A national healthcare provider faced significant challenges, including financial losses, due to suboptimal product strategy, pricing, medical cost management, and member churn. A&MPLIFY was brought in to lead a turnaround strategy, initially focusing on cost reduction for financial sustainability.

The client also recognized the need for sustainable revenue growth. To address this, A&MPLIFY implemented a member segmentation and churn analysis program, focusing on developing a data-driven understanding of member behaviors, needs, and motivations, creating actionable insights for targeted tactics, and establishing an analytical foundation to predict and reduce churn.


  • Analyzed three years of comprehensive member data to understand diverse aspects of member engagement and behavior.
  • Applied unsupervised machine learning techniques (K-Means, DBSCAN, GMM) to categorize 800,000 members into five distinct segments, each reflecting unique member needs and behaviors.
  • Developed a classification model to predict churn, pinpointing five key themes that influenced a member's decision to leave.
  • Designed four specific interventions based on the churn model's insights and quantified the annual revenue impact. These interventions focused on enhancing member engagement, improving service quality, and addressing specific needs identified in different segments.


  • 130M in annual revenue impact across four net new revenue generation opportunities.
  • 120GB first-party data and 10GB individual data points utilized to build an AI-Driven Member 360 View.
  • 50,000 at-risk members identified for targeted, data-driven retention interventions.


A&MPLIFY by Alvarez and Marsal accelerates growth and efficiency with artificial intelligence, customer experience and digital platforms.

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Originally Published by 20 February 2024

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