Can an LLM Make Better Google Ads Decisions Than a Human Reviewer?
Before
The company operated a portfolio of Google Shopping campaigns across apparel, accessories, and watches, generating thousands of new search terms every day.
The marketing team was responsible for reviewing these terms: deciding which queries to keep, which to exclude, and where spend could be better allocated. In practice, the volume made it difficult to keep up. Competitor brand names, wrong-category queries, adjacent product terms, and ambiguous long-tail searches were accumulating faster than any manual process could address.
There was no structured framework for evaluating term relevance at scale. Decisions depended on individual judgment, applied inconsistently across reviewers and campaigns. Terms that probably should have been excluded earlier lingered. Low-confidence terms sat in limbo because no one had bandwidth to assess them properly.
The team could see aggregate performance metrics, but had limited visibility into which search terms were actually contributing value at the individual query level. The question was whether there was a better, more systematic way to handle this.
What We Did
We designed and ran a structured assessment: could an LLM-powered classification pipeline handle search term review at a level that would be useful in a real campaign environment?
We built the pipeline against 6 days of live Shopping campaign data: approximately 50,000 search terms and 197,000 impressions.
For each search term, the system evaluated relevance against the client's actual product catalog and produced a structured, auditable output consisting of an action decision (KEEP, BLOCK, EVALUATE, or WATCH), a relevance score from 0 to 1, a category classification, plain-language reasoning explaining the decision, and suggested negative keywords where applicable.
The pipeline was not a black box. Every classification came with an explanation, written in language a marketing team could read, challenge, and act on. This was deliberate: if AI-assisted search term management is going to be useful, auditability matters as much as speed.
We also built in edge-case handling. Terms that were ambiguous or context-dependent were flagged as EVALUATE rather than auto-classified, preserving human oversight where it mattered most. Terms showing early signals but insufficient data were classified as WATCH for future reassessment.
What This Suggests
The pipeline classified all ~50,000 search terms with structured justification, flagged 178 for immediate blocking, and identified approximately 8,800 negative keyword candidates.
The core value is prioritization. In a campaign generating thousands of new terms daily, the most expensive problem isn't the obviously irrelevant query. It's the terms that are quietly burning budget while no one has time to look at them. A pipeline like this could surface those terms first, helping a team focus manual attention where the financial exposure is highest rather than reviewing terms in arbitrary order.
Beyond prioritization, the assessment suggests a team could potentially process the full volume of daily search terms without falling behind, build negative keyword strategies grounded in systematic analysis rather than spot-checks, and maintain structured visibility into the search term landscape at a level that manual review alone struggles to sustain.
The approach is technically viable: LLMs can produce structured, explainable, category-aware search term classifications at scale. The outputs are not deterministic (as with any LLM-based system, results can vary between runs), but the assessment showed that the classifications were useful, interpretable, and nuanced enough to distinguish between clear decisions and terms that still warrant human judgment.
Executive Takeaway
This was a structured, evidence-based assessment of whether LLMs could deliver useful search term decisions, tested on real campaign data at real scale.
The results were promising: the pipeline produced auditable classifications across 50,000 terms, at a pace and coverage level that manual review could not realistically sustain. It demonstrated that LLM-powered classification in paid search is technically feasible and produces outputs that are immediately interpretable by a marketing team.
Whether to operationalize this as part of an ongoing campaign workflow is a separate decision, but the assessment provides the evidence base to make that decision with confidence rather than speculation.