ACTION_ID: store_leads_enrich_company NAME: StoreLeads: Company Enrich CATEGORY: scrape CREDITS: 0 Enrich an e-commerce company using StoreLeads as the data source. Built specifically for online stores — given a store URL, returns e-commerce-specific signals you won't find in general firmographic providers: - Product count, vendor count, average product price. - Estimated traffic and estimated sales. - The e-commerce platform powering the store (Shopify, WooCommerce, BigCommerce, Magento, etc.). - Store features (search engine, email capture, etc.) and technologies installed on the site. - StoreLeads rank percentile (relative position vs other tracked stores) and Trustpilot review count + average rating. - Standard fields too: merchant name, description, location, employee headcount, contact emails, social URLs (Instagram, LinkedIn). Use this when the prospect is an e-commerce business and you want e-commerce-flavoured insights — platform / vendor footprint, store size, pricing band, traffic / sales scale — that other company enrich providers don't surface. 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_domain (type: url, required) — Company Domain URL or domain of the online store. Should be the actual storefront URL (e.g. "store.acme.com" or "https://www.acme.com") — StoreLeads is e-commerce-specific, so this won't resolve any arbitrary business domain. Pass the link a shopper would use to browse the store. ================================================================================ 2. OUTPUTS ================================================================================ merchant_name (type: string) — Merchant Name Merchant / store name. company_description (type: string) — Company Description Description of the company / store. tld1 (type: string) — TLD1 Top-level domain of the store. store_locator_page (type: string) — Store Locator Page URL of the in-site store-locator page (if any) — for retailers with physical locations. email_addresses (type: raw_array) — Email Addresses Contact email addresses found on the site. instagram_handle (type: raw_array) — Instagram Handle Instagram handle(s) listed on the site. linkedin_profile (type: raw_array) — LinkedIn Profile LinkedIn URL(s) listed on the site. categories (type: raw_array) — Categories Product / store category tags (e.g. "Electronics", "Fashion"). city (type: string) — City City of the merchant. administrative_area_level_1 (type: string) — Administrative Area Level 1 State / province / region (e.g. "California"). country_code (type: string) — Country Code ISO country code of the merchant. platform_type (type: string) — Platform Type E-commerce platform powering the store (e.g. "Shopify", "WooCommerce", "BigCommerce", "Magento", "Wix"). features (type: raw_array) — Features Store features detected on the site (e.g. "Search Engine", "Email", "Reviews"). technologies (type: raw_array) — Technologies Technologies installed on the site, with nested per-tech metadata. language_code (type: string) — Language Code ISO language code of the storefront. operational_state (type: string) — Operational State Current operational state of the store (e.g. "active"). currency_code (type: string) — Currency Code Currency the store transacts in (e.g. "USD"). avg_price_usd (type: number) — AVG Price USD Average product price in USD. product_count (type: number) — Product Count Total number of products listed in the store. vendor_count (type: number) — Vendor Count Total number of distinct vendors / brands sold in the store. estimated_visits (type: number) — Estimated Visits Estimated monthly visits to the store. estimated_sales (type: number) — Estimated Sales Estimated monthly sales for the store. employee_headcount (type: number) — Employee Headcount Approximate employee headcount. rank_percentile (type: number) — Rank percentile StoreLeads rank percentile — the store's position relative to all other StoreLeads-tracked stores. trustpilot_reviews (type: number) — Trustpilot Reviews Number of Trustpilot reviews. trustpilot_average_rating (type: number) — Trustpilot Average Rating Average Trustpilot rating (1-5). last_updated_at (type: string) — Last Updated At ISO date when StoreLeads last refreshed the store record. ================================================================================ 3. HOW TO CONFIGURE ================================================================================ Configure Action body: { "inputs": { "company_domain": "{{input.store_url}}" } } Pass the storefront URL or domain as `company_domain`. ================================================================================ 4. KEY NOTES ================================================================================ - E-commerce only: this action will not resolve arbitrary business domains — StoreLeads only tracks online stores. If the prospect isn't a real e-commerce merchant, the call will fail to return data. For non-e-commerce companies, use a general enrichment action like company_enrich_using_people_data_labs or enrich_company_linkedin_profile instead. - Pass the actual storefront URL — the link a shopper would use to browse the store — not just the parent company's marketing domain. - Strongest differentiator vs other company enrich providers: product_count, vendor_count, estimated_visits, estimated_sales, avg_price_usd, platform (Shopify / WooCommerce / etc.), features, and StoreLeads rank percentile. None of those are surfaced by the general firmographic providers. ================================================================================ 5. WHERE IT FITS IN A WORKFLOW ================================================================================ Pattern (e-commerce ICP filter on platform / features): score or filter e-commerce prospects on what platform they're running and which store features they have. input (storefront URL) -> store_leads_enrich_company (returns platform, features, technologies, product_count, etc.) -> filter / score on the relevant fields (e.g. platform = "Shopify", features contains "Reviews", product_count between 100 and 5000) -> outreach. Pattern (sizing / qualification): use estimated_visits, estimated_sales, avg_price_usd, and rank_percentile to qualify e-commerce prospects by store size before spending downstream enrichment / outreach credits. input (storefront URL) -> store_leads_enrich_company -> filter / rank on estimated_visits, estimated_sales, rank_percentile -> contact discovery + outreach on the qualified subset. Pattern (technographic insights): feed platform + features + technologies into a downstream LLM step to derive per-merchant talking points (integration angles, modernization signals, features the prospect is missing, etc.). input (storefront URL) -> store_leads_enrich_company -> llm_models (prompt: derive per-merchant talking points from platform + features + technologies) -> outreach. ================================================================================ 6. WHEN TO USE ================================================================================ Use store_leads_enrich_company when: - The prospect is an e-commerce merchant — this is the right enrichment provider when the target is an online store. - You need product_count, vendor_count, or any merchandising signal — the general firmographic providers don't surface these. - You're filtering on platform (Shopify / WooCommerce / BigCommerce / Magento / etc.) or store features — e.g. ICP targeting for an integration that plugs into a specific platform. - You're sizing or qualifying e-commerce prospects on traffic, sales, average price, or StoreLeads rank percentile. - You want technographic signals about the store's stack (technologies installed, features enabled) for outreach personalization. ================================================================================ 7. WHEN NOT TO USE ================================================================================ The prospect isn't an e-commerce merchant -> company_enrich_using_people_data_labs (https://floqer.com/docs/action-detail/company_enrich_using_people_data_labs.txt) -> enrich_company_linkedin_profile (https://floqer.com/docs/action-detail/enrich_company_linkedin_profile.txt) StoreLeads only tracks online stores. For B2B SaaS, services, or any non-e-commerce business, reach for a general firmographic provider instead. You need general firmographics with workforce-trend or funding depth (growth / churn / tenure / funding history) -> company_enrich_using_people_data_labs (https://floqer.com/docs/action-detail/company_enrich_using_people_data_labs.txt) PDL is the broader provider for non-e-commerce-specific firmographics. You need very specific or detailed data StoreLeads doesn't surface — e.g. a particular product detail, a recent press mention, named partnerships, copy from a specific page on the store -> llm_web_agents (https://floqer.com/docs/action-detail/llm_web_agents.txt) StoreLeads's schema is fixed; for narrowly-scoped lookups that don't need broad coverage, a web agent is a more flexible alternative. ================================================================================ This file is maintained manually. Last updated: 2026-04-30. Full interactive reference: https://floqer.com/docs/reference Action catalog: https://floqer.com/docs/action-catalog.txt