A long list is easy. A list where most names are reachable, relevant and currently buying takes four filters and about a day.
Trade data will hand you thousands of importers. Almost all of them are noise for your purposes. The work is in the filtering, and the order of the filters matters.
Start from the HS line, not the product name. Names vary by market and by company; the code does not. If you are unsure which line your goods travel under, read the descriptions on cleared consignments and work backwards.
Cut to markets where your certification is valid, your logistics work and your price lands. Twenty reachable buyers are worth more than two thousand you cannot supply, and this filter usually removes ninety percent of the list.
| Look for | Because |
|---|---|
| Shipments in most months | An operating requirement, not a project |
| Two or more years of history | A business, not an experiment |
| Multiple suppliers | Already comparing, so open to another |
| Volume flat or rising | Not a contracting account |
| Recent activity | Currently buying, not historically |
Check the declared unit values they buy at. If the band sits below your floor, they are not your customer regardless of how attractive the volume looks. Discovering that before outreach rather than after three calls is the entire point.
A steady mid-size buyer converts far better than an occasional large one, and is much less likely to already be locked into an exclusive arrangement.
Generic introductions get ignored. Referencing the product, the volume band and the origin they currently buy from demonstrates that you have done the work, and it changes the reply rate more than any other variable in the process.
Each filter is obvious in isolation. The reason lists come out badly is almost always sequencing. Rank by volume first and you build an impressive list of large buyers, most of whom sit in markets you cannot certify for or price bands you cannot supply. Filter for serviceability first and the list shrinks by ninety percent, which feels like a loss and is actually the entire value of the exercise: what remains is addressable.
The same applies to the price check. Doing it last, after outreach has begun, means discovering on the third call that the account buys well below your floor. Doing it as a filter means those names never reach the list, and the effort goes to prospects that can actually convert.
| Field | Why it is on the list | Where it comes from |
|---|---|---|
| Company | The prospect | Consignee on the record |
| Market | Serviceability already confirmed | Destination country |
| Tariff line | Confirms product relevance | Declared HS code |
| Shipments per year | The behaviour signal | Count of consignments |
| Typical consignment size | Whether you can supply it | Quantity per shipment |
| Declared unit value band | Whether you can price it | Value divided by quantity |
| Current origins | What you would be displacing | Country of origin mix |
| Last shipment date | Whether they are still active | Most recent record |
A good list is a depreciating asset. Contact everyone at once with the same message and you convert a researched prospect set into a burnt one in a week. Work it in waves, vary the opening based on what the record says about each account, and record the outcome against the name so the next attempt starts from something. Fifteen names worked properly over a quarter will out-perform three hundred contacted once.
Re-run the same query monthly and look only at what changed: new entrants, accounts that stopped, origins that shifted. That is a ten-minute review rather than a fresh project, and it is where the ongoing value sits.
Everything above is a framework, and a framework is only worth what it survives contact with. The useful discipline is to test each assumption against what consignments actually did, because customs data is one of the few commercial sources where the underlying event — goods crossing a border — physically happened and was documented under legal obligation at the time.
Fix the tariff line before anything else. Every filter, every duty figure and every comparison downstream depends on it.
Learn more ›A single period is a snapshot. Three years separate a trend from seasonality, and let you discount the incomplete recent periods.
Learn more ›Frequency and consistency beat size. A steady mid-scale counterparty is usually a better prospect than an occasional large one.
Learn more ›Declared unit values tell you the range you are entering before you quote into it.
Learn more ›Two failure modes account for most wrong conclusions drawn from trade data, and both are easy to avoid once named. The first is reading the incomplete tail of a series as a decline — authorities publish on a lag and revise afterwards, so the last one or two periods will fill in after you look. The second is reading a value movement as a demand movement, when declared value can move because volume moved, because unit price moved, or because the product mix inside a tariff line changed.
Customs data covers goods that crossed a border. It does not cover services, domestic trade, margin, contract terms or intent. Treat it as a dated, quantified observation to corroborate — not as a conclusion that arrives finished.
The difference between teams that get value out of trade data and teams that ran one interesting project is almost never analytical sophistication. It is whether the work became a routine. A saved query reviewed weekly, a short written note against each counterparty you assessed, and a standing habit of checking the period stamp before quoting a figure will out-perform an elaborate one-off study within a quarter, because markets move and a study does not.
The second habit worth building is writing down not just what you concluded but why and when. Records get revised, prices move, and counterparties change behaviour. Six months later nobody remembers whether a supplier was rejected on volume, on price band or on timing, and without that note the assessment simply gets repeated from scratch. A one-line rationale is what converts a list into institutional knowledge, and it costs seconds at the point where the thinking has already been done.
Finally, be explicit with colleagues about the confidence attached to any figure you circulate. A declared value from a complete period, controlled for origin and unit, is strong evidence. The same figure pulled from an incomplete recent period, averaged across a whole chapter, is barely evidence at all — and the two look identical once they are in a slide. Saying which one you have is what keeps trade data credible inside an organisation over time.
Small enough that every name can be qualified and personalised. Five to fifteen for deep outreach, fifty to a hundred for a working pipeline. Anything much larger becomes an artefact rather than a tool.
Usually no. The largest are most likely to be locked into established supply arrangements. Steady mid-scale buyers with several existing suppliers convert considerably better.
The record gives you the company, the product and the volume. The contact comes from normal business research, and the value of the record is that it tells you exactly what to say when you get there.
Monthly is enough for most categories. Look at the delta rather than rebuilding — new names, lapsed names and origin changes are where the actionable events are.
Markets refresh on their customs authority's own release cycle — monthly for most, 45 to 60 days for a few. The most recent one or two periods are always still filling in, so exclude them when you are reading a trend rather than treating the gap as a decline.
Yes. Give us the HS code or a product description and the market you care about, and we will return a sample of live customs records filed against it.
Keep reading
The next questions this one usually raises are covered in How to find buyers using import data, Seasonality in trade data and Negotiating with unit price data. Each picks up where this article stops, and together they cover the sequence a consignment actually goes through — classification and duty before anything moves, documentation and payment while it moves, and verification of the counterparty before any of it is committed to. Reading them in that order is usually more useful than reading them by topic.
Every company in an import record is already buying your product category.
Learn more ›Almost every traded product has a buying calendar.
Learn more ›A declared unit value is not a quotation, but it is the most credible number you can put on a table that you did not get from the other side.
Learn more ›