Collect the matching inputs.
Randomly assign stable units before exposure and analyze everyone assigned. Predefine the primary metric, duration and guardrails; check allocation and quantify uncertainty.
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Profitability · IncrementalityIncremental revenue lift.
Incremental revenue lift.
Find the sales your change actually adds.
Incremental revenue lift is the relative difference in revenue per eligible unit between a randomized treatment group and a comparable control group. The unit may be a visitor, customer or session, chosen before the test.
Start with your numbers.
Incrementality calculator
01 / The definitionWhat is
What is
incremental revenue lift?
Incremental revenue lift is the relative difference in revenue per eligible unit between a randomized treatment group and a comparable control group. The unit may be a visitor, customer or session, chosen before the test.
Attribution is not incrementality. Before-and-after comparisons can be distorted by seasonality, promotions and traffic mix; repeated peeking can produce false winners.
02 / The measurementA clear formula.
A clear formula.
A useful comparison.
Incrementality =(Treatment revenue per eligible unit − Control revenue per eligible unit) ÷ Control revenue per eligible unit × 100
Read the result in context.
There is no universal positive-lift target. Plan detectable effect and sample size from your baseline variance, then evaluate confidence intervals and contribution impact.
Document the comparison.
Keep the reporting dates, population, exclusions and calculation with every result. Show underlying counts as well as the average or rate, so a small sample does not look more conclusive than it is.
Illustrative example
From inputs to insight.
- Treatment revenue per eligible unit
- 3.3
- Control revenue per eligible unit
- 3
- Incrementality
- 10%
10% relative revenue lift: €3.30 versus €3.00 per eligible unit, a €0.30 absolute difference.
Try your numbers ↑03 / Industry benchmarksContext first.
Context first.
Targets second.
There is no universal positive-lift target. Plan detectable effect and sample size from your baseline variance, then evaluate confidence intervals and contribution impact.
We have not verified a public industry benchmark that matches the exact definition used on this page. A precise-looking generic range would hide important differences between businesses.
Build your own benchmark
Start with a comparable baseline.
01
Match the population.
Choose the category, channel or customer cohort you can compare consistently.
02
Let the data mature.
Use complete periods and allow the purchase, attribution or returns window in your definition to close.
03
Test a specific opportunity.
Set a target from your economics and observed variation. Keep conversion and contribution in view.
04 / Clerk customer evidenceReal stories.
Real stories.
Clear measurement limits.
We have not verified a published Clerk customer case with a quantified incremental revenue lift result matching this page’s definition.
What would count as useful evidence?
A case reporting treatment revenue per eligible unit and control revenue per eligible unit, with the time window and comparison method. General revenue growth or a related engagement metric does not establish this result.
Customer stories describe individual implementations. They are not industry benchmarks, guarantees or standardized causal tests.
05 / Put it into practice
Find the sales your change actually adds.
Test one meaningful experience change with a persistent control and monitor conversion, returns and contribution as well as revenue.
06 / Common questionsA little more
A little more
clarity.
How do I calculate incremental revenue lift?
(Treatment revenue per eligible unit − Control revenue per eligible unit) ÷ Control revenue per eligible unit × 100. 10% relative revenue lift: €3.30 versus €3.00 per eligible unit, a €0.30 absolute difference.
What should I check before comparing results?
Attribution is not incrementality. Before-and-after comparisons can be distorted by seasonality, promotions and traffic mix; repeated peeking can produce false winners.
What is a good incremental revenue lift?
There is no universal positive-lift target. Plan detectable effect and sample size from your baseline variance, then evaluate confidence intervals and contribution impact.
Does a higher attributed result prove incremental growth?
No. Attribution connects an interaction with an outcome under a reporting rule. To estimate what a change added, compare randomly assigned treatment and control groups using the same eligible population and a predefined measurement window.
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