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.
by Frederick Vallaeys (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.
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.
Finds identical keywords (same text and match type) active in multiple ad groups or campaigns, with per-instance performance so the keeper is obvious — ending the internal competition that splits history and muddies results.
Exact match hasn't been exact for years — plurals, synonyms and 'same meaning' rewrites all serve. Close Variant Monitor splits every exact-match keyword's spend into identical-term traffic vs close-variant traffic, reports the account-wide variant share, and lists the variants that spent money without converting. Because each variant is tied to the exact keyword it matched, adding the bad ones as exact negatives is surgical: it removes only that query, nothing else.
| Variable | Default |
|---|---|
DATE_RANGE | LAST_30_DAYS |
MIN_KEYWORD_COST | 20 |
MIN_VARIANT_COST | 5 |
TOP_N | 30 |
EMAIL_ADDRESS | (empty) |
=== Close Variant Monitor - EXACT match keywords (LAST_30_DAYS) === Spend on identical terms: 2110.40 Spend on close variants: 892.15 (30% of exact-match spend) Keywords analyzed (>= 20 spend): 42 --- Zero-conversion variants by cost (negative candidates) --- "free running shoes" (matched to [running shoes] in Shoes — Generic > Running) cost: 84.20 clicks: 96 "running shoe repair" (matched to [running shoes] in Shoes — Generic > Running) cost: 41.75 clicks: 38 Email sent to you@example.com
A 20-40% variant share is typical; the number itself isn't the problem — the zero-conversion variants are. Add irrelevant variants as exact-match negatives in the ad group where they matched. Variants with conversions are usually fine to keep (that's the useful side of close matching).