purchase and ultimately an obtainment of brand utility. (Figure 2)


Figure 2: Behavioral Economics Model

It is important to note that a brand needs to fulfill each element’s expectation in the decision template starting with the primary driver on the left. (Figure 3)


Figure 3: Understanding Utility Expectation For One Decision Type

In giving the majority of donors the option to engage with their particular charity, it was vital that the credit card not only serviced one charity, as it was typical, but as many charities as possible. In the end, Charity Charge designed a credit card that wasn’t only on par with regular credit cards, but instead of earning points for airline miles or hotel stays, customers carrying the card donate money by spending money.

Today, the Charity Charge credit card lets consumers spend money on anything they want, while simultaneously earning 1% on every purchase for donations to any nonprofit of their choice, including schools and religious organizations.

“When we started Charity Charge, we had intuitions about the reasons why consumers gave to charities. Behavioral Science Lab helped us uncover the qualitative and quantitative data-driven facts about why consumers give to charity and how they prefer to give,” said Garten, founder and CEO of Charity Charge. “This concrete data supported our hypothesis and was instrumental in Charity Charge’s ability to secure a partnership with MasterCard.” The Charity Charge credit card is available in the U.S. June 2016, and Behavioral Science Lab continue to monitor and partner in the next phase of the venture.


[1]Gohmann, T. and Goy, C., Cracking the Code on Why We Give to Charities, Behavioral Science Lab, 2014

[2] Holmes, CreditCard.com, Credit card ownership statistics, 2014.

[3] Colloquy, Buried Treasure: The 2011 Forecast of U.S. Consumer Loyalty Program Points Value, April, 2011. 2010 perceived value of points issued in the United States across industries was $48 billion. GDP in the U.S. in 2010 was 14.96 trillion. The resulting ratio of 0.0032 was then applied to 2014 data.

[4] Gohmann, T. and Goy, C., Cracking the Code on Why We Give to Charities, Behavioral Science Lab, 2014


Toward A Common Behavioral Economics Perspective

June 10, 2016



Toward A Common Behavioral Economics Perspective

June 10, 2016

by Timothy Gohmann, Ph.D., Chief Science Officer, Behavioral Science Lab

With so many researchers, government agencies and consultants claiming to use behavioral science techniques, interest is booming! The business media appear to be responding with new and varied applications, methodologies and the promise of quick behavioral change, often without fully understanding much about BE. For example, we at the Behavioral Science Lab are repeatedly asked by marketers to “just apply behavioral economics” with little or no clarification of the motivations of those whose behavior we are asked to modify.

This excited readiness to accept the application of BE but with little understanding may be the result of there being very few results in marketing as attractive as those in BE. The strict adherence to the scientific method, clear definition of terms and focus on decision making all make BE results attractive to practitioners, especially marketers. For example, where in marketing research can you find a glossary of “effects” or a literature of decision biases or Nobel Prize winners?

Nevertheless, some of the confusion with which BE is perceived today must be attributable to the practitioners themselves. For example:

  1. Do respondents express similar BE effects, or are there individual differences?
  2. How important are individual differences?
  3. How is BE best applied in the real world, how can it be improved?

By not answering these questions, are we investigators contributing to the confusion and misrepresentation of BE to potential users? If so, it behoves us to arrive at a common understanding of how BE works so that it reaches its full potential to enlighten and instruct.

To that end, this short discourse will attempt to offer a common perspective on BE and test it against selected findings. Most of the content will consist of “thought experiments,” intended not to minimize the value of past findings, but to place those findings in a context within which a more complete understanding of BE might be possible.

Basic Experimental Design

Many, but not all, BE studies use an experimental design that includes a choice decision with risk, payoff and often a reward. Results are interpreted as being reflective of how decisions are made and what decision commonalities or heuristics might be sufficiently consistent to be treated as new knowledge. In its simplest form, this design appears as the following:


Since any decision, even those in a laboratory setting, is not made in a vacuum, a more complete description of the decision process would be to include the presumed result of the Decision, which we will call the “Obtained Utility” as shown below.


Controlling Mental Process

Again, simply stated, we infer that selected options have greater utility than options not selected. From this behavior we infer the characteristics of a mental process or state guiding the observed choice behavior. Let’s add a component, Controlling Mental Process, to the model above about which we know neither its components nor method of operation.


Let’s try this Controlling Mental Process concept on some common BE findings.

Loss Aversion

One of the simplest BE effects to understand is Loss Aversion, the higher likelihood of selecting a choice option that avoids a loss of the same magnitude as an alternative that promises a gain. Often called the “losses loom larger than gains” phenomenon first reported by Kahneman and Tversky (1979), it is often used to explain the Endowment Effect. So applying our Controlling Mental Process concept, is there a state or process that guides loss aversion behaviour? If so, then there should be individual differences which affect decision making. In 2007, in a paper entitled “Individual-Level Loss Aversion in Riskless and Risky Choices,” Gächter, Johnson and Herrmann found that “ … in both choice tasks loss aversion increases in age, income and wealth and decreases in education.” Our conclusion from this and other studies is that some process or state affects loss aversion, exists prior to the choice task, and is related to these demographics. Operating in this way, this process or state would fit the definition of Controlling Mental Process.

Optimism Bias

This well-known BE effect is often described as the perceived higher likelihood of occurrence of events with positive, higher utility outcomes — in other words, “optimism” about the future. Unfortunately, not everyone experiences Optimism Bias equally. Strunk, Lopez and DeRubeis in “Depressive Symptoms Are Associated with Unrealistic Negative Predictions of Future Life Events” (2006) found that “A nonsignificant optimistic bias characterized participants with low depressive symptoms, whereas a significant pessimistic bias characterized participants with high depressive symptoms.” So we would conclude that differences in observed optimism appear related to the manner in which a yet unspecified Controlling Mental Process operates. A direct link between “unrealistic” optimism and physiology has been posited by Sharot, Korn and Dolan (2011), concluding that “… optimism is tied to a selective update failure and diminished neural coding of undesirable information regarding the future.” Our conclusions from these studies and others is that individual differences in Optimism Bias occur, i.e., not everyone behaves similarly as a result of it, and some pre-existing process or state “directs” it.

Intertemporal Choice (IC)

This is the well-known and researched higher likelihood of selecting a positive future outcome in the sooner it occurs. This effect does not follow the classic Discounted Utility model, but rather variable rate discounting. Intertemporal Choice suggests the higher selection likelihood of immediate versus delayed gratification, or Present Bias. So is there a uniform Present Bias effect across respondents or is there some process or state responsible for individual differences? In their 2007 paper, “Intertemporal Choice — Toward an Integrative Framework,” Berns, Laibson and Loewenstein suggest several control mechanisms for IC and cite neurological evidence for two decision states. Each of these states (control mechanisms) allow for individual differences in IC and may, themselves, conflict with each other according to the authors. So the concept of some Controlling Mental Process dealing with conflicting interactions and impacting the likelihood and degree of Present Bias appears not just feasible, but likely together with co-occurring differences in neural activity.

Finally, “internal” psychological variables such as self-control, and degree of innate risk aversion or acceptance appear to impact IC as do the “external” factors of time, stress, risk and payoff magnitude, and number of decisions.

So our premise that a Controlling Mental Process, as shown again below, regulates the liklihood of choice behavior (and perhaps the magnitude) associated with known BE effects (Loss Aversion, Optimism Bias and Intertemporal Choice) appears supportable.


What do we know about this concept? Daniel Kahneman in Thinking, Fast and Slow (2011) suggested that such a concept actually directs decision making in two ways, “fast” and “slow,” i.e., “System 1” and “System 2,” respectively. These “useful fictions,” as Kahneman refers to them, are aides in bundling the characteristics and mechanisms of such a controlling process. “Fast” decisions involve processes that are more intuitive, more impulsive, perhaps more related to perception than cognition and less likely to involve high-cost/-risk outcomes. Fast decisions are more likely to occur under time pressure, stress or an “overload” of the decision maker. One decision mechanism proposed for slow decisions is the Representativeness heuristic. This is the apparent comparison of current situations (decision options) to the utility of past situations so that an appropriate course of action can be selected with presumed greater “facility.” “Slow” decisions use mechanisms characterized by more deliberate “logical” processes with less impulsivity and more complete assessment of outcomes. Social impact and self-image also appear to impact these decisions. Anticipation (of outcomes) has been proposed as a slow decision mechanism in which an expected outcome (utility) is “forecasted.”

There is precedence for such an “expectational” basis of decision making. The Expectancy Theory of workplace performance proposed by Victor H. Vroom of Yale suggested that decisions were determined by a comparison of the effort required to achieve the expected increased salary and social prestige of advancement. Although the calculus for such decisions was not specified, Vroom was clear that the decision to assume advancement was dictated by the expectation of the sum of all rewards associated with it. This is a clear example of BE since there were both social and economic payoffs associated with the acceptance of advancement. Vroom also believed that the best strategy for management was to understand those nonfinancial “motivators” that were unique to each employee and contributed to individual higher performance.

Finally, Game Theory teaches us that feedback from the consequences of a decision must “feedback” to impact future decisions.

Utility Expectation Model

Since we are inferring something about a mental process that existed prior to the presentation of choice options, its function must be to direct actions that occur in the future together with their consequences. Its existence must precede the presentation of choice options even if its impact cannot be assessed until after a decision scenario is presented and behavior observed. So let’s relabel Controlling Mental Process the “Utility Expectation Model.” Such a model would have the following components:

  • Fast and slow decision processes
  • External factors impacting decisions (time, number of decisions, stressors, etc.)
  • Internal factors specific to the decision maker (risk aversion, self-control, wealth, etc.)
  • BE effects themselves (Loss Aversion, Optimism Bias, Intertemporal Choice, Endowment and others)
  • Feedback of experienced utility based either on the immediate result of a decision, the recollection of the results of prior decisions or the results of similar prior decisions.

Some educated guesses about how such a Utility Expectation Model would “manage” these components follow:

  1. Affective states (delight, dread, anxiety, social approbation, etc.) and environmental decision conditions (time, number of decisions, others) could play significant roles.
  2. “Conflict” (Approach-Avoidance) resolution suggests a systems structure with gates, paths and recursion.
  3. Psychological variables (self control, risk aversion, locus of control) may act as nodes in the system structure.
  4. BE effects may act as “moderators” or “accellerators.”
  5. Heuristics might be just be the “short form” of the full model.
  6. Mental constructs would have to have invariant meaning across respondents, so a new way of constructing and naming these constructs is probably necessary.
  7. To avoid “pollution” with experimenters’ beliefs and biases, most of the “work” to define such mental constructs would probably be done by respondents themselves using a formal process.
  8. Large scale sample sizes would be necsssary to capture the breadth of individual differences.
  9. Individual respondent models would be necessary to fully explain the role of individual differences.
  10. Respondents with similar individual models would be “aggregate-able” into common decision “types.”
  11. Model components may impact the model quite differently dependent on decision type.
  12. Validation on a respondent level and within decision type would be needed to confirm the results of multiple models.
  13. Different decision types may map to different game playing strategies.

Common Behavioral Economics Perspective

Our Common Behavioral Economics Perspective now appears as follows with a feedback loop:


When complete, this Common Behavioral Economics Perspective could be used to answer some interesting questions as follow:

  1. Are fast and slow process decisions really supported by the same underlying mechanism once the internal and external factors impacting decisions are known, or are they really two different mechanisms?
  2. Are decisions that appear strictly “irrational” actually “rational” when the complete set of decision elements are known, such as the internal and external factors above? Are there really any “irrational” decisions, or is “irrational” just an explanation of not knowing the true drivers of the utility expectation?
  3. What is the Model impact of known BE biases on the expectation of utility?
  4. How many types of similar decision types for a purchase in a given product class are in use by respondents?
  5. To what extent can respondents be disuaded of their own biases by “fixes” generated by understanding the bases of expected utility?
  6. What would intervention “fixes” look like if a decision process was fully understood? Would there be only one optimal intervention per process, or are optima possible?
  7. For decision processes involving investment, healthcare, education, risk protection, etc., could optimal decision set(s) be constructed for individuals?
  8. Is there a metric for how well a decision option satisfies the “requirements” of the Utility Expectation Model?
  9. How does culture impact the expectation of utility? What are the model’s components?

Let’s build on the content and scientific discipline of BE and expand it beyond its current limitations to form the basis of a broader understanding of human behavior. After all, isn’t our ultimate objective to better understand, ourselves, and our decisions?


Berns, G. S., Laibson, D. and Loewenstein, G. (2007). Intertemporal Choice–Toward an Integrative Framework. Trends in Cognitive Sciences 11(11), 482-488.

Gächter, S., Johnson, E. J. and Herrmann, A. (2007). Individual-Level Loss Aversion in Riskless and Risky ChoicesIZA Discussion Papers 2961, Institute for the Study of Labor (IZA).

Kahneman, D. (2011). Thinking, Fast and Slow, Farrar, Straus & Giroux.

Kahneman, D. and Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk, Econometrica, 47, 263-291.
Sharot, T., Korn, C.W. and  Dolan, R.J. (2011). How Unrealistic Optimism Is Maintained in the Face of Reality. Natural Neuroscience, October 9, 14(11), 1475-1479.
Strunk, D.R., Lopez, H. and DeRubeis, R.J. (2006). Depressive Symptoms Are Associated with Unrealistic Negative Predictions of Future Life Events. Behavioral Research Therapy, June, 44(6), 861-82.

Vroom, V. H. (1964). Work and Motivation. McGraw Hill.

Special thanks for contributions to this paper to Christian Goy, Co-founder & Managing Director, Behavioral Science Lab and Isabelle Zdatny, Temkin Group.


Does Behavioral Economics Apply To Voting?

October 22, 2015



Does Behavioral Economics Apply To Voting?

October 22, 2015

by Behavioral Science Lab

Historically, the calculus for voting behavior has centered on three elements: Candidate Preference driven by Issue Congruence (similarity between the candidate’s and voter’s position on the issues) and Turnout. Many an election has been won with just these three concepts driving strategy and tactics. Recently, however, the discussion of presidential candidates has used behavioral economics (BE) terms such as “risk,” “social acceptability” and “breaking the rules.” Does this mean that BE is really applicable to politics, or are these terms just part of the national vocabulary? Let’s actively try to apply the language of BE to the current presidential candidate discussion and determine whether value or obfuscation is the result.

Certainly Issue Congruence is a component, but what about toughness, leadership, fairness and other facets of personality? These should reflect some expectation of success regarding future crises, difficult-to-handle world leaders, sensitive domestic issues, their own party, etc. So there ought to be a calculation of Utility for each voter that would capture more than just Issue Congruence.

The uncertainty with which the candidate’s performance in office is perceived could offset their Value, including Issue Congruence with even the most closely held positions. It is conceivable that even the candidate with the highest Utility could be less preferred than one with fewer assets but more an expected, more predictable and, therefore, successful performance.

In BE experiments, the immediacy of the reward has much to do with the overall expectation of Utility. In the case of a presidential candidate, could the lead/learning time necessary to “get up to speed” impact their overall Utility?

So at least some of the major BE concepts do appear to fit a presidential election. Could these concepts be operationalized and a calculation of candidate Value be constructed? Could this estimate of Value be added to expected turnout and the ballot forecasted? Is there a methodology in existence into which these and other BE elements could be inserted and their effect estimated on each candidate? Yes, the Behavioral Science Lab’s MINDGUIDE℠ and BrandEmbrace℠ services are available today to estimate the voter decision process and likely vote outcome just as they do for buyers and their next likely purchase.

Chicago Auto Show 2010

What Volkswagen Should Do Next?

September 30, 2015


Chicago Auto Show 2010

What Volkswagen Should Do Next?

September 30, 2015

by Tim Gohmann, Ph.D.
Chief Science Officer, Behavioral Science Lab

Volkswagen Group AG has admitted to gaming U.S. Environmental Protection Agency diesel-emission control testing affecting some 2 million vehicles worldwide. As a result, Volkswagen has replaced its CEO, Martin Winterkorn, with former Porsche CEO, Matthias Mueller, continues to run TV ads for its non-diesel vehicles and prepares to weather a wide range of U.S. and German government fines and sanctions. It appears to not be dealing with its biggest risk, the continued and worsening “loss of confidence” in its vehicles by its consuming public. Such a loss of “faith,” if not dealt with directly, will become more severe as more details about the scandal become available and could require years, if not decades, to repair. Some are even speculating that its implications could spread to the German auto industry and Germany’s leadership role in the EU.

As behavioral scientists, we would define Volkswagen’s situation as a bias against the purchase of its vehicles resulting from the decline in the expected utility of its vehicles.

What does this mean for Volkswagen in the short and long term?

In the short term, the behavioral economic “causes” are:

Lack of Reciprocity and Fairness — Buyers were told that its diesel vehicles were both clean and efficient and now feel taken advantage of.

Loss Aversion — Buyers do not know who or how their vehicles will be brought into EPA compliance, thereby introducing the risk of additional repair cost and loss of resale value.

Violation of Social Norms — Potential buyers are being asked to assume the additional risk of social approbation with ownership.

In the longer term, the behavioral economic causes will be the result of generating “explanations” for wrongdoing and cognitive dissonance when no accurate explanations occur.

Negative Endowment — Simply stated, the “haves” (those running Volkswagen) appear “above me” because they still expect their vehicles to be bought without dealing with buyers’ concerns.

Cognitive Bias — These are full-blown explanations as to why the problem occurred and how and why it could happen again. It can include corporate culture and conspiracy theories and is the most difficult to overcome since it is both self-generated, based on gross generalizations, and projects them onto future events.

So what should Volkswagen be doing?

Step 1 — Take responsibility. Don’t wait for the U.S. EPA or Department of Justice to first make its case publicly, and then impose its fines, sanctions and punishments on the organization and individuals within it. Find out which employees did what and why and deal with them accordingly; explain it to the public and dealers; and let it be known that this type of behavior will not be tolerated. This isolates the problem, demonstrates it is not a “we-you” issue and clearly states that social norms are in common with the market and excludes other erroneous and more damaging explanations.

Step 2 — Apologize to current owners. Don’t sit back and give your owners time to distance themselves from the brand and communicate to others why they regret their purchase. This takes owners out of immediate loss aversion risk and demonstrates reciprocity and fairness in “making them whole.”

Step 3 — Stop promoting the sale of other vehicles. Don’t assume that the public is interested in buying your vehicles with so many unanswered questions. Wait until they hear the solution to the current problem. This negates negative endowment.

Step 4 — Take full corrective action. Communicate specific steps taken to ensure that this or a similar problem cannot occur in the future. Think big, on the order of an independent ethics audit, not just internal reviews. This minimizes cognitive bias by excluding speculation about future events.


The Behavioral Economics Guide 2015

July 16, 2015


Image Credit: Gary Knight (Bond Street in London)

Turning Luxury Shoppers into Luxury Buyers

June 15, 2015


Image Credit: Gary Knight (Bond Street in London)

Turning Luxury Shoppers into Luxury Buyers

June 15, 2015

by Christian Goy and Tim Gohmann, Ph.D.

A new report from the Boston Consulting Group suggests that global luxury goods and services will continue to grow at around 7 percent annually — handily outpacing the GDP of many countries.

Such growth is attributed to more brands reaching luxury status, higher disposable income and a larger number of buyers being able to afford luxury products. In the United States, the largest luxury market, about 46 million Americans (roughly the population of Spain), bought at least one luxury item in the past 12 months (Bob Shullman, 2015). In 2012, more than $1.8 trillion was spent on luxury items, with approximately $390 billion for traditional high-end apparel, cosmetics and jewelry.

However, with both size and growth, competition has become fierce with usually lower-priced e-commerce growing at three or four times the total market rate. How do marketers keep this massive market expanding and, more importantly, how do marketers turn affluent shoppers into buyers?

To get more persuasive “leverage,” some marketers, according to Russ Alan Prince (2013), have segmented the luxury market on the basis of personality, such as trendsetters, winners or connoisseurs. Others have used conspicuous consumption, ego gratification, projection of the “self” onto the purchase, and the expectation of happiness, pride or self-fulfillment as the presumed motivation for purchase. Although these psychological factors appear reasonable, do they really motivate purchase?

Liselot Hudders, Mario Pandelaere and Patrick Vyncke (2013) argue that even the fulfillment of the physical and psychosocial attributes of premium quality, aesthetics or even exclusivity might not be enough to convert luxury shoppers into buyers.

Is there a process or approach that really answers the question “What is the easiest way to convert luxury shoppers into buyers?”

We have learned through our MINDGUIDESM tool that there are a fixed number of requirements for luxury purchase for each product category. Some of these are psychological factors such as Impact on Others, Social Impact on Oneself (Effect of the Reaction of Others on the Buyer), Confirmation of Self-worth; others are product and feature related.

These requirements go together in multiple ways to form the expectation of utility of ownership. Each way is composed of multiple paths in which the requirements are used differently by luxury buyers to arrive at an expectation of utility. Each way is used by a segment of the luxury market with similar needs, forming similar consumer types. We found that purchase occurs when the expectation of utility rises above a threshold, which also differs for each consumer type and, of course, by product category.

In addition, there is a finite number of consumer types for a given industry or category. To date, two or three consumer types have always accounted for over half the buying audience but not necessarily over half of the total purchase value. Since 15 percent of the buying audience may account for 70 percent of luxury goods revenue, it becomes absolutely critical to know which consumer type is responsible for the largest source of business. In the luxury business, there is no “average” customer. In fact, one such customer type may be five to 10 times more profitable than an “average” customer, who, in fact, does not exist.

So what does this all mean for today’s luxury goods marketer? How can they best convert a luxury shopper into a luxury buyer?

Obtain the true “why” of purchases. Invest the time to understand the complex yet understandable reliable and useable model that fulfills luxury buyers’ expectation of utility.

Communicate the expectation of utility to those consumer types who are most likely to buy. Not only will those consumers resonate stronger with your brand when their expectations are reiterated, they also will become vital contributors to the short- and long-term success of your brand.

Understand which consumer type accounts for your largest source of revenue. This is likely a relatively small percentage of your total buyers but the consumer type whose patronage is most critical to your bottom line.

Build on the strength of your brand with the consumer types most responsible for your success before attempting to attract new buyers. Why? Because it’s easier to strengthen current luxury-buying relationships than it is to create new ones.

Special thanks to Tim Gohmann, co-founder and chief science officer, Behavioral Science Lab for his contributions to this article.

MINDGUIDE models the requirements and paths for each individual buyer and consumer types and determines what marketers have to do to drive purchase.

Image Credit: Gary Knight (Bond Street in London)

London Double Decker Bus

Glossary of Behavioral Economics Terms

June 3, 2015


London Double Decker Bus

Glossary of Behavioral Economics Terms

June 3, 2015

by Tim Gohmann, Ph. D.

We are often asked to explain behavioral economics terms and how our MINDGUIDESM and BrandEmbraceSM services “fit” with existing concepts. The following terms and their definitions are our first steps to do both:

Observable emotional response.

Affect Heuristic
Heuristic in which the emotion associated with a decision option impacts its likelihood of selection.

Manipulation of choice options vis-à-vis a reference point (or option) so that their likelihood of selection is affected.

Asymmetrically Dominated Choice
Heightened likelihood of choice option selection by adding one or more unattractive choice options. (See Decoy Effect.)

Availability Bias
Change in the likelihood of a choice option related to the apparent availability of options and not related to the respondent’s own expectation of utility.

Availability Heuristic
Decision shortcut in which the ability of the respondent to recall information about the choice options impacts their likelihood of selection.

Change in the likelihood of a choice option being selected not related to the respondent’s own Utility Expectation Model.

Behavioral Economics
Discipline that includes a psychological or sociological explanation of economic behavior.

Bounded Rationality
Decision process strategy limited by human ability to process information proposed by Herbert Simon.

Behavioral Science Lab service that computes an index of 0–100, indicating the degree to which a brand satisfies that buyer’s Utility Expectation Model.

Certainty/Possibility Effect
Likelihood of selection of choice options affected by the probability of gains or losses, determined not by the absolute value of the change, but by the degree of change relative to a base level. (See Saliency, Prospect Theory and Zero Price Effect.)

Choice Architecture
Method of or context within which the presentation of choice options affects the likelihood of option selection. (See Defaults, Framing and Decoy Effect.)

Choice Overload
Effect on the likelihood of choice option selection from a decision heuristic driven by a large number of decision options.

Cognitive Bias
Assessment of the selection of a decision option not conforming to norms, formal logic or external “rules.” (See Availability Bias, Representativeness, Optimism Bias and Confirmation Bias.)

Explicit or implicit agreement, with or without consequences, to the acceptance of a future behavioral change.

Confirmation Bias
Effect on the likelihood of current choice option selections that confirm the utility of past decision option selections.

Decision Fatigue
Impact of multiple and/or complex decision tasks on the heightened likelihood of using a heuristic.

Decision ElementsSM
Mental constructs that serve as components of the Utility Expectation Model on which decision options are evaluated. To date, we have found that there are no more than nine and no fewer than six such Elements. They “appear” at the time of the purchase decision and may not be “available” to the buyer outside of that context. They are neither demographic nor psychographic in nature, but are related to the functional, emotional and social consequences of purchase. Decision Elements may be at different levels of saliency to the buyer and may be perceived and used differently.

Decision Element GatesSM
Components of the Utility Expectation Model, these are mental “rules” that specify how decision alternatives are evaluated against each element for each buyer. For example, an element relating to social responsibility will have different definitions and uses for different buyers.

Decision Gate BlueprintsSM
Components of the Utility Expectation Model, these are mental strategies that specify how the Element Gates are used in a decision. They persist over time; our longest longitudinal study of 14 months confirmed the same Blueprints in use. For this reason we believe that Blueprints are mental templates, much like muscle memory but for the mind. Interestingly, there appears to be a finite number of Blueprints in each product category. Looking across all of the all product categories evaluated to date, the majority of buyers can be described with roughly half the number of Blueprints as there are Elements in their Blueprint, making the purchase decision processes of most buyers in a category describable with only three or four Blueprints.

Decoy Effect
Heightened likelihood of choice option selection by adding one or more unattractive/unappealing choice options. (See Asymmetrically Dominated Choice.)

Choice options “automatically” selected in the absence of a choice option selected by the respondent. (See Choice Architecture and Inertia.)

Discounted Utility (DU)
Utility of some future event or possession calculated as a reduction (discount) from its present value.

Reduction in the utility of choice options below their apparent utility. (See Choice Architecture and Time Discounting.)

Diversification Bias
Likelihood of selecting more choice options than are needed or useful. (See Projection Bias.)

Dual-system Decision Theory
Model of decision making that suggests two processes, System/Type 1 being faster, more “automatic” and less dependent on a cognitive heuristic; System/Type 2 process is slower, more complex and cognitive and used for more important/riskier choice options.

Empathy Gap (Hot-Cold/Positive-Negative) 
Impact of a prior emotional state on the likelihood of selecting choice options and appears most pronounced when the difference (gap) in the pre- versus post- (choice) state is maximized.

Endowment Effect/Bias
Effect of the apparent overvaluing of a possession, including a relationship. (See Loss Aversion, Inertia and Status Quo Bias.)

Expectancy Theory
First proposed by Victor H. Vroom, this conceptual model suggests that the selection of behavioral choice options are directed by their anticipated consequences.

Framing Effect
Impact on the likelihood of choice option selection associated with the method of or context within which options are presented. For example, risky choice framing would be represented by presenting the identical likelihood of winning but in terms of both winning and losing (i.e., the likelihood of winning is 30 out of 100 cases, but the likelihood of losing is 70 out of 100 cases).

Game Theory
Study of strategy, tactics (decisions), risks, rewards and the effect of learning in the playing a game.

Habit/Habit Bias
Heuristic characterized by apparent “automatic” decision making and rigid conformity to the respondent’s Utility Expectation Model.

Halo Effect
Diffusion of the perception of one characteristic of a person or thing to other characteristics of the same person or thing. For example, an attractive political candidate may also be considered warm and friendly.

Hedonic Adaptation/Treadmill
Belief that happiness/contentment reaches a stable and entropic level over time in spite of positive or negative emotional “ups” and “downs.” This steady-state level of happiness forms the basis by which the impact of upward or downward shifts are perceived.

Herd Behavior/Effect
Impact on decisions caused by participating in behavior and/or beliefs shared by a large number of others.

Mental decision-making shortcut often associated with a particular goal or purpose such as “price sensitive,” “brand loyal” and “discount driven.”

Hindsight Bias
Effect (‘knew it all along” effect) occurring when a rationale for a prior decision or conclusion is used to explain a current decision or conclusion. This may include the incorrect recollection of the circumstances of the prior decision or conclusion and/or the belief that the new decision or conclusion was the same or “predictable.”

IKEA Effect/Bias
Overvaluation of a product, belief or process in which there was participation in its development or completion. Related to the Endowment Effect but does not require ownership.

Inequity Aversion
Social approbation against participating in decisions that result in unequal distributions of wealth, value or prestige.

Stable state associated with little change in beliefs, behavior, commitments, relationships or decisions.

Intertemporal Choice
Study of the impact of different periods of time in the future on the likelihood of selecting choice alternatives differing in risk, payoff or likelihood of occurrence. In most cases, the likelihood of selecting a positive outcome is higher the sooner it occurs in the future. (See Present Bias.)

Licensing/Self-licensing Effect
Higher likelihood of selecting a choice option considered “bad” or immoral, after selecting an option considered “good” or moral.

Loss Aversion
Higher likelihood of selecting a choice option that avoids a loss of the same magnitude as an alternative that promises a gain. Often called the “losses loom larger than gains” phenomenon first reported by Kahneman and Tversky and used to explain the Endowment Effect.

Mental Accounting
Finding that the value of money differs depending on its origin and intended use, contrary to the concept of Fungibility, which states the opposite.

Behavioral Science Lab service that generates the Utility Expectation ModelSM, Decision ElementsSM, Decision Element GatesSM and Decision Gate BlueprintsSM.

Optimism Bias/Effect
Finding that choice options with positive, higher utility outcomes will be perceived as more likely to occur than those with negative consequences.

Overconfidence Bias/Effect
Finding that an individual’s subjective assessment of their performance exceeds their objective performance.

Planning Fallacy
Finding that an individual’s estimate of the length of time it will take to complete a task is always shorter than the actual time required.

Peak-end Rule
Refers to findings that the pleasantness or unpleasantness of past experiences are more related to peaks, valleys and the ends of experiences.

Desired choice alternatives, ordered on the basis of utility.

Present Bias/Effect
Higher likelihood of selecting choice options whose payoffs are nearer in time to the immediate present.

Priming/Creating a Psychological “Set”
Act of influencing subsequent tasks/measurements by inserting prior material overtly or subliminally.

Projection Bias/Effect
In behavioral economics, the degree to which one’s preferences, attitudes, beliefs and interests are believed to remain the same in the future.
In psychology, the degree to which others’ preferences, attitudes, beliefs and interests are believed to coincide with one’s own.

Prospect Theory
Model that describes the likelihoods of selecting choice options that differ in risk, probability of occurence and payoff. Some individuals may be more risk averse in order to be more loss averse, while others may be less risk averse to achieve a larger payoff.

Social norm suggesting that the nature of interpersonal interaction is best explained as a system of exchanges of like kinds, i.e. the most likely response to a social exchange is one that is similar or equivalent to the one that stimulated it, either positive and negative. This is the notion of “Equivalent Action.”

Representativeness Heuristic/Bias
This decision shortcut technique suggests that decision options will be compared against alternatives whose utilities are known and that the degree of “fit” with these alternatives will impact the decision. This is similar to the proposed method of operation of the Utility Expectation Model.

Risk as Feelings Theory
This systems model of behavior suggests that the anticipated (expected) utility of a decision is impacted by its emotional consequences, often related to the risk of the outcome. In this way, it is similar to the Utility Expectation Model in associating a utility with the expected emotional consequences of a decision.

Degree to which a Decision Element plays a role in the decision process. MINDGUIDESM surfaces low saliency but active Decision Elements through its proprietary data collection processes.

Social Norm
Accepted and appropriate rules of behavior for a group.

Social Proof
Influence of others to conform behaviorally.

Standard Economic Model
Expression of economic behavior with no reference to psychological or sociological concepts

Status Quo Bias/Effect
Increased likelihood of making the same (or similar) decisions in the future as were made in the past.

Sunk Cost Fallacy/Bias/Effect
Increased likelihood of deciding to continue a course of action due to the level of previously invested resources regardless of expected outcome.

Time/Temporal Discounting
Increased likelihood of selection of decision options whose positive consequences occur at an earlier date in the future, i.e., closer to the immediate present. This effect diminishes as the reward/utility alternatives occur further out in the future.

Benefits (satisfaction, happiness and/or well-being) derived from a good or service. Utility Expectation Model describes the template against which decision options are evaluated.

Utility Expectation Model
Computational model based on Expectancy Theory whose output is the expected utility values of purchase decision alternatives constructed on the basis of Decision ElementsSM, Decision Element GatesSM, and Decision Gate BlueprintsSM

Zero Price Effect
Relationship between the utility of products and services with zero price (free) and those with a non-zero price. Demand will usually be greatest for a zero (free) price option within a set of options with equal price reductions.

Does Your Marketing Pass This Simple Test?

Does Your Marketing Research Pass This Simple Test?

May 1, 2015


Does Your Marketing Pass This Simple Test?

Does Your Marketing Research Pass This Simple Test?

May 1, 2015

by Tim Gohmann, Ph. D.

Clients often ask for our opinions on what makes an up-to-date research function. Aside from the obvious (high share, margins and loyalty), we usually ask the following questions. Give yourself twenty (20) points for each “yes.” Our interpretation of the summed score is at the end.

  1. Are your techniques truly customer-/buyer-centric? If they are based on data that proceed the purchase, give yourself a “yes.” If they are based on past purchase behavior, it’s more difficult to gain a marketplace advantage since your competitors will have access to the same data, whether through your vendor or others, give yourself a “no.”
  2. Do your techniques consistently explain why your brand and its competitors are purchased? If you have heard the same reasons for purchase more than once, give yourself a “no.” Your competitors have heard them too. If you are hearing fresh explanations that are improving in their predictive ability, give yourself a “yes.”
  3. Are your research results directly linked to effective interventions including innovation?
    If this requires an analysis of a committee to answer, give yourself a “no.” If it’s clear which techniques are providing you with a demonstrable marketplace gain, give yourself a “yes.”
  4. Are the value of your deliverables demonstrably better than your competition?
    Although this sounds like something difficult to obtain, your competitor colleagues will tell you whether or not what they use is producing what they need without telling you what it is or how that insight is obtained. If yours are and theirs are not, give yourself a “yes.”
  5. Are your techniques proprietary with unique pedigrees of disciplines and concepts?
    If no, they or some variant are being duplicated or reverse engineered by your competitors, so give yourself a “no.” If “yes,” your intelligence is protected by heavy investment costs and a long development ramp-up, thereby enhancing their longevity and value.

Summed score interpretation:

80–100 Good job, eliminate that one “no” if you have one; gives you a competitive advantage in the market place
40–60 Look out, someone is selling you “damaged research goods;” consider getting a new research plan.
0–20 Danger, your competition accessed your insights last year; consider getting a new research team.


If you would like to know how MINDGUIDE℠ could improve your score on each of the five questions above, review our cases at the Behavioral Science Lab www.behavioralsciencelab.com or email us at info@behavioralsciencelab.com.

photo of Grand Central Station

Five Signs Your Marketing Team Might Be Failing

April 17, 2015


photo of Grand Central Station

Five Signs Your Marketing Team Might Be Failing

April 17, 2015

by Tim Gohmann, Ph. D.

Worried that your marketing and innovation costs are eating up more margin points than they are delivering? Your organization may be suffering from one of the following five symptoms we have seen over the years:

“No One Looks at the Data”
Marketing research data literally sits on the shelves or in digital archives, is rarely referred to or is believed to be so unrelated to the everyday requirements of the marketing group that it is ignored. This telltale sign is an indication that the value of whatever is in the reports is marginal. In this situation, everyone managing the procurement and dissemination of these useless data appear busy and engaged in their roles, but the game is one of “the emperor’s new clothes” (aka marketing research study). Presentations of the data may appear well thought through with much fanfare but with the actual goal of adding value where there is little. Marketing research departments operating in this way are often run for decades by the same management and staff. Attitude and usage, ad tracking and brand equity studies most often fall into this category. Their value is low because few, if any, better decisions are based on their results.

“No One Can Take Action Based on the Data”
In this case, the data may be frequently referred to, but one of the following occurs: No one can agree on the interpretation; additional analyses are required to extract the “true” meaning of the data; or the marketing staff is not capable of “keeping up” with the latest insights and additional consultants are needed. The net result is that few, if any, decisions made on the basis of the data will improve sales or net return. Unfortunately, the appeal of these studies is often linked to an in-vogue analysis technique, novel online data collection approach or even just “bigger” big data. Segmentation or media studies are often the culprit, many using complex algorithms to claim “validity.” Again, the net impact is that the bottom line improvement from the research investment is negligible and, in the case of some large studies, negative.

“If It’s New, It Must Be Better”
Often the organizational climate begins with the dictum to read a particular new book or investigate a new technique. This is often a new measure of loyalty, customer engagement, the effect of online or social media or a better “path to purchase.” For example, Behavioral Science Lab has been asked to just “ … apply behavioral economics” (BE) to a problem thought to be insoluble in any other way regardless of whether BE was even applicable. Frustration with the research staff in not finding the “best solution” often gets translated into the management prerogative of picking the newest solution regardless of its value. Long timelines are often required to “convert” to the new approach with this period often being exactly as long as the period required to assess the value of the new approach; so if it is not immediately helpful, the risk of poorer performance is multiplied.

“If It Wasn’t Developed Here, It Can’t Be Better”
A few companies have well-managed, long-standing insights teams focused on improving their own homegrown techniques. Annual enhancements to these approaches are often merchandised as proof of their value and the rationale for continued investment. Interestingly, BE Endowment Bias appears to correctly define this cause of poor insight — nothing new can replace the homegrown approach due to its overvaluation and marginal improvements in value. The net result is often nothing more than a circling of the internal research staff’s “wagons” and protection of its resources and staff. For example, one very famous beverage company used the same in-bred copy testing system for over a decade while watching the effectiveness of its ads decline.

“Don’t Need No Stinkin’ Insights”
Although more rare today than 20 years ago, some marketing groups still make decisions on the basis of their own wits with little or no support from an insights group. Some are able to get by because they manage niche brands, have unique market positions with little competition or a combination of all of these. Regardless of the cause, this is a dying breed. Eventually the “seat-of-the-pants” will be replaced as the strategic compass with something more accurate.