How We Turn Big Data
Into Big Impact
Our data is built on a multi-step data modeling process, powered by transaction data sourced from card issuers and processors.
1bn+
Transactions per Month
100m+
Debit & Credit Cards
Near Real-Time
Update Cycle
Our Transaction Data
Follow the Money
The core of our data are transaction data. All of the transaction data comes from card issuers or processors based on the transactions going over their networks.
Almost real-time data: The transaction data will be delivered with a delay of 7 days after the purchase event and will be immediately processed.
- Credit & Debit Cards
- Transaction data is captured directly from card issuers or processors when payments are made using credit or debit cards.
- E-Wallets
- Data from e-wallet transactions is sourced directly from processors handling payments through these digital platforms.
- Bank Accounts
- Transaction insights are obtained from bank account activity processed through financial networks.
- Privacy Regulations
- All transaction data collection complies strictly with privacy regulations, ensuring secure and lawful data handling.
Other Data Sources
Our Data Stands on Multiple Pillars
Beyond comprehensive transaction data, our insights are enriched by diverse data sources that provide a holistic understanding of the eCommerce landscape.
- Retailer Data
Many data points come directly from the retailers, providing key insights into sales and customer behavior.
- Product Categories & Brands:
What product categories and brands does the store offer? - Pricing Information:
What are the prices? - Marketplace Functionality & Activity:
Does the store offer market place functions? Or is the store active on other market places?
- Product Categories & Brands:
- Traffic Data
Traffic data helps us to understand the interaction between a customer and a store.
- Website Visitors:
How many visitors go to a website? - Session Duration:
How long do they stay? - Shopping Basket Activity:
How many items do they put into the shopping basket? - Pages per Session:
How many pages do they visit during a session?
- Website Visitors:
- Financial Data
About 5% of all stores are public companies, reporting quarterly revenues that help fine-tune our models. However, this data:
- Aggregation:
The data is highly aggregated and requires splitting by stores, brands, business models, and countries. - Time Distribution:
Revenues must be accurately allocated over time. - Processing:
Significant adjustments are needed to make the data usable for analysis.
- Aggregation:
Cube Approach
Multiple Data Sources for a Robust and Reliable Analysis
We see eCommerce data as a multidimensional cube, where each side represents key areas like retailers, markets, or transaction KPIs. All data points are interconnected, ensuring consistency and deeper insights. By analyzing data from multiple angles, we compare and validate values, enhancing accuracy and reliability. Our broad range of sources strengthens this structured approach, creating logical chains that help verify individual data points. Instead of viewing data as isolated figures, we see them as interconnected elements within a cube structure, forming a closed and coherent system.
- Consistency:
- Our cube-based, multidimensional approach allows us to cross-validate data, ensuring an unparalleled level of consistency.
- Robustness:
- By leveraging a wide range of data sources, we make our analysis resilient and less susceptible to fluctuations in individual sources.
- Logical Data Chains:
- Viewing data as interconnected chains enhances quality and enables data insights that would otherwise be difficult to obtain.
Transaction Intelligence
Data That Makes a Difference
With transaction intelligence, businesses gain deep insights into customer purchasing behavior, enabling more informed strategic decisions.
- 56,000+
Retailers
- 150+
Markets
- 250+
Product categories
Our Data Process
With a Multi-Stage Analysis Approach, We Ensure the Highest Quality
- Data Cleaning / Standardization
Thorough data preparation ensures strong foundation for modeling.
- Data Modeling
Experienced analysts and diverse data sources provide accurate results.
- Data Forecasting
Multiple inputs enable detailed and precise forecasting.
- Data Enrichment
Additional indicators refine the analysis of countries, categories, and retailers.
Data Cleaning / Standardization
Building a Strong Data Foundation
Our comprehensive approach to data preparation ensures a reliable foundation for market and company modeling. Through our meticulous processes, we address key aspects of data quality.
- Standardization:
- Different definitions are harmonized to create a unified data basis.
- Accuracy:
- Incorrect or unreliable data is identified and removed.
- Consistency:
- Comprehensive cleaning ensures a reliable and consistent data pool.
- Simplification:
- Processes reduce complexity, making the eCommerce market more accessible for our customers.
Data Modeling
Turning Raw Data into Meaningful Insights
What makes us unique in data modeling is our focus on expert insights, continuous refinement, and the integration of diverse and reliable data sources to deliver the most accurate results.
- Regular Updates:
- We continuously adjust our models and integrate new data points throughout the year.
- Expert-Driven Modeling:
- Our market specialists ensure optimal processing and contextualization of data.
- Human Intelligence:
- Experienced analysts are central to our process, supported by in-house tools.
- Data Diversity:
- We integrate all reliable market data to create a complete.
Data Forecasting
Forecasting eCommerce Developments with Precision
Accurate forecasts are built by combining multiple data sources to analyze trends, performance, and future developments.
- Historical Developments:
- Past market trends and patterns serve as a foundation for future projections.
- Macroeconomic Changes:
- Economic shifts are integrated to reflect their impact on market dynamics.
- Player forecasts:
- Information from companies reveals expected developments in markets and businesses.
- eCommerce KPIs:
- Metrics like conversion rates and cart abandonment highlight potential trends from our data pool.
Data Enrichment
Building a Full Picture with Data Enrichment
We enhance modeled data with expertly researched indicators to provide a complete picture of retailers, countries, and categories.
- Shipping Service Providers:
- Insights into shipping options used by online retailers.
- Payment Providers:
- Data on payment methods offered across online retailers.
- Marketplace Analytics:
- Analysis of performance and trends on marketplaces.
- Consumer Behavior:
- Metrics like AOV (average order value) and CVR (conversion rate).
- Shop Software:
- Information about eCommerce platforms used by online retailers.
- Marketing Performance:
- Evaluation of online advertising and marketing strategies.
- Key Financial Metrics:
- Critical financial indicators for business insights.
- Company & Contact Information:
- Detailed profiles of businesses and key contacts.
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