1B+
transactions / month
Row-level consumer transactions for research, modeling, and production workflows.
250K+
merchants & 800+ tickers
Broad coverage with merchant and ticker mappings ready for analysis.
1-day
lag
Yesterday’s consumer behavior in today’s pipeline.
97%
MARTS correlation
Tracks U.S. Census Advance Monthly Retail Trade Survey for an external check on the broader economic signal.
Aggregated data has already made decisions for you. What counts as a comparable-store cohort? How should returns be treated? Which merchants belong in a ticker? What makes someone a lapsed customer?
Spectrum leaves those choices with your team.
You get row-level transaction data with merchant, ticker mapping, transaction amount, timing, and consumer attributes without pre-aggregating the answer. It’s the difference between getting the report and getting the ledger it was built from.
Build your own cohorts, features, merchant groups, competitive sets, and aggregations instead of inheriting someone else’s definitions.
Packaged indices give every buyer the same view. Spectrum gives your team the raw material to build something truly differentiated.
Spectrum lands on a one-day lag, so a shift in consumer behavior doesn’t have to wait for a monthly file before it reaches your pipeline.
Consistent schema, point-in-time integrity, and delivery through Snowflake, S3, or API help you take an idea from evaluation to production without changing the underlying dataset.
Define revenue, traffic, retention, cross-shopping, customer, or category signals using your own methodology.
Roll transactions up by ticker, sector, geography, merchant, or custom cohort — then re-cut the view when the thesis changes.
Track where consumers spend across named companies and merchants to understand share shifts, customer movement, and competitive behavior.
Go from idea to backtest to production on one transaction stream, updated every single day.
Spectrum gives your team the row-level transaction stream — merchant, ticker mapping, amount, timing and consumer attributes — without pre-aggregating the answer.
Data type
Row-level consumer card transactions
Scale
1B+ transactions per month; 250K+ merchants; 800+ tickers
Granularity
Transaction, merchant, ticker mapping, consumer attributes
Refresh cadence
Daily, one-day lag
Validation
97% correlation to U.S. Census MARTS
Core use case
Custom signal engineering, research, production models, in-house competitive analytics
Delivery
Snowflake, S3, API, approved workflows
Built for
Investment teams and enterprise data science teams
Say you’re covering a retailer and the story is that new-store growth is hiding weakness in the existing customer base. A pre-aggregated signal gives you somebody else’s definition of the cohort. Spectrum lets you build your own.
Identify consumers who transacted with the retailer twelve months ago. See who stayed. Who increased spend. Who quietly left. Then follow where those customers went next.
The same rows can answer an entirely different question inside a company: from measuring customer defection after a competitor enters a market to building a proprietary demand signal for a forecasting model. When the question changes, the underlying stream doesn’t have to.
And because flexibility is only useful if the foundation holds up, Spectrum tracks U.S. Census MARTS at 97% correlation and sits on Facteus’s broader transaction foundation of 200M+ active U.S. cards from 18+ direct financial sources.
Work from the full row-level transaction stream to create proprietary signals and models.
A stable, ready-to-use ticker signal built for earnings and nowcasting.
Explore pre-aggregated consumer spending across companies, categories, markets, and competitors.
Explore Spectrum sample data with our team and see what you can build with more than a billion row-level transactions every month.
Book a demoHow is Spectrum different from Beacon?
Spectrum gives you the full row-level transaction stream to build your own signals. Beacon is a constant-card panel already engineered for stable ticker-level forecasting. Many teams that need both a ready-made forecast and deeper custom analysis use both.
Is Spectrum delivered daily?
Yes. Yesterday’s transactions are available today.
How do teams access the data?
Snowflake, S3, API or other approved delivery workflows.
Is Spectrum only for investors?
No. Investment teams are a core use case, but enterprise data science and analytics teams can also use Spectrum to build custom models, competitive intelligence, simulations, and other proprietary applications.
Why use row-level data instead of Chroma?
Use Spectrum when your team wants to define its own methodology, cohorts, features, and aggregations. Chroma is better suited to teams that want analytics-ready spend insights and faster answers without engineering directly from raw transactions.
Can Spectrum support backtesting?
Yes. Spectrum is designed with point-in-time integrity so teams can evaluate historical signals using the information that would have been available at the time.
How do you know the data is representative?
Spectrum tracks U.S. Census MARTS at 97% correlation. The broader Facteus transaction foundation is also checked against external economic benchmarks including BEA Personal Consumption Expenditures and company-reported earnings.
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