Study first
Review the ideas behind the questions
Check how each lift method earns trust before starting. The questions focus on method fit, comparison quality, and caveats when campaign evidence is useful but not clean.
Start With The Counterfactual
Advanced lift work starts by asking what would have happened without the campaign. The answer may come from a control group, matched comparison, synthetic comparison, time-series pattern, or theory-backed contribution claim.
- Incrementality evidence needs a credible counterfactual, not only a metric movement after launch.
- Evaluation should be designed early enough to improve understanding of who is affected and how.
- The method should match the business question and the level of causal rigor needed for the decision.
In Practice
Name The Decision First
A method that is enough for learning where to refine a campaign may be too weak for a large budget shift.
Plan Before Results Arrive
If the evaluation design is decided after the outcome is known, the readout needs a stronger caveat about bias and selection.
Common mistakes
Treating any post-launch improvement as lift.
Look for the counterfactual logic and possible bias before calling the change incremental.
Q&A
Can a weaker method still be useful?
Yes, but the readout should match the claim to the method and avoid using weak evidence for a high-stakes causal decision.
Why does planning timing matter?
Early planning protects the comparison frame and reduces the chance that the method is chosen to fit the result.
Choose The Design That Fits The Constraint
When randomization is not feasible, quasi-experimental methods can help. Each method has a different assumption, so the safest choice depends on rollout timing, data history, treated units, comparison availability, and assignment rules.
- Difference-in-differences depends on a credible assumption that the treated and comparison groups would have followed parallel trends.
- Synthetic control is strongest when a weighted comparison can match the treated unit's historical pattern.
- Regression discontinuity needs a real assignment threshold where people or areas near the boundary are plausibly similar.
In Practice
Check The Assignment Rule
A threshold method is not credible just because results can be sorted. The cutoff has to be part of the treatment assignment.
Respect The History
A synthetic comparison is weak if the pre-campaign history does not track the treated market closely enough.
Common mistakes
Choosing the most technical method before checking its assumption.
Choose the method whose assumption fits the rollout and data, then explain the remaining caveat.
Q&A
When does matching become risky?
Matching becomes risky when important selection factors are not observed, because the comparison may still be biased.
What makes interrupted time series fragile?
It needs enough pre- and post-campaign data and has to handle seasonality, autocorrelation, and unrelated trends.
Write The Caveat At The Same Level As The Evidence
Advanced measurement is not only method choice. The readout should state what the method supports, what it cannot prove, and what would make the next decision stronger.
- Contribution analysis can support a reasoned contribution claim, but it does not give definitive proof of causal effect.
- Theory-based methods can address causality questions, but they do not give precise effect-size estimates.
- If signal and noise are hard to separate, the recommendation should ask for better evidence before a larger budget move.
In Practice
Do Not Inflate Contribution
A contribution story can be valuable, but it should not be reported as a precise lift percentage unless the method supports that precision.
Make The Next Test Clear
When the evidence is not strong enough for a budget decision, the readout should name the cleaner comparison or assumption check needed next.
Common mistakes
Reporting every useful analysis as causal lift.
Use impact language only when the method and assumption support it, and label weaker evidence as contribution or directional learning.
Q&A
What should a caveat protect?
It should protect the comparison frame, the method assumption, and the strength of the claim the evidence can support.
When should the readout ask for another study?
Ask for another study when the current method cannot separate signal from noise well enough for the decision at stake.