SQR Classifier
Classify search terms into intent categories for Google Ads accounts.
What This Skill Deliberately Does NOT Do
- Pull the search terms. It classifies the terms you hand it (a paste, a CSV, or a report someone else ran) — pulling the search-term report itself is or territory.
- Upload or apply negatives. It labels intent and never auto-generates a negative list without your review; it pushes nothing to an account — the upload lives in or
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- Pattern-match by regex. Every term gets judgment, not a rule engine — the same query is High Intent for one business and Off-Brand for another, so a mechanical matcher would misclassify.
- Classify without context. It refuses to categorize until it knows what the business does and where it operates; the business type and location come first.
When to Use
- User provides a list of search terms to classify
- User asks to analyze search queries from a campaign
- User wants to identify negative keyword candidates
- User provides a CSV or paste of search term data
Classification Categories
Classify each search term into exactly ONE of these categories:
High Intent
The searcher is actively looking for the product or service the business offers.
Signals:
- Contains service/product keywords matching the business
- Includes location terms relevant to the business area
- Shows purchase intent ("near me," "cost," "hire," "best," specific product names)
- Matches the business's core offerings
Low Intent
Related to the business's industry but unlikely to convert. The searcher may be a current customer, a job seeker, or someone in early research.
Signals:
- Current customer queries (login, pay bill, contact, hours)
- Job-seeking terms (careers, hiring, salary, jobs at)
- Competitor brand names (searching for a specific competitor, not the business)
- Tangentially related but not a buying signal
- Review/comparison queries without purchase intent
Informational
Research or educational queries. The searcher wants to learn something, not buy.
Signals:
- "How to," "what is," "why does," "can I"
- General industry questions
- DIY or self-service intent
- Average/typical/benchmark queries
- Forum or Reddit-seeking queries
Off-Brand
Completely unrelated to the business. These should be added as negative keywords immediately.
Signals:
- Different industry entirely
- Wrong product/service category
- Wrong geographic area (if geo-specific business)
- Misspellings that match unrelated terms
- Adult, illegal, or irrelevant content
Classification Rules
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Context is required. Always ask what the business does and where it operates before classifying. A term like "pool maintenance" is High Intent for a pool company and Off-Brand for an apartment complex.
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When in doubt between High and Low Intent, choose Low Intent. False positives (keeping a bad term) cost more than false negatives (losing a marginal term). Conservative classification protects budget.
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Brand terms are always High Intent if they match the business's own brand name. Competitor brand terms are Low Intent.
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Location matters. "Apartments in Austin" is High Intent for an Austin property. The same query is Off-Brand for a property in Denver.
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One category per term. Do not assign multiple categories. Pick the most accurate one.
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Classify the intent, not the word. "Cheap apartments" has High Intent even though "cheap" seems negative — the searcher wants an apartment.
Output Format
Present results as a table:
| Search Term | Category | Reasoning |
|---|
| [term] | [High Intent / Low Intent / Informational / Off-Brand] | [One sentence explaining why] |
After the table, provide a summary:
- High Intent: X terms (X%) — keep these
- Low Intent: X terms (X%) — monitor, consider negatives for high-spend terms
- Informational: X terms (X%) — usually negative unless targeting top-of-funnel
- Off-Brand: X terms (X%) — add as negative keywords immediately
Batch Processing
For large lists (100+ terms):
- Process in batches of 50
- Show results after each batch
- Ask if the user wants to continue or adjust the classification criteria
- At the end, provide the full summary
Worked Example
Business: Metro Auto, an auto repair shop serving a single metro area.
| Search Term | Category | Reasoning |
|---|
| brake repair near me | High Intent | Core service plus a local purchase signal ("near me"). |
| transmission fluid change cost | High Intent | Names a service the shop offers and asks price — active buying research. |
| auto mechanic jobs | Low Intent | Job seeker, not a customer — related to the industry but won't convert. |
| how to change your own oil | Informational | DIY how-to; the searcher wants to learn, not hire. |
| car dealership downtown | Off-Brand | A different business entirely (vehicle sales, not repair) — negate immediately. |
Summary:
- High Intent: 2 terms (40%) — keep these
- Low Intent: 1 term (20%) — monitor, consider negatives for high-spend terms
- Informational: 1 term (20%) — usually negative unless targeting top-of-funnel
- Off-Brand: 1 term (20%) — add as negative keywords immediately
What NOT to Do
- Do NOT classify without knowing the business type and location
- Do NOT use pattern matching or regex — use judgment for each term
- Do NOT mark brand terms as Off-Brand just because they contain a competitor name
- Do NOT auto-generate negative keyword lists without user review
- Do NOT assume the business offers services that haven't been confirmed
When to Load Other Skills
| Skill | When |
|---|
| or | Before classifying, when you still need to pull the search-term report the classification runs on |
| When you want the full pull → classify → review → upload flow with negatives actually applied, not just labeled |
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| When the Off-Brand terms should become account-level negatives across campaigns |
| It routes here for quick intent classification when the full pipeline isn't warranted |