Incrementality Lift Calculator

Estimate incremental conversions, incremental ROAS, and incremental profit from a holdout test.

Attribution-reported ROAS can over-credit ads that would have converted anyway. Incrementality asks what lift ads caused compared to a no-ads baseline.

This calculator turns a simple holdout test (exposed vs control) into incremental conversions, incremental ROAS, and incremental profit using AOV and contribution margin assumptions.

Prefer an explanation- Read the guide.
 
 
 
 
 
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Tip: you can type commas (e.g., 10,000).

Example

Using the default inputs, the result is:
-$40,400.00
Exposed users (treatment)
100,000
Exposed conversions
1,200
Holdout users (control)
100,000
Holdout conversions
900
Ad spend (treatment)
$50,000
Average order value (AOV)
$80
Contribution margin
40%

How to calculate

  1. Enter exposed and holdout sample sizes and conversions.
  2. Enter ad spend for the exposed group and your AOV + contribution margin.
  3. Use incremental ROAS and incremental profit to decide what to scale.

Formula

Incremental conversions = exposed_conversions - exposed_users * (holdout_conversions/holdout_users); Incremental ROAS = incremental_revenue / ad_spend
  • Holdout group approximates the no-ads baseline for the exposed group.
  • AOV and contribution margin are constant for incremental conversions.
  • No statistical significance is computed; treat results as directional unless sample sizes are large.

FAQ

Why can incremental ROAS be lower than platform ROAS-
Platforms often claim credit for conversions that would have happened anyway (especially retargeting). Incremental ROAS isolates lift, so it's often lower but more decision-useful.
What if my holdout conversion rate is higher than exposed-
That implies negative lift. It can happen due to noise, non-random assignment, or true cannibalization. Check randomization, sample size, and whether holdout users were truly unexposed.

Common mistakes

  • Non-random assignment (exposed users differ from holdout users).
  • Too-small samples (results swing due to noise).
  • Using revenue but ignoring variable costs (use contribution margin).

Quick checks

  • Keep attribution model and window consistent when comparing campaigns.
  • Pair efficiency metrics (ROAS/CPA) with profit assumptions (margin, refunds, fees).
  • Validate tracking after site changes (pixels/events can silently break).