ChatGPT answers, unedited

I’d recommend a Search Query Waste Audit that combines query-level performance data with match-type and keyword-overlap analysis.

Recommended solution

Build a recurring audit that classifies every search query into three waste categories:

• Overlap waste: multiple keywords in the same account/campaign are eligible for the same query, causing internal competition, fragmented data, or higher CPCs.
• Irrelevant-match waste: broad/phrase matching is triggering queries that are outside the intended product, service, or audience.
• Low-value waste: queries that are technically relevant but generate spend without conversions or sufficient downstream value.

How I’d structure it

1. Export search-term data

• Query
• Triggering keyword
• Match type
• Campaign/ad group
• Spend
• Clicks
• Conversions
• Conversion value
• CPC/CPA
• Impression share where available
• Normalize the queries

• Lowercase and remove punctuation
• Normalize plurals/variants
• Group close semantic variants
• Separate brand, product, competitor, informational, and irrelevant intent
• Detect keyword overlap

Create a query → triggering-keyword map. Flag cases where the same query is being matched by multiple keywords, particularly when:

• Different match types are competing
• Different campaigns are targeting the same intent
• A broad keyword is capturing traffic intended for a more specific keyword
• Identical terms have materially different bids or performance
• Score wasted spend

A useful prioritization score is:

Waste Score = Spend × Irrelevance/Overlap Risk × Lack-of-Value Factor

This lets you focus on, for example, a $2,000 irrelevant-query cluster before a $15 anomaly.

5. Recommend an action for each cluster

• Add negative keyword
• Tighten match type
• Consolidate duplicate keywords
• Move query to a dedicated ad group/campaign
• Reduce bid/budget
• Keep as-is

The key output

Rather than producing a giant search-term spreadsheet, I’d create an opportunity table like:

The most valuable enhancement is to cluster queries by intent before calculating waste. Otherwise, an audit tends to flag individual queries without revealing that 50 slightly different searches are all wasting money for the same underlying reason.

If this is for Google Ads, I can also recommend a specific automated architecture (Google Ads → BigQuery/Sheets → query clustering → waste dashboard) and the rules I’d use to identify overlap and irrelevant match types.

A strong solution is a Search Query Waste Audit that combines query-level performance data with keyword/match-type overlap analysis.

Recommended approach

Build a recurring audit that answers three questions:

1. Are multiple keywords competing for the same searches?

• Map each search query to every keyword that can trigger it.
• Flag cases where broad/phrase/exact variants overlap heavily.
• Identify campaigns/ad groups competing against each other for essentially identical intent.
• Compare the winning keyword against alternatives using CPA, ROAS, conversion rate, and impression share.
• Are match types generating irrelevant traffic?

• Segment actual search queries by triggering keyword and match type.
• Flag queries with high spend but no conversions.
• Give extra scrutiny to broad match terms producing low-intent modifiers, informational searches, competitor names, jobs, free/cheap intent, etc.
• Calculate wasted spend % = spend on non-converting/irrelevant queries ÷ total search spend.
• Where should spend be consolidated or blocked?

Create recommendations such as:

• Negative keyword → irrelevant intent.
• Add exact keyword → valuable query currently captured inefficiently by broad/phrase.
• Pause keyword → redundant with a stronger keyword.
• Change match type → excessive query expansion.
• Consolidate ad groups → substantially overlapping keyword sets.
• Keep as-is → overlap exists but the economics justify it.

The key audit output

I'd use a query-level table with columns like:

The important distinction is not to treat all overlap as waste. If two keywords can trigger the same query but one consistently produces better economics, the issue is usually structure/optimization, not necessarily wasted spend.

Best implementation

If you're auditing Google Ads, I'd make this a weekly automated pipeline:

Google Ads search-term data → normalize queries → classify intent → identify keyword overlap → calculate waste → generate recommended negatives/match-type changes → prioritize by recoverable spend.

Then rank findings by potential savings, e.g.:

$18,400 spend → $6,200 estimated avoidable waste → 37% waste rate

That makes the audit much more actionable than a generic search-terms report.

If you're doing this at scale across many accounts, I'd recommend a rules + scoring engine rather than manually reviewing queries. The scoring can combine spend, conversion performance, semantic similarity, match type, query intent, and overlap frequency to produce a prioritized cleanup queue.

I’d recommend a Search Query Waste Audit that combines overlap detection with query-to-keyword relevance analysis.

How it should work

1. Pull search-term data - Search query
2. Matched keyword
3. Match type
4. Campaign/ad group
5. Impressions, clicks, cost
6. Conversions/revenue
7. Search term intent/category

1. Detect overlapping spend

Flag cases where the same or very similar queries are being captured by multiple keywords/ad groups—especially when: - A broad-match keyword captures a query that should belong to an exact/phrase keyword.

• Multiple campaigns compete for the same query.
• A lower-priority keyword is receiving spend instead of the intended keyword.
• Near-duplicate keywords create fragmented traffic.

1. Audit irrelevant match behavior

Classify search terms into:

• Relevant / converting
• Relevant / non-converting
• Irrelevant
• Ambiguous

Then identify match types disproportionately responsible for irrelevant spend. This is particularly useful for finding broad-match terms that generate expensive semantic variants you don't want.

1. Quantify the waste

For every issue, calculate:

Wasted spend = spend on queries judged irrelevant + avoidable duplicate spend

Rank findings by potential savings rather than simply by click volume.

1. Recommend an action

Each flagged query should get a specific recommendation:

The important part: don't just build a negative-keyword list

A good audit should distinguish true waste from intentional overlap. For example, two campaigns may legitimately target the same underlying term because of different audiences, geographies, products, or bidding strategies.

I'd therefore make the core output a waste opportunity report with columns like:

Query → Current match → Campaign → Cost → Conversions → Overlap reason → Relevance → Recommended fix → Estimated savings

For ongoing use, run it weekly, with a rolling 30–90 day view. That turns the audit from a one-time cleanup into a monitoring system.

If you're doing this at scale, the strongest implementation is a SQL/Python audit against your Google Ads search-term and keyword data, with similarity scoring for query overlap and rule/LLM-based intent classification for irrelevant queries.

A good solution is a Search Term Waste & Overlap Audit that combines query-level waste detection with keyword-overlap analysis.

Recommended approach

1. Pull search-term data
   • Query
   • Matched keyword
   • Match type
   • Campaign/ad group
   • Impressions, clicks, cost
   • Conversions, conversion value
   • Landing page

2. Flag irrelevant queries

Create rules for:

• High spend + zero conversions
• High clicks + zero conversions
• Low-intent modifiers (free, jobs, DIY, definition, etc., where applicable)
• Queries outside your product/service categories
• Poor-performing broad/phrase-match expansions

3. Detect overlapping search terms

Identify cases where the same or highly similar query is being captured by multiple keywords, ad groups, or campaigns. Then compare:

• Which keyword triggered it
• Match type
• CPC
• Conversion rate
• CPA/ROAS
• Campaign priority

This lets you find situations where, for example, a broad-match keyword is competing with a more intentional exact-match structure.

4. Quantify the opportunity

Give every finding a projected savings value:

Wasted spend = cost of qualifying non-converting/irrelevant traffic

Overlap opportunity = spend that could potentially be consolidated or redirected to the better-performing keyword/campaign

5. Turn findings into actions

Categorize each query as:

• Add negative
• Change match type
• Move to another ad group
• Consolidate duplicate keywords
• Keep/test
• Needs human review

Best implementation

If you're doing this at meaningful scale, I'd use a scheduled data pipeline + audit dashboard, rather than manually reviewing the Google Ads search-terms report.

A practical stack is:

Google Ads → BigQuery/warehouse → SQL rules + similarity matching → Looker Studio/BI dashboard → prioritized action list

The key is to have the audit produce a table like:

Most valuable feature: don't just report "wasted spend." Build a query-to-keyword map showing why each dollar was spent and whether a different keyword/match type should have captured the query. That turns the audit from a reporting exercise into an optimization workflow.

If you tell me whether you're auditing Google Ads, Microsoft Ads, or both, I can recommend a specific implementation and scoring model.

A strong solution is a Search Query Waste Auditor that combines query-level spend analysis with overlap detection and match-type diagnostics.

What it should audit

1. Overlapping search terms
   • Same or near-identical queries triggering multiple keywords.
   • Queries being served by the wrong keyword despite having a more specific/better-performing keyword available.
   • Cross-campaign/ad-group cannibalization.
   • Broad and phrase keywords competing with exact-match terms.

2. Irrelevant query matching
   • Search queries spending above a configurable threshold with no conversions.
   • Queries with poor CPA/ROAS relative to account or campaign benchmarks.
   • Broad/phrase matches generating semantically unrelated intent.
   • Queries containing known exclusions such as competitor, job-seeker, informational, or unrelated-product intent.

3. Wasted-spend scoring

Give every search query a score based on:

• Spend
• Conversions/revenue
• CPA or ROAS
• Match type
• Keyword overlap
• Relevance
• Statistical confidence

Recommended workflow

Search-term data → normalize queries → classify intent → detect keyword overlap → calculate waste → recommend action

For each query, the auditor could produce:

The key feature: an action queue

Rather than simply reporting waste, rank opportunities by recoverable spend:

• 🔴 Add negative — clearly irrelevant query
• 🟠 Add exact keyword — valuable query currently captured inefficiently
• 🟡 Change match type — broad/phrase producing excessive irrelevant traffic
• 🔵 Resolve overlap — multiple keywords/campaigns competing for the same intent
• 🟢 Keep — efficient and relevant

I'd also include a “Before → After” simulation estimating how much spend could potentially be eliminated or redirected if the recommended negatives, match-type changes, and keyword restructuring were implemented.

If this is for Google Ads, I can also recommend a concrete architecture using the Google Ads search-term/keyword data, including the overlap logic and a scoring formula.

A strong solution is a Search Query Overlap & Match-Type Audit that combines query-level analysis with automated waste detection.

Recommended approach

Build a recurring audit that ingests your Google Ads search-term and keyword data and flags four categories:

1. Overlapping search terms
   • The same search query is triggering multiple keywords, ad groups, or campaigns.
   • Identify cases where a broad-match keyword is competing with a more relevant exact/phrase keyword.
   • Quantify duplicated spend, clicks, conversions, and impression share.

2. Irrelevant query matching
   • Search terms that are clearly unrelated to the product/service.
   • Categorize them by intent/theme rather than reviewing thousands of terms manually.
   • Recommend negative keywords at the campaign or ad-group level, depending on whether the exclusion should be universal or localized.

3. Poor match-type efficiency
   • Compare broad, phrase, and exact match performance for equivalent intent clusters.
   • Flag broad-match queries generating spend without conversions or with materially worse CPA/ROAS.
   • Identify queries that should potentially be promoted into exact-match keywords.

4. Cannibalization / routing problems
   • Detect when multiple campaigns or ad groups are eligible for the same intent.
   • Recommend a preferred keyword destination based on conversion rate, CPA/ROAS, relevance, and campaign intent.

The audit output

I'd structure the result as a prioritized waste ledger, rather than simply dumping search terms:

The key improvement

Don't make the rule simply "broad match = bad." Instead, score each query using something like:

Waste score = spend × irrelevance probability × lack-of-conversion factor × overlap factor

Then rank recommendations by estimated recoverable spend.

For example:

• Immediate negative: high spend + clearly irrelevant intent.
• Routing fix: same query appears across multiple eligible keyword/ad-group paths.
• Keyword promotion: converting query deserves its own exact/phrase keyword.
• Monitor: broad-match query has conversions and acceptable efficiency.

Best implementation

If you have a meaningful amount of spend, I'd make this a weekly automated audit using Google Ads search-term data, with a dashboard containing:

Query → triggering keyword → match type → campaign/ad group → spend → conversions → CPA/ROAS → overlap count → relevance classification → recommended action → estimated savings.

The important part is retaining the recommendation and rationale, so the system doesn't repeatedly rediscover the same waste after every optimization.

If you're looking for a specific software/product solution rather than an analytical framework, I can also compare the best tools for automating this audit.

A strong solution is a search-query overlap + match-type waste audit that combines query-level performance data with keyword-level targeting.

Recommended approach

1. Export the right fields
   • Search query
   • Triggering keyword
   • Match type
   • Campaign/ad group
   • Impressions, clicks, cost
   • Conversions, conversion value
   • Date range

2. Detect overlapping coverage

Group queries that are being captured by multiple keywords/campaigns. Flag cases where:

• The same query is triggered by multiple keywords.
• Broad/phrase keywords are capturing queries that have a more precise exact-match equivalent.
• Multiple campaigns are competing for essentially the same intent.

3. Identify irrelevant match-type spend

Classify queries into:

• Relevant + converting → keep/scale.
• Relevant + non-converting → evaluate based on spend and intent.
• Irrelevant → add negative keywords.
• Duplicate/overlapping → consolidate targeting or tighten match types.

4. Quantify wasted spend

Create a dashboard with:

• $ spent on irrelevant queries
• $ spent on overlapping queries
• % of total search spend potentially wasted
• Cost per conversion for each match type
• Top waste-driving keywords and queries
• Recommended negative keywords

5. Prioritize actions by dollar impact

Rather than reviewing thousands of queries manually, rank recommendations by avoidable spend. For example:

Best solution architecture

If you're doing this repeatedly, I'd build a query audit engine rather than a one-off spreadsheet. It can ingest Google Ads search-term data, normalize queries, identify semantic/structural overlap, calculate wasted spend, and output a prioritized negative-keyword + match-type recommendation list.

The key is to distinguish true waste from intentional overlap. Simply flagging every query that appears under multiple keywords will produce a lot of false positives; the audit should consider intent, match type, campaign structure, conversion performance, and spend.

A good solution is a Search Query Waste Audit system that combines automated query mining, overlap detection, and match-type governance. The goal is to identify where multiple keywords compete for the same searches, where broad matching expands into irrelevant intent, and where budget is leaking.

Recommended approach: Search Term Intelligence Audit

1. Build a search term waste dataset

Pull the last 60–90 days of data from your ad platform (e.g., Google Ads):

Include:

• Search term
• Triggering keyword
• Match type
• Campaign/ad group
• Impressions
• Clicks
• Cost
• Conversions
• CPA / ROAS
• Quality Score
• Search intent category

Export at the search term level, not keyword level. Keyword reports hide the actual queries causing waste.

2. Detect overlapping keyword cannibalization

Look for cases where multiple keywords are matching the same queries.

Examples:

Create an overlap score:

Overlap Rate = Shared Search Terms ÷ Total Search Terms

Flag:

• >30% overlap = review
• >50% overlap = likely consolidation opportunity

Typical fixes:

• Consolidate duplicate keywords
• Move terms into tighter themed ad groups
• Use negatives to control traffic routing
• Prioritize the highest-performing keyword

3. Audit match-type leakage

Classify search terms by intent:

High intent

• "buy"
• "pricing"
• "quote"
• "near me"
• product/service names

Research intent

• "best"
• "reviews"
• "comparison"
• "how does"

Irrelevant intent

• jobs
• free
• DIY
• support
• login
• training
• unrelated industries

Example:

Keyword:

"commercial cleaning services"

Broad match triggers:

• "cleaning jobs"
• "house cleaning tips"
• "free cleaning checklist PDF"

Actions:

• Add negatives
• Move important terms to phrase/exact
• Reduce reliance on broad match where intent control matters

4. Create a negative keyword mining workflow

Set automated rules:

Add as negative if:

• Spend > $50–$100 with 0 conversions
• Clicks > 10–20 with poor engagement
• CPA > 2–3x target CPA
• Search intent is clearly irrelevant

Maintain negative keyword lists by theme:

Examples:

• Employment: jobs, careers, salary, hiring
• Education: course, certification, tutorial
• Free: free, template, download
• Competitors (if not targeting them)
• Consumer vs B2B mismatches

5. Use an overlap visualization dashboard

Build a dashboard in Looker Studio, Power BI, or a spreadsheet showing:

Waste metrics

• Spend on zero-conversion search terms
• Spend by match type
• Top wasted queries
• Cost from broad match
• Duplicate keyword coverage

Overlap metrics

• Search terms triggering multiple keywords
• Campaigns sharing the same queries
• Keywords with identical intent

6. Add automation

A scalable setup:

Weekly process

1. Export search terms
2. Score waste
3. Review top spenders
4. Add negatives
5. Consolidate duplicate keywords
6. Monitor conversion impact

Tools that can help:

• Google Ads Scripts for automated search term reports
• PPC management platforms with query mining
• Data warehouse + dashboard approach for larger accounts

A practical scoring model

Prioritize audits using:

Waste Score = Cost × Irrelevance Probability × Conversion Gap

Example:

Recommended operating model

For most accounts:

• Weekly: search term waste review
• Monthly: keyword overlap audit
• Quarterly: match-type strategy review

The biggest savings usually come from:

1. Broad match query cleanup
2. Duplicate keyword consolidation
3. Negative keyword expansion
4. Separating high-intent terms into exact/phrase campaigns

For larger accounts, I’d implement this as an automated Search Term Waste Scoring Engine rather than a manual audit, because the same leakage patterns recur continuously.

If your goal is specifically to audit wasted search spend caused by overlapping keywords and poor query/match-type coverage, I’d recommend Optmyzr as the strongest all-around solution.

Why Optmyzr

It addresses both sides of the problem:

• Irrelevant queries / wasted spend: its Negative Keyword Finder identifies query words and phrases that are spending without producing results and can push negatives back into the account. help.optmyzr.com
• Keyword overlap: its Traffic Sculptor identifies queries that are being matched by multiple ad groups and recommends exact-match negatives in the less-relevant ad group, effectively routing the query to the intended destination. help.optmyzr.com
• Duplicate keywords: Keyword De-Duper finds duplicate keywords within and across campaigns and recommends which to retain or pause. help.optmyzr.com
• Non-converting queries: its current Non-Converting Queries tool can flag queries with poor CTR or spend above the campaign's typical CPA without conversions. help.optmyzr.com

Optmyzr

How I'd structure the audit

I'd build a recurring query leakage + overlap audit with four buckets:

One important point: don't automatically treat every overlap as wasted spend. Google explicitly says match types overlap—phrase can reach queries that exact can reach, and broad can reach queries that phrase/exact can reach. support.google.com The audit should therefore determine which keyword/ad group is the best owner of a query, rather than simply eliminating duplicate-looking keywords.

If you don't want another platform

You can do a lighter-weight version directly in Google Ads using the Search Terms report. It exposes the actual search query, triggering keyword, and search-term match type, and Google recommends using that information to refine match types and add negatives. support.google.com

Google Ads Search Terms Report documentation

My recommendation:
Optmyzr for an automated, repeatable audit; Google Ads alone for a smaller account where a weekly manual review is practical. If you have a large account, the biggest value is its ability to turn the audit into actionable negative-keyword and query-routing recommendations rather than just giving you another spreadsheet.

I’d recommend a Search Query Waste Audit that combines query-level reporting with overlap detection and automated match-type diagnostics.

Recommended solution

1. Export actual search queries, not just keywords

Pull 60–90 days of:

• Search query
• Triggering keyword
• Match type
• Campaign/ad group
• Impressions, clicks, cost
• Conversions/revenue
• Quality/relevance metrics where available

2. Detect overlapping coverage

Group queries by normalized intent and flag cases where multiple keywords/campaigns are competing for essentially the same query. Prioritize overlap when:

• One keyword has substantially higher CPC/CPA
• Multiple campaigns target the same intent
• Broad/phrase terms are capturing queries already covered by exact terms
• Identical queries appear across multiple ad groups

3. Classify irrelevant queries

Create categories such as:

• Wrong product/service
• Wrong geography
• Informational/research intent
• Job/career queries
• Free/cheap intent
• Competitor-only intent
• Existing customers/support
• Ambiguous terms

Then calculate spend by waste category, rather than just producing a giant negative-keyword list.

4. Score each query for action

A useful prioritization score is:

Waste score = Cost × irrelevance probability × (1 − conversion efficiency)

This surfaces expensive, clearly irrelevant queries first while preventing the audit from overreacting to low-cost queries.

5. Turn findings into three action queues
   • Add as negative: clearly irrelevant or structurally wrong intent
   • Consolidate: overlapping keywords/ad groups/campaigns
   • Retain/test: potentially valuable queries where the data isn't conclusive

6. Measure savings after implementation

Track:

• Wasted spend removed
• CPA/ROAS improvement
• Conversion volume retained
• Impression/click loss from exclusions
• New irrelevant-query spend

Tooling

For a serious account, I’d build this as a recurring SQL/Python audit fed by Google Ads Search Query Performance data, with a dashboard showing the top waste opportunities. If you want a SaaS layer rather than building it yourself, tools such as Optmyzr or Search Engine Land-covered PPC tooling can help with account analysis, but I’d still keep the underlying query-level dataset under your control.

The key is not simply “find expensive keywords.” The audit should distinguish true irrelevance, cannibalization/overlap, and legitimate broad-match discovery. Otherwise you can reduce spend while accidentally eliminating profitable search demand.

If you tell me whether this is Google Ads, Microsoft Ads, or both, I can give you a concrete audit framework with the data schema, SQL logic, and recommended thresholds.

A strong solution is a Search Query Waste Audit that combines query-level performance analysis with overlap detection and match-type diagnostics.

Recommended approach

1. Pull search-term data at query level
For the last 60–90 days, export:

• Search term / actual query
• Keyword that triggered it
• Campaign + ad group
• Match type
• Impressions, clicks, cost
• Conversions, conversion value
• CPC and CPA/ROAS

2. Identify overlapping spend
Build a query-to-keyword map and flag cases where the same or near-identical search query is being triggered by multiple keywords/campaigns.

Prioritize:

• Same query appearing across campaigns
• Broad and phrase keywords competing with exact keywords
• Multiple ad groups targeting essentially the same intent
• Queries where different keywords produce materially different CPA/ROAS

A useful metric is:

Overlap waste = spend on duplicate query coverage where another eligible keyword/campaign already captures the same intent

3. Audit irrelevant match behavior
Classify queries into:

• Relevant + converting
• Relevant + non-converting
• Irrelevant
• Ambiguous
• Duplicate/overlapping

Then specifically examine broad/phrase-match queries that have:

• High spend with zero conversions
• CPA materially above account target
• Low engagement/poor downstream quality
• Intent unrelated to the keyword

4. Turn findings into actions
For each flagged query, assign one action:

The important part: don't just count duplicates

I'd score each opportunity by recoverable spend, rather than simply number of overlapping terms:

Waste score = spend × irrelevance/overlap confidence × lack of conversion efficiency

That gives you a prioritized list such as:

1. $8,400 — irrelevant broad-match queries
2. $5,100 — duplicate query coverage across campaigns
3. $3,700 — phrase-match queries that should be exact
4. $2,200 — high-CPA query clusters

Best implementation

If this is for a sizeable Google Ads account, I'd build a recurring query-audit dashboard that automatically:

• Normalizes search queries
• Groups semantic/near-duplicate queries
• Maps each query to every keyword/campaign that triggered it
• Calculates wasted spend
• Detects match-type expansion
• Recommends negatives, exact keywords, and consolidations
• Tracks savings after changes

The key is to make it action-oriented rather than another search-term report: every flagged dollar should lead to a specific optimization decision.

If you tell me whether you're auditing Google Ads, Microsoft Ads, or both, I can lay out the exact audit framework and scoring logic.

A strong solution is a Search Query Waste Audit that combines query-level performance data with overlap detection and match-type analysis.

Recommended approach

1. Pull search-term data
   • Search query
   • Campaign/ad group
   • Keyword that triggered it
   • Match type
   • Impressions, clicks, cost
   • Conversions, conversion value
   • Search term → keyword relationship

2. Identify irrelevant queries

Flag queries that:

• Spend above a threshold with zero conversions
• Have poor CPA/ROAS relative to account benchmarks
• Are clearly outside the product/service intent
• Repeatedly trigger broad-match keywords without producing value

3. Find overlapping search terms

Look for the same or highly similar queries being triggered by multiple:

• Campaigns
• Ad groups
• Keywords
• Match types

Then determine whether the overlap is actually harmful. For example, two campaigns competing for similar queries isn't automatically wasteful if one has a materially better business objective or economics.

4. Diagnose match-type waste

Create a matrix such as:

5. Calculate recoverable spend

Don't simply label all non-converting spend as waste. Assign each finding a confidence score:

• High confidence: clearly irrelevant or duplicate intent
• Medium: poor economics but potentially strategic
• Low: insufficient data

Then report confirmed waste, probable waste, and spend requiring review separately.

The key deliverable

I'd build a recurring dashboard with four views:

1. Query Waste

Top irrelevant search terms by spend

2. Query Overlap

Search terms triggered by multiple keywords/campaigns, ranked by duplicated spend

3. Match-Type Leakage

Spend from broad/phrase matching that could potentially be captured more efficiently through tighter targeting

4. Negative Keyword Opportunities

Recommended negatives, with estimated monthly spend that could be prevented

The most useful output isn't a giant list of bad queries—it's an action queue: query → source keyword → reason for waste → recommended negative/consolidation → historical spend at risk.

If you're doing this for Google Ads specifically, I can also recommend a technical implementation using Google Ads Query Language + BigQuery/Looker Studio, including the overlap logic and waste-scoring formula.

A good solution is a Search Query Waste Audit that combines overlap detection, irrelevant-match detection, and spend attribution into one recurring workflow.

Recommended approach

1. Pull search-term data
   • Search term/query
   • Campaign + ad group
   • Keyword that triggered it
   • Match type
   • Impressions, clicks, cost
   • Conversions, conversion value
   • Date range

2. Audit overlapping terms

Flag cases where essentially the same query is being captured by multiple:

• Campaigns
• Ad groups
• Keywords
• Match types

Then determine whether the duplication is intentional (e.g. brand vs. non-brand) or causing internal competition and fragmented spend.

3. Identify irrelevant query matches

Classify queries into categories such as:

• Clearly relevant
• Relevant but low intent
• Irrelevant
• Ambiguous / needs review

For irrelevant queries, prioritize by wasted cost, not simply query volume.

4. Calculate an opportunity score

A useful ranking is:

Waste Score = Cost × Irrelevance Probability × Lack of Conversion Signal

This puts expensive, clearly irrelevant traffic at the top rather than drowning the analyst in thousands of low-cost queries.

5. Recommend an action

For each flagged query, produce:

• Negative keyword — irrelevant intent
• Match-type restriction — overly broad matching
• Keyword restructuring — overlapping targeting
• Campaign restructuring — duplicated intent across campaigns
• Keep — overlap is intentional or performance justifies it

What the audit output should look like

Best implementation

If you're doing this at meaningful account scale, I'd build it as a repeatable audit pipeline rather than a spreadsheet exercise:

Google Ads search-term export/API → normalization → semantic query classification → overlap detection → spend/conversion scoring → recommended negative keywords/restructuring → analyst approval → change log.

The important distinction is that exact duplicate keywords aren't the only overlap worth finding. You want semantic overlap—e.g. “enterprise laptops,” “business laptops,” and “laptops for business”—while also distinguishing legitimate campaign segmentation.

If you tell me whether this is Google Ads, Microsoft Ads, or both, and roughly how many campaigns/search terms you're auditing, I can recommend a specific tooling architecture and scoring methodology.

A good solution is a Search Query Waste Auditor that combines query-level analysis with keyword-overlap detection. I’d structure it as a recurring audit rather than a one-off report.

What it should flag

1. Keyword cannibalization / overlap
   • Search terms triggering multiple keywords or ad groups.
   • Broad/phrase keywords capturing queries that should be owned by more specific keywords.
   • Multiple keywords competing for the same high-value query.
   • Near-duplicate keywords with different bids or match types.

2. Irrelevant query matching
   • Search queries with high spend but no conversions.
   • Queries semantically outside the product/service.
   • Informational, employment, support, DIY, free, competitor, or other unwanted intent.
   • Broad-match queries whose intent differs materially from the keyword.

3. Waste prioritization

Rather than simply producing a giant negative-keyword list, score each query using something like:

Waste Score = Spend × Irrelevance Probability × (1 − Conversion Quality)

Then prioritize:

• High spend + zero conversions
• High spend + poor conversion value
• Repeated irrelevant queries
• Queries being matched by several keywords

Recommended workflow

Export → Normalize → Classify → Detect overlap → Quantify waste → Recommend action → Review → Push changes

For each search query, retain:

The important part: distinguish waste from overlap

Don't automatically negative a query just because two keywords match it. Instead, determine which keyword should own the query.

For example:

Search query: "emergency commercial roof repair"

If it matches:

• commercial roofing — broad
• commercial roof repair — phrase
• emergency roof repair — phrase

the auditor should recommend routing the query to the most appropriate keyword/ad group rather than simply adding a negative everywhere.

This makes the system an intent-routing auditor, not just a negative-keyword generator.

My recommended implementation

If you're doing this at meaningful account scale, I'd build a lightweight pipeline that:

• Pulls Google Ads search-term data on a scheduled basis.
• Normalizes spelling, plurals, punctuation, and close variants.
• Uses semantic similarity to identify related queries/keywords.
• Calculates spend, conversion, and overlap metrics.
• Classifies query intent.
• Produces three action queues:
  1. Add negative
  2. Resolve keyword overlap
  3. Investigate / leave alone
• Gives every recommendation a reason and expected savings.
• Keeps a change log so you can measure whether negatives actually reduced waste without hurting conversions.

The key KPI shouldn't be "number of negatives added." It should be avoidable spend recovered while preserving qualified conversion volume/value.

If this is for Google Ads specifically, I can also lay out the exact audit logic, scoring model, and database/report schema I'd use to automate it.