STAGE 01
Capture
the raw signal
Put the listening apparatus in place — across customer conversations, product telemetry, and market data — so meaningful signals are caught, tagged, and retrievable instead of lost.
Artifacts
- signal inventory
- source map
- evidence log
STAGE 02
Cluster
find the pattern
Reduce scatter to a small number of recurring patterns with real frequency, severity, and segment lift.
Artifacts
- pattern library
- segment lift
- theme frequency
STAGE 03
Convert
signal → hypothesis
Translate each pattern into a falsifiable hypothesis a team can actually test inside a single product cycle.
Artifacts
- decision memo
- hypothesis set
- risk register
STAGE 04
Commit
stake the bet
Name the decision, the owner, the guardrails, and the number that would tell us we were wrong.
Artifacts
- bet card
- guardrail metric
- go / hold / kill
STAGE 05
Close
learning back in
Feed outcome data back into the signal layer so the next decision starts with a sharper prior, not a blank page.
Artifacts
- post-ship review
- prior update
- evidence ledger
Figure 2b — Decision cycle time, pipeline vs. baselinerelative index, baseline = 100
+ Clustered pattern library
74
+ Decision memo discipline
55
Signal → Ship, fully installed
38