Inventor of predictive personality trait software
Personality traits x size of the house matters.
Yes, there are big differences in dominate personality traits that show up.
What drives what?
Read more about our research into the personality traits of large and small house owners:
Personality Traits of Large House Owners
The psycholinguistic trait difference between small house owners and large house owners
They tend to be emotional, stressed and yet find ways to be positive. That is what the data suggest.
I looked at home purchases over five years in the US and appended a calculated personality trait to home ownership by house size. I find this to be challenging research, connecting house size to personality traits. Houses seem different than buying a product to get a job done yet home is a significant part of our daily life. In the US, we tend to buy what we can afford, stressing ourselves to the limits of reason.
Anyone who can afford a 4,000 sqft home has money to spare. It also means they likely have a spare room or two for things to do or keep a distance from who knows what. Their traits indicate the need to solve jobs to be done with added household space. 4,000 sqft is significant, even when house prices are relatively low.
Looking broadly at American homeownership in the 4,000 sqft range, we see a high biological process, related to food. Food becomes a means to solve things beyond hunger.
They are humane and helpful as opposed to suspicious and adversarial towards others.
They have a sense for biological process, understanding others behaviors, they sense well.
They have a high level of sensitivity to physical (via sound, sight, touch, or smell) and emotional stimuli, easily overwhelmed by too much information. Perhaps, larger spaces in the house give the flexibility to adjust.
They listen to upbeat songs and watch documentaries about celebrities. They are for the most part mindful of their peers, helpful to others, especially in need. They are warm, neighborly, and thoughtful. They have a hopeful perspective of human instinct and coexist well with others.
When looking at small house size (1,000 sqft) and very large, (10,000 sqft), we see dramatic differences in traits. I welcome anyone interested in traits and housing to take a look at the file and insights we uncovered. The trait research is University-based work.
Ken Favaro, David Meer, Samrat Sharma from HBR, May 2012 issue.
“Organic growth is not the inevitable result of a successful business model. All companies can become more skilled at growing organically with the business models they already have. But that requires active, engaged corporate leadership.”
That is a start. leadership allocates effort and capital against things needed to grow. Uninformed capitol allocates poorly. Capitol that has a technology core to help it find and allocate toward “where the puck will be” is smart leadership and money.
“Organic growth is not the inevitable result of a successful business model. All companies can become more skilled at growing organically with the business models they already have. But that requires active, engaged corporate leadership. The CEO and other senior executives don’t need to impose a lot of new processes or exercise a heavy hand.”
More skilled?…… If that includes a technology core to help inform decisions, eventually those decisions are more robust, sure.
“They just need to help the operating units keep an eye on the big picture, lead the fight against the business cycle, resist typecasting, and establish a common, rigorous language for organic growth. Respect those rules, and you will transform your company’s internal growth engine.”
In my opinion, organizations need technology that understands customers at a very deep level, independent of sales, marketing, and media. Especially data models of the past, building a true total addressable market based on ‘why people buy’ sets all the rest. It defines leadership choices, budget allocation based on a deep understanding of people.
When people are aligned, we have speed — and growth.
When people Inside of an organization understand the deep meaning of why people select them to fulfill a job to be done, they get the big picture. Things get concrete fast, it’s not consulting anymore. You’re removing the bias of why people make decisions, thus transforming the organization into a true growth engine based upon elements of a growth operating system.
Clayton Christianson teaches us that disruption needs a technology core. Having a technology that understands people that can be used across multiple departments as a source of truth is what is needed to fulfill the vision of a growth machine that David Meers and team describe.
In 1964, Daniel Yankelovich introduced in the pages of HBR the concept of nondemographic segmentation — thank God. By 2000, the idea of tracking clicks and cookies further distracted from the understanding of why people buy. Clayton Christensen introduced and expanded the concept of Job Theory, Jobs to be Done recently.
Daniel was a master and enlightened many.
It’s time for business to embrace an understanding of people and what is in their best interest, not what works for media or data. People don’t need things they don’t want. Bad products that don’t solve jobs to be done well are a waste of time and effort for everyone.
“The predictive power of marketing studies based on demographics was no longer strong enough to serve as a basis for marketing strategy, he argued. Buying patterns had become far better guides to consumers’ future purchases.”
Nothing has changed. It’s time to fundamentally change how we understand ‘why people buy’.
“But more than 40 years later, nondemographic segmentation has become just as unenlightening as demographic segmentation had been.Today, the technique is used almost exclusively to fulfill the needs of advertising, which it serves mainly by populating commercials with characters that viewers can identify with. It is true that psychographic types like “High-Tech Harry” and “Joe Six-Pack” may capture some truth about real people’s lifestyles, attitudes, self-image, and aspirations. But they are no better than demographics at predicting purchase behavior.”
“Now, Daniel Yankelovich returns to these pages, with consultant David Meer, to argue the case for a broad view of nondemographic segmentation. They describe the elements of a smart segmentation strategy, explaining how segmentations meant to strengthen brand identity differ from those capable of telling a company which markets it should enter and what goods to make. And they introduce their “gravity of decision spectrum,” a tool that focuses on the form of consumer behavior that should be of the greatest interest to marketers–the importance that consumers place on a product or product category.”
I trust this is an improvement. My bias is software must be a technology core for Jobs to Be Done to be effective and pervasive.
From: Branding in the Digital Age: You’re Spending Your Money in All the Wrong Places by David C. Edelman
When making purchasing decisions, consumers go on a “consumer decision journey” comprised of four stages: consider a selection of brands; evaluate by seeking input from peers, reviewers, and others; buy; and enjoy, advocate, bond. This journey replaces the famous funnel metaphor.
David’s excellent work helped me find my footing. My research indicates the customer decision journey varies, based on personality traits. Some personalities will have a much quicker cycle when aligned with the product/marketing message fit. Conversion rates of 10x to 20x vs. the least performing traits have been observed and duplicated many times.
Some personality traits will not advocate at all. It’s not in them to do so.
Evaluation stage can vary significantly based upon traits such as risk avoidance which can slow down decisions.
My research indicates people are very different and marketers make two fundamental decisions when reaching out to mass audiences.
Both of the above activities set up noise in the system that can be challenging.
FORD ownership is a club. Late models in one midwest State indicate consistent traits with some variation between models. What does this mean? If you want to sell more of these, get to know the Ford customer traits.
In summary, Ford owners are active, enthusiastic, always on the move. Ready for challenges and accept responsibility. They like to be in groups. They have a sense of orientation, they can connect relations between objects and people. There is a tendency to show more honesty, personal and disclosing behavior. They are authentic, emotional, open-minded, ready for a new experience.
You can see broad differences in ownership of a Ford Focus vs Mustang. Focus owners are more interested in health. University research, in summary, suggests a Focus owner is more sensitive to worries, competitive and ambitious, with a tendency to ignore symptoms of stress.
Mustang owners have a keen sense of time, place and motion — I would hope so.
Summary of all models:
FORD EXPLORER IOWA
FORD FOCUS IOWA
FORD MUSTANG
Personality Analytics is not just prediction and measurement.
Ever notice, when you meet somebody, within a few minutes, you can start to figure out who they are, what makes them tick? If you spend enough time with them, you become well versed at what they want to do. Is this just good guessing or are you forming theories about this person?
That’s the idea behind using personality analytics for creative and designing based on a 1:1, total addressable market.
So how does personality analytics drive the business?
First and foremost, making better recommendations. Every time I look at my apps that recommend things, I’m astounded that they want me to buy stuff I already own (in 2018!) or give a watered-down and safe suggestion list. It’s not intriguing. Sure, it does work, contributes billions to sales. It just could be so much more. Jeff Bezos says customer delight is a big deal. Why? He gets the bigger picture of understanding people is a pathway to success and helps avoid the pressures of disruption.
Second, why do we make people buy things so many times before we start treating them like the loyal customers they will be? The ability to predict who is loyal can be predicted, in large part based on personality traits. It’s not about tricking people and getting them to buy more things they don’t need; it’s about solving their ‘jobs to be done’.
Third, adapting your sales strategy. Ever been to a store and noticed that the rep always works the conversation, continually angling to form a connection? That’s a great sales rep. It’s a dying art, but it does work. Why can’t websites and apps do a better job than they do today understanding people through machine learning?
It’s possible with today’s technology to adapt the message before a person even buys. CMS and CRM give us this power to delight, not fright.
Forth, Create a look-a-like CRM based on personality traits. You know what works by looking at your CRM but why are so many visitors not buyers? Why can’t you buy a media list of buyers, not visitors? It is now possible to understand the customer at a deep level and find people on a 1:1 basis. It requires a mindset change with leadership and the organization must embrace change. A powerful example is Credit Karma. They changed the way banks acquire customers. Starting as a direct response marketing company, they are worth $4 billion (as of Q32018), based on deep understanding and excellent media execution. They are one of growing number of disruptors challenging and changing how we build organizations.
By matching what people need in life, you better fulfill their needs as they look for products to solve a job to be done.
Matching the needs of a person to their personality traits, you are;
It’s not about solving people’s needs, not selling them more junk. Figure that out and make lots of money.
In real life, you take into account three sets of data. You know certain routes will not be a good choice. Traffic will build up by the time you get to that location yet maps says its clear. How do you know? If it rains, you adjust. There is no data except a theory that some certain routes will not work for you. It is all memorization of past events or do you have a feeling about certain things? Maybe those feelings are theories.
In business, you are more likely to use data of the past. Theories sound like business school professors who never had a real job, right?
IMO, Theories are statements of causality. Something might happen. You’re reducing the probability of something happening while increasing the likelihood of success. If you have company series with data of the past, You are well on your way to having the makings of an operating system. If you’re trying to drive that three hours today you need an operating system to get there. Many things take place along that ride and in my opinion, it’s three sets of data along with the operating system that can determine which sets of data to use when.
But why do so many businesses Downplay theory? I believe theories are a very different set of math and logic, not taught in US schools. — not well. You just can’t mix the two easily. Excel does not have those functions. Imagine mixing poetry with accounting. It’s had to do.
Regardless, exceptional businesses add predictive theories to their models and operations. Its smart, it works and its time for more to do it.
I examined 4 million home purchases in the United StatesAnd look at a calculated psycholinguistic personality traits score based on square footage of the house.
Here are the preliminary results. In a later post, I will examine what high indexing personality traits mean based on square footage.
For houses’ owners less 1000 ft
1. Affect Words // Negative emotion // Anger
2. Relativity // Motion // motion
3. Relativity // Space // space
4. Personal Concerns // Death
5. Biological Processes // Health/illness
For houses’ owners over 4000 ft
1. Biological Processes // Body
2. Biological Processes // bio
3. Biological Processes // Health/illness
4. Social Words // Family
5. Biological Processes // Sexuality
For houses’ owners over 10,000 ft
1. Personal Concerns // Money
2. Relativity // Time
3. Cognitive processes // Cause
4. Perpetual Processes // Hearing
5. Summary Variable // Analytical Thinking
Each trait has well-defined definitions of the traits associated with each. As you can see above, many are obvious, while others might be surprising.
I have many questions myself.