Driving Multi-Modal Retail and Healthcare CX - with Brett Kiley of CVS Health
🎯 Summary
Summary of AI and Business Podcast Episode with Brett Kiley (CVS Health)
This episode of the AI and Business Podcast, featuring Brett Kiley, Executive Director of Customer Experience and Client Solutions at CVS Health, provided a deep dive into the strategic challenges and technological transformations occurring in customer experience (CX) within the complex retail and healthcare sectors. The core narrative focused on CVS Health’s journey to evolve from reactive problem-solving to proactive, hyper-personalized customer engagement amidst rising consumer expectations and significant regulatory oversight.
Key Discussion Points & Narrative Arc:
The conversation began by establishing the high bar set by modern consumers who demand 24/7 access and channel consistency across communication modes (chat, text, phone). Kiley highlighted the challenge of inconsistency across channels and the constant pressure to match the seamless experiences offered by non-competitor industries. The discussion then pivoted to the overwhelming volume of Voice of the Customer (VoC) data—from social media to internal surveys like Net Promoter Score (NPS)—and the difficulty in synthesizing it effectively, especially when balancing speed (seen in non-regulated industries) against heavy healthcare and financial regulation. A major theme was eliminating journey friction, ensuring that when a customer escalates from a bot to a human or switches channels, they do not have to restart the interaction.
Major Topics and Technical Concepts:
- Omnichannel Consistency & Journey Mapping: Focus on creating end-to-end seamless experiences where context and information transfer correctly between automated and human touchpoints.
- Data Governance and Prioritization: Moving beyond data overload by weighting characteristics (e.g., medication delivery timeliness matters more than geography) to inform operational strategy.
- Metrics and KPIs: Critiquing traditional metrics like Average Handle Time (AHT) in favor of context-aware KPIs tied directly to NPS and patient satisfaction, acknowledging that longer calls can sometimes indicate better resolution.
- Proactive vs. Reactive Service: A major strategic shift involving integrating demographic, interaction, and social data to anticipate needs rather than just responding to complaints.
- AI Application: Practical uses of Natural Language AI for handling routine inquiries, thereby freeing human agents for complex issues, and leveraging AI for hyper-personalization at scale (moving from broad buckets to “two million different buckets”).
Business Implications and Strategic Insights:
The strategic insight shared by Kiley is that superior CX is now the battleground for winning and transforming organizations, moving away from the perception of customer service as the “DMV.” Proactive service generates loyalty and positive viral messaging, which is impossible in a purely reactive model. Furthermore, the ROI for proactive, data-informed interventions (even small ones, like thanking a loyal customer) is often positive almost immediately.
Challenges and Controversies Highlighted:
The primary challenges are navigating the regulatory surface area (which inherently adds friction) while simultaneously implementing new technologies. There is also the persistent trust factor associated with automation, where human oversight is still perceived as more trustworthy, particularly in sensitive healthcare contexts. The difficulty in selecting the right KPIs that accurately reflect quality over mere speed was also a significant point of contention.
Actionable Advice and Recommendations:
- Define Contextual KPIs: Move beyond simple efficiency metrics (like AHT) and focus on drivers that align with regulatory requirements and core patient satisfaction goals.
- Prioritize Seamless Handoffs: Invest in technology that ensures context is maintained across all channels to eliminate customer effort (the need to repeat information).
- Leverage AI for Scale and Complexity: Use AI to automate routine tasks to enable human agents to focus on high-value, complex problem-solving, and use AI to achieve true hyper-personalization at massive scale.
Context and Industry Relevance:
This conversation is crucial for technology professionals because it illustrates how a massive, highly regulated entity like CVS Health is operationalizing AI and data science to meet consumer expectations set by frictionless tech giants. It underscores the necessity of balancing innovation speed with compliance, proving that strategic data governance and thoughtful KPI selection are prerequisites for successful digital transformation in CX.
🏢 Companies Mentioned
💬 Key Insights
"But imagine if I had two million customers and I had two million different buckets, and I could be that personal with my messaging. I see AI being able to do that for us."
"assuming we've got the deterministic anomaly detection use cases in place, all the surface level stuff, where do we see this going now that especially a lot of these communications are all language-based and we can leverage language a lot more easily for generative use cases."
"If you have a problem and you call us and we were able to solve that problem and we were able to solve the problem in the future or it's another really good one. Let's say you don't have a problem at all, but I just call you and say I really value you as a customer, you're really amazing. We're going to put $10 on your account. And I want to thank you for your business."
"Spending that extra time, making sure that you have all the information, and this is where data comes in, not just about the thing that you called about, but the other thing that you're going to call us about a week from now. And can we handle that right now?"
"So once we have these metrics that matter, then we can go to the marketing team and say, focus your efforts showing how we're better here. We can go to operations and say, focus your efforts on improving these because it has the biggest ROI."
"We've taken demographic data, we've taken interaction data, we've taken social media, as well as survey data, and put these together in a way that we can actually wait out... these matter the most and these matter the least."