N-gram Search Term Analysis for Google Ads Using AI

N-gram analysis breaks your search term report into individual word tokens (1-grams), 2-word phrases (2-grams), and 3-word phrases (3-grams) and aggregates performance metrics across all search terms that contain each token. The result is a frequency table showing which words and phrases appear most often in your triggered search terms, sorted by spend, clicks, conversions, or CPA. Patterns that show high spend and zero conversions across multiple search terms are strong negative keyword candidates, even if no single search term individually crosses a blocking threshold.

A typical active Google Ads account triggers thousands of unique search terms per month. Reviewing each one individually takes hours and misses the structural patterns. N-gram analysis surfaces that, for example, the word "free" appears across 847 different search terms and is responsible for $2,300 in spend with 0 conversions. That single negative keyword addition blocks all future variation of that pattern in one action, where individual search term review would only catch the terms you happened to scroll past.

PaidSync's analyze_search_term_ngrams tool runs the full n-gram breakdown against your live Google Ads account from within a Claude or ChatGPT conversation. There is no spreadsheet export, no Python script, and no manual pivot table. You prompt the AI, it calls the tool, and returns the n-gram frequency table with spend, clicks, CPA, and conversion data already aggregated. Then you can apply the negatives in the same session using add_negative_keywords.

A 1-gram (unigram) is a single word token. A 2-gram (bigram) is any 2-word phrase. A 3-gram (trigram) is any 3-word phrase. For negative keyword purposes, 1-gram analysis catches broad intent signals like "free", "DIY", or "jobs" that are irrelevant regardless of context. 2-gram and 3-gram analysis catches more specific patterns like "how to", "template download", or "near me free" that the 1-gram pass would miss. Running all three layers gives you the most complete picture of where search term waste is concentrated.

Yes. After analyze_search_term_ngrams returns the results, you can prompt the AI to stage a negative keyword addition for any n-gram at account, campaign, or ad group level. The add_negative_keywords tool shows you the complete change set before executing. You confirm once, and PaidSync applies all the negatives via the Google Ads API. The analysis-to-application workflow runs in a single conversation without switching between tools.

For accounts with $5,000 or more monthly spend, running n-gram analysis every 2 to 4 weeks is standard practice. The window should cover at least 14 days to give statistical weight to lower-impression n-grams. For accounts running broad match campaigns or Performance Max, the search term volume is higher and the review cadence should be closer to every 2 weeks. For accounts with tight keyword match types and low search term volume, monthly is sufficient.

Performance Max does not expose a traditional search term report at the same level of detail as Search campaigns. The search categories report and search terms insight view provide partial visibility. PaidSync's analyze_search_term_ngrams operates on the data Google makes available via the API, which for PMax includes search term insights but not the full match-level breakdown available for Search campaigns. For PMax accounts, the n-gram analysis on the Search campaigns that run alongside PMax still identifies the most actionable negative patterns.