February 3, 2025 • 18 mins
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https://g.page/r/CVs16A3t1J44EAI/review
This episode delves into the critical issue of fake reviews in the digital shopping landscape, emphasizing the growing prevalence and impact on consumer decision-making. We uncover insights from a study analyzing 73 million reviews, share tips to spot AI-generated reviews, and address the phenomenon of review hijacking affecting local businesses.
- The significance of reviews in modern purchasing decisions
- The rise of AI-generated reviews and implications for consumers
- Overview of the study and its findings on review fraud
- Tips for identifying suspicious reviews
- Discussion on the practice of review hijacking and its impact on businesses
ADDITIONAL RESOURCES
Original study:
https://askfortransparency.com/research/high-cost-of-review-fraud/
Google Business Profile monitoring by Whitespark:
https://whitespark.ca/local-platform/
---
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https://www.meredithshusband.com
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Would you say that shopping is in your DNA?
Speaker 2 (00:02):
That's a yes, if you're listening, that means yes
, yeah, I think it's in my ADHDDNA.
Speaker 1 (00:13):
I think it's in your DNA.
I think it's in all of our DNA.
Yeah, I think what shopping is,or what it represents, is in
our DNA.
Speaker 2 (00:20):
As Americans.
Speaker 1 (00:22):
As people.
It's like a human hunting andgathering type of thing.
Speaker 2 (00:25):
Oh, for sure, for sure, yes.
Speaker 1 (00:27):
So we have a problem to talk about today.
How do you, when you needsomething I'm not talking about
like luxury buys like when youneed a dress and you know the
designer you want and you knowwhat you want, then you know
what you want.
Yeah, but how do you choosewhat you want if you don't know
the exact item?
For example, let's say you needswim goggles, so you've been
(00:49):
picking up swimming recently.
Yes, and you need some goggles.
How do you choose which?
Speaker 2 (00:54):
goggles, to buy Best swimming goggles and I try and
look for what I would thinkwould be the least influenced.
If it's swimminggogglescom, wehave the best swimming goggles.
(01:17):
I try and look at some consumerreports or reviews you look at.
Speaker 1 (01:23):
Consumer reports is basically reviews.
Right, it's from consumers andit's also the company itself
consumer reports.
They do reviews of products.
Yes, that's my point yes youlook at reviews.
This article came across mydesk or it didn't come.
Speaker 2 (01:37):
I found a blog article okay, I was going to say
, like, were you, did it, did itdid it, did it say, like were
you this just came across mydesk.
Speaker 1 (01:45):
Tick, tick, tick, tick, yeah Newsflash.
Speaker 2 (01:47):
Exactly Muppet Newsflash.
Speaker 1 (01:54):
Let's say it popped up on my radar.
It was a blog article on WhiteSpark.
It was about reviews and it wasabout fake AI reviews.
Ai generated reviews.
Speaker 2 (02:00):
And if somebody is just listening for the first
time right now, what's WhiteSpark?
Speaker 1 (02:04):
White Spark offers SEO services for local
businesses.
Okay, I will actually talkabout them a little bit later.
Oh, and that's why it cameacross my desk, that's why it
popped up on my radar and I sawit that it has to do with SEO,
specifically with reviews aboutGoogle, but I think this can be
extrapolated to all reviews,anywhere there are reviews.
(02:25):
I think what we're going totalk about would be, in effect,
let's say, Okay.
Okay, so this article wassummarizing a study done by a
company having to do withreviews, having tried to
identify fake reviews andestimate how many of them are
out there and how do you spotthem, and blah blah blah.
Speaker 2 (02:44):
Couldn't you just say chat, gpt?
Write a good review based on mywebsite.
Speaker 1 (02:50):
Sure, it gets to be more complex than that.
I mean, there are traditionally, I think, a lot of us kind of
know when we might be reading afake review and traditionally
those reviews are just written.
That sort of work is outsourcedoverseas, so a lot of times
they're non-native Englishspeakers and that's why you can
kind of be like this reviewseems funny.
(03:12):
They have big companiesoverseas where it's just a
factory to create fake reviews.
Really, they create fake Googleprofiles, fake Google reviews,
fake Facebook likes, instagramlikes.
That's where all that stuff iscreated.
Speaker 2 (03:27):
Do we know where it is?
Speaker 1 (03:29):
Somewhere overseas where labor is cheap.
Speaker 2 (03:31):
Yeah, okay.
Speaker 1 (03:32):
So this article summarized some of the findings
from this study.
I'm going to cover some of them.
I think some of them are reallyinteresting.
Some of them are kind of likeyeah, no duh.
Interesting.
Some of them are kind of likeyeah, no duh.
And then a few tips for us asconsumers how to spot fake AI
reviews moving forward, becauseit's going to get trickier, it's
going to get more difficult tospot them.
Speaker 2 (03:52):
Do you think AI is going to learn how to look dumb
Right?
They're going to put ifs andthey're going to misspeak.
Speaker 1 (03:58):
They're going to have grammar mistakes, right,
because that's one way to spotthem.
It's like a perfectly writtenreview.
(04:19):
Okay, so I'm going to kind ofalso summarize those findings
here.
Okay, so I'm going to kind ofalso summarize those findings
here.
Okay, give you a couple tips,like I said, tips for us as
consumers and a tip for you as alocal business owner.
So first, this study reviewed73 million reviews and it
(04:42):
reviewed reviews of,specifically, google business
profiles, so it didn't look atreviews like on, say, amazon.
Or Yelp, or Yelp.
Okay, and it looked at threedifferent industries health
industry, legal industry andhome service industry.
Speaker 2 (04:53):
Okay, so home service like plumbing.
Speaker 1 (04:55):
Yes, plumbers, we need a plumber, electrician, a
contractor, whatever.
Speaker 2 (04:59):
Okay.
Speaker 1 (05:00):
Okay, so one of the biggest findings was that
reviews are the single mostinfluential factor on purchasing
decisions.
Not a big surprise, right?
Right, 98% of people respondthat they rely on reviews before
making a buying decision.
Again, not that surprising.
I've even seen this 98% figurebefore.
Speaker 2 (05:22):
Yeah, I don't doubt it, Because we trust other
people.
Speaker 1 (05:25):
Yeah.
Speaker 2 (05:26):
I don't doubt it, because we trust other people
and we, we, we just trust otherpeople.
Speaker 1 (05:29):
It's the closest thing to word of mouth marketing
, like if you were, if you wereto ask your best friend, uh, hey
, have you ever tried X, y, z?
And they said, oh, I like thisone, you would go get that one.
Like it's, it's the closestthing to that that you that can
be replicated online.
Speaker 2 (05:47):
Okay online.
I used to be the person whopeople would often come to for
those questions around theneighborhood, et cetera.
Speaker 1 (05:52):
I do not doubt it.
So this article estimated thatbecause of review fraud meaning
fake good reviews it estimated$300 billion in annual consumer
harm, so people buying stuffbased on reviews because those
reviews are good and thosereviews were fake.
Speaker 2 (06:13):
I don't know how it came up with that figure, but
Well, do you remember when Ibought that mouth guard cleaner
off of.
Speaker 1 (06:19):
Amazon.
Yes, yes.
Speaker 2 (06:20):
And then they sent me a form saying if I gave them a
five star review and sent them aphoto of proof, they'd give me
money and technically thatwasn't a fake review.
Speaker 1 (06:32):
I've had that too, by the way.
I had that with.
Speaker 2 (06:33):
Isn't that technically a fake review?
Speaker 1 (06:35):
That's not a fake review.
We're talking aboutAI-generated reviews here, so
that's not a fake review, but itshows how important those
reviews are.
Speaker 2 (06:42):
Yeah, yeah, the product that.
Speaker 1 (06:43):
I bought based on the good reviews.
The product was awful, yeah,and I got the same thing.
Hey, leave us a review and youget a free one.
I was like, okay, good idea,Okay.
So $300 billion in annualconsumer harm that averages out
to almost $2,400 per household,what so?
It's significant, right?
(07:04):
It's significant, right.
So, of all those 73 millionreviews that were analyzed, 14%
of them were categorized asextremely suspicious, ie, likely
, false, likely, fake.
So there were differentcategories.
Some reviews appear fine, someare a little suspicious, some
are like, obviously, well,that's really suspicious.
(07:25):
14% were in that category.
It's like they're obvious.
Speaker 2 (07:28):
Yeah, okay, right, written by cats, ai cats, uh-oh.
Speaker 1 (07:34):
And I thought this perhaps was the most interesting
and most alarming AI-generatedreviews.
Beginning in June 2023 towhenever this report ended,
sometime in 2024, thoseAI-generated reviews grew at 80%
, month over month.
What?
Every month, they grew 80%, 80,8-0.
(07:54):
, 8-0.
So they're almost doublingevery month.
So this is going to be a bigproblem.
It's going to be an issue.
I mean, we just won't be ableto trust reviews as much.
Unfortunately, in this articlethere were some things that you
can look for, some things thatwere common among those fake,
highly suspicious reviews,things that you and I can look
(08:16):
for when we're buying stuff.
Speaker 2 (08:18):
And just to clarify, this is different from sponsored
reviews that, for example, onYouTube.
They have to state we wereCorrect.
Speaker 1 (08:27):
This is not like an influencer.
Speaker 2 (08:29):
Right.
Speaker 1 (08:30):
Although, to be honest, I think at some point
you're watching influencers.
The influencer is going to befake.
Speaker 2 (08:35):
Yeah, they're going to have videos.
Speaker 1 (08:36):
Yeah, you don't need an actual person to sit there
and talk to the camera.
Anyway, I digress.
Like we said, I think a lot ofus kind of feel we can spot the
fake reviews.
We've had years of practice.
Speaker 2 (08:48):
Yeah, Like, for example, I just believe
everything I read, though that'smy problem.
Speaker 1 (08:52):
Yeah, I mean, for example, if a review has poor
grammar, like we were saying,it's probably real.
If a review is perfectlywritten, it could be a little
suspicious.
If a review has, like,professionally taken photos, I
always thought those were justplaced there by the actual
company, it could be could befake reviews.
Okay, how do we're going totalk about?
(09:13):
How do you spot fake reviews?
Okay, so they probably ran thisinformation through AI to
analyze it.
Some very specific things tolook for.
If you see the phrase Irecently had the pleasure of
working with, that evidently isa telltale sign of an AI
(09:33):
generated review.
So you read a review for, say,an attorney let's say you need
an attorney for some reason andthe and the review starts out.
I recently had the pleasure ofworking with blah, blah, blah
and blah blah.
That phrase is trending in fake, fake reviews.
So it's very common across thefake reviews Okay, Reviews that
refer to the other reviews.
(09:54):
So you've seen reviews that sayI don't know what these other
two-star reviews are talkingabout.
Always, yeah, Okay, that's also.
I'm not saying that can't be ina real review, it can be, but
it's very common among what theythought were fake reviews.
Review length was a signal.
Longer reviews tend to be fake,and this I would say you have
(10:19):
to use your judgment here.
I mean, sometimes reviews arelong and they're very helpful.
Sometimes they're long andmaybe they don't need to be.
Reviews with cliches in them,so kind of generic sayings like
the first thing that struck me.
These are examples from thearticle.
Quote the first thing thatstruck me.
The phrase game changer myfavorite delivers on its promise
(10:43):
.
The phrase delivers on itspromise, Wow.
Quote without the fuss, so kindof generic.
Speaker 2 (10:48):
Yeah Well, you know, when I have tried to use AI to
help write certain things, Ialways so generic.
It's really just cliche.
Speaker 1 (11:00):
That is an issue with the prompt.
The more specific you can bewith a prompt, the more you can
get around that, but I agreewith a relatively simple prompt.
Yeah, the response is going tobe generic, sounding like that
Okay, we're on number six now.
The review repeats the prompt,and so in this case that would
be the full product name or thefull name of the business.
(11:22):
So, for example, again, let'ssay you were looking at hiring
an attorney, if you had actuallygone out and worked with an
attorney and you were going towrite a review, right, and let's
say your attorney's name wasJames Clearwater, Jane
Clearwater.
Jane Clearwater.
Uh-huh, so let's say yeah, sothe name of her business might
be Jane Clearwater.
Speaker 2 (11:43):
Law.
Speaker 1 (11:44):
Law exactly.
Okay, if you were to leave areview for Jane Clearwater, you
would probably refer to her asJane, you and I.
We would say working with Janewas great, right.
An AI bot would say probablysomething like Jane Clearwater
Law was great to work with.
I enjoyed working with JaneClearwater Law, right, that
would be a giveaway.
Speaker 2 (12:05):
Yeah.
Speaker 1 (12:06):
Also highly structured writing.
So something that includes likean introduction, maybe bullet
points, includes a summary.
That's also a red flag.
So those are some things tolook for as a consumer, as a
business owner.
What does this mean?
So we've talked about theimportance yeah, we've talked
about the importance of likeyour Google business profile.
(12:28):
Yes, in 2023, I think it wasGoogle said they had blocked or
removed over 170 millionpolicy-violating reviews.
So not necessarily fake, butfake definitely fits into that
category.
So they also are making apretty concerted effort to get
rid of it.
They know this is a problem.
(12:48):
In the time span of I think itwas one year they got rid of 170
million reviews.
I will link to that quote.
I also linked to this article.
But what is specifically aproblem for you and I as local
business owners, we're not goingout and we're not buying AI
reviews, right, but somethingthat is happening is there's
(13:10):
something called reviewhijacking.
Review hijacking and what thisis is someone somehow takes over
your Google business profileand they change, say, the phone
number and the website addressbecause you already have good
reviews and they want to justuse your reviews because reviews
are so important, right,pirates?
(13:31):
Yes, now, I actually had thishappen to a client.
Speaker 2 (13:34):
And.
Speaker 1 (13:35):
I didn't realize this is what it was.
But yeah, I had a client prettysuccessful business, pretty
well-known, lots and lots ofGoogle, like hundreds of Google
reviews.
Someone came to them sellingsome sort of service I don't
know what it was, I wasn'tinvolved and for that service
they said oh well, we needaccess, we need to your Google
business profile.
Okay, that's sort of normal.
Next thing we know we don'thave access to the Google
(13:58):
business profile anymore and wedidn't catch this for months.
Speaker 2 (14:01):
Oh right, why would you?
Speaker 1 (14:02):
Why would you?
Yeah, there's, there's, there's.
No, it was only after theclient said, hey, we've hardly
been getting any calls, or callsare way down, and and we went
out and we looked at Google andwe did a search for their
company name and then we said,oh shit, your phone number is
wrong.
So, and then it took months.
It took months to get it, toget that profile back.
Now we got it back, but it wasa significant amount of time
(14:26):
that we lost and he lost asignificant amount of business
Of course.
Speaker 2 (14:29):
And then?
But how then would the like,don't the consumers realize that
it's a different name than whatthey were looking for?
Speaker 1 (14:36):
Don't the consumers realize that it's a different
name than what they were lookingfor.
I don't think it might occur tothem.
But at that point, why wouldyou suspect this Like this would
never occur to me to be likewait a minute, did you guys
steal this business profile?
And you're not the company Iintended to contact?
Like it would, it, justwouldn't.
You would overlook that.
(14:57):
Yes, it would be.
It should be a red flag, but itwould it just wouldn't.
Speaker 2 (15:00):
You would overlook that.
Yes, it would be.
It should be a red flag, butyou would overlook it.
Of course You'd be like, oh, Ithought that was an okay anyways
.
Speaker 1 (15:04):
So one of my points is that your reviews, especially
your reviews on your Googlebusiness profile, are so
important they're important forall the things that we just
talked about like 98%, soimagine 98% of your clients
looked at your reviews.
Okay, ding ding, that'simportant right there, the
review hijacking Ding ding.
That's also something to beaware of.
(15:25):
Monitor your Google reviews andwe've talked about this in
another episode a service thatWhite Spark offers.
Speaker 2 (15:34):
Aha, now bringing it home.
Speaker 1 (15:36):
And it's a dollar a month.
Like I, highly recommend it.
I will link to that too.
I'll link to that episode.
Speaker 2 (15:42):
For sure.
Speaker 1 (15:43):
And that's why I found this article on WhiteSpark
, by the way, of course.
But your reviews are alsoimportant for SEO.
Right, like, when is the lasttime you did a search for
something and those top fewresults pop up in the maps
section, the local?
When's the last time, like oneof those had a two-star average
review?
Like it's happened, but it'sbeen years.
(16:04):
So those reviews are good forSEO too.
Google is not going torecommend a company with poor
reviews to their users.
They're just not going to.
So also try to slowly build upyour reviews.
Put that into your workflow.
You don't need to build a tonovernight, and you shouldn't,
because that is a red flag toGoogle that they are fake.
(16:26):
But put it into your workflow.
What I do is after someone.
If I'm working with a client,at some point they are really
happy about something.
That's when I ask oh hey, canyou leave a review?
In my experience, that's thebest time to ask, and on that
note, if this podcast episodewas helpful to you, why don't
(16:49):
you go leave me a review onGoogle?
I will link to that also.
That would be awesome, lovely,as long as it's not AI.
Yeah, don't have AI, do it foryou.
Speaker 2 (16:56):
I will link to that also.
That would be awesome, as longas it's not AI.
Speaker 1 (16:58):
Yeah, don't have AI do it for you.
Speaker 2 (17:01):
I recently.
Speaker 1 (17:02):
Yes, I recently had the pleasure of working with.
Speaker 2 (17:06):
Meredith's husband I recently had the pleasure of.
It was a game changer.
Speaker 1 (17:09):
Jane Clearworth, jane Clearworth, jane Clearworth.
Would you say that shopping is in your DNA?
Speaker 2 (00:02):
That's a yes, if you're listening, that means yes
, yeah, I think it's in my ADHDDNA.
Speaker 1 (00:13):
I think it's in your DNA.
I think it's in all of our DNA.
Yeah, I think what shopping is,or what it represents, is in
our DNA.
Speaker 2 (00:20):
As Americans.
Speaker 1 (00:22):
As people.
It's like a human hunting andgathering type of thing.
Speaker 2 (00:25):
Oh, for sure, for sure, yes.
Speaker 1 (00:27):
So we have a problem to talk about today.
How do you, when you needsomething I'm not talking about
like luxury buys like when youneed a dress and you know the
designer you want and you knowwhat you want, then you know
what you want.
Yeah, but how do you choosewhat you want if you don't know
the exact item?
For example, let's say you needswim goggles, so you've been
(00:49):
picking up swimming recently.
Yes, and you need some goggles.
How do you choose which?
Speaker 2 (00:54):
goggles, to buy Best swimming goggles and I try and
look for what I would thinkwould be the least influenced.
If it's swimminggogglescom, wehave the best swimming goggles.
(01:17):
I try and look at some consumerreports or reviews you look at.
Speaker 1 (01:23):
Consumer reports is basically reviews.
Right, it's from consumers andit's also the company itself
consumer reports.
They do reviews of products.
Yes, that's my point yes youlook at reviews.
This article came across mydesk or it didn't come.
Speaker 2 (01:37):
I found a blog article okay, I was going to say
, like, were you, did it, did itdid it, did it say, like were
you this just came across mydesk.
Speaker 1 (01:45):
Tick, tick, tick, tick, yeah Newsflash.
Speaker 2 (01:47):
Exactly Muppet Newsflash.
Speaker 1 (01:54):
Let's say it popped up on my radar.
It was a blog article on WhiteSpark.
It was about reviews and it wasabout fake AI reviews.
Ai generated reviews.
Speaker 2 (02:00):
And if somebody is just listening for the first
time right now, what's WhiteSpark?
Speaker 1 (02:04):
White Spark offers SEO services for local
businesses.
Okay, I will actually talkabout them a little bit later.
Oh, and that's why it cameacross my desk, that's why it
popped up on my radar and I sawit that it has to do with SEO,
specifically with reviews aboutGoogle, but I think this can be
extrapolated to all reviews,anywhere there are reviews.
(02:25):
I think what we're going totalk about would be, in effect,
let's say, Okay.
Okay, so this article wassummarizing a study done by a
company having to do withreviews, having tried to
identify fake reviews andestimate how many of them are
out there and how do you spotthem, and blah blah blah.
Speaker 2 (02:44):
Couldn't you just say chat, gpt?
Write a good review based on mywebsite.
Speaker 1 (02:50):
Sure, it gets to be more complex than that.
I mean, there are traditionally, I think, a lot of us kind of
know when we might be reading afake review and traditionally
those reviews are just written.
That sort of work is outsourcedoverseas, so a lot of times
they're non-native Englishspeakers and that's why you can
kind of be like this reviewseems funny.
(03:12):
They have big companiesoverseas where it's just a
factory to create fake reviews.
Really, they create fake Googleprofiles, fake Google reviews,
fake Facebook likes, instagramlikes.
That's where all that stuff iscreated.
Speaker 2 (03:27):
Do we know where it is?
Speaker 1 (03:29):
Somewhere overseas where labor is cheap.
Speaker 2 (03:31):
Yeah, okay.
Speaker 1 (03:32):
So this article summarized some of the findings
from this study.
I'm going to cover some of them.
I think some of them are reallyinteresting.
Some of them are kind of likeyeah, no duh.
Interesting.
Some of them are kind of likeyeah, no duh.
And then a few tips for us asconsumers how to spot fake AI
reviews moving forward, becauseit's going to get trickier, it's
going to get more difficult tospot them.
Speaker 2 (03:52):
Do you think AI is going to learn how to look dumb
Right?
They're going to put ifs andthey're going to misspeak.
Speaker 1 (03:58):
They're going to have grammar mistakes, right,
because that's one way to spotthem.
It's like a perfectly writtenreview.
(04:19):
Okay, so I'm going to kind ofalso summarize those findings
here.
Okay, so I'm going to kind ofalso summarize those findings
here.
Okay, give you a couple tips,like I said, tips for us as
consumers and a tip for you as alocal business owner.
So first, this study reviewed73 million reviews and it
(04:42):
reviewed reviews of,specifically, google business
profiles, so it didn't look atreviews like on, say, amazon.
Or Yelp, or Yelp.
Okay, and it looked at threedifferent industries health
industry, legal industry andhome service industry.
Speaker 2 (04:53):
Okay, so home service like plumbing.
Speaker 1 (04:55):
Yes, plumbers, we need a plumber, electrician, a
contractor, whatever.
Speaker 2 (04:59):
Okay.
Speaker 1 (05:00):
Okay, so one of the biggest findings was that
reviews are the single mostinfluential factor on purchasing
decisions.
Not a big surprise, right?
Right, 98% of people respondthat they rely on reviews before
making a buying decision.
Again, not that surprising.
I've even seen this 98% figurebefore.
Speaker 2 (05:22):
Yeah, I don't doubt it, Because we trust other
people.
Speaker 1 (05:25):
Yeah.
Speaker 2 (05:26):
I don't doubt it, because we trust other people
and we, we, we just trust otherpeople.
Speaker 1 (05:29):
It's the closest thing to word of mouth marketing
, like if you were, if you wereto ask your best friend, uh, hey
, have you ever tried X, y, z?
And they said, oh, I like thisone, you would go get that one.
Like it's, it's the closestthing to that that you that can
be replicated online.
Speaker 2 (05:47):
Okay online.
I used to be the person whopeople would often come to for
those questions around theneighborhood, et cetera.
Speaker 1 (05:52):
I do not doubt it.
So this article estimated thatbecause of review fraud meaning
fake good reviews it estimated$300 billion in annual consumer
harm, so people buying stuffbased on reviews because those
reviews are good and thosereviews were fake.
Speaker 2 (06:13):
I don't know how it came up with that figure, but
Well, do you remember when Ibought that mouth guard cleaner
off of.
Speaker 1 (06:19):
Amazon.
Yes, yes.
Speaker 2 (06:20):
And then they sent me a form saying if I gave them a
five star review and sent them aphoto of proof, they'd give me
money and technically thatwasn't a fake review.
Speaker 1 (06:32):
I've had that too, by the way.
I had that with.
Speaker 2 (06:33):
Isn't that technically a fake review?
Speaker 1 (06:35):
That's not a fake review.
We're talking aboutAI-generated reviews here, so
that's not a fake review, but itshows how important those
reviews are.
Speaker 2 (06:42):
Yeah, yeah, the product that.
Speaker 1 (06:43):
I bought based on the good reviews.
The product was awful, yeah,and I got the same thing.
Hey, leave us a review and youget a free one.
I was like, okay, good idea,Okay.
So $300 billion in annualconsumer harm that averages out
to almost $2,400 per household,what so?
It's significant, right?
(07:04):
It's significant, right.
So, of all those 73 millionreviews that were analyzed, 14%
of them were categorized asextremely suspicious, ie, likely
, false, likely, fake.
So there were differentcategories.
Some reviews appear fine, someare a little suspicious, some
are like, obviously, well,that's really suspicious.
(07:25):
14% were in that category.
It's like they're obvious.
Speaker 2 (07:28):
Yeah, okay, right, written by cats, ai cats, uh-oh.
Speaker 1 (07:34):
And I thought this perhaps was the most interesting
and most alarming AI-generatedreviews.
Beginning in June 2023 towhenever this report ended,
sometime in 2024, thoseAI-generated reviews grew at 80%
, month over month.
What?
Every month, they grew 80%, 80,8-0.
(07:54):
, 8-0.
So they're almost doublingevery month.
So this is going to be a bigproblem.
It's going to be an issue.
I mean, we just won't be ableto trust reviews as much.
Unfortunately, in this articlethere were some things that you
can look for, some things thatwere common among those fake,
highly suspicious reviews,things that you and I can look
(08:16):
for when we're buying stuff.
Speaker 2 (08:18):
And just to clarify, this is different from sponsored
reviews that, for example, onYouTube.
They have to state we wereCorrect.
Speaker 1 (08:27):
This is not like an influencer.
Speaker 2 (08:29):
Right.
Speaker 1 (08:30):
Although, to be honest, I think at some point
you're watching influencers.
The influencer is going to befake.
Speaker 2 (08:35):
Yeah, they're going to have videos.
Speaker 1 (08:36):
Yeah, you don't need an actual person to sit there
and talk to the camera.
Anyway, I digress.
Like we said, I think a lot ofus kind of feel we can spot the
fake reviews.
We've had years of practice.
Speaker 2 (08:48):
Yeah, Like, for example, I just believe
everything I read, though that'smy problem.
Speaker 1 (08:52):
Yeah, I mean, for example, if a review has poor
grammar, like we were saying,it's probably real.
If a review is perfectlywritten, it could be a little
suspicious.
If a review has, like,professionally taken photos, I
always thought those were justplaced there by the actual
company, it could be could befake reviews.
Okay, how do we're going totalk about?
(09:13):
How do you spot fake reviews?
Okay, so they probably ran thisinformation through AI to
analyze it.
Some very specific things tolook for.
If you see the phrase Irecently had the pleasure of
working with, that evidently isa telltale sign of an AI
(09:33):
generated review.
So you read a review for, say,an attorney let's say you need
an attorney for some reason andthe and the review starts out.
I recently had the pleasure ofworking with blah, blah, blah
and blah blah.
That phrase is trending in fake, fake reviews.
So it's very common across thefake reviews Okay, Reviews that
refer to the other reviews.
(09:54):
So you've seen reviews that sayI don't know what these other
two-star reviews are talkingabout.
Always, yeah, Okay, that's also.
I'm not saying that can't be ina real review, it can be, but
it's very common among what theythought were fake reviews.
Review length was a signal.
Longer reviews tend to be fake,and this I would say you have
(10:19):
to use your judgment here.
I mean, sometimes reviews arelong and they're very helpful.
Sometimes they're long andmaybe they don't need to be.
Reviews with cliches in them,so kind of generic sayings like
the first thing that struck me.
These are examples from thearticle.
Quote the first thing thatstruck me.
The phrase game changer myfavorite delivers on its promise
(10:43):
.
The phrase delivers on itspromise, Wow.
Quote without the fuss, so kindof generic.
Speaker 2 (10:48):
Yeah Well, you know, when I have tried to use AI to
help write certain things, Ialways so generic.
It's really just cliche.
Speaker 1 (11:00):
That is an issue with the prompt.
The more specific you can bewith a prompt, the more you can
get around that, but I agreewith a relatively simple prompt.
Yeah, the response is going tobe generic, sounding like that
Okay, we're on number six now.
The review repeats the prompt,and so in this case that would
be the full product name or thefull name of the business.
(11:22):
So, for example, again, let'ssay you were looking at hiring
an attorney, if you had actuallygone out and worked with an
attorney and you were going towrite a review, right, and let's
say your attorney's name wasJames Clearwater, Jane
Clearwater.
Jane Clearwater.
Uh-huh, so let's say yeah, sothe name of her business might
be Jane Clearwater.
Speaker 2 (11:43):
Law.
Speaker 1 (11:44):
Law exactly.
Okay, if you were to leave areview for Jane Clearwater, you
would probably refer to her asJane, you and I.
We would say working with Janewas great, right.
An AI bot would say probablysomething like Jane Clearwater
Law was great to work with.
I enjoyed working with JaneClearwater Law, right, that
would be a giveaway.
Speaker 2 (12:05):
Yeah.
Speaker 1 (12:06):
Also highly structured writing.
So something that includes likean introduction, maybe bullet
points, includes a summary.
That's also a red flag.
So those are some things tolook for as a consumer, as a
business owner.
What does this mean?
So we've talked about theimportance yeah, we've talked
about the importance of likeyour Google business profile.
(12:28):
Yes, in 2023, I think it wasGoogle said they had blocked or
removed over 170 millionpolicy-violating reviews.
So not necessarily fake, butfake definitely fits into that
category.
So they also are making apretty concerted effort to get
rid of it.
They know this is a problem.
(12:48):
In the time span of I think itwas one year they got rid of 170
million reviews.
I will link to that quote.
I also linked to this article.
But what is specifically aproblem for you and I as local
business owners, we're not goingout and we're not buying AI
reviews, right, but somethingthat is happening is there's
(13:10):
something called reviewhijacking.
Review hijacking and what thisis is someone somehow takes over
your Google business profileand they change, say, the phone
number and the website addressbecause you already have good
reviews and they want to justuse your reviews because reviews
are so important, right,pirates?
(13:31):
Yes, now, I actually had thishappen to a client.
Speaker 2 (13:34):
And.
Speaker 1 (13:35):
I didn't realize this is what it was.
But yeah, I had a client prettysuccessful business, pretty
well-known, lots and lots ofGoogle, like hundreds of Google
reviews.
Someone came to them sellingsome sort of service I don't
know what it was, I wasn'tinvolved and for that service
they said oh well, we needaccess, we need to your Google
business profile.
Okay, that's sort of normal.
Next thing we know we don'thave access to the Google
(13:58):
business profile anymore and wedidn't catch this for months.
Speaker 2 (14:01):
Oh right, why would you?
Speaker 1 (14:02):
Why would you?
Yeah, there's, there's, there's.
No, it was only after theclient said, hey, we've hardly
been getting any calls, or callsare way down, and and we went
out and we looked at Google andwe did a search for their
company name and then we said,oh shit, your phone number is
wrong.
So, and then it took months.
It took months to get it, toget that profile back.
Now we got it back, but it wasa significant amount of time
(14:26):
that we lost and he lost asignificant amount of business
Of course.
Speaker 2 (14:29):
And then?
But how then would the like,don't the consumers realize that
it's a different name than whatthey were looking for?
Speaker 1 (14:36):
Don't the consumers realize that it's a different
name than what they were lookingfor.
I don't think it might occur tothem.
But at that point, why wouldyou suspect this Like this would
never occur to me to be likewait a minute, did you guys
steal this business profile?
And you're not the company Iintended to contact?
Like it would, it, justwouldn't.
You would overlook that.
(14:57):
Yes, it would be.
It should be a red flag, but itwould it just wouldn't.
Speaker 2 (15:00):
You would overlook that.
Yes, it would be.
It should be a red flag, butyou would overlook it.
Of course You'd be like, oh, Ithought that was an okay anyways
.
Speaker 1 (15:04):
So one of my points is that your reviews, especially
your reviews on your Googlebusiness profile, are so
important they're important forall the things that we just
talked about like 98%, soimagine 98% of your clients
looked at your reviews.
Okay, ding ding, that'simportant right there, the
review hijacking Ding ding.
That's also something to beaware of.
(15:25):
Monitor your Google reviews andwe've talked about this in
another episode a service thatWhite Spark offers.
Speaker 2 (15:34):
Aha, now bringing it home.
Speaker 1 (15:36):
And it's a dollar a month.
Like I, highly recommend it.
I will link to that too.
I'll link to that episode.
Speaker 2 (15:42):
For sure.
Speaker 1 (15:43):
And that's why I found this article on WhiteSpark
, by the way, of course.
But your reviews are alsoimportant for SEO.
Right, like, when is the lasttime you did a search for
something and those top fewresults pop up in the maps
section, the local?
When's the last time, like oneof those had a two-star average
review?
Like it's happened, but it'sbeen years.
(16:04):
So those reviews are good forSEO too.
Google is not going torecommend a company with poor
reviews to their users.
They're just not going to.
So also try to slowly build upyour reviews.
Put that into your workflow.
You don't need to build a tonovernight, and you shouldn't,
because that is a red flag toGoogle that they are fake.
(16:26):
But put it into your workflow.
What I do is after someone.
If I'm working with a client,at some point they are really
happy about something.
That's when I ask oh hey, canyou leave a review?
In my experience, that's thebest time to ask, and on that
note, if this podcast episodewas helpful to you, why don't
(16:49):
you go leave me a review onGoogle?
I will link to that also.
That would be awesome, lovely,as long as it's not AI.
Yeah, don't have AI, do it foryou.
Speaker 2 (16:56):
I will link to that also.
That would be awesome, as longas it's not AI.
Speaker 1 (16:58):
Yeah, don't have AI do it for you.
Speaker 2 (17:01):
I recently.
Speaker 1 (17:02):
Yes, I recently had the pleasure of working with.
Speaker 2 (17:06):
Meredith's husband I recently had the pleasure of.
It was a game changer.
Speaker 1 (17:09):
Jane Clearworth, jane Clearworth, jane Clearworth.
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