The skill
Install once in Claude. It scores any promotion on incremental contribution margin, runs the breakeven, estimates discount elasticity, designs a holdout, and ranks uplift targeting.
What's inside
Three files, one job: score the discount on margin
Built for growth, ecommerce and pricing 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 numbers to a go or no-go.
Step 1Bring your numbers
List price, unit COGS and variable selling costs for the margin check, or historical promo and non-promo sales for elasticity, or experimental data with a control for uplift.
Step 2Claude runs the math
It computes post-discount contribution margin and the breakeven volume, estimates elasticity, sizes the holdout, or ranks customers by uplift, in code.
Step 3Get a go or no-go and the fix
The incremental-margin verdict against your elasticity, the breakeven bar, the honest read on how much lift is incremental, and who to target or leave alone.
The skill works from the numbers you bring, it does not connect to your store or ad accounts. It also treats elasticity as correlational until a holdout confirms it, because promo timing usually rides a demand peak.
How to tell if a discount is profitable
A discount looks profitable on most dashboards because ROAS, revenue and conversion all rise during a promotion by construction. The only number that tells you whether it actually paid is incremental contribution margin, and once you run the breakeven and strip out the lift that was never incremental, most discounts lose money. Here is how to check before you launch.
Why ROAS and revenue lie about a promotion
A discount comes straight off contribution margin, so revenue-based metrics flatter it automatically. ROAS, top-line revenue and conversion rate all go up during a discount because more units sell at a lower price, which tells you nothing about whether the offer made money. The honest scorecard is incremental contribution margin per promotion, measured against a no-promotion baseline, and the skill reports exactly that instead of the vanity numbers.
How to run the discount breakeven
The breakeven is the volume increase a discount needs just to stand still, and it is brutal. Required volume increase equals the discount divided by contribution margin minus the discount, so at a 40 percent margin a 20 percent discount needs unit volume to double, and at a 30 percent margin the same discount needs a 200 percent lift. Price is the strongest profit lever, a 1 percent price move shifts operating profit about 8 percent and volume would have to rise about 18.7 percent to offset a mere 5 percent cut, which is rare. The skill computes the breakeven for your numbers and says no when it sits above your realistic discount elasticity.
Why most of the discount lift was never incremental
A large share of promoted sales would have happened anyway. On average about 58 percent of people who buy a brand on promotion had already bought it in the prior 26 weeks, and the promotion bump splits into roughly three equal thirds, brand-switching, purchases borrowed from other periods, and true category expansion, so only about a third is genuinely incremental and the borrowed third returns later as a post-promotion dip. The only honest way to separate the incremental sale from the subsidy is a holdout, a randomized control group that does not get the offer, which the skill designs for you.
How to target only persuadables with uplift
Sending a discount to everyone subsidizes people who would have bought anyway. An uplift model, unlike a propensity model, targets only persuadables, the customers whose probability of buying rises because of the offer, and avoids the sure things, the lost causes, and the sleeping dogs a discount actually pushes away. Profit-based and uplift targeting has beaten blanket discounting by wide margins in field experiments, and the skill ranks customers by uplift and tells you who to leave alone.
What the skill cannot do, and how to stay honest
It is an analysis tool, not a license to discount. Elasticity needs real price variation, so if you have always discounted at the same times the model cannot separate the discount from the seasonal peak it rode on. Uplift needs experimental data, because you cannot observe both outcomes for the same customer without a holdout. Personalized pricing carries fairness and legal risk, and a model maximizing this quarter's margin will ignore the reference-price and brand erosion that compounds for years, so the strategy call stays with a human. For the full argument and the evidence behind scoring promotions on margin, read Score Promotions on Margin, Not ROAS.