Transforming Digital Customer Feedback at Evernorth Health Services
Project Title: Retirement of Site-wide Digital NPS & Implementation of Journey-Based VoC Program
Role: Responsible, Accountable | Company: Evernorth Health Services | Timeline: 2019 – Present
Challenge
The existing site-wide Digital NPS (Net Promoter Score) survey at Evernorth Health Services was outdated and ineffective. It provided limited context, lacked actionable insights, and failed to capture meaningful feedback relative to specific member tasks. With a pop-up survey that disrupted user sessions, the feedback often reflected frustrations about the survey itself rather than the digital experience. This made it difficult to correlate member feedback with specific journeys, undermining efforts to improve digital containment, engagement, and overall member satisfaction.
Key Issues Identified:
Inaccurate feedback due to poorly timed, generalized NPS surveys.
Low-quality qualitative data with only 62% of respondents providing meaningful comments.
Inefficient analysis due to non-contextualized responses and a flawed NLP model.
Inability to track and improve specific member journeys.
High operational costs from digital leakage and unresolved pain points.
Solution
I spearheaded the retirement of the site-wide Digital NPS survey and led the design and implementation of a journey-based Voice of Customer (VoC) program. This new framework provides contextual, task-specific feedback, allowing for precise insights into member experiences.
Key Initiatives:
Journey-Based Feedback: Replaced generalized NPS surveys with targeted inline intercepts (not pop-ups) at key moments within specific user journeys.
Focus on Friction-Driven Sentiment: Friction results in poor experience. Identify the friction and resolve the root cause.
RUF Framework: Developed a Reliability, Usability, and Functionality (RUF) framework to assess product experiences, identify weak points, and prioritize improvements.
EASI Matrix: Introduced the Eliminate, Automate, Simplify, or Invest (EASI) Matrix to evaluate support contacts, quantify operational costs, and inform digital containment strategies.
AI-Driven Analytics: Implemented advanced sentiment analysis, anomaly detection, and predictive insights using AI and machine learning models.
