Ops & Supply

How to track broker and distributor performance with AI

Ops & Supply3 AI tools6 steps6 friction points

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

Tracking broker and distributor performance is one of the most operationally important — and most neglected — workflows in CPG. Your broker network is executing on shelf every day, opening new doors, filling voids, and ideally driving velocity. Your distributors are moving product through the chain. But most founders are reviewing this quarterly, with a deck their broker prepared, against numbers they can't independently verify. That's not performance management; that's a formality.

The workflow feels like an AI problem because it's fundamentally a pattern-recognition and synthesis task. You have data scattered across distributor portals, retailer POS reports, trade spend spreadsheets, and email threads with your field reps. An LLM seems like the right tool to ingest all that, find the signal, and surface which brokers are earning their commission and which aren't. The reasoning step is genuinely something AI can help with — if you can get the data in front of it.

ChatGPT, Claude, and Gemini can do real work here today. They can analyze performance data you paste in, flag underperforming territories, write structured scorecards, and help you build a consistent evaluation framework. The ceiling isn't the AI's reasoning ability — it's everything required to get clean, current data in front of the model, and to make the output durable enough to use month after month.

Skip the manual work → See the Starch version

Ops & Supply3 AI tools6 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 broker performance data manually — pull POS velocity by territory from your retailer portals, download distribution reports from UNFI or KeHE, and copy scan data from your trade spend spreadsheets into a CSV or Google Sheet.

2 Open ChatGPT or Claude and paste in the raw data alongside a description of your broker structure: which brokers cover which territories, what their KPIs are supposed to be (new authorizations, void closures, velocity targets), and how they're paid.

3 Ask the LLM to build a scorecard framework: score each broker on numeric performance against each KPI, weight the metrics based on your priorities, and produce a ranked summary with supporting detail.

4 Use a follow-up prompt to identify specific gaps — brokers who are meeting call frequency targets but not converting new stores, or territories where distribution is growing but velocity is flat, which may signal execution problems rather than distribution wins.

5 Ask the model to draft a summary you can use in your next broker review: what the data shows, what questions it raises, and what you want to see in the next 30 days. This is where LLMs genuinely save time — turning a spreadsheet into a structured conversation.

6 Repeat this process next month by re-exporting everything from scratch, re-pasting it, and re-running your prompts — because nothing from this session carries forward automatically.

Prompts you can copy

Here is POS velocity by territory for Q1, alongside new store authorization counts by broker. Score each broker 1-10 on velocity growth, new authorizations, and void closure rate. Weight velocity at 50%, authorizations at 30%, void closures at 20%. Show your work.

Broker A covers the Southeast and opened 12 new doors last quarter but velocity per store declined 8%. Broker B covers the Midwest, opened 4 doors, but velocity per store grew 14%. Which broker is performing better and why? What should I ask each of them in our next call?

I'm building a broker scorecard for a CPG brand selling through natural grocery and conventional. What are the 6-8 most important KPIs to track, and how should I weight them for a brand doing under $5M in retail sales?

Given this distributor performance data — cases shipped, depletion rate, out-of-stocks by region — flag the top 3 concerns I should bring to my distributor meeting next week and suggest specific asks.

Here is my broker commission structure and their Q1 results. Which brokers are generating positive ROI on their commission based on the sales data? Which ones are not, and what's the gap?

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, retailer POS systems, or trade spend spreadsheets — every run requires a fresh manual export, paste, and re-prompt.

Context window limits mean that if you're managing 5+ brokers across multiple territories with monthly data, the full dataset often gets truncated before the model can reason across all of it at once.

Nothing persists between sessions — the scorecard structure you carefully built in February isn't waiting for you in March. You're reconstructing the prompt and the framework from scratch each cycle.

Output format drifts between runs, even with the same prompt. The scorecard columns shift, the scoring logic varies subtly, and comparing this month's output to last month's requires manual normalization.

LLMs have no access to your actual broker contracts or commission structures unless you paste them in — so ROI calculations and performance-against-expectation analysis require you to manually compile the context every time.

There's no alerting or proactive monitoring — the model only sees what you show it, when you show it. A distribution void that opens in week 3 of the month doesn't surface until your next manual review cycle.

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 persistent software your broker performance workflow depends on, connected to your live business data, so you're not re-assembling the same report from scratch every month.

Broker Scorecard (coming soon — request beta access) will connect directly to your data sources and track retail execution, new store authorizations, distribution voids, and field activity across every territory, tied back to actual sales performance — so your quarterly broker review has numbers your broker didn't prepare.

Retail Analytics (coming soon — request beta access) will unify POS data from your retail accounts into one dashboard, showing sell-through velocity by store, regional distribution voids, and promotional lift — without waiting weeks for syndicated reports you can't afford.

Trade Spend Tracker (coming soon — request beta access) will plan promotions with budget allocation by account, track scan-backs and off-invoice allowances in real time, and measure actual sales lift after each event — so you know which promotions drove volume and which ones just wrote checks to retailers.

Connect your accounting data through QuickBooks or NetSuite — Starch syncs your actual financials on a schedule — and pair it with live-queried data from your CRM or sales tools to see broker cost against broker-attributed revenue in one place.

Describe the broker scorecard you want in plain English — 'score each broker on velocity growth, new authorizations, and void closures, weighted by territory size, and alert me when any broker drops below a 6' — and an agent builds it as a persistent app that runs continuously, not a one-off prompt.

Starch connects to 3,000+ apps through its integration catalog, plus any web portal through browser automation — so even if your distributor doesn't have a formal API, Starch can automate the login, pull the depletion report, and surface the data where you need it.

Get closed-beta access →

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