What is ProductLab?
ProductLab specializes in transforming unstructured consumer transaction data into structured intelligence. Leveraging a community of over 150,000 vetted consumer panelists who contribute documents like receipts, digital account details, and paychecks via highly-rated mobile applications, the platform ensures data is fully permissioned and sourced directly from end consumers. This approach, combined with a high retention rate and rigorous vetting process involving facial recognition and government ID verification, supports high-quality longitudinal research.
Using advanced machine learning algorithms, ProductLab processes this raw data, handling over 6 million documents and generating over 500 million data points across more than 50 document types. The platform delivers reliable, structured datasets with 99.9% accuracy and maintains a 99% SLA. Data is accessible 24/7 to data science leaders through integrations with AWS, BigQuery, Snowflake, and custom data feeds, ensuring seamless access to valuable consumer insights.
Features
- Direct Data Sourcing: Collects transaction data directly from over 150k vetted consumer panelists via mobile apps.
- Machine Learning Processing: Transforms unstructured data (receipts, digital accounts, etc.) into structured datasets.
- High Data Integrity: Utilizes facial recognition and ID verification for panelist vetting and ensures 99.9% data accuracy.
- Multiple Data Delivery Options: Delivers structured data 24/7 via AWS, BigQuery, Snowflake, and custom feeds.
- Longitudinal Research Capability: High panelist retention enables high-quality longitudinal studies.
- Wide Document Coverage: Processes over 50 types of transaction documents.
Use Cases
- Conducting market research using consumer transaction patterns.
- Developing data-driven business strategies based on real spending habits.
- Enhancing data science models with granular transaction data.
- Performing longitudinal analysis of consumer behavior.
- Gaining competitive intelligence through anonymized spending data.
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