Computes a false-positive rate, overall and per signal type, from a log of previously flagged or prioritized accounts and their eventual outcomes, surfacing which triggers actually predict real movement and which are generating expensive false alarms, only judging a signal once it has enough resolved volume to mean something.
A signal changing an account's priority is a bet, not a fact. Most teams get good at detecting activity and never circle back to check whether the flags it generated actually turned into anything.
Signal Accuracy Auditor
27 synthetic flagged-account events across 5 signal types, including two signal types with an identical 66.7% raw false-positive rate, one with enough resolved volume to judge and one deliberately too small, plus 2 still-pending flags used to check they get excluded from every rate.
1/1
High false-positive signal caught
1/1
Identical-rate small sample correctly withheld
2/2
Pending flags correctly excluded
Two signal types looked identically unreliable by raw rate alone. Only one of them had enough evidence to actually say so.
A flagged account that outcome-marked Stalled or Rejected. Converted or Progressed count as a true positive. A blank, still-pending outcome is excluded from every rate entirely, it hasn't had time to resolve yet.
Because a high raw false-positive rate on 2 or 3 flags isn't evidence of anything. The same discipline Attribution Builder and Enrichment Reliability Auditor apply elsewhere in the toolkit: report the number honestly, but don't judge it until there's enough volume.
Runs against your CRM using your own credentials, through your own AI instance. We don't copy, store, or retain your data.