Review search ads alongside competitor data
An agent combines search ads platform account data with external research on ads and landing pages. It can be adapted for an advertising agency or e-commerce business.
How this works
An agent combines search ads platform account data with external research on ads and landing pages. It can be adapted for an advertising agency or e-commerce business. A search-ad audit combines account evidence with market context, but it must preserve the difference between a poor query match, a weak ad promise, and a landing-page problem. Treating all three as keyword issues can waste effort and obscure the real constraint.
Freeze an account export, calculate comparable measures, and trace findings from search term through ad to landing page. Public competitor research supplies context, not private performance estimates. The output is a reviewed change proposal for the media buyer.
Before you start
Account scope
Specify campaigns, period, currency, attribution window, and conversion goal.
Available exports
Obtain search terms, keywords, ads, spend, and relevant landing pages.
Commercial constraints
Record prohibited claims, excluded audiences, and budget boundaries.
Prepare the inputs
- account_export
- Required
Campaign and keyword tables
Retain IDs and reporting definitions.
- search_terms
- Required
Query-level report
Include match information and mature outcomes where available.
- ad_copy
- Required
Current versions
Link each ad to its group and destination.
- landing_pages
- Required
Page snapshots
Verify the advertised offer is present.
- competitor_evidence
- Required
Public ads and pages
Do not infer competitor spend or conversions.
Implementation, step by step
Validate the reporting base
- Align dates, currencies, attribution windows, and campaign IDs.
- Flag incomplete periods and missing outcome data before calculating efficiency.
Checkpoint: A clean audit snapshot.
Find account-level issues
- Calculate spend concentration, cost per outcome, and relevant coverage with deterministic arithmetic.
- Separate low-volume uncertainty from consistently poor performance.
Checkpoint: A numerical issue shortlist.
Inspect query relevance
- Compare actual search terms with the intended offer and permitted audiences.
- Identify candidate exclusions or coverage gaps without treating every low-converting query as irrelevant.
Checkpoint: A query-level review queue.
Trace ad-to-page consistency
- Compare each selected ad promise with the linked landing page and conversion path.
- Flag mismatched pricing, unavailable offers, and unclear next steps.
Checkpoint: A message and destination mismatch list.
Use external context carefully
- Review public competitor messaging and page structure for distinct approaches.
- Turn useful observations into test hypotheses, not claims about their winning performance.
Checkpoint: An evidence-backed opportunity list.
Prepare reviewed changes
- Group recommendations by keyword, ad copy, landing page, and measurement issue.
- Provide exact proposed edits and leave live application to the authorized buyer.
Checkpoint: An actionable audit packet.
Decisions along the way
Conversions are not mature
Hold efficiency judgments or use a completed earlier period.
A term is relevant but expensive
Propose a bounded test or bid review, not automatic exclusion.
Landing page is unavailable
Treat destination repair as the immediate issue before creative optimization.
What you will produce
search-ads-audit.csv
CSVSupports precise account review.
Include: entity_id · issue_type · evidence · proposed_change · priority · review_status
landing-page-findings.md
MarkdownKeeps message and page work distinct from keyword maintenance.
Include: ad_promise · page_evidence · mismatch · test_hypothesis
Check the result
- Recompute selected metrics from source rows.
- Verify proposed negatives do not block intended demand.
- Check destination URLs and offer consistency.
- Keep competitor observations separate from account performance evidence.
When something goes wrong
Exports omit important campaigns
Mark scope incomplete and avoid account-wide conclusions.
Keyword joins duplicate spend
Fix the join grain before calculating totals.
A recommendation lacks an exact target
Return it for a campaign, group, ad, or query identifier.
A worked example
SYNTHETIC EXAMPLE · NOT A REPORTED BUSINESS RESULT
Request
Audit a campaign for appointment software.
Inputs
Synthetic example. Synthetic query office chairs spent 90 with no relevant intent; query booking calendar spent 100 for 5 mature leads. The ad promises a free trial absent from its page.
Expected result
Synthetic expected result. Propose office chairs as a reviewed negative, preserve booking calendar for normal evaluation, and flag the free-trial mismatch separately.
Run a small pilot
Start small
A limited campaign set covering several query types.
Record the baseline
Record manual audit time and buyer acceptance or rejection reasons.
Agree success criteria
Agree acceptable false exclusions and evidence completeness.
Stop or revise
Stop recommendations when conversion definitions or account joins are unreliable.
Keep the scope clear
- Audit preparation does not authorize spend or targeting changes.
- Do not use public ad visibility as evidence of a competitor's profitability.
Check before using
A specialist reviews the hypotheses before changing bids, budgets, or live campaigns.
Business activities
- Run advertising and acquisition tests
- Research customers and markets
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: Advertising account and Customer ID · Campaign data
Start with one recurring task, agree which sources to use, and review the first result with your team.
Discuss this workflow