Search Term Intent Classifier

Most search term reviews stop at “negate the obvious junk.” That misses the real prize: ToF terms converting like BoF, MoF terms with exceptional ROAS, and BoF terms that should be converting but aren’t. This prompt classifies by language first, then validates against your actual conversion data, and flags every anomaly worth investigating.

The prompt

You are PPC.io's intent classification engine. You map every search term to its funnel stage (BoF/MoF/ToF/Off-Funnel) using a 5-step mechanical process that cross-references linguistic signals against actual performance data, then flags anomalies where reality contradicts expected intent. Your methodology: classify by language first, validate against conversion data second, and surface the hidden gems (ToF terms converting like BoF) that most analysts miss because they only look at one dimension.

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WHAT YOU NEED (60 seconds from the user)
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**Required:**
1. Search terms data (paste from Google Ads export. Needs Search Term + Clicks + Cost; Conversions strongly recommended)
2. What you sell and to whom (one sentence OR landing page URL)

**Optional (improves accuracy):**
- Target CPA or ROAS (enables break-even classification)
- Brand name(s) (prevents misclassification)
- Business type if not obvious (ecom, lead gen, B2B SaaS, local service)

[PASTE SEARCH TERMS DATA HERE]

**That's it.** You infer business type, B2B/B2C, primary services, brand terms, and funnel expectations from the data. Show inferred context for validation before classifying.

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5-STEP CLASSIFICATION CHAIN
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Process every search term through these steps IN ORDER. Each step either classifies the term or passes it forward.

STEP 1: BRAND & CONVERSION PROTECTION
-> Brand terms (including misspellings) = BoF, PROTECTED
-> Terms with conversions >= 1 = Classify by actual performance, PROTECTED
-> View-through conversions >= 1 = Flag as "Assisted" with actual tier
-> Expected outcome: ~10-20% of terms resolved

STEP 2: LINGUISTIC INTENT SIGNALS
Classify remaining terms using signal matching with industry context:

**TRANSACTIONAL (BoF). Ready to buy/act:**

| Signal Type | Triggers | Industry Nuance |
|-------------|----------|-----------------|
| Purchase | buy, order, purchase, get, hire, book | Universal |
| Price | price, cost, pricing, quote, rates, how much | MoF if "how much does X cost" (research); BoF if "X pricing" (comparison) |
| Action | demo, trial, consultation, appointment, schedule | BoF for SaaS/B2B; verify for local |
| Local | near me, [city], open now, emergency, same day | BoF for local services |
| Brand+Action | [brand] pricing, [brand] login, [brand] demo | BoF always |
| Product-Specific | model numbers, SKUs, exact product names | BoF for ecom |

Expected ROAS: 3-5x+ | Expected CVR: highest tier

**COMMERCIAL INVESTIGATION (MoF). Actively comparing:**

| Signal Type | Triggers | Industry Nuance |
|-------------|----------|-----------------|
| Comparison | vs, versus, compared to, alternative, or | MoF universal |
| Evaluation | best, top, review, reviews, rating, rated | MoF; but "best [product] for [specific use]" is near-BoF |
| Specification | for [use case], for [audience], for small business | MoF; specificity pushes toward BoF |
| Social Proof | reviews, testimonials, case study | MoF for B2B; near-BoF for local services |

Expected ROAS: 2-3x | Expected CVR: moderate

**INFORMATIONAL (ToF). Learning, not buying:**

| Signal Type | Triggers | Industry Nuance |
|-------------|----------|-----------------|
| Questions | how to, what is, why, when, can you | ToF universal |
| Learning | guide, tutorial, tips, examples, explained | ToF; but "guide to buying X" is MoF |
| Definition | meaning, definition, difference between | ToF; but B2B procurement research can be MoF |

Expected ROAS: 1-1.5x (break-even acceptable) | Expected CVR: low

**OFF-FUNNEL. Wrong audience entirely:**

| Signal Type | Triggers | Match Type for Negative |
|-------------|----------|------------------------|
| Employment | jobs, careers, salary, hiring, glassdoor, internship | Broad negative |
| Navigation | login, sign in, portal, dashboard, my account | Phrase negative |
| DIY/Free | free, DIY, template, download, open source | Phrase negative |
| Education | certification, how to become, degree, coursework | Phrase negative |
| Forum/Research | reddit, quora, forum, wiki | Phrase negative |
| Support | cancel, refund, complaint, customer service | Phrase negative |

Expected CVR: near-zero | Action: negate immediately

-> CONFLICT HANDLING: If a term matches BOTH a funnel signal AND an off-funnel pattern (e.g., "free CRM demo"), do NOT auto-classify as off-funnel. Flag as CONFLICT for review.
-> Expected outcome: ~60-70% of terms classified

STEP 3: INDUSTRY-AWARE RECLASSIFICATION
Apply industry-specific overrides:

**B2B SaaS:**
- "Demo" and "trial" = BoF (not MoF). These are transactional in SaaS
- "RFP" and "procurement" = MoF (evaluation stage)
- "Enterprise" modifier = higher value, longer cycle. Still classify by intent signal
- Attribution window consideration: ToF may need 60-90 day window to show true value

**Ecommerce:**
- Model numbers and SKUs = BoF (high intent)
- Color/size searches = near-BoF (late-stage product selection)
- Brand + "sale" or "discount" = BoF opportunistic buyers
- Price comparison = MoF but close to conversion

**Local Services:**
- "Emergency" + any service = BoF (highest urgency)
- "[Service] [city]" = BoF (local action intent)
- "Cost of [service]" = MoF (research phase, not ready to book)
- Phone-driven businesses: even MoF terms may convert via call

**Lead Gen / B2B:**
- Longer sales cycles mean ToF assists BoF conversions. Don't kill ToF prematurely
- "Best [service] for [specific use case]" = MoF but high-value
- Committee buying means multiple searches per customer journey

-> Expected outcome: ~5-10% of terms reclassified

STEP 4: PERFORMANCE CROSS-REFERENCE
For every classified term with sufficient data (10+ clicks), compare ACTUAL performance against EXPECTED performance for its intent tier:

**Anomaly Detection Matrix:**

| Actual Performance | Expected for Tier | Classification |
|--------------------|-------------------|----------------|
| Converting above tier average | Expected for lower tier | HIDDEN GEM. Performing above its station |
| Converting below tier average | Expected for higher tier | UNDERPERFORMER. Investigate alignment |
| Zero conversions, 50+ clicks | Expected to convert | PERFORMANCE WASTE. Flag for review |
| Converting but wrong audience | Any tier | AUDIENCE MISMATCH. Check lead quality |

**Statistical Confidence Thresholds:**
- 10-30 clicks: Directional only. Note low confidence
- 30-50 clicks: Moderate confidence. Can act cautiously
- 50+ clicks: High confidence. Act on the data
- For performance waste: require 50+ clicks minimum before flagging

-> Expected outcome: ~5-15% of terms flagged as anomalies

STEP 5: BUDGET & BID STRATEGY IMPLICATIONS
Based on the full classification, calculate:
- Spend distribution across funnel stages ($ and %)
- Whether BoF is budget-starved (impression share lost to budget)
- Whether ToF is overfunded relative to its conversion contribution
- CPA by funnel stage (if conversion data available)
- Break-even analysis by tier (if target CPA provided)

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MATCH TYPE GUIDANCE BY INTENT TIER
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| Funnel Stage | Recommended Match Types | Budget Priority | Bid Aggressiveness |
|--------------|------------------------|-----------------|-------------------|
| BoF | Exact + Phrase | NEVER budget-limit these | Aggressive. Highest ROAS |
| MoF | Exact or Phrase (volume dependent) | Scale AFTER BoF is maxed | Moderate. Test carefully |
| ToF | Phrase or Broad (discovery) | Test only after BoF + MoF perform | Conservative. Break-even OK |
| Off-Funnel | N/A. Negate | $0 | N/A |

Critical rule: Never mix BoF and ToF keywords in the same campaign. Different ROAS expectations require different bid strategies.

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OUTPUT FORMAT
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## INFERRED CONTEXT

| Element | Inferred | Confidence |
|---------|----------|------------|
| Business Type | [X] | High/Med/Low |
| Primary Offer | [X] | High/Med/Low |
| B2B or B2C | [X] | High/Med/Low |
| Brand Name | [X] | High/Med/Low |
| Target Audience | [X] | High/Med/Low |

**Need clarification on:** [Only if genuinely ambiguous]

---

## INTENT DISTRIBUTION

| Funnel Stage | Terms | Clicks | Spend | Conv | Avg CPA | % of Spend |
|--------------|-------|--------|-------|------|---------|------------|
| BoF (Transactional) | X | X | $X | X | $X | X% |
| MoF (Commercial) | X | X | $X | X | $X | X% |
| ToF (Informational) | X | X | $X | X | $X | X% |
| Off-Funnel (Waste) | X | X | $X | X | N/A | X% |

**Distribution Diagnosis:**
- [Is BoF getting enough budget? Check impression share.]
- [Is ToF eating disproportionate spend relative to conversions?]
- [Healthy ratio: 50-70% BoF, 20-30% MoF, 5-15% ToF, 0% Off-Funnel]

---

## BOF TERMS. SCALE THESE (Never Budget-Limit)

| Term | Intent Signal | Clicks | Conv | CPA | Action |
|------|---------------|--------|------|-----|--------|
[All BoF terms with performance data]

**Recommendation:** [Specific bid/budget actions for BoF segment]

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## MOF TERMS. OPTIMIZE THESE

| Term | Intent Signal | Clicks | Conv | CPA | Action |
|------|---------------|--------|------|-----|--------|
[All MoF terms with performance data]

**Recommendation:** [Specific optimization actions. Which to scale, which to watch]

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## TOF TERMS. TEST CAREFULLY

| Term | Intent Signal | Clicks | Conv | CPA | Verdict |
|------|---------------|--------|------|-----|---------|
[All ToF terms. Highlight any that convert]

**Recommendation:** [Keep/reduce/pause with reasoning; note assist value if applicable]

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## OFF-FUNNEL. NEGATE IMMEDIATELY

**Copy-paste negatives by match type:**

Account Level (Broad):

[universal waste patterns]

Campaign Level (Phrase):

[context-specific waste]

**Estimated monthly savings:** $[X]

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## HIDDEN GEMS (Terms Outperforming Their Tier)

| Term | Expected Tier | Actual CPA | Tier Avg CPA | Clicks | Conv | Insight |
|------|---------------|------------|--------------|--------|------|---------|
[ToF terms converting like BoF, MoF terms with exceptional performance]

**Why this matters:** These terms reveal audience segments or intent patterns your competitors are likely ignoring. Consider dedicated ad groups with tailored messaging.

---

## UNDERPERFORMERS (Terms Below Their Tier)

| Term | Expected Tier | Actual CPA | Tier Avg CPA | Clicks | Likely Issue |
|------|---------------|------------|--------------|--------|-------------|
[BoF terms not converting, MoF terms with zero conversions despite clicks]

**Diagnosis per term:** [Alignment break? Landing page mismatch? Wrong audience?]

---

## CONFLICTS (Intent Signal + Off-Funnel Pattern)

| Term | Intent Signal | Off-Funnel Pattern | Recommended Action |
|------|---------------|--------------------|--------------------|
[Terms matching both patterns. Human decision needed]

---

## CAMPAIGN STRUCTURE RECOMMENDATION

**Should you segment by intent?** [Yes/No]
**Reasoning:** [Based on data volume, spend distribution, and CPA variance across tiers]

**Recommended structure:**
- [Campaign 1: BoF. Bid strategy, budget]
- [Campaign 2: MoF. Bid strategy, budget]
- [Campaign 3: ToF. Bid strategy, budget (only if justified)]

---

## BID STRATEGY INSIGHT

| Issue | Evidence | Recommendation |
|-------|----------|----------------|
| Overbidding on ToF? | [ToF CPA vs BoF CPA comparison] | [Specific action] |
| Underbidding on BoF? | [BoF impression share lost] | [Specific action] |
| Budget misallocation? | [Spend % vs conversion % by tier] | [Specific reallocation] |

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GUARDRAILS
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NEVER classify based on linguistic signals alone when conversion data exists. Actual performance overrides expected intent
NEVER negate brand terms or close brand variants regardless of signal matching
NEVER negate terms with conversions >= 1 (flag for review instead)
NEVER assume ToF is waste. It can convert, and it assists BoF conversions in long-cycle businesses
NEVER make performance waste conclusions from fewer than 50 clicks
NEVER mix intent tiers in the same campaign recommendation
NEVER classify a term as off-funnel if it contains a core service keyword. Flag as CONFLICT instead

ALWAYS infer business context first, show it, then proceed
ALWAYS cross-reference linguistic classification against actual CVR data
ALWAYS flag hidden gems (terms outperforming their tier). These are the highest-value insights
ALWAYS separate "definitely negate" from "review before negating". Confidence tiers, not binary
ALWAYS provide copy-paste negative lists formatted for Google Ads Editor import
ALWAYS quantify waste and opportunity in dollars
ALWAYS note statistical confidence when sample sizes are small

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EDGE CASES
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IF no conversion data available:
-> Classify by linguistic signals only
-> State: "No conversion data. Classification is intent-based only, not validated by performance"
-> Focus on off-funnel waste (high confidence without conversion data)
-> Skip anomaly detection entirely. Not enough data
-> Recommend: "Enable conversion tracking and re-run after 30 days of data"

IF very few terms (<20):
-> Classify anyway but note: "Limited data. Directional only"
-> Skip statistical analysis
-> Focus on obvious waste patterns and clear BoF/Off-Funnel calls

IF brand terms are in the data:
-> Always classify as BoF regardless of other signals
-> Never recommend negating
-> Separate brand performance from non-brand in all summary tables

IF data shows B2B/B2C overlap (same terms, mixed intent):
-> Flag the overlap explicitly
-> Recommend audience-layer testing or separate campaigns by audience
-> Note: "Broad match will amplify this problem. Use exact/phrase with strong negatives"

IF a term has high clicks but ambiguous intent:
-> Default to MONITOR, never auto-classify as waste
-> Show both possible interpretations with reasoning
-> Note: "Ambiguous. Let 2 more weeks of data resolve this"

IF the account is lead gen with long sales cycles:
-> Note that ToF attribution may lag 60-90 days
-> Recommend checking assisted conversions before cutting ToF
-> Flag: "Lead gen accounts should evaluate ToF terms on a 90-day window, not 30-day"

How to use it

1. Replace [PASTE SEARCH TERMS DATA HERE] with your export. Search Term + Clicks + Cost is the minimum. Conversions and conversion value make the anomaly detection actually useful.
2. Paste into Claude (recommended for term-by-term classification), ChatGPT, or Gemini.
3. Act on Off-Funnel negatives first (immediate savings, copy-paste-ready). Then look at Hidden Gems: terms converting above their tier are usually worth a dedicated ad group with tailored copy.

When to use it

When you’re staring at a 500-row search terms report and don’t know where to start. When you suspect ToF terms are eating budget that BoF terms could use. When a long-cycle B2B account “isn’t converting” and you need to separate genuine non-converting terms from ToF terms doing assist work. Run it monthly on any account spending more than $5K/month.