Map Firmographic + Technographic Filters for Outbound Lists

You are an outbound operations lead at a digital marketing agency. You translate vague ICP language into precise, tool-ready filter sets that produce lists of 500-2,000 qualified accounts (not 50,000 noise).

Our ICP: [ICP_DOC]
Services we sell: [SERVICES]
Tool we'll build the list in: [TOOL] (Apollo, Clay, ZoomInfo, LinkedIn Sales Navigator, or other)
List size goal: [LIST_SIZE]
Geo: [GEOS]
Known technographic signals that indicate fit (current stack, missing stack, recent installs): [TECH_SIGNALS]
Known intent signals if any (job postings, funding, leadership change): [INTENT_SIGNALS]

1. Build a primary filter set in the exact filter names/structure of [TOOL].
2. Build a secondary (broader) filter set for backfill if primary returns too few accounts.
3. List 3-5 firmographic filters (industry codes, employee count, revenue, geo, business model).
4. List 3-5 technographic filters (tools present, tools missing, recent installs).
5. List intent/trigger filters where the tool supports them.
6. Define exclusion filters (always exclude: e.g., agencies themselves, students, government).
7. Estimate how big the resulting list will be and flag if it's likely too large or too small.
8. Recommend the exact contact roles to pull at each account.

- Use the tool's actual field names where you know them; if unsure, say "approximate field name in [TOOL]".
- Don't filter so tightly the list is under 200 accounts. Don't go so broad it's over 5,000.
- Every filter must be traceable to a line in the ICP. No vanity filters.
- Flag any filter that will be unreliable in this tool (e.g., revenue is unreliable for private SMBs in most data tools).

Return:
1. Primary filter set (table: field | operator | value | source in ICP)
2. Secondary filter set
3. Exclusion filters
4. Contact roles to pull (with seniority)
5. Expected list size + reliability notes
6. 3 ways to enrich the list further in Clay if budget allows

• [ICP_DOC] — Paste your ICP definition
• [SERVICES] — What you sell
• [TOOL] — The data tool — Apollo, Clay, ZoomInfo, LinkedIn Sales Navigator, etc.
• [LIST_SIZE] — Target list size (e.g., 800 accounts)
• [GEOS] — Geographies in scope
• [TECH_SIGNALS] — Stack signals — e.g., 'uses HubSpot but no SEO tool', 'on Shopify Plus'
• [INTENT_SIGNALS] — Trigger events — funding, hiring, leadership change

ICP_DOC: Multi-location home services $5-30M revenue, 2-10 locations, Sunbelt US. SERVICES: Local SEO + Google Ads. TOOL: Apollo. LIST_SIZE: 1,000 accounts. GEOS: AZ, TX, FL, GA, NV, NC. TECH_SIGNALS: Uses ServiceTitan or HouseCallPro (operations maturity); no current SEO tool (no Semrush/Ahrefs traffic to their reports page). INTENT_SIGNALS: Hiring a marketing manager; opened a new location in last 12 months.

## Primary Filter Set (Apollo)
| Field | Operator | Value | ICP source |
|---|---|---|---|
| Industry | includes | Consumer Services > Home Services | vertical fit |
| Employee Count | between | 25-250 | proxy for 2-10 locations |
| HQ Location | includes | AZ, TX, FL, GA, NV, NC | geo |
| Keywords (company) | includes | HVAC OR plumbing OR electrical OR roofing | trade focus |
| Technologies | includes | ServiceTitan OR HouseCallPro | operations maturity |

## Secondary (broader) Set
Drop the technologies filter; expand to entire Sunbelt + Mountain West.

## Exclusion Filters
- Industry: Marketing Agencies, Consulting
- Employee Count: <10 (solo ops)
- Keywords: 'franchise corporate' (national HQs)

## Contacts to Pull
- Owner / President / CEO (decision-maker)
- Marketing Director or Marketing Manager (if exists)
- General Manager (operational buyer in some shops)
Seniority: Owner, C-Suite, Director.

## Expected List Size + Reliability
~900-1,200 accounts. Apollo's revenue data on private home services is unreliable — don't filter by revenue, use employee count as proxy. ServiceTitan technographic data is reasonably reliable in Apollo as of 2025.

## Clay Enrichment
1. Scrape each company website for # of location pages (validates location count).
2. Use Google PageSpeed API on their site for an instant talk-track.
3. Cross-reference job postings on Indeed for 'marketing' or 'sales' roles (intent signal).

• Always pull a sample of 25 accounts, eyeball them, then commit to the filter set. Saves you from finding out the filter is wrong after 1,000 records.
• Technographic data is great for SaaS, hit-or-miss for service businesses. Don't rely on it alone.
• Re-run quarterly. Data freshness rots fast in Apollo/ZoomInfo.