Define your personal value metric before measuring anything — answer "value for what purpose?" then select leading indicators that predict it
Before measuring anything, define your personal value metric (what 'value' means for your specific outputs) as the lagging indicator that ultimately matters, then select leading indicators that predict it—never measure without first answering 'value for what purpose.'
Why This Is a Rule
Most people start measurement by asking "What can I measure?" — a question that optimizes for measurability, not value. You can easily measure page views, word count, publication frequency, and follower count. But these are leading indicators (things that might predict value) not value itself. If your actual goal is "help people make better decisions," then views are a proxy at best and a distraction at worst. A post with 100 views that changes one person's decision-making is more valuable than a post with 10,000 views that changes nothing.
The rule inverts the measurement design: start with the lagging indicator that represents actual value to you ("people changed their behavior," "I deepened my understanding of X," "I got hired for Y"), then work backward to identify leading indicators that predict it. Leading indicators are useful only when they're connected to a defined value metric — otherwise, they're just numbers going up or down with no interpretive framework.
The "value for what purpose?" question prevents the common trap of measuring for its own sake. Without this question, you end up optimizing for whatever metric your tools show most prominently (platform analytics default to reach), which may or may not relate to what you actually care about.
When This Fires
- Before setting up any measurement system for your outputs
- When existing metrics feel meaningless or disconnected from your goals
- When you're measuring many things but none of them tell you whether your work matters
- Complements Track four output dimensions: reach, resonance, downstream action, personal growth — single metrics get gamed; balanced scorecards don't (four-dimension scorecard) with the foundational value definition that determines how to weight the dimensions
Common Failure Mode
Metric-first measurement: adopting whatever analytics your tools provide and treating them as value indicators. Google Analytics shows page views → you optimize for page views. Twitter shows impressions → you optimize for impressions. The metrics drive the goals rather than the goals driving the metrics.
The Protocol
(1) Before building any measurement system, answer: "What does 'value' mean for my outputs?" Write a concrete definition: "Value = people who implement the ideas," "Value = depth of my own understanding," "Value = consulting leads generated," "Value = community discussions sparked." (2) This is your lagging indicator — the thing that ultimately matters but may be hard to measure directly. (3) Identify 2-3 leading indicators that predict your lagging indicator: if value = implementation, then leading indicators might be "saves/bookmarks" (suggesting intent to use later), "questions about specific steps" (suggesting active engagement), "testimonials or case studies" (confirming implementation). (4) Measure the leading indicators as proxies for value, but regularly verify the proxy relationship: do outputs with high leading indicators actually produce the lagging indicator? (5) If leading and lagging indicators diverge (high saves but no implementation), the proxy has broken — redefine your leading indicators.