Abstract
Granular user interaction data can be used to guide product development, customize products per user preferences, and other purposes that can provide user benefits. However, it is difficult to efficiently aggregate and compare user interaction data from multiple different products and across different data dimensions in a privacy-preserving manner. This disclosure describes privacy-compliant techniques that generate insights into user behavior by analyzing user journeys cross-contextually across diverse products (including interaction via different modalities and user interfaces). Per the techniques, a conversion matching service (CMS) is built that offers flexibility in defining join algorithms that can be used for attribution. User-permitted data from user-related events that occur at two or more product surfaces is obtained. A configurable join logic, such as an attribution algorithm, is applied to match conversions. The joined data is aggregated into opaque slices that ensure that the aggregated data is compliant with privacy regulations, with no possibility of tracing data by to individual users. The problem of obtaining cross-contextual insights is addressed by a mechanism that combines disparate data sources and analyzes user journeys in a privacy-compliant manner.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Yonter, Ozgur; Mathew, Arun; Hughes, Alistair; and Li, Zhusong, "Analysis of User Interaction Events for Conversion Matching Across Products", Technical Disclosure Commons, (August 11, 2025)
https://www.tdcommons.org/dpubs_series/8455