Decoding Industry-Algorithm Asymmetry: The TEFF Framework for Contextual Congruence in FMCG and Luxury Marketing

This research examines how consumer decision-making patterns in different industries create fundamentally distinct effects when using standardized algorithmic advertising tools. While platforms such as Meta Ads and Google Performance Max claim universal efficiency, their one-size-fits-all approach often clashes with the unique temporal and emotional characteristics of specific markets. Fast-moving consumer goods (FMCG), characterized by impulsive, low-engagement purchases, benefit from algorithmic speed but may suffer brand loyalty degradation due to excessive optimization. Conversely, the luxury sector depends on high-involvement decision-making and rich storytelling, making it vulnerable to exclusivity erosion when algorithms favor broad reach over meaningful narrative engagement. To analyze this divide, we propose the Temporal-Emotion Fit Framework (TEFF), which evaluates three key aspects: Decision velocity (industry-specific purchase speed), Emotional persistence (the durability of sentiment-driven engagement), Platform-behavior synchronization (how algorithms align with industry needs). Findings indicate that FMCGs "instant-gratification" cycle matches well with real-time bidding on impulse-driven platforms. In contrast, luxurys "prolonged-engagement" nature requires curated, story-driven exposure on high-intent channels. By conceptualizing algorithms as industry trait amplifiers rather than neutral optimizers, TEFF disputes the assumption of AI impartiality. It instead advocates for ethical advertising strategies that ensure harmony between algorithmic functions and the distinct temporal-emotional dynamics of each sector.