Verifiable representativeness
Coverage should be validated against a known benchmark, not asserted. If you can't check it against the census, you're trusting it blind.
YipitData and Facteus both deliver row-level data that plugs into your models, so the comparison isn’t about delivery. It’s about what’s underneath the feed. YipitData assembles its consumer signal from email receipts, web scraping, and licensed third-party card panels. Facteus sources directly from 18+ banks, credit unions, and fintechs, which is exactly what lets us validate against the U.S. Census, within 2pp by state and 4.5pp by generation. Representativeness isn’t a layer you add after the fact. You can’t model a receipt panel into looking like America. You have to source it that way.
You’ve run this diligence before: a feed can look complete and arrive research-ready while the panel underneath was never built to represent the population you’re trying to read. Excellent modeling on top can’t fix a foundation that skews at the source. These are the four things worth checking before you trust any dataset with a call:
Coverage should be validated against a known benchmark, not asserted. If you can't check it against the census, you're trusting it blind.
Accuracy claims mean little unless they're measured and disclosed. Look for a stated correlation to ground truth.
Scraped pages, receipts, and licensed card panels inherit whatever those inputs are and whatever happens to them next. Direct sourcing is a foundation, not a rental.
8+ years of transaction history and 4+ years of UPC history – deep, continuous, and consistent so backtests reflect the market, not a chance in the panel.
The stability and representativeness of a spend panel is a function of where the data comes from. Panels assembled from licensed upstream suppliers inherit whatever risk and biases are inherent to those suppliers, and a single change at the source can ripple through coverage, representativeness, and continuity. Facteus sources directly from 18+ banks, financial institutions, and fintech partners, because we believe diverse sourcing is what keeps a panel representative and stable over time – and at Facteus, it’s compounded by years of data science tuning the panel to hold true against real economic benchmarks. That’s what makes census validation possible: 92% average correlation, within 2pp by state, 4.5pp by generation. And 1.8% MAPE against top 100 tickers.
Same category, different foundation. Where the comparison matters most for building signal you can trust:
| YipitData | Facteus | |
|---|---|---|
| Sourcing | Email receipts + web scraping + licensed third-party card panels | Sourced directly from 18+ banks, credit unions & fintechs |
| Who's in the panel | Inherent bias of opt-in panels; not census-benchmarked | Within 2pp of census by state, 4.5pp by generation |
| What accuracy is validated against | Company-reported KPIs, per ticker (~within 2% on covered names) | U.S. Census, at the population level with 92% avg correlation |
| Scale | 12M+ consumers | 200M+ active U.S. consumer cards |
| Product granularity | Item/UPC-level for covered tickers | Merchant, brand & product-level – 8M+ UPCs across 60k+ stores |
| Row-level access | Yes, via Snowflake & S3 | Yes, via Snowflake & S3 |
| Bias | Digital/receipt skew + inherited panel composition | No recall or self-selection bias; representativeness engineered at the source |
Coverage spans all U.S. geographies and economic classes, so the panel represents the country, not just those who opt-in to receipt sharing or rewards programs.
Representativeness you can verify against the population, not accuracy defined as fit to a single company's earnings, with 92% average census correlation.
A continuous panel you can cut by category, generation, and geography, so you can run the reads a per-ticker build never anticipated.
8M+ UPCs across 60k+ stores, delivered row-level via S3 and Snowflake for your own models.
Signal arrives while it's still actionable.
Built for funds that can't afford a break in their series and count on data reliability:
8+ years of transaction history and 4+ years of UPC history, so backtests hold.
Validate Facteus against your existing panel or research feed before you commit, so you're switching on evidence, not faith.
Transaction-level, aggregated, or dashboard for CC/debit (Ultra, Arbiter, Dashboard); row-level via S3 and Snowflake for UPC (Onyx). Fits your existing workflows.
Direct sourcing and ready-to-model delivery mean you're evaluating live signal in days, not stuck in a long integration cycle.
Facteus is more accurate than tools we've used in the past — letting us make decisions faster and with a higher degree of confidence.
Meet with our team to see a coverage comparison against your current panel.
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