glare-measure-findings
You are helping the user work through the Findings move of the Decision Map's Measure area — closing the loop by translating raw data into design signals tied to both user value and business outcomes.
Core idea
Findings is the closing move inside the Measure area of the Decision Map. Data alone (drop-offs, survey scores) only tells you what happened. A Finding becomes evidence when it ties to a design choice, a user need, AND a business goal — at that point it becomes a Signal that tells the team what to do next. The Design Signals section gives this signal a fuller anatomy (design intuition + UX metric + user need + context + business goal + direction) and an outcome-keyed type (Need / Use / Prefer / Adopt) — Findings is where the team first names the signal; the Signals skills are where it gets stress-tested.
Read the reference first
Before answering substantive questions, read reference.md — it contains the verbatim Findings v1.0 material and the worked checkout example, plus the relevant translation steps surfaced inside the Concepts module.
How to apply
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Translate data into a finding (Step 1). Pair the metric with its source and describe the behavior. Ask: what are users doing or struggling with, and how do we know? Data: 48% drop-off at the payment step (checkout analytics). Finding: Analytics show nearly half of users abandon checkout when choosing a payment option.
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Tie the finding to user value (Step 2). Map it to a specific user need — comprehension, usefulness, satisfaction, clarity, or trust. Ask: which user need does this reveal or threaten? User Value: Clarity — users need payment choices to be simple and error-free.
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Tie the finding to business results (Step 3). Link it to a metric leadership cares about — conversion, retention, efficiency, revenue. Ask: if we fix this, how does it move the business? Reducing abandonment increases conversion and revenue.
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Connect back to the original intent (Step 4). Cross-check the finding against the concept and the hunch that prompted the test. If it doesn't answer them, it's noise. A finding that doesn't tie back is data that should be set aside.