US retail kind-of-business sales intelligence (monthly)
Monthly US retail-sales intelligence at kind-of-business grain from the U.S. Census Bureau's Monthly Retail Trade Survey (12 FRED MRTSSM*USS series: NAICS 441, 442, 443, 444, 445, 446, 447, 448, 451, 452, 453, 454; millions of dollars, seasonally adjusted, 1992-01 ->). Each category-month row carries the sales level, its share of combined kind-of-business sales, month-on-month / year-on-year / 3-month-annualized momentum, YoY acceleration, a documented 0-100 category momentum score (50% winsorized YoY + 30% 3-month annualized + 20% MoM, min-maxed within each month) with deterministic rank and c1-c4 tiers, trailing-12-month record-high and contraction flags, plus month-level context (combined sales, total YoY, breadth of categories growing YoY, the month's top category, and the nonstore/e-commerce-adjacent share). An online shop joins monthly category sales to its own revenue on (date, category_code) for demand benchmarking; a sales team joins category momentum to pipeline value on date. Method: keyless FRED fredgraph.csv fetches with 0.3s polite pacing; a trailing-36-month verbatim transcription is embedded for offline safety; the transform is pure pandas in map_schema with no imputation (early-window momentum cells are honest nulls). Caveats: shares are shares of the 12 published kinds (which sum to combined retail-trade sales by construction), not of a separately published total; official e-commerce is quarterly (see companion dataset ecommerce_penetration) — the monthly nonstore share is the closest monthly proxy.
- Rows
- 4,980
- Columns
- 23
- Source cadence
- Monthly
- Last refreshed
- Sep 29, 2026
- Theme
- economics
| Column | Type | Description |
|---|---|---|
| date | date | Month of observation (Census Monthly Retail Trade Survey, monthly, seasonally adjusted). (unit: date) |
| country | string | Country name, always United States. |
| country_code | string | ISO 3166-1 alpha-3 country code, always USA. |
| category_code | string | NAICS kind-of-business code: 441, 442, 443, 444, 445, 446, 447, 448, 451, 452, 453, 454. |
| category | string | Kind-of-business label (Census MRTS kind names, NAICS 2022 subsector titles). |
| fred_series_id | string | FRED series id of the underlying Census MRTS series (MRTSSM*USS). |
| sales_musd | float | Monthly retail sales in the category, seasonally adjusted. Units: millions of US dollars. (unit: millions of USD) |
| share_of_total_pct | float | Derived: category sales as a percent of the combined sales of the 12 published kinds that month (sums to 100.0 per month). Connector-defined transformation. (unit: percent) |
| mom_pct | float | Derived: month-on-month percent change of sales_musd. Connector-defined transformation. (unit: percent) |
| yoy_pct | float | Derived: year-on-year percent change of sales_musd. Connector-defined transformation. (unit: percent) |
| mom_3m_ann_pct | float | Derived: 3-month percent change of sales_musd, annualized. Connector-defined transformation. (unit: percent (annualized)) |
| yoy_accel_pp | float | Derived: YoY acceleration — yoy_pct minus its own 12-month lag, in percentage points. Positive when the category's growth is speeding up vs a year ago. Connector-defined transformation. (unit: percentage points) |
| category_score | float | Derived: documented 0-100 momentum composite, min-maxed within each month across the 12 categories: 100*(0.50*min-max(winsorized YoY, +-25pp) + 0.30*min-max(winsorized 3-month annualized, +-30pp) + 0.20*min-max(winsorized MoM, +-10pp)). Connector-defined transformation; null for the first 12 panel months (YoY warmup). (unit: 0-100 score) |
| category_rank | float | Derived: within-month rank of category_score, 1 = strongest momentum (ties broken by category_code ascending); null for the first 12 panel months (YoY warmup). Connector-defined transformation. |
| momentum_tier | string | Derived: quartile tier of category_rank — c1 (ranks 1-3), c2 (4-6), c3 (7-9), c4 (10-12); null for the first 12 panel months (YoY warmup). Connector-defined transformation. |
| record_high_12m | integer | Derived: 1 when sales_musd is the maximum over the trailing 12 months (inclusive), else 0. Connector-defined transformation. |
| contraction_flag | integer | Derived: 1 when yoy_pct < 0 (category sales shrinking year-on-year), else 0. Connector-defined transformation. |
| total_sales_musd | float | Derived: combined sales of the 12 published kinds that month (the denominator behind share_of_total_pct). Units: millions of US dollars, SA. Connector-defined. (unit: millions of USD) |
| total_yoy_pct | float | Derived: year-on-year percent change of total_sales_musd. Connector-defined transformation. (unit: percent) |
| breadth_up_pct | float | Derived: share of the 12 kinds with yoy_pct > 0 that month — demand breadth. Connector-defined transformation. (unit: percent) |
| top_category | string | Derived: kind-of-business label of the rank-1 category that month; null for the first 12 panel months (YoY warmup). Connector-defined transformation. |
| nonstore_share_pct | float | Derived: share_of_total_pct of NAICS 454 nonstore retailers — the monthly e-commerce-adjacent channel lens (the official quarterly e-commerce share lives in the companion dataset ecommerce_penetration). Connector-defined transformation. (unit: percent) |
| row_hash | string | Deterministic 16-hex sha256 of (source_id, dataset_id, date, category_code, sales_musd) — stable row identity. Connector-defined. |
First 10 sample rows — a preview, not the complete dataset.
| date | country | country_code | category_code | category | fred_series_id | sales_musd | share_of_total_pct | mom_pct | yoy_pct | mom_3m_ann_pct | yoy_accel_pp | category_score | category_rank | momentum_tier | record_high_12m | contraction_flag | total_sales_musd | total_yoy_pct | breadth_up_pct | top_category | nonstore_share_pct | row_hash |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1992-01-01 | United States | USA | 441 | Motor vehicle and parts dealers | MRTSSM441USS | 32,679 | 22.946 | — | — | — | — | — | — | — | 1 | 0 | 142,419 | — | 0 | — | 3.803 | cb2562b28253888e |
| 1992-01-01 | United States | USA | 442 | Furniture and home furnishings stores | MRTSSM442USS | 4,087 | 2.87 | — | — | — | — | — | — | — | 1 | 0 | 142,419 | — | 0 | — | 3.803 | 5bcb7fb272423a0d |
| 1992-01-01 | United States | USA | 443 | Electronics and appliance stores | MRTSSM443USS | 3,743 | 2.628 | — | — | — | — | — | — | — | 1 | 0 | 142,419 | — | 0 | — | 3.803 | 66b6575ddec27ea2 |
| 1992-01-01 | United States | USA | 444 | Building material and garden equipment and supplies dealers | MRTSSM444USS | 10,698 | 7.512 | — | — | — | — | — | — | — | 1 | 0 | 142,419 | — | 0 | — | 3.803 | b94894b4c699db9e |
| 1992-01-01 | United States | USA | 445 | Food and beverage stores | MRTSSM445USS | 29,841 | 20.953 | — | — | — | — | — | — | — | 1 | 0 | 142,419 | — | 0 | — | 3.803 | 0e6ad25950b6c7de |
| 1992-01-01 | United States | USA | 446 | Health and personal care stores | MRTSSM446USS | 7,313 | 5.135 | — | — | — | — | — | — | — | 1 | 0 | 142,419 | — | 0 | — | 3.803 | 08d20427a8d07e66 |
| 1992-01-01 | United States | USA | 447 | Gasoline stations | MRTSSM447USS | 12,652 | 8.884 | — | — | — | — | — | — | — | 1 | 0 | 142,419 | — | 0 | — | 3.803 | 36c7c9bd16d2fd4b |
| 1992-01-01 | United States | USA | 448 | Clothing and clothing accessories stores | MRTSSM448USS | 9,459 | 6.642 | — | — | — | — | — | — | — | 1 | 0 | 142,419 | — | 0 | — | 3.803 | 36983829e2d58107 |
| 1992-01-01 | United States | USA | 451 | Sporting goods, hobby, book, and music stores | MRTSSM451USS | 3,103 | 2.179 | — | — | — | — | — | — | — | 1 | 0 | 142,419 | — | 0 | — | 3.803 | 4c9d4518a83334bc |
| 1992-01-01 | United States | USA | 452 | General merchandise stores | MRTSSM452USS | 19,813 | 13.912 | — | — | — | — | — | — | — | 1 | 0 | 142,419 | — | 0 | — | 3.803 | fa0d311c5c8833b1 |
- Current
20260929T050918Z-1df96a4c05c8 · sha256 1df96a4c05c8…
4,980 rows · first snapshot
Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).
curl "https://datazimuts.com/v1/datasets/census_retail_kind_intel/us_retail_kind_of_business_monthly" | jq '{title, rows, columns_count, license}'import requests
ds = requests.get("https://datazimuts.com/v1/datasets/census_retail_kind_intel/us_retail_kind_of_business_monthly").json()
print(ds["title"], ds["rows"], "rows")
# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
print(row)API endpoint: https://datazimuts.com/v1/datasets/census_retail_kind_intel/us_retail_kind_of_business_monthly
Tip: fetch /llms.txt for the full machine-readable catalog.
Where this data comes from and what was made from it. Other people's work shows as counts; only shared projects are named.
Cite this snapshot
Pinned to snapshot 20260929T050918Z-1df96a4c05c8 and its content hash, so readers get exactly the data you used.
US Retail Kind-of-Business Sales Intelligence (derived). (2026). US retail kind-of-business sales intelligence (monthly) [Data set, snapshot 20260929T050918Z-1df96a4c05c8, sha256 1df96a4c05c8]. Datazimuts. Retrieved 2026-09-30, from https://datazimuts.com/en/datasets/census_retail_kind_intel/us_retail_kind_of_business_monthly?snapshot=20260929T050918Z-1df96a4c05c8
@misc{dz_census_retail_kind_intel_us_retail_kind__1df96a4c,
title = {{US retail kind-of-business sales intelligence (monthly)}},
author = {{US Retail Kind-of-Business Sales Intelligence (derived)}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/en/datasets/census_retail_kind_intel/us_retail_kind_of_business_monthly?snapshot=20260929T050918Z-1df96a4c05c8}},
note = {Snapshot 20260929T050918Z-1df96a4c05c8, sha256 1df96a4c05c8739267c6373977daba9a454eabe14ce6cc2fa8f4f1bf731fa323; accessed 2026-09-30}
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