Jason Duke, Founder, Kronaxis
Tag: Insights
Every marketing brief starts the same way. "Our target audience is women aged 25 to 34, ABC1, living in urban areas." The media planner nods. The creative team nods. The research agency builds a recruitment screener around those parameters and delivers a report that confirms, unsurprisingly, that women aged 25 to 34 in cities like certain things and dislike others.
The problem is that this segment does not exist. Not as a coherent group with shared motivations, at least. Two women in that bracket, living in the same postcode, earning the same salary, working in the same industry, will make completely different purchase decisions if their personalities diverge. One is high Impulsivity and buys on the first visit because the product looks right and the price feels acceptable. The other is high Discipline and bookmarks the page, reads three reviews, checks a comparison site, waits for a discount code, and buys eleven days later.
Demographics describe who someone is. They do not describe what someone will do. And yet, demographic segmentation remains the default framework for consumer research, media buying, and campaign planning across almost every industry.
The demographic fallacy
The assumption behind demographic segmentation is that people who share observable characteristics share behavioural patterns. Age predicts taste. Income predicts willingness to pay. Location predicts lifestyle. Gender predicts preference.
These correlations exist. They are real. They are also weak. The variance within any demographic segment is vastly larger than the variance between segments. The average 28 year old woman in Manchester and the average 28 year old woman in Bristol are statistically similar on most purchase metrics. But the spread within "28 year old women in Manchester" is enormous. Some are impulsive spenders. Some are meticulous savers. Some are brand loyal. Some switch constantly. Demographics cannot distinguish between them.
This is not a theoretical observation. It is measurable. In a Panel Studio pricing test for a subscription product, the strongest predictor of willingness to pay was not income. It was not age. It was the interaction between two DYNAMICS-8 personality dimensions: Discipline and Mercuriality.
The finding was counterintuitive enough to be worth examining in detail.
When personality beats income
High income personas with high Mercuriality (emotional volatility, mood driven decision making) were more price sensitive than low income personas with low Mercuriality. That sentence reads as backwards, and it should. Income is supposed to predict price sensitivity. More money, less price sensitive. That is the demographic assumption.
But Mercuriality modulates how people experience price. A high Mercuriality persona reacts emotionally to price signals. A subscription at £9.99 per month feels like an ongoing commitment, a recurring drain, something that will nag at them. Their emotional response to the price is disproportionate to its actual impact on their budget. They hesitate, even when they can easily afford it.
A low Mercuriality persona on a lower income evaluates the same price rationally. £9.99 per month. £120 per year. The product delivers value worth more than £120. Purchase decision made. No emotional turbulence. The price is a number, not a feeling.
Discipline compounds the effect. High Discipline personas evaluate subscriptions as long term financial commitments. They calculate the annual cost, assess it against alternatives, and factor in the probability that they will actually use the product consistently. Low Discipline personas treat subscriptions as disposable: sign up when it seems useful, cancel when they forget about it, absorb the cost of months where they did not use it.
The demographic model says: target high income consumers with the premium tier. The personality model says: target high Discipline, low Mercuriality consumers with the annual plan, regardless of income, because they will convert at higher rates and retain for longer. These are different strategies with different media plans, different creative executions, and different performance outcomes.
Why demographics persist
If demographic segmentation is this limited, why does every marketing team still use it?
Three reasons. First, demographics are easy to measure. Every survey platform, every analytics tool, every ad platform collects age, gender, location, and inferred income. The data is ubiquitous. Personality data is not.
Second, demographics are easy to target. Facebook, Google, and every programmatic platform allows audience selection by demographic attributes. You cannot buy a "high Discipline, low Mercuriality" audience segment on Google Ads. You can buy "women 25 to 34 in London." The targeting infrastructure is built around demographics because that is what the ad platforms have.
Third, demographics are easy to explain. A board presentation that says "we are targeting women 25 to 34" is immediately understood. A presentation that says "we are targeting the high Discipline, low Impulsivity cluster that indexes 2.3x on annual subscription conversion" requires a fifteen minute explanation of personality frameworks before anyone knows what you mean.
These are real constraints. They explain why demographic segmentation persists despite its limitations. But they are constraints of convenience, not of effectiveness. The question is whether you want segments that are easy to define or segments that actually predict behaviour.
What psychographic segmentation adds
Personality does not replace demographics. It explains the variance that demographics leave on the table.
Consider a demographic segment: men aged 35 to 44, household income above £60,000, living in the South East. That segment has a measurable average response to any stimulus you test. But the spread around that average is wide. Some members of the segment convert at three times the average rate. Some never convert at all. Demographics cannot tell you which is which.
Add DYNAMICS-8 profiling and the segment splits into behaviourally distinct clusters. The high Impulsivity sub-segment converts on the first exposure to scarcity messaging ("only 3 left in stock"). The high Discipline sub-segment converts after receiving a structured comparison email that positions the product against two alternatives. The high Sociability sub-segment converts after seeing a testimonial from someone they perceive as similar to themselves.
Same demographic segment. Three different conversion paths. Three different creative strategies. The demographic model would show you the blended average and leave you guessing. The personality model shows you the components.
256 possible segments, 4 to 8 that matter
DYNAMICS-8 has eight dimensions, each continuous. If you simplify to high and low on each dimension, that produces 256 possible combinations. In practice, you do not need 256 segments. When you cluster a population panel by DYNAMICS-8 profile, four to eight meaningful clusters typically emerge. These are groups of personas whose personality profiles are similar enough that they respond to stimuli in recognisably similar ways.
These clusters are not arbitrary. They emerge from the data. And they are stable across demographic groups. The "cautious evaluator" cluster (high Discipline, high Acuity, low Impulsivity) shows up in every age band, every income bracket, every region. Its members behave similarly regardless of demographics, because the personality dimensions driving their decisions are the same.
This is the practical shift. Instead of reporting "women 25 to 34 preferred Option B by 52% to 48%," you report "the cautious evaluator cluster preferred Option B by 73% to 27%, driven by its structured feature comparison, while the impulse responder cluster preferred Option A by 68% to 32%, driven by its emotional headline."
The first report tells you what to launch. The second tells you how to sell it.
The practical step
Run your next pricing test, messaging test, or campaign concept through a personality segmented panel. Compare the results you get from demographic cuts alone against the results you get when you add DYNAMICS-8 segmentation. The difference in explanatory power is not subtle.
The average consumer is a statistical fiction. No individual sits at the mean of every dimension. The more precisely you can describe the personality segments that actually drive your category, the more precisely you can target, message, and convert them. Demographics got the industry this far. Personality is what gets it to the next level.