ProductLab develops a platform that transforms unstructured consumer transaction data from receipts, paychecks, and digital accounts into structured datasets using machine learning. This enables data science leaders to access high-quality, longitudinal insights for informed decision-making, backed by a vetted community of over 150,000 panelists.
Funding
Funding not disclosed
Founders
Product
Problem
Data science teams often struggle to access comprehensive and well-structured consumer transaction data due to the unstructured nature of receipts, paychecks, and digital account statements. Manually extracting and cleaning this data is time-consuming, costly, and prone to errors, hindering the ability to derive actionable insights.
Solution
ProductLab offers a platform that transforms unstructured consumer transaction data into structured datasets using machine learning. The platform sources data directly from a vetted community of consumer panelists who permission the use of their receipts, digital accounts, paychecks, and other transaction documents. Machine learning algorithms then process this data, delivering reliable, structured datasets to data science leaders through integrations with AWS, BigQuery, Snowflake, and custom data feeds. This enables data science teams to access high-quality, longitudinal insights for informed decision-making.
Target Audience
The primary users are data science leaders and teams in organizations that require consumer transaction data for research, analysis, and decision-making.
Features
- Data sourcing from a vetted community of over 150,000 consumer panelists
- Support for various document types, including receipts, digital accounts, paychecks, and emails
- Machine learning-powered data transformation into structured datasets
- Integrations with AWS, BigQuery, Snowflake, and custom data feeds for data delivery
- Panel vetting using facial recognition and government ID verification
- Data accuracy exceeding 99% through machine learning and operational processes
- Mobile apps for panelists to contribute transaction documents