In 2005, I was working with data from an online casino. The business had been running a promotion offering free tokens to new users, a fairly standard acquisition tactic designed to attract players, get them into the platform, and convert them into paying customers.
The promotion was working, in the sense that it was attracting users. What it was not doing was converting them.
I had been applying an early version of what would become the Resonance Engine, classifying users based on their psychological relationship with the brand rather than their behavioural profile. What I found was striking. The users who were taking the free tokens and never making a real deposit were clustering consistently in the Partial Resonator category, people who had a surface-level attraction to the brand but no genuine psychological commitment to it. The Core Resonators, the users with real brand connection, were showing genuine engagement and deposit behaviour regardless of the promotion.
The promotion was not attracting the wrong people by accident. It was structurally incapable of attracting the right ones, because it was optimised for surface behaviour rather than psychological commitment. No amount of promotional spend would convert a Partial Resonator into a Core customer, because the barrier was not awareness or trial. It was the depth of brand relationship.
That finding became the first validation of a framework I have spent the subsequent twenty years developing. This is the story of where the Resonance Engine came from, how it was validated, and why the distinction between genuine brand connection and promotional noise matters more now than it ever has.
The Intellectual Lineage
I need to go back to the beginning of my career to explain this properly.
In 2004 I joined Research Surveys, one of South Africa's most respected research firms, founded by Butch Rice and Henry Barenblatt. In my first years there, I received direct training from both Butch and Jan Hofmeyr on the Conversion Model, a framework that had been developed in the 1980s and was, at the time, genuinely unlike anything else in brand measurement.
Jan Hofmeyr's insight was rooted in an unlikely source: the academic study of religious conversion. His research explored how and why people shift their fundamental commitments, whether religious, political, or commercial, and found that those shifts do not happen gradually. They happen suddenly, when a psychological threshold is crossed. The mathematics he used to model this came from catastrophe theory, a branch of mathematics that describes systems that change discontinuously rather than smoothly.
Applied to brands, the implication was profound. Brand switching is not a gradual drift. Consumers do not slowly become less loyal and eventually defect. They hold their position, sometimes for a long time, and then shift suddenly when their psychological threshold is crossed. The Conversion Model captured where each consumer sat in relation to that threshold, classifying them into four states: Entrenched, Convertible, Ambivalent, and Available.
This was not a behavioural segmentation. It was a psychological one. And it predicted brand switching behaviour with a precision that purely behavioural models could not match, because it was measuring the underlying state that would determine future behaviour rather than the historical behaviour that had already occurred.
I later worked with Butch again at Pondering Panda. Those ideas, seeded at the very start of my career, never left me. The Resonance Engine is a different model, developed independently and extending the framework in significant ways. But the foundational conviction, that brand relationships are psychological phenomena not statistical ones, and that measuring psychological state predicts commercial behaviour more reliably than measuring past behaviour, comes directly from that lineage.
What the Resonance Engine Measures
The Resonance Engine measures brand equity through four dimensions: Signal, Fit, Affinity, and Gravity.
Signal measures the cognitive strength of brand connection. How clearly and consistently does the brand occupy a defined space in the consumer's mind? A brand with strong Signal is one that consumers can describe precisely and confidently, one that has a clear identity that is not confused with competitors.
Fit measures the perceived alignment between the brand's identity and the consumer's self-concept. This is not about whether consumers like the brand. It is about whether they see it as a brand for someone like them. Fit is the dimension that explains why technically superior brands sometimes lose to emotionally resonant ones.
Affinity measures the warmth of the emotional relationship between the consumer and the brand. This goes beyond awareness and beyond preference to capture something closer to the quality of feeling the brand evokes. Brands with high Affinity are ones that consumers would genuinely miss.
Gravity operates at the category level rather than the individual brand level. It measures how much brand matters as a purchase driver within the category itself. In some categories, like paper napkins or commodity household goods, brand has low Gravity. Consumers buy what is available and brand is rarely a primary consideration. In others, like soft drinks or premium spirits, brand has high Gravity and is central to the purchase decision. Understanding category Gravity is the strategic precondition for brand equity investment. Before asking how strong your brand is, the Resonance Engine asks whether brand strength even matters in this category, and to what degree.
The combination of these four dimensions produces a brand equity picture that is simultaneously richer than standard awareness and preference measures and more directly connected to commercial decision-making. The Resonance Engine does not ask "do you know this brand" or "do you prefer this brand." It asks about the nature, depth, and force of the relationship between the consumer and the brand, and whether the category creates the conditions for brand relationships to matter at all.
First Validation: Online Gaming
The casino finding I described at the opening of this piece was not a designed experiment. It was a discovery made by applying the framework to real commercial data and finding that the classifications predicted behaviour that the business had not expected them to predict.
Partial Resonators took the free tokens. They explored the platform superficially. They did not make deposits. Core Resonators showed genuine engagement regardless of promotional incentives. The classification, derived entirely from survey-based psychological measures, mapped almost perfectly onto actual deposit behaviour.
This was important for two reasons. First, it suggested that the framework was measuring something real, something that had predictive validity beyond the survey context in which it was collected. Second, it suggested a commercially actionable insight: the business should stop optimising its acquisition strategy for surface behaviour and start measuring psychological commitment as a leading indicator of customer value.
Second Validation: Grocery Retail
Several years later, I had the opportunity to validate the same framework in a completely different context.
Working in a role that gave me access to a major grocery retailer's loyalty programme transaction data, I was able to test whether consumers classified as Core Resonators through survey-based measures showed different actual purchase behaviour from those classified as Partial Resonators.
They did. Measurably and consistently.
Core Resonators showed higher total monthly grocery spend and more frequent store visits than Partial Resonators, controlling for demographics and household size. The survey-based psychological classification predicted real commercial behaviour in a grocery context with the same pattern it had shown in an online gaming context a decade earlier.
The data belongs to the retailer and I cannot disclose the specifics. But the methodology, the questions, the classification framework, the analytical approach, is entirely mine. And what matters for the validity of the Resonance Engine is not the specific numbers from any single study. It is the cross-category consistency of the pattern.
Online gambling in 2005 and grocery retail in a later role are about as different as two consumer contexts can be. Finding the same fundamental relationship between psychological brand commitment and real commercial behaviour in both contexts is what gives the framework its credibility. Most brand measurement frameworks are validated within a single category, if they are validated behaviourally at all. The cross-category consistency of the Resonance Engine validation is what separates it from methodologies that are theoretically interesting but commercially untested.
Identifying High-Value Brand Investment Targets
One of the most strategically useful outputs of the Resonance Engine is the identification of consumers who are psychologically proximate to genuine brand commitment: those who sit close enough to the threshold between Partial and Core Resonator that investment in the right brand dimensions could shift them.
Most brand investment is poorly targeted because it treats all non-loyal consumers as equivalent. Some of those consumers are deeply committed to a competitor and will not move regardless of what you do. Others are psychologically available and would respond to the right brand signal. Treating them the same way wastes significant marketing resource.
The Resonance Engine identifies where each consumer sits in relation to that threshold. The consumers closest to it are not always the biggest segment, but they are typically the highest-value target for brand investment, because they are the ones most likely to shift from surface attraction to genuine loyalty in response to the right brand action. Knowing who they are, and which equity dimensions would move them, is the difference between targeted brand investment and undifferentiated brand spend.
From Insight to Decision
Market research has traditionally produced insight. Someone else produces the decision.
The industry is now moving toward a different model, one where the output of research is not a report to be interpreted but a decision-ready input that maps directly onto the choices a business needs to make. This shift from insights to decisions is becoming one of the defining conversations in our field, and it is one the Resonance Engine is well positioned for.
Most brand tracking produces outputs that require significant interpretive work before they connect to decisions. Awareness is up three points. What do you do? Net Promoter Score has declined in one segment. Which investment would address it? The gap between what the tracking shows and what the business should do is wide, and bridging it requires analytical work that many brand teams do not have the resource or capability to perform consistently.
The Resonance Engine is structured differently. Because it measures the psychological mechanisms that produce commercial outcomes rather than the outcomes themselves, its outputs map directly onto decision variables. If Signal is weak, the investment case is for clarity of brand identity. If Fit is low among a high-value segment, the investment case is for relevance and self-concept alignment. If category Gravity is low, the entire strategic question shifts from "how do we build the brand" to "where should we compete on dimensions other than brand."
These are not insights that require further interpretation before they become decisions. They are decision inputs, structured around the variables a brand team can actually control and the levers most likely to produce commercial return. That is what decision-ready brand intelligence looks like in practice.
Resonance Radar: Scaling to the Open Web
The Resonance Engine measures brand equity through consumer research. But the same analytical lens can be applied to a different data source: the open web.
Resonance Radar is the web-scale extension of the Resonance Engine. Rather than asking consumers how they relate to a brand, it analyses how brands are positioned across web content: the articles, analyses, commentaries, and narratives that form the ambient information environment around a brand.
Where the Resonance Engine measures brand equity as consumers experience it, Resonance Radar measures it as the web constructs it. The two perspectives are complementary. A brand can have strong consumer equity but weak web positioning, which creates a vulnerability as consumers increasingly form first impressions from digital content rather than direct experience. Or a brand can have strong web positioning but weak consumer equity, which suggests that the narrative around the brand is not translating into genuine psychological connection.
Together, the Resonance Engine and Resonance Radar provide a complete picture of brand health: what consumers think and feel, and how the broader information environment is shaping those thoughts and feelings.
Why This Matters Now
Brand measurement has been dominated for decades by awareness and preference tracking. These are not useless measures. But they are measures of outputs rather than inputs. They tell you where your brand is. They do not tell you why it is there or what would change it.
The market research industry is now facing a version of the same challenge described in the previous post about the analytical bottleneck. Dashboards full of brand tracking data give you numbers but not understanding. Awareness is up. Preference is flat. Consideration has declined slightly among the 25 to 34 demographic. What does any of that mean? What should you do about it?
The Resonance Engine answers those questions by measuring the psychological mechanisms that produce awareness, preference, and consideration outcomes. It tells you not where your brand sits but why it sits there, which of the four equity dimensions is driving or constraining your brand's performance, and where investment would produce the most meaningful shift.
That is the difference between brand measurement and brand intelligence.