OBJECTIVES: This analysis explored the application & effectiveness of “Digital Twinning” hyper modelling & personalization in Healthcare.

METHODS: The analysis showcased the interoperability of “Digital Twinning” in HealthCare Marketing. Health digital twins are the virtual representations (“digital twin”) of patients (“physical twin”) and its application have been primarily focused to aid robotic surgery, accelerated precision medicine applications, accelerating the evaluation of clinical interventions, manage inventories and complex supply chains. We in digital analytics world, attempted to explore Digital Twinning Hyper-modelling over static cohorts/persona, tapping in actions, behavior, thoughts, mindset, triggers, outcomes with help of Real-World Data & web APIs. Time frame for this analysis was from July 2020 to August 2022.

RESULTS: Real Time Member/Prospect Segmentation/Output on visiting web-traffic (sample size ~50K average per day). Persona-led Journey Outcome Propensity Modelling & Event-based decisions/paths nurturing/personalization on a sample set (0.1%) as a test resulted in conversion upliftment (+18%). Accuracy of the model was 73% as per the test period. This study would be focusing on multiple Healthcare Digital Platforms based services for better understanding of the personalized behavior of members.

CONCLUSIONS: Digital twins hyper modelling represents the logical evolution of cohort segmentation and holds lots of potential to target, retarget or suppress targeting as per the nature/need of the Digital Twin. This engine provides next best actions to prevent churn, uplift cross-selling, rapid personalization, dynamic-segmentation and nurturing for meaningful experiences