Ming Ooi, the co-founder of Fakespot.com and a mastermind in unveiling the opaque reality of Amazon reviews, joins us for an illuminating conversation that will change how you perceive online shopping. Born from a blend of Singaporean roots and Oklahoma upbringing, Ming’s fascinating journey through the e-commerce landscape led him to confront the challenge of deceptive reviews head-on, birthing a platform dedicated to consumer enlightenment. Our discussion traverses the origin story of Fakespot.com, Ming’s revelations on his pre-marketplace days with Amazon, and the critical mission to inject honesty into the digital consumer experience.
As Ming narrates the astonishing rise of Fakespot, from a burgeoning user base to over a million, we peel away the layers of strategy and resilience that propelled the startup’s growth. He candidly shares the initial forays into affiliate marketing with Amazon, the unexpected twists that forced a pivot away from traditional revenue models, and the inner workings of leadership dynamics that shaped the company’s trajectory. This adventure through the monetization labyrinth reveals not just the trials of a tech startup but also the ingenuity required to thrive in the competitive world of online review verification.
Our episode culminates with a forward-looking gaze into the ever-shifting sands of e-commerce, where Ming offers profound insights into the role of AI in discerning the authenticity of reviews and the future landscapes where these digital critiques will evolve. We exchange anecdotes with the online seller community, dissect the enigma of ‘supernova products’, and confront the stark realities of review manipulation. Ming leaves us contemplating the need for a vigilant consumer ethos in an AI-enhanced marketplace and extends an invitation to dive further into this critical dialogue. Join us as we unravel the complex tapestry of trust, deception, and innovation that defines the modern e-commerce review ecosystem.
In episode 393 of the AM/PM Podcast, Kevin and Ming discuss:
00:00 – Exploring Fake Amazon Reviews With Ming Ooi
03:14 – Review Authentication Startup Collaboration
07:01 – Amazon’s Impenetrable Firewall Explained
11:17 – Utilizing Social Media for Fakespot’s Marketing Success
11:47 – Monetizing a Review Website With Data
15:38 – Monetizing High Conversion Rates
17:57 – Monetizing Data for Financial Companies
20:03 – Amazon Review Management Analysis and Challenges
23:17 – Navigating Amazon Seller Feedback and Product Reviews
29:21 – Amazon Product Listing Strategy Failure
31:05 – Amazon Acquisition Talks and Fake Reviews
34:53 – Fake Reviews in E-Commerce Industry
36:09 – AI, Reviews, and FakeSpot Analysis
41:44 – Trusting Product Reviews
45:01 – The Future of E-Commerce Reviews
47:25 – Trust in Technology and AI
52:00 – Kevin King’s Words Of Wisdom
Transcript
Kevin King:
Welcome to episode 393 of the AM/PM Podcast. My guest this week is Ming Ooi. Ming is the co-founder of Fakespot.com, a site that analyzes Amazon reviews to determine what’s fake and what’s not, and there’s some pretty interesting stuff that he’s going to reveal, including some things that Amazon’s doing when it comes to reviews that might not make you too happy. It’s going to be a great episode full of some fascinating information when it comes to reviews. Enjoy this episode with Ming. Ming Ooi, welcome to the AM/PM Podcast man. I’m glad to have you here today.
Ming:
Thank you very much. Thanks for having me. This is going to be a real treat, I hope. I think.
Kevin King:
I think so too. I think we might open a few eyes today. There might be some cool, interesting stories that people have always wondered about but nobody really ever talks about, so I think that’s going to be going to be pretty cool. Now, you’re Singaporean or Chinese, Singaporean or Singapore, or what. What are you? What are you?
Ming:
I grew up between Singapore and Oklahoma.
Kevin King:
Oh, that’s a big difference Oklahoma.
Ming:
Shout out, shout out to the town.
Kevin King:
Oklahoma and Singapore. That’s quite a big difference. I think, like you mentioned earlier, you know Abe. Abe was on the podcast a couple months ago. Abe’s one of the top guys in the PPC, runs a PPC agency. If you haven’t heard his podcast, go back and listen to Abe Chimale’s podcast here on AM/PM Podcast. You can find it. It’s a really good episode.
Kevin King:
But Abe told me this interesting story I met you at Rich Goldstein’s party I think, a suite party in the Barbra Streisand suite during Prosper, and Abe had told me this story about you, about how he had a little office for his company and you had a little office right next to it. And one day you guys bumped into each other and you’re like, hey, what do you do? Hey, what do you do? Uh, hey, what do you do? And it just like y’all became friends because there was some, you were doing two different, radically different things. But in this e-commerce industry and that was interesting. And so, you were the co-founder. Was it co-founder or one of the early guys? Co-founder for Fake Spot? So fakespot.com for those of you don’t know. What started? What about 2014? 2015, somewhere around in there?
Ming:
Yes, 2015 it came to us and in 2016 is the official. I think, January 2016 we incorporate, you know, formed the LLC around to take it sort of official, but yeah, 2015,
Kevin King:
So what was the concept? You probably, when you started it, what started it was were you and your co-founder searching for stuff on amazon or somewhere like these reviews just aren’t real.
Ming:
I had a tech partner and I that we incubated a bunch of different startups at the time, and so I was reworking on two different startups of my own and my tech partner, Jeremy Petuto. Jeremy said, hey, I have somebody like late 2015, or somewhere middle 2015. He said, hey, I have somebody that is working with me that brought this to my attention and it’s a review authentication website that he created but he doesn’t know what to do with it. It’s interesting and he knew I came from the retail space.
Ming:
So, before I get to Fakespot, in a prior career I had ran a kids division of a consumer goods product company, and that product company that claimed the fame is we invented the memory foam bath mat, which at one point was ubiquitous and it could be still ubiquitous in every American household. Every household across the country and I ran the kids division of that company. So you know we had worked with Amazon and this was pre-Amazon market days where you, you know, had to go see the buyers and you do all that stuff. But so I had a fair amount of experience in retail and Amazon a little and Amazon, and so when they brought it to me, they were like, okay, what we would this and it was a great idea.
Ming:
I was like okay, reviews, and the thing is we had sort of arrived at the same problem from different angles. My wife had bought beauty products that were supposed to be from Korea and they were starting to be shipped from Ukraine and different parts of the world and we couldn’t figure out what was going on and why things are like this. And we couldn’t figure out. The reviews said something different and Saoud, the founder, had the same thing. He had the same experience of. He had bought all these supplements that he thought was supposed to be like this because he was really into health and fitness and he couldn’t figure out why the reviews. And Saoud, to put it mildly, is a prodigy in terms of programming, to put it mildly. I think in the circles of people who know this guy is a bonafide genius in terms of programming and coding and he managed to create this system of authenticating reviews and he just put it out there and just with that, when it came to me, he had like 10,000 users maybe with no marketing, no advertising, just and Jeremy and Saoud came to me and was like, okay, you understand retail, you understand 10 000 users, maybe with no marketing, no advertising just. And Jeremy and saw would came to me. It’s like, okay, you understand retail, you understand tech, what do we do with this? And we spent a little bit of time tinkering, figuring out and then finally we thought, okay, there might be a plan here. I think there’s a plan of what we can do with this. And then so we incorporate a company and the idea was like we will work together on it. He focused on the tech side, I would focus on the business side and Jeremy sort of also helped because I would focus on the tech side. And that’s kind of how we got started.
Kevin King:
So you guys were frustrated with reading reviews on Amazon, primarily that just were fake, obviously, clearly fake. So he developed, was he using early AI back then, or he just developed some sort of algorithm where he could actually determine based on several parameters, that this is most likely not real or?
Ming:
When I finally learned how the whole thing was done, I was gob smacked at how he had managed to pull this off and it’s to say that it’s like a model. Algorithm is a simple thing. That there are elements of AI in it, there are elements of machine learning, you know, it goes so deep that it was crazy how somebody could see that and think of it and execute it. So I’ll give you a rudimentary kind of thing of how it works right. Number one Amazon has a firewall that people may or may not realize didn’t execute it. So I’ll give you a rudimentary kind of thing of how it works right.
Ming:
Number one Amazon has a firewall that people may or may not realize. If you ping the website too much, it triggers a whole bunch of different things on it, because they don’t want competitors coming to scrape all their information about, like their pricing and stuff like that. So Amazon has a near like their pricing and stuff like that. So Amazon has a near impenetrable firewall and I say near impenetrable because Saoud was able to get it to ping Amazon. You know when you’re averaging. You know 10, 20 million users at any given time. You know, you’re pinging Amazon a lot and just to get past that firewall is an art form in and of itself.
Ming:
So that was number one which surprised me that he could do it. And then the second bit of it is that we, well I say we Fakespot, so most review things will look at the reviews and then they’ll do a few criterias. What Fakespot did was take all the reviews that you could see, apply it, and then there were a whole bunch of different criteria behind it, which I’ll get to later, and then they would go to every reviewer and then go grab the reviews from every reviewer that was available. So now, all of a sudden, you have multiple data sets. You have one data set of all the reviews for this product and then you have another data set of all the reviewers that left reviews. And then they were able to.
Kevin King:
Just to be clear, it’s all the reviews for one ASIN and then whatever people wrote, whether it’s Tom, Jerry, Sherry, Mary, whatever what they wrote, and then also for Mary, it went and looked at all her individual reviews for everything that she wrote on every product. She wrote everything that Jerry wrote on everything that he wrote. Okay, so I just want to make sure that people are clear with that.
Ming:
And then you roll the whole thing up and then you put it through every different criteria and you can catch cadence. You can catch, cut and paste. You can catch if they reviewed the same thing multiple times. If they’re like, you can catch all that like now. You can catch if they reviewed the same thing multiple times. If they’re like, you can catch all that like now, you can imagine how deep this goes.
Kevin King:
That’s big. That’s a lot of
Ming:
All that back in less than a minute.
Kevin King:
In real time.
Ming:
Yeah.
Kevin King:
So this wasn’t scraped data that was stored somewhere. This was like, if I go there and like I’m in, I want to know if this right now, this product, product B, whatever ASIN it is. It’s like calculating it almost in real time. It’s not like score that we scraped this last week or two weeks ago or something like that.
Ming:
So now what happens is like, once you scrape all that stuff, you’re able to put it somewhere, you’re able to store it out, and then you can pick okay, this was X amount point in time, and then we’ll only pick this X amount point in time.
Kevin King:
New, and that’s why it gets better and better, because you don’t have to run the same thing.
Ming:
You can just add it incrementally, but there are new products, all the time there’s new stuff, and so you’re always constantly doing the new stuff all the time, like all the time and it goes that deep every single time so when you launched this, was it mostly aimed at?
Kevin King:
It was aimed at consumers, right? So the consumers that were shopping on Amazon would like. You were like, hey, install this extension. I don’t remember when y’all eventually had an extension. I don’t know if you had an extension at the first, did you have an extension at the beginning?
Ming:
No, we did it in the first couple of years, no.
Kevin King:
So at the beginning, they had to actually go to fakespot.com, cut and paste a URL or type it in or whatever. Yeah, down the road, you actually had a chrome extension. I think that actually would just show up. It made it a lot easier and give it a little rating. Yeah, y’all did what. Was it like a through F or something like that?
Ming:
Yes. So, I’m going to take credit for that one. That was one of my things that when we first started people will remember this. We started as a percentage thing as like, this is 80, reliable, whatever it was. And my thing to Saoud and given my consumer goods background and a lot of training working with Walmart was you’ve got to simplify it for the masses. It has to be the lowest common denominator. And I told Saoud I was like you’ve got to change it to A through F, because we Americans understand A through F. You give me percentages and all that things like that. I don’t understand. But if you tell me this is an A review, these products are, these reviews are A, I’ll know what that means, I know what B means, I know what C means, etc. etc. And so that has been one of the things that we changed almost immediately we changed that, I think.
Kevin King:
How did the word get out? Was it just viral because y’all weren’t really, were you doing any advertising or anything? Or is it just like some social media posts and just one person telling the next person telling the next person?
Ming:
So no money, mostly everything so social media. So I would wrote a script when we were good enough, where, like he wrote a script where every time somebody ran something it would send out in either not so much, Facebook, but it was sent out on Twitter like, hey, this person did this review and stuff and we’re able to tag it that way. So if people were doing looking online for reviews and stuff somehow, usually they would find us one way or the other. So it was a lot of social media stuff.
Kevin King:
Wait, wait, wait. So that way. That’s a cool thing. So if I, if Kevin, if I go and I typed in on Fakespot X, Y, Z, ASIN, it would give me back and say, okay, this is A, B and then somehow he took that and then posted that to your Twitter channel.
Ming:
And Fakespot Twitter channel just send it out.
Kevin King:
Fakespot Twitter channel and put some hashtags or whatever, if he knew who I was, or whatever hashtag it.
Ming:
On the product or the brand whatever it needs that to be and automatically hashtag it by taking the same tags from the product title. And that’s how people found us, because when they’re doing searches, they were like, oh, this came up. Let me double check this.
Kevin King:
Oh, that’s brilliant. That’s really smart.
Ming:
Yeah, and we started literally when I say we started with no money, it was no money, and we built all this with no money for the first three years of change.
Kevin King:
So how many users ended up? What’s the like have gone. Unique users have gone through it over time.
Ming:
So when I started with them in 2015, with Saoud in 2015, we had 10,000 uniques, and when I stepped away in 2018, we were a million uniques.
Kevin King:
Okay, and it’s still going today.
Ming:
I think they’re probably up to like 20, 25 million unique users minimum, if not more.
Kevin King:
I mean, obviously I’m not privy to that information anymore, but yeah, they’re still doing very well today. Now you said you exited that’s because the company was sold.
Ming:
No, I exited because the agreement with Saoud was always for him to be CEO in training and because I had run my own startups and stuff and I knew this space, the retail space. He was a great tech guy, still is, but you know what I’ve learned in years over tech is you can over tech something, especially if you’re looking at something that’s a consumer based and you are not thinking about the consumer so much, you get enamored with the tech so much that you miss out on what you’re supposed to deliver. And so what we had talked about was I was sort of like more like the product officer, the strategy officer and the CEO, where he was a CEO in training, and we told him look, we’ll do all this with you and at some point you will be CEO, but you just have to trust us and we’ll work on the product, we’ll work on the business plan together and get us and the business model, because we had to find a way, how to make money and then when everything gets to a certain point, even if we’re early, you get the keys back to the car.
Kevin King:
How did you make money? Because it was free service. So what was it? Was it a data play?
Ming:
So when we started the first six months I think we thought we were going to be affiliate marketing, because I was like, oh, you know what? There’s this thing, affiliate marketing. So we tried affiliate marketing and we thought, oh, so we started at Fakespot was an affiliate marketing entity to Amazon and we hooked it up and our convert and I think we were making ten thousand dollars the first week right off the bat, like we had like 10 grand and they got. I was like oh, this will be great with limited amount of users we already had ten thousand dollars in in affiliate marketing.
Kevin King:
So someone would come and type in an ASIN to check it. Then you would link you would like link them. Link it back with an affiliate link, link them back to Amazon.
Ming:
Yeah.
Kevin King:
Okay, and you have a hot lead in your hand.
Ming:
So we had 50,000 the first month and I thought, oh, this is it, We’ve hit the jackpot and we’ve got the gold mine and Amazon probably cut us off. They would let. It was funny because they would keep the dashboard showing how much money we were accruing by our links so we could see the money growing. But they would tell us by this account you’re not getting any access to it. We’ve locked this account, but you could see the money growing because
Kevin King:
Oh man.
Ming:
It’s crazy.
Kevin King:
So that didn’t pan out. But so then what was the next step to actually monetize it?
Ming:
So we didn’t realize it at the time, but it didn’t pan out because our conversion rates were so high like who? Because by the time you clicked on us to do research on the review and then you click through, you almost, and you get an, A grade, you are almost guaranteed to buy it. So our conversion rates were easily north of 50 percent. Yeah, and I think for whatever reason, Amazon caught on to and then so they shut that account. And then we thought, okay, let’s try a different way. So we then tried a third party. So we found another company that was an affiliate link of the Amazon and we tried doing that and routing it through them and they caught on. And that’s when we realized, oh, our conversion rates are too high that there’s nobody that sends 50% of their links, you know, through Amazon that way, and in terms of affiliate money, and nobody’s making this level of money that has this little volume. I mean, you got to think about it. We only had like, let’s say, even at the end of the first year, we had like 100,000 users, maybe tops. But we’re like, you know, you’re making a quarter, you’re like all that. But we’re like you know you’re making a quarter. You’re like oh, it keeps running at how much you’re converting.
Ming:
So our last ditch was to try and open up another company that we thought could be a sister company, and at the end it wasn’t possible. And Amazon claimed that two things. The first one they claimed that we were routing people away from Amazon and then sending them back, which, in their terms of agreement, they claim is illegal. And I was like, well, they’re not leaving your site, they go to your site, they come to our site and then we send them back. And then, after that, we had launched the within the next year, we had launched a sort of an early version of the chrome extension, of the browser extension, and then they said well, you’re now trying to affect our product via extension, so that disqualifies you from doing anything with us as well.
Kevin King:
Okay, so that didn’t work. So then, how did you monetize it?
Ming:
We realized that we had all this data, people around the world, and now it’s been slowly, like you know, a year and a half in we’re like, into different countries, and that’s also another thing is to make this work in the UK, to make this work in Germany, all different sorts of things you have to do to make it work elsewhere, because Amazon is different in every region in the world. So we realized we had all this data and you know. So we started knocking on some doors and financial companies started knocking on doors because we had what they deemed as alternative data sets, and this was when data 2017, 2018, data was like the big magic word of like what do you have?
Ming:
What can you do with data? And so we managed to license out one of the world’s biggest quant funds, quant data shops in the world. We got in contact with them and negotiate a deal where they were willing to trial this for like six months to a year, where they were paying us for us to keep sending them data on what people were tracking and what they were looking, and they were looking to see if there were some trends based on what people are buying, what people are, you know, leaving reviews about and what people are checking and what. Basically a way to check on what hot on Amazon without you know and you know, as sellers, you guys do this because you get like what’s hot on Amazon, what you’re selling and stuff like that, so it was a different way of tracking that, and so that was the last sort of stage of the monetization.
Ming:
And then the other thing we were developing was sellers tools. How do you help the seller manage not manage as much, but like figure out what’s going on reviews, clean up the reviews if need to or challenge the reviews if they needed to be challenged.
Kevin King:
Also, you had an early system. That’s a hot thing right now is there’s companies out there that are doing it for you or showing you how to use ChatGPT to see what the violations were. So you guys were doing that years ago.
Ming:
Yeah, we were trying to do that early and at that time the only review management system companies are out there and there were a few big ones, but they were all only interested in managing reviews, as in terms of getting people to write it or getting you know, or putting bad reviews in competitive. I’m not saying everybody does that but I’m just saying like they were.
Ming:
Just that was their review management. There was no real analysis of like, what’s your velocity? What are you looking at? Why are things coming the way they’re coming? Is there a seasonality to it? You know, based on how you sell. Is there? You know, what are? How long does somebody take before they buy to give you a review? So we were trying to do things like that to the small seller in a sort of platform format. But what we found is like a lot of sellers are busy fulfilling orders and just running the day-to-day of their inventory and all that. And as Amazon got bigger to manage, they were worried about reviews but they weren’t really worried about reviews to that level at that point yet. So we were a little too early.
Kevin King:
And don’t forget to sign up for my Billion Dollar Sellers newsletter. It’s free every Monday and Thursday. a little too early. It’s like a $25,000 mastermind in a box billiondollarsellers.com, and in just a few weeks I’ll be doing BDSS Billion Dollar Seller Summit number 10 in Hawaii. Get information on that if you want to join us last minute at billiondollarsellersummit.com.
Kevin King:
Yeah, they were not worried about them being so much fake as they were just how do I get more on my-.
Ming:
Right. You can remember the days where, like 5,000 reviews was a big deal, or 1,000 reviews was a big deal. Now you’re like 1,000 reviews. Is anybody even buying this thing, right?
Kevin King:
Well, in 2015, 2016, there was how did you guys deal with the Back back? Then you could actually use services like Zonblast or Vowel Launch or a whole bunch of these there’s tons of them that would charge you 300 bucks, 500 bucks, 700 bucks to run a promotion and you would set up a coupon code on Amazon for free you can’t do this stuff anymore or 99% off or 9% off or whatever. And then they would have a list of people that they curated off of Facebook or wherever they curate them. And they would say hey, Kevin’s got a new dog bowl. Slow feed dog bowl. If you want it, it’s free. All you got to do for getting this for free use this coupon code and agree to write a review, to go back and post a review, and in that review, you need to make sure you put this terminology as something to the effect of I received this product in exchange for my honest opinion or something like that. And then I remember it was October 3rd or 4th or something, 2016. Amazon overnight shut that down. Everybody was freaking out, like how am I going to rank that because you could literally use one of these services and be number one the next day, the very next day, and then the reviews would start trickling in to help build that moat and Amazon shut that off. So how, when y’all are analyzing these, clearly those are fake. You know, some people are saying their honest opinion, but some of them are just writing whatever.
Kevin King:
And those, some of these services had ways to filter it out, like they would first have the right to review on their service and if it was positive, they would pass it through. If it was negative, they would pass it through. If it was negative, they would filter it out. There was all kinds of stuff that was going on. How did you guys? And then sometimes people would leave. I remember I used to do this. I don’t do this anymore, but people would accidentally. I would guide people to leave it on the seller feedback, because you on Amazon, you have a product feedback where people write a review and give the product to store and then there’s a separate one that’s based on the seller and the seller one is supposed to be for like, oh, he didn’t ship it on time, or it arrived damaged, it wasn’t packed very well or something very much delivery-based, not about is this a good quality product or is it as advertised. And so I would guide people to, actually, I would tell them if you got a positive view, use this link. And if you got, if you’re not happy, you know, let us know here and we’ll go to the seller feedback one which you could easily get removed. Amazon would cross them out, you can report them and you can knock out all your negative reviews. So it was a way to game the system.
Kevin King:
None of that works anymore, so don’t do that. But how are you guys filtering with so many? There were tens of thousands, hundreds of thousands of these. How did that come into effect? How did that affect the Fakespot analyzation?
Ming:
So the good news is like again, the part one of it is like when you have the history you’re able to tell, because so every so remember, we said like we go to the product, and every product has their own sort of history, and then you go to every user. So now every user has their own sort of history and then you go to every user. So now every user has their own history of who we find, because everybody has their unique.
Kevin King
But you can see, this is Soccer Mom in Kansas City. When you look at the history of Mary and all her reviews are almost the same because she’s doing one of these services, things.
0:24:41 – Ming:
Yeah all the reviews are the same and you’ll find that most of them have the same cut and paste language. So because we were able to do cadence and language and do that kind of matching where you can tell these are all the red flags if they cut and paste the same. So I used to give interviews for this. So a lot of these examples are still in my head. There was one person I remember that only reviewed automotive products and used the same language every time, the same three, four lines, oh this thing. So the first line would always be different. They would call out the name of the product oh this, this, this, this, this and then the second paragraph would be the same thing. I really like it. I use it.
Ming:
You know, I would recommend this to all my friends and family. Cut and paste and just on that one thing we were able to track, oh, this person has done this, this, this. So just on that level, you can track when people use the same language. So, even if they don’t put the first bit upfront anymore that this is a paid review, which a lot of people stop doing you can then see when they’re lazy and they’re cutting and pasting. And for us, if you’re cutting and pasting and saying the same things, it’s not a reliable review. Now, if you’re cutting, pasting and saying the same things, it’s not a reliable review. Now, there are different levels of reliability, of course. So then, instead of an A, maybe you’re B, and then, instead of a B, maybe you get down to a C, and if you see a lot of this, then it’s obvious that it’s some sort of bot or something. Then it’s a D or an F, you know, however that goes.
Kevin King:
So this was happening back then and it still happens today. But there’s a lot of black cat sellers from China and Russia, China especially that they’ll use exchange students. So a Chinese exchange student comes to the US to go to university, they will hire them and they will create an avatar for this person and they’ll say, okay, Ming, you’re really into cats, you’re a cat person and so anything that’s being launched in the cat space. We’re going to be forwarding you these links and we want you buying anything that a cat person would buy, and some of that’s cat stuff.
Kevin King:
Some of it might be, you know, cat people, I don’t know cat people buy a certain type of pan or whatever, and so they would create so that you wouldn’t be detected as, oh, you’re all of a sudden you’re buying all this random stuff. That’s all over the freaking place and it’s kind of obvious.
Ming:
Do you remember in 2016, 2017, where there was a brief moment in time where people were getting random packages from Amazon that they didn’t order? Yeah, it’s called brushing.
Kevin King:
Yeah, it’s called brushing yeah.
Ming:
That was like and I remember getting a lot of calls and interviews on that and they’re like why we get packages. Well, because they want to review it.
Ming:
They have to send the product somewhere and they just want to review it, and it’s always like crap, like well crap.
Kevin King:
The way that was working was there’s someone had been, someone over in China. I know a specific I know of I don’t know the person, but I know of a specific group that had somehow gotten 50 million Amazon customer buyers data. So I think they got it from the back door from India or from China, but they had their actually addresses, the addresses, you know. They had my address and so they would just go in and they would create a virtual credit cards. They would sign up for a bank of America and create you could do like a, you know, one off credit cards that are meant for security purposes, for your employees or for whatever. But they would go and they’d have multiple banks and there were services that popped up that would let you do a thousand virtual credit cards. So they’d use these virtual credit cards and you could put in whatever your address you wanted. So, so the AVS matching and everything would match and so they would. They would match those credit cards and then use people to just say, hey, make sure it’s either spoof it, but better than spoofing.
Kevin King:
It was to one of these students to say go buy this product and just ship it to this random address, or they do it from China and ship it to a random address. So people are getting it’s called brushing. People will get this stuff just showing up. You know, 35 packages show up at their door. I didn’t order any of this stuff, but they didn’t care, because what they were doing was they would have 10 different seller accounts selling the same product. So one account they would keep completely clean, totally clean, totally above board, and then the other nine would be variations, and those they’re doing they’re burner accounts and so they would have the same product and that’s where they do all their malicious stuff build up the reviews and then transfer those to the main account or use it to get the main account rocking and rolling, and then just abandon the nine and have one account that’s stable. There’s a whole process there.
Ming:
Yeah, and every year we would find these new things, just the way that we were looking at reviews and we’re like, you see the patterns, yeah, and it’s weird that you said that, because you’re right they would transfer, like they would sell one thing and then transfer the ace into something else, and then the reviews, would you know? The reviews would be talking about, like, let’s say, a t-shirt, and then next thing, the reviews would be talking about pair of shorts or whatever, something like that.
Kevin King:
That’s called zombie accounts. Those are zombies, so what those were was somewhat I go on 2016. I saw some guru on YouTube telling me how I can get my own Lamborghini and live in a mansion. I launched a product on Amazon and it would sell on a blender, let’s say and it just, it didn’t work. I just lost my ass because I know what the hell I’m doing. So I just said, screw it, I ran out of inventory, I just let it go, but I did not delete the listing and so and so what would happen? But maybe I had some decent reviews. I just didn’t have enough money or whatever. So there was tools that would go in and scrape Amazon and find these zombies that are just dead men, dead listings walking, and they would take them over with a 1P account and then take them over and then merge those reviews. And they figured if we get enough reviews at the top, nobody scrolls all the way down.
Ming:
Yeah, nobody scrolls down, correct.
Kevin King:
Get enough of what the product is at the top. Nobody scrolls all the way down. Yeah, nobody scrolls down. Correct, we get enough of what the product is at the top, we’ll be okay. And a lot of people don’t even read the reviews, they just look at the stars. So they’re not worried. If it’s about if I’m not selling a blender, if I’m selling a pair of socks, who cares? That was what that was. I remember Slick, which is one of the biggest sites out there for deals. They actually I don’t know if they partner with you guys or they just use you, but you had to have a certain score on Fakespot.
Ming:
Yeah, to get on Slick Deals. Yeah, I think in 2018, we actually talked about them either buying us or buying a stake in Fakespot or doing some sort of alliance with Slick Deals at some point. So I had some pretty deep conversations with those guys.
Kevin King:
So what did? Did amazon always treat you like the brother from another mother they don’t want to talk to? Or do they ever like, say, hey, it’s kind of cool what you’re doing, we’re going to knock you off, or we want to, we’re interested in it, or they just like you? I wish you guys wouldn’t do this. What was their general attitude, or did it evolve over time?
Ming:
I think it evolved over time because in the beginning we were just like a little speck, you know, like a drop of in the ocean, so they didn’t really care, like you know you’re like, and then every six months or so, we would get some sort of contact and then gradually it would get more and more like hey, this is Amazon, you better cease and desist, or we’re going to do this, or this is this. But what’s funny and this is the inside story scoop for you guys. Right before the pandemic, Amazon called us to start acquisition talks and then the pandemic happened and the whole thing, you know, went away. But I think that they knew that they were. They had a lot of bad publicity over that point and I think we would. The company would have probably ended up in Amazon’s hands had the pandemic not happened and nobody ended up caring because all they cared about was that things could get delivered and that you could, you can ship it back one way the other, and so they were.
Ming:
So after a while that went away, but for the longest time, and then they even, you know they even complained to Apple to try and get Fakespot booted off the App Store. So you couldn’t look at it because we had, you know, our app was essentially an overlay onto Amazon’s on mobile so it was kind of interesting as well. So you could look at it, it would turn us on and it would overlay onto it and do a review. So they were like complaining left and right.
Kevin King:
Did Apple do anything?
Ming:
They took it off for a while. I think it went back up after a while. But yeah, it was the more attention we got, the less desirable we became to them or the less fixed spot we came to them in their eyes.
Kevin King:
So you became. You said you did a lot of interviews. So you became. You were on every reporter from the Wall Street Journal and Business Insider and all these guys CNBC you’re on their hot list. So anytime there’s any kind of story about reviews or brushing or whatever you were on their speed dial right.
Ming:
Yeah, you know, I did the Bloomberg E-commerce Podcast with Spencer Sloan. Is this his last name, Spencer? Like so a bunch of those Wall Street Journal and then some international stuff, and then you’ll be surprised, a lot of local TV stations, like you know, region like Kansas City, Denver, some of the ones I can’t remember. They were always doing these little every time somebody complained to their TV news about fake reviews and all this stuff, like somebody.
Kevin King:
Probably one was like Channel 5 is on your side if you’ve been ripped off, call us, we’ll do a story.
Ming:
Yeah, I did a lot of remote interviews for things like that. Yeah.
Kevin King:
So what’s some of the craziest stuff you saw people doing like when your system, like you said, was, you could see all these patterns? What was something you’re like, oh my God, this is just like I can’t believe they’re getting away with this, or something along those lines?
Ming:
You know, I think the thing for us was that you know, most of these companies, mostly sellers, were using services, so the patterns were always the same and we taught everybody. I was like look, if you don’t believe us, look at it that way. And you said just now, nobody scrolls through, they look at the top, or usually they don’t even look at the top, they don’t even filter it.
Ming:
They just look at what are the top reviews and they give it even filter it. They just look at what are the top reviews and they give it a passing glance. And the most egregious ones we always see were like that they were repeat of the same reviews with the same language. With this, because if you’re going to do hundreds and thousands of reviews, you’re not going to get creative, you’re just going to throw it up against the wall and there’s going to be so much that it won’t. It won’t matter. There’s going to be so much it won’t matter.
Ming:
So for us it wasn’t that, it was just the sheer volume of it. At one point we were doing some analysis for different media companies and we found that like, let’s say, phone accessories some categories like phone accessories were like pretty much 70% of the reviews of phone accessories were all fake, like the, especially like cases from China that were being made in China and shipped over here, and stuff like that. Like they’re all fake, like not, not one of them is credible in that way because, let’s face it, you buy a phone case. Are you really going to leave a review if it’s a $7 item? But you’ll find that these $7 items have tons and tons of reviews and you’re like, why is that the case when it’s a throwaway item? So there are things like that that we found.
Kevin King:
Because reviews are a moat. I mean so that the number is a moat. And I know now, even with Vine reviews, there’s a lot of people I don’t know how Fakespot’s addressing this but quite a few of the reviews from Vine reviewers are no longer real. They’re all AI written. I know Vine reviewers that are not writing their own reviews anymore. They’re just typing in giving a ChatGPT or Gemini or something here. Here’s the link to the listing write a review, yeah, and they’re just, they’re posting, they’re fake. How do you deal with that?
Ming:
So the AI aspect, and because we started with the AI aspect of it early, like I remember, the first time I really worked with Saoud was like when I heard the terms machine learning and how he was training, how he was training them to learn about how people write and cadence and language and like how language of somebody with a different writing style will mean differently than somebody with a from writing style, will mean differently than somebody from a different background and stuff. So the and part of the reason why when Fakespot got sold last year or it got bought last year, it was bought by Mozilla’s AI arm and you’re like, why is Mozilla’s AI arm buying this? Because of that, because the machine learning and the AI and Fakespot has to outpace all these things that are going on so if you’re using ChatGPT, the right reviews.
Kevin King:
Well, you can tell every LLM has a pattern, right, every LLM has a pattern. Now you can affect that pattern if you’re good at prompting, right, but the average person is not so you’re going to catch 80 to 90%.
Ming:
You have to get even deeper into your AI as to how to catch these guys. But we Fakespot has a little bit of head start, because they started this a little bit earlier. And I’ll tell you another sort of funny anecdotal story. In 2018, we got a call to go present to the NSA and to present to Darpa, and we’re like why does the government want to see us. So we thought we had done something wrong and as it turns out, they were looking for a tool to analyze research papers and figure out which were real and which were fake, and which were plagiarized and which weren’t plagiarized. Because they were getting so many research papers on these kind of topics from around the world, they couldn’t tell which was real, legitimate to put their attention on. So the government was well, the NSA was interested in licensing our engine to like do something with this to spot fakes in the research world.
Kevin King:
What do you think about? There’s been talk and Amazon tested this a little bit last year and they went away from it. But I know it’s Casey Goss, like when he had Viral Launch young kid, super smart guy, I think he was 20, 21 when he started Viral Launch. But one of the things he came out with this is years ago now.
Kevin King:
He said Amazon needs to change the way they’re doing their reviews. He said that Amazon, the way they’re doing the reviews is it’s not fair because something that has 10,000 reviews has a moat over a new product. Maybe I’ve come in, I’ve developed a new product and I’ve actually got a better product that actually solves a problem that everybody’s complaining about and actually should be given a chance. And I have no chance against something that has 10,000 reviews. It’s almost 95% of the time it’s going to always beat me. So he said that Amazon should change the way they do reviews and only show like the last year or something like that. So something that has 10 000 only show 820. What’s what are the last years worth of reviews and that should be the brown or not this lifetime number. So Amazon kind of checked, tested that a little bit in India and the US last year and then they went away from it.
Ming:
He’s correct. I think there’s got to be a better way of doing reviews. But I also think that we, you know, coming from the retail side, like in retail, everybody complains that we train the consumer to only focus to buy something when it’s a sale time. We train the consumer to only buy something when it’s high reviews. All right. How many people do you know look at restaurants or product reviews, as you said just now? They don’t even read the most recent reviews. They don’t bother to filter it. They’re like oh, this has a thousand reviews at four and a half stars, the other one has ten thousand reviews at four and a half stars. I’m going with a ten thousand review one.
Ming:
I think part of it has to be how do you train people to read, to understand reviews better? And then the other part of it, yes, you have to change, because I do that now, like whenever I look at anything on amazon or what I’m buying, I only read the last two, three months because I know the product might have changed somehow. You know, because you know these things especially they’ve been on the market for a couple years or whatever it is. So I don’t bother a review from 2019. I don’t bother reading, I’m like, okay, give me what. What happened the last three months, six months, and then the velocity.
Kevin King:
Yeah, a lot of times what I do is I look at the star rating. Okay, this one’s 4.5. And if they’re equal, and one is 10,000, one is 1,000, 10,000 reviews with a 4.5 is better than 1,000 with a 4.5. And then I will look through the last handful of reviews. I don’t go three months or whatever. I’ll look through the last handful and then I look for the worst negative ones, because and sometimes the worst negative ones are not necessarily bad, you know, sometimes it’s, you know, even like on not just on Amazon, but if it’s on a hotel website, it’s someone bitching that you know. You know they gave it a one star because their next door neighbor was having an argument all night long. That’s not on the hotel.
Ming:
Right. But even like Amazon, like how many times somebody will say, oh, the box came squished a little bit and I’m like, okay, but did the product survive this? But all you talk about is the box came squished. Or oh, they put so much packaging for a little thing. I’m going to give this one star because I’m an environmentalist.
Kevin King:
I throw those out. Yeah, I throw those out, but there is a, I think, it’s statistically maybe correct me if I’m wrong on this I think it’s. Is it 21 reviews, statistically, is the number needed to actually start to trust it? Someone actually put that out. This was a stat that I can’t remember it came from, but some statistician said you need 21 actual, real reviews. You know, if it’s a Fakespot F 21 account. But 21 real reviews can actually give you an indication of what this product is.
Ming:
That’s a misnomer, because that and the word I’ve used a couple times is the velocity of it. If you get 21 reviews in a short, short, short period of time and so that’s another thing that Fakespot would. Compare is like okay, this is a competitor one and you get these reviews in X amount of time. From a comparable product perspective, should you be getting those? And so we caught a lot of people like that.
Ming:
We had people write into US and said complain as sellers. They’re like how dare you give me a C grade? You know, I got all my reviews legitimately, blah, blah, blah. And then we were able to say well, you got all your reviews in a seven day span, within launch of, and you had X amount. And we can tell from sales, because we were able to track all these things, how much you sold versus how many and your conversion rate of reviews. And like, if you sold 21 products in the first week and all 21 people left you a review, we can pretty much tell that they’re not all going to be reliable. And then you know there are other things to look at but the velocity alone of that will tell you. So just saying that, oh, 21 is legitimate. It’s true to some point but not really true to some point, because the velocity of it counts, the time period that you get it counts.
Kevin King:
Hey, what’s up everybody? Kevin King here, you know one of the number one questions I get is how can you connect to me? How can I, Kevin, get some advice or speak with you or learn more from you? The best way is with Helium 10 Elite. If you go to h10.me/elite, you can get all the information and sign up for Helium 10 Elite. Every month I lead advanced training where I do seven ninja hacks. We also have live masterminds every single week. One of those weeks I jump on for a couple hours and we talk shop, we talk business, do in-person events. Helium 10 Elite is where you want to be. It’s only $99 extra on your Helium 10 membership. It’s h10, h10.me/elite. Go check it out and I hope to see you there.
Kevin King:
If I’m not doing any hanky panky on my listing or any kind of, if I just selling my product and leaving it at that, I’m not doing a follow-up sequence with a tool, I’m just letting Amazon do their thing. They send out, hey, review this product, and people just naturally go and review products. What kind of rate should I get? So I cause people always come to me and they say, Kevin, how do I get the number one. Still the number one thing how do I get reviews? How do I get reviews? What’s the best way to get reviews? And I just tell them sell more and put out a good product. Yeah, and so what is the number you should that you guys figured out? Okay, this about a two percent rate of reviews organically is pretty natural. Or is it a four percent, or is it one percent or what?
Ming:
So we looked at it, at depending on the type of product and I’ll get into the we always thought that between a five and 10% conversion rate made sense.
Kevin King:
Now, five to 10% review rate.
Ming:
Yeah, it makes sense. But again, it depends on the price of the product and it depends on the sort of like, the trends of the moment of in terms of like you know, is this a summer product, and so people are more liable to leave a summer review and stuff. And then every so often you will get something that’s like a supernova kind of product that’s so new. So we were. So I had a couple of different sellers where I would call and like we would talk about what you, what you’re seeing in reviews, and why and versus, and they’re all like top sellers. I always like to pick the brains of sellers. That’s how I got to know a, because I knew it was a seller past. I just wanted to pick his brain on how it worked and she was the person that invented the mattress clips, that clipped your sheet to the mattress, and they were the number one seller. They were made in the USA and stuff. And then all these copycats started showing up overnight and they took a hit and stuff.
Ming:
But when I was looking at their velocity. In the beginning there was nothing like it and people were so ecstatic that a $12.99, well, I guess now $8.99, whatever the price is a cheap product like this made such a difference in their lives that they were willing to like share it. So every time you get this sort of like supernova product and then you have to discount for the fact that you have to then take an account that this is a supernova product and so that’s why you’re getting that right. And so and we experienced that ourselves with our memory foam bath mat when we first launched memory foam bath mat and I was like I don’t employee number 11 of the company or something like that, and our sales kept going through the roof and people kept leaving reviews or word of mouth because they were like, oh, this thing is insane. And at that time there were no reviews on sites but there were blogs and blogs would start talking about this and people then commented on the blogs how great this was and stuff, and it was just a supernova product.
Ming:
But you can’t compare a supernova product with something as basic. Let’s say you were launched a new battery product. Today, you’re not going to get that kind of velocity. In fact, you’re probably going to be lucky if you convert 1% of your sales into a. There’s gotta be a compelling reason why people are leaving a review for you, you know, especially for a lot of throwaway products or a lot of products people think like eh, you know. So there are these things that we have to take into account, and so one of the things that I would do was like look at the competitors and what kind of velocity everybody has. So what should you expect in this category for this kind of product?
Kevin King:
So what’s the future of reviews? Reviews are a very important element when it comes to e-commerce and selling online, not just e-commerce and anything you buy. Where’s this going? Are we going to trust anything, especially with AI now? Or are we going to have a fake spot little icon in their Apple Vision Pro that says that this? That says Ming, yes, this is the real Ming. This is not the fake Ming, or what’s? Where’s this going? Where do you see this going?
0:47:45 – Ming:
I think it’s like an arms race on either side right, let’s say, the dark side and the light side, and like the guys who wanted to be very useful and very functional and sort of like, where it’s a community feeling, where you can really trust that person who you don’t know but you know enough context where you trust it enough, where then you make a decision, purchase of it. And then you always have the dark side of like well, I’m just going to game this as best I can, because this is an irrelevant product and as you spend what’s 10, 15 dollars if you don’t like it, there’s a throwaway. I’m not sure there’s a good answer for either one of these. I think it’s always going to be like an arm series that way. I also think that you know we as consumers have to be not lazy, and you know, yeah, we as computers have to be not lazy or like look, it takes you a couple extra clicks to look at something. It’s just like when you’re in the store, you buy it, you pick it up, you read the, you know again, this is my training for Walmart. They were like people come in, they pick up the product, they look at the call outs and the stuff and then they put it. Then they look at what’s around it and all the shelves are selling committed products. What are the committed products saying about it? And then you put it in the cart. If this is the one you want, you put it in the car. We don’t do that now, instead of looking at compared products because we just want it and you want it. You want to make that purchase in seconds but an extra minute isn’t going to kill you, and I think we just have to get back to like be a smarter consumer, like you know, like you said you read the worst ones and you’re like are these really that bad?
Ming:
Do they really matter? You know they. They shipped it a day late. Oh, this is a one star. You’re like is that really an issue? I mean it is and it isn’t.
Kevin King:
Awesome. Well, Ming, this has been really fun. We could keep talking for a while but we’ve been going an hour here and my editor’s gonna kill me. He’s like wants me to keep these a little shorter, but when it’s so good, you got to keep going. So, Mel, don’t cut this because this has been really good stuff. Really appreciate you taking time. If people want to learn more about you what you’re up to these days or what’s happening is there a good way for them to do that?
Ming:
Generally everybody can find me on LinkedIn. I try to use that to sort of like my background. I’m running a different tech company. I run a different tech company now that’s in real estate tech. That’s not really in the space. I’m not going to push it too much. And then obviously you know for me, the better Fakespot does in the long run, the better it is for me selfishly. So I would encourage everybody to like keep looking at it, keep using it, and you know. But yeah, look for it. Look for me on LinkedIn. I think Ming Ooi there are not that many Ming Ooi’s on LinkedIn, so you can always find me. Happy to chit-chat anytime. This has been a lot of fun. This is great.
0:50:39 – Kevin King:
Awesome. Thanks, Ming, and that’s spelled M-I-N-G for those of you driving or working out right now M-I-N-G and then O-O-I, the last name’s, O-O-I, Ming. I really appreciate it, man. This has been awesome. Hopefully I’ll see you again soon. We can have another chat on something. Yeah, in a very short while.
Ming:
I’ll see you next year at Prosper and we’ll have a cigar with Abe.
Kevin King:
There we go. It’s a deal, it’s a date. Thanks, man.
Ming:
Thank you very much, Kevin. Talk to you soon.
Kevin King:
Really fascinating stuff with Ming. He was an open book just talking information. I hope you really got a lot from this episode. Don’t forget to sign up for my newsletter, billiondollarsellers.com, and we’ll be back next week with another great episode. We get talking about some PPC stuff that’s going to really help you out. I think you’re going to really like it. But before I let you go today, I’ve got some words of wisdom. People pay in anticipation of what they’re going to get instead of gratification for what they did get. People pay in anticipation of what they think they’re going to get instead of gratification for what they did get. See you again next week for the 100th episode of me hosting the AM/PM Podcast. Bye.
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