The Paradox of Digital Invisibility: Quantifying the Impact of Algorithmic Blindness in Corporate Data (n=150 Analysis)
Year: 2025
Type: article
Abstract: Current digital marketing metrics rely heavily on "traffic volume" as a proxy for relevance. However, the rise of Large Language Models (LLMs) requires a shift from popularity-based metrics to structure-based retrieval. This study analyzes N = 150 corporate websites across 10 heterogeneous industries to test a new hypothesis: that "Structural Hygiene" (e.g., canonical tags, schema markup) is a stronger predictor of Generative AI visibility than brand popularity. Using an OLS regression model, we achieved a predictive power of R² = 91.82%, but uncovered a critical "Statistical Collapse" (VIF > 42) due to multicollinearity between brand noise and technical signal. This paper presents the "QCSM Paradox": high-value/low-traffic sites remain invisible to LLMs due to architectural flaws, creating "AI Hallucinations" based on data ingestion failures. We propose the Quality Certification Scoring Model (QCSM) as a new engineering standard to isolate technical merit from brand noise.
Author: Jurado Peralta, Carlos Ricardo
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7117664679
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