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In private rollout: onboarded personally, usually live within 24 hours.
Proof

Attribution & proof

See the revenue she actually earned, measured against a holdout, so it's causal not correlation.

Measured against a holdout

The revenue she actually earned.

  • A control group never sees her
  • Five attribution models on one journey
  • Lift you can trust, not assumed credit
Holdout-verified · 95%

Good afternoon, Northbrook Coffee Co.

Sarah influenced revenue in the last 30 days.

Revenue influenced

Last-widget attribution activeLift measured

€12,840

218 orders assisted in the last 30 days

Carts saved

214

+24%

AOV with Sarah

€94.50

+11.6%

Conversion lift

+23%

vs. holdout

A control group never sees her, so the lift above is what she added, not what she stood next to.

Illustrative: the Northbrook Coffee Co. demo store

Most tools tell you what they touched. Sarah tells you what she changed. Her revenue is measured against a control group that never saw her, so the number is lift you can trust.


01

The problem

When an assistant gets credit for every order it brushed against, the dashboard reads great and means little. You can't tell the sales she caused from the sales that would have happened anyway. Without a control, attribution is a story, not a measurement.

02

How it works

  1. Multi-touch journey

    Every order is traced from the first widget touch through to purchase, so the full path is on record, not just the last click.

  2. Five attribution models

    The same journey is scored five ways: last-widget, linear, time-decay, first-touch, and position-based (U-shaped). You pick the lens that fits how you think about credit.

  3. A 30-day window

    Touches count for 30 days, built deliberately long for slower gifting and jewelry cycles where the decision takes weeks.

  4. An A/B holdout

    A percentage of visitors never see Sarah. Their order rate is the control. Her lift is measured against them, so the headline is incremental revenue, not assumed credit.

03

What makes it real

Ten distinct touch types are tracked across the journey, and attribution is computed at order time against the recorded path. The holdout is the honest part: because a real slice of traffic is held back as a control, the revenue figure reflects causation, not correlation. A note on scope: the holdout is opt-in and set up by you, and the email and SMS touch types are framework-only today, not yet wired into the live measurement.

One journey, five lenses

The same touches, scored five ways.

  • A 30 day window for slow decisions
  • Last widget, linear, time decay, first touch, position
  • Pick the lens that fits how you think about credit

One journey · 5 models

The same path, credited five ways

30-day window
Day 1Chat opened
Day 6Product view
Day 19Added to cart
Day 23Checkout
Day 24Purchase

Credit per touch, by model

Export evidence
Last-widget
100%
Linear
20%
20%
20%
20%
20%
Time-decay
10%
16%
26%
42%
First-touch
100%
Position-based
40%
40%

Every touch is recorded; attribution is computed at order time. The same journey totals to one purchase. Each model just splits the credit differently.

Illustrative: the Northbrook Coffee Co. demo store

Your next hire

Ready to put a real associate on your storefront?

Book a 30-minute demo with the founder over Google Meet and watch Sarah work your own catalog. We onboard you personally, usually live within 24 hours. Not ready for a call? Request an invite.

Private rollout · one-click uninstall if it doesn’t earn its keep