US e-commerce penetration index, quarterly
Quarterly US e-commerce penetration index, 1999Q4 through 2026Q2, from the U.S. Census Bureau's Quarterly E-Commerce Report (FRED series ECOMPCTSA: e-commerce retail sales as a percent of total retail sales, seasonally adjusted). Ships ML-ready derived features: share_qoq_pp and share_yoy_pp (quarterly and year-on-year changes in percentage points), share_accel_yoy_pp (YoY acceleration), and share_vs_5y_median_pp (deviation from the trailing 20-quarter median). Base values read verbatim from FRED's keyless endpoint 2026-09-28; derived columns are this connector's transformation.
- Rows
- 107
- Columns
- 8
- Source cadence
- Quarterly
- Last refreshed
- Sep 29, 2026
- Theme
- economics
| Column | Type | Description |
|---|---|---|
| date | date | Quarter of observation (first day of quarter). (unit: date) |
| country | string | Country name, always United States. |
| country_code | string | ISO 3166-1 alpha-3 country code, always USA. |
| ecom_share_pct | float | FRED series ECOMPCTSA: E-Commerce Retail Sales as a Percent of Total Sales — the Census Bureau's Quarterly E-Commerce Report estimate of e-commerce sales as a share of total US retail sales. Units: percent, seasonally adjusted, quarterly. (unit: percent) |
| share_qoq_pp | float | Derived: quarterly change of ecom_share_pct in percentage points. Connector-defined transformation. (unit: percentage points) |
| share_yoy_pp | float | Derived: 4-quarter (year-on-year) change of ecom_share_pct in percentage points. Connector-defined transformation. (unit: percentage points) |
| share_accel_yoy_pp | float | Derived: YoY acceleration — share_yoy_pp minus its own 4-quarter lag. Positive when the channel shift is speeding up vs a year ago. Connector-defined transformation. (unit: percentage points) |
| share_vs_5y_median_pp | float | Derived: ecom_share_pct minus the trailing 20-quarter median (min 8 quarters). Positive when penetration is above its own last-five-year baseline. Connector-defined transformation. (unit: percentage points) |
First 10 sample rows — a preview, not the complete dataset.
| date | country | country_code | ecom_share_pct | share_qoq_pp | share_yoy_pp | share_accel_yoy_pp | share_vs_5y_median_pp |
|---|---|---|---|---|---|---|---|
| 1999-10-01 | United States | USA | 0.6 | — | — | — | — |
| 2000-01-01 | United States | USA | 0.8 | 0.2 | — | — | — |
| 2000-04-01 | United States | USA | 0.9 | 0.1 | — | — | — |
| 2000-07-01 | United States | USA | 1 | 0.1 | — | — | — |
| 2000-10-01 | United States | USA | 1 | 0 | 0.4 | — | — |
| 2001-01-01 | United States | USA | 1.1 | 0.1 | 0.3 | — | — |
| 2001-04-01 | United States | USA | 1.1 | 0 | 0.2 | — | — |
| 2001-07-01 | United States | USA | 1.1 | 0 | 0.1 | — | 0.1 |
| 2001-10-01 | United States | USA | 1.2 | 0.1 | 0.2 | -0.2 | 0.2 |
| 2002-01-01 | United States | USA | 1.3 | 0.1 | 0.2 | -0.1 | 0.25 |
- Current
20260929T021218Z-50364faeac42 · sha256 50364faeac42…
107 rows · +0 rows vs previous
20260929T021055Z-50364faeac42 · sha256 50364faeac42…
107 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/ecommerce_penetration/us_ecommerce_penetration_quarterly" | jq '{title, rows, columns_count, license}'import requests
ds = requests.get("https://datazimuts.com/v1/datasets/ecommerce_penetration/us_ecommerce_penetration_quarterly").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/ecommerce_penetration/us_ecommerce_penetration_quarterly
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 20260929T021218Z-50364faeac42 and its content hash, so readers get exactly the data you used.
US E-commerce Penetration Index (derived). (2026). US e-commerce penetration index, quarterly [Data set, snapshot 20260929T021218Z-50364faeac42, sha256 50364faeac42]. Datazimuts. Retrieved 2026-09-30, from https://datazimuts.com/en/datasets/ecommerce_penetration/us_ecommerce_penetration_quarterly?snapshot=20260929T021218Z-50364faeac42
@misc{dz_ecommerce_penetration_us_ecommerce_penet_50364fae,
title = {{US e-commerce penetration index, quarterly}},
author = {{US E-commerce Penetration Index (derived)}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/en/datasets/ecommerce_penetration/us_ecommerce_penetration_quarterly?snapshot=20260929T021218Z-50364faeac42}},
note = {Snapshot 20260929T021218Z-50364faeac42, sha256 50364faeac421c74d637477a739857eca4b8427cac3b9d6272bb8a8a829dd3b1; accessed 2026-09-30}
}Embed a table or a chart
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<iframe src="https://datazimuts.com/embed/chart?dataset=ecommerce_penetration%2Fus_ecommerce_penetration_quarterly&lang=en&theme=auto&snapshot=20260929T021218Z-50364faeac42&x=date&y=ecom_share_pct&agg=avg" title="US e-commerce penetration index, quarterly" width="100%" height="380" style="border:0" loading="lazy"></iframe>
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