Precise metrics require instrumentation investment. Instrumentation adds complexity to the codebase, increases event volume, and creates data storage requirements. Teams that want high-resolution product metrics must pay an engineering cost. Teams that want to avoid that cost operate with lower resolution visibility into product behaviour.
The inverse tradeoff is over-instrumentation. A system that emits events for every user action produces so much data that the signal is lost in the noise. Useful measurement requires selecting the right instrumentation points, which requires understanding the user flows that matter before building the measurement infrastructure.