New-to-Bank Checking Propensity Scores
The likelihood that a household opens a checking account at a bank it doesn't use today.
Across 139.6M U.S. households in the Semcasting file, 15.4M clear the high-fit bar for new-to-bank checking propensity (score 66+, the top 10% nationally). Among metro areas they concentrate in Colorado Springs, Clarksville, Greeley: in the Colorado Springs metro area, 40.6% of households are high-fit, 3.67× 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, FDIC, BLS and CFPB 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 66+)
- 15.4M
- Qualified (score 60+)
- 35.8M
- U.S. zipcodes indexing 120+
- 1,065
Share of households clearing the high-fit bar in the top metro area vs the nation. Qualified tier (score 60+, the top 25%): 35.8M households. Average score 52.3.
States highest high-fit share
| State | High-fit | Share |
|---|---|---|
| District of Columbia | 116K | 34.0% |
| Colorado | 679K | 26.9% |
| Nevada | 267K | 20.3% |
| Texas | 2.3M | 18.6% |
| Rhode Island | 79K | 17.5% |
| Arizona | 531K | 17.3% |
Zipcodes highest index, 2,000+ households
| ZIP | Households | Index |
|---|---|---|
| 75454Melissa, TX | 10,085 | 146.8 |
| 30620Bethlehem, GA | 6,249 | 146.2 |
| 79413Lubbock, TX | 8,920 | 142.1 |
| 66043Lansing, KS | 3,283 | 139.9 |
| 80920Colorado Springs, CO | 15,605 | 138.3 |
States with at least 50,000 scored households. The largest high-fit audiences by count are in Texas (2.3M), Florida (1.6M), California (1.3M), Georgia (697K), New York (688K).
Metro areas highest high-fit share, 100,000+ households
| Metro area | High-fit | Share | vs U.S. |
|---|---|---|---|
| Colorado Springs, CO | 131K | 40.6% | 3.67× |
| Clarksville, TN-KY | 54K | 38.0% | 3.44× |
| Greeley, CO | 57K | 36.1% | 3.26× |
| Panama City-Panama City Beach, FL | 42K | 35.7% | 3.23× |
| Lubbock, TX | 52K | 34.1% | 3.09× |
| Crestview-Fort Walton Beach-Destin, FL | 54K | 33.9% | 3.07× |
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: 12.7% high-fit across 2.5M households.
Common questions
Where do new-to-bank checking propensity households concentrate?
By share of households, in the Colorado Springs, Clarksville and Greeley metro areas. In Colorado Springs, 40.6% of households are high-fit, 3.67x the national rate. By count, the largest high-fit audiences are in Texas (2.3M), Florida (1.6M), California (1.3M).
What does high-fit mean for new-to-bank checking propensity?
A score of 66 or higher, the top 10% of U.S. households for this outcome. Qualified (score 60 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 new-to-bank checking propensity 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 new-to-bank checking propensity
- Semcasting dataSemcasting household file
- Regulatory filingFDIC Summary of Deposits
- Federal statisticsCensus ACS (mobility, internet)
- Federal statisticsCensus ZIP Code Business Patterns
How the score is built
Each household's new-to-bank checking propensity score (0 to 100) combines its own household fields (50%: home-purchase recency 35%, digital activity 45%, mid-range income 20%) with market signals for its zipcode (50%) from FDIC Summary of Deposits, Census ACS (mobility, internet), Census ZIP Code Business Patterns. Each zip signal is converted to a national percentile. Weights follow Semcasting's acquisition driver model and are refined as campaign and account-opening results come in. 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. As account-opening and campaign outcome data comes in, each score is fitted to actual results and lift is reported by score decile against the holdout.
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.