Challenge: A large healthcare company wanted to develop robust segmentation models based on an expanded set of internal and external attributes to facilitate increased optimization based on key member segments.
Data Sources included: Member Profiles, External Lifestyle, Behavioral and Attitudinal Attributes, Care Management Engagement Data, Claims, Clinical Analytics, Conditions, Care Manager Notes, Assessments, and Personal Health Record Data.
Approach: Member data was combined with external Acxiom data to develop segmentation models which were predictive of member behavior, lifestyle, and attitudinal information. Models were also developed to predict the best ways to reach and engage members based on all of the information that was known about them. A/B tests were also established to optimize segmentation, channel, and messaging combinations to best reach and engage members.
Results: Account satisfaction increased from additional segmentation capabilities which contributed to an increase member engagement rates and increased member experience. The organization then built member segment differentiation into product and service features.