Amazon Big Deal Days: Benchmarks & prompt guide
See how Amazon performed across various event days, then run 25 prompts built for capturing more revenue this Big Deals Day
Prime Big Deal Days runs October 6 to 7, and the traffic it creates reaches well beyond Amazon. To help you plan, we pulled cross-channel benchmarks from the last three Amazon events: Prime Day 2025, Big Deal Days 2025, and Prime Day 2026. You'll see how Amazon stacked up against Meta, Google, TikTok, and AppLovin on efficiency and conversion, and how sharply ad spend climbs once the deals go live. It's a clear picture of what good looks like, so you can judge your own results in context.
We also reviewed hundreds of anonymized Moby conversations from Amazon sellers during those same events to learn what they needed most. Sellers spent the events separating Amazon from Shopify, reconciling gross and net sales, and figuring out which products actually made money. We turned those questions into 25 refined prompts you can run against your own account this Big Deal Days, covering everything from live pacing and forecasting to discount decisions and executive recaps.
What’s inside this comprehensive guide:
Benchmarks from the last three Amazon events:
Compare median ROAS, CPA, CPM, CTR, and conversion rate across Amazon, Meta, Google, TikTok, and AppLovin for Prime Day 2025, Big Deal Days 2025, and Prime Day 2026.
How Amazon ad spend moves during event days:
See how much average daily spend climbed during each event and which days carried the peak.
What sellers asked Moby when it mattered most:
Learn which questions dominated each event, from data lookups and reconciliation to a sharp rise in requests for recommendations last October.
25 prompts ready for Big Deal Days 2026:
Run prompts for pacing, forecasting, SKU profitability, Amazon versus Shopify reporting, discount modeling, ad diagnostics, and executive recaps.
Prompts built to give you trustworthy answers:
Use prompts that ask for clear metric definitions, flag missing data, and separate correlation from causation so you act on evidence, not assumptions.
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