Category: Personality

A look at a select few companies and a comparison of Opportunity mindset vs Results driven mindset of leadership teams. Jamie Dimon is a classic example of Results driven mindset. Larry Ellison is a classic example of Opportunity driven mindset. Both leaders have a leading edge of systems thinking. They seek answers to the same questions in very different ways. No one person is 100% of any one mindset but clusters and patterns do exist. We all have dozens if not hundreds of traits and states that prioritize how we solve questions. Each company hires and promotes certain mindset types different than competitors and industry. When companies get this right, they promote and hire mindsets in consistent ways while preserving diversity and complimentary thinking. 

Disciplined vs Systematic mindset of leaders in a select list of companies. A sample of 22,500+ leaders (titles include Director, President and/or Chief). No wrong or right answer. Each company needs different mindsets to operate in the environment they are in. Too much can serious impact performance. For example, too much variation in Signal to Noise ratio in hiring often drives lower PE Ratios for publicly traded companies. For example: Companies with too much Systems thinking might not settle down fast enough to productize, opening the,selves to disruptive innovation. Focusing on mindset first, allows companies to plan complimentary thinking and more diversity in multiple ways.

How do you make decisions when the data does not support what you intuitively know. Data-driven decision making is a great idea and it works really well when there’s stability. But how do you deal with things when stability doesn’t exist?

There is always a lack of perfect data. Those that can find data and minimize ego, bias and guessing are often those that climb the ladder of success.

So what are some of the things you can do to address this head on:

We saw the extreme importance of online and mobile well before the herd. So much so we built the first online platform in 1997. Today, we create and leverage our own digital research solutions.  

It is said that all decisions are emotional. We quantify emotions. Quantifying data that’s hard to get is one good place to start.

We solve consumer psychology questions for our customers. What personality behaviors, emotions, and traits are driving revenue? We can answer that. using linguistic empowered technology, You can start to create predictive data sets were no data existed before. Sometimes this data is sitting right inside of your own company it’s just hidden and needs to be unlocked. For example, customer service data has a lot of language usage to uncover. Predicting what people do connected to what they buy from you is also critically important.

Our 10 years of research have shown that traits are responsible for 80% of your company’s revenue and profits. By focusing on this group of people, the user experience goes up, bad ratings go down because you’re aligned.

Branding: Who gets you is a matter of connecting. What does connecting mean? It’s the emotional ties that we formed between people. As a brand more and more, you are a personality.

Creative: It is said that all buying decisions are emotional, then quantifying emotion can be beneficial to defining creativity, language usage, marketing, and markets.

Apps and websites: The right language, down to verb usage, can impact conversion rates in a profound way. We make that data actionable. We also help you with tonality and language using computational linguistics. Our research connects to decades of university work to the realities of revenue and profit.

Product: Certain features and functionality work well with certain personalities and emotions while others don’t. Having the research about which product features will impact how your product is used and perceived can make a product launch work. For example, a major retailer was using technical language to describe products to a non-technical audience. Simple but who was measuring to say anything different? The language was subtly different enough to fail several product launches.

Advertising and messaging performs best when there is alignment between the writer and the reader. Just like reading a good book that your friend can’t get their head around, how language works for one person may not work for the other. By understanding who will be reading your material including video, color, and design, you can better flow the right customer through advertising and messaging.

Market to unmet needs: Is it just a media issue?  Something tells me there is a better way that briefs potential customers on some ‘trust and respect’ issues. Trust and respect of your brand It’s seriously impacted when the wrong personalities meet up. It’s kind of like blood types. (https://www.businessinsider.com/harvard-psychologist-amy-cuddy-how-people-judge-you-2016-1) If you have the data, why would you attempt to acquire people at a profoundly higher cost or would you drop that audience, focusing on the best potential customers.

Modeling: An example: I recently ran a study for a major chain and polled 22 million vitamin buyers from my data set. There are so many different reasons to buy vitamins and you can see it in the results. For example, in New Jersey where there is a high density of vitamin purchases, overall people in that state see vitamins as “little batteries”. It’s the lifestyle that powers the need. In Wyoming, which has the lowest per capita vitamin consumption, all the signals came back as biological. I believe it’s because of the environment. Using massive data sets, you can discover much more accurate ways to reach an audience and determine a realistic market share and cost of acquisition.

Sampling: This could help with defining where to best get primary data. It can also be used to compare to primary data results.

Media: One of my favorite areas of work is looking at market share density maps. People who are expected to buy based on personality traits versus who is buying (if existing sales) Indicates markets to put resources against and where to avoid.

Pricing: If you have access to sales data, you can clearly see the best customer versus not so good against trait differences. In my 10 years of operating this software for myself, only a handful of traits drive the majority of sales. The differential from the best customer to worse customer is at least 10 to 1 in terms of profits. Price elasticity is not about averages. When you fine-tune who is the best customer you have more of a chance to understanding where price elasticity truly works

I get accurate assessments based on personality traits, along with a number of other data sources. There are a number of ways to acquire the data but step one is vital – get something that is informative, so you can use your exec brain to execute.

Stop averaging your work. Missing data implies make you risk averaging.

When we attract the wrong customer is when we get bad reviews. There’s no reason to attempt to align your mission and vision with someone that does not agree. You’re trying to solve a problem they have and sometimes products don’t match the personalities of the buyer.  Find your audience, execute what is important and go.

How are you feeling? Nope, not a personality question. Behaviors? Temporary. What are you like? Yes, that is a hint at personality. What did you buy last week, what did you click on….. no. What will you buy in a year? Personality driven.

Personality is the combination of traits that form an individual’s distinctive character. Some aspects of personality are inherited, but most are learned through experience, much of it early in a person’s life. Once formed, personality changes very little during later life. Personality is a major determinant of choices, preferences, and behavior. Therefore, being able to assess aspects of personality can be a powerful tool for marketing, sales, and customer relationship management.

Our approach is to help organizations develop and continually refine marketing, sales, and customer relationship management strategies using a unique and easy-to-understand personality-based predictive analytics model of buyers. This will enable everyone in the organization to work together to optimize customer acquisition, revenue growth, and retention.

It is an exciting time to have the horsepower needed to truly execute predictive and prescriptive analytics and marketing automation. How is your organization using personality, trait or some other theory formed data set to guide profit growth? Can you separate what Clayton Christensen calls ‘data of the past’ from ‘data of the future’?

Each State has a variety of personality types.

Some States have more types than others. If you look at the distribution of types of personality by State you have to wonder how this translates to your business? Do you have this same diversity by State? It turns out, likely not.

We have seen many examples where distribution of personality types by State is highly influenced by the brand’s personality and limited to the products they sell.

We have found that few brands have more than 3 or 4 ‘profitable’ personalities when you look at the means they acquire them today.

So if your best, most profitable customers are only ESTJs, how do you change your marketing… or do you?

Alphabetically, size, price, type, similarity, and even random are just some of the ways companies with large distribution centers arrange their warehouses in order to optimize time between order creation and fulfillment. Similar to how UPS revolutionized the logistics of home delivery with their “no left turn” strategy; is there an optimal way to arrange products within a warehouse to reduce the time-to-fulfillment thereby lowering overhead and increasing customer satisfaction?

There just may be, and the arrangement is by personality!

Our findings show that people buy primarily based on desire and justify the details in order to satisfy inherent feelings. Is it logic? Are we just being human? The creative types have been saying this motivation has existed for years but it has been difficult to verify. What about the camera you just bought? Yes, you are likely to add a lens cap and case to your purchase which intuitively means fulfillment centers should be arranged in such fashion. But is it possible that something that appears irrational on the surface is actually the most optimal in practice?

If products were located based on personality in fulfillment centers, much shorter distances may be traveled by warehouse staff. Even in the wishful days when machines are extracting all product, a ton of energy and time can be cut if we physically locate products based on desire, personality, and traits. In addition to cutting time, accuracy of orders will increase dramatically.

It’s time for warehousing, distribution and marketing (recommendation, merchandising, etc.) to get on the same page. Time to locate the wine next to glasses, strawberries next to chocolate and make everyone’s lives more efficient and profitable. On the surface our logic appears random but the data backs it up. Products that seem very distant should be collocated in the warehouses, on the shelves, and in marketing plans. Just like dating, timing and personality is everything.

Let’s take a look at how two well-known e-commerce sites with millions of orders arrange and operate their fulfillment center. Their actual names will be genericized for discretionary reasons. We’ll review their initial intake/stocking methodologies and fulfillment processes in order to gage whether the hypothesis that stocking initial intake of products by their correlation to Myers–Briggs Type personality can reduce time of fulfillment. As a reminder; Myers–Briggs Type Indicator (MBTI) is designed to indicate psychological preferences in personality and decision making by which humans experience the world – sensation, intuition, feeling, and thinking – and that one of these four functions is dominant for a person most of the time. The MBTI was constructed for normal populations and emphasizes the value of naturally occurring differences. “The underlying assumption of the MBTI is that we all have specific preferences in the way we construe our experiences, and these preferences underlie our interests, needs, values, and motivation.[1].

Site A Operations:

Intake and Initial Stocking Methodology: Random. As products arrive they are placed anywhere in the warehouse that has space and the location is scanned into the system.

Fulfillment Methodology: Prior to an order being filled an algorithm based on the quickest time to fulfillment generates the optimal path for individuals to take within the warehouse.

Figure A provides an elementary optical overview of Site A’s warehouse operations. In this example, items are stocked randomly. When an order came in for a two CD’s, a baseball bundle and a pair of shoes, based on where the items were randomly place upon intake, an algorithm generated the optimal path to take around the warehouse to collect the items to satisfy the order.

The benefits to Site A’s operations are that minimal time is spent during the intake and stocking process because any product can go anywhere, as long as the products location is identified while stocking. The algorithm that generates the optimal path for fulfillment is the key component to the success of this process. The questions are; does the time that Site A saves during the disregard for intake and sorting supersede the time spent on fulfillment even if the fulfillment path is optimized? Should consumers’ action and personality be a factor in these logistics?

Site B Operations:

Intake and Initial Stocking Methodology: Product based. As products arrive they are placed in designated areas in the warehouse; traditionally by Global Product Classification (GPC) or United Nations Standard Products and Services Code (UNSPSC) which classify products by grouping them into categories based on their essential properties as well as their relationships to other products.

Fulfillment Methodology: Prior to an order being filled an algorithm based on the quickest time to fulfillment generates the optimal path for individuals to take within the warehouse.

Figure B provides a simple visual overview of Site B’s warehouse operations. In this example, items are stocked by products and product similarity (i.e. CD’s with electronics, shoes with clothing, etc.). When the same order came in as in Site A’s example (two CD’s, a baseball bundle and a pair of shoes) based on where the items were place upon intake, an algorithm generated the optimal path to take around the warehouse to collect the items to satisfy the order.

The benefits to Site B’s operations is that the layout of the warehouse is logical and products are easier to find if an algorithm is not available. If an algorithm has been developed to generate the optimal path for fulfillment it is still a key component to the success of this process but not as dependent as Site A. Site B has some flexibility in the algorithms accuracy due to the logical layout of the warehouse. The questions are: did the upfront sorting and stocking by product classification save anytime when making fulfillments? And as with Site A, should consumers’ action and personality be a factor in these logistics?

The simple answer is yes and yes! We have been stocking warehouses relatively the same for the past 50 + years, it may be time to arrange by our multiple personalities.

If we understand how each of the 16 Myers–Briggs Type Indicator (MBTI) are attracted and related to each product we may be able to arrange a warehouse based on personality and likelihood to purchase as illustrated in Figure C. For instance, the ISTJ personalities are defined as responsible executors. Individuals with this type of personality are generally attracted to logical and conservative products such a simple black or blue suit, tasteful furniture, and standard cleaning supplies. So can products be grouped into personality likelihood of purchase? The answer is yes! MakeBuzz uses proprietary computational linguistics software to translate the hundreds of data points they’ve aggregated on a persons’ interests, demographics and activities into a set of traits that make up Myers-Briggs types. Their database contains the personality profiles of over 219 million people, which means they can generally match ~50 % of any given CRM filled with products and consumers actions.

So what if arranging products by personality in a warehouse and could save 1, 5, or even 10 seconds off the average time of fulfillment. What could that do to operating costs? Table A illustrates just that.

If a well know e-commerce site accepts on average 35 orders every second based on an 8 hour day and arranging their warehouse by personality reduces time to fulfillment by only 1 second, the site can save almost $80,000 per day or over $28 million in efficiencies per year. If the fulfillment time can be reduced by a mere 10 seconds the annual efficiency savings could surpass $86 million.

Without leveraging consumers’ personality traits and how they relate to potential product purchases we may missing out on an opportunity to optimize operations in large distribution facilities. If we are able to combine current organizations CRM’s with personality traits we begin to paint a more robust picture of consumers which ultimately proved better experiences and increased revenue.

This hypothesis can easily be integrated into an organizations current operations and tested prior to any warehouse reorganization. Historical data can be used to measure actual results and actual savings via a virtual warehouse configure by personality type. So, to ensure success in the future, perhaps it is time to arrange us all by our multiple personalities in order to be placed on a million dollar date.

Co-Authors:

Tom Stanek: Versatile, dynamic leader and team builder offering record sales with top level consumer and client support.

Christopher Skinner: Personality software Inventor – connecting personalization to personality.

[1] Myers, Isabel Briggs with Peter B. Myers (1995) [1980]. Gifts Differing: Understanding Personality Type. Mountain View, CA: Davies-Black Publishing. ISBN 0-89106-074-X.