Small Business Banking Audiences
Owner-operator households likely to need business checking, lending and treasury services.
Across 139.6M U.S. households in the Semcasting file, 14.4M clear the high-fit bar for small business banking (score 62+, the top 10% nationally). Among metro areas they concentrate in Boulder, Ann Arbor, Charlottesville: in the Boulder metro area, 41.3% of households are high-fit, × the national rate. At $1.50 to $3.00 CPM, these audiences are priced at approximately 50% or less of the market rate, with results reported every week.
Fair Lending qualified
No race, sex, age, marital, familial or public-assistance inputs, and no proxies for them.
Household + zip signals
Semcasting household fields combined with Census, HMDA, IRS data for the zipcode.
State and zip indexes
Scores roll up to every U.S. state and zipcode, indexed to the national average (100).
Households national audience
- U.S. households scored
- 139.6M
- High-fit (score 62+)
- 14.4M
- Qualified (score 52+)
- 36.6M
- U.S. zipcodes indexing 120+
- 5,575
Share of households clearing the high-fit bar in the top metro area vs the nation. Qualified tier (score 52+, the top 25%): 36.6M households. Average score 41.9.
States highest high-fit share
| State | High-fit | Share |
|---|---|---|
| Colorado | 565K | 22.3% |
| Connecticut | 318K | 21.6% |
| New Hampshire | 119K | 20.5% |
| District of Columbia | 67K | 19.6% |
| Vermont | 50K | 17.2% |
| Maryland | 392K | 15.5% |
Zipcodes highest index, 2,000+ households
| ZIP | Households | Index |
|---|---|---|
| 53033Hubertus, WI | 2,298 | 168.9 |
| 48130Dexter, MI | 6,661 | 168.1 |
| 06419Killingworth, CT | 2,605 | 167.4 |
| 04021Cumberland Center, ME | 2,729 | 165.7 |
| 06013Burlington, CT | 3,706 | 164.9 |
States with at least 50,000 scored households. The largest high-fit audiences by count are in California (1.7M), Texas (1.6M), Florida (934K), Illinois (623K), Pennsylvania (621K).
Metro areas highest high-fit share, 100,000+ households
| Metro area | High-fit | Share | vs U.S. |
|---|---|---|---|
| Boulder, CO | 52K | 41.3% | 4.00× |
| Ann Arbor, MI | 59K | 39.3% | 3.81× |
| Charlottesville, VA | 31K | 30.4% | 2.95× |
| Austin-Round Rock-San Marcos, TX | 313K | 28.7% | 2.79× |
| Fort Collins-Loveland, CO | 44K | 27.1% | 2.62× |
| Bridgeport-Stamford-Danbury, CT | 99K | 27.0% | 2.62× |
District of Columbia is a single urban core, so it is shown among states but is better compared with metro areas. The Washington metro area as a whole: 23.0% high-fit across 2.5M households.
Common questions
Where do small business banking households concentrate?
By share of households, in the Boulder, Ann Arbor and Charlottesville metro areas. In Boulder, 41.3% of households are high-fit, 4.00x the national rate. By count, the largest high-fit audiences are in California (1.7M), Texas (1.6M), Florida (934K).
What does high-fit mean for small business banking?
A score of 62 or higher, the top 10% of U.S. households for this segment. Qualified (score 52 or higher) is the top 25%. These are percentile cuts across all scored households, not a count of households that need the product.
Is the small business banking score Fair Lending qualified?
It uses no race, sex, age, marital status, familial status or public-assistance inputs, no fields that stand in for them, and no credit bureau data. It is not a credit decision. Zip-level targeting should still get a disparate-impact review before media or offer allocation.
Evidence base data behind small business banking
- Semcasting dataSemcasting household file
- Federal statisticsIRS SOI 2022 ZIP data
- Federal statisticsCensus ACS 2024 5-year
How the score is built
Each household's small business banking score (0 to 100) is 60% its own household fields (income range 38%, home value 23%, homeownership 23%, digital activity 15%) and 40% Fair Lending qualified statistics for its zipcode from IRS SOI 2022 ZIP data, Census ACS 2024 5-year, each converted to a national percentile. State and zip indexes compare an area's average score with the U.S. average (100).
How it is confirmed
Every scored file carries a fixed 10% holdout. Campaign results are compared with the holdout at the same score cutoffs, and the difference is reported as lift. Weights are refined against campaign response and booking data as it accumulates.
What it is not
The score is not a credit decision and uses no credit bureau data. It excludes race, sex, age, marital status, familial status and receipt of public assistance, and any field that stands in for them. Zip-level targeting should still get a disparate-impact review before media or offer allocation.