Beta

Attribution Builder

Reads your deals tagged with a primary trust signal, referral, customer proof, review, expert content, prior familiarity, and compares win rate, cycle time, and deal size across them. Flags small samples instead of calling a lucky streak a pattern, and flags deals with no trust signal recorded at all.

Any CRM

The problem

A single lucky streak on a small sample can look exactly like a real pattern until someone checks the sample size.

How it works

  1. 01Reads deals tagged with a primary trust signal: referral, customer proof, review, expert content, or prior familiarity
  2. 02Compares win rate, cycle time, and deal size across signal types
  3. 03Flags small samples instead of presenting them as reliable, and flags deals missing a trust signal entirely

Verified on real test data

14 synthetic deals tagged with a primary trust signal (Referral, Customer Proof, no signal recorded, and a deliberately tiny Expert Content sample), run twice: once at the default 5-deal reliability threshold, once at 3.

75%

Referral win rate

25%

Untagged deal win rate

4

Deals missing a signal

The tool's job is knowing when a number is too small to trust, not just computing the number.

See the full test run →

Questions about Attribution Builder

What's the minimum sample size it trusts?

Configurable. The default threshold in testing was 5 deals per signal before a result counts as reliable.

What happens to deals with no trust signal recorded?

They're separated into their own list as a CRM hygiene gap, not folded into a misleading average.

Runs against your CRM using your own credentials, through your own AI instance. We don't copy, store, or retain your data.