The Marketing Abstraction Crisis: A Five-Part Series

Part 2: The Brand Value Paradox - Why Measurement Destroys What Matters Most

(Part One is here.)

The most devastating consequence of data abstraction in marketing isn't just that we've lost touch with customers - it's that we've made the most valuable marketing activities impossible to justify.

I've watched this race to the bottom unfold across multiple industries over 20+ years of advisory work. Companies abandon brand investment for performance marketing, celebrate improved short-term metrics, then wonder why their business fundamentally weakens over time.

The culprit isn't digital marketing itself, but our failure to distinguish between data as representation versus data as abstraction.

The Representation vs. Abstraction Divide

Here's the crucial distinction that marketing has lost: data can serve as representation - maintaining meaningful connection to the reality it describes - or it can become pure abstraction, disconnected from any underlying truth about customer experience.

When a focus group participant explains their frustration with your product, their words represent actual experience. When that same frustration gets processed into a sentiment score and appears on a dashboard as "customer satisfaction trending down 0.3%”, it has become pure abstraction - mathematically derived from reality but no longer representing it meaningfully.

We've systematically replaced representational data with abstracted data, then optimised our entire function around these abstractions. We measure engagement rates instead of genuine engagement. We track customer lifetime value calculations instead of understanding what customers actually value.

The False Choice

The marketing world has accepted a false dichotomy: brand marketing versus performance marketing, long-term versus short-term, creativity versus data. But this division only exists because we've confused data abstraction with data representation.

Performance marketing appears measurable because it deals in immediate digital abstractions - clicks, impressions, conversion rates. These feel scientific because they produce numbers quickly, but they've lost connection to customer reality.

Brand building seems unmeasurable because we've forgotten how to collect representational data about brand impact. We can't track brand value in real-time dashboards, so we assume it can't be measured at all. But this confuses measurement difficulty with measurement impossibility.

The real choice is between optimising for data abstractions that feel measurable but don't represent reality, versus developing representational data that maintains connection to actual customer experience and long-term business value.

The Abstraction Trap in Action

Attribution modelling perfectly illustrates our confusion of abstraction for representation. These systems promise to trace customer journeys scientifically, generating precise percentages and dollar values that seem to represent marketing impact.

But attribution models are pure abstractions. They reduce the messy, context-rich reality of human decision-making - conversations with friends, offline experiences, emotional associations - to mathematical formulas about trackable digital interactions.

When marketers optimise against these models, they're optimising for mathematical abstractions that exist only in their measurement systems. The precision is false, the causation is assumed, but the numbers look scientific.

True representational data about customer decision-making would preserve context, acknowledge complexity, and maintain connection to actual human experience. It would be messier and less precise, but it would represent something real.

The Innovation Consequence

This confusion has devastated marketing's role in driving innovation. Data abstractions reveal patterns in mathematical models rather than problems in the world. They optimise current offerings for current customers through current channels, but can't reveal breakthrough opportunities because they've abstracted away from customer reality.

Representational data - information that maintains connection to actual customer experience - can guide innovation because it preserves the context, friction, and unmet needs that create innovation opportunities.

The Long-Term Destruction

Companies trapped in abstraction optimisation systematically underinvest in activities that create lasting competitive advantage. Meanwhile, performance marketing provides immediate abstracted metrics that feel like proof of impact.

Deceptively, this shift often looks good in the short-term. Performance marketing extracts value from existing brand equity - the customer goodwill and positive expectations that previous marketing investment has created. Companies celebrate improved abstract transactional metrics while devouring the relational foundation their growth depends on.

The Competitive Blindness

Abstraction optimisation creates competitive blindness. When all companies optimise for the same abstract metrics, they converge toward the same solutions, creating strategic herding behaviour.

Real competitive advantage comes from representational understanding - serving customer needs that competitors can't see because those needs exist in the unmeasurable reality between data points.

In our third article, we'll explore the profound paradox that Agentic AI can help us to break free from these abstraction traps - using artificial intelligence to cut through abstractions back to representational understanding of customer reality.