AI Can Now See All Your Amazon Data. Here’s What Sellers Ask First — And What They Find.

You know your ACoS. You can rattle off which campaigns hit 25% and which ones are sitting at 40%.

But here’s a question most sellers can’t answer without spending an afternoon in spreadsheets:

Which of your campaigns are actually profitable once you factor in real product costs, FBA fees, returns, and shipping?

Not ACoS profitable. Actually, money-in-your-pocket profitable.

If you paused on that — you’re not alone. That question is the single most common thing Amazon sellers ask when they finally get AI connected to their real business data. Not inventory questions. Not listing optimization. Not product research.

Advertising. Every time.

The Margin Squeeze Nobody’s Talking About

2026 is hitting Amazon sellers from every direction.

FBA fulfillment fees went up an average of $0.08 per unit in January. That might sound like pocket change until you’re shipping 20,000 units a year and it adds up to $1,600 in extra costs — with zero improvement in service.

But the fulfillment hike is just the headline. Amazon also killed all FBA prep and labeling services in the U.S., started delaying payouts, introduced defect-based fees up to $1.74 per unit for labeling errors, and raised AWD West region storage by 19%.

Meanwhile, ad costs keep climbing. PPC bids in competitive categories have jumped dramatically over the last two years. Sellers in forums are reporting 600% bid increases in some niches. One veteran seller on the Amazon forums put it bluntly: he’d spent over $627,000 on ads lifetime and was questioning whether PPC even makes sense anymore.

And Amazon now takes over 50% of sellers’ revenue through fees — up from 40% five years prior, according to Marketplace Pulse. The margin you had two years ago doesn’t exist anymore.

So where does that leave you? Making faster, smarter decisions about where every dollar goes — especially your ad dollars. And that’s exactly where most sellers are flying blind.

The Daily Grind That Eats Your Time

Be honest about what your Tuesday afternoon actually looks like.

You’re in Campaign Manager. You’re pulling search term reports. You’re adjusting bids on keywords that might be working or might be burning cash — you won’t know for 48-72 hours because Amazon’s reporting is delayed. You’re toggling between tabs trying to figure out if a campaign with a decent ACoS is actually contributing to your bottom line once real costs are factored in.

Listing optimization? You’ll get to it. Product scouting? When there’s time. Profitability analysis at the SKU level? Maybe quarterly, if you’re disciplined.

Advertising is the constant. It’s where the money moves in real time. It’s the part of the business that demands daily attention and punishes you the fastest when you look away.

This isn’t a hot take. It’s the pattern we saw play out in real time when we connected Amazon sellers’ actual Seller Central data to AI.

What Happened When Sellers Could Finally Ask AI About Their Real Numbers

At Seller Labs, we built an MCP server — a secure, read-only bridge that connects your actual Amazon data (sales, advertising, profitability, inventory, keywords, margins) directly to AI models like Claude. No exporting. No spreadsheets. Just ask a question in plain English and get an answer backed by your real numbers.

We expected sellers to explore everything.

They didn’t. They went straight to advertising. Keyword performance. Campaign effectiveness. ACoS. Ad spend. ROAS.

Out of 50+ data tables covering every dimension of an Amazon business, advertising was the overwhelming focus. The question sellers asked most:

“Which campaigns are actually profitable after all costs?”

And the answers surprised people. Campaigns that looked healthy on an ACoS basis were underwater once real product-level COGS, FBA fees, returns, and shipping were factored in. Some sellers’ “best” campaigns turned out to be their most expensive mistakes.

Before AI had access to real data, answering that question meant exporting ad reports, matching them against profitability data in a separate spreadsheet, building pivot tables, and praying the numbers lined up. A full afternoon for one answer.

Now it takes ten seconds and one question.

5 Questions You Should Be Asking AI About Your Ads Right Now

You don’t need our MCP server to start using AI more effectively for your advertising. You can do most of this today with ChatGPT, Claude, or Gemini and a CSV export from your ad reports. The key is asking the right questions.

1. “Which campaigns have the highest ACoS but lowest actual profitability?”

ACoS alone doesn’t tell you if you’re making money. Upload your ad report alongside your COGS data and ask AI to cross-reference them. You’ll find campaigns that look efficient but are actually losing money once all fees are included.

2. “What search terms am I paying for that never convert?”

Export your search term report for the last 60 days. Ask AI to flag every term with more than 20 clicks and zero conversions. These are your negative keyword candidates — and they’re silently draining your budget every day.

3. “How has my TACoS trended over the last 90 days, and what’s driving the change?”

TACoS (Total Advertising Cost of Sale) is the metric that tells you how much of your total revenue depends on advertising. If TACoS is climbing, your organic sales are shrinking relative to your ad-driven sales — which means you’re becoming more dependent on paid traffic, not less.

4. “Which of my products should I stop advertising entirely?”

Not every product should have an active campaign. Ask AI to identify products where the advertising cost exceeds the margin ceiling — meaning no amount of bid optimization will make the campaign profitable. Sometimes the best ad decision is turning one off.

5. “What would happen to my total sales if I cut my bottom 20% of campaigns?”

This is a scenario question that would take hours to model manually. AI can estimate the impact based on your historical data — and the answer is often that the bottom 20% of campaigns contribute almost nothing to total sales while consuming a meaningful chunk of budget.

What the Smartest Sellers Are Building Next

The sellers who started with advertising questions didn’t stop there. They started building systems.

One seller in our community built what he calls a “digital CFO” — AI running daily financial investigations across his entire catalog, digging into shipping costs, margin trends, and product-level performance. His goal: replace the third-party subscription software he’s been paying for monthly with AI conversations connected to his own data.

Another seller built automated agents that check ad performance and business data every morning, then send a summarized briefing to his Slack channel. No dashboards. No manual pulls. Just a daily report waiting before his first cup of coffee. He’s now moving it to the cloud so it runs entirely on its own.

These aren’t features anyone built into a product. They’re experiments sellers designed once they realized what’s possible when AI can actually see their numbers.

The Honest Truth About AI for Amazon Sellers

We could tell you AI is ready to run your Amazon business. But experienced sellers would see through that immediately.

Here’s where things actually stand: AI still makes mistakes. It gets math wrong sometimes. It can drift in longer conversations. One seller in our community told us he was nervous to let his team use it because his employees wouldn’t push back on a wrong answer the way he would.

That’s a valid concern. AI is a tool, not an autopilot.

But it’s improving fast. The latest Claude model retrieves relevant data 76% of the time, up from 18% just months ago. And perhaps most importantly — it now stops and asks for clarification instead of making something up.

The sellers who are furthest ahead didn’t wait for the technology to be perfect. They started building the skill while the competitive advantage was still wide open. By the time everyone else catches up, they’ll have months of experience asking better questions, building better systems, and making faster decisions.

What You Can Do This Week

1. Export your last 60 days of ad data. Campaign reports, search term reports, and if you have it, product-level profitability. Upload it to Claude or ChatGPT and start asking the five questions above.

2. Track TACoS, not just ACoS. If your Total Advertising Cost of Sale is climbing, your business is becoming more ad-dependent — and that’s a fragile position in a year when costs are rising everywhere.

3. Identify your bottom 20%. The campaigns, keywords, and products that consume budget without contributing meaningful revenue. Cut or pause them and redirect that spend to what’s actually working.

4. Ask one question you’ve been avoiding. Every seller has it — the analysis that would take too long, the spreadsheet that never gets built, the number you suspect is bad but haven’t confirmed. That’s your first AI question. Ask it.

5. Build a Monday morning prompt. One saved question that summarizes your ad performance for the week. Run it every Monday. Refine it every week. That’s the beginning of a system — not just a one-time analysis.

FAQ

I don’t use Seller Labs. Can I still use AI for my ad analysis?

Absolutely. Export your campaign and search term reports from Seller Central as CSVs. Upload them to ChatGPT, Claude, or Gemini. Ask specific questions about your data. You won’t have the real-time connection that an MCP server provides, but you’ll still get answers that would take hours to find manually.

What’s the difference between ACoS and TACoS, and why does it matter?

ACoS measures how much you spend on ads relative to ad-attributed sales. TACoS measures how much you spend on ads relative to total sales (including organic). If your ACoS is flat but TACoS is climbing, it means your organic sales are declining — you’re becoming more dependent on paid traffic to maintain revenue.

Won’t AI give me wrong answers about my business?

It can, especially with vague questions. The fix is specificity. “How are my ads doing?” gets you a generic answer. “Show me my top 10 campaigns by spend last 30 days with ACoS above 35% and compare against product-level margins” gets you something actionable. And always ask: “What assumptions did you make?” That one question separates useful analysis from hallucinated noise.

What’s a good prompt to start analyzing my ad data with AI?

Try this. Upload your last 60 days of campaign and search term reports as CSVs to ChatGPT or Claude, then paste this prompt:

“Analyze my Amazon ad campaigns. For each campaign, show me: campaign name, total spend, total sales, ACoS, and total clicks with zero conversions. Sort by spend descending. Then flag any campaign where ACoS is above 35% and highlight search terms with more than 15 clicks and zero orders — those are my negative keyword candidates. Summarize the top 3 actions I should take this week to reduce wasted spend.”

That single prompt replaces hours of spreadsheet work. Save it, refine it each week, and you’ve just built the foundation of an AI-powered ad review system.

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The Full Story: When We Gave Sellers AI Access to Their Real Data

Everything above started with a real experiment. We connected Amazon sellers’ actual Seller Central data — sales, ads, profitability, inventory, all of it — to Claude AI through the Seller Labs MCP Server. We gave them access and watched what happened.

They all asked about the same thing. The full story — why advertising dominated every conversation, what sellers discovered when they finally saw their real numbers, and what the smartest sellers are building now — is right here:

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The Sellers Who Win in 2026 Won’t Outspend. They’ll Out-Ask.

Amazon sellers have more data than ever and less time to make sense of it. Costs are rising. Margins are thinning. And the sellers who are pulling ahead aren’t the ones working harder — they’re the ones asking better questions, faster.

AI doesn’t replace your judgment. It gives you the answers you need to use it.

Start with advertising. It’s the question every seller asks first — because it’s the one that matters most right now.

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Keep Reading

• Why Target Charges $5.99 for Shipping — And What Amazon Sellers Can Learn From It — A VA mistake revealed a $200K shipping insight. The same behavioral pricing framework Target, Best Buy, and Walmart use — applied to your Amazon business.
• Top 10 Strategies for Amazon Sellers in 2026 — The playbook for navigating rising fees, evolving algorithms, and increasing competition on Amazon this year.
• Amazon MCP Server: How Seller Labs + Claude AI Deliver Insights — How to connect your Amazon data to Claude and ask questions in plain English for instant, data-backed answers.

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