The skill
Install once in Claude. It computes incremental lift per segment from a holdout, runs the power calculation, fits an uplift model and ranks segments by Qini, and returns a keep, merge or revert-to-generic decision.
What's inside
Three files, one job: keep only the segments that pay
Built for lifecycle, CRM and growth teams, not data scientists.
Install guide
A short setup walkthrough, plus a no-install option if you cannot add skills.
Plan B prompt
A single prompt you paste into any Claude chat to run the same analysis without installing anything.
How it works
Three steps, from your data to a keep-or-merge verdict.
Step 1Bring your data
Randomized treatment and control by segment for lift and uplift, or per-segment conversion counts and a target effect for the power check.
Step 2Claude runs the analysis
It computes incremental lift and an incrementality factor per segment, runs the two-proportion power calculation, fits an uplift model, and computes the Qini curve, in code.
Step 3Get the verdict
Incremental lift with confidence intervals, the segments too small to power, the persuadable-rich segments versus the cosmetic ones, and a keep, merge or revert-to-generic call for each.
The skill works from the data you bring, it does not connect to your ad platform or ESP. The holdout and uplift work needs a real randomized control, otherwise the model is a propensity score dressed up as uplift, not a causal read.
How to measure incremental lift by segment
Incremental lift is the share of conversions a segment would not have produced without the treatment, and it is the only honest scorecard for a segmentation. A personalized-versus-generic comparison credits conversions that would have happened anyway, and splitting a list into many segments destroys the statistical power to measure any of them. Here is how to measure lift per segment and decide which segments are worth keeping.
Why personalized versus generic is the wrong test
The common test, does the personalized version beat the generic one, credits the sure things, the people who would have converted regardless. Uplift modeling splits any audience into persuadables, sure things, lost causes, and sleeping dogs, and only persuadables produce incremental lift. In a worked example a platform reports 8,000 attributed purchases, but a holdout shows only 2,200 were incremental and the other 5,800 would have happened anyway, so a real incrementality test almost always returns a smaller number than your reported ROAS.
How to measure incremental lift with a holdout
The honest measure is a holdout, a randomized control that receives the generic experience while the treatment receives personalization, with incremental lift read as treatment rate minus control rate per segment. The skill computes that lift with confidence intervals and an incrementality factor, incremental divided by attributed conversions, so you can see at a glance which segments produce real lift and which are just harvesting conversions that were already coming.
Why splitting your list destroys measurement
The granularity math is unforgiving. The minimum detectable effect shrinks only with the square root of the sample, so splitting one list into k segments cuts each segment's sample by k and inflates each segment's minimum detectable effect by the square root of k. A common floor for a valid holdout is a couple of hundred conversions in the control arm, and most micro-segments never reach it, so the skill flags any segment too small to power and tells you to merge it upward. If you cannot power a holdout for a segment, you cannot manage it.
How to rank segments by Qini and prune
Once you have a holdout, fit an uplift model and rank segments by the Qini curve, the area between your uplift-ranked targeting and random targeting. A steep Qini means the model is finding persuadables, so personalize there, and a near-diagonal Qini means the segmentation is cosmetic, so prune it and revert to a strong generic experience. The skill runs this with libraries like causalml, scikit-uplift and EconML, then collapses the segmentation toward four to eight coarse behavioral buckets that capture most of the durable lift.
What the skill cannot do, and how to stay honest
It is a measurement tool, not a magic lift generator. An uplift model trained on observational, non-randomized data confuses correlation with causation and is a propensity model dressed up as uplift, so randomization is required. True one-to-one generative content cannot be A/B validated, because there is no counterfactual for a single person, so individual lift is unfalsifiable. Product recommendations and triggered lifecycle flows pay because they are grounded in revealed behavior and measured, while message micro-segmentation usually does not. For the full argument and the evidence behind scoring segments on lift, read Score Segments on Lift, Not Relevance.