Eurostat Wholesale Trade Turnover Data: Dataset Structure and Field Coverage

The series you want is sts_trtu_m — Eurostat's harmonised short-term business statistics table for turnover and volume of sales in wholesale and retail trade. Filter it to NACE Rev.2 division G46 and you have European wholesale trade turnover, month by month: roughly 20 million cells in the unfiltered cube, spanning 42 geographies (EU27_2020 and euro area aggregates plus every member state and the EFTA and candidate countries that report) with monthly history from January 1991 to the latest reference month, observed to June 2026.

Datadory catalogs this record as Short-term business statistics — Wholesale & Retail Trade (NACE G46) with a quality score of 9/10, one of only four 9-scored datasets in the trading companies & distributors slice alongside Companies House's UK register, Statistics Canada's Table 20-10-0074-01 and BEA's industry accounts. The granularity is country × indicator × NACE division × adjustment × unit, at monthly frequency — the same shape national statistical institutes publish, but harmonised so a German series is comparable with a Portuguese one without any rebasing work on your side.

Turnover is not the only measure on offer. The cube carries deflated volume-of-sales indicators next to nominal ones, which matters because wholesale revenue moves with prices; ranking countries or months on nominal turnover alone conflates inflation with demand.

Every observation decomposes along six axes, and getting them wrong is the difference between one clean series and a multi-megabyte dump:

Two practical consequences follow from that structure. First, because values are indices rather than euros, sts_trtu_m answers "is wholesale activity rising or falling, and how fast" but never "what is the German wholesale market worth" — for absolute sizing you pair it with another source (see below). Second, because adjustment type sits on its own axis, the seasonally adjusted and raw versions of the same series are separate rows; pick one deliberately or your charts will double-count nothing but your joins will break.

Four workflows use the series repeatedly in practice:

1. Demand-timing dashboards. Plot monthly G46 turnover and volume-of-sales by country to see where European wholesale demand is accelerating; 42 geographies let you rank markets in one chart.
2. Territory scoring. Rank target countries on three-month momentum in the adjusted volume series before setting quotas or routing territories, then refresh monthly.
3. Nowcasting anchors. Feed the index into models of distributor revenue, since the harmonised definition holds across borders and reaches back to January 1991 — over 35 years of monthly history for backtests.
4. Cross-source reconciliation. Because values are indices, they calibrate other sources: align a private shipment feed's growth rate against G46 volume-of-sales to sanity-check coverage claims.

Workflow 4 points at the real boundary. sts_trtu_m tells you how much wholesale activity changed, not which firms drove it, and stops at the EU's statistical perimeter entirely. Closing either gap takes different sources from the same slice — Statistics Canada for dollar-denominated provincial detail, WTO Stats for country-pair merchandise flows, Companies House for the firm-level register behind the aggregate.

The three flagship macro series in this industry answer different questions, and picking wrong wastes a week: