Algorithmic Homogenization: Evidence That Platform Optimization Compresses Brand Differentiation

Marketing platforms promise differentiation through ever-finer optimization, yet practitioners increasingly report that brands are becoming harder to distinguish. Using large-scale longitudinal data on advertising creatives, media allocation, and performance across multiple platforms, this empirics-first study documents a counterintuitive regularity: as algorithmic optimization intensifies, between-brand variance in creative features, strategic choices, and outcomes compresses, even while experimentation activity increases. The convergence is platform-specific and accelerates following shifts toward automated bidding and creative optimization. The pattern is not explained by imitation or budget concentration. Instead, the evidence indicates selection pressure from platform reward surfaces that consistently favor a narrowing set of features, penalizing deviation. Conceptually, these results suggest that algorithmic optimization functions as a market-level selection mechanism, shaping not only individual firm performance but the distribution of strategies observed across firms. By documenting this phenomenon without presupposing theory, the study contributes empirically grounded insights to research on platform governance, algorithmic control, and competitive differentiation. The findings reveal an unintended consequence of algorithmic marketing systems: they scale efficiency while structurally constraining differentiation. The results have direct implications for brand strategy, creative governance, and performance measurement, and motivate new directions for research on platform-mediated markets.