The Mystery of the Midnight Search Term Report
I’ll never forget the first time I opened a Google Ads search term report at 11 PM on a Tuesday. My campaign was bleeding budget—$3,000 spent in two weeks with barely any conversions. As I scrolled through hundreds of search queries, I noticed something strange: certain word combinations kept appearing together, but Google’s interface showed them scattered across different rows.
“coding classes near me free trial no commitment” “coding classes near me free download” “coding classes near me free consultation”
My brain started connecting dots. These weren’t random searches—they were patterns. Clusters of meaning hiding in plain sight. That’s when I discovered n-grams, and honestly, it changed everything about how I manage search campaigns.
If you’ve ever felt overwhelmed by search term reports, confused about which keywords to add or exclude, or frustrated watching your ad spend disappearing into irrelevant clicks, you’re in the right place. This guide will show you how to use n-gram analysis to find the signal in the noise, optimize your negative keyword strategy, and actually understand what your audience is really searching for.
The Core Philosophy: Patterns Over Individual Words
Here’s what most advertisers get wrong: they treat each search query as a unique snowflake. They add keywords one by one, exclude terms reactively, and never see the forest for the trees.
N-gram analysis flips this approach. Instead of obsessing over individual searches, you’re looking for recurring word sequences—the phrases and combinations that appear again and again across your search terms.
An n-gram is simply a sequence of “n” words:
- 1-gram (unigram): Single words like “coding” or “online”
- 2-gram (bigram): Two-word sequences like “coding classes” or “online learning”
- 3-gram (trigram): Three-word sequences like “coding classes online” or “learn programming kids”
The magic happens when you aggregate these patterns. Instead of seeing 500 unique search queries, you suddenly see that 200 of them contain the word “free,” 150 include “download,” and 80 mention “job” or “salary.” Now you’re working with actionable intelligence, not just data.
Think of it like this: if you’re trying to understand what people want at a restaurant, you could analyze every single order individually. Or you could notice that “with extra cheese” appears in 40% of orders, “gluten-free” shows up in 15%, and “spicy” is a common modifier. That’s n-gram thinking—finding the recurring building blocks of intent.
Understanding N-Gram Types and Their Applications
1-Grams (Unigrams): The Foundation Layer
Where it applies
Unigram analysis is your starting point for any search term optimization. It’s particularly valuable when you’re launching new campaigns, entering unfamiliar markets, or dealing with broad match keywords that are generating hundreds of varied searches. Use 1-grams when you need to quickly identify the most frequent individual words appearing in your search terms—both positive and negative.
What to do
Export your search term report and create a frequency count of every individual word. You’re looking for the terms that appear most often, then categorizing them into buckets: relevant, irrelevant, intent-modifiers (like “free,” “cheap,” “professional”), and location-based terms.
The key is volume—you want to see which single words are eating up your budget. If “free” appears in 300 search queries that generated clicks but zero conversions, that’s a clear signal. If “certification” shows up 200 times with a 12% conversion rate, you’ve found gold.
Real-world example
Let’s say you’re running campaigns for personalized online coding classes for children. Your 1-gram analysis might reveal:
- “free” (428 occurrences, 0.2% conversion rate)
- “university” (156 occurrences, 0% conversion rate)
- “bootcamp” (89 occurrences, 1.1% conversion rate)
- “kids” (312 occurrences, 8.4% conversion rate)
- “children” (201 occurrences, 9.2% conversion rate)
For a company like ItsMyBot, which offers 1:1 personalized instruction for specific age groups, this immediately tells you to add “free” and “university” as negative keywords, while recognizing that “kids” and “children” are high-intent terms worth bidding more aggressively on.
Tips & best practices
- Set a minimum threshold (e.g., words appearing at least 10 times) to avoid noise
- Combine impression data with conversion data—high impressions + low conversions = negative keyword candidate
- Watch for misspellings and variations that should be grouped together
- Don’t just look at cost; a word with 50 clicks at $2 CPC but 5 conversions might be more valuable than one with 200 clicks at $1 CPC and 3 conversions
2-Grams (Bigrams): Where Intent Becomes Clear
Where it applies
Bigram analysis is where you start understanding actual search intent. This is crucial for mid-campaign optimization when you’ve gathered enough data to see patterns but need more specificity than single words provide. Use 2-grams when refining ad copy, creating more targeted ad groups, or building out your negative keyword list with precision.
What to do
Extract every two-word sequence from your search terms and count their frequency. You’re looking for phrase patterns that reveal what users want, how they describe your service, and what qualifies or disqualifies them as customers.
This is where context emerges. “Coding free” tells you something different than “coding certification.” “Online classes” has different intent than “online degree.” The two-word combination reveals the user’s specific need or constraint.
Real-world example
Analyzing bigrams from an educational service campaign might show:
- “free trial” (156 instances, 4.2% conversion rate)
- “free download” (89 instances, 0% conversion rate)
- “near me” (234 instances, 6.8% conversion rate)
- “for kids” (198 instances, 11.3% conversion rate)
- “job placement” (67 instances, 0.8% conversion rate)
This tells a story. “Free trial” converters are tire-kickers but some convert—maybe worth keeping with bid adjustments. “Free download” seekers want software or resources, not classes—exclude it. “Near me” suggests local intent even for online services, which could inform your location targeting strategy. “For kids” is clearly your sweet spot if you’re targeting parents of young learners.
For a business offering personalized 1:1 online coding instruction, understanding these patterns helps you differentiate between parents seeking quality education versus people looking for free resources or professional certifications.
Tips & best practices
- Look for bigrams that appear with multiple prefixes or suffixes—these are stable intent signals
- Identify contradictory bigrams (e.g., “beginner advanced”) that might indicate confused searchers
- Use bigram data to inform ad copy—if “personalized instruction” appears frequently with high conversion rates, feature it prominently
- Cross-reference with your landing pages to ensure message match
- Pay attention to action-oriented bigrams like “sign up,” “book now,” “get started”—these indicate bottom-of-funnel intent
3-Grams (Trigrams): Precision Targeting Territory
Where it applies
Trigram analysis is for mature campaigns where you’re optimizing for efficiency and hunting for incremental gains. This level is particularly powerful for competitive markets, high-CPC keywords, or when you’re trying to carve out a specific niche. Use 3-grams when you need surgical precision in your targeting and exclusions.
What to do
Extract three-word sequences and look for complete thought fragments that reveal specific user situations, concerns, or requirements. At this level, you’re essentially reading the user’s mind—seeing the full context of their search intent.
Trigrams help you identify multi-faceted requirements (e.g., “affordable online classes”), specific objections (e.g., “no long term commitment”), or exact scenarios (e.g., “beginner coding for kids”).
Real-world example
For an online coding education provider, revealing trigrams might include:
- “coding classes for kids” (89 occurrences, 12.1% conversion rate)
- “free online coding classes” (134 occurrences, 0.4% conversion rate)
- “best coding bootcamp for” (45 occurrences, 2.1% conversion rate)
- “1 on 1 coding” (28 occurrences, 18.7% conversion rate)
- “personalized coding instruction for” (19 occurrences, 21.4% conversion rate)
This is incredibly actionable. “Coding classes for kids” is your core audience with solid conversion rates. “Free online coding classes” is budget-draining—exclude the phrase entirely. “Best coding bootcamp” suggests comparison shopping for intensive professional training, not your offering. But “1 on 1 coding” and “personalized coding instruction” are small in volume but converting at premium rates—these deserve dedicated ad groups with higher bids.
For ItsMyBot’s model of personalized 1:1 instruction, finding that “1 on 1” trigram with high conversion rates validates the core value proposition and suggests making it more prominent in ad copy and landing pages.
Tips & best practices
- Don’t dismiss low-volume trigrams with exceptional conversion rates—they might be hidden gems
- Use trigrams to create exact match keywords for your highest-intent searches
- Build dedicated landing pages for high-performing trigrams
- Create “mirror” negative keyword phrases for problematic trigrams (e.g., if “free trial no commitment” converts poorly, exclude that exact phrase)
- Trigrams often reveal seasonal or trending topics—monitor them monthly
4-Grams and Beyond: Advanced Pattern Recognition
Where it applies
Four-word sequences and longer are primarily useful for extremely high-volume campaigns, enterprise-level accounts, or when you’re dealing with very specific, long-tail searches in technical or professional services. This level of analysis reveals complete user questions and highly specific intent that might be invisible in aggregate data.
What to do
At this level, you’re essentially reading full micro-sentences. Extract 4+ word sequences and look for complete questions, specific scenarios with multiple qualifiers, or exact pain points. These often appear less frequently but with remarkable intent clarity when they do.
The practical approach here is to look for recurring longer phrases rather than analyzing every possible combination. You’re hunting for the “unicorn” searches—the ones that perfectly describe what you offer or perfectly describe what you don’t want.
Real-world example
In educational services, illuminating 4-grams might include:
- “online coding classes for kids near” (32 occurrences, 14.2% conversion rate)
- “personalized 1 on 1 coding instruction” (12 occurrences, 33.3% conversion rate)
- “coding classes with live teacher for” (8 occurrences, 37.5% conversion rate)
- “free coding course with certificate download” (41 occurrences, 0% conversion rate)
These longer phrases are remarkably specific. That “personalized 1 on 1 coding instruction” phrase with 33% conversion rate? That’s someone typing exactly what you offer. Create an exact match keyword for it, even with just 12 searches. Meanwhile, “free coding course with certificate download” is someone looking for completely different content—exclude the entire phrase.
Tips & best practices
- Focus on 4-grams with conversion data—even one or two conversions can be significant at this granularity
- Use these to inform your keyword research—what other variations might exist of these high-performing phrases?
- Consider these as ad copy ideas—if users are searching this specifically, they might respond to ads using nearly identical language
- Don’t create hundreds of 4-gram keywords; cherry-pick the best performers
- Monitor these quarterly rather than weekly—they don’t change fast
Advanced N-Gram Applications: Going Deeper
Cross-Campaign Pattern Analysis
Once you’re comfortable with basic n-gram analysis, you can apply it across multiple campaigns to identify broader patterns in your account. This is where you start seeing strategic insights rather than tactical adjustments.
Export search terms from all campaigns and analyze them together. You might discover that certain 2-grams or 3-grams perform consistently well across different product lines, suggesting expansion opportunities. Or you might find that specific problematic terms appear everywhere, indicating account-level negative keywords you should add.
For instance, if you’re running campaigns for different age groups in coding education (ages 5-7, 9-12, 13-15), cross-campaign n-gram analysis might reveal that “game development” appears frequently across all age groups but only converts in the 13-15 segment. This tells you to make “game development” a negative keyword for younger age campaigns while bidding more aggressively on it for teens.
Seasonal and Temporal N-Gram Patterns
Search behavior changes throughout the year. Back-to-school season brings different n-grams than summer break. Running month-over-month n-gram analysis helps you identify these shifts before they impact your budget.
Create monthly n-gram reports and compare them. You might notice that “summer coding camp” explodes in May-June, while “after school coding program” peaks in August-September. This allows you to prepare campaign adjustments, ad copy variations, and negative keyword additions proactively rather than reactively.
Sentiment and Qualifier Analysis
Beyond just identifying phrases, analyze the emotional or qualifying terms within your n-grams. Words like “best,” “top,” “affordable,” “cheap,” “luxury,” “beginner,” “advanced” tell you where users are in their decision-making process and what they value.
Create buckets for different sentiment categories:
- Price-conscious: cheap, affordable, budget, discount, deal
- Quality-seeking: best, top, professional, expert, premium
- Urgency: now, today, immediate, fast, quick
- Research-phase: review, comparison, vs, versus, difference
This helps you segment audiences and adjust bidding strategies. Research-phase n-grams might get lower bids since they’re earlier in the funnel, while urgency n-grams deserve aggressive bidding.
Practical Implementation: Tools and Workflows
Manual Excel Method
For smaller campaigns or those just starting with n-gram analysis, Excel or Google Sheets work perfectly. Here’s the workflow:
- Export your search term report with columns for search term, clicks, conversions, and cost
- Use formulas to extract n-grams from each search query
- Create pivot tables to count frequency and sum metrics for each n-gram
- Sort by frequency and conversion rate to identify winners and losers
- Cross-reference with your existing keyword and negative keyword lists
The advantage here is complete control and transparency. You see exactly what’s happening with your data and can customize the analysis to your specific needs.
Python Scripts for Scale
If you’re managing multiple campaigns or dealing with tens of thousands of search queries, Python scripts become essential. Libraries like pandas make n-gram extraction and analysis straightforward, and you can automate the entire process to run weekly.
A basic script can:
- Import search term data from CSV
- Extract all n-grams (1-4 words)
- Calculate frequency, cost, conversion, and performance metrics
- Flag high-priority adds and excludes based on your thresholds
- Output prioritized recommendations
Even if you’re not a programmer, tools like ChatGPT or Claude can help you create these scripts. The time investment pays off exponentially once you’re analyzing at scale.
Dedicated Tools
Several tools specialize in n-gram analysis for Google Ads:
- Optmyzr has built-in n-gram analysis features
- PPCexpo’s N-Gram analyzer is specifically built for this purpose
- Microsoft Excel Power Query can be configured for automated n-gram extraction
- Google Ads Scripts can be customized to generate n-gram reports
The right tool depends on your budget, technical comfort level, and campaign complexity. For most advertisers managing 5-10 campaigns with moderate budgets, the manual Excel method supplemented with occasional script-based deep dives works well.
Cultural and Market-Specific Considerations
Language Variations and Regional Differences
N-gram analysis becomes more complex when dealing with multiple languages or regional variations. German-language campaigns, for example, feature compound words that might appear as single terms in English but combine multiple concepts.
In German markets, words like “Programmierkurse” (programming courses) or “Einzelunterricht” (individual instruction) might need to be analyzed differently than their English equivalents. The word order also differs—German often places descriptive terms after nouns, which affects how you should think about 2-grams and 3-grams.
For international campaigns, run separate n-gram analyses for each language rather than translating everything to English. The search patterns, common qualifiers, and intent signals differ meaningfully across languages and cultures.
Industry-Specific Terminology
Educational services, healthcare, legal, and financial industries each have their own lexicon that appears in search queries. Understanding industry-specific n-grams helps you avoid false positives and negatives.
In children’s education, for instance, “coding” might appear alongside terms like “STEM,” “curriculum,” “montessori,” “waldorf,” or “homeschool.” Each of these suggests different parent philosophies and priorities. A Montessori-focused parent searching for “montessori coding curriculum” has very different expectations than someone searching “competitive programming for kids.”
Build industry-specific n-gram dictionaries that categorize terms by parent philosophy, teaching methodology, or educational goals. This helps you create more nuanced audience segments and messaging.
Parent vs. Child Search Behavior
When your end users are children but your paying customers are parents, n-gram patterns reveal this dual audience. Parents search differently than kids would—they use terms like “safe,” “educational value,” “screen time,” “age-appropriate,” while kids might search for “fun coding games” or “minecraft programming.”
Understanding which n-grams indicate parent searches versus child-influenced searches helps you adjust bidding and messaging. Parent-oriented n-grams typically convert better because they control the purchase decision, even if child-influenced searches show higher engagement.
Building Confidence: From Data to Decisions
N-gram analysis isn’t about achieving perfection—it’s about making progressively better decisions with the information you have. Your first n-gram analysis will feel awkward and uncertain. You’ll question whether you’re setting the right thresholds, whether you should exclude certain terms, whether you’re reading the patterns correctly.
That’s completely normal and actually healthy. The goal isn’t to immediately optimize every campaign to peak performance. The goal is to see patterns you couldn’t see before, test hypotheses based on those patterns, and learn what works for your specific business.
Start with your highest-spending campaigns. Run a basic 1-gram and 2-gram analysis. Identify the 10 most obviously problematic terms and add them as negatives. Track the impact over two weeks. Then go deeper with 3-grams. Then try cross-campaign analysis. Build your skills and confidence incrementally.
Remember: even experienced advertisers are constantly learning. Consumer search behavior evolves, new competitors enter markets, and Google’s algorithms change. N-gram analysis is a practice, not a one-time optimization. The value comes from making it a regular part of your workflow, not from executing it perfectly once.
The most successful advertisers I know run n-gram analysis monthly for major campaigns and quarterly for stable, lower-volume campaigns. They keep historical reports to spot trends over time. They’re not looking for dramatic revelations—they’re looking for the small 2-3% improvements that compound into significant performance gains over months and years.
Frequently Asked Questions
How much search term data do I need before n-gram analysis is useful?
You can start finding patterns with as few as 100-200 search queries, but the real value emerges around 500-1,000+ queries. If you’re just launching campaigns, wait 2-4 weeks to accumulate enough data. For lower-volume campaigns, you might need to look at 60-90 day periods instead of 30 days to get sufficient sample sizes. The key metric isn’t just quantity but diversity—you want to see recurring patterns, not just one-off searches.
Should I analyze all search terms or just converting ones?
Analyze both, but separately. Non-converting search terms tell you what to exclude or deprioritize. Converting search terms tell you what to amplify and expand. Your biggest wins often come from identifying high-volume, zero-conversion n-grams that are draining budget—eliminating waste is sometimes more impactful than finding new opportunities. Run two analyses: one focused on cost and clicks to find waste, another focused on conversions to find opportunities.
How often should I add negative keywords based on n-gram findings?
Weekly additions for active campaigns with healthy spend, monthly for stable campaigns, and immediately for any n-gram that’s consuming significant budget with zero conversions. However, be thoughtful about excluding terms too quickly—some n-grams need 50-100 clicks before you can confidently assess their performance. Set minimum thresholds based on your average conversion rate (e.g., if your CVR is 5%, wait until an n-gram has at least 40-50 clicks before excluding it based on performance).
Can n-gram analysis help with match type decisions?
Absolutely. High-performing 3-grams and 4-grams are excellent candidates for exact match or phrase match keywords. If you discover a specific 3-gram that converts at 20%+ compared to your account average of 5%, create an exact match keyword for it with a higher bid. Conversely, if your broad match keywords are generating n-grams that are consistently off-target, it might signal that you need to tighten match types or add more specific phrase match variations.
What’s the relationship between n-grams and audience signals?
N-grams reveal intent; audience signals reveal identity. Together they’re powerful. For example, if your n-gram analysis shows “after school program” converting well, and your audience data shows parents of 8-12 year-olds performing best, you can create campaigns or ad groups that combine these insights. Look for n-grams that align with specific audience segments—like “homework help” appearing more often from searches by parents versus “coding games” from family households with kids who influence device usage.
Should I use the same n-gram analysis approach for Shopping and Display campaigns?
No—n-gram analysis is specifically powerful for search campaigns where you have actual query data. For Shopping campaigns, you’d analyze search terms similarly but focus more on product-related n-grams within titles and descriptions. Display campaigns don’t have search queries, so the approach doesn’t apply directly. However, you can analyze which ad copy phrases (essentially your n-grams) perform best across different placements, which is a related but different technique.
