Key Takeaways
- UK country-by-commodity import data is free and useful for import planning, especially when you need evidence for sourcing, market sizing, and risk checks.
- ONS publishes monthly, non-seasonally adjusted country-by-commodity import data, while HMRC’s uktradeinfo service lets you build custom overseas trade tables.
- Commodity-code data is usually better for duty, customs, and product planning; SITC data is better for broader trend comparisons across countries and time.
- HMRC explains that trade statistics use commodity codes at the 8-digit level, with the first 6 digits coming from the international Harmonized System.
- The data is not a demand forecast on its own. You still need supplier capacity checks, landed-cost modelling, Incoterms, lead times, and operational constraints.
- For low-value customs-declaration trade, GOV.UK’s June 2025 methodology notes say imports and exports of individual value £873 or less are aggregated under SITC group 931.
Country-by-commodity trade data lets you see what the UK is already importing, from which countries, and in which product groups. Used well, it gives importers a practical evidence base before committing to a new supplier, switching origin, or increasing order volume.
For UK import planning, the value is in combining trade data with customs knowledge. The same dataset can help a commercial team estimate market demand, help a customs team sense-check commodity classification, and help an operations team spot origin concentration before disruption exposes it.
This guide explains where to get the data, what each source is good for, and how to turn it into decisions that support importing rather than just reporting.
What country-by-commodity trade data shows
Country-by-commodity data breaks UK goods trade into two dimensions: the country involved and the goods category being traded. For imports, that means you can look at a product group and see the countries from which the UK has been receiving those goods. You can also start with a country and see which goods categories dominate trade from that origin.
The Office for National Statistics publishes a monthly “Trade in goods: country-by-commodity imports” dataset. The research notes for this article identify it as non-seasonally adjusted and covering all countries with selected commodities. That matters because the raw monthly numbers can move for seasonal, operational, or one-off reasons, so you should avoid treating a single month as a trend.
HMRC’s uktradeinfo service provides another route into UK overseas trade data. Its custom table builder lets you choose the flow, period, partner country, and commodity detail you need, then export the result. According to HMRC’s uktradeinfo guidance, users can build overseas trade data tables and export full commodity data as CSV, with Open Document Spreadsheet export being introduced gradually from 11 July 2025.
The strongest import-planning use case is comparison. You are usually asking whether one origin is becoming more important, whether a substitute origin has enough visible trade, whether a product category is growing, or whether your assumed commodity code lines up with the way the product appears in official data.
ONS data versus HMRC custom tables
ONS and HMRC data can both support import planning, but they fit slightly different jobs. ONS country-by-commodity files are useful when you want a ready-made official dataset that is updated monthly and easy to cite in board papers or planning notes. HMRC’s custom tables are better when you want to build your own extract around a precise product, flow, country, and period.
Use the ONS dataset when you need a fast country-by-commodity view. It is especially useful for market context, country concentration, and short briefing notes because it packages the series in a consistent publication.
Use uktradeinfo when you need a more tailored slice. If you are comparing imports for a particular product family across several countries, the custom table builder avoids downloading more data than you need. It also keeps you closer to HMRC’s trade-statistics classifications, which can matter when customs colleagues are using the output to support classification or duty checks.
The practical rule is simple: ONS is often the best first look; HMRC custom tables are often the better working extract. If the decision affects duty, product classification, or declaration preparation, involve the person responsible for customs clearance before the analysis becomes a buying plan.
Commodity codes, HS codes, and SITC
Commodity codes and SITC are not interchangeable. They answer different questions and sit at different levels of detail.
HMRC’s uktradeinfo guidance says trade-in-goods data is published under two classification systems: commodity codes and the Standard International Trade Classification, Revision 4. Commodity codes are the product codes most importers recognise from declarations and tariff work. SITC is a statistical classification designed for economic analysis and international comparison.
For trade statistics, HMRC explains that commodity codes are compiled at the 8-digit level. The first 6 digits are the Harmonized System: chapters at HS-2, headings at HS-4, and subheadings at HS-6.
Use commodity-code data when you care about duty, restrictions, licences, origin preferences, product-level purchasing, or import duty. Use SITC when you are presenting broad trade trends, comparing categories across countries, or analysing long-term economic patterns. If you need both, keep the two views separate and document the mapping choice.
How to use the data in import planning
Start with a specific planning question, not with the dataset. “Should we add Vietnam as a second source for this product family?” is a useful question. “What does trade data say about Vietnam?” is too broad and will pull you into interesting but unfocused analysis.
Once the question is clear, define the product scope. If the buying team uses a supplier description and the customs team uses a commodity code, reconcile those before you download anything.
Next, choose the countries and periods. A 12-month view can show current trading reality, while a three-year view can show whether a source is consistently present or only appears after a temporary disruption. If the product is seasonal, compare the same months across years rather than reading month-on-month movement too literally.
Then turn the extract into a planning table. At minimum, include product scope, country, period, value, quantity if available, share of total imports, and an operational note such as “strong growth, check supplier base” or “visible trade but duty treatment needs review”.
Sourcing and country-risk decisions
Country-by-commodity data is useful for spotting concentration before it becomes a procurement problem. If most UK imports for a product group come from one or two origins, your own supplier strategy deserves a closer look. The risk may still be acceptable, but it should be conscious.
The data can also identify realistic alternative origins. If a country already exports meaningful volumes of the relevant goods to the UK, it suggests the origin has established trade routes, freight patterns, compliance experience, and buyers already active in the category.
Use trade data alongside freight and contract terms. A country may look attractive in trade statistics but become less attractive once you model lead time, minimum order quantities, port options, insurance, and responsibility under Incoterms.
The most useful sourcing output is a short ranked watchlist rather than a sprawling country report. For each potential origin, record what the trade data shows, what you still need to validate, and the next operational action. That might be requesting supplier references, checking testing requirements, reviewing rules of origin, or asking your forwarder for lane options.
Demand signals and market sizing
Import data can help you check whether a market exists at national level. If UK imports of a product group are consistently growing, that is a useful signal for a distributor, wholesaler, or overseas manufacturer considering a UK route to market. If volumes are flat or volatile, the market may still be good, but you need another explanation.
Do not mistake import value for sales demand. Trade data normally reflects goods crossing the border, not sell-through, margin, stock levels, or channel performance. A spike could mean genuine demand growth, front-loading before a rule change, currency effects, stock rebuilding after disruption, or a small number of large consignments.
Value and quantity should be read together where possible. If value rises while quantity is stable, the change may reflect higher unit prices, product mix, exchange rates, or higher specification goods.
For planning, use trade data to set a range rather than a single answer. It can support assumptions in a business case, but it should sit beside customer research, competitor checks, stock-turn data, and channel feedback.
Duty and compliance checks
Trade data is not the same as the UK Trade Tariff, but it can still improve customs planning. If your analysis depends on a product group, the commodity-code structure forces the commercial team to confront classification early. That is usually a benefit, because duty, VAT, controls, and documentation all depend on the correct code.
Before you use a code in a landed-cost model, check it against the product specification. GOV.UK guidance on finding commodity codes says the code is used for declarations, duty and VAT checks, and finding reliefs.
Trade data can also expose where rules of origin may matter. If a product category shows significant imports from countries covered by UK trade agreements, preferential duty may be part of the commercial picture. Our rules of origin guide explains the compliance work behind that claim.
For planning, separate three numbers: the visible trade value, the expected customs value of your shipment, and the landed cost after duty, VAT, freight, insurance, and fees. Trade data helps with market and sourcing context. Tariff classification and import costing decide what the shipment will actually cost you.
Limitations that can change the answer
The first limitation is timing. Monthly trade data is retrospective, so it records what has already crossed the border. If your decision depends on a port strike, a sanctions change, or a sudden duty amendment, official trade statistics may lag the commercial reality you need.
The second limitation is classification. Statistical categories can hide product differences that matter commercially. Two goods under the same heading may have different quality levels, margins, supplier bases, regulatory controls, or final customers.
The third limitation is aggregation. GOV.UK’s June 2025 UK overseas trade methodology notes state that, for trade collected from customs declarations, imports and exports of individual value £873 or less are aggregated under SITC group 931. That means low-value flows may not appear in the product detail in the way an e-commerce importer expects.
The fourth limitation is Northern Ireland treatment. HMRC’s uktradeinfo guidance notes that the EU Combined Nomenclature is still used for Northern Ireland trade statistics and that CN commodity codes are the same as the UK Tariff. If your planning includes Great Britain and Northern Ireland routes, make sure the geography and classification basis match the decision.
A simple workflow for import teams
Use a repeatable workflow so each planning exercise can be reviewed later. The output should be a short evidence pack that explains the product scope, data source, period, countries compared, and the decision it supports.
Begin with the buying question and product definition. Agree the commodity code or statistical category, then record who approved it. If the code is uncertain, treat the analysis as provisional and ask for classification support before using it in a duty model.
Download the data from ONS or build a custom HMRC table. Keep the raw extract unchanged, then create a working copy for filters, charts, and notes. This makes it easier to answer later questions about whether a figure came from the official data or from your own calculation.
Finally, translate the findings into actions. The action may be “continue with current supplier”, “qualify two alternative origins”, “request duty advice”, or “test route cost from a new port”. Trade data is only useful when it changes the next operational step.
Common mistakes to avoid
The most common mistake is starting too broad. A country-by-commodity dataset can answer hundreds of questions, but an import plan usually needs one or two. Keep the analysis tied to a buying, customs, or operations decision.
Another mistake is assuming the biggest exporting country is automatically the best source. Large trade flows can indicate capacity and established lanes, but they can also mean intense competition, long lead times, high exposure to disruption, or limited supplier leverage. A smaller origin may still be better if it offers reliability, compliance quality, or tariff preference.
Teams also over-read short-term movement. One month of import growth can be noise, especially with non-seasonally adjusted data. Look across several periods, compare the same months where seasonality matters, and annotate known shocks such as strikes, regulatory deadlines, or freight disruption.
The final mistake is separating trade analysis from customs compliance. A sourcing option that looks promising in the data still needs an EORI number, accurate declarations, correct Incoterms, and a checked duty position. Import planning works best when commercial and customs teams use the same evidence from the start.
Frequently Asked Questions
Is UK country-by-commodity trade data free?
Yes. ONS publishes country-by-commodity trade datasets, and HMRC’s uktradeinfo service provides access to overseas trade data and custom table building. You may need spreadsheet or data-analysis tools to handle larger files comfortably, but the underlying public data is available without paying a data vendor.
Should I use commodity-code data or SITC data?
Use commodity-code data when the decision is close to customs, duty, product classification, or landed cost. Use SITC when the question is about broader economic trends or high-level comparison across countries and periods. If a decision involves both, keep the outputs separate and explain the classification basis.
Can trade data tell me which supplier to choose?
No. It can show whether an origin is active in a product category and whether UK imports are rising, falling, or concentrated. Supplier choice still needs commercial due diligence, sample checks, factory capacity, compliance evidence, contract terms, freight options, and payment-risk assessment.
How current is the data?
ONS publishes the country-by-commodity imports dataset monthly, according to the research notes for this article. Treat it as an official view of recent trade rather than a live operational dashboard. For urgent disruption, regulatory changes, or port issues, use official notices and carrier or port updates alongside the trade data.
What is the £873 aggregation threshold?
GOV.UK’s June 2025 methodology notes say imports and exports of individual value £873 or less, where collected from customs declarations, are aggregated under SITC group 931. For import planners, that means low-value trade may be less visible at detailed product level than higher-value consignments. This is especially relevant if your business model depends on small parcels or lower-value shipments.