When this topic matters
You have data. You can measure almost everything. Question is: what is worth optimizing?
Some metrics are vanity metrics — look good but do not correlate with success. Others are counter-productive — optimizing them leads to wrong behavior.
What happens in practice
Examples of problematic metrics: 1) Call count (leads to fast, low-quality calls). 2) Call duration (leads to prolonging). 3) Connection rate (depends mainly on database quality, not operator).
These metrics have place as diagnostics, but not as optimization targets.
Why it fails
Goodhart's Law: "When a measure becomes a target, it ceases to be a good measure." People optimize the number, not the substance.
Example: operator maximizes call count → shortens calls → fewer qualifications → less revenue. Metric rises, outcome falls.
How to think about it
Test: "If we maximize this metric, will it lead to outcome we want?" If not, it is not a good target.
Optimize outcomes (meetings, pipeline, revenue), not activities (calls, emails). Activities are diagnostics.
- Optimize: conversion, qualified meetings, revenue
- Do not optimize: call count, call duration, connection rate
- Diagnostics: activities as explanation of "why"
- Test: does maximization lead to desired outcome?
What you gain and what you lose
Focus on outcomes: right motivation, but harder control. You cannot "check" revenue daily.
Focus on activities: easy control, but may lead to optimizing wrong things.
When to apply
Always when setting targets or KPIs. Test: does maximization lead to desired outcome?
Regularly review: what made sense a year ago may not make sense today.
Optimize outcomes, not activities. Test: does maximizing metric lead to desired outcome? If not, it is diagnostics, not target.