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/

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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

👉 Connect with Destaney Wishon: https://www.linkedin.com/in/destaney-wishon/
👉 Learn more about BTR Media: https://www.btrmedia.com/
👉 Check out the report: https://cdn.amazon.science/e8/5c/f3531e25435494a35483c62028a8/scipub-approval152129-40664328-design-and-evaluation-of-wholepage-experience-optimization-for-ecommerce-search.pdf

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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

👉 Connect with Ritu Java: https://www.linkedin.com/in/ritujava/
👉 Learn more about PPC Ninja: https://www.ppcninja.com/

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Welcome to RetailPlaybook, hosted by Andrew Bell and provided by ReFiBuy, equips brands with cutting-edge research, original playbooks, and future-proof strategies to optimize for agentic commerce on Amazon's Alexa for Shopping, Walmart's Sparky, Target Shopping Assistant and beyond.

If you’re interested in brand strategies for Agentic Commerce on Amazon, Walmart and beyond, you’re in the right spot.

RetailPlaybook is published with a sister podcast, Retailgentic, every other week

👉 Connect with Andrew Bell: https://www.linkedin.com/in/andrew-bell-540403275/
👉 Learn more about ReFiBuy: refibuy.ai

🧠 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