#680 – AI Toolkit for Amazon Sellers

Andrew Bell, a standout figure in the realm of AI and custom GPTs, kicks off our very first installment of AIM, our Monthly AI workshop on all things artificial intelligence. In this episode, Andrew opens up about his journey from the Moody Bible Institute to becoming an e-commerce maestro on platforms like Amazon. Growing up in the quirky town of Santa Claus, Indiana, and now living in Kentucky, Andrew captivates with his unique perspective, connecting the dots between local sports rivalries and the debates surrounding artificial intelligence. His pioneering work with Touch of Class and his learnings from the Freedom Ticket course by Helium 10 laid the groundwork for his success, making him a voice worth listening to in the fast-paced world of e-commerce.

As we engage with Andrew, we discover the fascinating world of AI-driven tools designed for Amazon listing optimization and product research. With over 350 custom GPTs under his belt, Andrew discusses the art of prompting and how robust strategies can mitigate errors. The conversation takes a turn into the challenges faced when AI relies on outdated data, such as the myths surrounding the Amazon A10 algorithm. By blending AI with reliable resources like Helium 10’s data, the episode reveals how sellers can enhance the accuracy and trust of their listings, amidst a landscape of evolving AI capabilities.

Rounding out the episode, Andrew shares strategic insights for maximizing AI in product research and listing optimization. From the intricacies of using Amazon’s generative AI-powered shopping assistant Rufus to the innovative application of AI reasoning models, Andrew highlights ways to maintain accurate and engaging Amazon listings. We explore the benefits of visual label tagging and new features for price alerts, all aimed at boosting conversion rates during key sales events. Don’t miss Andrew’s reflections on the continued evolution of AI technology and its growing impact on both personal and professional realms.

For those eager to try out Andrew Bell’s custom GPTs firsthand, listeners of the Serious Sellers Podcast get exclusive access. While many of his 350+ custom GPTs are still gated for private workshops and Helium 10 events, Andrew makes a few powerful ones available for our audience. Go to https://h10.me/andrewbellgpt to access a curated selection, including GPTs for product research, demand validation, e-commerce script agents, and even one cleverly titled NoMDashes, designed to remove those pesky AI giveaway dashes from your copy. These GPTs aren’t available to the general public yet, so this is your chance to explore cutting-edge AI tools before they’re widely released. Just one more perk of tuning in.

In episode 680 of the Serious Sellers Podcast, Bradley and Andrew discuss:

• 01:00 – Andrew Bell’s Backstory
• 04:55 – Success in Amazon Seller Optimization
• 09:03 – Pricing Strategies Across Different Niches
• 11:57 – Leveraging AI for Amazon Listing Optimization
• 14:35 – Trust in AI and Misconceptions
• 17:29 – Keyword Generation in ChatGPT
• 20:41 – Maximizing AI for Product Research
• 22:36 – The Future of Data Augmentation
• 26:29 – Leveraging AI for Comprehensive Product Research
• 34:07 – E-commerce Sellers Optimizing for AI in 2025
• 34:19 – Amazon Listing Optimization Strategies
• 37:51 – Visual and Textual Content for Conversions
• 40:39 – Click Training Data and Customer Questions
• 46:04 – Advancements in Reasoning Models and AI
• 47:00 – Amazon Rufus and AI Reasoning Models

Transcript

Bradley Sutton:

Today we’ve got one of the top minds in the whole world when it comes to ChatGPT, custom GPTs, Rufus and more, and on this show he’s even going to give a special link of some of the private Amazon custom GPTs that he has exclusively for our Serious Sellers Podcast audience. How cool is that? Pretty cool, I think. Hello, everybody, and welcome to another episode of the Serious Sellers Podcast by Helium 10. I’m your host, Bradley Sutton, and this is the show that’s completely BS-free, unscripted and unrehearsed organic conversation about serious strategies for serious sellers of any level in the e-commerce world and for the first time in our podcast. He’s been on the AM/PM podcast before, he’s been on webinars with us before, but the first time on the Serious Sellers Podcast we’ve got Andrew Bell coming live and direct. From where were you even at right now?

Andrew:

Owensboro, Kentucky.

Bradley Sutton:

From Kentucky, live and direct from Kentucky. That’s right. We had some running jokes in our Elite workshop. There were some strange like keywords that you had never heard of, and then your go-to excuse was you’re from Kentucky, but I think you’re selling the, wait, Kentucky is, like you know, there’s the blankety blank state. There’s the blank state, Kentucky bluegrass state, right.

Andrew:

 That’s right.

Bradley Sutton:

Yeah, so you’re selling the bluegrass, uh, your bluegrass compatriots. A little short there, yeah, but anyways, welcome. Welcome to the show. You have been in the e-commerce world for a little while, but let’s go before e-commerce. We talked a little bit about this on the webinar, but people on the podcast might be the first time listening to you. Were you born and raised there in Kentucky?

Andrew:

Just across the river. So yeah, basically I was actually from a small town called Santa Claus Indiana, which is real, it’s not fake, it’s a Christmas-themed town. Live on Blue Spruce Drive. There’s a theme park called Holiday World, but Owensboro is just about 30 minutes away, so just across the river.

Bradley Sutton:

Now I know sports, that direction is very popular, so is everybody like Kentucky Wildcats fan out there.

Andrew:

Yeah, Kentucky fans, and my wife’s brother runs at Louisville. He does like the 800 meter and stuff, and so I have a Louisville like a plate on the back of my car and I’m pretty much Louisville

Bradley Sutton:

Cardinals right, Is it Cardinals?

Andrew:

Yeah, that’s right, Louisville Cardinals, and let’s just say I’m not very welcome in Owensboro for that.

Bradley Sutton:

Yeah, in certain places, you know people understand this kind of thing in the UK for, like soccer teams, where you know if you go to a neighborhood that’s all Manchester City, you better not be bringing your Manchester United stuff there. And then in Alabama, if you’re, like you know, wearing your auburn, you know jerseys, like you could get beat up in some Alabama, like you know hot beds, and so I think here in California people don’t care too much. There’s a lot of sports teams out here, but we have some rivalries like Padres versus Dodgers and things like that, and we’re actually gonna be talking about rivalries like controversy about what is happening with AI and stuff. So this is kind of goes to it. But anyways, back to your backstory. You did not go to neither Louisville nor Kentucky for university. Where did you go?

Andrew:

Went to Moody Bible Institute, which is a Bible college, a place strictly for, they don’t even have business majors there, it’s strictly for preaching, missionaries and then I, of course, studied ancient Greek and preaching.

Bradley Sutton:

But of course you did.

Andrew:

Yes.

Bradley Sutton:

All right. So was it four years?

Andrew:

Yeah, four-year degrees. It’s actually called biblical languages, so it’s a little bit of Hebrew, but mostly ancient Greek. I, even to this day, I’ll read in my Greek New Testament for my devotionals too, so I like keeping up with it. Think of it as like my Sudoku for the day.

Bradley Sutton:

All right. So how in the world do you go from majoring in Ancient Greek and Biblical Studies to E-commerce? Fill in the gap there.

Andrew:

Oh, yeah, obviously, it’s like, yeah, no, it’s a natural transition, right?

Bradley Sutton:

Yeah, of course, natural segue.

Andrew:

Yeah, exactly. But no, basically what I did is I had come home from school, graduation and such, and met my wife and pretty awesome, started working for a church, and then I decided I don’t know if I want to do this anymore. I think I’m going to go into e-commerce world. So there was this company at the time called Touch of Class that wasn’t really doing Amazon and I thought, oh man, this is a goldmine for products to go up on there and to sell, and they’re very niche, they’re always the highest priced items, but I believe there was a lot of, like you know, demand for it as well. And so that’s when I enrolled in this is not a promotion, necessarily, but enrolled in Freedom Ticket. So you were my first teacher, so that’s. Yeah, you and Kevin, yeah, yeah, that’s right.

Bradley Sutton:

Well, I’m wearing. We used to have an old Freedom Ticket. We had the original Project X and I’m wearing a hat that has an X on it. That’s why I’m wearing it. This is not just a Mighty Ducks hat. I get hats sometimes if it has tie-ins to Helium 10, and Project X, as many people know, was in the original Freedom Ticket. A lot of people learned about the coffin shelf from those videos.

Andrew:

To this day, you still give that example. I think that says a lot.

Bradley Sutton:

It’s still selling on Amazon five years, five, six years later. Still active, still the number one selling, or it wasn’t for a time, but now it’s back to the number one selling coffin shelf on Amazon. So this was a company that had a large catalog of products. They just weren’t really optimized or weren’t even selling really well on Amazon. And so you came in and what were you able to do for them?

Andrew:

I was basically able to take all their ASINs so about 4,000 ASINs and I basically started everything from scratch. I’m talking pick packing, shipping, doing FBA, to learning everything on Seller Central, to building a brand store that eventually made a million dollars. Out of the 7 million we made on Amazon annually. Within four years, I was able to get us there and so, yeah it, it took. You know one of the one of the things too I guess it leads into this as well, as you know Amazon listing optimization. Once you know, I went through helium 10 and did all the training. I was the guy that was doing Cerebro, you know, 30 times on every product and that that’s just probably a minimum of what I would do, right.

Bradley Sutton:

That’s a lot of products. Like, yeah, I’m assuming you’re doing keyword research on all these products, you’re editing, you’re making the listing, maybe commissioning some other graphics. Is this a project that went on for like a couple of years, or how long did it take you to do all that?

Andrew:

Oh yeah, 40, 50 hours a week just spent on things like that with listing optimization. But nothing was more fun than that. I would just sit back and go to a Starbucks and just literally do optimization and just have a lot of fun. One day I remember I was doing Tiffany style table lamps and someone was asking what I was doing. I was like, oh, I’m doing Tiffany style table lamps and I was kind of showing what was going on. By the end of that day I had 28 Tiffany style table lamps done for optimizations and I kind of scaled the strategy for myself where I would use Magnet, use Cerebro, built keyword lists for every single product.

Andrew:

So I had a unique keyword list for every product. Of course I would reuse general things like metal, wall art, let’s say, and wall decor. And then if I was doing accent tables even though I had a vintage accent table, I would still use the accent table list. So I had varying degrees of lists as well that I would apply. And this is back when I would just use Scribbles. This is before Listing Builder. If you remember Scribbles, I love using Scribbles. Scribbles could show you what keywords were used and what weren’t. It doesn’t have quite the same sophistication as Listing Builder, but nonetheless I really like that.

Bradley Sutton:

It’s an oldie, but goodie.

Andrew:

Yeah, it is. It’s tried and true. That’s right.

Bradley Sutton:

Now where these Amazon lists like these products, all 4,000 of these, did they exist on Amazon, but they were just like thrown up there or they weren’t even on Amazon at all. They were just on the company’s website.

Andrew:

Yeah, they weren’t necessarily on Amazon.

Bradley Sutton:

Well, so you were basically doing it from scratch. It was like somebody who makes a brand new product. It was almost like you were doing 4,000 product launches then, as it were.

Andrew:

Yeah, absolutely yeah. So if, like, we had a ton of Tiffany style table lamps, those are the ones that I would optimize. And that was kind of like a one man, you know team, and of course I had departments at my disposal as well, because this was a very seasoned company as well, you know, and everything we did was our exclusive. So we had no competition whatsoever in the Buy Box. So we had to compete with other products on price and it came down, for me it came down to like we have to show up over those people, because we have no other option, because they’re going to go with the other product because much higher priced.

Bradley Sutton:

So it had to be competing products or distributors of the actual brand product that were on your piggyback and on your listing.

Andrew:

No, no, no, so, no. So we did not have to be on Buy Box. We had a hundred percent Buy Box because they’re exclusives, only ones in our listings, right, I’m talking more people within our niche competitors that we had. So per category, I would track about 10 to 15 different competitors. So, for example, with metal wall art, we have like the largest collection metal wall art possibly on the planet. I know we do on Amazon for sure and everything is handcrafted, steel, all that stuff and all sorts of different themes, and so I tracked all the competitors related to that and we were always the highest price item in there. But one thing I noticed is our keyword research was always better and that made us stand out over others and that’s why we still sold.

Bradley Sutton:

Yeah, I mean, sometimes people are like, oh no, I don’t want to get into this niche, everybody’s so low price. Well, it’s not always a matter of playing the price game. You know there’s some niches where you actually play the price game. Or playing the price game means opposite of what you would think it does. It means, like you should be the highest price. You know, like people in the baby category there’s a big niche for, like, there’s parents out there like, no, I want the best for my newborn, I’m going to go buy some cheap. You know, I don’t want to be that parent that buys the $8 stroller. You know, give me that $80 one, you know.

Bradley Sutton:

So it’s not always about Amazon success. It’s not always about fighting, like you know, Chinese sellers, as some people think, or sellers from factories in India or wherever, where we’re. You know they’re barely breaking even. Oh, you don’t have to play that, you don’t have to play that game. Okay, interesting. So I mean, I think another takeaway is for all you listeners out there maybe you guys have been long-term listeners of the podcast and, for whatever reason, you haven’t started selling on Amazon yourself. You know, Andrew’s path is very similar to my path. I actually, before I started selling on Amazon, I was a consultant for others. So if you take in a lot of knowledge even without selling your own product, that doesn’t mean the only application of what you guys have learned in Freedom Ticket or this podcast or other places can only be applied to your own brand. Become a consultant. You know, like Andrew made a career out of helping other companies and even getting employment, for you know putting it into practice without ever having sold his own product. So that’s a very, very viable path.

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Bradley Sutton:

Now, after being all in the game, as it were, at what point did you start getting obsessed with all things AI?

Andrew:

Well, as soon as my wife actually introduced me to ChatGPT and she had been talking about it she’s a content creator, this would have been November 2022 when it launched and she was like right on it. And I’m like, oh my gosh, it seems like she’s always ahead of me teaching me things. But yeah, basically the message limits just went up and up and up and I just kept hitting those limits every single time. Once new models and such came out. What ChatGPT 4 costs back 2023, I believe April when it came out is so cheap. Now, in fact, I don’t think they even have it. They’ve retired regular ChatGPT 4, at least when you go and you have the regular account and such, and they have ChatGPT 4.0 instead. So I definitely got interested in doing that, and so then that led me to think, oh my gosh, how can I apply this to my job? How can I scale already a robust strategy that I have for listing optimization with keyword research, marry the two together and create a prompting philosophy that would back all of this? And this was well before.

Bradley Sutton:

You’re probably wishing you had it before you started those 4,000 listings.

Andrew:

But because of that I was able to develop a really good prompting philosophy. I was able to use those SOPs and such and put them into a type of logic that would scale listings for other sellers in a way that was both keyword rich but also narrative style driven, so built to convert as well. And that’s the success that I had saw before. And so I did that, married also, too, with best practices. And this was well before GPTs came out. So then I decided you know, I’m going to, you know, make a GPT out of this a year later, and that’s you know. By then I’d already, like, fine-tuned all the prompts across different models. So when GPTs came out, the GPT store, it made perfect sense for me to turn everything into GPTs. And before you know it, and to this day, I have about 350 GPTs I actually counted today around 350 and 30 of which are Amazon related, and now I have the top.

Bradley Sutton:

Only 30 out of 300 are Amazon related.

Andrew:

Yeah, only well, explicitly Amazon. Yeah, all the others are like for fun stuff. Like, for example, I think I told you about the poetry GPT.

Bradley Sutton:

I know about the poetry cause you got the number one poetry, custom GPT and right.

Andrew:

Yeah, yeah it’s actually funny too. I actually have the number one, you wouldn’t think this, number one pregnancy GPT too. You’re thinking how well, when my wife-

Bradley Sutton:

What does it do?

Andrew:

Well, I had twin girls and so I coordinated when I was, you know we and so we had to go into the office frequently for her to get like looked at and stuff even more that I was there. I really made use of that, cause when you have OBGYNs in like more critical care unit type stuff, you get, I think, like the best ones. So I was able to use that, put them into like a really good knowledge base, like use what they had and put it in a really good knowledge base and create a really good GPT, which is actually my highest rated GPT 4.8 out of five stars, of 5 stars for 200 ratings and reviews. So it’s backed up by a significant amount of data, which is cool. So I did that. I had a passion, kind of doing it for everything.

Bradley Sutton:

Well, while we’re on this topic, there are some people who might have misconceptions about AI, or they’re new to it. They don’t know how it works, and they might have some initial negative reactions, such as like, oh, I tried it for the first time and I asked it about me and it like made up some stuff. Or maybe somebody has seen, you know, asking ChatGPT about Amazon SEO and it’s making up nonsense that literally doesn’t exist, like the Amazon A10 algorithm and stuff like that. So, like, how can I trust AI and stuff like this is my Amazon listings? How in the world can I trust my business to this thing that makes mistakes?

Bradley Sutton:

And so, first of all, we’re going to talk a little bit. You’re an expert on how to use AI in a way that lessens the number of like, mistakes, hallucinations, whatever you want to call it. But, in the first place, why do things like that happen? Like, why is Amazon? Or why is Amazon, why is ChatGPT talking about an A10 algorithm that literally doesn’t exist? I know the answer to this, but there might be people that are out there that don’t know. So, you can probably explain it better than me.

Andrew:

It’s because when the AI is trained over time, it’s trained on the articles that have been written at the time. And A10 was a way to use to boost themselves in the search ranking, because everybody was searching for it, because it sounds really good. Like, oh, the next model we’re going to type in A10. And so tons of articles started coming out. Well, by the time recently when that happened, it was already trained on all those articles that said A10. So AI really thinks that A10 is like the most important thing and even when you search for it, it’ll actually default to that until you press it. You press it and say, hey, I don’t think that’s correct, and then it’ll say, well, I think it is. I’m like you need to search a little deeper and then finally it corrects itself. With that, so and that.

Andrew:

But that has a lot to do with like just having bad data to like bad background information and kind of banking off like the hallucinations existed before. But like with keyword with the prompting philosophy I had that I developed because it was already so robust and really good, I was able to then, like you said, reduce the number of hallucinations that exist. But when I was using Helium 10, that gave me the trust that I needed to go forward and create the best listings possible. So what you shouldn’t do when you’re looking at my GPTs is say I don’t need Helium 10. No, if anything, you need them more than ever, right?

Bradley Sutton:

Well, let’s talk about that a little bit, not just about Helium 10. But if somebody just use your free and by the way, everything that Andrew is talking about today poetry, GPTts, I guess, the pregnancy one, all this stuff is free, he does this for fun, kind of, and out of kindness of his heart. But if you went and found one of Andrew’s 30 Amazon GPTs whether it’s listing, optimization or whatever and don’t connect it to anything, it’s not terrible. It’s going to give some decent information. It can write a decent listing, but why wouldn’t it be fully optimized? First of all, Helium 10’s database is not open to the world, let alone GPT or OpenAI. So what is ChatGPT basing a listing on? Because it’s obviously going to be based on keywords, no matter what keywords make up a listing, since it’s not connected to Helium 10, and if somebody doesn’t put the right keywords in, where is it even getting the keywords? Is it like Google? Is it just like common sense or what?

Andrew:

Yeah, I think it’s a mixture of both. If you don’t have it, use the tool of search. Typically it’ll just kind of, like you said, make it up on its own. You can tell when it’s like kind of bull crapping. You guys are like, no, that’s not true, but who’s you know if you don’t. You search.

Andrew:

It’s giving you, hey, top 10 keywords for Google and then it uses ones from Google’s top 10, from Walmart, bringing Walmart oh, these are the top 10 from Amazon, but it’s not even in your category. So it’s like it doesn’t have enough intelligence to know that. Because when it’s writing it doesn’t have your proprietary data, for example, or Amazon proprietary data to back it up. So it has to rely on something else. And because the number one policy really for ChatGPT, for OpenAI, is to assist the user and give something useful to the user, no matter what, it’ll go behind your back and make you think very confidently that these are the keywords that are going to help you show up. Yeah.

Bradley Sutton:

So, again, I’m not going to try to BS people out there, but unless Helium 10, which we’re not going to do opens up our entire billion dollar database to the world, including AI, or unless Amazon all of a sudden makes which it’s not going to makes search query performance available to be crawled by AI and opens up Brand Analytics to everybody, what are you always going to need to pair with the use of AI. Whether we’re talking product research, whether we’re talking keyword research, whether we’re talking listing optimization, what is the limitations of AI in this world that we live in, where a lot of data sources, be it from Amazon or other places, are gated?

Andrew:

Yeah, data is limited to the amount of information someone’s willing to disclose through their own source. That’s not gated right that no paywall exists and that kind of depends. But if you’re willing to provide the information from Helium 10, plug in the keywords and have it trained on that and then it memorizes that, that’s a good thing. However, you don’t want to rely strictly on their memory because sometimes they’ll hallucinate with multiple things. It can be worse for hallucination they start combining hallucinations from before with real data and then make wrong hallucinating interpretations and whatnot.

Andrew:

So when you marry the data, keyword research that you’ve done through or let’s say this, a workflow, because it’s not just about you asked about what you should be doing. If you don’t want AI to hallucinate as much, you need to be mimicking real world workflows that actually produce something. That’s good, because otherwise AI is going to assume a framework that might not even be relevant to your niche, right, and what you need is you need a workflow that’s best practices from the industry, because even though Helium 10, let’s say you’re not paying for Helium 10, let’s say you’re not paying for another one, even though they still come out with articles that talk about how to do keyword research nonetheless, and applying those methods is a significant way to reduce the number of hallucinations and to give you a better answer. But what makes it even better is having the actual data behind the framework itself and the workflow there. That’s where you get. The holy grail of all optimizations is when you have a superior prompting logic behind superior data going forward, and in this case we’re talking keyword research, product research and the like.

Bradley Sutton:

It’s like the old adage garbage in, garbage out and then whatever the opposite of that is AI can only work with what it can work with and it does a darn good job even if you don’t give it anything. Like you know it’s better. It’s better than like just hey, let me ask Google something that you know has no idea about. It has so much more data in it than like you know what people are used to with using Google and stuff, but it’s limited with the ability. So, like there’s nothing wrong with doing product research on there for, like hey, what’s trending out there, but is that going to give you the best Amazon you know knowledge? No, like here, I just did. Let me share my screen here.

Bradley Sutton:

I just did an example, right when you were talking here about. I asked ChatGPT what are the top selling wooden egg trays on Amazon, right? So it gave just some random like answers here and then I clicked on each of these links and, first of all, it’s not even showing me wooden egg trays. These are like the stackable egg racks. I also sell one of these and these aren’t necessarily the top selling ones. Interestingly enough, it says based on customer reviews and popularity like well it would not know anything about popularity, you know per se.

Bradley Sutton:

But here’s the search for wooden egg tray. A lot of those don’t even come up here and our product here here’s one of the project X products right here is one of the top selling ones and it’s nowhere on there. Now, what could have changed this is if I had downloaded like for maybe Amazon product opportunity, explore some click data of all the stuff that say wooden egg tray, if we’re talking Helium 10, I could have gone to Black Box, typed in wooden egg tray, downloaded the CSV file that has all the sales. And now, if I say the same exact question, I probably could do that right now, but I’d probably take five minutes, so I’m not going to do it. But if I say what are the top selling wooden egg trays on Amazon, here’s the file of the data, what’s going on. It would tell me exactly the top five. You know ones that I’m looking for. So again, it has to do with the data that you put in.

Bradley Sutton:

And personally, I don’t think there’s ever going to be a world where it’s just going to be able to function on its own. When we’re talking about different marketplaces that have proprietary information. What’s the best ones from Shopify or Google, who knows, maybe that one? You know like there’s public domain information about what’s the most searched pages and maybe how many pages are viewed. It’s not as private as like Amazon or Walmart.com or TikTok shop. But, guys, you know, stop thinking you could just replay. Oh, I don’t need search query performance anymore. I’ve got ChatGPT. I don’t need Helium 10. I have ChatGPT. There’s never going to be a world where you don’t need to augment it with data. Now, along the lines of the hallucinations in general, maybe, if you can share your screen and if you can give.

Bradley Sutton:

I will try and describe this as much as we can for those who are listening, like on their morning running to work, your morning jog before work, or we’ll try to verbally describe it for you, but can you give some examples and tips on how people can use ChatGPT in general or with Amazon related tasks that will help give them better results, because this is something that you are super good at.

Andrew:

I think one thing I want to show is the I can screenshot it here. The product research kind of shows you like the market and if it’s worth, like putting a product up to begin with. So we did this in one of the others, Bradley.

Bradley Sutton:

Which GPT, which version of GPT, are we looking at here?

Andrew:

So this is the product research GPT, and it’s actually functioning off O3 reasoning model, which that means it spends a significant amount of time basically thinking through every different facet. So what this is going to do is it’s going to go through 10 different possible dimensions in the research process for product research and within each of those, it has its own research prompt. So it’s not just a hey, find me the best product, right, the next best hit. It’s like something you can, something you can put in your own product, or a product that you want to try on Amazon or possibly launch, and it’ll produce for you product research that’s based on 10 different research prompts across 10 dimensions, each of which provides its own search as well.

Bradley Sutton:

So, first of all, my first takeaway is one way you can get better with just using ChatGPT is actually using a custom GPT Like you’ve created that is doing like 75 million not that much, but a lot of prompts that you’ve programmed. So that’s somebody who doesn’t have to go and type out 17 paragraphs of instructions. You’ve kind of like automated it here to go that extra mile.

Andrew:

Exactly. It saves you the work. You don’t have to store a prompt somewhere either which is pretty nice, I think as well and you can then plug in whatever product you want, and it keeps you in context no matter what, and that’s the beauty of a GPT Always keeps you in context. You don’t have to store a prompt anywhere else, and it’s extremely reliable as well.

Andrew:

So here with the prompt, let’s talk about. I’m going to kind of talk you through what’s happening with, like the reasoning process behind it. Okay, what product you want to do?

Bradley Sutton:

Let’s just do the. What about the one I just showed, like a wooden egg tray or something?

Andrew:

Yeah, okay, so I’m going to type in wooden egg tray and basically it’s going to go through its thinking process. This is the distinction between a reasoning model and non, is it? It doesn’t like think through everything all the way, it’ll produce an answer that is immediate with the regular model, but with the reasoning one, it’s not the case.

Bradley Sutton:

I like how it’s kind of thinking out loud, as it were. It’s like showing you what exactly it’s showing you the work that’s different than yeah.

Andrew:

And that fosters trust in my mind of what was happening. So you need to follow and track it all the way. So it’s like the user mentioned this, but there’s no real direct question attached. So then it thinks through. It says okay, for each dimension I’ll create two to three search queries, run them through a web search task. Each search query will target specific aspects of the product. I’ll proceed with the structure for a thorough investigation.

Andrew:

And then it’s talking about okay, I got the three searches here, as you see, the first search and then it goes through five different sources, domains. So it goes through Trends Google, Data Bridge, Market Research, Amazon, Spur, Research, Rule Handmade Calm, which would make sense to search something like that. So niche, because we’re talking about. So then it keeps going and does the same thing and then, once it retrieves the results, it summarizes the findings, gathers the top sources with citations, and then it says I’ll do this for each dimension. So it’s repeating the same step consistently, you’ll notice here. So here you have three search queries about the same number of domains and then, as you continue going down, three search queries, same number of domains, and then you go down again.

Bradley Sutton:

So many steps here. This is amazing.

Andrew:

Yes, and so it goes very thoroughly. And that’s the thing. Using reasoning models. I highly encourage it because it cultivates and fosters trust with the model, and that’s what you need, because otherwise it’s like you’re kind of trusting it in the dark, especially when you’re talking about data, which is, by the way, the number one reason why people are unwilling to use generative AI, with PPC as an example because you’re trusting with your money. So now it’s looking at competitors, and so it’ll go through and look at wooden egg trays in different retail channels, so for Amazon, Walmart, Etsy, wholesale suppliers so it’s searching Etsy here, bear.com, Alibaba, backyard, barnyard.com I mean these obviously sounds like something that it would go into. Then dives into several other areas competitor brands, pricing strategies, distribution channels. So you see the consistency with which it’s going. It hasn’t stopped the consistency.

Bradley Sutton:

Now, what did you have to do that this custom GPT is going into so much depth, with just somebody typing in wooden egg tray, like how many lines of prompts did you even give this? Is going into so much depth, with just somebody typing in wooden egg tray, like how much, how many lines of prompts did you even give this? Or is most of this just it’s using its own common sense of what it thinks it should do?

Andrew:

No, this is something that has to be intricately put in, because otherwise you could take it a million other directions.

Bradley Sutton:

Yeah, okay. I was about to say like this is pretty good for just making it up on its own, okay.

Andrew:

Yeah, absolutely so. Yeah, as you keep going. Then it goes in I’m digging into future trends for the wooden egg trays, similar products, search for insights on these various things. And then it keeps going and at that point it’s gone through all the dimensions. So it faithfully went through all requested dimensions for the product idea of wooden egg tray. So each dimension now is presented with a research prompt that was used. The web search query is actually executed, so it shows its work here.

Andrew:

Even more concise insight summary and then the key findings. So here we find out the general egg tray market all materials is valued at $18.8 billion at 2025. Yeah, what is a small but growing premium sub-segment. Keyword tools show 3K, 5K average monthly US searches for wooden egg trays, steady interest, but not yet saturated. Google trends indicate mild but consistent five-year growth for wooden egg tray, peaking each spring. But even here, look it cited this and I’m sure it’s citing from another source, but like, notice it just, it’s just the Amazon page that it shows. So this is another thing where, like having even better data, like, yes, sure this helps because obviously yeah, wooden egg holder listing does not say in there, hey, this.

Bradley Sutton:

The search volume is this like that’s not exactly on Amazon so, yeah, that’s why people have to be careful about you know, no matter how good the prompt is, it still can. You know, no matter how good the prompt is, it still can you know like wants to it’s kind of like that seven-year-old kid when there’s guests coming to the house and they want to kind of like show off, hey look what I can do you know it’s like hey, I don’t want to disappoint you know, so let me, you know, show off a little stuff here.

Bradley Sutton:

But then what I would do right here is I would be like I would have maybe, if we’re not talking about products and we’re just talking about the general market, I would have maybe gone to historical Cerebro and downloaded, like maybe a few of the months, or Magnet actually Magnet would be better in this case. Any keyword that has egg tray in it, and then some of that historical search volume.

Bradley Sutton:

If I were to manually download in Excel all these things from Helium 10 and find out which keywords are increasing in search volume over time or decreasing. That would take forever, if it’s even possible, but I can just literally just download it and give the raw data and then now all of a sudden, this prompt would probably show me even better data based on what Helium 10 is giving.

Andrew:

Absolutely way better data, I would argue yeah, absolutely. And then, as you go down, competitor landscape says market is fragmented boutique, Etsy makers, DTC farmhouse brands, large import resellers on Amazon, Ebay, no dominant national brand. Differentiation comes from wood species Kaia, walnut, bamboo capacity and aesthetic. Ceramic, metal shelter holders compete on price. And then you have the customer pain points.

Andrew:

So surface negative reviews, complaints, and it gives the again. This is important because it gives the research prompt and the key findings are common complaints shallow holes that let eggs roll, rough machining that splinters, finishes that aren’t truly food safe and trays too large for standard fridges as well. It looks like it’s taken from forums as well. So, like reputable forums have come before and you notice too, it’ll take a little bit from Reddit as well. And here’s the thing with this product research tool, you can actually be able to fine tune it with your sources. So there’s a new version that I’m going to have coming out, where you can actually be able to use whatever sources you think are most reputable for your product research. And imagine again having like putting your Helium 10 data into a product research prompt like this.

Bradley Sutton:

Yes, yeah, I mean we’re going to pricing strategy like elasticity and remember guys those of you watching on YouTube all he did was type in wooden egg and he didn’t type in a question or anything. Now you can’t go to ChatGPT guys and type in wooden egg tray and expect this. The reason again this is coming out is because this is one of his 30 chat or custom GPTs. Real quick for the people who are taking notes where can they go to see this exact GPT to use it? And your others? How can they find it on the interwebs out there?

Andrew:

Well, you’re going to get a link too. We’re going to share the link with you.

Bradley Sutton:

In the description. We’ll put that in the description. Perfect

Andrew:

yeah in the description yeah, I would say that’s best.

Bradley Sutton:

And then if for some reason I can’t get that or I don’t remember, it’s there. If I type in Andrew Bell, custom GPTs, Amazon or something, would that probably take me to the right place.

Andrew:

Not this one, because it was it’s been gated for the webinars and workshops

Bradley Sutton:

That you’re giving like, yeah, that not even everybody else has access. Wow. Okay. Look at that guys. See what happens when you listen to the podcast. You get special freebies too that nobody else can get. I love it.

Andrew:

Yeah, absolutely. You’ll get a couple too. There’s like two or three that are not open to the public yet, in particular, and not even distributed through many other channels other than like Helium 10, for example. So, yep, you’ll get this product research one for free. In fact, I have several custom GPTs you can use. I have one, two that I haven’t released product research, product demand, ideas and e-commerce script agent, and then a special one called NoMDashes, which, if you’ve ever heard of AI, you’ve probably heard oh, having dashes is a sign of AI-generated content. So if that’s something you don’t like, it’s like okay, you just plug it into NoMDashes and it’ll take care of it.

Bradley Sutton:

Cool, all right, let’s switch gears a little bit. I mean, still talk about Amazon, but another thing you talk extensively about is new things that have to do with AI that actually pop up in like Amazon search results, and probably the most notable one would be Rufus.

Bradley Sutton:

Now, I’ve done some studies where you can kind of see that in search at least, or in my opinion, for me personally, I don’t use Rufus that much, but when I do, it’s on the product page because I’m like trying to like say, hey, give me a summary of the reviews or give me the price history, and then when I did a poll out there, it still seems like that’s the predominant, because that actually that’s, in my opinion, that’s where it’s most useful is on the product page to do stuff that just the human eye can’t do.

Bradley Sutton:

But still, Rufus is coming up in, like, you know, search bar and search results bar and search results, and there’s many ways, so it’s something that you know, you know shouldn’t be the very first thing I think that people optimize for, but anything that comes up in Amazon that affects how your product could be found, even if it’s only like one out of a hundred customers or we’ll use it. It doesn’t matter, guys, you need to be optimizing for this thing. So, in your opinion, what are the top things that sellers today should be optimizing for in today’s world? Obviously, we can have a podcast. Next year, I’m sure the answer is going to be different, but right now, in 2025, how should sellers be optimizing their listings? When it comes for better visibility, like with Rufus.

Andrew:

Absolutely. Well, there’s a lot of things you can do techniques of where you ask all sorts of questions about your product and wherever it says, well, the product information doesn’t provide this, but the customer reviews say this, you can basically take that information, that question, it’s like okay, if that’s not answered somehow in the bullet points, here’s, here’s a big thing. Rufus takes from everything on your product detail page, from your title to your product description, to your attributes, bullet points, no matter how long they are, rufus will take it. Rufus basically sees your product page as a knowledge base and takes from that, specifically when it’s on there. Now, what’s pretty cool is when you use Rufus, let’s say on the homepage, it’ll provide you much more general things.

Andrew:

But then when you search with, go to the search page and then ask questions from the search results page, you’re going to get something deep within for that search term. So, for example, if I’m typing in metal wall art, you’re going to see options like hey, compare these metal wall art to one another. But then when you go into a specific product, it actually mines down even deeper and you can see things like show price history. You can see customer reviews with images that are provided to as well. There’s a new feature, actually, that you can set the price of what you want, like, if it’s 5% off and that’s what you want to see, you can set it now and Rufus will keep an eye on price for you and give you a notification when a price on a particular product has changed, whether it’s 5%.

Bradley Sutton:

Where does that notification come through on? Is it just like your Amazon app, or what?

Andrew:

Yeah, it’s like on the Amazon app, and so, Rufus, yeah, but you can only do it on the mobile app.

Bradley Sutton:

For deals like Prime Day, depending on when this comes out, that’s like maybe I need to buy something. I’m like you know what. I’m going to wait a couple days because it’s going to be Prime Day or Black Friday. Let me set a notification of this product is, Rufus can do that. That’s very cool.

Andrew:

Yeah, absolutely, but it only works on the mobile app right now. For that in specific. And then one thing too is Rufus I would definitely do. It’s called visual label tagging, so putting text on your images because Rufus can actually read and take into factor. In fact, this is pretty cool.

Andrew:

When Rufus is asked a question, it’ll take not just in all the texts, but it’ll take every image, look at every image at once, find the ones that’s most relevant, right, and the most relevant one’s going to be the one that answers the question with the text or implicitly does it, and then chooses an image based on that. If it’s a certain score above, I think it’s like points. You know it’s 70% likelihood. I think it’ll like actually bring that image into the chat. So having text on your image helps you do that.

Andrew:

And what’s really cool is Rufus is actually seeing the image itself too. So one time when I typed in for a rug, I said, hey, I want to see this rug next to a chair and a couch, because I saw an image of that, and it actually brought that image in. Now it took four tries, but that’s because it’s deciding how relevant it is to bring it in. So when you have both the image and text that answer the question. It actually helps you bring it in. So if someone’s asking what does this look like up close, don’t just put you know this is, you know here’s what it looks like up close. Here’s the different, you know fibers, let’s say, of a rug, but actually show the image of it up close and it’ll take both into factor, the text and the image, giving you a higher likelihood to bring that in the chat, which would then lead, I think, to better conversions, because then the person’s seeing it from a visual point of view as opposed to not.

Bradley Sutton:

Yeah, okay, that’s good to know. Now I’ve given examples, I think on this podcast or at least on stage before, where one of the things mentioned is making sure that the questions are answered in the correct way and in a positive way for your company and your own listings. And if not, you go in and change your listing. And so those of us who have been doing listing optimization on Amazon for years know that sometimes, hey, I want to be indexed for a new keyword. You edit your listing, you put the keywords in. Sometimes it takes at fastest, maybe like three, four hours to be indexed.

Andrew:

That’s true.

Bradley Sutton:

Maybe a day or two. Guys, what I did something for Rufus? Like there was a question that was not being answered correctly or no, I’m sorry, it was being answered correctly, but my listing didn’t have the right answer. Like it was something like hey, is this coffin shelf durable, or something like that. And it said, well, the listing doesn’t say, but the reviews say no, I like that. And it said, well, the listing doesn’t say, but the reviews say no. I was like, oh, crap. I don’t want this.

Bradley Sutton:

I don’t want this to be the number one thing, and so I literally changed one of my bullet points to say this coffin shelf is very durable because of its wood, high quality wood finish guys, 10 minutes later, 10 minutes, I asked Rufus the same question or it was the auto complete question it said, yeah, you know what, according to this listing, it’s very durable because of this and that.

Bradley Sutton:

So, like it is super fast, so it’s something super easy to do. Guys like to. There’s only, like you know, so many auto questions that Rufus has in the auto complete you could like optimize for like less than five minutes here. So it’s something that you definitely should do now. As far as other ways of influencing Rufus like to me like something in the future that would be, or even now, if it’s possible I don’t know, I’ve never tried it, but is being able to influence, what are the auto-complete things that come up like in the search results, because that’s where it’s kind of like wide open, somebody who goes on your listing. They already must have some buyer intent, but have you found any way to kind of like, change what those auto-suggest Rufus questions are, either on a page or on a search results?

Andrew:

No, there’s no known ability or way to do that, now, that being because okay. So that being said, Rufus runs on. According to its patent, is run on click training data. In other words, when a question’s clicked that’s immediately recorded when it’s on a product page. So, for example, if I’m on a metal wall art piece let’s say it’s abstract, et cetera and I’m asked a question and I very frequently ask about the size of the item, that click training data with that product will be taken into factor. So, yes, that question type of question might not show up when I’m looking at a different type of product. Let’s say we’re coffin, a coffin shelf, right, but if I go to another wall decor piece, it’s going to make the assumption, based on that click training data, that I’m going to want to ask about size and then it’s going to put size first.

Andrew:

Now, the validity of that, we are not 100% sure it’s coming up. However, there’s evidence recently just came out I just posted about it recently on LinkedIn where the customer questions that people are asking on the search results page are actually becoming a frequently asked section bar on the search result page on Amazon. So it says customers also ask and it’s the Rufus questions there. So the click training data has actually paid off because and it’s showing you the most popular ones that are being clicked for Rufus and so that’s actually showing up now across a number of different categories and your brand I’ve noticed across almost every brand. It’s had that. So if you guys have a brand type in your brand into Amazon right now this works only on the mobile app and scroll down just a little bit and you’ll see customers ask and it’ll give questions and answer questions about that brand and those are answers that likely do come from Rufus because of that click training data.

Bradley Sutton:

Okay. So, guys, I mean, this is still I mean, I know it’s been around for over a year now, but it’s still. That’s infancy when it comes to Amazon, and it’s changing, and you can see tests that are happening, every day, where somebody on LinkedIn will say, hey, look what’s showing up in my browser and nobody else has that. Well, what does that mean? That means Amazon is doing some kind of test because they do a lot of tests with new and emerging technologies and so, guys, this is something that Amazon is, going to continue to iterate on, and right now, there’s nothing from like the Amazon API or Brand Analytics that gives you data. But hopefully, fingers crossed, in the future we’re going to have some better data from Amazon that helps us to optimize even more and helps us to understand what we need to do. But in the meantime, guys, it’s super simple what Andrew has talked about that you guys need to be doing now. But, trust me, guys, this is going to affect later on how you might do your advertising and things like that. I personally don’t think it’s ever going to affect later on how you might do your advertising and things like that. I personally don’t think it’s ever going to take over the shopping experience, and the reason why is because Amazon is like, if I go to Amazon and because I’m looking for a and I literally bought this last week a 64 ounce insulated water bottle there is nothing that can ever be made other than like a mind reader. That is going to beat the experience of me typing in to search 64 oz insulated water bottle and then seeing the results. Like I am not going to sit there and have a three minute conversation with Rufus, Claude or any other thing. If I already know what I want or I have a good idea, like to type that and see the results instantly. Nothing is going to beat that.

 Bradley Sutton:

As far as having like some two-way conversation where I think people are going to start or you know and you can. You, I’d like to get your opinion on this, but my personal opinion none of us know anything, like we can just make guesses and stuff. We’re not. We don’t know what’s going to happen in the future, but my opinion is, the thing that AI in Amazon or in general is going to change the shopping experience on is traditionally if you don’t know what you want. The traditional path is hey, let me go to Google or Bing or whatever and start doing research on it. Like my kids are doing a theme party here next week. I think it’s a tropical theme party so like they might do stuff like start Googling, hey, tropical theme party ideas, or what is the best? What are the best decorations to have. If I’m doing like, they’re literally having kind of like conversations even in Google.

Andrew:

That’s a perfect example.

Bradley Sutton:

And I think that if Rufus and other things get more sophisticated, Google is now out of the question. You can now just go to Amazon and start in Rufus some of these conversations when you don’t know what you want. You’re like hey, Rufus, what do you think I should get? I’m doing a tropical theme party. What do you think I should get? Oh, you know you should do this and that. That, to me, is what the future of search is. Not necessarily. The shopping experience is going to change, but the initial research that you might have done is going to change. What are your thoughts?

Andrew:

No, I think that’s an interesting point and I think, like with this, it goes beyond Rufus. I want to touch on two different points. One that you said with you said the theme party you’re doing. Okay, well, one of the GBTs I created was actually based on a paper recently came out of Amazon science and it’s backed up by mostly authors that come from both the Rufus team, which there’s I think there’s 60 people now on the Rufus team, which is much more than anything else. Like, as an example, there’s no one on the Amazon Inspire team, so it’s like they got that out, and there was only one person on Amazon Post team, so that was a telltale sign that they were going to go out too, but there’s 60 people now on the Amazon team. In fact, they’re just now hiring somebody that’s going to be a manager for both Rufus and Alexa and how Alexa is going to take Rufus product intelligence Because when you look at the job, you’re seeing the future of what Amazon is creating, and so I really see what this science paper. It’s really cool because it’s taking that reasoning concept we talked about in product research and it’s actually going to apply it at scale on Amazon.

Andrew:

Now we’re a little bit farther from that, but I’ve said that before and yet things accelerate at a pace that you couldn’t believe. Reasoning models are expensive now, but they’re going to be very cheap later on, because once upon a time GPT4 was really, really expensive and I could do 10 messages every three hours, but now I barely ever hit the limit on GPT, GPT4. So all that to say, with your tropical style theme party you could type that into Rufus It’ll reason through everything that you know you’ve ever searched right, then making assumptions of how you ask questions, of how you do filters, and bring that in into a cohesive whole to give you everything that you would need for a tropical style, um, or at least suggestions. It’s not saying everything that, oh, this is like something that you, you know we picked for you, that we didn’t think that you know you could do in yourself, necessarily, but I think you’ll be surprised by like, having that proof of work with the reasoning models married to having products at every stage of question.

Bradley Sutton:

Okay, this is interesting, like we can go on and on. Who knows, 20% of what we talked about today might be different, because you know the world of AI, Rufus and everything else is changing so, so rapidly. So, Andrew, how we actually drop your link in our YouTube version of the video? So we’re going to drop the link so people can see it in the YouTube version to get these custom GPTs. But how can people find you on the interwebs out there, just if they want to follow you and see what you do a little bit more?

Andrew:

On the interwebs LinkedIn. Linkedin’s the best. Yeah, just type in Andrew Bell, hopefully I show up. I hope I look like my picture.

Bradley Sutton:

I love it. I love it, love it. All right. Well, Andrew, I would say Eucharisto for coming on the podcast today. I know I butchered the pronunciation. Hey, I can’t use the excuse that I’m from Kentucky, but hey, I’m from California. We might be not as smart as those in Kentucky, but anyways, thank you for coming on the show this time and we look forward to, in a few weeks, bringing you back.

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