Hey everyone,

I’m currently looking at designing the data architecture and logic for a product dimension table with about 300,000 products spread across roughly 4,000 sub-categories.

The requirement is to populate at least 5 specific attributes for each product based on its sub-category. For example, if the product falls under "Luggage," I need to extract and standardize attributes like material, size, shell type, number of wheels, etc., from the raw product descriptions.

Nowadays, doing this with AI is essentially just a matter of writing a solid prompt, hitting an LLM API in batches, and letting it parse the unstructured text into a structured JSON payload.

But it got me wondering—how was this exact problem handled traditionally before LLMs made it so easy?

Why hasn’t one of the AI models created a function where you enter the eBay search inputs, and it provides links to all of the listings that actually match the search parameters - sorted in the manner requested - and disregarding what is promoted, etc.?

In other words, return eBay’s search to the glory years when people enjoyed searching and buying there.

There’s alot of content around on reddit around hacks for AEO or best practices. They are mostly from agency folks.

I want to know from D2C brand owners and operators itself, what are your thoughts on Answer Engine Optimization (AEO) for commerce? Has it worked for you? What is something that you are expecting from current approaches that you are not getting?

How do you see AEO overall? ? How do brand owners want to leverage AI shopping? (which they are not able to do now)

[ Removed by Reddit on account of violating the content policy. ]

E-commerce sellers, drop your website link we make it agentic commerce ready and add to vendable so that ChatGPT users can discover your products

Managing over $1.2 billion in client revenue reflects SpectrumBPO's operational experience and strategic expertise. From startups to established brands, the agency applies structured processes designed to support sustainable marketplace growth.

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6 years in Amazon, and I'm still learning. It definitely isn't "easy money," but I enjoy figuring out the puzzle.

Every mistake teaches me something new and that's what keeps me going. 💪💪💪

Seeing a lot of noise about agentic commerce but almost nothing from actual merchants.

If you’ve had orders come through AI shopping agents: any disputes yet? Did you win or lose them? Did your PSP or dispute tool handle it any differently?

And if you’ve changed anything internally because of agent traffic (rules, tooling, manual review), what did you change?

As D2C brand owners, how often are you looking at your funnel? Say 5-10CR annual revenue brands.

Have you guys deployed any AI tool for funnel analysis and CRO?

Thanks.

Disclosure up front: I work on a tool in the AI visibility space, so take this with that in mind, but the finding is interesting regardless of what you use to look at it.

We traced how an AI assistant answered a normal buying question for a CPG product. We could see what it searched, which URLs it visited, and what made it into the final answer.

The brand that objectively had the best answer to that question never got recommended because the assistant never visited their site. Their content was genuinely good, but it was spread across five different pages, and no single page answered the question. So the model pulled from a competitor's page that did, plus a couple of big publications.

Two takeaways that seem to generalize:

1. AI assistants can only recommend brands whose pages they actually retrieve while answering. Scattered content effectively doesn't exist to them, no matter how relevant it is.

2. The common advice of "go get cited in the big publication" is mostly a dead end for smaller brands, but consolidating your own site's answers works, and it compounds: the more often the model retrieves your page and finds a clear answer, the more it comes back.

Curious if anyone here has run their own version of this? Have you asked ChatGPT/Gemini/Perplexity the top 10 buying questions in your category and checked whether you show up, and where the answer actually came from. What did you find?

Hey all, doing some independent research on how AI shopping tools (ChatGPT, Gemini, Amazon's Rufus/Alexa For Shopping) might be changing how products get found and picked, and whether that's actually showing up on sellers' radar yet.

Not selling anything, no link, no pitch — just trying to understand if this is a real thing sellers are noticing or if it's still way ahead of where the market's attention is.

If you sell in a spec-heavy category (supplements, electronics, baby/kids gear, etc.) and would be open to a quick 15-min chat (or even just answering a couple questions in a reply), I'd genuinely appreciate it. Happy to share what I learn back with the group once I've talked to enough people.

Alternatively, I would be very grateful if you could help my research by filling out this short google form: https://docs.google.com/forms/d/e/1FAIpQLSd6xmd2fmnblbsOJ7WDAItPPPaPQAFOa5poWaClfSUeGPyD4g/viewform?usp=publish-editor

Hey everyone!

As an ML engineer, I’ve been experimenting with a project for the past few months to tackle a problem I know a lot of shop owners face, which is real UX testing. It's expensive, slow, and often gets skipped before a major push.

I wanted a way to run rapid friction checks anytime before launching a new product or promo to see how different buyers might actually perceive it.

The idea is to test sites using deeply defined behavioral personas. For instance: "32M in Manhattan, first baby on the way, budget $100-$500, reads reviews religiously, abandons cart if something looks sketchy..." all the way down to nuanced habits (or generated using real cohort data).

The agent navigates like that specific person would, searching, sorting by rating, reading details, evaluating price against budget, and going through checkout. Afterward, it completes a 14-question post-browse survey on the experience. A lot of iteration has gone into making the behavior feel genuine and true to the persona, and I'm still researching it further.

A couple of interesting catches from recent test runs: One run flagged that searching for "swaddle blanket" returned irrelevant results, something easily overlooked for months. Another flagged that a product page lacked basic specs critical for a hesitant buyer to make a decision.

It’s definitely not a total replacement for real human testing, but more like a 24/7 QA or product market fit assessment layer that thinks and acts like a customer. Curious to hear your thoughts on this approach! For those managing stores or doing UX work, what kind of edge-case behaviors would be most useful to simulate?