Cube is Facteus’s pre-processed, aggregated consumer transaction dataset for businesses and agencies that want answers fast. It delivers segmented consumer spend, key KPIs, and shopper cohorts in an analytics-ready format built for decisions, not cleanup.
Aggregated, analytics-ready consumer transaction dataset.
Built for businesses and agencies that need fast insight without raw-row overhead.
Best for competitive benchmarking, cohort analysis, and KPI tracking.
On this page: what Cube is, what it solves, specs, proof, use cases, and FAQs.
Cube is Facteus’s aggregated transaction data product for enterprise and agency users. It provides pre-processed consumer spend data with shopper cohorts and key KPIs, making it easier to analyze market share, retention, cross-shopping, and competitive trends. Compared with Ultra, Cube is more ready-made. Compared with Audiences, it is designed for analytics, not activation.
Cube reduces prep work with analytics-ready structure.
Cube supports cross-functional consistency across strategy, finance, marketing, and agencies.
Cube helps answer multiple business questions from one signal.
Cube is built for teams that want usable outputs quickly.
Track spend by cohort, geography, merchant, and other business-relevant dimensions.
Use KPIs and category views to understand market share, share of wallet, and competitor momentum.
Cube’s aggregated structure helps teams get to dashboards and decisions with less transformation work.
Strategy, finance, marketing, and agency teams can work from the same external signal.
Cube is designed for business users who want fast, analytics-ready consumer spend data.
| Specification | Detail |
|---|---|
| Data type | Transaction data |
| Granularity | Aggregated / segmented |
| Refresh cadence | Daily / one-day lag |
| Core use case | Competitive benchmarking and business analytics |
| Delivery | Analytics-ready datasets and approved integrations |
| Audience | Businesses & agencies |
Segmented consumer spend data is valuable because business decisions rarely happen at the total-market level alone. Teams need to know which cohorts are growing, which geographies are shifting, which competitors are winning, and how those patterns connect. Raw transactions can answer some of that, but they also create a lot of work.
Cube is built for teams that want the answer without the detour. It packages observed consumer spend into an aggregated, analytics-ready format with key KPIs and shopper cohorts already in view. That makes it easier for enterprise teams and agencies to move quickly from question to insight, whether the job is market share analysis, audience planning, or competitive reporting.
Because Cube sits on the same verified Facteus signal used across the platform, it retains the methodological rigor that higher-stakes teams need. It is fast to use, but not flimsy. That is the point.
Cube gives business users speed without losing the foundation.
185M+ cards
30+ diverse sources
One-day lag delivery
Benchmarking against authoritative external measures
Monitor category and competitor movement.
See how spend changes by income, generation, or geography.
Give clients real purchase-based market context.
Create a shared external view across functions.
Cube is aggregated and analytics-ready. Ultra is row-level and more flexible for technical teams.
Cube is designed for businesses and agencies that want fast answers from segmented consumer spend.
Cube supports market share, share of wallet, cross-shopping, retention, and competitive benchmarking.
Cube is refreshed daily with one-day lag signal.
Yes. Cube is positioned for practical analytics and dashboard workflows.
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