Trade Promotion & ROI Planner

How to use it

1. 1Paste the prompt into your AI model of choice.
2. 2Provide the product, retailer, mechanic and your margin and price.
3. 3Answer its clarifying questions for a tighter read.
4. 4Ask it to model two or three mechanics side by side.

See it in action

Trade promo read — CrunchaBar 6-pack @ [National grocer]

Objective:
Drive trial during the holiday window and beat LY volume — without training the shopper to only buy on deal.

Mechanic & depth:
25% off (≈$3.74) for 2 weeks. That's a deep cut for granola bars; expect strong display pull but watch margin.

Breakeven math (directional, using your numbers):
- A 25% price cut on a 35% margin item compresses unit margin hard. To hold total margin dollars you need a large unit lift — roughly a 2x+ volume increase just to stand still on profit. [Confirm with your exact margin waterfall.]
- Translation: this only pays back if it recruits genuinely new buyers, not just rewards planned purchases.

Likely sources of volume:
- Incremental: new triers drawn by display + price.
- Pulled-forward: existing buyers stocking up (no net gain).
- Cannibalized: shoppers trading down from your single bars.

Risks:
- Discount-training: 25% twice a year teaches "wait for the deal."
- Pantry-loading masking true incrementality in the post period.

KPIs (pre / during / post):
- Lift vs baseline (500 u/store/wk) during the deal.
- Dip below baseline immediately after (pull-forward signal).
- New-buyer % and 4-week repeat.

Recommendation: REWORK.
A shallower cut (15-20%) plus a second-unit mechanic or display likely protects margin while still driving trial. Test the depth, don't default to 25%.

Want me to model 15% vs 20% vs 25% side by side?

The prompt

Here's the start of the prompt. Download the free bundle for the full, ready-to-paste version — plus the installable Claude Skill and Custom-GPT instructions.

# Role
You are a CPG trade and shopper marketing analyst who treats every promotion as an investment that must pay back. You work the economics backward from margin and you separate real incrementality from pulled-forward and cannibalized volume.

# Context I'll provide
- Product: [PRODUCT]
- Retailer / channel: [RETAILER]
- Mechanic considered: [MECHANIC e.g. % off, BOGO, multibuy, display]
- Price & margin: [REGULAR PRICE, MARGIN %]
- Objective: [OBJECTIVE e.g. trial, volume, defend share]
- Baseline & any history (optional): [BASELINE UNITS, LY RESULT]

# Your task

Frequently asked questions

How do you calculate trade promotion ROI?

Trade promotion ROI compares the incremental margin a promo generates against its cost — the discount funding plus any display or fees. The key is isolating truly incremental volume from sales that were just pulled forward or cannibalized from your other SKUs. This skill works the breakeven backward from your margin and flags those volume sources so the ROI read is honest.

Why does a deeper discount need so much more volume?

Because a price cut comes straight off your margin. If you discount a thin-margin item heavily, each unit makes far less, so you need a disproportionately larger unit lift just to hold the same total margin dollars. The skill makes this explicit so a tempting 'big number' discount doesn't quietly destroy profit.

Will it just approve whatever promo I'm planning?

No. It's designed to give an honest go / rework / no-go call and will recommend a shallower mechanic or skipping the promo when the math doesn't support it. A good promo plan sometimes concludes 'don't run this,' and the prompt explicitly tells the model not to rubber-stamp the deal.

Can it compare different promo mechanics?

Yes. Give it the options — a straight % off, a BOGO, a multibuy, or a display-led deal — and ask it to model them side by side so you can see which delivers the objective at the lowest margin cost. Feeding it real velocity data makes the lift assumptions behind each option far more reliable.