Prepare recurring search-ad keyword cleanup
Turn repeated keyword and negative-keyword analysis into a reviewable maintenance workflow.
How this works
Turn repeated keyword and negative-keyword analysis into a reviewable maintenance workflow. Keyword maintenance is a recurring control process, not a one-time hunt for low-converting phrases. The same search term can be irrelevant, under-observed, or relevant but served through the wrong match structure, and those situations require different actions.
Use a current search-term report and existing keyword configuration to propose precise additions, exclusions, or restructuring. Apply deterministic thresholds only after relevance and observation sufficiency are established. Keep proposed negatives reviewable because an overly broad exclusion can block valuable demand.
Before you start
Account taxonomy
Know campaign intent, product scope, and protected query themes.
Review policy
Define observation requirements and permitted match types.
Current configuration
Obtain existing keywords and negative lists to detect overlap.
Prepare the inputs
- search_terms
- Required
Query-level export
Include campaign, group, spend, and mature outcomes.
- keywords
- Required
Current keyword records
Retain match type and status.
- negative_lists
- Required
Existing exclusions
Identify account-level versus campaign-level scope.
- relevance_rules
- Required
Offer and audience criteria
Use concrete examples of acceptable and irrelevant intent.
- review_window
- Required
Dates and attribution settings
Avoid incomplete conversion periods.
Implementation, step by step
Validate the report grain
- Check duplicate rows, date coverage, and query normalization.
- Preserve original search text alongside normalized forms.
Checkpoint: A clean search-term table.
Classify intent relevance
- Compare queries with the actual offer and campaign purpose.
- Separate unrelated intent, ambiguous intent, and relevant demand before considering performance.
Checkpoint: A relevance queue with evidence.
Assess observation sufficiency
- Calculate spend and outcome measures using the approved window.
- Keep low-volume or immature terms pending rather than automatically excluding them.
Checkpoint: A mature-performance assessment.
Draft exact changes
- Propose keyword additions, negatives, or group changes with match type and scope.
- Check whether existing exclusions or keywords already implement the desired behavior.
Checkpoint: A deduplicated change set.
Review exclusion effects
- Test proposed broad or phrase negatives against valuable example queries.
- Narrow the scope where a term has both useful and irrelevant meanings.
Checkpoint: A reviewed import or action file.
Reconcile after application
- Record approved changes and actual account receipts.
- Review subsequent query coverage and unintended blocks in the next cycle.
Checkpoint: A maintenance history and follow-up list.
Decisions along the way
Term is relevant but has little data
Continue observation within existing limits.
One word appears in both useful and irrelevant queries
Use a narrower phrase or campaign-specific exclusion.
Desired keyword already exists
Avoid creating a duplicate and inspect its current routing.
What you will produce
keyword-maintenance.csv
CSVSupports exact reviewed changes.
Include: query · entity_id · action · match_type · scope · reason · review_status
negative-impact-check.md
MarkdownShows why an exclusion is appropriately scoped.
Include: proposed_negative · blocked_examples · protected_examples · decision
Check the result
- Verify every proposed negative against the offer definition.
- Recalculate performance using mature outcomes.
- Check duplicates across shared negative lists.
- Inspect whether important query examples would be blocked.
When something goes wrong
Report excludes hidden or low-volume queries
Disclose limited coverage rather than claim exhaustive maintenance.
A negative blocks intended demand
Revert the specific authorized change and review its scope.
Account IDs differ from the export
Refresh configuration before applying the file.
A worked example
SYNTHETIC EXAMPLE · NOT A REPORTED BUSINESS RESULT
Request
Clean search terms for business appointment software.
Inputs
Synthetic example. Synthetic free dentist jobs is irrelevant; appointment software for dentists is relevant. Both contain dentist.
Expected result
Synthetic expected result. Propose an exclusion around jobs where appropriate, not dentist. Keep the relevant query eligible and review match scope before applying.
Run a small pilot
Start small
One campaign with a mix of relevant, irrelevant, and ambiguous terms.
Record the baseline
Record manual review effort and later unintended exclusions.
Agree success criteria
Agree relevance accuracy and protected-query coverage.
Stop or revise
Stop application if the proposed file cannot be mapped to current account entities.
Keep the scope clear
- A maintenance recommendation does not authorize changing live targeting.
- Do not turn unknown conversions into zero merely to justify exclusions.
Check before using
Review false exclusions, conversion delays, and campaign scope. Keep account changes bounded and reversible.
Business activities
- Run advertising and acquisition tests
FROM A RECIPE TO YOUR WORKFLOW
Bring the company context together.
This workflow needs reliable inputs before it needs a better prompt. Moat8 Vault brings working information into a readable archive for the AI tools you use.
For this recipe: Search-term and keyword performance · Campaign intent and exclusions · Change rules
Start with one recurring task, agree which sources to use, and review the first result with your team.
Discuss this workflow