ACTION_ID: company_enrich_using_people_data_labs NAME: People Data Labs: Company Enrich CATEGORY: scrape CREDITS: 5 Enrich a company with People Data Labs's full firmographic and workforce-intelligence dataset. PDL is the broadest company-enrich provider in the catalog and is most useful when you need data points that the leaner enrichment actions don't return. Unique to PDL (vs other company enrich actions): - Workforce dynamics: 12-month employee churn rate, 12-month employee growth rate, average employee tenure overall and by job level (cxo, director, manager, vp, entry, etc.), average tenure by department/role, and yearly employee growth rate by role. - Headcount over time: employee headcount by month (multi-year history), gross hires by month, gross departures by month — the per-month signals that surface hiring sprees, layoffs, or growth/contraction trends. - Headcount by department and country: employee count broken out by job role (engineering, sales, marketing, etc.) and by country (US, Canada, …). - Funding history depth: full funding-stages array, current funding stage, total funding, number of funding rounds, latest funding date — beyond just a single funding-stage label. - Corporate structure: parent company ID, immediate parent, affiliated company IDs, all subsidiaries, alternate company names and websites. - Identifiers across systems: SIC + NAICS + GICS classifications, MIC exchange code, stock ticker, LinkedIn ID + slug, country code. Standard firmographics (name, domain, headcount range, industry, HQ, social URLs, description, founded date, revenue) are also returned, but those are available from cheaper providers — reach for PDL when the workforce-trend and corporate-structure signals are what matter. INDEX: 1. Inputs 2. Outputs 3. How to configure 4. Key notes 5. Where it fits in a workflow 6. When to use 7. When not to use ================================================================================ 1. INPUTS ================================================================================ company_name (type: string, optional) — Company Name Name of the company. company_domain (type: string, optional) — Company Domain Company domain (e.g. "salesforce.com"). Must be used together with Company Name if Social Media Profile URL is not provided. social_media_profile_url (type: url, optional) — Social Media Profile URL Social profile URL — LinkedIn, Twitter, Facebook, etc. ================================================================================ 2. OUTPUTS ================================================================================ company_id (type: string) — Company ID PDL's internal identifier for the company. sic_industry_classification (type: raw_array) — SIC Industry Classification SIC industry-classification details. Each item exposes: - industry_group (type: string) — Industry Group - industry_sector (type: string) — Industry Sector - major_group (type: string) — Major Group - sic_code (type: string) — SIC Code company_name (type: string) — Company Name Name of the company. employee_headcount_range (type: string) — Employee Headcount Range Headcount band (e.g. "1000-5000"). company_categories (type: raw_array) — Company Categories PDL category tags for the company. company_type (type: string) — Company Type Company type (e.g. "Public", "Private"). naics_industry_classification (type: raw_array) — NAICS Industry Classification NAICS industry-classification details. Each item exposes: - industry_group (type: string) — Industry Group - naics_code (type: string) — NAICS Code - naics_industry (type: string) — NAICS Industry - national_industry (type: string) — National Industry - sector (type: string) — Sector - sub_sector (type: string) — Sub Sector company_stock_ticker (type: string) — Company Stock Ticker Public stock ticker symbol. date_founded (type: number) — Date Founded Year the company was founded. company_description (type: string) — Company Description Long-form company description / summary. company_website (type: url) — Company Website Company website URL. company_headline (type: string) — Company Headline Short company tagline / headline. company_industries (type: string) — Company Industries Industry label(s) for the company. company_region (type: string) — Company Region Macro region (e.g. "North America"). country (type: string) — Country Country of HQ. company_postal_zip_code (type: string) — Company Postal/Zip Code Postal / ZIP code of HQ. company_headquarters (type: raw_array) — Company Headquarters Nested HQ object with keys: address_line_2, continent, country, geo, locality, metro, name, postal_code, region, street_address. social_profiles (type: raw_array) — Social Profiles Array of social-profile URLs (LinkedIn, Twitter, Facebook, etc.). gics_sector (type: string) — GICS Sector GICS sector classification. company_linkedin_id (type: string) — Company Linkedin ID LinkedIn's internal company ID. company_twitter_url (type: url) — Company Twitter URL Twitter / X profile URL. company_display_name (type: string) — Company Display Name Display-friendly company name. company_facebook_url (type: url) — Company Facebook URL Facebook page URL. company_linkedin_url (type: url) — Company LinkedIn URL LinkedIn company page URL. mic (type: string) — MIC MIC (Market Identifier Code) for the exchange the company trades on. company_linkedin_slug (type: string) — Company LinkedIn Slug LinkedIn slug (the part after /company/). employee_headcount (type: number) — Employee Headcount Exact employee count (vs the band in Employee Headcount Range). funding_stages (type: raw_array) — Funding Stages Array of all funding stages the company has raised at. estimated_revenue (type: string) — Estimated Revenue Inferred revenue band (e.g. "10B+"). alternate_company_names (type: raw_array) — Alternate Company Names Other names this company is known by. latest_funding_date (type: string) — Latest Funding Date Date of the most recent funding round (ISO format). alternate_company_websites (type: raw_array) — Alternate Company Websites Other domains the company is known by. employee_churn_rate (type: raw_array) — Employee Churn Rate Employee churn rate. Nested field: 12_month (number). employee_growth_rate (type: raw_array) — Employee Growth Rate Employee growth rate. Nested field: 12_month (number). current_funding_stage (type: string) — Current Funding Stage Current / latest funding stage label. company_funding (type: number) — Company Funding Total funding raised, in dollars. number_of_funding_rounds (type: number) — Number of Funding Rounds Total number of funding rounds raised. average_tenure_by_role (type: raw_array) — Average Tenure by Role Average employee tenure broken out by role. Nested numeric fields: customer_service, design, education, engineering, finance, health, human_resources, legal, marketing, media, operations, public_relations, real_estate, sales, trades. employee_count_by_job_role (type: raw_array) — Employee Count by Job Role Headcount broken out by role. Same nested role keys as Average Tenure by Role. average_employee_tenure (type: number) — Average Employee Tenure Average tenure across all employees, in years. average_tenure_by_job_level (type: raw_array) — Average Tenure by Job Level Average tenure broken out by job level. Nested numeric fields: cxo, director, entry, manager, owner, partner, senior, training, unpaid, vp. employee_headcount_by_month (type: raw_array) — Employee HeadCount by Month Total headcount over time. Nested fields keyed by month (e.g. "2021-01") with numeric counts. company_linkedin_follower_count (type: number) — Company LinkedIn Follower Count LinkedIn page follower count. gross_additions_by_month (type: raw_array) — Gross Additions by Month New hires per month. Nested fields keyed by month. employee_headcount_by_country (type: raw_array) — Employee Headcount by Country Headcount broken out by country. Nested numeric fields per country (e.g. united_states, canada, …). gross_departures_by_month (type: raw_array) — Gross Departures by Month Departures per month. Nested fields keyed by month. yearly_employee_growth_rate_by_job_role (type: raw_array) — Yearly Employee Growth Rate by Job Role Year-over-year growth rate broken out by role. Same nested role keys as Average Tenure by Role. naics_code (type: string) — NAICS Code Top-level NAICS code. naics_insustry (type: string) — NAICS Industry NAICS industry label. country_code (type: string) — Country Code ISO 3166 2-digit country code. ================================================================================ 3. HOW TO CONFIGURE ================================================================================ Configure Action body: { "inputs": { "company_domain": "{{input.company_domain}}", "social_media_profile_url": "{{input.linkedin_url}}" } } Identifier rules — PDL needs `company_domain` or `social_media_profile_url` to resolve a company. Either can stand alone, or be paired with `company_name` for disambiguation. `company_name` ALONE will NOT resolve a company — the action returns `Missing input data`. Name is supplemental, not a primary key. Most reliable single-field identifier: `social_media_profile_url` (LinkedIn / Twitter / Facebook). `company_domain` resolves cleanly when the domain maps unambiguously to one company (e.g. `apple.com` → Apple). For multi-tenant or ambiguous domains (regional subsidiaries, parent / child relationships), pair the domain with `company_name` so PDL has disambiguation context. ================================================================================ 4. KEY NOTES ================================================================================ - `company_name` ALONE will not resolve a company — PDL rejects it as `Missing input data`. Pass `company_domain` or `social_media_profile_url` (either can stand alone), or pair the name with one of those two. Most reliable single-field identifier: `social_media_profile_url`. - Workforce-trend signals (`employee_growth_rate`, `employee_churn_rate`, `employee_headcount_by_month`, `gross_additions_by_month`, `gross_departures_by_month`) are PDL's unique value vs cheaper providers. If you only need basic firmographics, use `enrich_company_linkedin_profile` instead. ================================================================================ 5. WHERE IT FITS IN A WORKFLOW ================================================================================ Pattern (workforce-trend ICP scoring): use PDL's growth, churn, and hiring-velocity signals to filter or score companies before spending credits on contact discovery. input (company domain or LinkedIn URL) -> company_enrich_using_people_data_labs (returns employee_growth_rate, employee_churn_rate, employee_headcount_by_month, gross_additions_by_month, average_employee_tenure) -> format_data_using_js_expression (compute a growth / hiring-velocity score from the workforce signals — e.g. 12-month growth above 20% AND > 5 hires in the last 3 months) -> filter or rank rows on the score -> contact discovery (e.g. get_employees_by_company_using_floqer_native) -> outreach. Pattern (org-structure-aware outreach): pull the company's parent / subsidiary structure to consolidate or split prospect coverage — useful when targeting a parent and its subsidiaries differently, or when avoiding double-touching the same corporate group. input (company domain) -> company_enrich_using_people_data_labs (returns alternate_company_names, alternate_company_websites, company_linkedin_id, naics / sic / gics classifications) -> format_data_using_js_expression (decide treatment per row based on parent / subsidiary relationships) -> outreach with the right segmentation. Pattern (per-prospect insight via LLM): feed the breadth of PDL data into an LLM step to generate per-company insights (growth narrative, hiring focus, recent funding signals) used in outreach. input (company domain) -> company_enrich_using_people_data_labs -> llm_models (prompt: feed employee_growth_rate + gross_additions_by_month + funding fields, return per-company growth signals) -> outreach. ================================================================================ 6. WHEN TO USE ================================================================================ Use company_enrich_using_people_data_labs when you need PDL's unique data points — particularly: - Workforce trends: employee growth / churn rate, average tenure, employee count by role, employee headcount over time, monthly hires and departures. Useful for ICP scoring on momentum, spotting hiring sprees / layoffs, or building "growth account" lists. - Department-level headcount: when role mix matters (e.g. "target companies with > 50 engineers and < 20 in sales"). - Country-level headcount: when geographic ICP filters need more granularity than HQ country. - Full funding history: number of rounds, current stage, latest funding date, total raised — useful for funding-triggered outreach. - Corporate structure: parent / subsidiary / affiliate IDs — for ABM segmentation across corporate families. If you only need standard firmographics (name, headcount, industry, HQ, social URLs, description) and none of the above, use a cheaper provider — see WHEN NOT TO USE below. ================================================================================ 7. WHEN NOT TO USE ================================================================================ Need cheaper, leaner firmographic enrichment (just the basics — name, headcount, industry, HQ, social URLs) -> store_leads_enrich_company (https://floqer.com/docs/action-detail/store_leads_enrich_company.txt) -> enrich_company_linkedin_profile (https://floqer.com/docs/action-detail/enrich_company_linkedin_profile.txt) Both return the standard firmographic shape without PDL's workforce-trend or corporate-structure depth, at lower credit cost. Need only headcount distribution by country -> company_headcount_distribution_by_country (https://floqer.com/docs/action-detail/company_headcount_distribution_by_country.txt) Cheaper if country headcount is the only thing you need. Need a department-level tech-stack breakdown -> company_tech_stack_floqer (https://floqer.com/docs/action-detail/company_tech_stack_floqer.txt) Need very specific or unstructured data PDL doesn't expose (e.g. number of physical locations in a specific state, recent press mentions, named-customer logos on a homepage) -> llm_web_agents (https://floqer.com/docs/action-detail/llm_web_agents.txt) PDL's schema is fixed; if the data point you want isn't in the outputs above, fall back to an open-ended web-research agent. ================================================================================ This file is maintained manually. Last updated: 2026-05-20. Full interactive reference: https://floqer.com/docs/reference Action catalog: https://floqer.com/docs/action-catalog.txt