Data Sources

A Rich Set of Independent Sources, With Transactions at the Core

Transaction Data

Billions of Real Purchases Every Month

Transaction data from credit and debit cards, e-wallets, and bank accounts gives us the most direct view into actual consumer spending. All data is anonymized and processed in full compliance with privacy regulations.

Payment instruments: Cards, e-wallets, bank accounts
Source: Card issuers and processors
Privacy: Anonymized, regulation-compliant
Scale: Billions of purchases per month

Data Cube

The Analytical Approach Behind Our Data

Multiple Dimensions

The ECDB Cube is our core data model, a multi-dimensional framework interlinking markets, retailers, consumers, and more. Our analysts use it to organize, cross-validate, and model data consistently across every dimension we cover.

Revenue Equation

At the core sits the ECDB Revenue Equation, the logic connecting all dimensions of the Data Cube. It systematically links key metrics across retailers, markets, and consumers, ensuring every figure fits a consistent structure and enables reliable, source-independent market intelligence.

Questions?

Get the Full Picture on Our Methodology

Our team knows the data inside out. Whether you have questions about sourcing, modeling, or coverage, talk to an expert directly.

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Approach

A Structured Path to High-Quality Data

01Data CleaningStep 1

Preparing Data Before It Enters the Model

Before any modeling begins, incoming data is cleaned, normalized, and validated. Our analysts identify inconsistencies, remove noise, and resolve conflicts across sources, ensuring only reliable, well-structured data feeds the modeling pipeline.

Benefits

Advantages of Our Approach

Every data point in ECDB has passed through a multi-stage process built for accuracy. Here is how our approach compares.

Other Providers

Data Foundation
Modeling Approach
Consistency
Validation
Transparency
Output

Transaction data at the core, enriched by multiple independent sources
Analysts in the driving seat, supported by proprietary models and AI
Revenue Equation links all dimensions across markets, retailers, consumers
Outlier detection, sanity checks, and multi-perspective cross-verification
Confidence indicators per profile, visible in the platform
Structured intelligence across markets, retailers, categories, and consumers

Other Providers

Traffic estimates, surveys, or single-source scraping
Automated pipelines, limited expert validation
Metrics often inconsistent across dimensions and time
Limited or no post-modeling validation
Limited or no accuracy indicators
Traffic rankings and visit counts, no purchase-level depth

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Frequently Asked Questions

Yes, ECDB's data is built on billions of anonymized real-world transaction records provided by official partners, offering a highly accurate and near real-time view of actual purchasing behavior. To ensure full market coverage, this data is complemented by modeling and forecasting where direct observations are not available. This combination allows us to deliver both precision and completeness. If you'd like to see how this works in practice, feel free to book a demo with our team.

ECDB combines multiple data sources to ensure broad and reliable market coverage. A key foundation is the analysis of billions of transactions each month, providing a strong view of real market activity. This is complemented by additional data sources and validation, allowing ECDB to also cover smaller markets and companies where data is typically limited. If you'd like to learn more, our team would be happy to guide you, feel free to book a demo.

Yes, ECDB provides a broad view of global e-commerce, including emerging markets and smaller economies. This is made possible by combining multiple data sources, with a strong foundation in the analysis of billions of transactions each month, offering a reliable view of real market activity. This data is further enriched and validated to ensure coverage even where data availability is typically limited.

ECDB uses a dual approach: Bottom-up aggregation of store- and retailer-level data. Using a combination of weighted traffic (adjusted by purchasing power parity), AI-based product portfolio clustering, manual analysis, and near real-time transaction data. That resulting figure is validated top-down against external market research, association data, and macro indicators (GDP, internet penetration, consumer spending). Both estimates are reconciled into a weighted final figure. All published market figures are model-based estimates.

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The Data Engine Behind Our E-Commerce Intelligence