sqr-classifier

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 google-ads-query or gaql-query-patterns 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 sqr-pipeline or add-account-negative-keywords.
• 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

1. 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.

2. 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.

3. Brand terms are always High Intent if they match the business's own brand name. Competitor brand terms are Low Intent.

4. Location matters. "Apartments in Austin" is High Intent for an Austin property. The same query is Off-Brand for a property in Denver.

5. One category per term. Do not assign multiple categories. Pick the most accurate one.

6. 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):

1. Process in batches of 50
2. Show results after each batch
3. Ask if the user wants to continue or adjust the classification criteria
4. 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
google-ads-query or gaql-query-patterns	Before classifying, when you still need to pull the search-term report the classification runs on
sqr-pipeline	When you want the full pull → classify → review → upload flow with negatives actually applied, not just labeled
add-account-negative-keywords	When the Off-Brand terms should become account-level negatives across campaigns
geo-conflict-analyzer	It routes here for quick intent classification when the full pipeline isn't warranted