ImportYeti
PowerQuery
GET

Rank US importers from shipment data

1 per 10 records

Aggregate US import bills of lading into a ranked list of companies (volume-oriented). Optional sort_by for weight or TEU.

Related:Rank buyers & suppliers by product

Parameters#

Query parameters

30 parameters
  • page_sizenumberoptionaldefault 10

    Number of returned results

  • offsetnumberoptionaldefault 0

    Results starting position

  • start_datestringoptionaldefault 01/01/2021
  • end_datestringoptionaldefault 08/31/2026
  • pagenumberoptional

    Page number (1-indexed). Takes precedence over offset.

  • companystringoptional

    Company identifier

  • supplierstringoptional

    Supplier identifier

  • product_descriptionstringoptional

    Supports boolean operators and wildcards

    AND / OR / NOTwildcards * ?
  • weightstringoptional

    Supports ranges

    ranges [a TO b]
  • teustringoptional

    Supports ranges

    ranges [a TO b]
  • company_total_shipmentsstringoptional

    Supports ranges

    ranges [a TO b]
  • supplier_total_shipmentsstringoptional

    Supports ranges

    ranges [a TO b]

fields, exclude, and format work the same way on every endpoint — Response shaping has worked examples.

Response#

A volume-ranked list of US importers that match your filters.

Response shape
{
  "requestCost": 1,
  "creditsRemaining": 9998.5,
  "data": {
    "data": [
      {
        "key": "Ikea Supply",
        "doc_count": 191251,
        "total_shipments": 1311940,
        "name_variations": ["Double Ace Cargo C/O", "Double Ace Cargo Network"],
        "company_address": [{"key": "4104 Industrial Parkway Drive Lebec Ca 93243 United States", "doc_count": 12733}],
        "company_country_code": "US",
        "company_country": "United States",
        "company_link": "/company/ikea-supply"
        // …14 more fields
      }
    ],
    "totalCompanies": 59583
  },
  "executionTime": "224ms"
}

Rows are nested one level deeper

PowerQuery routes put the records at data.data — not at data. The total number of matches is on data.totalCompanies, which is the value you page against. Note that this key is named per route family (totalCount, totalCompanies, totalSuppliers, totalBrokers).

Top-level fields#

  • data — an object holding the result rows (data.data) and the match total (data.totalCompanies)
  • requestCost — Data credits used for this call (0 if none)
  • creditsRemaining — Your data credit balance after the call
  • executionTime — How long the request took
  • totalCompanies — Total matching companies, not just this page

Fields on each row in data.data#

FieldExample
22 fields
  • keystringIkea Supply
  • doc_countnumber191251

    How many documents matched your filters for this bucket — not the company's lifetime total.

  • total_shipmentsnumber1311940

    The company's total shipments on record, independent of the filters in this query.

  • name_variationsstring[]["Double Ace Cargo C/O", "Double Ace Cargo Network"]
  • [{"key": "4104 Industrial Parkway Drive Lebec Ca 93243 United States", "doc_count": 12733}]

    key, doc_count

  • company_country_codestringUS
  • company_countrystringUnited States
  • [{"key": "inter.ikea.com", "doc_count": 5822}, {"key": "ikea.com", "doc_count": 5391}]

    key, doc_count

  • emails, phone_numbers

  • company_main_phone_numberstring983874848039015
  • [{"key": "Geodis Usa Llc", "doc_count": 70932}]

    Who the carrier notifies on arrival — frequently the freight forwarder or customs broker rather than the buyer.

    key, doc_count

Errors#

Failures come back as JSON with a statusCode and message. The ones worth handling here are 401 (bad or missing key), 403 (out of credits), and 404 (no such record). See all error responses.