Shane Barker Podcast Transcript
Shane Barker joins host Brian Thomas on The Digital Executive Podcast.
Brian Thomas: Welcome to Coruzant Technologies, Home of the Digital Executive Podcast.
Welcome to the Digital Executive. Today’s guest is Shane Barker. Shane Barker is the CEO and founder of Trace Fuse, the first Amazon terms of service compliant negative review removal platform. With more than 25 years of experience in digital marketing and a track record of working with Fortune 500 brands, Shane has been recognized as a top influencer in the industry today.
His mission is to help Amazon sellers protect their brand reputation and revenue by offering a white hat solution to remove policy of violating reviews.
Well, good afternoon, Shane. Welcome to the show.
Shane Barker: Thank you for having me, man. This is exciting.
Brian Thomas: Absolutely my friend. I appreciate it. And you’re in the great city of Reno, Nevada.
I love that place. I’m in Kansas City, so we got a couple hours difference, but I appreciate you. Doing what you do to get on a podcast. ’cause just like you today, both of us, I think had a lot of busy schedules for podcasts, so thank you. Of course. Of course. And Shane, jumping into your first question, what was the moment or case that made you realize that Amazon sellers badly needed a terms of service or TOS compliant, scalable solution to removing negative or fake reviews?
Why did existing tools or processes fall short?
Shane Barker: Yeah, the aha moment for me was about five years ago when I was doing a lot of direct to consumer right through my brand, sheen broker.com through the website and clients that I was working with. And I’d always wanted to have like a SaaS based product and, but really couldn’t, didn’t find anything that wasn’t already like saturated and had a lot of software companies and so.
I was asked my clients, Hey, what are the biggest pain points that you guys deal when it comes to Amazon? And everybody said, reviews. And I said, Hey, you know, not a problem at all. Let me take a look at that. I was a little too cocky and thought I could figure it out in a few weeks. I’ve got big developers and all this fun stuff.
It took us two years to crack the code, which tells me that, it’s obviously not an easy process. And so I realized that by doing that, obviously we’re tapping into a market that, and I found this out later, is that a lot of sellers. Just couldn’t get reviews removed. And the fact that we were able to crack the code was a, a big pivotal point for ourself as for our business.
But it was also great ’cause a lot of sellers didn’t believe it was possible. ’cause they say, well, these things violate Amazon’s guidelines. These are things that, where they mentioned a competitor pricing, cussing hate speech, anything FBA related. So if it got shipped out. By Amazon. Those were all things that we file on.
And so once people found out that we could actually get it done, things just absolutely took off. So that was kind of a dream for me to be able to do a SAS product and for, to be really the first to market to be able to remove these reviews. There’s, we have over 600 brands that we work with and we’ve removed over 14,000 reviews, so there’s really nobody in this space even close to us.
Brian Thomas: That’s awesome. And again, starting out the podcast with an amazing question. A story, a pain point, as you mentioned is a pain point here. Amazon reviews bad reviews, fake reviews, whatever they are. I liked how you, again, rolled up your sleeve, dove in and honestly have really turned this whole thing upside down and I think that’s amazing.
So thank you. And Shane, you emphasize that trace fuse is 100% Amazon terms of service compliant. What does that mean in practice? What actions aren’t allowed? And what specific criteria or policy points do you decide if a review is eligible for removal?
Shane Barker: Yeah, I mean, really what we do is everything we do is like the AI that we’ve created, we’re looking for things that violate Amazon’s guidelines.
So we actually collect the reviews. We run those through our AI software, and the AI will tell us whether it’s compliant with Amazon’s guidelines or not. If it is not compliant, as I said, mentioning competitors pricing cussing, then what we do will be filing a case with Amazon looking to get that review removed.
When we talk about being TOS compliant. That just means that when we’re filing cases in the way that we do things, we wanna make sure that we stay above board and are doing the rules.
Brian Thomas: Great. Thank you so much. I appreciate that. What I really like is you are leveraging AI technology. I think that’s been the whole topic of this podcast for the last 18 to 24 months.
But every time someone gets on to talk about ai, it’s how they’re using it in a different way and how they’re applying it. In your case, this is totally new space, this e-commerce and removing negative reviews. I think that’s awesome and I’m glad that yet you are leveraging. AI to help you get through all this 14,000 reviews is no easy feat to, to have removed.
So appreciate that. And Shane, absolutely. Trace Fuse uses AI to detect negative reviews that violate policies, then human teams file cases. Can you maybe walk us through a typical process from detection, case preparation, removal? What are the hardest parts, there’s false positives in there and you know, of course.
How do you figure that all that out?
Shane Barker: Yeah the way that we go about it is when we’re looking at reviews, we collect those reviews, and then what we’re doing is when we grab those from Amazon, we actually run ’em to the ai. As I said, the AI just says whether it’s compliant with Amazon not compliant.
If it is not compliant, then guess what? We’re gonna be, we’ll take a look at it. If it is compliant, we don’t do anything with those reviews. So what happened with us, the reason why we integrated AI into the program about three, three and a half years ago is because. We were looking at this, we were manually looking at reviews through these things called humans, right?
I’m being a little facetious here. And so we had filers or people that would take a look at the reviews and they could only work about four hours because you start to run into errors, right? You start to, you’re looking at so many reviews, your eyes start bleeding. Not really bleeding. Nobody was hurt in our process.
But, the idea of it is that, you know, you start to get the human air factor, and we said, wait a second. Is this something that we could train AI on? And this was before chat, GPT and Perplexity and Gemini and all the other fun AI software’s out there. And so that’s, we really helped us streamline it.
I mean, at this point, we, I mean with 14,000 reviews that we. Have gotten removed. You have to realize there’s hundreds of thousands of reviews that we monitor on a daily basis. So when we monitor those reviews, there’s no way to be able to scale. We had to use ai. And so what the AI does, we’ve trained the model over the last three years.
It really just looks at, hey, if somebody uses the S word in a review, we know that that’s a cuss word and we use keywords. And then what it does, it just looks and it flags those reviews. Right. And then. Once it flags those reviews, obviously we have humans or people that will go and take a look at that.
We’re obviously gonna take a look at that. We’re gonna file a case, but everything that we file with Amazon is done manually through humans. And one of the reasons that is, is because Amazon doesn’t want you to just automatically file and, and there’s no automation allowed like that on Amazon. It has to be done through a human.
Amazon doesn’t want you to send, 10,000. You know, cases to them, obviously in a day that’s gonna be a nightmare for them. So we do ’em all through humans. And so the humans, it also helps us to just double check that whatever the AI said, to make sure that it’s, you know, there’s no, as you said, false positives or anything like that.
So it’s just another line of protection for us to be able to get those cases in and get those reviews removed for our clients.
Brian Thomas: That’s amazing. And having your process, right, and obviously AI does a lot of this work, but reviewing a hundred thousand reviews daily is a big feat. Now, humans couldn’t do that unless you had an army.
But even then, it’s subject to human error and people do get fatigued. But I like how you are keeping the human in the loop for part of that, but you’re also part of Amazon, that process. You also have that handoff to the human, and the human works those cases through Amazon, so I appreciate that.
Yeah. And Shane, the last question of the day, from your work with, again, 600 plus brands and over 14,000 removed reviews, what patterns have you noticed? What kinds of reviews most often violate Amazon guidelines and how often do you encounter reviews that feel unfair or harmful, but don’t clearly violate policy?
Maybe you can share some of that.
Shane Barker: Yeah, absolutely. So, the, probably the reviews that we see most, depending on categories, like if you see like beauty brands, a lot of the times what we’ll see is a lot of them being compared to other beauty brands, right? They’ll compare that and that either can be a strategy by the competitor putting in their name in there, or it could also be just somebody saying, Hey.
I use Maybelline and I tried it this time, but I usually always use L’Oreal. Right? Which I think is the same company, but you get my point. It’s, we’re looking at a situation where those reviews are gonna, those are gonna be up there and people are, just mentioning, being honest about a review, not knowing that they’re violating Amazon’s guidelines, right?
So that happens a lot. Pricing is very, very, happens all the time. Somebody says, Hey, I paid $35 for this on Prime Day, now it’s $50. I’m not gonna buy it. Right. Well that’s obviously a special on Prime Day. Amazon doesn’t want people to know about the lower price because that was, then people won’t buy it at a $50 price point.
Right. So those are types of things. And then also cussing. I mean, that does happen quite often. If somebody’s very emotional and mad about something, then they’ll throw in a cuss word here and there, and that will negate any review in the sense that now it makes it so that it’s non-compliant with Amazon.
And so those are the ones that we would take a look at. I definitely have seen a lot of reviews that I think are, potentially unfair or harmful. What I mean by that is reviews that maybe you’re looking at it clearly somebody’s getting attacked or the brand’s getting attacked, but they didn’t write anything that violates Amazon’s guidelines.
So those kind of break my heart because. We obviously can’t file on ’em because there’s nothing that violates Amazon’s guidelines. So there’s really nothing we can do. So those kind of hurt a little bit, but it kind of is what it is.
Brian Thomas: Thank you, I appreciate that. And just to highlight some of those, the terms of service there, you talked about looking at some in specific categories.
You brought up the example of the makeup Right. Obviously calling out. Competitor or calling something out specifically could violate terms of service. Pricing is a big one, right? That’s a big one there. Of course, the curse words and the one, and I’ll say the one that broke your heart, the hard ones.
When people truly just attack the company or the product line, yet they don’t violate the terms of service. And so you’re, it’s kind of a catch 22 there, and it is hurtful for the company that’s working hard to do a good job of what. Their, whatever their product is they’re trying to sell.
So I appreciate that.
Shane Barker: Yeah,
Brian Thomas: Shane, it was such a pleasure today and I can’t wait to get you back on the podcast.
Shane Barker: And Brian, thank you so much for having me today. I really appreciate it for taking the time. And once again, man, keep up the good work. You guys are absolutely crushing over there. I know you’re at what, at 1100 episodes at this point.
So keep the magic going my friend. You guys are doing an awesome job.
Brian Thomas: Bye for now.
Shane Barker Podcast Transcript. Listen to the audio on the guest’s Podcast Page.
