This paper introduces Spectral Brand Theory (SBT), a computational framework that models brands as multi-dimensional signal sources perceived through observer-specific spectral profiles. Unlike traditional brand frameworks that treat brand perception as a uniform property of the brand itself, SBT formalizes the observer as an active assembler of brand meaning — each observer cohort perceives a structurally different brand from the same signal environment. The framework decomposes brand signals across eight perceptual dimensions (semiotic, narrative, ideological, experiential, social, economic, cultural, temporal), defines observer cohorts through formal spectral profiles (sensitivity, weights, tolerances), and models brand perception as probabilistic cloud formation that collapses into conviction through evidence accumulation. We validate the framework through structured analysis of five brands spanning luxury (Hermès), mass-market (IKEA), mission-driven (Patagonia), technology (Tesla), and hyperlocal niche (Erewhon). The structured analysis identifies nine candidate mechanisms, four of which are reported in depth: (1) structural absence as a brand strategy, where designed signal restriction generates value through what is not emitted; (2) a five-type coherence taxonomy, where brands with identical coherence scores exhibit fundamentally different resilience properties; (3) asymmetric conviction resilience, where evidence-free negative convictions are more stable than evidence-rich positive ones; and (4) the independence of brand power and brand health as measurable variables. The framework is computationally implementable: the entire analytical pipeline operates as a structured prompt sequence executable by large language models, compressing analysis that would require a multi-week consulting engagement into a single analytical session. Cross-model replication (Claude Opus 4.6 and Gemini 3.1 Pro across all five brands) confirms that structural conclusions are framework-driven rather than model-specific. The analyses constitute a structured demonstration of the framework’s analytical capacity; empirical validation against independently collected consumer data remains for future work. An open-source toolkit is publicly available.
Dmitry Zharnikov (Sat,) studied this question.