Product Validation Measurement Plan Prompt

Overview

A success metric you can't measure is just a wish. Once you've defined what success means for a shipped product, this prompt breaks it into the concrete signals that confirm or deny it: the behavioral events to watch, the funnel and cohort cuts that isolate the answer, the feedback sources to pull from, and how to read each one against the target. It plans the measurement, not the plumbing — you supply the live data; the plan tells you what to look at, how to slice it, and what counts as hit, partial, or miss before the numbers arrive.

Workflow

1. 1
   Start from the metric, not the dashboard

   Feed in the success-metric definition first; the plan should derive signals from the target, not from whatever happens to be tracked already.

2. 2
   Lock the thresholds before the data

   Fill in what hit/partial/miss looks like up front — thresholds set after seeing the numbers are just a story.

3. 3
   List the confounders out loud

   Name the launch spike or seasonality now, so the read on the data accounts for them instead of being fooled.

Why This Works

• Deriving signals from the metric stops the measurement from drifting toward easy-to-track vanity numbers
• Pre-committing hit/partial/miss thresholds removes the room to rationalize the result later
• Naming confounders up front keeps a launch spike from being mistaken for product-market fit

Use cases

• Turning a post-launch success metric into a concrete list of signals to watch
• Deciding the cohorts and funnel cuts that isolate whether the metric was hit
• Setting hit/partial/miss thresholds before the data is collected