Use Cases for Amazon's New Shopping Data Tools

Amazon is rewriting the baseline for how sellers compete: measure sharper, fulfill smarter, automate faster. That was part of the underlying messages I saw in Seattle. Accelerate’s opening sessions made clear that the focus is shifting: from chasing single-metric wins to mastering full-funnel measurement, hybrid fulfillment, and automation as core disciplines. TL;DR from Amazon Accelerate Advertising is moving past RoAS into full-funnel measurement Seller Central is surfacing sharper customer behavior data APIs are becoming essential infrastructure for operations ✅ Advertising measurement is being rebuilt around the full funnel. For years, last-touch attribution was the default: measure clicks, calculate RoAS, and double down. New tools like Conversion Path Reporting and Multi-Touch Attribution now expose the combinations of ads that lead to a purchase and the value of brand interactions long before the final click. Amazon shared cases where optimizing to Multi-Touch Attribution delivered a 62–64% sales lift. That kind of data forces sellers to think differently about budget allocation. ✅ Seller Central and Brand Analytics are surfacing sharper insights. Two features stood out. ASIN-level shopping journeys show where products sit in the funnel (awareness, consideration, intent, purchase), making it easier to spot leaks. Keyword-level impression trends highlight visibility shifts before conversion drops. Together, they give operators a more direct view of how catalog performance maps to customer behavior. ✅ APIs are becoming the default operating system. From A+ Content APIs that streamline updates across hundreds of ASINs, to Multi-Location Inventory APIs that make FBM delivery promises more accurate, Amazon positioned SP-API as non-optional. Enterprise sellers using these tools report saving 10–15 hours a week and reacting to market shifts up to 90% faster. The subtext: manual workflows won’t scale in the environment Amazon is creating. Also, AI is here to help us. I feel that these changes show Amazon’s deeper intent to reward sellers who can act with precision across advertising, catalog, and fulfillment simultaneously. The risk? Sellers who treat these updates as “nice to have” add-ons will fall behind 100%  The opportunity? Those who build automation and measurement into their workflows gain both speed and resilience. If you are a brand, ask how your ad reporting links to customer retention metrics. If you are an operator, test hybrid fulfillment strategies before Q4 to see where they can hedge risk. If you are a leader, invest now in SP-API integration so your team isn’t stuck in manual workflows when the stakes rise. The broader lesson is simple: in Amazon’s ecosystem, optionality and automation are the foundation for staying visible and profitable. #AmazonSellers #EcommerceStrategy #FBA #AmazonAccelerate

Search Query Performance Report is the most powerful Amazon tool you've never heard of. When I ask DTC brands if they've looked into SQP reports for their brand, the answer is always the same: "What's that?" After managing $200M+ on Amazon for brands like Ridge Wallets and HexClad, I can tell you this is the biggest missed opportunity. Search Query Performance Report literally gives you ALL the data you'd ever dream of: • What search queries lead to conversions for your brand • How your brand compares to the market on EVERY keyword • Your click-through rate vs. market average • Your conversion rate vs. market average • Sales volume for each keyword It's like lifting up the hood and seeing the engine of your brand. Most DTC brands are shocked to discover 80-90% of their purchases come from branded searches. But the real gold is finding those non-branded keywords where you're converting better than the market. For example, when we found "frying pan" was a top search term for HexClad, we: - Optimized the title (most important keyword at the beginning) - Called out key features in the image stack - Adjusted our PPC strategy to target it more aggressively - This data is so valuable that we built our own custom dashboard that scrapes - SQP weekly to track trends over time. The dashboard makes this complex data easy to read and actionable. Amazon isn't like Shopify. It's not about creating awareness — it's about capturing high-intent buyers who are already searching. SQP reports show you exactly where those buyers are and how well you're converting them. Are you using this data to drive your Amazon strategy?

Amazon just gave brands the clearest signal yet about what matters next. On the Q3 earnings call last week , Andy Jassy said: “Rufus, our AI-powered shopping assistant, has had 250 million active customers this year… Customers using Rufus are 60% more likely to complete a purchase. Rufus is on track to deliver over $10 billion in incremental annualized sales.” That’s $10 billion in AI-driven shopping behavior happening inside Amazon. As a brand, how do you get “Rufus-ready” — optimizing for conversational discovery, natural-language content, and AI-era buying behavior. Here’s the 5-step to-do list you can start acting on right now 👇: ✅ 1. Audit PDPs for conversational discovery Make sure your listings answer shopper-style questions like “Which is best for me?” and “Does it work for ___?” ✅ 2. Scale authentic UGC & reviews Feed Rufus what it loves — real human voice, use-case content, and side-by-side comparisons. ✅ 3. Leverage natural-language private feedback Use real consumer comments (not marketing copy) to rewrite PDPs in their words. Rufus — and shoppers — reward authenticity. ✅ 4. Map the intent-chain & pre-media momentum Build content for every stage (research → compare → decide) and seed early before ads run. ✅ 5. Engineer “trigger phrases” into content Weave shopper-style questions (“Is this better than…?”) into reviews and FAQs — it’s the new AI SEO. 💡 Bonus: Track new AI KPIs — how often your content mirrors real shopper language, and how conversion changes when it does. Brands that adapt to Rufus now will have an early edge the next era of eCommerce. If you’d like the full report — including templates for the AI-Discovery Scorecard, Intent-Chain Grid, and Natural-Language Feedback Framework — drop a note in the comments or DM me “Andy Jassy Rufus Report.”