[Research Round-Up] New Study Shows the Continuing Value of B2B Thought Leadership

Source:  Edelman and LinkedIn

(This month's Research Round-Up discusses the sixth edition of the B2B thought leadership impact study by Edelman and LinkedIn. This research has consistently provided valuable insights about the impact of thought leadership content on the perceptions and behaviors of business decision-makers.)

Edelman and LinkedIn recently published the findings of their 2024 B2B thought leadership impact study. The study involved a survey of 3,484 business leaders (all LinkedIn members) from a wide range of industries and company sizes. The survey was conducted November 30 - December 14, 2023, and included respondents from the United States, Canada, the United Kingdom, Germany, Singapore, Australia, and India.

The study defined "thought leadership" as "content that offers expertise, guidance or a unique point of view on a topic or in a field."

Some of the study results were reported by type of survey respondent. The three categories used in the study were: 

• B2B Decision-Makers - "Company executives who consume thought leadership and are involved in making final decisions on their company's choice of professional service providers or products."

• C-Suite Executives - "Company owners, partners and founders who consume thought leadership and who have complete or partial ownership of a company, or C-Suite-level executives with responsibility for a business function."

• Producers of Thought Leadership - "Managers (and higher) who both consume thought leadership and work for an organization that produces free thought leadership."

Here's a recap of some of the study's major findings.

As with previous editions of the study, the researchers asked survey participants about their consumption and overall view of thought leadership content.

• 52% of B2B Decision-Makers and 54% of C-Suite Executives said they spend (on average) an hour or more per week reading thought leadership content.

• 73% of B2B Decision-Makers said an organization's thought leadership content is more trustworthy than its other marketing materials for assessing the organization's capabilities and competencies.

• 48% of B2B Decision-Makers said the overall quality of the thought leadership content they read is good, but only 15% said it is very good or excellent.

Thought Leadership and Out-of-Market Buyers

A major theme of this study is the value of using thought leadership to engage potential buyers who aren't ready to buy. Findings in the 2024 study indicate that good thought leadership content can stimulate demand among out-of-market buyers by prompting them to rethink their status quo.

For example, more than 75% of B2B Decision-Makers and C-Suite Executives said thought leadership content had led them to research a product or service they hadn't previously considered, and of these respondents, 60% said thought leadership content had made them realize their organization could be missing out on a significant business opportunity.

The study also addressed the importance of providing thought leadership content to existing customers. The survey found that good thought leadership content (from a competitor) can lead many B2B buyers to question whether they should continue working with an existing supplier and realize that other suppliers had a better understanding of their challenges.

What Drives the Quality of Thought Leadership

The Edelman/LinkedIn study also asked participants about the attributes of effective thought leadership content.

Sixty-two percent of B2B Decision-Makers said that average or above average thought leadership content is produced or written by prominent or well-respected experts, and 66% said it has a unique format or style that makes it stand apart from other thought leadership content.

B2B Decision-Makers also identified three other attributes that characterize the highest quality thought leadership.

• 55% said it is supported by strong research and data

• 44% said it helps them better understand the challenges and opportunities their business is facing

• 43% said it includes concrete guidance and case studies

The Takeaway

Like its predecessors, the 2024 Edelman/LinkedIn B2B thought leadership impact study provides marketers important insights into the impact of thought leadership on the perceptions and behaviors of B2B buyers. It also confirms the continuing power and value of authoritative and well-crafted thought leadership content.

[Book Review] Jonah Berger Unveils the Hidden Power of Words

 " Saying you 'recommend' rather than 'like' something makes people 32 percent more likely to take your suggestion."

"Adding more prepositions to a cover letter makes you 24 percent more likely to get the job."

"And saying 'is not' rather than 'isn't' when describing a product makes people pay three dollars more to get it."

Source:  HarperCollins Publishers

These are just a few examples of the power of language cited by Jonah Berger in his latest book, Magic Words:  What to Say to Get Your Way (HarperCollins Publishers, 2023).

Jonah Berger is a professor of marketing at the University of Pennsylvania's Wharton School and the best-selling author of Contagious, Invisible Influence, and The Catalyst. 

He has published over 80 articles in top-tier academic journals, and his work is frequently covered in popular media outlets like The New York Times and Harvard Business Review.

Berger is a recognized authority in the field of consumer language research, which can be generally defined as research concerned with the language used and consumed by marketplace participants such as consumers and marketers.

Recent advances in natural language processing and machine learning, together with affordable access to massive computing power, have raised interest in the field of consumer language research and made larger, more meaningful studies technologically feasible and economically practical.

Most marketers recognize that effective content is essential for marketing success. However, marketers don't always realize that minor changes in the specific words they use can have a major impact on content effectiveness. Magic Words is a worthwhile read because it raises marketer awareness of this important topic. 

What's In the Book

Jonah Berger spells out his rationale for writing Magic Words in the Introduction.

". . . while we spend a lot of time using language, we rarely think about the specific language we use. Sure, we might think about the ideas we want to communicate, but we think a lot less about the particular words we use to communicate them . . .

The right words, used at the right time, can change minds, engage audiences, and drive action . . .

This book uncovers the hidden science behind how language works and more important, how we can use it more effectively."

(Emphasis in original)

Berger devotes most of the book to a discussion of six categories of magic words. Specifically, he focuses on words that:

• Activate identity and agency (Chapter 1)
• Convey confidence (Chapter 2)
• Ask the right questions (Chapter 3)
• Leverage concreteness (Chapter 4)
• Employ emotion (Chapter 5)
• Harness similarity (and difference) (Chapter 6)

In Chapter 7 of Magic Words, Berger argues that words ". . . not only influence and affect the people who listen to or read them, they also reflect and reveal things about the person (or people) who created them." (Emphasis in original) Therefore, he contends, language science techniques can be used to detect and reveal societal beliefs and biases.

Chapter 5 of Magic Words exemplifies the kinds of insights found throughout the book. In this chapter, Berger explores why emotional language enhances engagement in most circumstances. He discusses the value of tapping into both positive and negative emotions and why it's important to move frequently between positive and negative emotions whenever possible. He also explains why creating some uncertainty can enable your content to hold attention.

My Take

Magic Words is a thought-provoking book that would be useful for anyone who needs to communicate more effectively and persuasively. The fact that just about everyone has this need at least occasionally explains the widespread appeal and popularity of the book.

Magic Words is a particularly valuable resource for people like marketers and salespeople whose professional success is largely dependent on their ability to be effective and persuasive communicators.

The book is also an easy read because Jonah Berger's writing style is engaging. He is an academic with the rare ability to make a complex topic accessible to a non-academic audience. He uses real-world examples and anecdotes throughout the book that any reader can relate to.

One of the book's most important lessons for marketers is that the "magic words" discussed in the book aren't equally magical in all circumstances. Which words will work best depends on the context in which the words are used.

In Chapter 4, for example, Berger writes that concrete words are usually more effective than generic words. Concrete language signals that you understand the specific needs of a prospect or customer, and it makes your message easier to understand and more memorable. However, Berger also writes that abstract language often works better when the goal is to convey the potential of an idea, a new product, or a new business.

In every category of the magic words he discusses, Berger points out that there are "exceptions to the rule," or more accurately, that some circumstances will call for a different, and perhaps contradictory, approach.

So, in the end, Magic Words does two things that make it a valuable read for marketers. First, it demonstrates the power of specific language choices. And second, it reinforces the over-arching principle that context should ultimately dictate the choices you make. If you're involved in creating content, Magic Words should be on your reading list.

Halos, Horns, and Content Marketing

Source:  Shutterstock

If you've ever bought or sold a house, you're probably familiar with the concept of curb appeal. Curb appeal is the visual attractiveness of a house as seen from the street, and it's what creates a potential buyer's first impression of the house. Real estate professionals know curb appeal plays a big role in determining how quickly a house will sell and what the selling price will be.

Good first impressions are also important for successful B2B marketing. Today, most potential buyers will form their first impression of your company based on the content you produce. If your content doesn't create a good first impression, potential buyers will quickly turn elsewhere, and you may not get another chance to connect with those buyers. 

In the words attributed to Will Rogers, "You never get a second chance to make a first impression."

When your content creates a good first impression, potential buyers are more likely to come back for more, and they will be more inclined to view the rest of your content - and your company - favorably.

Enter the Halo Effect

This inclination results from a cognitive phenomenon known as the halo effect. The American Psychological Association defines a halo effect as, "a rating bias in which a general evaluation (usually positive) of a person, or an evaluation of a person on a specific dimension, influences judgments of that person on other specific dimensions."

Put more plainly, a halo effect exists when we transfer our perceptions about one attribute of a person or an organization to other attributes of that person or organization without having a rational basis for the transfer. In other words, if we perceive that a company is good at "A," we will tend to think the company is also good at "B," even though we actually know nothing about the company's capabilities at "B." 

The halo effect was first identified by psychologist Edward Thorndike in 1920, and it's been widely studied since that time. Although the halo effect was first applied to the evaluation of people, we now know that halo effects influence how we evaluate inanimate objects including products, services, brands, and companies.

The most important thing to remember about the halo effect is that it magnifies the influence of first impressions beyond what would be justified on a purely rational basis.

Halos Are Everywhere

The halo effect can be found in a wide range of human judgments. For example:

• If I meet a likable person, I will be inclined to believe he or she is also generous and ethical, even though I know nothing about the person's generosity or ethics.

• If I have a good experience with a Honda automobile, I'll be inclined to believe I will also be happy with a Honda lawnmower, even though I know nothing about the quality of Honda lawnmowers.

• If I find one of your company's white papers to be valuable, I'll be inclined to believe other content produced by your company is likely to be valuable. I'll also be inclined to believe your company is probably good at what it does even if I know little about your company.

Halo Effect's Evil Twin

The halo effect is most frequently discussed in the context of irrational positive evaluations, but the same cognitive mechanism can also produce irrational negative judgments.

If I attend a webinar hosted by your company and find the content to be poor, I'll be inclined to think the other content produced by your company probably isn't very good. In addition, my webinar experience may lead me to form a negative overall impression of your company.

This negative manifestation of the halo effect is called, appropriately, the horn effect. 

Implications for Marketing

As a B2B marketer, it's important to recognize that almost every content resource you publish has the potential to trigger (or contribute to) a halo effect or a horn effect. Therefore, one obvious lesson is that you can benefit from halo effects (and avoid horn effects) if you consistently produce content that will create a good first impression with potential buyers.

I would also argue that the potential benefits of halo effects should influence how you think about content distribution. Marketers have been debating the use of gated vs. ungated content for the past several years. While opinions vary, the conventional view is that it's appropriate to gate very-high-value content resources, while keeping other resources ungated.

I contend this is the wrong approach. Suppose you have created a content resource that is truly outstanding, one that is likely to make a good impression on potential buyers. In that case, you should want that resource to reach (and be consumed by) as many potential buyers as possible. The last thing you want is to put any hurdles between your content resource and your target audience.

If a potential buyer is impressed with your content, he or she is likely to seek out other content you've produced. And when the potential buyer is ready to begin an active buying process, your company will likely be included in his or her initial consideration set of potential vendors.

The benefits of halo effects aren't always immediate, but they can be powerful.

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).

The Right Customer Promises Drive Better Marketing Results

One of the more infamous quotes in marketing is usually attributed to John Wanamaker, who reportedly said, " Half the money I spend on advertising is wasted. The trouble is, I don't know which half."

Cracking the code on what drives marketing effectiveness can be incredibly difficult. One TV ad, webinar, or ebook may be hugely successful, while another - based on the same theme and having similar creative elements and comparable distribution - fails to move the needle. In many cases like this, there's no readily apparent way to explain the difference in performance.

An article appearing in the current issue of the Harvard Business Review offers a potential solution for this conundrum, at least when it comes to brand advertising. "The Right Way to Build Your Brand" was written by Roger L. Martin, Jann Schwarz, and Mimi Turner.

Martin is the former dean of the Rotman School of Management and the author of several books on business strategy and management. Schwarz and Turner are both executives at The B2B Institute, a B2B marketing think tank funded by LinkedIn.  

The authors clearly state their central message early in the article:  " . . . the key to successful brand building is a clear and specific promise to the customer that can be demonstrably fulfilled. Advertising that makes such a promise almost always results in better performance than advertising that does not - even if the latter creates greater name awareness."

This conclusion was based on an analysis of a large database of advertising case studies maintained by the World Advertising Research Centre (WARC). The WARC database includes over 24,000 case studies drawn from global ad competitions. These competitions typically require their entrants to provide information about how well their ads worked.

Specifically, the authors analyzed data relating to more than 2,000 ad campaigns entered in competitions from 2018 to 2022. The first step of the analysis was to classify the campaigns based on whether they had made "an explicit and verifiable promise to customers." Forty percent of these campaigns (the "CP campaigns") included such a promise, while 60% (the "non-CP campaigns") did not.

Advertising that Included Customer Promises Performed Better

The authors then compared the performance of the CP campaigns with the non-CP campaigns on a variety of metrics and found that the CP campaigns outperformed the non-CP campaigns across most of the metrics. For example, the analysis revealed that:

• 56% of the CP campaigns (vs. 38% of the non-CP campaigns) produced improvement in brand perception, brand preference, and purchase intent.

• 45% of the CP campaigns (vs. 38% of the non-CP campaigns) resulted in increased market penetration.

• 27% of the CP campaigns (vs. 17% of the non-CP campaigns) resulted in market share growth.

The article also compared the performance of the CP campaigns vs. the non-CP campaigns based on the rating system used by WARC to rank campaign performance. The following table shows the results of that comparison.

As this table shows, the CP campaigns did better than the non-CP campaigns on all but the lowest level of performance.

Martin, Schwarz, and Turner also looked at what made the promises in the CP campaigns attractive to customers. They found that the most effective promises shared three important attributes. They were memorable, valuable, and deliverable.

Why Customer Promises Work

The authors have built a compelling case for including customer promises in brand advertisements. But what makes such promises effective? Martin, Schwarz, and Turner gave this answer:

"When one person makes a promise to another, it creates a relationship between the two. If the pledge is fulfilled, it builds trust, resulting in a valuable connection."

I don't disagree with this rationale, but established decision science principles provide an even more compelling explanation for why the right kinds of customer promises will deliver better business outcomes. This explanation is based on the interplay of rewards, goals, and motivation.

I wrote about this topic earlier this month, but here's an abbreviated recap of the relevant decision science principles.

• Motivation is a willingness to exert mental or physical effort in pursuit of a goal, and motivation is the primary driver of all human behavior.

• As humans, we pursue a goal because we expect to receive a reward if the goal is achieved. Neuroscience research has shown that our brain has a "reward system" that's activated when it processes information that signals a reward we value.

• When our brain's reward system is activated, we become motivated to pursue the goal that will enable us to reap the expected reward.

So, a customer promise in a marketing message will be effective when it signals a reward the recipient values. Martin, Schwarz, and Turner allude to this when they write, "Customers must want what the promise offers."

"The Right Way to Build Your Brand" is an important article for marketers. It's well worth the few minutes you will spend reading it.

Top image courtesy of Kevin Simmons (Mayberry Health and Home) via Flickr (CC).

[Research Round-Up] What CEOs Think of Marketing/CMOs and How Much Tech Buyers Trust Marketing

(This month's Research Round-Up features a study by Boathouse that reveals what CEOs actually think about marketing and CMOs, and a survey by Informa Tech that addresses how much trust B2B technology buyers actually place in marketing.)

The Third Annual CEO Study on Marketing and the CMO by Boathouse 

Source:  Boathouse

• Based on a survey of 150 CEOs at U.S. companies; 55% were with public companies, and 45% were with private companies
• Survey respondents were with companies having $250 million to more than $1 billion in annual revenue
• Survey respondents represented 17 industry sectors
• The survey was in the field September 9, 2023 - October 4, 2023

This survey explored the perspectives of U.S. CEOs regarding the performance of their marketing function and their CMO. It also addressed how CEOs view their job and the major issues they are facing.

Overall, this survey contains good news for CMOs and marketers. On most points, the survey found that CEOs have a more favorable opinion of their marketing team and CMO than they did when earlier versions of the survey were conducted in 2022 and 2021.

To set the stage, the survey asked participants about the problems they want marketing to help them solve. The top five problems selected by respondents (from a list of 15) were:

1. "Create new customers, retain existing customers, and drive revenue growth" (52% of respondents)
2. "Drive sales and grow market share" (45%)
3. "Stay ahead, differentiate, grow faster than our competition" (44%)
4. "Improve our brand/reputation" (41%)
5. "Transform the company's narrative in the marketplace" (40%)

Nearly half (49%) of the surveyed CEOs rated the performance of their marketing function as Best in Class. That was up from 24% in the 2022 edition of the survey.

The latest survey also found that CEOs view their CMO more favorably. In the 2023 survey, 26% of the respondents gave their CMO a grade of "A" for the overall performance of their role. That was up from 16% in the 2022 survey.

Concerning artificial intelligence, over half (57%) of the surveyed CEOs in the 2023 survey gave their CMO a grade of "A" or "B" on their ability to integrate AI/machine learning into their marketing efforts.

Despite the high grades for overall performance, the latest Boathouse survey identified areas where CEOs aren't as pleased with CMO performance. For example, only 23% of the surveyed CEOs gave their CMO a grade of "A" on strategy, and the lowest number of "A" grades given to CMOs was on their "ability to drive company growth."

2023 Trust in Marketing Index:  How B2B marketers can fill the trust gap by Informa Tech

Source:  Informa Tech

• Based on a survey of 150 B2B technology buying decision-makers
• 68 of the respondents were at the C-level or executive level of seniority; 82 were at the director level
• Respondents were located in the United States and the United Kingdom
• The survey was conducted in the summer of 2023

The purpose of this research was to assess the level of trust that B2B technology buyers have in marketing and identify factors that will increase or reduce that level of trust. To quantify the level of trust, Informa Tech created a "Trust in Marketing Index."

The survey used to develop the index included five index questions with numerical values assigned to each potential answer. The researchers calculated the average score for each index question and then added the average scores together to create the overall index score.

The resulting index showed that B2B technology buyers' level of trust in marketing is at 61 on a scale of 1 to 100. So, while the level of trust isn't horrible, there is significant room for improvement.

Here are the five index questions and the key survey finding for each.

• "In general, how much do you trust the information marketers provide in B2B content?" - 62% of the survey respondents said they trust all or a majority of the content B2B marketers provide.

• "How often are you disappointed with the value of B2B gated content?" - 71% of the respondents said often or sometimes.

• "How much do you trust personalized content . . . from B2B marketers you've already shared your data with?" - 59% of the respondents said they trust all or a majority of such personalized content.

• "How good of a job are all B2B brands doing in general when targeting you with content and offers?" - 62% of the respondents said good or outstanding.

• "How good of a job are all B2B brands in general doing when it comes to sending content and offers at the right time?" - 64% of the respondents said good or outstanding.

The survey also identified several factors that increase or reduce buyer trust in marketing. For example, 85% of the respondents said high-quality B2B thought leadership content improves the perception of a brand. In contrast, 42% of the respondents said content that is too general reduces trust.

[Book Review] "Escape from Model Land" by Erica Thompson

Source:  Basic Books

Predictive mathematical models touch our lives virtually every day. Every weather forecast we watch, hear, or read is formulated based on multiple atmospheric models. And that's just one example.

Predictive models have also become an integral part of modern marketing. For example, marketers use mathematical models to determine the optimal mix of marketing programs (marketing mix models), identify the attributes of their best prospects, and personalize marketing communications and other forms of marketing content.

The primary function of most mathematical models in marketing is to identify patterns in existing data and then apply those patterns to predict the likely future outcomes or results of marketing decisions or programs.

The use of predictive models in marketing is poised to increase significantly because of continuing advances in artificial intelligence. If you need proof of this growth, just look at the explosion of generative AI applications since the public release of OpenAI's ChatGPT in November 2022.

All this makes it vital that marketers have a basic understanding of how mathematical models are constructed, how they work, and why they don't always produce accurate forecasts. This makes Escape from Model Land:  How Mathematical Models Can Lead Us Astray and What We Can Do About It (Basic Books, 2022) a book all marketers should read.

Escape from Model Land was written by Erica Thompson, an associate professor at University College London (UCL) and a Fellow at the London Mathematical Laboratory. Previously, she was a senior policy fellow at the Data Science Institute at The London School of Economics and Political Science. Thompson holds a PhD in physics from Imperial College.

What's In the Book

Escape from Model Land contains ten chapters. In the first six chapters, Thompson focuses on the attributes and limitations of mathematical models. She observes that people who design and build models work in a wonderful place she dubs "Model Land." In Model Land, she writes, all the assumptions that underlie a model are "literally true," and all the uncertainties are quantifiable.

The problem is that these conditions don't exist in the real world. Thompson writes, "Deep or radical uncertainty enters the scene in the form of unquantifiable unknowns:  things we left out of the calculation that we simply could not have anticipated . . . In that case, your carefully defined statistical range of projected outcomes would turn out to be completely inadequate."

Escape from Model Land discusses several other limitations of models. For example, Thompson observes that all models are oversimplifications of the real world, which means they provide an incomplete picture of reality. She writes, " We might think of models as being caricatures . . . Inevitably, they emphasize the importance of certain kinds of features . . . and ignore others completely."

Thompson also points out that a model builder makes numerous choices when developing a model - what to put in, what to leave out, what scientific and mathematical approach to take, etc. Therefore, a model will reflect the values, education, and culture of the model builder, which means that it only presents one perspective of a given situation when, in fact, several perspectives are possible.

Throughout the book, Thompson exposes the limitations and "blind spots" of predictive models, but she does not argue they should be relegated to the junk pile. Near the end of Chapter 1, Thompson includes a passage that describes the challenge she hopes the book addresses. She writes:

"I have tried to find a balanced way to proceed in between what I think are two unacceptable alternatives. Taking models literally and failing to account for the gap between Model Land and the real world is a recipe for underestimating risk and suffering the consequences of hubris. Yet throwing models away completely would lose us a lot of clearly valuable information."

Thompson uses the final chapter of Escape from Model Land to offer five suggestions for addressing this challenge.

• Define the Purpose - "As a starting point for creating models, we need to decide what purpose(s) they are supposed to be put . . . Most models are not adequate for the purpose of making any decision, although they may be adequate for the purpose of informing the decision-maker about some parts of the decision."

• Don't Say "I Don't Know" - "If we can give up on the prospect of perfect knowledge and let go of the hope of probabilistic predictions . . . there are alternative narratives in each model which in themselves contain useful insights . . . We know nothing for certain, but we do not know nothing."

• Make Value Judgements - "All models require value judgements . . . When you understand the value judgements you have made, write them down . . . Allow for representations of alternative judgements without demonising those that are different from your own."

• Write About the Real World - "When you're explaining your results to somebody else, get out of Model Land and own the results . . . in what ways is this model inadequate or misinformative? What important processes does it fail to capture?"

• Use Many Models - ". . . gathering insights from as diverse a range of perspectives as possible will help us to be maximally informed about the prospects and possibilities of the future."

My Take

Escape from Model Land is well-written, accessible, and engaging. Erica Thompson does an excellent job of making the complex, technical aspects of mathematical models easy for those of us who aren't trained data scientists to understand.

This book is not specifically about marketing, but it contains a message that is important and timely for marketers. Over the past several years, marketers have increasingly relied on data to inform their decisions, and recent advances in artificial intelligence will likely increase this reliance.

There's no doubt that data analytics and AI can help marketers make more evidence-based decisions, but these tools also have limitations that often go unrecognized - or at least underappreciated.

The apparent precision of numbers and the halo of scientific validity surrounding AI can easily create an illusion of certainty that gives us a false sense of confidence in the outputs these tools produce.

Escape from Model Land reminds us that marketing should always be "data-informed," but never totally "data-driven." 

Leverage Buyer Goals to Drive Breakthrough Marketing Results

Source:  Shutterstock

I've always been skeptical of claims that using any one technique or tactic will consistently result in superior marketing performance. Simple, "silver bullet" solutions for big, complex challenges are incredibly rare in the real world.

But, if there is one key to decoding the formula for effective marketing, it is the ability to understand how people make decisions and what drives human behavior.

Understanding what will cause a potential buyer to respond to your marketing messages and ultimately buy your product or service is a prerequisite for developing an effective marketing strategy and creating persuasive marketing messages and content.

When you can't identify the factors that underlie human decision-making and behavior, it's nearly impossible to design marketing programs that are consistently successful. It's like trying to navigate by the stars on a cloudy night. 

The good news is, you can use established principles of decision science to identify and better understand the mechanisms that drive your potential buyers' decision-making and behavior.

The Critical Role of Buyer Goals

Recent advances in decision science have established that motivation is the primary driver behind all human behavior, including buying behavior.

The American Psychological Association defines motivation as, "a person's willingness to exert physical or mental effort in pursuit of a goal or outcome." Put another way, motivation is the willingness to take action to achieve a goal. The goal may be to solve a problem, satisfy a need, or get a particular "job" done.

As humans, we pursue a goal because we expect to receive a reward if the goal is achieved. Neuroscience has shown that the human brain has a "reward system," which is a group of structures and neural pathways that are activated when our brain processes sensory inputs that signal a reward we value.

When our brain's reward system is activated, we are motivated to pursue the goal that will enable us to reap the expected reward. And the more we value the expected reward, the more motivated we become to achieve the goal.

Our goals also largely dictate what we pay attention to. Research has shown that our brain automatically scans our environment for information that aligns with our goals. So, in essence, our brain causes us to pay attention to information that is closely related to our goals.

Lastly, goals can be explicit or implicit. Explicit goals are those we set and pursue at a conscious level. An implicit goal operates primarily at a subconscious level. These goals arise out of basic human physical, psychological, and social needs, things like safety, security, and autonomy. We are motivated to pursue implicit goals even when we aren't consciously thinking about them.

Implications for Marketers

These principles of decision science have major implications for marketers. The most important lesson is that the ability of any marketing message to provoke a response from a potential buyer is determined by how closely the message aligns with the buyer's goals. That degree of "fit" is what makes the message relevant to the buyer and what will prompt him or her to respond.

This means you need to identify what the goals of your potential buyers are and then craft messages that are linked to those goals. Unfortunately, this is easier said than done for two main reasons.

First, buyer goals are highly individualistic. They can differ even among buyers who have similar demographic attributes, work in similar types of businesses, and have similar job titles and functions. Therefore, even well-constructed buyer personas may not reveal what goals are most important for an individual buyer.

Second, the goals of a business buyer can and will change as the opportunities and challenges facing the buyer's organization change. This means that a buyer who doesn't respond to a particular marketing message today might well respond to the same message received a month from now.

The challenges presented by these two factors are always present, but they are more pronounced when you're seeking to acquire new customers.

If you are properly nurturing your relationship with an existing customer, you should be well-positioned to understand what your customer's high-priority opportunities and challenges are at any point in time. And that gives you greater insight into the goals your customer's buyers are likely to have.

When you're seeking to acquire new customers, the most effective strategy is to ensure that your marketing messages feature links to one or more of the implicit goals I discussed earlier. This approach has two main advantages.

First, implicit goals are universal because they arise out of fundamental psychological and social needs that all humans share. And second, implicit goals are durable; they don't change much over time. Therefore, marketing messages linked to these goals will likely resonate with most of your buyers whenever they are used.

The bottom line is:  If you want to achieve consistent marketing success, there's no substitute for understanding your buyer's goals.

Are the 4P's Still Relevant for Today's Marketers?

Source:  Shutterstock

(The concept of the "marketing mix" has been a staple of marketing for over 70 years. It's discussed in virtually all marketing textbooks and taught in virtually all introductory marketing courses. But does the marketing mix idea still have a place in 21st-century marketing? The answer is "yes," and here's why.)

The marketing mix construct has been part of the marketing landscape for more than seven decades. The origin of the concept can be traced to 1948 when James Culliton, a marketing professor at Harvard, wrote an article in which he described the marketing executive as a "mixer of ingredients."

Culliton's article inspired Neil H. Borden, another Harvard marketing professor, who began using the phrase "marketing mix" in his teaching and writing in 1949.

Borden developed a model of the marketing mix that included 12 elements - product planning, pricing, branding, channels of distribution, personal selling, advertising, promotions, packaging, display, servicing, physical handling, and fact-finding and analysis.

In his 1960 marketing textbook, Basic Marketing:  A Managerial Approach, E. Jerome McCarthy introduced a simpler model of the marketing mix that contained only four elements - product, price, place, and promotion. McCarthy's model quickly became popular and has been so widely adopted by academics and practitioners that the "4P's of marketing" have become synonymous with the concept of the marketing mix.

Despite its popularity and longevity, the 4P's model has been criticized for several reasons. Given how much marketing has changed over the past several decades, it's legitimate to ask whether a sixty-year-old marketing mix model is still relevant. My answer to this question is an emphatic "yes," provided you keep a few things in mind. 

The 4P's Include More Than the Terms Suggest

One criticism of the 4P's is that the ingredients used in the model don't adequately capture the complexity of today's marketing environment.

The response to this criticism is that the terms used in the model should be viewed as flexible category labels that can encompass more than the literal or common meanings of the words would suggest. For example:

• Product - The "product" element can be used for both products and services, and for complex "solutions" that consist of multiple products and services. In essence, this element can refer to whatever a company sells.

• Price - This element can encompass any type of price and virtually every aspect of pricing strategy - for example, cost-plus vs. market-based vs. value-based pricing, premium vs. discount pricing, unit pricing, subscription-based pricing, and pay-for-performance pricing.

• Place - "Place" can encompass any method or channel of distribution a company is (or could be) using. Importantly, place can also encompass distribution via the cloud.

• Promotion - This element is intended to encompass all of the ways a company can communicate with its customers and potential buyers. This would include all online and offline "marketing" communication channels and tactics, and personal selling, but it would also encompass communications that are "non-promotional," such as customer service and customer success communications.

The 4P's Describe Factors Marketers Can Manipulate and Control, Not What They Must Achieve

Another criticism of the 4P's model is that it focuses on the decisions and actions of the selling company, but doesn't address what is required to be successful with customers. This criticism is factually accurate, but that doesn't mean the model is flawed. It simply means the model was never designed to prescribe what will be effective with customers.

The 4P's model is like a list of available ingredients a chef can use to prepare a variety of dishes in a variety of ways, but it doesn't provide recipes for specific dishes that diners are guaranteed to like. It's up to marketers to decide what specific ingredients will produce a "meal" that will appeal to their target buyers.

To make these decisions wisely, marketers will need to use other methods and tools to identify the needs and preferences of their potential buyers. It's noteworthy that, in his marketing textbook, E. Jerome McCarthy did not discuss the 4P's model until after he had explained the importance of understanding the needs and attributes of the potential customers in the selling company's target market.

The Marketing Mix Concept Is Still Relevant

Even if you think the 4P's model is outdated, it's important to recognize that the basic idea of marketing leaders as "mixers of ingredients" is even more valid today than it was when it was introduced more than 70 years ago.

Regardless of company size, the resources available for marketing are rarely sufficient to enable marketing leaders to do everything they'd like to do. Deciding how and where to invest finite marketing resources has never been easy, but these decisions have become more complex because today's marketing leaders have more options than ever.

The challenge facing marketing leaders is to use their finite resources to implement the combination of marketing activities and programs that will produce maximum results. Therefore, the task of a marketing leader is similar to that of a professional money manager.

The job of an investment manager is to construct a portfolio of investments that will produce the highest risk-adjusted rate of return. In today's environment, as in the past, a primary job of a marketing leader is to construct a portfolio of marketing activities and programs that will maximize the return on marketing resources.

So, James Culliton's 76-year-old description of marketing executives as "mixers of ingredients" is still accurate.

[Research Round-Up] AI vs. Humans - Round 1

Source:  Shutterstock

(This year, I plan to devote some of my Research Round-Up posts to a discussion of academic research papers about artificial intelligence. Some of these scientific papers will likely focus on comparing the capabilities of AI to those of humans at performing tasks related to marketing. This month's Research Round-Up features an unpublished paper that compares the performance of AI vs. humans at generating ideas for new products.)

"Ideas are Dimes a Dozen:  Large Language Models for Idea Generation in Innovation"

• Authors - Karan Girotra, Cornell Tech and Johnson College of Business, Cornell University; Lennart Meincke, Christian Terwiesch, and Karl T. Ulrich, The Wharton School, University of Pennsylvania

• Date Written - July 10, 2023

This paper describes the results of an experiment designed to compare the performance of generative AI and humans at producing ideas for new consumer products.

The task used in the experiment was to generate ideas for a new product for the college student market that would sell at retail for less than $50. The AI application used in the experiment was OpenAI's ChatGPT-4.

The experiment used three "pools" of new product ideas.

• First pool (200 ideas) - Ideas created without AI assistance by students enrolled in a product design course at an elite university.

• Second pool (100 ideas) - Ideas generated by ChatGPT based on the same "prompt" as that given to the students.

• Third pool (100 ideas) - Ideas generated by ChatGPT based on the same prompt and a sample of highly-rated product ideas. 

All 400 product ideas were evaluated by a panel of college-age individuals in the United States. The quality of the product ideas was based on purchase intent. Panel members expressed their purchase intent by selecting one of five options - definitely would not purchase, probably would not purchase, might or might not purchase, probably would purchase, or definitely would purchase.

The Results

The average quality of the product ideas produced by ChatGPT was higher than the average quality of the human-generated ideas. The average purchase probability of a human-produced idea was 40.4%, while the average for a ChatGPT idea (without examples) was 46.8%, and the average with examples was 49.3%.

Of the 40 highest-rated ideas in the experiment, 35 (87.5%) were ideas produced by ChatGPT.

The researchers also asked members of the evaluating panel to rate the novelty of the new product ideas. In this experiment, the mean novelty value of the human-generated ideas was higher than that of the ideas generated by ChatGPT. However, the researchers noted that novelty did not appear to be significantly correlated with purchase intent.

Implications for Marketers

The Girotra et al. paper has important implications for marketers because it adds to our understanding of the capabilities of AI applications like ChatGPT.

The results of the experiment described in the paper are similar to the findings of other recent research, including an experiment conducted by Boston Consulting Group (GCG) and scholars from four elite universities. I described this study in a post I wrote last fall.

In the BCG study, participants were tasked to generate ideas for a new shoe for an underserved market. They were also required to develop a list of the steps needed to launch the product, create marketing slogans, and write a press release for the product. The researchers found that participants who used an AI tool to complete the tasks outperformed those who didn't by 40%.

The results of these studies suggest that AI tools based on large language models may be better than humans at performing "brainstorming-like" tasks where the objective is to generate a large number of diverse ideas relating to a topic.

This result should not be that surprising. Large language models are trained on a voluminous amount of data from incredibly diverse sources. The ability to generate responses based on such a vast repository of training data enables an AI tool like ChatGPT to excel at brainstorming-like tasks.

For marketers, the findings described in the Girotra et al. paper and similar findings in other studies suggest that AI tools powered by large language models can be particularly well suited to perform content ideation tasks such as generating potential topics for blog posts or producing potential social media posts.