Search Term Analysis

Search Term Analysis is the process of examining the actual search queries that trigger your paid search ads to understand user intent and optimize campaign performance. This analysis reveals the gap between the keywords you're bidding on and the real search terms people use, helping you identify new opportunities, eliminate wasteful spending, and better align your campaigns with customer behavior. By analyzing search term data, marketers can make informed decisions about keyword expansion, negative keyword lists, and bid adjustments to improve both relevance and return on ad spend.

When search term analysis reveals high-performing queries with strong conversion rates and low costs, it indicates your campaigns are effectively capturing qualified traffic. Conversely, if analysis shows irrelevant or low-converting search terms consuming budget, it signals the need for tighter keyword targeting and expanded negative keyword lists. This metric works closely with Quality Score and Keyword Performance Analysis, as search term relevance directly impacts ad quality and overall campaign effectiveness.

Understanding how to do search term analysis effectively requires regular review of search query reports, identifying patterns in user behavior, and translating insights into actionable optimizations. This practice connects directly to Search Query Performance and Negative Keyword Analysis, forming a comprehensive approach to paid search optimization that drives better targeting and improved campaign results.

Search Term Analysis involves systematically examining the actual search queries that triggered your ads to identify optimization opportunities and understand user behavior patterns.

Approach: Step 1: Export search term data from your advertising platform with metrics like impressions, clicks, conversions, and costs Step 2: Categorize search terms by relevance, intent, and performance to identify high-value queries and irrelevant traffic Step 3: Create actionable recommendations including new keywords to target, negative keywords to add, and bid adjustments to implement

Worked Example

Consider a fitness equipment retailer running ads for "home gym equipment." After analyzing 30 days of search term data, they discover:

High-performing terms:

• "compact home gym equipment" (50 clicks, 8% CTR, 12% conversion rate, $45 CPA)
• "apartment workout equipment" (35 clicks, 6% CTR, 15% conversion rate, $38 CPA)

Poor-performing terms:

• "gym membership prices" (120 clicks, 2% CTR, 0% conversion rate, $0 revenue)
• "free workout videos" (85 clicks, 1.5% CTR, 0% conversion rate, $0 revenue)

Analysis insights: The compact/space-saving angle drives quality traffic, while informational queries about memberships and free content waste budget. Actions include adding "compact" and "apartment" as new keywords, implementing negative keywords for "membership," "free," and "prices," and increasing bids on space-related terms.

Variants

Time-based analysis compares search terms across different periods to identify seasonal trends or performance changes. Segmented analysis breaks down terms by campaign, ad group, or device type to understand context-specific performance. Intent-based analysis categorizes terms by commercial intent (transactional, informational, navigational) to align bidding strategies with user motivation levels.

Common Mistakes

Insufficient volume thresholds lead to making decisions on statistically insignificant data—ensure terms have adequate impressions and clicks before drawing conclusions. Ignoring match type context means treating broad match irrelevant terms the same as exact match variations, when they require different optimization approaches. Reactive-only optimization focuses solely on adding negative keywords without identifying positive expansion opportunities from high-performing search terms.

It's natural to want benchmarks for search term analysis performance, but context matters significantly. These benchmarks should guide your thinking and help you identify when performance deviates from expectations, rather than serve as rigid targets to hit at all costs.

Search Term Analysis Benchmarks

Dimension	Segment	Match Rate	Irrelevant Query %	New Negative Keywords/Month
Industry	SaaS	85-92%	8-15%	50-150
	Ecommerce	80-88%	12-20%	100-300
	Fintech	88-95%	5-12%	30-80
	Healthcare	90-96%	4-10%	20-60
Company Stage	Early-stage	75-85%	15-25%	20-80
	Growth	85-92%	8-15%	80-200
	Mature	90-95%	5-10%	150-400
Business Model	B2B	88-94%	6-12%	40-120
	B2C	82-89%	11-18%	100-250
	Enterprise	92-97%	3-8%	20-50
	Self-serve	80-87%	13-20%	80-200

Source: Industry estimates based on paid search performance data

Understanding Context

These benchmarks provide a general sense of healthy search term analysis performance—you'll know when something feels off. However, search term metrics exist in constant tension with each other and broader campaign objectives. As you optimize one area, others may naturally shift. Consider these metrics holistically rather than optimizing any single metric in isolation.

Related Metrics Interaction

Search term analysis performance directly impacts and reflects other key metrics. For example, if you're aggressively adding negative keywords to improve match rate and reduce irrelevant queries, you might see your impression volume decrease and cost-per-click increase as you compete for more specific, higher-intent terms. Conversely, if you're expanding into new markets or testing broader match types, expect your irrelevant query percentage to temporarily spike while you gather data to refine targeting. The key is understanding these trade-offs and ensuring your search term optimization aligns with broader campaign goals like conversion rate, customer acquisition cost, and lifetime value.

When your search term analysis reveals declining performance, several interconnected factors could be at play. Here's how to diagnose what's going wrong:

Irrelevant search queries are increasing You're seeing more traffic from queries that don't match your target audience or product. Look for high impression volume from terms with low click-through rates and poor conversion rates. This often cascades into wasted ad spend and diluted campaign performance. The fix involves expanding your negative keyword lists and tightening match types.

Keyword cannibalization is occurring Multiple campaigns or ad groups are competing for the same search terms, driving up costs and confusing your optimization efforts. Check for overlapping keywords across campaigns and declining Quality Scores. This internal competition reduces overall account efficiency and makes search term analysis optimization more difficult.

Search intent has shifted User behavior patterns have changed, but your keyword strategy hasn't adapted. You'll notice previously high-performing terms showing declining conversion rates or engagement metrics. Seasonal trends, market changes, or competitor activity can trigger these shifts, requiring you to realign your targeting strategy.

Match type settings are too broad Broad match keywords are attracting irrelevant traffic, making your search term reports noisy and less actionable. Signs include high impression volumes with low relevance scores and increased cost-per-acquisition. This makes it harder to identify genuine optimization opportunities.

Competitor activity is intensifying Increased competition for your target terms is driving up costs and reducing impression share. You'll see declining ad positions and higher CPCs for previously profitable keywords. This external pressure requires more sophisticated bidding strategies and potentially expanding into long-tail opportunities.

Each issue compounds the others, making systematic diagnosis crucial for effective search term analysis optimization.

Implement negative keyword campaigns systematically When irrelevant queries drive up costs, create comprehensive negative keyword lists by analyzing your search term reports weekly. Export queries with low conversion rates or high costs, then add variations as negative keywords at campaign and ad group levels. Validate impact by comparing cost-per-conversion before and after implementation—you should see improved relevance scores within 2-3 weeks.

Refine match types based on query patterns If broad match keywords trigger unrelated searches, analyze query-to-keyword mapping in cohorts. Group similar irrelevant queries and either switch to phrase/exact match or add specific negative keywords. Use A/B testing by splitting similar campaigns with different match type strategies to measure which approach delivers better qualified traffic.

Optimize landing page alignment with search intent When conversion rates drop despite relevant traffic, examine the user journey from query to landing page. Create intent-based landing pages that directly address the search terms driving traffic. Test page variations against your top-performing queries and track bounce rates alongside conversion metrics to validate relevance improvements.

Establish query performance segmentation Categorize search terms by intent (informational, commercial, navigational) and performance metrics. Create cohort analyses comparing high-converting query types against poor performers to identify patterns. This segmentation reveals which query categories deserve budget increases and which need immediate optimization or exclusion.

Monitor competitor keyword encroachment Track branded vs. non-branded query performance trends monthly. If competitor terms increasingly trigger your ads, adjust bidding strategies and create dedicated competitor campaigns with tailored messaging. Use impression share data to validate whether you're maintaining visibility for your core terms while filtering out costly competitor spillover.

Stop calculating Search Term Analysis in spreadsheets and losing valuable optimization opportunities. Connect your data source and ask Count to calculate, segment, and diagnose your Search Term Analysis in seconds, uncovering the insights that drive better campaign performance.