Depth below the merchant
Merchant- and brand-level data tells you how much was spent at a store. Product- and UPC-level data tells you what, exactly sold — which is where category, assortment, and share-of-item questions get answered.
Affinity Solutions and Facteus are built on the same foundation: bank-direct transaction data, sourced from financial institutions rather than surveys, receipts, or panels. So the comparison isn’t about where the data comes from — both start from real, deterministic purchases. It’s about the details that decide whether the data answers your actual questions: how deep it goes below the merchant, how specifically its accuracy is validated, how far back it runs, and how much of the U.S. spend it sees. On those specifics, the two diverge — and for a team making a forecast, a benchmark, or a plan, the details are the decision.
When two datasets both source directly from financial institutions, the surface pitch looks identical. Before you commit a forecast or a plan to either, these are the things worth checking:
Merchant- and brand-level data tells you how much was spent at a store. Product- and UPC-level data tells you what, exactly sold — which is where category, assortment, and share-of-item questions get answered.
"Strong census correlation" is a claim. A stated figure — a specific correlation, a stated variance by state and generation — is something you can actually check. Ask for the number, not the adjective.
Benchmarking and seasonality work depends on depth of history. A few years covers recent cycles; more covers the comparisons that put a current read in context.
The more U.S. card spend a panel sees, the more confidently it represents categories, geographies, and cohorts beyond the largest brands.
Where we extend further is in depth, disclosure, and reach:
Same foundation, different reach. Where the comparison matters most for decisions you have to defend:
| Affinity Solutions | Facteus | |
|---|---|---|
| Sourcing | Opt-in, rewards linked sourcing | 18+ bank-direct, spanning income-levels & regions |
| Validated accuracy | "Strong census correlation" (specific figures not published) | 92% average census correlation, within 2pp by state, 4.5pp by generation |
| Scale | 150M+ cards / 100M consumers, self-selection bias from opt-in | 200M active U.S. consumer cards, no recall or selection bias |
| Product granularity | Merchant level, no UPC | Merchant, brand & product-level with 8M+ UPCs |
| History depth | Up to 5 years | 8+ years transactions, 4+ years UPC |
| Delivery | Platform, audiences, feeds | Analytics-ready KPIs, natural-language chat, dashboards, row-level feeds |
| Freshness | Lagged data supply | Daily; ~70% fill within 1 day |
Market share, share of wallet, cross-shopping, and brand affinity delivered as ready-to-use KPIs, so analysts spend time in the insights, not building.
By category, named competitor, geography, generation, income, or loyalty status, and much more — from brand health down to UPC- and store-level.
8M+ UPCs answers assortment, item, and category questions brand-level data can't reach.
Insight arrives while the decision still matters.
Built for teams that have to defend the forecast, the plan, and the budget and for agencies to win with targeting.
Market share, share of wallet, cross-shopping, and brand affinity come modeled and aggregated.
Drill from brand health to UPC- and store-level truth without changing tools.
Segmented audiences from real consumer spend transactions available off-the-shelf or built custom – at the speed and granularity needed for action.
Pre-built dashboards and ready-to-use insights mean your team is acting in days, not stuck in a long integration.
Facteus has transformed how our FP&A team forecasts, benchmarks and allocates — all based on competitive consumer transaction data.
Meet with our team to see a coverage comparison against your current data.
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