Metrics like high Net Promoter Scores fail to capture genuine human connection in digital interactions. Instead of chasing vanity KPIs, pharma should seek the "digital equivalent of a smile"—behavioral signals that indicate a truly positive and human customer experience.
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For Nike's innovators, the ultimate measure of success isn't market performance but the user's genuine joy upon experiencing the product. This "athlete's smile" confirms that a meaningful problem has been solved, serving as a leading indicator that commercial success will naturally follow.
Metrics like "Marketing Qualified Lead" are meaningless to the customer. Instead, define key performance indicators around the value a customer receives. A good KPI answers the question: "Have we delivered enough value to convince them to keep going to the next stage?"
Metrics like product utilization, ROI, or customer happiness (NPS) are often correlated with retention but don't cause it. Focusing on these proxies wastes energy. Instead, identify the one specific event (e.g., a team sending 2,000 Slack messages) that causally leads to non-churn.
Vanity metrics like views don't drive business results. A better approach is to focus on "conversation metrics"—the quality and quantity of interactions in comments and DMs. Speed and personalization in responses build relationships and are a stronger indicator of impact.
To evaluate AI's role in building relationships, marketers must look beyond transactional KPIs. Leading indicators of success include sustained engagement, customers volunteering more information, and recommending the experience to others. These metrics quantify brand trust and empathy—proving the brand is earning belief, not just attention.
Elliot Cohen posits that the healthcare system is broken because it optimizes for financial relationships, not the patient. He argues the key metric should be Net Promoter Score—how much consumers love the experience. A system that people enjoy engaging with would inherently solve many cost and quality issues.
Open and click rates are ineffective for measuring AI-driven, two-way conversations. Instead, leaders should adopt new KPIs: outcome metrics (e.g., meetings booked), conversational quality (tracking an agent's 'I don't know' rate to measure trust), and, ultimately, customer lifetime value.
To prevent digital engagement from feeling robotic, teams must connect with the real world. Accompanying field reps on visits provides invaluable, direct feedback from HCPs, leading to more human-centric content formats like 30-second videos instead of text-heavy emails.
When AI can directly analyze unstructured feedback and operational data to infer customer sentiment and identify drivers of dissatisfaction, the need to explicitly ask customers through surveys diminishes. The focus can shift from merely measuring metrics like NPS to directly fixing the underlying problems the AI identifies.
Instead of focusing solely on CSAT or transaction completion, a more powerful KPI for AI effectiveness is repeat usage. When customers voluntarily return to the same AI-powered channel (e.g., a chatbot) to solve a problem, it signals the experience was so effective it became their preferred method.
