Shipment Intelligence

Customs = means data is provided by customs of that country and we have full data of its trade.  For example, India makes its data available directly for all shipments by road, sea, and air so it includes data of all importers in India.

Mirror = refers to additional Bill of Lading data and customs records given by trade partners. Additional Bill of Lading data is provided by sources other than customs.

DataSur = US Imports and Argentina Exports

TradeAtlas = 73 other countries

Filter out irrelevant shipments incorrectly categorized under a certain HS – too often it happens that shipments that doesn’t have anything to do with the labelled HS code end up it the wrong category. Not correcting for this will have an impact on data quality and misconceptions about trade volumes and values

Deepening Product classification – based on more than 1,000+ mappings dividing the HS Code 200410 (Frozen Potato Products) into Frozen French Fries, Frozen Potato Specialties and Other

Exporter and Importer names mentioned in product descriptions

Brands of exporters or importers mentioned in branding columns

Notify party for importers to get the end customer in scope instead of the logistic partner

Derive exporter and importer based on the combination of port of origin or departure and country of arrival

Cleaning unit of mass (e.g. kilogram, pounds, tons) in order to ensure that all data can properly be presented in kilograms as the default unit of mass

Take the Net Weight Original if filled – when net weight is applicable we prioritize the net weight as presented

Search for net weights mentioned in the product description – for all known net weight values historically present in the data, a search is carried out together with the word “Net weight” or synonyms like “Net WT” Quantity to Net Weight – for all values in the “quantity” column and having a “quantity unit” of kilogram, the net weight is calculated