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.

Highlights

• 𝗧𝗵𝗲 𝘀𝘁𝘂𝗱𝘆: 100,000 shopping prompts across several categories, tested from Amazon’s homepage context, tracking which products Alexa for Shopping showed, how they were grouped, where they were positioned, and what the explanation said.
• 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝘀𝗵𝗲𝗹𝘃𝗲𝘀: A group of products organized around a particular need or reason to choose. One request like “budget laptops for school” splits into groups such as all-around MacBook, Windows laptops, two-in-one convertibles, and ultra budget picks.
• 𝗧𝘄𝗼-𝘁𝗵𝗶𝗿𝗱𝘀: Roughly two-thirds of products leading their own shelf were still below the first overall position.
• 𝗔 𝗵𝗮𝗿𝗱 𝗹𝗶𝗺𝗶𝘁 𝗼𝗻 𝗰𝗼𝗽𝘆: “Better copy can explain a product’s capabilities. It can’t give the product a capability it doesn’t have.”
• 𝗪𝗵𝗶𝗰𝗵 𝗴𝗿𝗼𝘂𝗽𝘀 𝗮𝗽𝗽𝗲𝗮𝗿 𝗳𝗶𝗿𝘀𝘁: Groups built around ratings, popularity, trust, and value showed up first more often than the baseline. Premium groups showed up first less often. These are presentation patterns, not ranking weights.
• 𝗧𝗵𝗲 𝘁𝗵𝗿𝗲𝗲 𝗹𝗲𝗻𝘀𝗲𝘀: Review every response for the comparison, the explanation, and the reason to choose.

Showing up in the answer is only half the job, and this one is about the other half. Enjoy the episode.

Timestamps:

01:10 The study: 100,000 Alexa for Shopping prompts
01:40 What the research can and can't tell you
02:00 Preview: the three takeaways
03:00 Takeaway 1: Competing inside a product group
03:30 Example: budget laptops for school
04:10 Semantic shelves explained
04:30 First in group vs. fifth overall
05:35 Takeaway 2: Persuasive, but is the requirement answered?
06:00 The machine-washable running shoe example
06:50 How to audit product info and shopping responses
08:00 Takeaway 3: Your role in the shopping decision
08:15 Which groups appear first (Chart 18)
09:30 Reviews, price, and the New Balance comparison
10:10 When fewer reviews win
11:00 What a brand team should do next
12:15 Visibility vs. sales
12:50 Walking through the interactive study

👉 Read the full study: https://alexaforshopping.refibuy.ai/

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This one is a hands-on, screen-shared deep dive. John has become one of the sharpest practitioners in AI-generated retail imagery, and in this episode he opens up his actual workflow (files, folders, skills, QA loops and all) and runs live demos against real Amazon listings.

Along the way we get into the question every brand is asking right now: do AI shopping assistants actually read the text on your product images, and what should you do about it?

Highlights

• 𝗧𝗵𝗲 𝟲𝟱-𝘆𝗲𝗮𝗿-𝗼𝗹𝗱-𝗼𝗻-𝗮𝗻-𝗶𝗣𝗵𝗼𝗻𝗲 𝘁𝗲𝘀𝘁: John optimizes every image stack as if the shopper is a 70-year-old man walking outside, squinting at Amazon on his phone. “If he can see it, and he can understand it, then everyone else can.”
• 𝗬𝗲𝘀, 𝘁𝗵𝗲 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁𝘀 𝗿𝗲𝗮𝗱 𝘆𝗼𝘂𝗿 𝗶𝗺𝗮𝗴𝗲𝘀: Open the PDP, ask Alexa+ something that only appears as a callout in an image, then ask it to show you where it got that. Sometimes it returns the image itself as proof.
• 𝗕𝘂𝘁 𝗵𝘂𝗺𝗮𝗻𝘀 𝘀𝘁𝗶𝗹𝗹 𝗰𝗼𝗺𝗲 𝗳𝗶𝗿𝘀𝘁: “Even though AI can read text on images, that’s a secondary use case. What about the primary one of the customer actually being able to see?” Nobody is telling Alexa to find and buy a DSLR camera strap unattended yet. It’s on its way. It isn’t here.
• 𝗜𝗺𝗮𝗴𝗲 𝗦𝘁𝗮𝗰𝗸 𝗥𝗼𝗹𝗹𝗼𝘂𝘁: A skill that locks four approved images as high-fidelity source of truth, then regenerates the entire stack for new flavors or variants changing only the color and flavor props, leaving badging, layout and copy structure untouched.
• 𝗧𝗵𝗲 𝗤𝗖 𝗹𝗼𝗼𝗽 𝗶𝘀 𝘁𝗵𝗲 𝘄𝗵𝗼𝗹𝗲 𝘃𝗮𝗹𝘂𝗲 𝗽𝗿𝗼𝗽: The skill analyzes each generated image against the original, catches its own hallucinations and regenerates. On the live demo it caught a glass that wasn’t tucked behind a circle (a detail both John and Andrew had missed).
• “𝗢𝘄𝗻 𝘁𝗵𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗶𝗻𝘀𝘁𝗲𝗮𝗱 𝗼𝗳 𝗿𝗲𝗻𝘁𝗶𝗻𝗴”: There are good third-party image tools and John endorses some of them. He still thinks brands should bring the process in-house.
• 𝗙𝗶𝗹𝗲𝘀 𝗮𝗻𝗱 𝗳𝗼𝗹𝗱𝗲𝗿𝘀 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗺𝗼𝗮𝘁, 𝗻𝗼𝘁 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹: John’s “life OS” is plain files and folders he points at whatever tool he’s using, sometimes three at once.
• 𝗔𝘂𝗴𝗺𝗲𝗻𝘁, 𝗱𝗼𝗻’𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲: For brands with in-house designers who are hostile to AI creative, have the designer build one image that is gospel, then use AI to scale it. “A designer shouldn’t be spending time and energy” making the same layout eleven times in eleven colors.

If you own creative for a brand on Amazon, watch this one instead of listening to it. Enjoy the conversation.

Timestamps:
00:48 Welcome to RetailPlaybook
01:05 Andrew introduces John Aspinall
02:20 How Andrew and John first met
03:15 Why John keeps switching models
05:30 The file-and-folder "life OS" as your source of truth
07:05 Local or cloud? John's Mac Mini setup
08:00 Backing up to GitHub at every session end
10:10 AI shopping and the image indexation debate
11:05 How to test whether Alexa+ reads text on your images
12:15 Which image in the stack actually matters
13:40 The 65-year-old-on-an-iPhone test
17:50 Live demo: the Image Stack Rollout skill
19:25 Why Codex: QA and checks built into the skill
21:15 The step before: Image Stack Creation
24:20 QC catches a hallucination and regenerates it
27:30 Set it running, go to lunch: 20 to 30 ASINs at a time
30:45 Hot sauce, Tabasco, and a full stack from one bottle shot
33:25 "Own the process instead of renting"
35:10 Main Image Lab: 60+ tactics, top 5 picked for you
36:20 Where to find John, and a free image stack for listeners

👉 Connect with John: https://www.linkedin.com/in/jaspinall/

🧠 Want to stay ahead in AI commerce? Subscribe and follow along:
📰 Subscribe to the free Substack: retailplaybook.ai
📺 Watch episodes on YouTube & Subscribe for updates: https://www.youtube.com/@Retailplaybooks