Kelly Services, Inc.: revenue
Kelly Services revenue: $5.36 billion for the year ending 2019-12-29, down 2.9% on the year. 3 values since 2017 from SEC filings, each linked to its filing.
All Kelly Services, Inc. financial histories
At a glance
Kelly Services reported revenue of $5.36 billion for the year ending 2019-12-29, down 2.9% from $5.51 billion a year earlier. Across the last three reported years it ranged from $5.36 billion (2019-12-29) to $5.51 billion (2018-12-30).
Computed from the values in the table below, in USD; each links to the filing it came from.
What this measure means
Revenue under this specific accounting concept. A missing value is not zero; filers can use other revenue concepts.
Exact concept: us-gaap:Revenues. Each value covers an annual-duration reporting interval, shown with both start and end dates. Different units remain separate; no currency conversion or interpolation is applied.
Coverage of this history
Selected reporting periods run from 2017-01-02 to 2019-12-29. The SEC response was captured on 2026-09-19.
This selected history ends more than two years before capture. Do not treat its final value as a current balance or current annual result. More recent filings may use another accounting tag; inspect the filings before drawing conclusions about the company.
Selected filing history
| Period start | Period end | Value | Unit | Filed | Source filing |
|---|---|---|---|---|---|
| 2018-12-31 | 2019-12-29 | 5,355,600,000 | USD | 2020-02-13 | 10-K · 0000055135-20-000006 SEC |
| 2018-01-01 | 2018-12-30 | 5,513,900,000 | USD | 2020-02-13 | 10-K · 0000055135-20-000006 SEC |
| 2017-01-02 | 2017-12-31 | 5,374,400,000 | USD | 2019-02-14 | 10-K · 0000055135-19-000006 SEC |
Related KELLY SERVICES, INC. histories
Inspect the source
- Entity
- KELLY SERVICES, INC. / CIK 0000055135
- Captured
- SEC response SHA-256
8b40b85cf8ec33b3e0ea8fae68f843b38e6ab6e5481a61ecf4bd4cd4411bad4e
Current SEC company facts · Download the original response snapshot (gzip) · Download the selected JSON
Latest-filed annual-report facts per unit and reporting period at capture time. Duration facts cover 300 to 400 days. This selection can include restatements and is not a point-in-time backtest dataset. Missing concepts are omitted, never zero-filled. Values retain original units and are not currency converted. Extended concepts require compatible unit shapes and at least three reporting ends with changing values within one unit. Constant or incompatible added histories are omitted.
Public company accounting reference, not market prices, returns, an investment recommendation, or ALPHAC performance. Validate a separately constructed return series with the validation API; accounting values are not returns.
Use this in research
A financial period ends before its results become public. Use the filing date as a minimum availability boundary, inspect amendments, and retain the original filing vintage when testing historical signals. This latest-filed selection can contain information unavailable at the time.
These pages do not supply prices, total-return histories, corporate-action adjustments or a tradable universe. Build those inputs separately before evaluating a strategy. A profitable backtest can still reflect selection bias or costs that were left out.
Research methodology · Execution and cost assumptions · Check backtest overfitting
Build with the open-source tools
Use these accounting records as inspectable inputs. When you have constructed a return series, the validation tools can help test its statistical evidence and preserve the result with its limitations.
- Get an API key and run your first validation
- Connect the MCP server to your coding assistant
- Inspect the ALPHAC engine on GitHub
- Read the MCP server source and integration examples
The MCP server is free and MIT-licensed: npx -y canli-mcp. If these records help your research, a star on GitHub helps other researchers find them.
Read the published dataset with Python
import json
from urllib.request import urlopen
with urlopen("https://canlicapital.com/company-data/0000055135.json") as response:
record = json.load(response)
print(record["fetched_at"])
print(record["policy"])
for concept in record["concepts"]:
print(concept["tag"], next(iter(concept["observations"])))