Aetna, a leading U.S. health insurer under CVS Health, aims to improve its Medicare Advantage (Part C) Star Ratings by optimizing member engagement through targeted marketing campaigns. These campaigns directly influence CMS performance metrics, particularly compliance gaps, which significantly impact star ratings and, in turn, higher bonus payments from the federal government. However, limitations in campaign tracking, data integration, and member targeting hinder accurate performance assessment. This project applies causal inference techniques, machine learning models, 14
and clustering algorithms to evaluate the effectiveness and ROI of various outreach channels. We develop predictive tools to quantify campaign effectiveness, enhance interpretability through SHAP and LIME, and synthesize our findings in an interactive front-end for our client. Expected outcomes include improved campaign targeting, higher satisfaction scores, and a scalable decision-support dashboard to guide Aetna’s future outreach strategy. This data-driven framework offers a replicable model for enhancing healthcare marketing effectiveness while aligning with regulatory and ethical standards.
Watch the team present this project at 01:59:01 in the session recording here.
Keywords: Medicare advantage, causal inference, machine learning, member engagement, campaign optimization, healthcare, healthcare analytics, SHAP interpretability, CVS, Aetna
Faculty Advisor
Shree Bharadwaj works at West Monroe, a management consulting company. As the AI Center of Excellence lead for M&A, he advises private equity and venture capital firms and C-level executives on value creation, post-close synergies, and data and analytics (BI/AI) strategies focused on business outcomes. In his previous executive leadership roles at Syndigo and IRI, he led the product ownership, data strategy, data science, next-gen platform and M&A integrations. His expertise revolves around growth and innovation, leadership and operational excellence. Additionally, his focus revolve around automation and driving decisioning using AI/ML, data engineering at scale using on-premise and cloud platforms, effective data visualizations, model-driven design, and algorithimic thinking.
Bharadwaj was elected to the Global Standards Architecture Board at GS1, where he worked with global industry leaders to develop standards, road maps, and governance and compliance requirements relating to food services, healthcare, retail, supply chain, and CPG/FMCG verticals. His experience spans across multiple industries that include AdTech, EdTech, Fintech, healthcare, MarTech, public safety, retail, and telecom in organizations that range from startups to Fortune 100 companies. His interests include His interests include Intelligent Systems and Robotics, Machine Learning at scale, Data Engineering, Data Visualization & Knowledge Engineering.