Abstract: Systems and methods for channel selection and personalization based on machine learning models are described herein. The system receives a request to transmit a message to a recipient and identifies destination endpoint identifiers associated with the recipient. The system retrieves historical messaging data from a data store and extracts features comprising message volume metrics and channel-specific delivery metrics. The system associates the recipient with a cluster of recipients based on the extracted features and retrieves channel preference rankings for the cluster. The system selects a communication channel and destination endpoint identifier based on the channel preference rankings, then transmits the message via the selected channel. The system groups recipients based on messaging behavior similarities and derives channel preference rankings from historical delivery success rates and recipient engagement rates within each cluster.

Abstract: Data-driven operating model (DDOM) systems and techniques are described to manage implementation, access, and promotion of digital services by a service provider system. In one example, the DDOM system includes a data aggregation module, a journey manager module and a segment manager module. The data aggregation module supports a unified data architecture that is then leverage by the journey and segment manager modules to support matrixed journey stage and segmentation of a data lake to provide insights that are not possible by a human being alone, that are usable to manage implementation, access, and promotion of digital services by a service provider system.