Traditional business intelligence methods miss the complex interplay between review content, structure, and perceived helpfulness. Scoop’s machine learning surfaced several non-obvious patterns: for example, dissatisfaction is not only a function of negative sentiment but directly correlated with theme specificity—especially with size-related complaints (which dominated both incidence and predictive power for 1-star outcomes). Safety references, while less common, were uniformly fatal for product reputation, indicating that even isolated mentions of risk must be preemptively addressed. Positive comments clustered around vague or non-specific praise, showing that absence of complaint rather than explicit acclamation characterizes satisfied buyers. Engagement metrics—such as helpfulness voting—were almost entirely uncorrelated with rating trends, except in the special case of medium-length, image-supported reviews. This suggests low overall review system engagement, yet identifies a clear opportunity: driving more high-quality, image-rich reviews could amplify shopper confidence and sway purchasing decisions. These subtle but actionable linkages evade dashboard summarization and require advanced, contextual learnings from agentic AI—approaches that go well beyond keyword counts or simple sentiment scoring to highlight root causes and product perception levers.