Finding Gold in Your Google Ads Data: AI-Powered Search Term Analysis

January 27, 2026 11 min read

# Finding Gold in Your Google Ads Data: AI-Powered Search Term Analysis

Your Google Ads search term report contains hidden treasure. And hidden waste.

Every query that triggered your ads tells you something: what people actually search for when looking for your product, what terms you're paying for that will never convert, what keywords you're missing that your competitors capture.

Most advertisers export the report, sort by spend, skim the top 20 terms, add a few negatives, and move on. They're leaving money on the table.

AI-powered search term analysis goes deeper—finding patterns humans miss, opportunities buried in long-tail data, and systematic waste that compounds monthly.

Here's how to extract every dollar of value from your search term data.

The Hidden Value in Search Terms

Your search term report isn't just a list. It's a map of customer intent.

What search terms reveal:

Data Point	What It Tells You
New converting terms	Keywords to add to campaigns
High-spend non-converters	Negatives to eliminate waste
Question-format queries	Content opportunities
Competitor mentions	Competitive positioning needs
Misspellings that convert	Broad match opportunities
Long-tail variations	Exact match expansion candidates
Intent signals	Landing page optimization targets

The average account has 10,000+ unique search terms per quarter. Manual analysis catches the obvious ones. AI analysis catches everything.

The Manual Approach (And Its Limits)

Typical search term management:

1. Export last 30 days
2. Sort by cost
3. Review top 50-100 terms
4. Add obvious negatives ("free," "jobs," competitor names)
5. Maybe add a few high-performers as keywords
6. Done until next month

Time invested: 30-60 minutes Terms reviewed: 0.5-1% of total Waste eliminated: Obvious offenders only Opportunities captured: Whatever stood out

This approach misses:

• Low-volume terms that collectively represent 40%+ of spend
• Patterns across related terms (not just individual queries)
• Emerging trends before they become obvious
• Negative keyword themes (not just individual terms)
• Keyword opportunities in the long tail

The AI-Powered Approach

Our system analyzes every term, identifies patterns, and generates prioritized actions.

Phase 1: Data Preparation

Export search terms with all available metrics:

• Search term
• Match type triggered
• Impressions, clicks, cost
• Conversions, conversion value
• Campaign and ad group

For meaningful analysis, pull at least 90 days of data. Short timeframes miss patterns.

Phase 2: Pattern Recognition

AI analyzes the data looking for:

Negative keyword candidates:

• Terms with spend > $X and zero conversions
• Terms with high impressions but CTR < 0.5% (low relevance)
• Terms containing irrelevant modifiers ("free," "diy," "cheap" if you're premium)
• Terms indicating wrong intent (informational when you want transactional)
• Terms mentioning competitors (if you don't want to bid on them)

Keyword opportunities:

• Converting terms not yet added as keywords
• Terms with CTR > campaign average (high relevance)
• Long-tail variations of your best performers
• Question-format queries (potential featured snippet targets)
• Terms with high conversion rate but low impression share

Match type optimization:

• Exact match candidates (proven converters to protect)
• Phrase match candidates (proven themes to expand)
• Broad match risks (high spend from irrelevant queries)

Phase 3: Prioritized Recommendations

AI doesn't just flag issues—it prioritizes by impact:

Example output:

NEGATIVE KEYWORD RECOMMENDATIONS (Priority: High)

1. Add "jobs" as campaign negative
   - 47 matching terms, $1,234 spend, 0 conversions
   - Pattern: Users searching for employment, not product
   - Impact: Est. $400/month waste elimination

2. Add "free" as campaign negative
   - 23 matching terms, $890 spend, 0 conversions
   - Pattern: Users seeking free alternatives
   - Impact: Est. $290/month waste elimination

3. Add "how to" as ad group negative (Brand campaign only)
   - 12 matching terms, $456 spend, 1 conversion at $456 CPA
   - Pattern: Informational intent, poor conversion
   - Impact: Est. $400/month CPA improvement

KEYWORD OPPORTUNITIES (Priority: Medium)

1. Add "[product] for small business" as exact match
   - Current: Triggered via broad match
   - Performance: 34 conversions, $12 CPA (vs $28 account avg)
   - Recommendation: Protect with exact match, increase bids

2. Add "[product] vs [competitor]" as phrase match
   - Current: Triggered via broad match
   - Performance: 12 conversions, $18 CPA
   - Recommendation: Create dedicated ad group with comparison ads

MATCH TYPE ALERTS (Priority: Low)

1. Broad match "[product]" generating irrelevant traffic
   - 23% of queries are off-topic
   - Consider tightening to phrase match or adding negatives

Phase 4: Implementation

Recommendations export directly to Google Ads Editor format:

Campaign,Ad Group,Keyword,Match Type,Action
All Campaigns,-,jobs,Negative Broad,Add
All Campaigns,-,free,Negative Broad,Add
Brand Campaign,-,how to,Negative Phrase,Add
Product Campaign,Core Terms,[product] for small business,Exact,Add
Product Campaign,Competitor,[product] vs [competitor],Phrase,Add

Import to Editor, review, apply. What took hours of manual work happens in minutes.

Real Impact Examples

Let me show you what this analysis uncovers.

Example 1: The Compound Waste Pattern

Manual analysis found: "Free trial download" - added as negative

AI analysis found:

Pattern: "Free" modifier queries
- 47 unique terms containing "free"
- Combined: 2,340 clicks, $4,680 spend, 0 conversions
- Terms include: free alternative, free version, free trial,
  free download, free software, open source free, etc.

Recommendation: Add "free" as campaign-level negative
Annual savings: $56,160

The manual approach found one term. AI found the pattern across 47 terms.

Example 2: The Hidden Gem

Manual analysis found: Nothing notable in position 73 of cost-sorted list

AI analysis found:

High-efficiency opportunity:
- Term: "[product] for accountants"
- 12 conversions, $96 total spend ($8 CPA vs $32 account avg)
- Currently: Triggered via broad match, no dedicated targeting
- Impression share: 23% (missing 77% of searches)

Recommendation:
1. Add as exact match keyword
2. Create dedicated ad with accountant-specific messaging
3. Create landing page for accountants
Projected impact: 4x conversions at same CPA

This converting term was buried in the long tail. AI surfaced it because of anomalous efficiency.

Example 3: The Competitor Intelligence

Manual analysis found: A few competitor brand terms to negative out

AI analysis found:

Competitor mention analysis:
- [Competitor A]: 234 queries, 12 conversions, $18 CPA
- [Competitor B]: 156 queries, 8 conversions, $22 CPA
- [Competitor C]: 89 queries, 2 conversions, $67 CPA

Insight: Users comparing you to Competitor A convert well.
Users searching Competitor C have different needs (poor fit).

Recommendations:
1. Create Competitor A comparison campaign (opportunity)
2. Add Competitor C as negative (poor fit)
3. Research why Competitor B traffic underperforms

Not all competitor traffic is equal. AI identifies which competitors to embrace and which to avoid. This analysis feeds into our broader competitive intelligence system.

Building Your Search Term Analysis System

You can implement this approach with available tools.

Step 1: Export Comprehensive Data

From Google Ads:

• Reports > Predefined reports > Search terms
• Date range: Last 90 days (minimum)
• Columns: Search term, match type, campaign, ad group, impressions, clicks, cost, conversions, conv. value

Export as CSV.

Step 2: Prepare Analysis Prompt

Feed the data to Claude or GPT-4 with this prompt:

Analyze this Google Ads search term data and provide:

1. NEGATIVE KEYWORD RECOMMENDATIONS
Identify terms to add as negatives based on:
- High spend with zero conversions
- Patterns indicating wrong intent
- Irrelevant modifiers appearing across multiple terms
Format: Term, reason, total spend, recommended match type

2. KEYWORD OPPORTUNITIES
Identify terms to add as keywords based on:
- Conversions at better-than-average CPA
- High CTR indicating strong relevance
- Volume potential if given dedicated targeting
Format: Term, current performance, recommended match type, projected impact

3. MATCH TYPE RECOMMENDATIONS
Identify keywords where match type should change based on:
- Broad match generating excessive irrelevant queries
- High-performing terms that need protection with exact match
Format: Current keyword, current match type, recommended change, reason

4. PATTERNS AND INSIGHTS
What themes or patterns exist in:
- Converting terms (what do winners have in common?)
- Non-converting terms (what do losers have in common?)
- Emerging trends (new query patterns worth watching?)

Prioritize all recommendations by estimated monthly impact.

Here is the data:
[paste CSV]

Step 3: Review and Implement

AI recommendations need human review:

• Does the negative make sense for our business?
• Is the keyword opportunity actually relevant?
• Do we have capacity to create dedicated campaigns/landing pages?

Review takes 15-20 minutes. Then export to Google Ads Editor and apply.

Step 4: Establish Cadence

Weekly: Quick review of high-spend non-converters (15 min) Monthly: Full AI analysis and implementation (60 min) Quarterly: Deep dive on patterns and strategic opportunities (2 hours)

Consistent attention compounds. Monthly analysis catches waste before it accumulates.

The Math of Systematic Analysis

Let's quantify the impact:

Typical account inefficiency:

• 15-25% of spend goes to irrelevant queries
• On $10,000/month spend, that's $1,500-2,500 waste

Manual analysis catches:

• Obvious negatives (the "jobs" and "free" queries)
• Maybe 30-40% of waste
• Savings: $450-1,000/month

AI analysis catches:

• Pattern-based negatives
• Long-tail waste
• 70-85% of waste identified
• Savings: $1,050-2,125/month

Additional value from opportunities:

• Converting terms promoted to keywords
• Dedicated campaigns for high performers
• Typical improvement: 10-20% more conversions at same spend

Annual impact of AI-powered analysis: $15,000-35,000 on a $10,000/month account. This is one of many ways our AI-amplified marketing approach drives measurable ROI.

Start Today

If you're running Google Ads, here's your immediate action:

Today:

1. Export last 90 days of search terms
2. Run AI analysis using the prompt above
3. Identify top 10 negative keyword candidates

This week:

1. Implement negative keywords
2. Identify top 3 keyword opportunities
3. Create new keywords or campaigns for opportunities

This month:

1. Establish weekly review cadence
2. Track impact of changes (before/after spend efficiency)
3. Refine analysis prompts based on what's useful

Let Us Analyze Your Campaigns

Want professional search term analysis? Our paid media team offers it as a service.

What you get:

• Comprehensive analysis of all search terms
• Prioritized negative keyword recommendations
• Keyword opportunity identification
• Match type optimization recommendations
• Implementation support

Investment: Starting at $500 for one-time analysis, or $300/month for ongoing optimization.

Contact us:

• Email: hello@wedoworldwide.com
• Website: wedoworldwide.com

Share your search term export (anonymized if preferred). We'll send back a sample analysis showing what you're missing.

---

About the Author: Mike McKearin is the founder of WE-DO Growth Agency. His team manages $2M+ in annual Google Ads spend, using AI-powered search term analysis to reduce waste by an average of 23% while improving conversion volume.