ChatGPT and Claude Prompts for Amazon Sellers

Independent sellers generated more than 60% of Amazon store sales in 2025, so chatgpt and Claude prompts for Amazon sellers have to do more than produce polished copy. The strongest library combines report-based prompts for static analysis with connected prompts that let Claude or ChatGPT query the seller's own synchronized Amazon data through Agent Central, while Agent Central returns facts and previews and the seller or workflow decides what to do.

Amazon's seller base is large enough that prompt quality now affects real operating decisions, not just content drafts. U.S. independent sellers averaged more than $375,000 in annual Amazon-store sales in 2025, and more than 75,000 sellers exceeded $1 million in sales, which is why prompts for listings, ads, inventory, finance, and returns need to be structured around measurable seller outcomes, not vague brainstorming alone. A prompt works best when it names the marketplace, date range, currency, source report, and whether the result is analysis or a preview that needs approval. Agent Central fits that operating model because it connects via Amazon authorization and scheduled account syncs, not by pretending the data is real-time.

Practical rule: If the prompt can't identify the source of truth, it's too loose for Amazon work.

For sellers who want a broader prompt database for AI workflows, a useful external starting point is the prompt database for AI workflows. The rest of this library stays focused on copy-ready Amazon workflows, what the assistant should inspect, and where human review still matters.

Table of Contents

• 1. Campaign Performance Analysis from Advertising Reports
  • Ready to copy
  • What works and what doesn't
• 2. Live Sales and Traffic Insights from Connected Account Data
  • Ready to copy
  • What to ask for
• 3. Inventory Levels and Restock Priority Planning
  • Ready to copy
  • What to pair it with
• 4. Inbound Shipment Tracking and FBA Logistics Planning
  • Ready to copy
• 5. Listing Optimization and Catalog Data Analysis
  • Ready to copy
  • What to ask the connected assistant for
• 6. Fees, Refunds, and Profitability Analysis from Seller Central Reports
  • Ready to copy
  • Good habits for this workflow
• 7. Live Profitability and Financial Metrics from Connected Account Data
  • Ready to copy
  • What makes the prompt strong
• 8. Order Data and Returns Analysis from Seller Central Reports
  • Ready to copy
  • Useful follow-up prompts
• 9. Live Order Management and Returns Processing Workflow
  • Ready to copy
• 10. Multi-Account and Cross-Marketplace Performance Comparison
  • Ready to copy
  • What to include
• ChatGPT vs Claude: 10 Amazon Seller Prompt Use Cases
• Turn Prompts Into Controlled Operating Workflows

1. Campaign Performance Analysis from Advertising Reports

Paste the report, keep the scope tight, and ask for a ranked readout. This is the cleanest use case for chatgpt and Claude prompts for Amazon sellers because the report itself is the source of truth, so the assistant can sort spend, clicks, orders, conversion, and efficiency without needing a live account connection.

Ready to copy

Review this Sponsored Products report for the last 30 days. Show campaigns, ad groups, and keywords ranked by spend, then flag anything with high spend and weak conversion. Group the findings by match type, call out search terms that need negatives, and separate the items that are underperforming from the ones that are stable but not scaling. Return the output as a table with columns for entity, spend, clicks, orders, conversion rate, and action note. If any field is missing, write unknown.

A good answer contains a clean ranking, a short list of obvious waste, and a separate list of terms worth testing in exact match, without pretending to know anything outside the pasted report.

What works and what doesn't

A report-based prompt works best when the export includes enough history to show trend, not just noise. Three months of Sponsored Products data often reveals whether a keyword is weak or just in a seasonal dip. The model should also be told to use the columns that exist in the report, because invented fields create unusable advice.

If the seller wants bid changes, the prompt should ask for a preview of the suggested changes, not a live action.

The weak version is “analyze my PPC.” That usually returns generic commentary. The stronger version names the campaign type, the date range, and the output structure, then tells the assistant to separate stable winners from items that need manual bid work in Ads Manager.

2. Live Sales and Traffic Insights from Connected Account Data

Connected prompts work best when the seller needs repeated reads from current account data, not a one-time paste. Agent Central can connect Claude or ChatGPT to the seller's Amazon data so the assistant can return sales, traffic, and conversion metrics without rebuilding the same report every time. For setup, use the Claude quickstart and the ChatGPT connection guide.

Ready to copy

Show the top 10 SKUs by revenue for the selected marketplace and period, then rank them again by conversion rate. Include sessions, orders, and session-to-order ratio for each SKU. If traffic source data is available, split it by source and mark anything where sessions are high but orders lag. Return only facts from the connected account and label any missing field as unknown.

A good answer gives a clean SKU ranking, a traffic split, and a clear view of where sessions are not turning into orders.

Run the same SKU ranking on a fixed cadence, daily or weekly, so shifts in conversion show up early. That matters when the team is watching a small set of products and needs to know whether traffic is holding while purchase intent slips. The prompt is strongest when it keeps the assistant inside the connected data and forces it to say unknown instead of filling gaps with guesses.

This approach is better than a pasted summary when the team checks the same panel often. It also works for marketplace comparisons because Agent Central syncs account data on a schedule, so the assistant can answer from prepared records instead of waiting on Amazon's reporting flow.

What to ask for

• Top performers by revenue, units, or conversion: Useful for finding the SKUs that deserve budget, content, or inventory attention.
• Traffic source breakdowns: Useful when direct traffic, search traffic, or another source behaves differently from the rest.
• Trend windows: Useful for month-over-month or year-over-year checks, especially when the seller wants to spot a shift without rebuilding a spreadsheet.
• Explicit missing-data handling: Useful because connected data can still have gaps, and the assistant should say unknown rather than estimate.

For a deeper sales workflow example, see the Amazon sales data analysis workflow.

3. Inventory Levels and Restock Priority Planning

Inventory prompts need the current position, not just a guess at demand. When Claude or ChatGPT is connected through Agent Central, the assistant can read inventory facts from the seller's synchronized Amazon data and return current units, sell-through, and days of supply so the operations team can decide what gets reordered first.

Ready to copy

Show the current inventory position for these ASINs. Include FBA units, reserved units, inbound units if available, sell-through rate, and estimated days of supply. Rank the SKUs from most urgent restock risk to least urgent, and separate anything with excess stock from anything that is close to stockout. Do not estimate missing fields. Return only connected account facts.

A good answer contains a ranked list, a clear restock priority order, and a separate excess inventory view that doesn't blur into urgency.

This prompt is useful because inventory decisions usually need a threshold, not a narrative. If the seller wants reorder triggers, the assistant should be told to identify the SKUs below the chosen day-of-supply cutoff, then leave the final purchase decision to the operator. That keeps the model in a diagnostic role, which is safer than asking it to decide inventory actions on its own.

What to pair it with

• A velocity ranking: Helpful for finding which SKUs consume stock fastest and therefore deserve earlier replenishment planning.
• A minimum threshold prompt: Helpful when the team wants the assistant to surface only the SKUs below a chosen supply level.
• A seasonal comparison: Helpful when Q4 demand and off-season demand need to be handled differently.

For a practical inventory framework, the inventory management workflow for Amazon is a relevant companion read.

4. Inbound Shipment Tracking and FBA Logistics Planning

Inbound shipment prompts work well when the seller needs a status check and a preview before submitting a change. Amazon's own reporting and shipment workflows are asynchronous, so the assistant should summarize what's already in the pipeline and present any adjustment as a preview the seller approves.

Ready to copy

Show the current inbound shipment status for all open FBA shipments in this marketplace. Include shipment name, units, current status, receiving facility if available, and any delays or exceptions. If a quantity or destination change is possible, prepare a preview only and do not submit anything. Return the exact fields from the account and label unknown values as unknown.

A good answer contains shipment status, inbound units, and an explicit preview of any change instead of an automatic submission.

Logistics teams often need a fast read on whether a shipment is late, split, or still in transit. Amazon's reporting for shipments and inventory is not an instant lookup, so the operational move is to ask the connected assistant for the current known state, then use the preview step for any correction. That keeps the workflow auditable.

Practical rule: Any change to inbound quantity or routing should be previewed before approval, especially when the shipment affects stockout risk.

A clean inbound prompt should also ask for receiving facility and units at risk if the seller wants to connect logistics timing to demand planning. That gives the team one place to see whether a delay is dangerous or just inconvenient.

5. Listing Optimization and Catalog Data Analysis

Listing prompts are best when they operate on catalog fields, search terms, and source-provided content, not on generic rewriting. Agent Central can expose current listing data, so Claude or ChatGPT can compare the existing title, bullets, and backend terms against the seller's own catalog records before drafting a preview.

Ready to copy

Review these listings and identify missing keyword coverage, weak title structure, and redundant phrases. Compare the current title, bullets, and backend terms across the selected ASINs, then produce a preview of revised copy that keeps the brand name, fits the category, and avoids unsupported claims. Return the proposed edits separately from the current fields, and mark any uncertain phrase as needs review.

A good answer contains a clean gap analysis, a separated preview, and a warning anywhere the copy might overreach the available facts.

This is one of the easiest places to make a bad prompt look productive. A shallow prompt says “improve this listing,” which usually returns polished but unusable prose. A better prompt tells the assistant exactly which fields to inspect, which claims are off-limits, and what the output should look like. That is especially useful for brands that need version control across multiple ASINs.

What to ask the connected assistant for

• Keyword gaps: Useful when the seller wants to know which relevant terms are absent from title or backend fields.
• Variation consistency: Useful when related ASINs need aligned naming and attribute structure.
• Preview-only rewrites: Useful when the seller wants copy options before approving a change.
• Policy-risk flags: Useful when the listing contains a phrase that needs human verification.

For a broader catalog workflow, the Amazon listing analysis workflow helps frame this in operator terms.

6. Fees, Refunds, and Profitability Analysis from Seller Central Reports

Use the report first, then let the model structure the readout. Payments files, fee lines, reimbursements, and settlement exports usually give Claude or ChatGPT enough detail to separate margin pressure from refund noise and odd fee behavior, as long as the seller pastes clean rows into the prompt.

Ready to copy

Analyze this Seller Central report by SKU and order date. Break out referral fees, FBA fees, refunds, reimbursements, and net proceeds. Rank the SKUs by true profitability after fees and returns. Flag any unusual fee pattern or reimbursement candidate, and mark any row with missing or unclear values as needing review. Keep the answer tied to the pasted report only.

A useful answer gives SKU-level margin visibility, a fee breakdown, and a short list of rows that need human review in Seller Central. It should also separate what the report shows from what still needs approval before any claim or correction is filed.

This prompt works well for sellers who know gross sales but not net margin by product. It also helps when reimbursement claims need a first-pass triage, because the assistant can surface candidate rows by SKU and date for manual review before submission. Traceability matters more than polish here. If the numbers cannot be tied back to the pasted report, the answer is not ready for action.

A fee-focused operating lens helps keep the prompt grounded. See the Seller Central fees analysis guide for a practical reference point.

Good habits for this workflow

• Use a longer lookback to spot margin drift instead of one noisy month.
• Keep the output at SKU level so the team gets action-ready rows, not just a summary.
• Ask for claim candidates separately so reimbursement eligibility gets a second pass.
• Request fee comparisons by category when fee structure and category classification need closer inspection.

7. Live Profitability and Financial Metrics from Connected Account Data

Connected finance prompts work best when the seller needs recurring reads on margin, payout status, and fee movement without pasting a fresh export every time. Agent Central syncs financial data on a schedule, so Claude or ChatGPT can read the seller's own account data instead of relying on a manual report loop.

Ready to copy

Show the current profitability view for these SKUs. Include net margin, fee totals, and any fee trend that changed in the selected period. If payout status is available, include it too. Return the facts only, separate the data from any interpretation, and label unknown when the account doesn't expose a field. If a field is missing or stale, call that out before making any recommendation.

A useful answer gives current margin facts, fee totals, and a clean split between observation and interpretation. It should also show where the connected account data is incomplete, since a missing field can change how a seller reads the result.

The operator value is speed with guardrails. A finance manager can reuse the same prompt each week and compare the output without rebuilding the request. That matters when the question is not what should happen, but what changed since the last review. Pricing moves, cost cuts, and fee checks still stay in human hands, because the seller must approve any action that depends on the numbers.

What makes the prompt strong

• A fixed date range: Keeps week-to-week or month-to-month comparisons consistent.
• A SKU list: Stops the assistant from drifting into unrelated products.
• A separate interpretation step: Forces the answer to stay factual before action is discussed.
• A payout or fee trend field: Helps the team see whether the change looks structural or temporary.
• A validation note: Tells the assistant to flag missing, stale, or partial account fields before anyone acts on them.

For recurring financial reviews, this is one of the cleanest uses of connected data. It replaces the paste-and-check cycle with a controlled read from the seller's own account, while keeping approval for any fee dispute or pricing change with the operator.

8. Order Data and Returns Analysis from Seller Central Reports

Returns data needs its own read. A pasted Orders report, Returns detail file, or Refund report gives Claude or ChatGPT enough structure to find repeated issues, rank the highest-return ASINs, and separate product defects from fulfillment problems.

Ready to copy

Review these orders and returns by SKU and date. Rank the SKUs by return rate, summarize the most common refund reasons, and flag any pattern that points to product quality, fulfillment, or customer expectation mismatch. Do not estimate rates that are not visible in the report. Keep the output tied to the pasted data, and separate the facts from any suggested investigation.

A useful answer gives a ranked return view, a reason breakdown, and a clean split between product issues and fulfillment or expectation issues.

The value here is diagnosis before action. A returns prompt can show whether one SKU is out of line, whether packaging problems keep repeating, or whether the listing is setting the wrong expectation. That gives the seller a tighter review path and avoids broad fixes based on a noisy sample.

Useful follow-up prompts

• A reason grouping prompt: Use this when refund reasons need to be clustered into cleaner operational buckets.
• A before-and-after comparison: Use this after a copy, pricing, or packaging change to see whether returns shifted.
• A SKU ranking prompt: Use this when only the highest-risk products should be audited first.

For a broader read on seller-side order workflows, connected account data is more useful than repeated exports once the team checks the same metrics regularly.

9. Live Order Management and Returns Processing Workflow

Order management needs preview-first language because the output may affect a customer, a refund, or a fulfillment decision. When Agent Central is connected to Claude or ChatGPT, the assistant can read order details and return requests from the seller's own account, then present a preview before any supported change is submitted.

Ready to copy

Show all pending returns and open order exceptions for this marketplace. Include order ID, reason, age, status, and any fulfillment detail that is already available. For any refund or return action, prepare a preview first and do not submit until approved. Return the facts separately from the preview.

A good answer contains the current case list, the age and reason for each case, and a preview-only action path.

That structure works because support teams need a queue, not a lecture. The assistant should surface what is pending, what is old, and what can be handled in a batch, then leave the approval step to the seller or the team lead. Duplicate-submission protection and audit logging are valuable here because refunds and cancellations can become messy fast if the same action is triggered twice.

A good operations prompt should make “no action taken” a valid outcome when the data is incomplete.

A practical version of this workflow also asks for orders with fulfillment exceptions, because those often need outreach before a customer escalates. The assistant's job is to sort the cases, not to overreach into final judgment.

10. Multi-Account and Cross-Marketplace Performance Comparison

Cross-marketplace prompts are where connected access starts to look different from pasted reports. Agent Central can separate linked account data, so Claude or ChatGPT can compare sales, profitability, and advertising performance across multiple accounts and marketplaces without blending the records together.

Ready to copy

Compare performance across these connected accounts and marketplaces. Show revenue, profitability, and advertising efficiency for each region, then rank them from strongest to weakest. Keep each account separate, use the same date range and currency assumptions where possible, and label any field that isn't available. Do not recommend actions, only return the comparison facts.

A good answer contains a clear regional ranking, separate account boundaries, and a stable comparison frame.

This prompt is useful for agencies and multi-marketplace operators because the issue is rarely a single SKU in a single market. The underlying question is often where budget belongs, where margins are strongest, and where advertising is getting more expensive relative to return. The assistant should surface the comparison cleanly, while the human decides whether to shift spend or adjust pricing.

What to include

• Marketplace-level ranking: Useful when investment needs to follow the strongest region.
• Per-account separation: Useful when agencies must avoid cross-account contamination.
• A consistent period: Useful when comparing like with like.
• Currency handling: Useful when marketplaces differ and the output needs to stay interpretable.

For multi-account operators, the main value is consistency. The same prompt can be reused across markets, as long as the account scope and date range stay explicit.

ChatGPT vs Claude: 10 Amazon Seller Prompt Use Cases

Turn Prompts Into Controlled Operating Workflows

The safest rollout is simple. Start with pasted reports, standardize the date range and output format, then connect Agent Central for repeated reads across Seller Central and Amazon Ads when the team is ready for faster review cycles. Setup is straightforward, sign up at agentcentral.to, connect Seller Central and Amazon Ads through Amazon authorization, then add the connection to the AI client using the Claude quickstart. From there, keep the rules tight, scoped access, explicit marketplace and account context, source-field preservation, and a preview before any supported change.

That operating discipline matters more than clever wording. Amazon Ads reporting is asynchronous, SP-API access is scoped by roles and authorization, and Seller Central workflows still need human approval for anything that changes bids, budgets, listings, prices, inventory, or shipments. The strongest prompt system treats the model as a structured analyst, not an automatic decision-maker.

A practical next step is to separate the library into two buckets. One bucket handles pasted reports for one-off analysis, the other handles connected account reads for repeated checks across sales, traffic, inventory, orders, finance, and fulfillment. That split keeps chatgpt and Claude prompts for Amazon sellers useful without letting them drift into guesswork.

For current pricing, see Agent Central pricing. Plans are priced by monthly Amazon order volume, and the 14-day free trial needs no card. For teams building broader agent workflows around Amazon data, the same operating model also aligns well with scalable agentic browser workflows.

Agent Central connects Claude and ChatGPT to Amazon Seller Central and Amazon Ads with structured, scheduled reads, guarded writes, and audit logs. For sellers who want prompt-driven workflows that stay tied to real account data, it gives a practical way to move from pasted reports to connected analysis. Visit agentcentral to set up the connection and start with controlled Amazon workflows.