Why AI Product Teams Should Track User Depth, Not Just Click Frequency

A piece published on DEV Community argues that standard usage metrics like click counts and LLM call frequency are insufficient for evaluating the real impact of AI features in SaaS products. The author contends that a meaningful distinction exists between users who run shallow, occasional queries and those who embed multi-step AI workflows into their daily routines. To capture this difference, the author advocates for tracking dimensions such as power user density, feature depth, and conversion likelihood rather than raw engagement numbers. The article introduces a tool called the AI Power User Analytics Engine, built as a Model Context Protocol connector, which is designed to measure these more nuanced behavioral patterns. The author argues that an MCP-based approach enables autonomous agents to detect and respond to shifts in user behavior in real time, rather than relying on after-the-fact dashboard reports.