Breaks all search terms into 1-, 2- and 3-word patterns and aggregates performance per pattern — surfacing the systemic waste your negative keywords miss and the converting themes worth breaking out.
by Brainlabs & Nils Rooijmans (concept), Chiliad (rewrite)·chiliad.io
Uses Claude AI to analyze your search terms and recommend negative keywords with clear reasoning. Each term is evaluated for business relevance, intent, and conversion potential — then exported to a Google Sheet with actionable suggestions.
Finds brand search terms leaking into non-brand campaigns (inflating their stats and misleading Smart Bidding) and generic traffic riding in brand campaigns — with the misrouted spend quantified.
Measures how much of your exact-match spend actually flows through close variants and lists the zero-conversion variants — the most surgical negative keyword candidates in the account.
Reads negative keywords and placement exclusions from a Google Sheet and syncs them to multiple campaigns via shared negative keyword and placement lists. Optionally filters to campaigns with a specific label.
Quantifies spend on search queries containing competitor names, with conversion performance and a period-over-period trend — whether competitor bidding is your strategy or your broad-match accident.
Shows what your Dynamic Search Ads decided on their own: waste queries to negate, the landing pages absorbing DSA spend, and converting queries to promote into real keywords.
Individual search terms are too noisy to act on; the words inside them are not. The N-Gram Analyzer splits every search term from the chosen date range into 1-, 2- and 3-word combinations and aggregates cost, clicks and conversions per combination. The output is two lists per n-gram size: WASTE (patterns that spent real money with zero conversions — negative keyword candidates) and WINNERS (the highest-converting patterns — themes worth their own ad groups or bids).
| Variable | Default |
|---|---|
DATE_RANGE | LAST_30_DAYS |
MIN_WASTE_COST | 50 |
TOP_N | 20 |
MAX_SEARCH_TERMS | 50000 |
EMAIL_ADDRESS | (empty) |
Analyzed 18342 search terms (LAST_30_DAYS). === 1-WORD N-GRAMS - TOP WASTE (0 conversions, cost >= 50) === "free" cost: 412.80 clicks: 391 conversions: 0.0 search terms: 214 "jobs" cost: 187.22 clicks: 96 conversions: 0.0 search terms: 41 === 1-WORD N-GRAMS - TOP WINNERS (by conversions) === "buy" cost: 1204.10 clicks: 890 conversions: 64.0 search terms: 156 === 2-WORD N-GRAMS - TOP WASTE (0 conversions, cost >= 50) === "near me" cost: 240.15 clicks: 168 conversions: 0.0 search terms: 77 Email sent to you@example.com
Each n-gram row aggregates every search term containing that pattern, so review before excluding — 'free' in 'sugar free' is not the same as in 'free download'. Conversions are attributed to every n-gram in the converting term, so columns don't sum to account totals. A classic PPC analysis technique, implemented fresh for GAQL.