Digital Direct Banking Audiences
Households likely to open and run accounts through mobile and online channels.
Across 139.6M U.S. households in the Semcasting file, 14.4M clear the high-fit bar for digital direct banking (score 57+, the top 10% nationally). Among metro areas they concentrate in Albany, Boulder, Raleigh: in the Albany metro area, 27.3% of households are high-fit, 2.65× 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 57+)
- 14.4M
- Qualified (score 47+)
- 36.9M
- U.S. zipcodes indexing 120+
- 4,690
Share of households clearing the high-fit bar in the top metro area vs the nation. Qualified tier (score 47+, the top 25%): 36.9M households. Average score 37.3.
States highest high-fit share
| State | High-fit | Share |
|---|---|---|
| District of Columbia | 139K | 40.5% |
| Vermont | 57K | 19.7% |
| Colorado | 497K | 19.7% |
| Connecticut | 281K | 19.1% |
| New Jersey | 660K | 18.5% |
| Massachusetts | 509K | 18.0% |
Zipcodes highest index, 2,000+ households
| ZIP | Households | Index |
|---|---|---|
| 11791Syosset, NY | 8,177 | 159.2 |
| 20011Washington, DC | 26,464 | 159.2 |
| 11803Plainview, NY | 10,499 | 158.6 |
| 20017Washington, DC | 9,011 | 157.9 |
| 10504Armonk, NY | 2,823 | 156.9 |
States with at least 50,000 scored households. The largest high-fit audiences by count are in California (1.7M), Texas (1.3M), New York (1.0M), Florida (780K), New Jersey (660K).
Metro areas highest high-fit share, 100,000+ households
| Metro area | High-fit | Share | vs U.S. |
|---|---|---|---|
| Albany-Schenectady-Troy, NY | 106K | 27.3% | 2.65× |
| Boulder, CO | 34K | 26.8% | 2.59× |
| Raleigh-Cary, NC | 169K | 26.7% | 2.59× |
| Fort Collins-Loveland, CO | 43K | 26.4% | 2.56× |
| Bridgeport-Stamford-Danbury, CT | 92K | 25.1% | 2.43× |
| Rochester, NY | 114K | 25.1% | 2.43× |
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: 19.2% high-fit across 2.5M households.
Common questions
Where do digital direct banking households concentrate?
By share of households, in the Albany, Boulder and Raleigh metro areas. In Albany, 27.3% of households are high-fit, 2.65x the national rate. By count, the largest high-fit audiences are in California (1.7M), Texas (1.3M), New York (1.0M).
What does high-fit mean for digital direct banking?
A score of 57 or higher, the top 10% of U.S. households for this segment. Qualified (score 47 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 digital direct 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 digital direct banking
- Semcasting dataSemcasting household file
- Federal statisticsCensus ACS 2024 5-year
How the score is built
Each household's digital direct banking score (0 to 100) is 60% its own household fields (digital activity 64%, income range 14%, homeownership 14%, home value 7%) and 40% Fair Lending qualified statistics for its zipcode from 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.