1Angle
Customer Blind Spot Analysis
We reveal where your marketing misses what truly drives customer decisions, the gap between what you optimize and what consumers actually respond to.
SUPRA Methodology
The subconscious mind is the driving force of decision making.
That’s why we made it the center of our proprietary research approach.
The methodology
Deep Implicit Research is SUPRA’s proprietary framework for measuring what consumers can’t or won’t say, a modern, evidence-based form of implicit and motivational research that accesses the System 1 layer driving roughly 95% of decisions. It merges qualitative neuroscience, quantitative implicit measurement, and Causal AI across three stages: Frame, Measure, Infer.
1Stage 1
Frame
Deep Implicit Download is our proprietary method to qualitatively reveal subconscious drivers of customer decision making.
Stated-preference qualitative methods (focus groups, traditional in-depth interviews) ask consumers to articulate what they cannot access. We use neuroscience-informed projective techniques to reveal what conscious questioning can’t reach.
It helps us fill the (often vast) white spots of expert knowledge, the place where strategy meets the limits of stated preference.
Methods
Deep Implicit Download · Projective techniques · Neuroscience-informed interviewing
2Stage 2
Measure
Implicit Association Testing (IAT) is used to quantify the subconscious, brand associations, touchpoint contacts, willingness to buy and to pay.
Reaction-time-based methodology measures cognitive associations that the conscious mind can’t deliberately mask. The faster the response, the stronger the underlying association.
This is where stated preference gets replaced with measured behavior, at scale and in milliseconds.
Methods
Implicit Association Test · Single-Category IAT · Reaction-time pricing · Behavioral measurement
3Stage 3
Infer
SUPRA Causal AI finds the causal links between marketing actions, perceptions, and market outcomes, ultimately discovering causal subconscious mechanisms.
Facts can be measured. Cause-effect can not. It needs inference. Most marketing analytics confuses correlation with causation. Causal AI doesn’t. It’s the difference between describing the past and choosing the future.
The output isn’t a survey result or a regression coefficient. It’s a decision-grade map of what drives behavior in your category, and the levers you can pull.
Methods
Causal graphs · Counterfactual modeling · Instrumental variables · SUPRA Causal AI
The methodology principle
“Facts can be measured. Cause-effect cannot. It needs inference.”
Dr. Frank Buckler, Founding Partner
Frequently asked
Where we usually look first
Which one applies depends on the decision in front of you.
1Angle
We reveal where your marketing misses what truly drives customer decisions, the gap between what you optimize and what consumers actually respond to.
2Angle
An AI roadmap: where can AI improve your marketing effectiveness and efficiency, and where is it just expensive theater?
3Angle
We identify the single focus theme that will unlock maximal growth, the one decision that, if made right, changes the trajectory.