At Kikoff, we publish original research and data stories to help people understand what’s really happening with the credit, spending, and overall financial health of everyday Americans. Not guesswork, but real numbers.
This page explains exactly where those numbers come from, how we protect the people behind the data, and how we make sure what we publish is accurate and fair. If you’re a reader, journalist, or researcher trying to understand or cite our work, you’re in the right place.
Our data sources
Our editorial team draws on many different types of data for our original research, and we’re transparent about each one in the stories we publish:
- Our own user data. As a fintech company helping people build credit, we have access to aggregated data on how real people are managing their credit, spending their money, and staying on top of their finances. When we publish a story using this data, we base it on anonymized, aggregated information. We never use an individual users’ personal information.
- Public and third-party data. Where it helps put our own findings in context, we reference data from independent sources like the U.S. Census Bureau, the Bureau of Labor Statistics, and the Consumer Financial Protection Bureau. We link directly to the original source, so you can check the numbers yourself.
- Commissioned research. For some stories, we work with independent research firms to run surveys on topics our own data can’t address alone. When we do this, we name the research partner directly in the story.
How we protect the privacy of our users
Nothing we publish includes personally identifiable information. Before any data becomes part of a published story, it’s aggregated and anonymized. That means it reflects patterns across large groups of people, and not any individual’s information.
We also don’t publish findings about groups so small that a user could reasonably be identified or identify themselves.
For details on how we handle user data, see our privacy page.
How we define and calculate our numbers
Some terms show up across multiple stories, and we want them to mean the same thing every time:
- Approval odds — how likely a user is to be approved for a given financial product based on their profile
- Credit desert — A city, region, or ZIP code where credit invisibility is unusually common compared with the rest of the country
- Credit invisible — users with no credit file on record with the credit bureaus, so there’s no history to generate a score from
- Exit rate — the share of credit-invisible users who go on to have a scorable credit file within a set time period (specified in each story)
- Hard inquiries — a formal credit checks that happens when a user applies for credit, usually counted over a trailing period (like “the past 12 months”)
- Seasonal stress — financial pressure that reliably rises during the same time each year, year after year, like the holidays or back-to-school
- Unscoreable — users who have a credit file but not enough activity (too few accounts, or nothing recent enough) to generate a reliable score
Each story clearly defines terms specific to that piece, right where the number appears. No need to hunt through fine print.
What every data story includes
To keep things consistent and clear, every Kikoff data story states plainly and transparently:
- Sample size — how many people, accounts, or transactions the finding is based on
- Comparison groups — who’s included (think: “U.S. adults in metro areas” or “Kikoff members with an active Credit Account”)
- Time period — the specific window of data we analyzed
- Key data terms — how we calculated any specific metric mentioned in the story (like “credit utilization” or “average balance”), so there’s no guesswork about what a number actually means
We won’t publish a finding based on a sample too small to be meaningful, and we’ll note the specific sample size for every story, so you can judge its reliability yourself.
How we check our work
Before a data story goes live, it goes through editorial review to confirm the numbers are calculated correctly, and by an editor to make sure the story represents those numbers accurately and clearly. If a claim can’t be confirmed against the underlying data, it doesn’t get published.
Errors and corrections
We work hard to get every number, every calculation, and every conclusion right before it’s published. But we won’t get it right every time.
If we find an error in a published data story, or if updated source data changes our findings, we correct it and note the correction directly in the story with the date it was made. We won’t quietly edit the numbers and move on.
Spot something that looks off? Email research@kikoff.com to flag it. A member of our editorial team will take a look, and if you’re right, you’ll see the correction on the page.
Questions or want to cite our work?
If you’re a journalist, researcher, or reader with questions about a specific finding, reach out to research@kikoff.com. We’ll help you get it right.
If you’re citing our work, please credit us:
[Report/Story Title], Kikoff. [Publication date]. [URL]
Want to know more about the people and standards behind everything we publish? See our editorial guidelines and meet our team.
