The Yobi Knowledge Graph is a product of our thesis that businesses don’t require raw behavioral data to optimize performance or ROI. One of Yobi’s goals is to reduce the proliferation of identifiable consumer data. Rather than broker raw data, Yobi commercializes a non-identifiable, machine-readable representation of behavior. This representation maintains statistical equivalence with raw data without ever revealing personal information. As a result, businesses can integrate Yobi’s Knowledge Graph into all data acquisition and customer science capabilities.
Clean, Aggregated Data
For each of our datasets, we use machine learning to reduce the raw consumer behavioral data to a representation of each identifier as a vector of numbers.
Capture Signal & Preserve Privacy
These numbers contain the statistical information needed to make predictions about future behavior, while revealing none of the details about past behavior.
Safe Machine-Readable Data
This approach results in a representation that can be used in the models that we or our clients develop, but doesn’t make sense to a human and can only be interpreted in the context of other vectors generated by the same process.
Further Ethical Modeling
Our vectors can also be tuned to improve prediction of specific behaviors, and to remove information about protected characteristics to reduce risks of violating privacy or supporting discrimination.