Credit Card Audiences
Households likely to open and actively use a bank credit card.
Across 139.6M U.S. households in the Semcasting file, 14.3M clear the high-fit bar for credit card (score 59+, the top 10% nationally). Among metro areas they concentrate in Appleton, Rochester, Lancaster: in the Appleton metro area, 34.9% of households are high-fit, 3.42× 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 59+)
- 14.3M
- Qualified (score 51+)
- 35.1M
- U.S. zipcodes indexing 120+
- 3,935
Share of households clearing the high-fit bar in the top metro area vs the nation. Qualified tier (score 51+, the top 25%): 35.1M households. Average score 41.6.
States highest high-fit share
| State | High-fit | Share |
|---|---|---|
| Utah | 252K | 20.8% |
| Vermont | 58K | 20.1% |
| Wisconsin | 537K | 19.9% |
| Minnesota | 475K | 19.2% |
| Iowa | 251K | 17.6% |
| Nebraska | 145K | 17.3% |
Zipcodes highest index, 2,000+ households
| ZIP | Households | Index |
|---|---|---|
| 48072Berkley, MI | 7,014 | 147.7 |
| 17520East Petersburg, PA | 2,224 | 146.8 |
| 19033Folsom, PA | 3,075 | 146.8 |
| 52340Tiffin, IA | 3,052 | 146.1 |
| 48128Dearborn, MI | 4,357 | 145.9 |
States with at least 50,000 scored households. The largest high-fit audiences by count are in Texas (1.1M), California (981K), Pennsylvania (826K), Illinois (731K), Florida (700K).
Metro areas highest high-fit share, 100,000+ households
| Metro area | High-fit | Share | vs U.S. |
|---|---|---|---|
| Appleton, WI | 40K | 34.9% | 3.42× |
| Rochester, MN | 27K | 26.5% | 2.60× |
| Lancaster, PA | 56K | 25.9% | 2.54× |
| Grand Rapids-Wyoming-Kentwood, MI | 115K | 25.1% | 2.46× |
| Ogden, UT | 57K | 24.7% | 2.41× |
| Provo-Orem-Lehi, UT | 54K | 24.2% | 2.37× |
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: 6.8% high-fit across 2.5M households.
Common questions
Where do credit card households concentrate?
By share of households, in the Appleton, Rochester and Lancaster metro areas. In Appleton, 34.9% of households are high-fit, 3.42x the national rate. By count, the largest high-fit audiences are in Texas (1.1M), California (981K), Pennsylvania (826K).
What does high-fit mean for credit card?
A score of 59 or higher, the top 10% of U.S. households for this segment. Qualified (score 51 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 credit card 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 credit card
- Semcasting dataSemcasting household file
- Federal statisticsCensus ACS 2024 5-year
- Regulatory filingFFIEC HMDA 2024
How the score is built
Each household's credit card score (0 to 100) is 60% its own household fields (income range 42%, digital activity 33%, homeownership 17%, home value 8%) and 40% Fair Lending qualified statistics for its zipcode from Census ACS 2024 5-year, FFIEC HMDA 2024, 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.