Ops & Supply

How to plan trade spend and retail promotions with AI

Ops & Supply3 AI tools7 steps6 friction points

Want an agent to handle this on your live data? See the Starch version →

Trade spend and retail promotions planning means deciding where to put your promotional dollars — scan-backs, off-invoice allowances, TPRs, display fees, slotting — and building a promo calendar that accounts for retailer timing, distributor requirements, and your own margin floor. For most CPG brands, this is one of the highest-dollar decisions they make, often 15–25% of gross revenue, and it's done in a patchwork of spreadsheets, broker emails, and gut instinct.

The reason operators reach for AI here is obvious: the workflow is analytically dense but structurally repetitive. You're working with tables of accounts, SKUs, dates, and spend figures — exactly the kind of structured reasoning where a large language model seems like it should be useful. You want to model lift assumptions, allocate budget across accounts, compare plan-to-actual from the last promotion, and come out with a defensible calendar. That's a lot of spreadsheet logic that a good prompt should be able to shortcut.

ChatGPT, Claude, and Gemini can genuinely help with parts of this. They're good at structuring a promo calendar template, writing lift-assumption formulas, stress-testing a budget allocation against margin targets, and drafting the retailer-facing promo sell sheets. Where they fall short is everything that requires your actual data — your real scan-back history, your current account list, your distributor portal numbers. That data lives somewhere else, and getting it into the model is on you, every single time.

Skip the manual work → See the Starch version

Ops & Supply3 AI tools7 steps6 friction points

AI walkthrough

How to do it with AI today

A practical walkthrough using ChatGPT, Claude, and other off-the-shelf LLMs — what they're good at, what you'll have to do by hand.

Tools that work for this

ChatGPTClaudeGemini

Step-by-step

1 Export your trade spend history from wherever it lives — a distributor portal, your broker's spreadsheet, QuickBooks — and paste it into Claude or ChatGPT as a CSV block. Ask the model to categorize spend by account, promotion type, and time period.

2 Paste in your planned promo calendar for the next quarter — even a rough list of events, accounts, and estimated spend — and ask the model to flag where your budget concentration is highest and which accounts have no promotion planned.

3 Ask ChatGPT or Claude to build a lift-assumption model: given a historical baseline velocity and a planned price reduction percentage, calculate expected incremental units and whether the promotion is margin-positive at your current COGS.

4 Feed the model your top 10 accounts by volume, your available trade budget, and your margin floor, then ask it to produce a recommended budget allocation table ranked by expected ROI. Expect to iterate on the assumptions manually.

5 Use the model to draft your promo sell sheet or internal promo brief — account name, event dates, mechanics, funding source, expected lift, and retailer ask. Claude tends to produce cleaner structured documents here than raw ChatGPT.

6 Run a plan-to-actual comparison by pasting last quarter's planned spend next to your actual scan-back data and asking the model to calculate variance by account and identify the three biggest underperformers.

7 Export the outputs manually into your spreadsheet or shared doc. Nothing the model produces persists — close the window and it's gone.

Prompts you can copy

Here is my trade spend by account for Q1 [paste CSV]. Categorize by promotion type (scan-back, off-invoice, display, slotting), calculate total spend per account, and flag any account where trade spend exceeds 30% of that account's net revenue.

I have $80,000 in trade budget for Q3 across 12 retail accounts. My margin floor is 42%. Allocate budget across accounts to maximize expected lift, assuming 8% velocity lift per 10% price reduction. Show your assumptions.

Here is my promo calendar for Q2 [paste table]. Identify gaps — accounts with no promotional activity in any 6-week window — and suggest where to add events based on retailer reset timing patterns for natural grocery.

Compare my planned trade spend [paste] to my actual scan-back receipts [paste]. Calculate variance by account, rank accounts from largest negative variance to largest positive, and summarize what drove the top three misses.

Draft an internal promo brief for a 4-week TPR at Sprouts in June: 15% price reduction on our 12oz SKU, $6,200 funded via scan-back, baseline velocity 42 units/store/week. Include expected lift, total cost, and net margin impact at $4.80 COGS.

Reality check

Where this gets hard

The walkthrough above works — until your numbers change, the LLM hallucinates, or you have to re-paste everything next month.

No live connection to your distributor portals, scan-back history, or QuickBooks data — every session starts with a manual export and paste, which means you're always working on data that's already stale.

Promo calendars span multiple accounts, SKUs, and time windows. Once the dataset gets past ~200 rows, you hit context limits and the model starts dropping rows or summarizing instead of calculating.

Outputs aren't consistent across sessions. The allocation table you carefully structured last month won't match what the model produces this month unless you re-paste all your formatting instructions every time.

Nothing persists. Your promo plan, lift model, and variance analysis exist only in a chat window. Next quarter you rebuild from scratch, re-paste all the same data, and re-explain all the same context.

The model has no visibility into your broker network or retail execution — it can't tell you whether a promotion actually ran in-store, whether a display got set up, or whether a distribution void killed your lift numbers.

Sharing outputs with a broker or buyer requires manual copy-paste into a doc or spreadsheet. There's no connected surface — just a chat transcript that nobody else can access or update.

Tired of the friction?

Starch runs the whole workflow on live data — no copy-paste, no hallucinated numbers, no re-prompting next month.

See the Starch version →

Starch alternative

The same workflow on Starch

Starch is an agentic operating system — an agent builds and runs the software your trade planning depends on, connected to your live business data, so you're not re-running prompts from scratch every month. For trade spend and retail promotions, that means a persistent planning and tracking system that reflects your actual numbers.

Trade Spend Tracker — coming soon, request beta access — will connect to your distributor portals, track scan-backs and off-invoice allowances in real time, and measure actual sales lift against plan, so you're not piecing this together manually after every promo event.

Retail Analytics — coming soon, request beta access — will unify POS data from your retail accounts into one dashboard showing sell-through velocity by store, promotional lift by event, and distribution gaps — the exact numbers you need before a buyer meeting.

Broker Scorecard — coming soon, request beta access — will track retail execution and new store authorizations by territory and tie broker field activity back to actual sales performance, giving you data to push back on anecdote-heavy quarterly reviews.

Connect QuickBooks from Starch's integration catalog and describe what you want — 'show me trade spend as a percentage of net revenue by account, updated weekly' — and an agent builds that dashboard and keeps it current without you re-exporting anything.

Starch automates browser-reachable distributor portals through browser automation — no API needed — so scan-back data and deduction records can flow into your planning surface on a schedule instead of sitting in a portal you log into manually.

Describe your promo calendar logic in plain English — 'flag any account with no promotional event in a 6-week window and calculate whether our Q3 budget allocation stays above a 40% margin floor' — and an agent builds an app that runs that logic against your live data continuously.

Get closed-beta access →

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