Remembering the Life and Work of Daniel Kahneman

Daniel Kahneman, the renowned psychologist best known for his groundbreaking work on the psychology of human judgment and decision-making, died this past Wednesday at the age of 90.

Dr. Kahneman is widely regarded as one of the intellectual founders of the behavioral science discipline now called behavioral economics. He was awarded the Nobel Prize in Economic Sciences in 2002 even though he never took a course in economics.

Dr. Kahneman earned his PhD in psychology at the University of California, Berkeley. He began his academic career as a lecturer in psychology at The Hebrew University of Jerusalem in 1961. He later taught at the University of British Columbia, the University of California, Berkeley, and Princeton University.

At the time of his death, Dr. Kahneman was Professor of Psychology and Public Affairs Emeritus at the Princeton School of Public and International Affairs, and the Eugene Higgins Professor of Psychology Emeritus at Princeton.

Dr. Kahneman gained prominence in the 1970s when he and fellow psychologist Amos Tversky published several scientific papers describing their research on human decision-making under uncertainty. 

Kahneman and Tversky challenged the long-standing notion that people make economic decisions on a purely rational basis. They argued that people regularly rely on mental shortcuts known as heuristics that make us subject to several cognitive biases.

In his 2011 best-selling book, Thinking, Fast and Slow, Dr. Kahneman described his views regarding human judgment in non-academic terms and introduced his now-famous "System 1-System 2" model of human decision-making. Thinking, Fast and Slow has achieved seminal status, and in my opinion, it should be required reading for all business, marketing, and sales leaders.

Like thousands of others, I have been greatly influenced by the views advanced by Daniel Kahneman. When I learned of his death, I looked back at the posts I've published here and found that I've discussed or referred to his work in no fewer than 20 posts.

One of my earliest discussions of Dr. Kahneman's work was published in March 2015, and to commemorate his life and work, I've reproduced that post below. Even after nine years, the material in the post is still remarkably relevant.

Fair winds and following seas, Professor Kahneman.

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Why You Need Marketing Content for Two Ways of Thinking

This is the second of several posts about the role of behavioral economics in marketing, particularly in content marketing. In my first post, I introduced the topic of behavioral economics and argued that it's critical for marketers to understand the psychological aspects of human decision-making and to incorporate those factors into marketing strategy and marketing communications.

Behavioral economics challenges a fundamental assumption of mainstream economics. For decades, economists have assumed that people make economic decisions rationally. The traditional view says that people weigh the economic costs and benefits of proposed actions, have relatively stable preferences, and usually act to maximize their economic self-interest. Behavioral economics holds that people don't always make rational economic choices because they unconsciously use heuristics (mental shortcuts) that produce several cognitive biases.

In his landmark book Thinking, Fast and Slow, psychologist Daniel Kahneman - whose research with fellow psychologist Amos Tversky laid the foundation for behavioral economics - argues that heuristics and biases originate in the ways we think and learn. Kahneman says that the cognitive processes used by humans can be thought of as two "systems."

• System 1 (fast thinking) operates automatically, quickly, with little or no effort, and with no sense of voluntary control.
• System 2 (slow thinking) consists of thinking processes that are reflective, controlled, deliberative, and analytical. 

According to Kahneman, when we think of ourselves, we identify with System 2, our rational self, but System 1 actually originates many of the impressions and feelings that are the sources of the explicit beliefs and deliberative choices of System 2. 

Kahneman puts it this way:  "System 1 continuously generates suggestions for System 2:  impressions, intuitions, and feelings. If endorsed by System 2, impressions and intuitions turn into beliefs, and impulses turn into voluntary actions. When all goes smoothly, which is most of the time, System 2 adopts the suggestions of System 1 with little or no modification."

Therefore, System 1 exerts a powerful influence on the economic decisions we make, including decisions about the purchase of products or services.

System 1 thinking is valuable and, in fact, essential to our well-being. We live in a world that is both complex and rapidly changing, and we don't have the time, energy, or information-processing capacity to consciously and deliberately analyze every event or circumstance that we encounter. If we didn't have a mechanism for thinking fast, automatically, and effortlessly, we simply couldn't function effectively. The good news is, System 1 is generally good at what it does - the "suggestions" that it makes to System 2 are usually accurate and appropriate.

However, System 1 also has biases. It tends to make systematic and predictable logical errors in certain circumstances. In Thinking, Fast and Slow, Kahneman identified 22 characteristics of System 1 thinking that can contribute to biased decision-making. All of these characteristics are important for marketers, but some of them are particularly relevant for creating engaging and persuasive content. For example, System 1:

• Links a sense of cognitive ease to illusions of truth - if something is familiar and easy to understand, we are more likely to believe it is true
• Responds more strongly to losses than to gains, which makes framing content messages the right way particularly important
• Infers and exaggerates consistency (the halo effect)
• Sometimes substitutes an easier question for a difficult one

In upcoming posts, I'll discuss how marketers can use these characteristics of human thinking to make marketing content more compelling.

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Image courtesy of nrkbeta via Flickr (CC). 

The Powerful Head Start B2B Marketers Shouldn't Ignore

Imagine you're a world-class athlete about to run a 100-meter dash. Your competitors are also world-class athletes, so the outcome of the race would normally be far from certain.

But in this race, you'll have a major advantage. You'll be allowed to leave the starting line two seconds before the other runners. World-class track athletes usually run a 100-meter dash in about ten seconds. So, with a two-second head start, you're almost certain to win.

In the race to win business and grow revenue, some companies have a significant head start over their competitors. I'm referring to the head start that results when a company, product, or service (which I'll call collectively a brand) is included in the initial consideration set for a prospective purchase.

The importance of the initial consideration set is hard to overstate. In most cases, a B2B buying process begins when a trigger event causes a business person (the potential buyer) to feel a need or desire to solve a problem or seize an opportunity that may require a purchase.

When such a need or desire arises, a potential buyer will quickly create a mental list of the brands he or she feels are worth considering, i.e. an initial consideration set.

This initial consideration set is based on the mental impressions of brands the potential buyer has formed through personal experiences with the brand, marketing messages, news reports, and conversations with colleagues and friends.

Several studies have shown that potential buyers are very likely to select vendors that were in their initial consideration set. Here are two recent examples.

The Bain & Co./Google Survey

Bain & Co. and Google recently surveyed 1,208 people at US companies who were involved in buying several types of business products and services. The researchers also conducted extensive interviews with ten buyers to explore their habits at each stage of the buying journey.

In this survey, 80% - 90% of the respondents (depending on what they were buying) said they had a set of vendors in mind before they did any research. And, 90% of those respondents said they ultimately chose a vendor that was in their initial consideration set.

The WSJ Intelligence/B2B International Survey

In a 2021 survey of business decision-makers by WSJ Intelligence and B2B International, the researchers divided the B2B customer journey into three stages.

The study defined the Pre-Decision stage as ". . . the time between when they had selected a supplier [for a given product/service category] and when the 'trigger' occurred that prompted them to actively begin searching for and deciding on a new supplier."

The survey contained several questions about a recent purchase and asked the participants to reflect on the vendor that was ultimately selected (the winning vendor) and on a vendor that was considered but not selected (the losing vendor).

The survey findings revealed that mental impressions existing during the Pre-Decision stage have a significant impact on purchase decisions.

• Survey respondents were more than twice as likely (79% vs. 37%) to say they were very familiar with the winning vendor versus the losing vendor before their active buying process began.

• At the Pre-Decision stage, respondents had a higher level of pre-existing trust (57% vs. 37%) and confidence (52% vs. 37%) in the winning vendor than in the losing vendor.

The Importance of Mental Availability

So, the research clearly shows that the initial consideration set has a major impact on final purchase decisions. Therefore, marketers should be focused on having their brand(s) included in the initial consideration sets of as many potential buyers as possible. To achieve this objective, marketers need to run marketing programs that will increase the mental availability of their brand(s).

The concept of mental availability was popularized by Byron Sharp and his colleagues at the Ehrenberg-Bass Institute for Marketing Science. According to Sharp, mental availability is the likelihood that a potential buyer will think of a brand in the context of a specific buying situation.

To design marketing programs that will increase mental availability, marketers must keep two important points in mind.

First, increasing general brand awareness isn't enough. Potential buyers create their initial consideration set based on the specific context of each buying situation. Therefore, marketers need to run programs that will build and refresh the memory structures that connect their brand(s) to the specific needs and desires their potential buyers are most likely to experience.

Second, because potential buyers create their initial consideration set quickly after a trigger event occurs, marketing programs designed to increase mental availability need to reach potential buyers before they have started an active buying process. This explains why reaching "out-of-market" buyers is vital for effective marketing.

Increasing mental availability and being included in the initial consideration set of a larger number of potential buyers won't, in itself, guarantee success. The rest of the B2B buying process still matters. But being included in more initial consideration sets provides a head start that B2B marketers can't afford to ignore.

Top image courtesy of tableatny via Flickr (CC).

[Research Round-Up] The Effectiveness of AI-Generated Images for Marketing

Source:  Shutterstock

(This year, I'm devoting some of my Research Round-Up posts to academic research papers relating to the use of artificial intelligence for marketing purposes. This post features an unpublished paper that compares the performance of AI-generated vs. human-made images across three marketing use cases.)

"The power of generative marketing:  Can generative AI reach human-level visual marketing content?"

• Authors - Jochen Hartmann and Yannick Exner, Technical University of Munich; Samuel Domdey, Technical University of Hamburg-Harburg

• Date Written - July 12. 2023

This paper describes the results of three studies designed to evaluate the performance of AI-generated vs. human-made images used for marketing purposes. Specifically, the studies evaluated image performance across three dimensions relevant to marketing.

• Human perception of image quality and realism
• Social media engagement
• Click-through rates of banner ads

The studies used AI-generated images created with 13 text-to-image diffusion models, including DALL-E2, Jasper, Midjourney v4, and several versions of Stable Diffusion. Altogether, these studies collected more than 17,000 human evaluations of over 1,500 AI-generated images.

All of the AI-generated images in these studies were created using a two-step process. In the first step, the researchers employed an image-to-text AI model to create a textual description of each human-made comparison image. These textual descriptions were then used (without modification) as the prompts to produce the AI-generated images.

Here are abbreviated descriptions of the three studies and the high-level results of each study.

Study 1 - Human Perception of Quality and Realism

The objective of this study was to compare the perceived quality and realism of AI-generated vs. human-made images across three marketing use cases - product design, social media, and print ads. 

Each image was rated by five human evaluators for quality and realism using a 7-point Likert scale (1 = low, 7 = high), resulting in a total of 7,830 ratings.

The ratings for quality and realism varied depending on the specific image being evaluated and on the model used to create the AI-generated image. Overall, however, the study revealed that the AI-generated images outperformed or were on par with the human-made images in the product design and social media use cases.

In the print ad use case, the AI-generated images were significantly less likely to perform on par with the human-made images in terms of perceived quality and realism.

Again, the ratings varied significantly depending on the model used to create the AI-generated image. So, the choice of model matters.

Study 2 - Social Media Engagement

This study's objective was to compare the ability of AI-generated images vs. a human-made image to produce engagement in a social media setting. In this study, engagement referred to the "likelihood to like" an image and the "likelihood to comment" on an image.

This study included one human-made image and 13 AI-generated images. The researchers recruited 701 participants who were randomly assigned to one of the 14 images. Each participant was asked to rate how likely they were to like or comment on an image using a 7-point Likert scale (1=low, 7=high).

The results of this study showed that the AI-generated images generally performed on par with the human-made image in terms of social media engagement.

Study 3 - Click-Through Rates On Banner Ads

The objective of this study was to compare the effectiveness of AI-generated images vs. a human-made image when used in an online banner ad. The measure of effectiveness used was click-through rates (CTR).

This study was a randomized field experiment that consisted of a real-world online banner ad campaign run on a leading display advertising platform. The human-made image was a professional photo purchased from Adobe Stock. The campaign ran December 28-29, 2022, and generated 702 clicks on 86,809 impressions.

Of the 14 images tested, the human-made image ranked 10th in terms of CTR. The best-performing AI-generated image achieved a 21.5% higher CTR compared to the human-made image.

This study also demonstrated that model choice matters. The best-performing AI model (Stable Diffusion v1-3) outperformed the worst model (Disco Diffusion) by 65.5%.

My Take

The three studies described in the Hartmann et al. paper demonstrate that generative AI models can create visual content that is on par with - and often better than - human-made images for a variety of marketing use cases.

If anything, these studies probably underestimate the ability of generative AI models to produce human-level visual content. The prompts used to create the AI images for these studies were produced by an image-to-text AI model, and the researchers didn't modify those prompts. Prompts engineered by experienced marketers would likely have resulted in more effective AI images.

These studies also probably underestimate the quality of images generative AI models can currently produce because new, more capable versions of some of the models used in the studies have been released since the studies were conducted. For example, these studies used DALL-E2 and Midjourney v4, but DALL-E3 and Midjourney v6 are now available.

At minimum, the results of these studies suggest that AI-generated images are likely to play an increasingly important role in marketing.

[Book Review] Why Marketers Should Think Like World-Class Poker Players

Source:  Penguin Random House

The idea that marketers need to think like world-class poker players may seem a little odd, but that's the primary lesson I take from Annie Duke's book, Thinking in Bets:  Making Smarter Decisions When You Don't Have All the Facts (Portfolio/Penguin, 2018).

Thinking in Bets isn't specifically about marketing, but it describes an approach to thinking about decisions that would serve marketers well. So, if you haven't read Thinking in Bets, I recommend you add it to your 2024 reading list.

Annie Duke is a recognized authority in the field of decision-making, but her professional journey has been a little unusual. She graduated from Columbia University with degrees in English and psychology, and she has a master's degree in cognitive psychology from the University of Pennsylvania. She had finished her PhD coursework at UPenn when she became ill and was forced to take a leave of absence.

During her leave of absence, Duke moved to Montana and began to play poker. She became a professional poker player and, over a twenty-year career, she won numerous high-level poker tournaments, including the prestigious World Series of Poker. During her career, Duke won over $4 million in poker tournaments.

Duke retired from professional poker in 2012 and just last year completed her doctoral work and earned a PhD in cognitive psychology from UPenn. She's now a sought-after corporate speaker and a consultant on decision strategy.

What's In the Book

Annie Duke describes the primary purpose of Thinking in Bets in these terms:

"The promise of this book is that if we follow the example of poker players by making explicit that our decisions are bets, we can make better decisions and anticipate (and take protective measures) when irrationality is likely to keep us from acting in our best interest."

Duke's core argument is that the significant decisions we make in life are essentially bets on the future, and she elaborates on this argument in the first three chapters of the book. She writes:

". . . our decisions are always bets. We routinely decide among alternatives, put resources at risk, assess the likelihood of different outcomes, and consider what it is that we value. Every decision commits us to some course of action that, by definition, eliminates acting on other alternatives."

According to Duke, uncertainty is the factor that makes our decisions like bets in a poker game. When you place a bet in poker, you can't know for sure that you will win the hand. And, when we make any significant decision, we can't know with certainty that our decision will produce the desired results.

One key to becoming a better decision-maker is developing the ability to effectively cope with the uncertainty that's inherent in all significant decisions. She writes:

"What good poker players and good decision-makers have in common is their comfort with the world being an uncertain and unpredictable place. They understand that they can almost never know exactly how something will turn out . . . instead of focusing on being sure, they try to figure out how unsure they are, making their best guess at the chances that different outcomes will occur."

Duke acknowledges that becoming comfortable with uncertainty is easier said than done. She observes that the human brain evolved to create coherence and certainty, and this makes us prone to illogical thinking and several cognitive biases. Duke describes the hazards of such illogical thinking and cognitive biases throughout Thinking in Bets.

Lastly, Duke devotes more than half of her book to a discussion of several tactics that will help us develop our ability to "think in bets" and make better decisions.

In Chapters 4 and 5, Duke describes how we can use a "decision group" or a "decision pod" to help us maintain our decision-making discipline and thus improve our decision-making skills. In Chapter 6, she discusses scenario planning, backcasting, premortems, and several other valuable tactics that she calls forms of "mental time travel."

My Take

Thinking in Bets is a valuable resource for any marketer. Annie Duke's writing style is informal and engaging, and she makes liberal use of stories that are always on point and often amusing.

As I mentioned earlier, Thinking in Bets isn't specifically about marketing. However, the decision-making principles described in the book are universal. I would argue that Thinking in Bets is especially relevant for marketers because the outcomes of most significant marketing decisions depend on the reactions and responses of other human beings. This means that marketing decisions often involve greater uncertainty than other kinds of business decisions.

Thinking in Bets is a self-help book in the sense that it focuses primarily on how we can improve our individual decision-making. However, Duke offers several suggestions for how business leaders can improve decision-making in their organization.

Duke argues that it's particularly important for business leaders to encourage skepticism and the expression of dissenting views in their decision-making processes. One way to operationalize skepticism and dissent is by using "red teams."

Duke describes the role and value of red teams in these terms:

"Just as the CIA has red teams and the State Department has its Dissent Channel, we can incorporate dissent into our business and personal lives. We can create a pod whose job (literally, in business, and figuratively, in our personal life) is to present the other side, to argue why a strategy might be ill-advised, why a prediction might be off, or why an idea might be ill informed. In so doing, the red team naturally raises alternative hypotheses."

Thinking in Bets won't teach you how to make specific marketing decisions, but it will help you make better marketing decisions.

Decision Science Explains the Power of Strong Brands

Marketers have long argued that a strong brand can induce customers to pay premium prices, increase customer loyalty, and drive growth. But until recently, it's been difficult for marketers to explain exactly why and how a strong brand produces these results. Read on to learn why established principles of decision science can explain the power of a strong brand.

Numerous studies conducted over many years have demonstrated that strong brands produce significant benefits for their owners. A strong brand can make customers more willing to pay premium prices, increase customer loyalty, and drive revenue and market share growth.

While the benefits of strong brands are well established, we haven't had a clear understanding of why or how they produce these proven benefits. But thanks to advances in the decision sciences, this mystery has now been solved.

Last fall, I reviewed and strongly recommended Phil Barden's book, Decoded:  The Science Behind Why We Buy. In Chapter 1 of his book, Barden discusses several decision-making principles derived from cognitive and social psychology, behavioral economics, and neuroscience. Then, he uses these principles to explain how people make buying decisions and how brands influence those decisions.

The Science of Human Decision-Making

Barden's explanation of how brands influence buying decisions is grounded in the model of human decision-making developed by psychologist Daniel Kahneman, who won the 2002 Nobel Prize in economics.

Kahneman's model posits that people use two types of cognitive processes to make decisions.

• System 1 (which Barden calls the "autopilot") is fast, intuitive thinking that operates automatically, quickly, and with little or no conscious effort. System  1 essentially integrates perception and intuition.

• System 2 (which Barden calls the "pilot") is slow thinking that consists of processes that are reflective, deliberative, and analytical.

Together, these two cognitive systems determine all the purchase decisions that people make.

The human autopilot is "always on." It automatically processes all the information that is perceived by our senses, even if we aren't consciously focusing on those sensory inputs. And all of those sensory inputs have the potential to influence our decision-making and behavior.

The human brain uses sensory information to learn through a process called associative learning. Our brain builds neural connections between sensory inputs that occur repeatedly in the same context, creating associative memory. Or, to put it more informally, "What fires together wires together."

These associative memories (many of which we aren't consciously aware of) are the basis of human intuition, which can be described as our ability to "know" something without knowing exactly why or how we know it.

Associative memories also exert a major influence on what we buy, and this largely explains the power of strong brands.

How Brands Influence Purchase Decisions

In Decoded, Phil Barden argued that brands influence buying decisions because they provide "frames" that affect how we perceive products and services. Barden doesn't provide a definition of "brand," but it's clear that he means more than just a product or service. In Barden's model, "brand" refers to all of the perceptions and linkages relating to a product or service (or the business that provides it) that a person has stored in his or her associative memory.

To demonstrate the impact of framing, Barden used the illustration that I've reproduced below.

In this illustration, two large squares frame two smaller squares. When people see this drawing, most will immediately say the two small squares are different shades of gray. In fact, they are exactly the same color.

Our perception that the two small squares are different shades of gray is due to the differences in the color of the two large squares. So, the color of the frame changes how we perceive the color of each small square.

Barden argues that this is how brands work. He writes:

"The framing effect is crucial for marketing . . . We know that they [brands] have an impact, but how brands work is hard to grasp . . . Framing explains how brands influence purchase decisions:  brands operate in the background, framing the perceptions and, with it, the experience of the product."

It's important to note that many of the associative memories that are linked to a brand aren't about the functional attributes of the product or service. More often, the most powerful perceptions stored in our associative memory are about psychological goals (e.g. security, autonomy, excitement) or past emotional experiences.

Barden's explanation of how brands influence purchase decisions is compelling, and it provides two lessons for marketers. First, it reinforces the importance of effective branding and brand marketing. And second, it should remind us that most significant purchase decisions involve both deliberative/rational and intuitive/non-rational thinking.

Top image courtesy of Affen Ajlfe (www.modup.net) via Flickr (PD).