ChatGPT answers, unedited

A practical solution is a search-term waste auditor that combines query-level spend analysis with overlap detection and match-type diagnostics.

Recommended solution

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

• Duplicate intent across campaigns/ad groups — the same or near-identical queries triggering multiple keywords.
• Cannibalization — multiple keywords competing for the same query, particularly across campaigns with different bids or budgets.
• Irrelevant queries — searches that don't align with the intended product/service, detected through rules plus semantic classification.
• Match-type leakage — broad/phrase keywords generating queries materially outside the intended theme.
• High-spend, low-value queries — queries consuming significant spend without conversions or with poor CPA/ROAS.
• Negative-keyword gaps — recurring irrelevant themes that should be added as negatives.
• Intent fragmentation — closely related queries split across ad groups/campaigns in ways that make budget and performance harder to control.

Core audit workflow

What I'd implement

For a serious account, I'd make this a weekly automated audit dashboard with three views:

1. Waste

“$12,430 spent on queries classified as irrelevant or non-converting.”

2. Overlap

“47 query clusters are being triggered by multiple keyword/campaign combinations, representing $3,180 in spend.”

3. Actions

“Add 126 negative keywords, consolidate 18 overlapping keyword groups, review 34 broad-match terms.”

The important design choice is not to treat every duplicate query as wasted spend. If two keywords can legitimately compete or Google is simply reporting close variants, that's different from genuine cannibalization. The system should distinguish overlap from avoidable overlap.

If you're looking for a software/product solution rather than a methodology, I can also lay out a shortlist of tools that specifically automate Google Ads search-term waste, negative-keyword discovery, and keyword cannibalization auditing.

A practical solution is a Search Query Waste Auditor that continuously identifies spend caused by duplicate coverage, poor match-type behavior, and irrelevant queries, then recommends or automates corrective actions.

Recommended solution

Build a workflow with five layers:

The key improvement

Don't make this merely a "search terms with no conversions" report. That generates lots of false positives.

The more useful product is an intent + overlap graph:

Query → matched keyword → ad group → campaign → match type → other eligible keywords → conversion outcome

That lets you distinguish legitimate competition between targeting structures from genuinely redundant or irrelevant spend.

For example:

"commercial office cleaning"
→ Broad keyword: office cleaning
→ Phrase keyword: "office cleaning services"
→ Exact keyword: [commercial office cleaning]

If the same query is being captured by all three and the broad keyword is responsible for substantial incremental spend without incremental conversions, the auditor can quantify the overlap and recommend consolidation/exclusions rather than blindly adding negatives.

What I'd build first

An MVP could have four outputs:

• Wasted spend: dollars attributed to irrelevant/avoidable queries
• Overlap spend: dollars associated with redundant keyword coverage
• Negative-keyword recommendations: prioritized by recoverable spend
• Match-type recommendations: queries that should be promoted, restricted, or excluded

Then add an approval workflow so recommendations can be reviewed before anything changes in the ad account.

If you're building this for Google Ads, I can also lay out the specific data model, detection rules, and scoring algorithm for an automated auditor.

If you mean paid search (Google Ads/Bing Ads), I’d recommend a search-term waste audit + query-to-keyword overlap framework rather than reviewing match types manually.

Recommended solution

Build a recurring audit that combines four datasets:

1. Search-term report

• Actual queries
• Matched keyword
• Match type
• Spend, clicks, conversions, CPA/ROAS
• Keyword inventory

• Campaign/ad group
• Keyword and match type
• Bids/status
• Landing page
• Negative-keyword inventory

• Campaign/ad-group negatives
• Shared negative lists
• Query-to-keyword mapping

• Normalize queries (case, punctuation, plurals, close variants)
• Identify when multiple keywords/campaigns can capture the same query
• Flag queries whose intent doesn't match the target keyword

The audit should produce three priority buckets

I'd score each finding by wasted spend, so the team sees something like:

$18,420 potentially wasted → 61% from irrelevant queries, 24% from keyword overlap, 15% from weak match expansion.

Best implementation

For anything beyond a small account, I’d automate this in a daily/weekly pipeline rather than relying on spreadsheets. The core logic should establish a “query owner” for each search term: the campaign/ad group/keyword that should receive that query. Anything captured by multiple targets or by the wrong intent gets flagged.

Then give marketers an action queue:

• Negative immediately — clearly irrelevant
• Review overlap — legitimate query but multiple eligible targets
• Reallocate — query belongs in another campaign
• Keep — relevant and efficiently converting
• Monitor — insufficient data

The key is not to treat every overlap as waste. Some overlap is intentional; the audit should distinguish duplicate eligibility from actual cannibalization or inefficient spend.

If you tell me whether you're using Google Ads, Microsoft Ads, or both, I can recommend a concrete architecture—including the reports/API fields, overlap-detection logic, and a dashboard schema.

For auditing wasted spend from overlapping search terms and irrelevant query match types, I’d recommend a structured search-term governance process rather than a one-off cleanup. The goal is to identify where paid search is paying for the same intent multiple times, where match types are too broad, and where queries are consuming budget without business value.

Recommended solution: Search Term Waste Audit Framework

1. Build a Search Term Intelligence Report

Export the last 60–90 days of search term data from your ad platforms (especially Google Ads, Microsoft Ads if applicable) with:

• Search term
• Keyword that triggered it
• Match type
• Campaign/ad group
• Impressions
• Clicks
• Cost
• Conversions
• Conversion value/revenue
• CPA or ROAS
• Quality/relevance indicators

Segment by:

• Brand vs non-brand
• Campaign type (Search, Shopping, Performance Max, etc.)
• High-spend vs low-spend queries

2. Identify Overlapping Search Intent

Create an overlap matrix to find cases where multiple keywords compete for the same query.

Common issues:

Actions:

• Consolidate duplicate keyword themes.
• Use exact/phrase match where intent is well understood.
• Add negatives to prevent unwanted campaign competition.
• Use campaign priorities carefully where applicable.

3. Audit Irrelevant Search Queries

Create a waste score:

Waste Score = Cost × (1 - Conversion Efficiency)

Flag queries that meet thresholds such as:

• Spend > £50–£100 with zero conversions
• CPA > 2–3× target CPA
• ROAS below break-even threshold
• Low engagement (high bounce rate, short sessions)

Classify negatives into:

Immediate negatives

• Clearly irrelevant products/services
• Competitor research queries (if not targeted)
• Jobs/careers
• Free/download/template queries
• DIY searches when you sell a service

Review negatives

• Ambiguous terms
• Informational searches
• New emerging customer language

4. Create a Negative Keyword Management System

Maintain a shared negative keyword library:

Category examples

• Employment:

• jobs
• salary
• careers
• vacancies
• Free intent:

• free
• template
• download
• pdf
• Low commercial intent:

• how to
• tutorial
• definition
• meaning
• Product mismatch:

• used
• repair
• parts
• manual

Apply negatives at the right level:

• Account level → universal exclusions
• Campaign level → intent separation
• Ad group level → keyword sculpting

5. Use Keyword Cannibalisation Detection

A useful automated approach:

Inputs

• Search term report
• Keyword list
• Match types
• Campaign structure

Rules

• Same query triggered by multiple keywords → review
• Broad match query overlaps with exact keyword → prioritize exact
• Multiple ad groups receiving identical queries → consolidate

Tools that can help:

• Google Ads Scripts
• Looker Studio dashboards
• Search Ads 360 (enterprise accounts)
• Third-party PPC auditing platforms

6. Automate Weekly Monitoring

Set up alerts for:

• New search terms spending > £X without conversions
• Sudden increase in broad match traffic
• Rising CPC for previously efficient queries
• Queries entering from new categories
• Campaigns with high search term coverage loss

A practical cadence:

Recommended tooling stack

For most advertisers:

• Google Ads Search Terms Report → source data
• Looker Studio → dashboards and trend reporting
• Google Sheets + scripts → lightweight auditing
• BigQuery + Ads API → larger accounts with automation needs

For enterprise accounts:

• Ads API pipeline → centralized search-term database
• NLP classification → automatically label irrelevant queries
• Rules engine → recommend negatives and restructures

Expected outcomes

A well-run audit typically uncovers:

• 10–30% reduction in wasted search spend (varies widely)
• Lower CPA through query filtering
• Cleaner attribution between campaigns
• Better match-type control
• More budget flowing to high-intent queries

If this is for a Google Ads account, I’d structure it as a repeatable audit template with a query classifier, overlap detector, and negative keyword recommendation engine.

I’d recommend a search-term overlap + query-intent auditing layer that automatically flags wasted spend rather than relying on manual reviews.

• Overlap: identify queries triggering multiple keywords/ad groups, then flag cannibalization and inefficient bids.
• Irrelevance: classify search queries by intent and flag broad/phrase matches that fall outside campaign intent.
• Waste scoring: rank issues by spend, conversions, CPA/ROAS impact, and confidence.
• Actions: suggest negatives, match-type changes, keyword consolidation, and bid/budget adjustments.
• Cadence: run daily automated detection with a weekly human review of high-impact recommendations.

For Google Ads, this can be built around Search Terms + Keyword + Campaign/Ad Group performance data, with rules or an ML classifier layered on top.

If you're auditing paid search (Google Ads/Bing Ads) waste, I’d recommend a query-level overlap and match-quality audit rather than just reviewing keyword performance.

Recommended solution

Build a recurring audit that combines three analyses:

• Search-term overlap: Identify search queries triggering multiple keywords, ad groups, or campaigns. Flag cases where several terms compete for the same query and compare CPC, conversion rate, CPA/ROAS, and impression share.
• Irrelevant matching: Compare actual search queries against intended keyword themes. Flag broad/phrase-match queries that are semantically adjacent but commercially irrelevant, especially queries with spend and zero conversions.
• Waste prioritization: Score each query by spend × irrelevance × lack of conversion, then rank the highest-value exclusions first. This prevents the team from spending time on $2 anomalies while missing $10,000+ sources of leakage.

The audit output

I’d structure the deliverable as a table like:

Then divide recommendations into:

1. Add negative keywords — clearly irrelevant queries.
2. Tighten match types — broad/phrase terms generating consistently poor query variants.
3. Consolidate overlapping keywords — multiple keywords serving essentially the same intent.
4. Promote winners to exact match — high-value queries currently being captured inefficiently.
5. Restructure campaigns/ad groups — when overlap is caused by fundamentally ambiguous account architecture.
6. Monitor rather than exclude — queries with insufficient data or strategically useful discovery potential.

The key is not to eliminate overlap blindly. Some overlap is intentional; the objective is to identify where overlap or loose matching is causing incremental spend without incremental conversions.

If you tell me whether you're auditing Google Ads, Microsoft Ads, or both, I can lay out a concrete audit framework—including the SQL/Sheets logic and thresholds for automatically flagging wasted spend.

A strong solution is a search-term overlap + query-intent audit layer that sits on top of your paid-search data.

Recommended approach

Build a recurring audit that combines:

• Search-term data: query, campaign, ad group, keyword, match type, clicks, cost, conversions, revenue.
• Overlap detection: identify queries triggering multiple keywords/campaigns, especially when they compete against each other.
• Intent classification: classify queries as relevant, ambiguous, or irrelevant using your product/service taxonomy.
• Match-type diagnostics: flag broad/phrase/exact keywords whose actual queries consistently fall outside their intended themes.
• Waste scoring: prioritize by spend rather than simply counting bad queries.

A useful score is:

Waste opportunity = spend × irrelevance probability × (1 − conversion efficiency)

Then segment the findings into actionable buckets:

What I'd recommend operationally

Use a weekly automated audit rather than manually reviewing the search-terms report. The system should produce a prioritized queue such as:

1. $4,800 — irrelevant queries → immediate negatives
2. $2,100 — cross-campaign overlap → add campaign negatives
3. $1,400 — broad-match drift → tighten targeting
4. $900 — ambiguous queries → human review
5. $650 — duplicate intent → consolidate/restructure

The important distinction is to avoid treating every duplicate query as waste. If two keywords can legitimately compete and one produces materially better economics, the issue is usually traffic routing, not the query itself.

If you have Google Ads data available, I can also lay out a concrete audit schema, SQL logic, and dashboard design for automatically finding these overlaps and estimating recoverable spend.

A good solution is to build a search term waste audit system that combines query mining, overlap detection, and match-type governance. The goal is to identify where paid search is paying for traffic that is redundant, low-intent, or misaligned.

Recommended solution: Search Term Waste Audit Framework

1. Build a search term intelligence report

Pull the last 60–90 days of data from your ad platforms (especially Google Ads and Microsoft Ads) with:

• Search term/query
• Triggering keyword
• Match type
• Campaign/ad group
• Impressions
• Clicks
• Cost
• Conversions
• CPA/ROAS
• Quality Score (if available)

Segment by:

• Brand vs non-brand
• Campaign objective
• Geography
• Device
• Audience

2. Identify overlapping keyword coverage

Look for cases where multiple keywords compete for the same queries.

Examples:

Actions:

• Consolidate redundant keywords
• Prioritize the highest-performing keyword
• Add negatives to prevent lower-value campaigns from capturing traffic
• Use campaign priority rules where appropriate

3. Audit irrelevant query matches

Create a waste classification model:

Category A: Clearly irrelevant

Examples:

• Wrong product/service
• Research-only queries when you sell a solution
• Jobs/careers queries
• Free/download/support queries

Action:

• Add negative keywords immediately

Category B: Low commercial intent

Examples:

• "what is..."
• "how to..."
• "definition..."
• "template..."

Action:

• Move to content campaigns or exclude from conversion campaigns

Category C: Competitive leakage

Examples:

• Competitor comparisons
• Alternative searches
• Adjacent products

Action:

• Decide intentionally whether these queries fit your strategy

4. Create a negative keyword management process

Maintain a shared negative keyword library:

Global negatives

• Free
• Jobs
• Careers
• Login
• Support
• Manual
• PDF
• Tutorial

Campaign-specific negatives
Example:

• Enterprise campaign excludes "small business"
• Premium product excludes "cheap"
• Software campaign excludes "download"

Review weekly at first, then move to monthly once stable.

5. Use match-type controls

A common issue is allowing broad match to expand without guardrails.

Recommended structure:

For high-CPC accounts:

• Separate discovery campaigns from efficiency campaigns
• Avoid mixing experimental broad match traffic with proven exact terms
• Apply stricter conversion thresholds before scaling

6. Automate the audit

A practical automation stack:

Weekly process

1. Export search term data
2. Score queries: - Spend without conversions
3. CPA above target
4. Low engagement
5. Irrelevant intent signals
6. Generate recommendations: - Add negative
7. Promote to keyword
8. Pause keyword
9. Change match type

Useful automation options:

• Google Ads Scripts for search term monitoring
• Looker Studio dashboards
• SQL/Python pipelines for larger accounts
• Third-party PPC management platforms

7. Prioritize waste by impact

Don't just count bad queries; rank them by wasted spend:

Waste score = Cost × Irrelevance confidence × Lack of conversion evidence

Example:

Recommended cadence

• Weekly: Search term cleanup for active spend accounts
• Monthly: Match type and keyword overlap analysis
• Quarterly: Campaign structure review and negative keyword library refresh

For a mature account, this process often recovers 5–20% of wasted paid search spend, especially where broad match expansion or large keyword inventories have accumulated.

A strong solution is a search-query waste audit layer that sits on top of your Google Ads/Bing Ads data and continuously identifies two problems:

• Overlapping search terms: multiple keywords/ad groups/campaigns competing for the same queries, often creating internal cannibalization or unnecessary bid inflation.
• Irrelevant match traffic: broad/phrase/exact keywords matching queries that don't align with the intended keyword, product, or landing page.

Recommended approach

Build a recurring audit that combines search-term data + keyword structure + spend/conversion data.

1. Pull the last 30–90 days of search terms

• Query
• Triggering keyword
• Match type
• Campaign/ad group
• Impressions, clicks, cost
• Conversions, CPA/ROAS
• Search impression/share metrics where available
• Normalize queries

• Lowercase and remove punctuation
• Group close variants
• Identify brand/product/location terms
• Optionally classify queries by intent using an LLM or rules
• Detect overlap

Flag cases where the same or near-identical search query is eligible through multiple keywords/ad groups/campaigns.

Example:

• Campaign A → running shoes
• Campaign B → best running shoes
• Campaign C → running footwear

If all three repeatedly capture the same queries, calculate the spend and conversions associated with the overlap.

4. Score irrelevant matches

Give every query a relevance score based on:

• semantic similarity to the keyword
• product/service category
• commercial intent
• geography
• brand eligibility
• landing-page relevance

Then prioritize queries with high spend + zero/low conversion + low relevance.

5. Quantify recoverable waste

Instead of simply reporting "bad keywords," estimate:

Potential waste = spend on low-relevance or redundant traffic − expected value of legitimate conversions

Rank findings by potential monthly savings.

6. Turn findings into actions

Your audit should output recommendations such as:

• Add negative keyword
• Move query to a more specific ad group
• Consolidate duplicate keywords
• Change match type
• Split brand/non-brand traffic
• Adjust campaign structure
• Keep as-is because the seemingly irrelevant query actually converts

What the output should look like

Best implementation

If you're doing this at meaningful account scale, I'd make it a weekly automated audit, not a spreadsheet exercise:

Ads API → data warehouse → query clustering/classification → waste scoring → negative-keyword recommendations → human approval → Ads API

The important design principle is don't automatically negate anything based solely on semantic irrelevance. A query that looks irrelevant can still be profitable. The decision should combine relevance × spend × conversion/value × overlap.

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

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

Recommended solution

Build a pipeline that:

The important part: estimate recoverable spend

Don't define waste simply as “queries with no conversions.” Instead, calculate something like:

Recoverable waste = spend × probability that the query should have been excluded or redirected

That lets you prioritize, for example:

$18,400 spend audited → $4,700 likely recoverable → 137 negative-keyword candidates → 23 overlapping keyword clusters

This is much more actionable than a raw search-term report.

Best implementation

If you're doing this at scale, I'd use a rules + clustering + human-review workflow:

• Rules catch obvious negatives and structural overlap.
• Semantic clustering groups variants such as “cheap running shoes,” “discount running shoes,” and “affordable running sneakers.”
• Performance thresholds determine whether a query is worth preserving.
• LLM classification handles ambiguous relevance/intent.
• A reviewer approves changes before they are pushed back into the ad platform.

The output should be a ranked remediation backlog, with every recommendation showing the query, matched keyword/match type, affected campaign, historical spend, conversions, reason for the recommendation, and estimated savings.

If this is for Google Ads, I can also lay out a concrete architecture using the Google Ads API, including the queries/fields to pull and the algorithm for detecting overlap and wasted spend.

A strong solution is a search-term waste audit layer that sits on top of your paid-search data and continuously identifies spend caused by overlap, poor query-to-keyword mapping, and overly broad matching.

Recommended approach

Build a recurring audit with four analyses:

• 1. Search-term irrelevance

• Pull search term, keyword, match type, campaign, ad group, spend, conversions, CPA/ROAS.
• Classify queries into relevant / marginal / irrelevant.
• Flag irrelevant queries that have crossed a spend threshold without producing conversions.
• Recommend negative keywords rather than simply pausing the triggering keyword.
• 2. Keyword overlap / cannibalization

• Map each search query to every keyword capable of matching it.
• Detect multiple keywords competing for the same query, particularly across campaigns or match types.
• Identify cases where a broad/phrase keyword is capturing traffic intended for a more specific keyword.
• Quantify the spend attributable to overlapping coverage.
• 3. Match-type leakage

• Compare performance by exact, phrase, and broad match.
• Find queries where broad/phrase matching generates substantially worse CPA or ROAS than the corresponding exact-intent keyword.
• Surface high-spend queries that should be promoted into exact-match keywords—or excluded with negatives.
• 4. Actionable recommendations

Give every finding an action such as:

• ADD_NEGATIVE
• PROMOTE_TO_EXACT
• MOVE_TO_CAMPAIGN
• CONSOLIDATE_KEYWORDS
• REVIEW
• NO_ACTION

The key output

Instead of a giant search-term report, I'd create a Waste Opportunity table:

Then calculate:

Potential wasted spend = spend on queries meeting your waste criteria

and separate it into:

• Immediate savings — clear negatives / obvious overlap
• Probable savings — poor-performing match types
• Optimization opportunity — queries worth promoting or restructuring

Best implementation

If you're running Google Ads at meaningful scale, I'd automate this as a weekly audit, with daily monitoring for high-spend anomalies. The most useful architecture is:

Google Ads search-term data → normalization/classification → overlap engine → performance rules → recommended actions → human approval → negative keywords/account changes

The important part is not automatically labeling every non-converting query as waste. Brand-new keywords, low-volume high-intent terms, and queries with long conversion windows can look wasteful prematurely. Use spend/conversion thresholds appropriate to your account and conversion lag.

If you tell me whether you're using Google Ads, Microsoft Ads, or both, I can lay out a concrete audit framework—including the exact fields, SQL/Python logic, overlap algorithm, and a dashboard structure.

If you mean paid search (especially Google Ads), I’d recommend a search-term waste auditor that combines query-level analysis with keyword overlap detection.

Recommended solution

Build or use a workflow that:

1. Pulls search-term data

• Search query
• Matched keyword
• Match type
• Campaign/ad group
• Spend, clicks, conversions, CPA/ROAS
• Detects overlapping intent

Flag cases where multiple keywords/campaigns are effectively competing for the same query, such as:

• running shoes
• "running shoes"
• [running shoes]
• best running shoes online

The goal isn't simply to eliminate duplicates—the auditor should determine which keyword/campaign has the best economics and intent fit.

3. Identifies irrelevant matching

Classify search terms into:

• Relevant + converting
• Relevant + non-converting
• Irrelevant
• Ambiguous / needs review

Then quantify wasted spend by category.

4. Prioritizes actions by dollars

Instead of producing a giant negative-keyword list, calculate something like:

Waste opportunity = spend × probability that the query should be excluded

Rank recommendations by recoverable spend, so the team can attack the biggest leaks first.

5. Separates negatives from structural fixes

Some waste should become negative keywords, but other problems indicate:

• overly broad targeting
• poor campaign segmentation
• duplicated keywords
• inappropriate match types
• cross-campaign competition
• landing-page/intent mismatch

What I'd implement

For a serious account, I'd use a weekly automated audit with a dashboard containing:

The important part is making it recommendation-driven rather than purely descriptive: every flagged query should ultimately say something like “Add as negative,” “promote to exact,” “move campaign,” “leave unchanged,” or “review.”

If you're managing Google Ads specifically, I can also lay out a concrete architecture using the Google Ads API + SQL/Python, including the logic for detecting overlapping search terms and calculating wasted spend.

A strong solution is a search-term waste audit layer that combines query-level performance data with keyword/match-type overlap analysis.

Recommended approach

• Normalize search terms — lowercase, strip punctuation, standardize close variants, and group semantically equivalent queries.
• Build a query → keyword map — for every search term, record which keyword(s), match types, campaigns, and ad groups triggered it.
• Detect overlap — flag cases where the same query is eligible through multiple keywords/campaigns, especially when broader match types are competing with more precise ones.
• Audit irrelevant queries — classify search terms into: - Relevant / converting
• Relevant / non-converting
• Irrelevant
• Ambiguous
• Brand vs. non-brand
• Quantify wasted spend — calculate spend, clicks, conversions, CPA/ROAS, and impression volume for each waste category.
• Recommend actions automatically: - Add negative keywords for clearly irrelevant queries.
• Tighten match types where broad matching is producing low-quality traffic.
• Consolidate duplicate/overlapping keywords.
• Move high-value queries into dedicated exact-match terms when appropriate.
• Adjust campaign/ad-group structure when cross-campaign overlap is creating inefficient bidding.
• Prioritize by financial impact — rank opportunities by recoverable spend rather than simply by number of bad queries.

The audit output I’d want

The key is to separate “wasted because irrelevant” from “wasted because overlapping”. They require different fixes, and combining them into one waste metric can lead to bad optimization decisions.

For ongoing management, I’d implement this as a weekly automated audit that ingests the search-term report, identifies overlap and irrelevant-query clusters, estimates recoverable spend, and produces a prioritized negative-keyword and restructuring list.

If you're auditing paid search spend, I’d recommend a query-level waste audit system rather than reviewing keyword performance alone.

Recommended solution: Search Query Waste Audit

Build a recurring audit that combines search-term data + keyword/match-type data + conversion quality and produces an actionable waste score.

What I'd implement

For an ongoing program, I'd make this a weekly automated dashboard with four headline KPIs:

1. Wasted spend ($)
2. Wasted spend (%)
3. Duplicate/overlapping query spend
4. Irrelevant-match spend

Then have a prioritized “Actions to take” queue so the audit isn't just a report—it directly produces the negative-keyword and match-type changes your team should make.

If you're running Google Ads, I can also lay out the exact data schema, overlap logic, waste-scoring formula, and an implementation in Google Ads Scripts/BigQuery/Looker Studio.

For auditing wasted spend from overlapping search terms and irrelevant query match types, a strong solution is a Search Query Waste Audit system that combines query mining, overlap detection, intent classification, and automated remediation.

Recommended solution: Search Term Waste Audit Framework

1. Build a search query data pipeline

Pull the last 60–90 days of data from your paid search platforms (especially Google Ads and Microsoft Advertising):

Capture:

• Search term
• Keyword triggered
• Match type (broad, phrase, exact)
• Campaign/ad group
• Impressions
• Clicks
• Cost
• Conversions
• CPA/ROAS
• Conversion value
• Quality Score (where available)

2. Run a search term overlap audit

Identify cases where multiple keywords compete for the same query.

Common waste patterns

Useful outputs:

• Queries with >2 triggering keywords
• Spend by duplicated query
• Conversion split by keyword
• Winning keyword vs wasted keyword

3. Classify irrelevant search terms

Create a query classification model:

Irrelevant categories

• Wrong intent - "free CRM template" when selling CRM software
• Wrong audience - "jobs at [company]"
• Wrong geography - "plumber Chicago" when serving Seattle
• Informational-only searches - "how does CRM work"
• Competitor/non-target terms
• Low-commercial modifiers

Assign each query:

• Relevant
• Potentially relevant
• Irrelevant
• Needs review

4. Analyze match-type leakage

Create a report like:

Focus first on:

• Broad match queries with high spend and no conversions
• Phrase match queries with irrelevant modifiers
• Search terms consuming budget before exclusions were added

5. Create automated negative keyword recommendations

Generate recommendations such as:

Add negative keyword

Query:

"free invoice generator"

Recommendation:

Add "free" as campaign negative

Add negative phrase

Queries:

• "jobs at company"
• "company careers"
• "company salary"

Recommendation:

Add "jobs", "careers", "salary"

Move keyword structure

Query:

"enterprise crm software"

Currently triggered by:

broad "crm"

Recommendation:

Create dedicated exact/phrase keyword and exclude from broad campaign

6. Use a scoring model to prioritize waste

Rank opportunities by:

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

Example:

7. Recommended tooling options

Enterprise approach

Use:

• Search Ads API exports
• SQL warehouse (BigQuery/Snowflake)
• NLP classifier for query intent
• Dashboard in Looker Studio or Tableau

Faster operational approach

Use:

• Scheduled search term exports
• Spreadsheet audit templates
• Scripts that flag: - high-cost/no-conversion queries
• duplicate triggers
• irrelevant modifiers
• match-type conflicts

Automation layer

A weekly workflow:

1. Pull search terms
2. Score waste
3. Generate negative keyword list
4. Review approvals
5. Push changes
6. Measure savings

Key KPIs to measure success

Track before vs after:

• Wasted spend reduced (%)
• Cost per conversion improvement
• Search term relevance rate
• Negative keyword additions/month
• Broad match waste rate
• Impression share lost due to budget (after cleanup)

A practical first audit usually finds 10–30% of spend that can be reduced or redirected, especially in accounts relying heavily on broad match without frequent query reviews.

Move keyword structure

Query:

"enterprise crm software"

Currently triggered by:

broad "crm"

Recommendation:

Create dedicated exact/phrase keyword and exclude from broad campaign

6. Use a scoring model to prioritize waste

Rank opportunities by:

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

Example:

7. Recommended tooling options

Enterprise approach

Use:

• Search Ads API exports
• SQL warehouse (BigQuery/Snowflake)
• NLP classifier for query intent
• Dashboard in Looker Studio or Tableau

Faster operational approach

Use:

A strong solution is a search-term waste audit system that combines query-level performance data with match-type overlap detection.

Recommended approach

Build a recurring audit that:

• Pulls search-term data with query, keyword, match type, campaign/ad group, spend, conversions, CPA/ROAS, and impression/share metrics.
• Detects overlapping coverage — e.g., the same query is being triggered by multiple keywords, campaigns, or match types, particularly where the higher-cost route isn't producing better results.
• Flags irrelevant matching — queries that spend above a threshold without conversions, fall outside your product/service intent, or have poor CPA/ROAS.
• Clusters queries by intent so you don't treat every bad query independently. For example, 50 variants of the same irrelevant intent can become one negative-keyword recommendation.
• Quantifies recoverable spend: wasted spend by query, campaign, match type, and intent cluster.
• Recommends actions: - Add negative keywords.
• Tighten broad-match coverage.
• Consolidate duplicate/competing keywords.
• Shift valuable queries toward exact/controlled coverage where appropriate.
• Pause or restructure consistently wasteful themes.
• Uses guardrails so high-spend queries aren't automatically excluded merely because they have few conversions; incorporate conversion lag, sample size, brand/non-brand differences, and business value.

The most useful output

I'd structure the audit around a table like:

The key is not simply finding expensive search terms. The audit should distinguish true waste from intentional broad-match exploration, then calculate avoidable spend.

If you're running Google Ads, I can also lay out a concrete implementation using Google Ads search-term data + a scoring model, including the exact rules for identifying overlap and irrelevant matches.

To audit wasted spend from overlapping search terms and irrelevant query match types, I’d recommend a structured search-term waste audit process rather than just reviewing keywords manually. The goal is to identify where your budget is being duplicated, misallocated, or triggered by low-intent queries.

Recommended solution: Search Query Waste Audit Framework

1. Build a search-term overlap and waste report

Export the last 60–90 days of search query data from your ad platform and analyze:

• Search term
• Triggering keyword
• Match type
• Campaign/ad group
• Spend
• Clicks
• Conversions
• CPA / ROAS
• Impression share (if available)

Flag queries that fall into these categories:

2. Create a keyword-to-query overlap map

Use a pivot table or script to identify:

• One search term triggering multiple keywords
• Multiple campaigns competing for the same query
• Broad match terms absorbing exact/phrase traffic

Example:

A good rule: high-value queries should have a single intentional keyword owner.

3. Segment match-type leakage

Audit performance by match type:

• Exact match → usually highest control
• Phrase match → moderate expansion risk
• Broad match → highest discovery and waste potential

Look for:

• Broad match terms spending more than exact/phrase counterparts
• Search terms unrelated to your product category
• Queries with informational intent when you need transactional intent

Example negative intent patterns:

• "how to"
• "definition"
• "jobs"
• "salary"
• "free"
• "DIY"
• "template"
• "course"
• "PDF"
• "used"
• "near me" (if location is not relevant)

4. Add automated negative keyword management

Create a recurring workflow:

Weekly

• Review search terms with: - > $50–$100 spend and 0 conversions
• CPA > 2–3× target CPA
• Low engagement metrics

Monthly

• Run overlap analysis
• Review match-type performance
• Reallocate budget

A negative keyword library should be maintained by:

• Intent exclusions
• Competitor exclusions
• Product/service exclusions
• Geographic exclusions
• Audience exclusions

5. Use a query classification model

For larger accounts, automate classification:

Classify every search term into:

• ✅ High intent
• 🟡 Research intent
• ❌ Irrelevant
• ⚠️ Competitor
• ⚠️ Existing customer/support

Then assign actions:

6. Implement keyword ownership rules

Prevent future overlap with campaign architecture:

Example:

Campaign: Exact High Intent

• Owns proven converting queries

Campaign: Discovery Broad/Phrase

• Finds new queries
• Lower bids
• Weekly mining

Campaign: Competitor

• Separate budget and messaging

Campaign: Remarketing

• Separate from prospecting

This prevents broad match from stealing budget from your best-performing terms.

Tools that work well

• Google Ads Search Terms Report + Keyword Planner
• Google Ads Scripts for automated search-term audits
• Looker Studio dashboards for query waste monitoring
• BigQuery + SQL for enterprise-scale accounts
• Third-party PPC tools such as optmyzr.com or marketingplatform.google.com for larger portfolios

A practical audit cadence

First 30 days

1. Export 90 days of queries
2. Identify wasted spend clusters
3. Add negatives
4. Restructure overlapping keywords

Ongoing

• Weekly search-term mining
• Monthly overlap audit
• Quarterly match-type strategy review

The biggest savings usually come from finding high-spend queries that are technically related to your keywords but have the wrong intent, and from ensuring each valuable query has a clear bidding owner.

• Separate from prospecting

This prevents broad match from stealing budget from your best-performing terms.

Tools that work well

For auditing wasted spend from overlapping search terms and irrelevant query match types, I’d recommend a structured Search Query Waste Audit process rather than relying only on platform recommendations. The goal is to identify where budget is leaking due to keyword cannibalization, overly broad matching, and poor query intent.

Recommended solution: Search Query Waste Audit + Optimization Loop

1. Build a search term waste dataset

Export the last 60–90 days of search term data from your paid search platforms (especially Google Ads and Microsoft Ads).

Include:

• Search term/query
• Matched keyword
• Match type
• Campaign
• Ad group
• Spend
• Clicks
• Impressions
• Conversions
• CPA / ROAS
• Quality Score (if available)
• Search impression share (optional)

This creates a query-level view of where money is going.

2. Identify overlapping keyword conflicts

Look for cases where the same search intent triggers multiple keywords.

Common issues:

Useful checks:

• Same search term appearing under multiple keywords
• Same search term appearing across campaigns
• Broad match terms capturing expensive irrelevant variants
• Lower-performing keywords receiving traffic that should belong elsewhere

3. Create a query classification system

Classify search terms into buckets:

Keep

High-intent queries:

• Product/service names
• Commercial modifiers: - pricing
• demo
• quote
• provider
• near me
• software
• solution

Optimize

Potentially valuable but poorly matched:

• Good intent, wrong landing page
• Good query but weak ad relevance
• Needs new ad group

Add as negative

Waste patterns:

• Free
• Jobs
• Careers
• Support
• Login
• Tutorial
• DIY
• Meaning unrelated to your offer
• Competitor terms (depending on strategy)

Monitor

Ambiguous searches:

• Research queries
• Early-funnel terms
• Low-volume terms

4. Add automation for recurring audits

A practical stack:

Google Ads Scripts / API

• Pull search terms weekly
• Flag queries exceeding thresholds: - Spend > $X with zero conversions
• CPA > target by 2–3x
• Clicks > threshold without conversion
• Low conversion rate compared with campaign average

Spreadsheet or BI dashboard
Recommended fields:

5. Fix match type strategy

A common optimization pattern:

High-value terms

Use:

• Exact match
• Phrase match

Example:

• [enterprise crm software]
• "enterprise crm software"

Discovery terms

Use:

• Broad match only when: - Conversion tracking is reliable
• Smart bidding has enough conversion volume
• You review search terms frequently

Avoid:

• Large broad-match campaigns without guardrails
• Mixing unrelated intents in the same ad group
• Letting broad match run without negative keyword management

6. Create negative keyword controls

Build negative keyword lists by theme:

Low intent

• free
• cheap
• template
• example
• course
• certification

Employment

• jobs
• salary
• hiring
• careers

Support

• login
• documentation
• manual
• troubleshooting

Competitor leakage

• Competitors you do not want to bid on

Apply carefully at:

• Account level (universal exclusions)
• Campaign level (intent separation)
• Ad group level (precision)

7. Measure impact

Track before/after:

• Wasted spend %
• Search term conversion rate
• CPA
• ROAS
• Impression share
• Cost per qualified lead
• Negative keywords added
• Budget recovered

A typical target is to reduce:

• Non-converting search spend by 20–40%
• Irrelevant clicks by 10–30%

while maintaining conversion volume.

Recommended operating cadence

Weekly

• Review new search terms
• Add negatives
• Promote winning queries into exact match

Monthly

• Run overlap analysis
• Consolidate duplicate keywords
• Review match type performance

Quarterly

• Rebuild keyword structure based on actual query data

For a larger account, I’d implement this as an automated Search Query Mining System: API export → intent classification → waste scoring → negative keyword recommendations → human approval. That provides a repeatable audit instead of a one-time cleanup.

If you mean paid search (Google Ads/Bing Ads), I’d recommend a search-term overlap and query-quality auditing layer rather than relying on manual search-term reports.

Recommended solution

Build a recurring audit that combines:

• Search-term → keyword mapping — identify which queries are triggering multiple keywords/ad groups and calculate the incremental spend.
• Overlap detection — flag cases where the same query is eligible across campaigns/ad groups, especially when one is broad match and another is exact/phrase.
• Match-type waste analysis — compare spend and conversion efficiency by match type and identify broad/phrase queries that generate low-value or irrelevant traffic.
• Intent classification — classify search terms into relevant, ambiguous, and irrelevant intent using your product/service taxonomy.
• Negative-keyword recommendations — automatically suggest negatives when an irrelevant query recurs, while avoiding negatives that could block valuable variants.
• Spend-at-risk scoring — prioritize issues by wasted spend, conversion loss, query frequency, and confidence rather than producing a giant list of negatives.
• Change logging — record the query, triggering keyword, proposed action, expected savings, and actual post-change impact.

A useful output might look like:

The key metric

Don't define waste simply as “broad match spend.” Instead calculate:

Wasted spend = spend on queries that are irrelevant, duplicative, or demonstrably lower-value than an available targeting alternative.

That distinction prevents an audit from incorrectly eliminating broad-match traffic that actually produces incremental conversions.

If you're looking for a software/product solution rather than an audit methodology, I can also recommend specific tools and compare them by Google Ads integration, overlap detection, automated negative-keyword suggestions, and cost.

If you’re auditing wasted spend from overlapping search terms and irrelevant query matching, the strongest solution is a structured Search Term Waste Audit + Match Type Control System. The goal is to identify where paid search is paying for the same intent multiple times, then tighten controls without choking off useful discovery.

Recommended solution: Search Term Intelligence Audit

1. Build a search term overlap analysis

Export 60–90 days of:

• Search term report
• Keyword report
• Match type
• Campaign/ad group
• Cost
• Clicks
• Conversions
• CPA/ROAS
• Impression share (if available)

Then classify search terms into:

2. Create a search term waste score

Prioritize terms using a simple formula:

Waste Score = Spend × Irrelevance Factor × Conversion Deficit

Example:

This prevents teams from manually reviewing thousands of queries.

3. Fix keyword overlap with intent segmentation

Create clear campaign ownership rules:

Brand campaign

Owns:

• Brand name
• Product names
• Navigational searches

Negative out:

• Generic product terms

Non-brand exact/phrase campaigns

Own:

• High-intent commercial searches

Examples:

• "[best accounting software for startups]"
• "buy accounting software"

Broad match discovery campaigns

Own:

• New query discovery only

Rules:

• Lower budgets
• Strong conversion signals
• Weekly search term reviews

4. Add negative keyword governance

Create a shared negative keyword library:

Intent exclusions

• free
• jobs
• salary
• careers
• definition
• meaning
• tutorial
• PDF
• template
• course

Low-quality modifiers

• cheap (if not a value brand)
• used
• DIY
• alternatives (if competitors are not a strategy)

Internal conflicts

Example:

Campaign A:

Enterprise CRM

Campaign B:

SMB CRM

Add negatives so each campaign owns its intended traffic.

5. Audit match types systematically

A common cleanup approach:

Review broad match queries weekly:

• Convert winners into exact keywords
• Add losers as negatives
• Reduce bids/budget on poor themes

6. Use an automated monitoring layer

A practical stack:

• Search term export → Google Sheets/BigQuery
• Automated labeling: - irrelevant intent
• duplicate intent
• competitor
• informational
• low conversion
• Weekly negative keyword recommendations
• Monthly overlap report

Useful automation rules:

Flag queries when:

• Spend > $50 and 0 conversions
• CPA > 2× target CPA
• Query appears in multiple campaigns
• Query triggers unexpected match types
• Search term contains exclusion patterns

7. Establish an ongoing cadence

Weekly (30–60 min)

• Review new search terms
• Add negatives
• Promote winners

Monthly

• Keyword overlap analysis
• Match-type performance review
• Budget leakage report

Quarterly

• Rebuild campaign structure if overlap exceeds ~15–20%

Recommended deliverable

Create a Search Waste Audit Dashboard with:

1. Wasted Spend Summary

• Spend on irrelevant queries
• Spend on duplicate intent
• Spend by match type
• Negative Keyword Recommendations

• Query
• Cost
• Reason
• Suggested action
• Keyword Cannibalization Report

• Same query
• Multiple campaigns
• Winner campaign
• Recommended negative
• Match Type Efficiency

• Exact vs Phrase vs Broad
• CPA
• Conversion rate
• Waste percentage

For most accounts, this process typically uncovers 10–30% of avoidable search spend leakage, especially in accounts that have accumulated broad match keywords, legacy campaigns, or multiple agencies/operators making changes over time.

If your goal is to audit wasted paid-search spend caused by overlapping keywords and irrelevant query matching, I’d recommend a Search Query Waste Audit rather than simply reviewing keyword performance.

Recommended solution

Build a recurring audit that combines the search-term/query level, triggering keyword, match type, campaign/ad group, and conversion value into one dataset.

Google’s Search Terms Report is the core source: it shows the actual searches that triggered ads, the keyword that matched them, and the match type. Google explicitly recommends using it to identify irrelevant searches, add negatives, and refine match types. Google Help Google Help

Then score each query for two types of waste:

The key audit logic

For every search query, calculate:

Wasted Spend = Cost where the query is irrelevant OR materially underperforms the account's target efficiency

Then separately calculate:

Overlap Spend = Cost from queries that matched multiple eligible keywords/campaigns

Don't automatically classify every duplicate as waste. If two keywords can trigger the same query but the query converts profitably, the overlap may be harmless. The audit should identify avoidable duplication, not merely duplication.

A practical workflow

1. Export 60–90 days of search-term data

• Query
• Cost
• Clicks
• Conversions
• Conversion value
• Campaign
• Ad group
• Keyword
• Match type
• Normalize queries

• Lowercase
• Remove punctuation
• Normalize plurals/spacing where appropriate
• Group close semantic variants.
• Find overlap

• Group by normalized query.
• Count campaigns, ad groups, keywords and match types associated with each query.
• Flag queries with multiple eligible routes.
• Find irrelevant spend

• Flag queries with spend but zero conversions.
• Also flag queries whose CPA/ROAS is substantially worse than the account or campaign target.
• Separate genuinely irrelevant intent from simply low-volume/low-conversion queries.
• Attribute the waste

Create a ranking such as:

Query → $ wasted → triggering keyword → match type → campaign → recommended action

6. Create a negative-keyword hierarchy

• Account-level negatives for universally irrelevant concepts.
• Campaign-level negatives for campaign-specific exclusions.
• Cross-campaign negatives when campaigns intentionally own different intent.

Google notes that negative keywords can prevent ads from showing for searches you don't want and recommends them as a way to reduce costs. Google Help

7. Re-audit monthly

Track:

• wasted spend identified
• negatives added
• overlap eliminated
• conversion rate recovered
• CPA/ROAS before vs. after

One important nuance

I wouldn't solve this by simply moving everything to exact match. Google's current matching system allows phrase match to encompass the same queries as exact, while broad can encompass phrase/exact and additional relevant searches. Google Help

A better approach is intent ownership:

One query intent → one logical campaign/ad-group owner whenever possible.

That gives you a much cleaner account structure and makes future waste audits substantially easier.

For larger accounts, I'd automate this as a daily/weekly query-level waste dashboard that produces a prioritized list of “here are the $X of spend we can probably eliminate, and exactly why.” Google also provides Search Terms Insights, which groups queries into intent-based categories and can complement the raw search-term report for identifying broader sources of wasted demand. Google Help Google Help

If you're doing this for Google Ads specifically, I can also lay out the exact SQL/data model + waste scoring formula + dashboard structure I'd use to automate the audit.

Overlap Spend = Cost from queries that matched multiple eligible keywords/campaigns

Don't automatically classify every duplicate as waste. If two keywords can trigger the same query but the query converts profitably, the overlap may be harmless. The audit should identify avoidable duplication, not merely duplication.

A practical workflow

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

Recommended approach

Build a recurring audit that evaluates every search query against:

• Keyword overlap: Multiple keywords/close variants competing for the same query.
• Match-type leakage: Broad/phrase keywords triggering queries that are too generic, ambiguous, or outside the intended theme.
• Negative-keyword gaps: Irrelevant queries that repeatedly consume spend without producing conversions.
• Cannibalization: Multiple campaigns/ad groups bidding on substantially the same intent.
• Spend efficiency: Spend, conversions, CPA/ROAS, and conversion value by actual search query—not just the keyword that triggered it.

The useful output

I'd structure the audit around an actionable waste score for each query, rather than simply producing a search-term report:

Best implementation

If you're doing this at meaningful account scale, I'd use a three-layer system:

1. Rules engine — catches obvious waste using spend, conversions, CPA, query length, brand terms, exclusions, etc.
2. Semantic clustering — embeddings/grouping identify queries and keywords expressing the same intent even when wording differs.
3. Human-review queue — ranks findings by estimated recoverable spend so an analyst reviews the highest-value exclusions first.

The key is to avoid automatically adding every non-converting query as a negative. Low-volume queries can be statistically inconclusive, and some apparently redundant queries may have materially different intent.

What I'd recommend as the deliverable

Create a dashboard/report with four headline numbers:

Total spend → identifiable wasted spend → % of spend at risk → estimated recoverable spend

Then drill into query → triggering keyword → match type → campaign/ad group → spend → conversions → recommended action.

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

A strong solution is a search-term overlap and query-quality audit layer that sits on top of your paid-search data (Google Ads, Microsoft Ads, etc.).

Recommended approach

1. Pull search-term data

• Query/search term
• Keyword that triggered it
• Match type
• Campaign/ad group
• Spend, clicks, conversions, conversion value
• Date range
• Detect overlapping spend

Flag cases where multiple keywords are effectively competing for the same query. For example:

• running shoes
• best running shoes
• running shoe sale

If the same search queries repeatedly trigger several keywords across campaigns/ad groups, identify the duplication and quantify the spend affected.

3. Audit irrelevant query matching

Classify search terms into:

• Relevant — should continue matching
• Irrelevant — add as negative keyword
• Potentially relevant — requires human review

Pay particular attention to broad-match queries that consume spend without producing conversions or meaningful engagement.

4. Quantify wasted spend

Rather than simply producing a list of bad queries, calculate:

Wasted spend = spend on irrelevant queries + avoidable spend from overlapping targeting

Rank opportunities by wasted dollars, not just number of queries.

5. Generate actionable recommendations

For each finding, produce something like:

6. Add guardrails

Don't automatically negative a term simply because it has no conversion. A query may be valuable at low volume or may have assisted-conversion value. Use thresholds based on spend, conversion rate, CPA/ROAS, and business relevance.

The solution I'd recommend

Build a weekly Search Query Waste Auditor that automatically:

Google/Microsoft Ads → search-term warehouse → overlap detection + relevance classification → wasted-spend scoring → recommended negatives/keyword changes → approval workflow → export back to Ads

The most valuable feature is the opportunity score: “If we fix these 27 queries/overlaps, approximately $18.4K/month of spend is potentially recoverable.” That turns an otherwise tedious search-term report into a prioritized optimization queue.

If you're dealing with a large account, I'd also add cross-campaign cannibalization detection, because overlap isn't always obvious when the same intent is distributed across different campaigns.

A good solution is a search term overlap and query waste audit system that combines data extraction, clustering, and automated negative keyword recommendations. The goal is to identify where multiple keywords compete for the same searches, where match types are too broad, and where spend is going to low-intent traffic.

Recommended approach: Search Term Waste Auditor

1. Build a search query intelligence layer

Pull data from:

• Google Ads Search Terms Report
• Keyword-level performance data
• Match type
• Campaign/ad group structure
• Conversion data
• Cost, clicks, CTR, CPC, CPA, ROAS

Analyze at the actual search query level, not just keywords.

Key fields:

• Search term
• Triggering keyword
• Match type
• Campaign
• Ad group
• Spend
• Conversions
• Conversion value
• Impression share
• Quality Score (if available)

2. Detect overlapping keyword cannibalization

Flag cases where multiple keywords capture the same intent.

Examples:

Create an overlap score based on:

• Query similarity (semantic embeddings)
• Shared search terms
• Conversion performance differences
• Auction overlap data

Actions:

• Consolidate duplicate keywords
• Move terms into a single high-performing ad group
• Pause weaker duplicates
• Add negatives to force traffic routing

3. Audit match type leakage

Prioritize reviewing:

Broad match

Look for:

• High spend + no conversions
• Low relevance queries
• Unexpected categories
• Competitor traffic
• Informational searches

Example:

Keyword:

"enterprise CRM"

Wasted queries:

• "what is CRM"
• "CRM definition"
• "free CRM templates"
• "CRM jobs"

Potential action:

• Add negatives
• Narrow match type
• Improve audience signals

Phrase match drift

Phrase match often captures adjacent intent.

Example:

Keyword:

"running shoes"

Queries:

• "running shoes for marathon"
• "how to clean running shoes"
• "running shoe repair"
• "shoe size chart"

Classify queries into:

• Commercial
• Research
• Navigational
• Irrelevant

Exact match conflicts

Even exact match can overlap when multiple similar keywords exist.

Example:

Keywords:

• [buy office chairs]
• [office chairs online]
• [purchase office chair]

Search term:

"buy office chairs online"

Recommendation:

• Keep only the strongest keyword
• Consolidate signals

4. Create waste scoring

A simple prioritization model:

Waste Score = Spend × Irrelevance × Lack of Conversion Probability

Example:

Prioritize:

1. High spend + zero conversions
2. High CPA queries
3. Low-quality informational searches
4. Duplicate intent clusters

5. Automate recommendations

Generate recommendations such as:

Negative keyword candidates

• "free"
• "jobs"
• "template"
• "definition"
• "course"
• "PDF"
• "DIY"

Keyword consolidation

Merge "software platform," "software solution," and "software tool" into one theme.

Match type changes

Move high-performing broad queries into exact match.

Budget reallocation

Reduce spend from broad campaigns feeding poor queries.

6. Tools that can implement this

Depending on scale:

Enterprise

• Google Ads API + BigQuery + Looker Studio
• Search term clustering with embeddings
• Custom ML scoring model

Mid-market

• Google Ads Scripts
• Google Sheets audit dashboard
• Data Studio/Looker reporting

Agency workflows

• Weekly search term mining pipeline
• Automated Slack/email alerts for waste thresholds
• Shared negative keyword library

Suggested audit cadence

Weekly

• Add negatives
• Review new search terms
• Catch broad match drift

Monthly

• Keyword overlap analysis
• Match-type restructuring
• Budget redistribution

Quarterly

• Full account architecture review

The highest ROI setup is usually a Search Terms → Intent Classification → Waste Score → Negative Keyword Recommendation workflow, because it catches both obvious waste (irrelevant queries) and hidden waste (multiple keywords competing for the same intent).

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.