ACTION_ID: company_enrich_using_store_leads
NAME: StoreLeads: Company Enrich
CATEGORY: Company Enrichment
CREDITS: 1

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.

1. INPUTS

storeleads_api_key (type: boolean, optional) — StoreLeads API key
  Whether to use a personal StoreLeads API key for this run instead
  of Floqer's StoreLeads connection. Allowed values: "true" /
  "false" (default "false"). Set to "true" only if you've connected
  your own StoreLeads account on the Floqer Connections page;
  otherwise leave default. Runs on your own key are not charged
  Floqer credits.

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}}",
    "storeleads_api_key": false
  }
}

Pass the storefront URL or domain as `company_domain`. Leave
`storeleads_api_key` false (or omit it) to run on Floqer's
StoreLeads connection; set it true to run on your own key.

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)
    -> company_enrich_using_store_leads (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)
    -> company_enrich_using_store_leads
    -> 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)
    -> company_enrich_using_store_leads
    -> llm_models (prompt: derive per-merchant talking points from
       platform + features + technologies)
    -> outreach.

6. WHEN TO USE

Use company_enrich_using_store_leads 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
  -> action-detail/company_enrich_using_people_data_labs.txt
  -> 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)
  -> 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
  -> 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.

Last updated: 2026-08-13.