Category: Psycholinguistics

Why do some companies enjoy accelerated and sustained growth in defiance of supply-chain glitches, rising inflation and political disruption? Are they just lucky?

After analyzing 30,000 businesses and millions of leaders over a 15-year period, business model expert Christopher Skinner believes he has the answer. It doesn’t involve capital, a stellar business plan or genius marketing.

“It’s because of who they hire,” he says.

In analyzing businesses and their employees, Skinner pondered how the most successful companies recruited ace teams with members akin to the “PayPal Mafia,” a dozen-plus entrepreneurs (Elon Musk, Reid Hoffman and Peter Thiel, among others) who once worked at PayPal and later launched major companies.

“It turns out that it’s not random,” says Skinner, founder and managing partner of New Orleans-based Stealth Dog Labs, which builds organizations using disruptive innovation technologies. “It takes purpose,” he says.

Analyzing Language Data to Identify Personality Types

But how does a company identify and then hire “good people?”

When Skinner started his career, he knew he needed a methodology, one that uses the psychology of language to determine individual and team mindsets. By studying vast amounts of speech and text found online, it would be possible to identify archetypal personality types and, indeed, the personalities of entire organizations.

Opportunists, for example, are known to excel at sales, and results-driven individuals can make superb leaders. By contrast, some personalities—self-saboteurs, narcissists and martyrs—can become company tripwires.

The problem he faced, however, was how to gather and then evaluate millions of words and phrases—language puzzle pieces that he could assemble into portraits of personality types.

“I built a crawler,” Skinner says.

Specifically, Skinner, a designer and builder of search-engine algorithms, built his first psychology-focused search engine in 2010. “Really, no different than what Google does,” he says. “It just collects words.”

Skinner has become one of the few people to study language density at scale, his work utilized by Google, Vodafone, Bose, Target, Oreck, United Airlines and SpiderOak cybersecurity, among others. When studying such data, he identifies words and phrases that organizations and individuals frequently repeat, removing personal factors. He then categorizes the findings into basic personality types.

For example, results-driven mindsets favor such words as “complete,” “obtain” and “secure,” as well as future-tense words. Such types quickly get to the point, are highly outcome oriented and are not distracted. “It’s the right role for leadership,” Skinner says.

Sales whiz opportunists use hedge words such as “because,” “therefore,” “if” and “then.” Skinner notes that analyzing language usage is a nuanced art, so his findings might not be obvious to others.

“I didn’t invent language psychology,” says Skinner, who has a degree in abstract mathematics along with three tech automation patents. “But over the last hundred years, psychologists have learned that if you use too many of certain words, it means something.”

Qualities of Successful Leaders

Successful leaders are usually results-driven, but they’re also system thinkers, a mindset that values relationships and varied interactions among teams and networks. Examples of system thinkers are Jamie Dimon; Steve Jobs; Whitney Wolfe Herd, co-founder and now chairman of the online dating platform Bumble; and author Reshma Saujani who founded Girls Who Code, which aims to close the tech gender gap.

“Systems thinkers can float and comprehend very ambiguous things,” Skinner says. “A person at that level combines things and sees how they relate, a process that doesn’t normally make sense to most of us. If you have enough of that, and blend it with results-oriented thinking that targets outcomes, it’s going to be very profitable for everybody. These are win-win types. They figure out how to make it all work.”

Skinner, who has ingested a wealth of language data, sometimes observes similarities between individuals. He notes that FTX cryptocurrency exchange founder Sam Bankman-Fried and biotech blood-testing entrepreneur Elizabeth Holmes, both convicted of fraud, share similar language density patterns.

The pair, and others like them (“the list is quite long,” Skinner says), use fewer emotional words and have an “increased use of cognitive processing words like ‘think,’” he says. They also tend to use the words “possibly” and “navigate.”

Skinner has used various methods of assessing personality, including the Myers-Briggs Type Indicator and the Five Factor model, commonly called the Big Five, but he prefers the work of William R. Torbert. “Torbert describes people very simply,” he says. “Once you learn Torbert’s concepts about mindset, you have a very good idea of what kind of person you’re talking to.”

Skinner advises employers to give personality tests to potential hires, adhering to laws that prevent violation of privacy.

Every Company Has a People Operating System

A company’s “people operating system” is ultimately what determines continuing success, Skinner says. He arrived at the phrase after observing the ecosystem of employees, vendors and customers that surround companies. “All these people should be working toward a common purpose, all of them more or less in sync,” he says.

Skinner offers the example of AutoZone, which sells automotive replacement parts. The company has nimbly adapted to changing markets and technologies over its 45-year history. In 1999, it made its debut on the Fortune 500 list, and in 2021, Forbes ranked it No. 39 out of 750 multinational companies and institutions on its World’s Best Employers list. AutoZone has more than 7,000 stores in the U.S. and other countries.

“I don’t doubt that the CEO and who he’s surrounded himself with are exceptional,” Skinner says. “He’s got an operating system that’s spot on.”

Linking Personality Types to Selling Luxury Real Estate

Filling the right slots with the right people is the optimal way to develop such an operating system. Sales-minded opportunists, for example, excel at making things happen. “They find a way to win,” Skinner says. “And they’re very good at emergencies. But that’s a very self-oriented position. Their purpose, and really their subconscious purpose is, frankly, themselves.” They do best when sticking with sales.

Placing personality types in the wrong position can create friction, both within the individual and the company. Opportunists, for example, might be good at selling conventional real estate, but not brokering elite deals. “They’re not going to survive long. Do they exist? Yes, they’re out there. But are they closing the volume of deals they could if they would just think a bit more customer-centric?”

In luxury real estate, the customer often has infinite choices, “and if a potential buyer doesn’t feel like they have a team player, they’re going to walk,” Skinner adds. Rather, top-tier brokers are often system thinkers who are results-driven. Empathy also helps in understanding affluent, multifaceted mindsets.

Much like the “PayPal Mafia,” “once you get that team right, then it takes off,” Skinner says. “PayPal was a force precisely because of that team. They didn’t join forces haphazardly; it was with a purpose. Before PayPal, you went to your bank. They created an industry.”

Results-Driven Leaders Drive a Company’s Success

Companies that lose too many results-driven leaders usually suffer. After the 2008 subprime mortgage crisis, JPMorgan Chase Bank struggled. “If you look at the top 1,000 leaders in the company, they lost a disproportionate number of results-driven leaders,” Skinner says. “It was just a bad time to be in the banking business, and they were paying for that.”

Around 2013, Chase, the largest bank in the United States, “started hiring results-driven leaders again and it’s paid off,” Skinner says.

Skinner considers Chase’s CEO and chairman Jamie Dimon to be “one of the highest results-driven system thinkers running a company today. I’ve analyzed his text for two decades (speeches and other communication). His mental capacity has matured. He’s as skilled as Jeff Bezos. He’s in a rare, rare club, and he no doubt surrounds himself with capable people.”

Other companies don’t bounce back like Chase did. Skinner cites Unisys, a global technology solutions company. “Unisys’ stock has declined over the past 20 years,” he says. “But what’s interesting is, almost every year they do a little worse. And they’re not alone. When you look at how they think, how they use language, it’s not results-driven. They tend to be a great group of experts who are highly disciplined. But they’re not solving customers’ problems.”

Disciplined experts often resist change, have limited flexibility and are slow decision makers, according to Skinner. Some businesses do fine with disciplined experts, he adds, because they’re not driven to innovate.

“But I don’t know many businesses that are not on pins and needles over the next 10 years,” Skinner says. “If you feel that decisions are not being made fast enough, and the company is suffering from limited flexibility and resistance to change, then bringing in results-driven systems thinkers could be an answer.”

In an age of IoT, smartphones, and dozens upon dozens of connected devices in our home, there is more data than ever for marketers to use and make informed decisions about customers, right?

In fact, I get many calls from a lot of data companies that can’t make sense (meaning, making valuable) of all the data there ingesting. The old saying goes, “water, water everywhere but not a drop to drink”.

Data-driven decision-making is not a bad idea but when the wrong data is built into that methodology or system, it’s not going to work well. The best case, it’s reactive. Knowing when customers are going to be loyal after you see a bunch of people visiting the shop or coming to our website over and over again is not a prediction. Some people might value it but they’re going to value it low. That’s not a scalable businH

The problem as I see it, people are complicated. Even though we have a lot of data about people’s actions, we’re not really looking at the root cause reasons why they make decisions. We’re looking at evidence of past decision-making and in many cases, it’s just a bunch of data that’s correlating to nothing valuable.

What’s more important than the data is how you think about data. Executives are often relied upon to make decisions and to use what’s in front of them. Most always they don’t have enough of the right information to make non-bias, good decisions that are predictive in nature. I was once told by an executive who said I wait for perfect information before I make a decision. Wow.

Two things can get you out of this mess. One, create data where there was none. Be predictive. That is, create information that is causal in nature and is a hypothesis about the future. There are ways to do it. Two, learn to be less wrong. What I mean by that is there are ways to be less wrong than to try to be right all the time. It does not sound like a great idea but how are you going to manage risk when there are more unknowns than you can ever quantify? If we think events are certain, there’s a way to manage that and plan for it but chances are there’s a lot of uncertainty in what you’re attempting to do. We think it’s uncertain times now, just realize that over 90% of all products fail – every decade for the last 30+ years. Imperfect data has always been the case and it’s not any better even if we have all of this so-called information.

The concept of being less wrong comes from Thomas Bayes who was an English mathematician and in 1763 came up with the idea of Bayesian mathematics. It’s a simple idea with profound mathematics behind it. When we accumulate more data with unproven evidence how can we predict the probability of an event if you only have partial information?

It turns out you can collect a lot of partial data points. The right ‘math glue’, you can determine where not to go. For business leaders, it’s kind of hard to imagine that 98+% of all those people out there will never be your customer. You can construct the model to prove why (its sometimes best to ignore this and market to 98% of the population) If we narrow down the area of focus, this increases the probability of success for what remains. This allows you to not necessarily proven outcome but removes the noise. Over time you will incorporate new data, further increasing the likelihood of success – Call it systematic intelligence. Now you were approaching evidence of causality because you can truly predict outcome. As less wrong keeps getting better soon becomes prediction.

Alan Turing use this thinking to develop the machines necessary to break the German codes during World War II. Also, during World War II this method was used well to predict German military production which allowed planners in the United States to allocate resources for more accurately. Fast forward to today, the same thing holds true.

Now I had artificial intelligence and you have a system that can truly move fast. Just don’t give it a bunch of junk. In a wanabee deterministic world, good decision making under duress, in a complex and forever-changing world requires a better approach.

I have previously documented my methodologies, connecting human activity data of large groups of people to a series of psycholinguistic based personality traits. 

Recently, we began experimentation with personality traits that are used to calculate a theoretical Big Five personality type Using Spearman’s and Pearson’s correlations

The results are quite different using the different correlation methodologies. It appears Spearman is a better predictor of outcome. My goal is to better predict who will buy things without any online click-based or cookie base data. The same goes for sessions or IP addresses.

Below are some of the early-stage results. Below is a summarization of people throughout the United States who have bought these types of automobiles using Pearson’s correlation. This is not predictive in nature but looking at historical auto data sales connected to human activities of buyers. The calculations do not connect to people but two groups of people, removing personal information in the process.

My next set of reports will examine more detailed data showing some last degrees of differences between cat and dog owners and how it connects to personality traits.

Psychometrics works well and can be ethically used but when fools are added to the equation, things go very bad.

Recent headlines have put many of us in a reflective move. We are collectively retracting our steps, discovering the power of data and how it can be used for us and against us.

When you write on Facebook, you should not be surprised when they use it to sell better ads to a targeted audience. But you should be surprised when they allow the machine to hand it off to others. An election is a lot bigger than advertising a clothes.

Marketers have to segment. If they did not, they would be marketing shotguns to nuns. Marketers have to segment to cut the possible audience down to a small ‘tribe’ of people. Since 1890, this has been done by the punched card system method. Almost everything sitting in your house today was bought by a company using the foundations of this system. Now, marketing needs to reflect on the mess it made.

A map of German ancestry in 1890. The American 1890 census was the first to be machine-tabulated. Because of this, the bureau was able to add new, detailed questions and observe micro-trends in the American population.

A Brief History of Machine Personalities The Character & Ephemera From a Century of Machine Thinking.

What should be in place starts with ownership and control of our data. You choosing to provide it or not. If you provide it, it should come at a cost. Maybe building an blockchain enabled technology would allow for 1:1 marketing and a fair exchange of value.

Sadly, Facebook is struggling right now. They made poor decisions that many marketers and publishers before avoided. It’s a bad situation to be in. The market and world will now decide its fate.

Modern psychometrics at scale – the United States by State.

When I decided to invent my version of psychometric analytics, I chose to live by a few rules:

1. Never use it to make people poor, hurt, or do anything evil.
2. Psychoanalytics must give better choices to business and consumers. It’s not about manipulation of one side but balance. Providing better choices helps people. It limits the bad buys, the unhappy results when you bought something that does not fit who you are. Since almost all our buying decisions are ’emotion’, why not match what fits.
3. Never market to anyone under 18 years old.
4. Be non-invasive. Never ask people to give up information. What we did is create theories of people and how those activities could bridge to personality traits. There is no need for cookies or surveys. You can create a great tribe with very little data. Having too much data, weak in value, is a bad crunch.
5. All sources of data are publicly available sources. Example: a list of people who scuba dive, own horses, like art, etc. By creating a bridge between these activities, we found a way to calculate what likely traits are associated with people. Then we re-cluster the data into meaningful tribes that help business describe products, reach the right people. The goal is to create multiples of higher value than using ‘data from the past’ methods (cookies, browser history, etc).
6. Combining theories of people with business data, you have a solid picture of the business, that helps create macro-economic business predictions. For a business to function well, it needs to have a reasonable prediction of its own future.
7. Avoid cookie data, browser history and mobile location data to be a better marketer. Invasive data has limited value if you adopt predictive systems, based on sources of truth. Too much data leads to indecision and bad decisions.

Ethical use of data can be used to help consumers choose products wisely, but when data is used to manipulate people, trust is violated and we get to where we are now.

There are several major technical areas to solve in order to quantify customer delight.  Customer delight is a well-documented, but hard to achieve KPI. It is a form of “measured happiness”. Jeff Bezos understands how important it is and has 5 teams working on it at Amazon. It’s that important.  

Most organizations prioritize deep understand of “data of the past”, a term Clayton Christensen, the well-known Harvard professor, uses when describing the shortcomings and ultimate demise of market leading organizations. By focusing on data of the past, even if you are applying predictive analytics to that data, can lead to shortcomings and misleading results. Many organizations predict the obvious because it is safe to do and creates the least amount of risk to the culture of the organization. Just ask Kmart, Sears and GoPro to start. Reinventing oneself is not new and it should not be so hard.

While Clayton is fascinated why great organizations fail, we would like to see that issue avoided. If technology can help, the better we all would be.

Data at scale is a start. Having massive data at scale on people activities means one has the building blocks of a better way. When you understand what people do in their lives and work helps form the basis of understanding why they make certain purchasing decisions.  

Having a job to be done is fine, understanding why the job is to be done is much better. People are different and combining Big Data with predictive characteristics of people based on that data drives real results back to CRMs.

Many companies can quantify human activities, demographics, income and wealth data. Many sources of data can tell us what types of occupations and cars we drive. What this lacks is what does it mean to be a scuba diver, a plumber, drive a certain type of car and on and on. By converting human activity and demographic data into predictive psychometric data, one approaches a much better understanding of who our customers are and why they buy. It leads to quantifying what delights people. When Clayton talks about “jobs to be done”, the lack of a psychometric context of the person limits the choice that person will make. By understanding people psychometrics at scale, you can see CRM patterns that are otherwise invisible.

We have discovered that what drives sales, psychometrically, can be described by a handful of variables closely associated with a person’s personality and traits. Those variables are much greater indicators of what will be purchased in the future than past sales data, cookie data and the like. Just because you went to Paris does not predict you will keep going. Today, companies like Amazon do very well “predicting” ink sales if we buy a printer. They recommend we buy lens caps if we buy a camera, cases if we buy a hard drive. While these are good reminders, they are not delightful. Lacking delight opens up the risk of price shopping and loss of brand equity of the organization, something Jeff Bezos understands well. No one can win a price war in the long run.  

The words people use can be converted into a rich understanding of their beliefs, desires, relationships and personalities. By understanding cognitive patterns and connecting to CRM data, we find the psychometrics of the CRM. Doing so creates certainty which drives speed and alignment with the organization. If you ever fretted over an analytics program, you understand how so many choices often do not lead to a clear path.

Once we understand why people buy and are delighted, we have a clear path to creating customer delight and remove complexity and uncertainty within the organization. Customer delight is broken down into 3 key elements:

1. Create customer loyalty. Instead of rebuying your customers through paid media, how can we get people back easier?
2. More profits. Easy to say, hard to execute. While many businesses are offering discounts right away, having customer delight slows down the coupon bus. Apple does not have coupons, you don’t need them either.  
3. Reviews. A customer who gets it, says nice things, online and to friends. They own it, flaunt it and you win.  

According to a Bain & Company report on Net promotor score and profits, only 9% of organizations surveyed could sustainable profits and growth for 10 years. By understanding the psychometrics of people within our CRM, we eliminate complexity, the enemy of certainly.

Our research has found that only 2 to 4 personality types are profitable for any given SKU or product line found in an entire CRM. When we look at market share of most companies and we examine the details of the Bain & Company search on sustainable profits, we see a reason why organizations are limited. They spend most of their funds focused on people they cannot delight. It creates anxiety and frustration with the organization, expending resources with no outcome. We found that a 10 to 1 difference exist between the most profitable personality vs the least. Organizations that understand why we have a job to be done are most likely to scale and outperform competitors.  

Organizations that focus with the end in mind and prioritize what is important most, can create customer delight. A 5% increase in customer retention alone can yield 25% to 100% increase in profits if they have the predictive data appended to their CRM. Companies that go beyond the CRM and create look-a-like models can locate audiences that while harder to reach, prove to be a better path to sustainable profits and long term happiness, both inside and outside the organization.

We know this is a complex topic and we left out a lot of secret sauce. Contact us if you want to solve real problems, we are delighted to share how and help you reach that better place. To learn more and see how we can help you, please contact us.  We promise a call is worth the effort.

Cutting lawns means many different things to many different people.

Electric equipment has the possibility to change how people see lawn care: the days of gas and long extension cords are coming to an end. The right company that focuses on understanding customer segments in a very different way will seize market share and own the market.

Currently, equipment is sold by facts and figures. But this is not the way people emotionally embrace change.

Psycholinguistics tells us along with the trait theory that people are buying products for very different reasons.

What is your reason for choosing this product?

• Environment
• Convenience
• Noise
• Safety versus gas and the electric wire
• Status
• Efficiency
• Less maintenance

Each reason may or may not have a big connection to the other reason. For instance, somebody interested in reducing emissions and creating more efficiency when cutting the grass might not have any connection to a person interested in reducing noise and safety concerns.

Marketing needs products based on facts and figures. This solves some conversion rates but not all. The dramatic change in conversion rates reminds me of when Walmart promoted the CFL in 2006. They dramatically shifted the market from incandescent to CFL by launching a series of campaigns they change the perception of the light bulb.

The same can be said for Apple over a variety of products, over many years. They began selling products based on an idea to “think different”. The RAM, storage, and capabilities were second to the philosophy that Apple sells to this day.

The manufacturer or the reseller is in position to do this now.

Understanding psycholinguistics changes the ad copy, but more importantly, it changes conversion.

By segmenting people based on why they decide and why they would change from traditional to battery-powered lawn care is where the manufacturer and the reseller need to go.

Psycholinguistics of gardening in the State of Iowa (just the over-indexing traits):

Gardening on a surface level may be a relaxing hobby, but when you go into the psycholinguistics of it, it says a lot about the people who garden.

Gardeners focus on personal concerns and feelings. They’re socially oriented around family, and they are perceptive. They live in the present often have high expertise and confidence. They tend to be not as concerned about money and do not think analytically or logically, thus using feelings to guide their decisions.

Over-indexing in certain personality traits has many different meanings the quick review of gardeners in Iowa indicate intellectual curiosity open to emotion sensitive to beauty, considerate trusting and trustworthy, yet open to or are vulnerable to stress. Sometimes ordinary situations can be threatening. Generally, they have positive emotions, are assertive and see stimulation from others. There’s great appreciation for art, adventure, imagination, and curiosity.

They don’t make fast decisions. They’re not controlling or focused on themselves.

By understanding psycholinguistics and personality traits, we can better understand what makes a gardener garden. By understanding the gardener at a deeper level, we better understand what marketing copy and messaging we’ll convince them to move from traditional power equipment two battery. It’s not always about saving the environment nor is it still about efficiency. It just depends on the person. By segmenting people and messaging hey brand stands a far better chance to improve conversion and market share dramatically. Dominating this market allows your brand to expand to other areas in the future and build a long road toward success for years to come.

We plan on helping your brand succeed. Please write so we can provide more information about how to approach the market and how to create market share growth. By doing these things, we create customer delight along with customer equity. We predict what comes next and we build potent experiences for both the company and your customers.

Early adopters of subscription boxes are the visionaries. They try new things for the sake of trying new products, but your biggest hurdle is crossing the chasm into the mass middle. How do you convince the pragmatists that your product is delightful and exciting? And how do you venture outside of targeting your core customer who will also be receptive to your product?

There is a high degree of disappointment which converts into poor ratings. In many cases, the ratings are not appropriate because the type of customer receiving the box is a mismatch. The overreach of marketing drives many correct customers but can lead to some big potholes. It’s hard to reach a broad audience and satisfy everyone.

Box disappointment can lead to poor ratings which impact sales tremendously. It is not a marketing or product problem, but like finding a place for a Tetris piece. It’s about matching the right customer in the perfect spot. By designing the right box for the right customer, you create high degrees of delight social sharing.

The results you find online can be terrifying to a company. There is an oversupply of subscription box companies right now, and the company who wins this battle will seize market share now in a challenging environment later to win volume. Subscription boxes are a powerful way to capture market share brand awareness and sales. For many reasons, this is why we shop in stores to be delighted and surprised at what we pass. This is a new way of thinking, but it needs work.

Below is a snippet of activities in Dallas, Texas, which is our example city. We see here that people interested in cooking are nothing like people who travel, while travel and home decorating have similar psycholinguistic traits. Our research indicates that writing specific content based on your goals drives a better conversion rate.

We know that this is a somewhat unconventional way of improving conversion, but we have found that understanding why people buy is critical and an efficient way to grow sales and profits. Contact us to see how we can help you improve knowing your customer.