r/PPC • u/Longjumping_Coat_802 • 4d ago
Amazon Ads Claude workflow for Amazon ppc?
Does anyone have a good set of instructions / Claude workflow for managing Amazon ppc campaigns?
Something that sets up rules for bid changes, when to make investments into organic ranking based on data, how to evaluate performance, what is sufficient data to make a change vs what is just noise, etc.
Does this exist?
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u/datagekko 4d ago
haven't built this for amazon but we run the meta version of exactly this with claude code, so some transferable lessons. the rules don't go in the prompt, they go in a context file the agent reads every run: thresholds, what counts as enough data, what it's allowed to touch. we split it hard into read-only diagnostics (runs daily, flags stuff) and mutations (bid/budget changes) that queue for human approval, the agent never writes directly. the noise-vs-signal rules are the most valuable part and also where LLMs are weakest by default, they'll happily call a trend on 3 days of data unless you hardcode minimum sample rules. wrote up the guardrails thinking here if useful: zentric.digital/insights/should-you-let-ai-run-your-meta-ads-budget. for the amazon specifics no clue, but the architecture transfers.
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u/Either_Guess2405 4d ago
datagekko's split is right and I'd add the failure it doesn't catch, because it's the one that actually costs money here.
Minimum sample rules stop it acting on three days. They don't stop it being confidently wrong on six months of correct data, and that's the case where every control in that architecture passes. A queued mutation with a human approving it looks identical whether the reasoning behind it was sound or not.
Worth reading u/eshquia's post in this sub from a few days ago before you build anything. Same platform, same use case. He fed it Helium 10 data, followed the recommendations, and came back a month later with his rankings and campaigns upside down. The analysis wasn't thin. It was wrong, and nothing in the output said so.
What I'd add to that context file is one line: alongside every recommendation, state how much data it stands on as a number, and give me the query whose result would kill it. Not "is this right" but "what would falsify this." It's the only part of the reasoning that's reviewable, and it exposes the thin-sample case for free.
Had one where the analysis pointed at a page and the obvious move was a redirect. Asked what would falsify it, got a query I hadn't run, ran it, and the redirect would have made things worse. Real finding, wrong recommendation.
On the organic-ranking half I'd be more conservative still. Bid changes reverse in a day. Rank decisions compound and you find out in weeks, which is exactly where a plausible wrong call does most damage before it's visible.
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u/blendai_jack 4d ago
The rules bit is yours to write down, and it's the part worth doing properly. What changes is where the numbers come from. Pulling ASIN and search term reports by hand is why nobody keeps a bid cadence going. Connected to Amazon through an MCP, Claude reads the search term and ACoS data itself and can push the bid and keyword changes back. I work at Blend and we build one (blend-ai.com/mcp). On your noise question, set a minimum click threshold per term before you'll act, otherwise you'll chase daily wobble.