Gtm contact filter

Contact Filter

Classify and rank contacts from people-search output using a three-tier ICP scoring system. Rejects non-ICP contacts before enrichment to save credits.

Read .agents/skills/_shared/conventions.md before executing.

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When to Use

• After people-search, before people-enrichment
• Input: csv/intermediate/contacts_found.csv
• Output: csv/intermediate/contacts_filtered.csv
• Even small batches (10 contacts) benefit from ICP ranking

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Prerequisites — Company Data

Contact-filter classifies on company-level fields (company_employee_count, company_hq_location, company_industry, company_specialities) alongside each contact. These come from upstream skills.

Source	Fields Provided	Cost
PB LinkedIn Profile Scraper (enrichWithCompanyData: true)	employee_count, hq, industry, specialities	Free (PB plan + LinkedIn account)
PB Sales Navigator Account Scraper	employee_count, hq, industry, growth metrics	Free (PB plan + SN account)
PB LinkedIn Company Scraper	Full company page data	Free (PB plan + LinkedIn account)
FullEnrich Company Finder	employee_count, hq, industry	0.25 credits/company
Pipe0 companies:profiles:amplemarket@1	employee_count, hq, industry	2.00 credits/page (100 results)
Pipe0 companies:profiles:crustdata@1	employee_count, hq, industry	2.00 credits/page (100 results)

Where company data typically comes from:

• Company-First workflow: Already enriched in the company-enrichment step.
• Persona workflow: Run PB Profile Scraper with enrichWithCompanyData: true during/after people-search.
• Standalone: Run one of the API providers above before contact-filter.

If company data is missing: The filter falls back to job-title-only classification. It skips company-dependent dimensions (industry tier, location tier from HQ, size filter) and relies on job_tier and person location only.

See _shared/conventions.md for the canonical field name mapping (snake_case across all skills).

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Step 1 — Load ICP Configuration

Read {client-slug}-gtm/context/icp.md for client-specific tier definitions, thresholds, and sort order.

If icp.md does not yet contain contact-filter configuration, ask the user:

1. What are your target job titles / role keywords?
2. What seniority levels are you targeting? (manager, director, VP, C-level?)
3. What industries are highest priority? Lowest priority?
4. What geographies are highest priority?
5. What company size range? (min and max employee count)
6. Any ICP keyword signals to look for in company specialities?
7. Which dimensions matter most for ranking? (e.g. location first, then role, then industry)

Save the answers to context/icp.md under a ## Contact Filter section.

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Classification System

A. Job Tier (1–6, lower = better)

Fully parameterized — client defines role keywords. Example structure:

Tier	Description	Example
1	Primary target roles, mid-level seniority	RevOps Manager, Sales Ops Lead
2	Primary target roles, other seniority	RevOps IC, Head of RevOps
3	Leadership in target domain	VP Sales, Director of Revenue
4	General domain roles	Account Executive, Sales Manager
5	C-level (promote to tier 3 if company < threshold employees)	CRO, CSO
6	Reject (non-target roles, junior, students)	Intern, Coordinator, Marketing Analyst

C-level promotion rule: If company_employee_count < {client_clevel_threshold} (e.g. 50), promote C-level from tier 5 → tier 3 (they're operational at small companies).

Hard reject: job_tier >= hard_reject_threshold (default: 6).

Missing title: tier 6 (Unknown). Default threshold rejects tier 6. To allow missing-title contacts through, set hard_reject_threshold: 7 in your ICP config.

B. Industry Tier (0–5, lower = better)

Client defines 5 industry tiers ranked by fit for their offering.

• Match logic: exact match first (full industry name), then keyword fallback
• Tier 0 = Unknown (company_industry missing) — sorted between tier 1 and tier 2
• Missing industry → tier 0, NOT rejected

C. Location Tier (A–D, A = best)

Client defines geographic priority tiers.

• Primary: company HQ location (company_hq_location field — format: "City, CC", extract country code CC)
• Fallback: person location field
• Both missing: tier D (lowest)
• Hard reject option: client can specify only tier A (or A+B) passes

D. Company Size Filter

Client defines min and max company_employee_count.

• Outside range AND field is present → hard reject
• Field missing/empty → skip size filter, set employee_count_missing: true, deprioritize in sort

E. ICP Keyword Scoring

Scan company_specialities for client-defined domain keywords. Count matches → icp_kw_score (0 = no match, no penalty). Missing specialities → icp_kw_score = 0.

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Hard Filters (Rejection Rules)

Prefer not to reject solely on missing data — use the hard_reject_threshold to control strictness per client. The exception is missing job title (tier 6), which is rejected at the default threshold; raise the threshold to 7 to keep them.

Filter	Condition	Action
Job tier	>= hard_reject_threshold (default: 6)	Reject
Employee count	outside [min, max] AND field present	Reject
Location tier	worse than threshold AND field present	Reject (optional)

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Soft Sort (Ranking)

Sort order is configurable per client — stored in context/icp.md.

Default composite sort key (used if no preference stated):

job_tier → industry_tier → company_loc_tier → person_loc_tier → employee_count_missing (penalty last) → icp_kw_score (desc) → follower_count (desc)

Other valid orderings:

• Geography-first: company_loc_tier → job_tier → industry_tier → ...
• Company-fit-first: industry_tier → icp_kw_score → company_loc_tier → job_tier → ...

Ask the user at setup which ordering fits their priorities. Store it in context/icp.md.

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Output Columns Added

The following columns are appended to all passed records:

Column	Description
job_tier	1–6 (1 = best fit)
persona	Label for the role category
seniority	Detected seniority level
ind_tier	0–5 (0 = unknown, 1 = best fit)
ind_label	Matched industry label
comp_loc_tier	A–D based on company HQ
person_loc_tier	A–D based on person location
effective_loc_tier	Best of comp_loc_tier / person_loc_tier
employee_count_missing	true/false
icp_kw_score	Count of ICP keyword matches in specialities
priority_label	Human-readable priority (e.g. "High", "Medium", "Low")
sort_key	Composite sort value used for ranking

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Missing Data Handling

Field	Missing Behavior
job_title	tier 6 (Unknown) — rejected at default threshold (6); raise to 7 to keep
company_industry	tier 0 (Unknown) — sorted between tier 1 and tier 2
company_hq_location	Fall back to person location; if both missing → tier D
company_employee_count	Skip size filter, flag employee_count_missing: true, deprioritize in sort
company_specialities	icp_kw_score = 0 (no penalty)
Follower count	0 (lowest tiebreaker priority)

Default policy: Don't hard-reject on missing data unless the corresponding tier is explicitly above threshold. Adjust hard_reject_threshold per client.

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Execution Protocol

1. Read ICP Configuration

Load from context/icp.md. If contact-filter section is missing, ask the setup questions above and save the answers.

2. Classify All Contacts

Run the classification logic over all rows in contacts_found.csv. Add the output columns listed above.

3. Apply Hard Filters

Reject rows that fail hard filter conditions. Log rejection reasons.

4. Sort Passed Records

Apply the client's configured sort order (or default) to rank passed contacts.

5. Print Run Summary

## Contact Filter Summary
- Input: {n_input} contacts
- Hard rejected (job tier): {n_job_rejected}
- Hard rejected (employee count): {n_size_rejected}
- Hard rejected (location): {n_loc_rejected}
- Passed: {n_passed} contacts

## Tier Distribution (passed contacts)
Job tiers: tier1={x}, tier2={y}, tier3={z}, tier4={w}
Industry tiers: tier1={x}, tier2={y}, tier3={z}, ...
Location tiers: A={x}, B={y}, C={z}, D={w}

## Top 10 by Priority
[name | company | job_title | job_tier | ind_tier | loc_tier | icp_kw_score]

6. Write Output

Save passed contacts (ranked) to csv/intermediate/contacts_filtered.csv. All input columns preserved. Output columns appended.

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ICP Configuration Format (in context/icp.md)

## Contact Filter

### Job Tier Definitions
- Tier 1 (Primary mid-level): keywords = [revops, sales ops, revenue operations], seniority = [manager, senior, lead]
- Tier 2 (Primary other): keywords = [revops, sales ops], seniority = [head of, director, IC]
- Tier 3 (Leadership): keywords = [sales, revenue, commercial], seniority = [VP, director, head of]
- Tier 4 (Domain): keywords = [account, sales, business dev], seniority = any
- Tier 5 (C-level): keywords = [CRO, CSO, chief revenue, chief sales]
- Hard reject (Tier 6): keywords = [student, intern, junior, coordinator]
- C-level promotion threshold: 50 employees
- Hard reject threshold: tier >= 6

### Industry Tiers
- Tier 1 (Best fit): [B2B services, manufacturing, logistics, wholesale, consulting]
- Tier 2 (Good fit): [retail, hospitality, consumer-facing with B2B sales]
- Tier 3 (Neutral): [SaaS, software, IT services]
- Tier 4 (Low fit): [deeptech, aerospace, biotech]
- Tier 5 (Hard): [finance, healthcare, pharma, legal, government]

### Location Tiers
- Tier A: [DE, AT, CH, NL, BE, LU, DK, SE, NO, FI, GB, IE]
- Tier B: [FR, ES, IT, PL, CZ, HU, RO, PT, GR, BG, HR, SK, SI]
- Tier C: [US, CA, AU, NZ]
- Tier D: all other
- Hard reject threshold: none (all tiers pass, used for sorting only)

### Company Size Filter
- Min: 10 employees
- Max: 500 employees

### ICP Keywords (company specialities)
- keywords = [automation, workflow, crm, sales enablement, revops, salestech]

### Sort Order
job_tier → industry_tier → comp_loc_tier → person_loc_tier → employee_count_missing → icp_kw_score (desc)

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Reference Patterns

The classification logic follows these patterns:

• classify_job(title, employee_count) → returns (tier, persona, seniority) based on keyword matching + seniority detection + C-level promotion rule
• Industry: exact set match first, keyword fallback
• Location: parse country code from "City, CC" format in HQ field
• Composite sort key as tuple: (job_tier, ind_tier, loc_tier_ord, ...) — Python's natural tuple ordering does the ranking

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What's Missing (To Document)

• Benchmark: credit savings (% reduction) from filtering before enrichment across different client ICPs
• Native integration with downstream tooling (n8n, custom scripts)