negative-keyword-miner

name	negative-keyword-miner
description	Mine, categorize, and manage negative keywords from search term reports. Use when analyzing search queries, building negative keyword lists, detecting match type bleeding, or resolving cross-campaign keyword conflicts.

Negative Keyword Miner

Systematic approach to extracting, categorizing, and managing negative keywords across Google Ads and Microsoft Ads campaigns. Covers search term report analysis, AI-assisted categorization, match type strategy, shared lists, cross-campaign conflict detection, and industry-specific templates.

Capabilities

1. Search Term Report Analysis - Pull and analyze search queries that triggered ads
2. AI Categorization - Automatically classify irrelevant queries by category
3. Match Type Analysis - Detect broad match bleeding and close variant issues
4. Negative List Management - Build campaign-level and shared negative keyword lists
5. Cross-Campaign Conflict Detection - Find negatives that block desired traffic
6. Industry Templates - Pre-built negative keyword lists by industry vertical

Workflows

Workflow 1: Search Term Report Mining

Step 1: Pull search term report via GAQL

SELECT
  search_term_view.search_term,
  campaign.name,
  ad_group.name,
  metrics.impressions,
  metrics.clicks,
  metrics.cost_micros,
  metrics.conversions,
  metrics.conversions_value,
  search_term_view.status
FROM search_term_view
WHERE
  segments.date DURING LAST_30_DAYS
  AND metrics.impressions > 0
ORDER BY metrics.cost_micros DESC

Step 2: Calculate efficiency metrics

For each search term:

CPA = Cost / Conversions
ROAS = Conversion Value / Cost
Waste Score = Cost × (1 - Conversion Rate / Campaign Avg Conversion Rate)

Step 3: Flag candidates for negatives

Criteria	Threshold	Priority
High spend, zero conversions	Cost > 2× CPA target, 0 conversions	Critical
Very low CTR	CTR < 0.5% with 100+ impressions	High
High CPA	CPA > 3× target	High
Irrelevant intent	Contains job/free/DIY terms	Medium
Competitor terms (unintentional)	Contains competitor brand names	Medium
Informational queries	Contains "what is", "how to"	Low-Medium

Step 4: Categorize flagged terms

Group into categories for systematic addition:

Category	Examples	Negative Match Type
Job seekers	"marketing manager jobs", "hiring ads"	Phrase: "jobs", "careers", "hiring", "salary"
Free seekers	"free ad maker", "free templates"	Phrase: "free"
DIY/Education	"how to make ads", "ad tutorial"	Phrase: "how to", "tutorial", "course"
Wrong product	"TV ads" when selling digital ads	Exact: [TV ads], [television advertising]
Wrong location	"ads agency london" for US business	Phrase: "london", "uk"
Competitor	"hubspot ads tool"	Exact: [hubspot], [competitor name]

Workflow 2: Broad Match Bleeding Analysis

What is broad match bleeding? Broad match keywords trigger on loosely related searches that waste budget. This has intensified with Google's expanded close variants and broad match modifier deprecation.

Step 1: Identify broad match keywords

SELECT
  ad_group_criterion.keyword.text,
  ad_group_criterion.keyword.match_type,
  campaign.name,
  ad_group.name,
  metrics.impressions,
  metrics.clicks,
  metrics.cost_micros,
  metrics.conversions
FROM keyword_view
WHERE
  ad_group_criterion.keyword.match_type = 'BROAD'
  AND segments.date DURING LAST_30_DAYS
ORDER BY metrics.cost_micros DESC

Step 2: Cross-reference with search terms

For each broad match keyword, compare triggered search terms:

Broad Keyword: "running shoes"
Search Terms Triggered:
  ✅ "buy running shoes online" — relevant
  ✅ "best running shoes 2026" — relevant
  ⚠️ "running shoe repair near me" — tangential
  ❌ "running shoes drawing" — irrelevant
  ❌ "free running shoes giveaway" — irrelevant
  ❌ "running shoe factory jobs" — irrelevant

Step 3: Calculate bleed rate

Bleed Rate = Irrelevant Search Term Impressions / Total Search Term Impressions × 100

Interpretation:
  < 10%: Healthy — normal broad match behavior
  10-25%: Moderate — add negatives for top offenders
  25-50%: Severe — consider switching to phrase match
  > 50%: Critical — switch to exact match or pause keyword

Step 4: Remediation decision matrix

Bleed Rate	Conversion Rate	Action
< 10%	Above avg	Keep broad, monitor
< 10%	Below avg	Add negatives, monitor
10-25%	Above avg	Add negatives, keep broad
10-25%	Below avg	Switch to phrase match
25-50%	Any	Switch to phrase match + negatives
> 50%	Any	Switch to exact match

Workflow 3: Negative Keyword List Management

Campaign-level vs Shared lists:

Type	Use When	Pros	Cons
Campaign-level	Specific to one campaign's theme	Precise control	Hard to maintain at scale
Shared list	Applies across multiple campaigns	Centralized management	May over-block niche campaigns
Account-level	Universal exclusions	One place for all	No exceptions per campaign

Recommended list structure:

Shared List: "Universal Negatives"
  → Apply to ALL campaigns
  → Contains: jobs, careers, salary, free, cheap, DIY, tutorial, wiki

Shared List: "Competitor Brands"
  → Apply to non-competitor campaigns
  → Contains: exact match competitor brand names

Shared List: "Geographic Exclusions"
  → Apply to geo-targeted campaigns
  → Contains: irrelevant cities, countries, regions

Campaign-Level:
  → Campaign "Running Shoes" excludes: "basketball", "hiking", "dress shoes"
  → Campaign "Running Apparel" excludes: "shoes", "sneakers"

Managing list size:

• Google Ads limit: 5,000 negative keywords per list
• Google Ads limit: 20 shared lists per account
• Campaign-level limit: 10,000 negative keywords per campaign
• Practical recommendation: Keep each list under 1,000 terms

Workflow 4: Cross-Campaign Conflict Detection

What is a negative keyword conflict? A negative keyword in Campaign A blocks a search query that Campaign B is actively bidding on. This causes lost traffic.

Step 1: Export all keywords and negatives

-- Active keywords
SELECT
  campaign.name AS campaign,
  ad_group.name AS ad_group,
  ad_group_criterion.keyword.text AS keyword,
  ad_group_criterion.keyword.match_type AS match_type,
  'POSITIVE' AS polarity
FROM keyword_view
WHERE ad_group_criterion.status = 'ENABLED'

-- Negative keywords
SELECT
  campaign.name AS campaign,
  '' AS ad_group,
  campaign_criterion.keyword.text AS keyword,
  campaign_criterion.keyword.match_type AS match_type,
  'NEGATIVE' AS polarity
FROM campaign_criterion
WHERE campaign_criterion.type = 'KEYWORD'
  AND campaign_criterion.negative = TRUE

Step 2: Cross-reference for conflicts

For each negative keyword, check if it would block a positive keyword:

Conflict Type	Example	Severity
Exact vs Exact	Neg: [blue shoes] blocks Positive: [blue shoes]	Critical
Phrase vs Exact	Neg: "blue shoes" blocks Positive: [buy blue shoes]	High
Broad neg vs Phrase pos	Neg: blue shoes blocks Positive: "blue running shoes"	Medium
Shared list vs campaign	List neg blocks campaign positive	Variable

Step 3: Resolution

1. Remove the conflicting negative
2. Add the negative as campaign-level instead of shared
3. Use exact match negatives to be more precise
4. Move the positive keyword to the campaign with the negative to consolidate

Workflow 5: Automated Negative Mining Cadence

Weekly routine:

Day	Task	Time
Monday	Pull 7-day search term report	15 min
Monday	Flag high-spend zero-conversion terms	10 min
Monday	Add critical negatives immediately	10 min
Thursday	Review moderate-priority candidates	15 min
Thursday	Check for cross-campaign conflicts	10 min
Friday	Update shared negative lists	10 min

Monthly routine:

• Full 30-day search term analysis
• Broad match bleed rate calculation
• Shared list audit (remove overly aggressive negatives)
• Cross-campaign conflict resolution

Negative Keyword Match Types

How Negative Match Types Work (Different from Positive)

Negative Type	Blocks	Does NOT Block
Broad (default)	Queries containing ALL negative words (any order)	Queries with only some of the words
Phrase	Queries containing negative words in exact order	Queries with words between or rearranged
Exact	Only the exact query	All other variations

Critical difference from positive match types:

• Negative broad match does NOT include close variants, synonyms, or related searches
• Negative keywords don't match plurals, misspellings, or similar terms
• You must add each variation explicitly

Example:

Negative broad match: running shoes

• ❌ Blocks: "running shoes", "shoes running", "red running shoes"
• ✅ Still shows: "running", "shoes", "run shoe", "running sneakers"

Negative phrase match: "running shoes"

• ❌ Blocks: "buy running shoes", "running shoes sale"
• ✅ Still shows: "shoes for running", "running shoe"

Negative exact match: [running shoes]

• ❌ Blocks: "running shoes" only
• ✅ Still shows: "buy running shoes", "running shoes sale"

Industry Negative Keyword Templates

E-Commerce (General)

Broad negatives: free, cheap, used, refurbished, wholesale, bulk, diy, 
  repair, fix, manual, instructions, jobs, careers, salary, hiring,
  review, reviews, reddit, forum, comparison, vs, alternative

Phrase negatives: "how to make", "how to build", "how to fix",
  "near me" (if online only), "open box", "second hand"

Exact negatives: [ebay], [amazon], [walmart], [alibaba], [craigslist]

SaaS / B2B Software

Broad negatives: free, open source, template, tutorial, course,
  certification, training, salary, jobs, intern, internship,
  download, crack, pirated, github, stackoverflow

Phrase negatives: "what is", "how to use", "definition of",
  "vs", "alternative to", "open source", "free trial expired"

Exact negatives: [competitor names as exact match]

Local Services (Plumbing, HVAC, Legal)

Broad negatives: diy, tutorial, youtube, jobs, careers, salary,
  hiring, school, certification, course, training, license,
  requirements, exam, practice test

Phrase negatives: "how to fix", "how to install", "how to repair",
  "near me" (for excluded cities), "DIY guide"

Exact negatives: [cities you don't serve], [states you don't serve]

Healthcare / Medical

Broad negatives: jobs, careers, salary, nursing, school, degree,
  certification, free, symptoms, wiki, wikipedia, webmd, mayo clinic,
  home remedy, natural cure, reddit

Phrase negatives: "how to become", "medical school", "nursing program",
  "is it normal", "home treatment"

Exact negatives: [competitor names], [irrelevant specialties]

Financial Services

Broad negatives: free, jobs, careers, salary, scam, fraud, complaint,
  reddit, review, lawsuit, class action, calculator, formula, excel,
  template, worksheet

Phrase negatives: "how to calculate", "what is the formula",
  "free calculator", "excel template", "is it a scam"

Exact negatives: [competitor names], [irrelevant product types]

Real Estate

Broad negatives: jobs, careers, salary, license, exam, course,
  school, training, zillow, trulia, realtor.com, free, craigslist,
  for rent (if selling), for sale (if renting)

Phrase negatives: "how to become", "real estate school",
  "real estate license", "average salary"

Exact negatives: [cities not served], [competitor agent names]

Reference Data

Negative Keyword Impact on Performance

Typical improvements after systematic negative keyword mining:

Metric	Before	After (30 days)	Improvement
CTR	3.2%	4.8%	+50%
CPC	$2.40	$1.95	-19%
Conversion Rate	2.1%	3.4%	+62%
CPA	$114	$57	-50%
Wasted Spend	25%	8%	-68%
Quality Score (avg)	5.2	6.8	+31%

Budget Waste Benchmarks

Account Maturity	Typical Waste %	Target Waste %
New (0-3 months)	25-40%	< 20%
Established (3-12 months)	15-25%	< 10%
Mature (12+ months)	8-15%	< 5%
Well-optimized	3-8%	< 3%

Examples

Example 1: SaaS Company Search Term Audit

Scenario: B2B SaaS spending $30K/month on Google Ads. CPA target: $80. Actual CPA: $135.

Analysis of 30-day search term report (2,400 unique terms):

Category	Terms	Cost	Conversions	CPA	Action
High intent	480	$12,000	210	$57	Keep
Mid intent	620	$8,000	48	$167	Optimize
Informational	540	$5,500	8	$688	Add negatives
Job seekers	180	$2,000	0	∞	Add negatives
Competitor	320	$1,800	5	$360	Move to competitor campaign
Irrelevant	260	$700	0	∞	Add negatives

Negatives added: 145 terms across 6 categories Projected savings: $8,200/month (27% of spend) Projected new CPA: $80 (hitting target)

Example 2: E-commerce Broad Match Bleeding Fix

Scenario: Online shoe store, keyword "running shoes" on broad match.

Bleed analysis:

• Total search term impressions: 45,000
• Relevant impressions: 28,000 (62%)
• Irrelevant impressions: 17,000 (38%) ← Severe bleed

Top bleed categories:

1. "running shoes drawing" (art related) — 4,200 impressions, 0 conversions
2. "running shoes for flat feet" (medical) — 3,100 impressions, 2 conversions (acceptable)
3. "free running shoes" — 2,800 impressions, 0 conversions
4. "running shoes factory outlet near me" — 2,400 impressions, 0 conversions (online only)
5. "running shoes repair" — 1,900 impressions, 0 conversions

Actions:

1. Added negatives: drawing, free, repair, factory outlet, near me
2. Kept "flat feet" (relevant variation)
3. Moved keyword from broad to phrase match
4. Bleed rate dropped from 38% to 11% in 2 weeks

Example 3: Cross-Campaign Conflict Resolution

Scenario: 3 campaigns competing with conflicting negatives.

Conflict found:

• Campaign "Brand" has negative phrase: "reviews"
• Campaign "Non-Brand" bids on keyword: "product name reviews"
• Result: "product name reviews" blocked everywhere (Brand neg + Non-Brand doesn't target it)

Resolution:

1. Changed Brand campaign negative from phrase to exact: [reviews]
2. Now "product name reviews" routes to Non-Brand campaign correctly
3. Impressions for review-related queries increased 340%
4. Added 12 high-intent review queries to Non-Brand campaign

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