What is the purpose of each dataset?

Each dataset provides insights into global trade by tracking shipments entering and leaving the U.S. and Mexico, as well as customs clearance data. These datasets help businesses analyze supply chains, monitor competitors, and optimize logistics.

Key Data Points and Limitations

Dataset	Key Data Points	Key Limitations
U.S. Import	Importer and exporter Container details Product details Ports details	Some shipments are confidential, affecting companies like Tesla & NVIDIA. No value (except for shipments using CIF incoterm).
U.S. Export	Exporter Product details Destination country Transport details	No consignee and notify party name.
Mexico Import	Importer Product details and Value Customs details Transport details	85% of shipments lack foreign entity name.
Mexico Export	Supplier Product details and Value Customs details Transport details	85% of shipments lack foreign entity name.
Customs Clearance	Company and notify party Port details Customs clearance status Broker details	No product-level or supplier details.

Transportation Covered

U.S. Import/Export

You can see

You can understand

FTL/LTL outbound locations

Parcel distribution

Over dimensional & overweight freight customers

Drayage Lanes

Inland rail terminals

You can't see

Domestic Lanes

Mexico Import/Export

You can see

You can understand

Final destination based on terminal crossing

You can't see

No restrictions

Customs Clearance

You can see

You can understand

Canadian Cross-border

Fill in manifest confidentiality

You can't see

Goods shipped

Foreign Entity

Final Destination

How is the data collected for each dataset?

U.S. Data

Collected through a combination of U.S. Customs and Border Protection (CBP) and Homeland Security.

Mexico Data

Collected through a third-party vendor that sources the data from port authorities. The exact third-party method is kept undisclosed, but the data comes from the ports.

Customs Data

Data is collected after liquidation (settling of taxes and fees on shipments) and comes from government records.

Dataset Update Frequency & Availability

Dataset	Update Frequency	Available Date From
U.S. Import	Monthly, no delay (around the 3rd—4th)	2015
U.S. Export	Monthly, with a 30—60 day delay	2015
Mexico	Monthly, with a 60 day delay	2021
Customs Clearance	Monthly, with a 365 day delay	About 3 years back

What are the main use cases for each dataset?

U.S. Import Dataset

Helps businesses understand the flow of goods into the U.S., identify potential suppliers, and assess competition. Useful for identifying trade trends and managing supply chain logistics.

U.S. Export Dataset

Helps track U.S. exports and identify potential markets for U.S. goods, although it's limited for sales prospecting due to a lack of detailed information.

Mexico Dataset

Crucial for understanding cross-border trade between the U.S. and Mexico, especially for supply chains related to agriculture, automotive, and chemicals.

Customs Clearance Data

Useful for analyzing cleared shipments and identifying trends or issues in compliance, as well as verifying shipments that have already gone through customs.

What are the most common mistakes users make when using the datasets, and how can they avoid them?

Overestimating Data Accuracy

Users may assume that the data is 100% accurate or that missing data is a sign of poor service. However, many gaps (like missing consignee names) are inherent due to manifest confidentiality or incomplete customs records. To avoid this, users should understand the limitations of each dataset and focus on trends rather than specific shipments.

Mistaking U.S. Data for Real-Time Data

Users often mistakenly assume that data from the U.S. import dataset reflects real-time shipments. Since the data is updated monthly and sometimes with a delay, this can be misleading. It's important to remember that the dataset reflects historical shipments.

Relying on Incomplete Information

Users may overlook incomplete consignee or product data. It's essential to double-check and use multiple data points when making decisions.