Author: christopherskinner4136

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Scientists identify 4 personality types

On Sept 17th, Ben Guarino published an article called ‘Scientists identify four personality types’. This is valuable research into an important subject matter

“In a report published Monday in the journal Nature Human Behavior, researchers at Northwestern University in Illinois identify four personality types: reserved, role models, average and self-centered.

Social psychologists dispute whether personality types exist. Traits are another matter. Personality traits “can be measured consistently across ages, across cultures,” said Amaral, co-director of the Northwestern Institute on Complex Systems. The five best-established traits, or Big Five, are openness, conscientiousness, extroversion, agreeableness and neuroticism.

My research indicates the 4 and 5 model personality types are too few and wide when used to quantify decision making, specifically buying things. Myers-Briggs, 16 type model is too narrow. MBTI works well but is hard to describe and execute. My software calculated the traits of millions of people, connecting a theory of traits to why people decide. I am pleased to see further evidence from this work and curious who else is exploring trait theory and decision making.

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Using Jobs to be Done theory to create better products

Jobs to be done is a great way of understanding customer intent in a very different way. It is really about not fooling people, giving people better choices that align with their needs. Less junk in the closet, less returns and more positive reviews. I like to say it is a means to achieving a balance of customer delight (KPIs) with customer equity.

JTBD is a new way to do ‘product market fit’ but I think it is so much more, given its predictive, causal relationship to outcomes and focus on ‘jobs’.

Every brand I have ever measured have customers that fit into a “trait sequence”. That sequence is driven by multiples of “trait density” compared to the US population. It is like a DNA sequence, ordered by density, from top to bottom. In the example below, I have connected traits to revenue, as each make and model has a well know price tag. Connecting predictive traits to real revenue sets the stage to connect predictive traits to predictive profits. That means the making of a Growth operating system.

What makes “trait sequence” interesting to me is reversing the engine and producing a named addressable audience (look-a-like CRM) based, primarily on traits, which for the basis of jobs to be done. The output can be used in many ways. It’s a lot of theory mixed with the real world of a CRM as a source of truth. If you can predict an outcome based on a theory of a person, something went right. If that theory results in a much better way to do business, happier customers that are delighted and have high customer equity, you got something scalable. It might be a bit like cave diving, where the only thing you can be certain of is the oxygen at the beginning and end. Matching traits to profits and using this as a basis for predicting growth works well for those who want to disrupt markets. You may have to deal with predictive models that are uncomfortable along the way.

For me, traits tell me why people hire products. It defines jobs to be done. It sets the stage to disrupt yourself and the competitors around you.

Once a brand understands its customer base, they can see the world through their eyes — both those who like the brand and the people who were ‘sold’ by past efforts. Defining why we buy means less bad marketing, better connecting to future customers and less junk we never needed. Alignment = scale.