Own the Process Instead of Renting It: Velocity Sellers Creative Director John Aspinall

This is a research-and-playbook deep dive on AI-generated product imagery for Amazon. Andrew Bell sits down with Kamaljit Singh, who runs the creative agency AMZ One Step and the software listingoptimization.ai, to walk through how brands can use AI images without losing credibility. Kamaljit also screenshares his workflow, so you can see the strategy in practice.

This is a solo research-and-playbook deep dive. Andrew Bell walks through a study of 100,000 shopping prompts tested from Amazon’s homepage context, looking at which products Alexa for Shopping shows, how it groups them, where they land, and what the explanation says. He turns the findings into three takeaways and a starting plan any e-commerce or product content team can use.

This is a solo deep dive with Andrew Bell, walking through the newest layer of Amazon Agentic Commerce Optimization (ACO). Andrew introduces inference optimization, the discipline of engineering a product page so Alexa for Shopping can move from raw facts to a defensible conclusion about whether an ASIN actually fits a shopper’s mission. It builds directly on two earlier RetailPlaybook frameworks, Noun Phrase Optimization and Semantic Bridging. Highlights: 𝗧𝗵𝗲 𝗳𝗼𝗿𝗺𝗮𝗹 𝗱𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻: Inference optimization is the discipline of identifying the commercially important conclusions Alexa for Shopping may need to reach about a product, then reverse-engineering the factual, functional, contextual, evidentiary, and cross-surface pathways that make those conclusions defensible. 𝗧𝗵𝗲 𝗽𝗮𝘁𝗵𝘄𝗮𝘆: Product fact to functional consequence to customer benefit to context to desired outcome to mission-fit conclusion, with evidence and boundary conditions surrounding every step. 𝗣𝗿𝗲𝗺𝗶𝘀𝗲𝘀 𝘃𝘀. 𝗰𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻𝘀: A 9.8-inch table depth is a premise. “This fits your narrow entryway” is a conclusion, and nothing connects the two automatically. 𝗥𝗲𝗱𝘂𝗻𝗱𝗮𝗻𝗰𝘆 𝘃𝘀. 𝗰𝗼𝗻𝘃𝗲𝗿𝗴𝗲𝗻𝗰𝗲: “Perfect for apartments” repeated five times across the listing is one claim, not five. Verified dimensions, storage config, capacity, and cleaning specs addressing different parts of the mission, that’s convergence. 𝗧𝗵𝗲 𝘀𝗲𝘃𝗲𝗻 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗴𝗮𝗽𝘀: Factual, functional, benefit, context, evidence, contradiction, and distance gaps, the recurring failure patterns Andrew says show up on nearly every listing audit. “𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗱𝗶𝘀𝘁𝗮𝗻𝗰𝗲”: An operator construct (not a disclosed Amazon metric). The farther a conclusion sits from verified product truth, the more evidence it needs to earn it. 𝗪𝗼𝗿𝗸 𝗯𝗮𝗰𝗸𝘄𝗮𝗿𝗱: Alexa reasons forward (facts to mission fit). Andrew’s workflow has operators start from the desired conclusion and reverse-engineer back to which PDP surface has to carry each fact. 𝗙𝗶𝘅 𝗰𝗼𝗻𝘁𝗿𝗮𝗱𝗶𝗰𝘁𝗶𝗼𝗻𝘀 𝗳𝗶𝗿𝘀𝘁: If an attribute says 9.8”, an infographic says 11.2”, and a bullet says 10”, every downstream conclusion gets weaker. Fix the record before expanding copy. 𝗧𝗵𝗲 𝗽𝗮𝗴𝗲 𝗮𝘀 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝘀𝘂𝗿𝗳𝗮𝗰𝗲: Item Name (identity), Item Highlights (decisive facts plus close bridges), priority bullets, attributes, images, A+, video, reviews and Q&A: each surface has one distinct evidence job. 𝗧𝗵𝗲 𝗴𝗼𝗮𝗹: Ask what Alexa would need to believe in order to recommend your ASIN. Then ask what she would need to see in order to reasonably believe it. Work backward from there. This one’s a bit of a mental workout, but it’s the layer I think separates brands that just get found from brands that actually get recommended. Enjoy the episode! Timestamps:

This week's we dig into a recent Amazon science paper on whole-page optimization and what it means for brands. It's less founder-journey, more roll-up-your-sleeves strategy with how the search results page is quietly becoming personalized per shopper, the framework Amazon's ranker uses, and where advertising dollars should actually go. If you sell on Amazon (or Walmart, or Target), this one is tactical. Highlights 𝗧𝘄𝗼 𝘀𝗵𝗼𝗽𝗽𝗲𝗿𝘀, 𝘁𝘄𝗼 𝗦𝗘𝗥𝗣𝘀: Amazon’s whole-page-optimization research shows the same query can return distinctly different search pages depending on who’s searching. 𝗧𝗵𝗲 𝗻𝘂𝗺𝗯𝗲𝗿𝘀: The paper reported a 1.87% lift in brand relevance and a 0.05% revenue uplift. Small on paper, but “in Amazon terms, huge,” and a preview of what fuller personalization could unlock. 𝗧𝗵𝗲 𝟯 𝗖𝘀: Amazon’s page ranker leans on Context, Customer, and Content, the same three things Destaney says she’s been preaching for years. 𝗢𝗻𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁, 𝗺𝗮𝗻𝘆 𝘀𝗵𝗼𝗽𝗽𝗲𝗿𝘀: The same protein gets shown to a bodybuilder, an 80-year-old needing nutrients, and a mom on the go and Amazon has the inputs to tell them apart. “𝗥𝗢𝗔𝗦 𝗶𝘀 𝗻𝗼𝘁 𝗮 𝗯𝗶𝗹𝗹𝗯𝗼𝗮𝗿𝗱”: Ad spend directly influences BSR and organic rank. If you spend strategically, drive sales and reviews, and Amazon repositions you on the shelf. 𝟯𝟬% 𝗮𝗳𝘁𝗲𝗿 𝟮𝟰 𝗵𝗼𝘂𝗿𝘀: On one ~$30-AOV account, over 30% of sales happened more than a day after the click. 𝗦𝗽𝗼𝗻𝘀𝗼𝗿𝗲𝗱 𝗽𝗿𝗼𝗺𝗽𝘁𝘀: Lots of potential, thin results so far with one or two sales per campaign, not even live in half of Destaney’s accounts. But, users are being retrained by ChatGPT to search by prompt, and Amazon will follow. Destaney is exactly the kind of practitioner this show is built for: no guru talk, just hard-won reps across hundreds of categories. If you want to understand where Amazon’s search page is heading and what to do about it, start here. Timestamps: 00:22 Welcome to RetailPlaybook 00:40 Andrew's intro 01:11 Meet Destaney Wishon, CEO of BTR Media 01:23 From Bentonville: optimizing bids without software 02:26 Customized SERPs, explained 02:57 Inside Amazon's whole-page-optimization paper 04:32 The numbers: brand relevance and revenue uplift 04:54 From "the everything store" to the next advantage 06:26 One product, many shoppers: the protein example 07:11 Beyond ROAS: long-term sales 08:37 The 3 Cs: context, customer, content 13:05 The bumblebee problem 14:07 The risk: authenticity and killing discovery 17:11 The $200 birdhouse: audiences as bid modifiers 21:34 Why ROAS isn't a sufficient objective 24:16 30% of sales after 24 hours: rethinking day-parting 25:27 Sponsored prompts: hype or opportunity? 27:31 Move money upper funnel, drive branded search 28:16 Alexa for Shopping and the search bar merge 32:09 The future of Amazon in two buckets 33:39 Wrap-up

Episode 2 is a tactical deep dive into how brands stay visible on Amazon as AI reshapes search. Andrew sits down with Ritu to map how the old keyword world (A9) and the new conversational one (Alexa for Shopping) actually work together rather than replacing each other. It's part strategy, part myth-busting, and full of concrete moves you can apply to your listings this week. Highlights The dual flywheel: Ritu’s core framing, keyword search (A9) and conversational search (Alexa) are two flywheels you have to spin at the same time. “We’ve gotta spin both these flywheels simultaneously.” A9 isn’t going anywhere: “If there’s one thing Amazon cares more about than AI, it’s its bottom line.” They won’t torch years of proven search science overnight. Cosmo isn’t A9: Cosmo is one knowledge graph among many at Amazon, it adds semantics, inference, and personalization, but it did not “replace” A9. Different animals running in parallel. Three step-ups from A9: What Alexa does that keyword search can’t: semantics (”cozy blanket” = soft blanket), inference (”best shoes for mountain climbing” → hiking boots), and personalization (a gamer and a commuter searching “headphones” get different results). Noun phrase optimization: Ritu’s five-part recipe for a title that AI understands determiner, adjective (pre-modifier), noun adjunct, head noun, and prepositional phrase (post-modifier). “The ultra-lightweight, waterproof hiking boots with durable rubber soles” beats a comma-stuffed keyword string. The Prompts Report: There’s already a report in the advertising console showing which prompts triggered which campaigns, plus impressions, clicks, and attributed sales, though data is still sparse with a short look-back window. 66% and above the fold: More than 66% of sales happen on page one, and the majority above the fold, which is why organic alone won’t cut it and the organic-to-ads (OA) ratio matters. Page one is the gate: “You have to be on page one in A9 to even be considered in the pool” Alexa draws from and personalizes. “Ambient” and ubiquitous: Ritu’s prediction: Alexa for Shopping becomes ambient, reachable from every surface (phone, watch, product page, Lens Live), not just a search box. Ritu is one of the sharpest minds working in Amazon today, and this conversation is a masterclass in staying visible as search gets smarter. Enjoy the conversation. Timestamps: 00:30 Welcome to RetailPlaybook 00:49 Andrew introduces the show and guest Ritu Java 01:31 "We say yes to both", A9 and Rufus as a dual layer 02:29 Ritu's background: engineer, 17 years in Japan, Etsy 04:11 Data science school and discovering Amazon 05:14 Building PPC Ninja and the listings flywheel 07:12 The dual flywheel, explained 08:23 Two AI nerds: "fabling" before the show 08:40 A9 vs. Alexa, organic and ads on both 10:04 How ads pushed organic below the fold (66% on page one) 13:01 Getting comfortable with conversational search 14:33 The "shopping mission" and cognitive load 15:47 Search hasn't changed, it's gotten intelligent 17:35 The Prompts Report in the ad console 20:28 A9 history and the A10 myth 23:16 Cosmo: one knowledge graph among many 24:59 Why page one of A9 is the gate to Alexa 25:46 The Bumblebee problem 26:50 Where Alexa is headed: ambient and ubiquitous 28:56 Agentic ads and off-site ads 31:29 The dual flywheel diagram: keyword vs. conversational 32:55 Step-up 1: semantics 33:38 Step-up 2: inference 34:03 Step-up 3: personalization (gamer vs. commuter headphones) 35:33 Noun phrase optimization: the 5 parts 39:47 Item name vs. item highlights: does SEO power move? 42:39 Why hero image optimization matters more now 43:52 Wrap-up and where to follow Ritu