To use google ads negative keywords with claude, export your search terms, ask Claude to classify irrelevant queries and recommend a negative match type, then verify each suggestion against campaign intent before adding it. This workflow turns a search-terms report into a reviewable exclusion list while keeping the final decision—and any account changes—with your team.
Choose the right search-term data
Start with queries that could change a decision
In Google Ads, open the search terms report and select a date range that reflects your conversion cycle. The report shows searches that triggered ads, helping you find evidence of mismatched intent rather than guessing from keyword wording; see Google’s search terms report documentation.
Export the fields needed to judge relevance: search term, campaign, ad group, match type, clicks, cost, and conversions. Add conversion value or lead-quality information if your team uses it to distinguish valuable from low-quality leads. Do not send customer details or other unnecessary personal information to an AI tool.
- Include the campaign or ad group so a suggestion can be scoped correctly.
- Keep the date range and attribution context with the export.
- Remove rows that contain sensitive information not needed for classification.
A query can be irrelevant in one campaign and useful in another. Preserve enough context for Claude to tell whether the account is selling a product, providing a service, recruiting, or targeting a particular audience.
Set rules before asking Claude to classify
Define what counts as irrelevant
Give Claude a short account brief: what the business sells, who it serves, which locations it targets, and which types of searches should remain eligible. Then define your decision rules. For example, “Exclude job-seeking queries from customer-acquisition campaigns, but do not exclude informational searches unless the query clearly asks for a free alternative.”
Ask for candidate recommendations, not automatic changes. Claude can help interpret rows, but it does not know your margin, sales qualification criteria, or campaign strategy unless you supply that context. Anthropic’s prompt engineering guidance recommends making instructions and desired output clear; apply that by asking for evidence and a structured response.
Review these Google Ads search terms using the account brief below. For each term, label it relevant, irrelevant, or uncertain. Recommend a negative keyword only when the query conflicts with the stated offer or targeting. Suggest campaign or ad-group scope and a negative match type. Explain the reason using the query and brief. Do not infer missing facts. Return: search term, label, reason, suggested negative, match type, scope, and confidence. Flag uncertain cases for human review.
Use “uncertain” as a valid result. If the model is forced to make a yes-or-no call, ambiguous terms are more likely to become costly exclusions.
Review intent and match type, not just the word
Check what the exclusion would actually block
Read each query as a whole. A term containing “cheap,” “free,” or “jobs” is not automatically irrelevant: it may reflect a comparison shopper, a legitimate offer, or a distinct campaign objective. Confirm that the recommendation follows your brief, not a broad assumption about the word.
Google Ads negative keywords support broad, phrase, and exact match types, and their matching behavior differs from positive keywords. A negative broad match can block a search containing all the negative terms; phrase match requires the terms in the same order; exact match targets the query without additional words. Check Google’s negative keyword documentation before applying a broad exclusion. The wider the match, the more carefully you should check for useful queries it could suppress.
Google also notes that negative keywords do not cover close variants in the same way as positive keywords, so spelling variations may need separate review. Don’t respond by adding every imaginable variant: first check whether those searches occur and whether they are genuinely unwanted.
Turn recommendations into a decision list
Use scope and evidence to control risk
Keep Claude’s proposed action separate from the action you approve. Use this table as a review artifact; the thresholds are illustrative starting policies, not universal benchmarks.
| Evidence in the report | Review decision | Possible action |
|---|---|---|
| Query clearly asks for something the business does not offer | Confirm the intent conflict and check campaign context | Consider a tightly matched negative at the narrowest useful scope |
| Query has spend above one target CPA and no conversions | Illustrative starting policy: inspect the query, conversion lag, and lead quality before acting | Exclude only if the intent is also a poor fit; otherwise gather more evidence |
| Query is ambiguous or has too little data | Keep it in a review queue | Do not exclude solely because it has no recorded conversion |
| Several clearly irrelevant queries share a theme | Check whether the theme applies across campaigns | Choose campaign scope or a shared list only when the rule is genuinely common |
Adjust that illustrative spend policy upward if conversion lag is long or the account has sparse data; adjust it downward only when the query is plainly outside the offer or the cost of wasted traffic is material. The signal is new conversion and sales-quality evidence, not a desire to make the report look cleaner.
Before applying, compare each candidate with existing negatives and check whether the proposed scope would affect other campaigns. Google’s instructions for adding negative keywords explain the available account workflow.
Apply changes in a controlled way
Separate approval from execution
For a small review, add only the negatives you approved in Google Ads and record the date, scope, reason, and source query. For a larger workflow, an MCP connection can let an AI client call tools exposed by a server; the Model Context Protocol introduction describes that client-server model. A connection alone does not establish that a particular tool is available or that a change is safe.
If you use an AI client connected to the Google Ads MCP, check which account tools and approval steps are available before relying on it. NotFair describes its agents as approval-gated and campaign changes as reversible; still, verify the proposed account, scope, and change details before approving an action.
- Preview the negative, match type, and target campaign or ad group.
- Reject actions that lack a clear reason or exceed the approved scope.
- Save a change log so another manager can understand and reverse the decision.
Do not let a model turn an unreviewed classification into a bulk account change. For API-based workflows, Google’s Google Ads API guide to negative keywords is the relevant reference for how negative criteria are represented.
Check the consequences and refine the rules
Look for blocked intent, not just fewer clicks
After applying approved negatives, review subsequent search terms and campaign outcomes using a comparison window that accounts for your normal conversion lag. Check whether irrelevant queries declined and whether qualified traffic, conversions, or lead quality also changed. A drop in clicks alone does not show that the exclusions improved performance.
Use what you learn to update the account brief and prompt. If a supposedly irrelevant category produces qualified leads, narrow the rule or remove the negative. If unrelated variants continue to appear, review those actual queries and add only justified exclusions. Keep the decision trail so future reviewers can distinguish a deliberate policy from an old one-off fix.
- Did the excluded query truly conflict with the offer or targeting?
- Did the change affect only the intended campaign or ad group?
- Did qualified traffic or lead quality worsen after the change?
Start with one reviewed batch
First, export a focused set of search terms with campaign context, then ask Claude to label each row and explain its recommendation without making changes. Approve only clear, well-scoped exclusions, and use the next review to check whether the evidence supports keeping them. If you want an approval-based AI workflow for advertising changes, NotFair provides hosted MCP servers for connecting AI clients with marketing platforms.
Authored with NotFair SEO