Matt Edmundson: Welcome to the e-Commerce podcast with
me, your host, Matt Edmundson.
The E-Commerce podcast is all about helping you deliver e-commerce.
Wow.
And to help us do just that, I am chatting with today's very special
guest, Valon Xhafa from Behamics about how AI and consumer behavior can
reduce, uh, shopping cart abandonment.
Yes, we are getting into all of that good stuff.
AI shopping, cart abandonment, What is not to like, but before Valon and
I jump into that, let me suggest a few of the e-commerce podcast episodes that
I think you will enjoy listening to.
Check out why you should be using AI in your e-commerce business with Shanif.
That was a great conversation, uh, with him and about how AI is changing shopping
product recommendations, specifically with Oliver Edholm, who was just, he
blew my mind, uh, with that conversation.
So do check those out.
You can find these and our entire archive of episodes on our website
for free ecommercepodcast.net.
Yes, you can.
You can also sign up for our newsletter, and each week we will email you
these links along with the notes and the links from today's conversation
with Valon directly to your inbox.
Totally free.
It's all amazing.
Now, this episode is brought to you by the e-commerce cohort,
which helps you deliver e-commerce.
Wow to your customers.
Yes, it does.
Valon.
I'm sure you've come across a whole bunch of folks stuck with their
eCommerce websites, or they've just got siloed into working into just
one or two areas of their business and miss the big picture, well enter
eCommerce cohort to solve this problem.
It's a lightweight membership group with guided monthly sprints that cycle through
all the key areas of e-commerce, the sole purpose of which is to provide you
with clear, actionable jobs to be done so you will know what to work on, uh, and
get the support you need to get it done.
So whether you're just starting out in e-commerce or if like me, you are
a well established e-commercer, I will definitely encourage you to check it out.
Head over to www.ecommercecohort.com for more information.
Or you can email me directly, matt@ecommercepodcast.net with
any questions and I'll try my level best to answer them.
Head over to ecommercecohort.com.
Honestly, you're gonna wanna check it out.
So Valon is a top bloke.
Yes, he is.
He has previously worked as an AI scientist at Google and other research
institutes developing sophisticated AI techniques and algorithms.
Uh, Valon is always looking for innovative methods to use artificial
intelligence to improve the online shopping experience at Behamics.
Valon, it is great to have you on the show, zooming in
all the way from New York.
Uh, thanks for being with us.
Great to have you.
Valon Xhafa: Hey, Matt.
Um, thanks for having.
Matt Edmundson: Oh no.
Brilliant, brilliant, brilliant.
So Valon, you have an impressive resume, uh, or CV as we
like to say here in the uk.
Uh, an AI scientist that would make a really interesting business card.
Do you know, what I mean, where you've got your name, I'm an AI
scientist, and the conversations that that would create at dinner parties.
Um, so you've obviously done some interesting stuff.
You find yourself now at Behamics.
So what is Behamics?
What does it do?
Valon Xhafa: Yeah, I saw Behamics, or I found Behamics right after I left
Google as a way to apply AI in eCommerce.
So what I saw is that, um, AI is widely applied in all industries, like
I know driving healthcare, finance, uh, but not a lot in eCommerce.
And I expected that AI could make a huge impact in eCommerce too.
So I saw Behamics as a way to apply AI in eCommerce to reduce
shopping car abandonments, you know, like shopping cart abandonments.
It's a, it's a big deal.
Um, and it's like 70% of cards are abandoned.
Um, and it, there is no like feedback or, or clear facts or statistics.
Why do we have shopping car abandonments?
Mm-hmm.
So this was as a, as a challenge.
And that's why I started beginning to understand why do we have
shopping cart abandonments?
How can we prevent shopping cart abandonments?
Not just like recover them through emails and all these retargetings and
everything that you usually do, but also be able to understand before they happen
or predict before a cart is abandoned.
Matt Edmundson: So you wanna predict shopping cart
abandonment before it happens.
Um, which is, uh, if I can put it bluntly, sounds almost a little
bit like witchcraft, right?
It's that kind of, uh, it's this sort of, it's this black hole, uh, of stuff
where I think people like me to sort of get lost in our thinking a little bit.
But AI has made some interesting advancements, but before
we get into that uh, Valon.
Let's just, for those that are new to e-commerce, what are cart abandonments?
Um, what do you mean when you say cart abandonments,
there's 70% cart abandonments?
Valon Xhafa: Yeah, the, there is a, there is a kind of like a
definition of the cart abandonment.
A simple one, which is like when a user visits your shop and they, they add
a product to the cart and then they leave without buying that product.
So that's usually, it's called cart abandonment.
And because of that you have like 70% of users, they abandon their carts.
Mm-hmm..
So we're talking about like 70% of users who added something to the cart.
You know, and not 70% of your whole traffic.
Right?
Because there are a lot of users who just come because they just
like, yeah, just wanna kill some time and just like enjoy themselves.
Like just looking at products and everything, you know, like everyone,
all of us pretty much we do that.
Uh, but cart abandonments is like this group of consumers or users who add
something to the cart and then they don't end up buying them, but they just leave.
So that's the cart abandonments.
Matt Edmundson: So people have added stuff to their shopping cart,
but they've just not purchased.
And this is, um, this is a big problem, isn't it?
I say it's one of these things that I've heard talked about off and on
for a long time now, you know, it's, it's one of the sort of the key
areas that people like to focus on.
It's like, what is stopping the people once they've added it to cut
what stops them paying me money?
And I want to understand that process.
So why, why is this, um, And, and the other thing that I've noticed,
and Valon, correct me if I'm wrong here, is the percentage of cart
abandonment seems to be going up.
It doesn't seem to be generally falling.
It seems to be, it's becoming more and more common.
Have I, Is that a fair reflection or, or have I got that
entirely the wrong way around?
Valon Xhafa: It is.
Its a, its a, it is a statistic.
It is a trend.
Uh, so cart abandonments are going up.
And connected to that also product returns, because these two concepts
are like tightly related to each other.
Um, so these are like, these are like statistics, uh, or KPIs that
are unfortunately like going up, um, because of a lot of issues, a lot of
reasons out there that we are gonna explore them during this podcast.
Um, but yeah, that's, that's the reality.
Matt Edmundson: It's interesting actually, that you've linked there, uh,
product cart abandonment and returns.
Uh, something that I, I want to come back to.
Um, so I've, I've jotted it down.
Uh, whenever I look down, I'm making notes.
By the way.
I'm not checking my email.
Just being clear.
Checking the football score or playing Sudoku.
So what are, what are some of the main reasons that you have
found, uh, for cart abandonment?
Why do, why do we still partake in this?
And let me be frank, right?
I catch myself doing it, uh, adding stuff to cart and then not buying.
Why do, what are some of the reasons we do it?
Valon Xhafa: Uh, so there are like, uh, external and internal factors.
Um, and we're talking about like the soar external factors regarding the
soar, And internal factors, at Behamics, we, we mainly focus ourself on internal
factors because these are like factors that we can actually impact and we can
improve and prevent cart abandonments.
And these factors usually account for the majority of the reasons.
So if we're talking about some of the main reasons, they are like, we group them into
two categories, like behavioral issues or behavioral reasons and technical.
Technical issues within your shop, right?
Yeah.
Um, so behavioral issues are simple.
Put like users as we as humans, we have hard time making decisions when we
have a lot of options to choose from.
Imagine, for example, for you or for me, or for anyone else who had a lot
of options to go to good universities, you know, or got a lot of offers.
Mm-hmm.
, it was a really hard decision to make, you know, But when you have only two
offers, it's easier to make a decision.
It's the same story everywhere, right?
If you wanna buy a car or jeans or t-shirts or whatever you wanna buy,
the more options you have to choose from the hard it is to make a decision.
And going back to the shops, usually within a shop for a t-shirt, you have like
20 different T-shirts within the page, you know, which looked pretty similar.
Mm-hmm . And you only wanna buy one t-shirt.
So you're gonna be like, Okay, which one should I pick here?
Because there are a lot of them.
Why is it different from this?
Why is the price changing?
I mean, this looks a bit better, but the price is higher, or the price is lower.
You know, all these different confusion points.
So what happens here is that what usually users do is that they
shortlist some of these T-shirts.
Let's say out 20.
They pick like two or three, they add them to the cart.
But here's the catch.
Most of the brands, most of the people think that the user has the
intention to buy this product just because they added these three T-shirts
to the cart, which is not true.
Mm-hmm., because users actually, they think the cart as a wishlist, you know,
the wishlist that usually have that you don't usually use, and most users they
don't use, you know why they don't use it?
Because the cart itself, it's actual wish list.
Yeah.
It's a short list for the users..
So when a user adds three T-shirts to the to the cart, they're not adding
because they wanna buy all of them.
They, they're adding because then they have a shortlist out of which
they can make an easier decision.
Mm-hmm.
And then when they go the cart, they have three different t-shirts, and then
they can think about it, and here's where returns are related, because if
the user cannot make a decision, To pick one T-shirt out of these three
different t-shirts, they will say, Okay, maybe I can pick two and then buy them
and then make the decision at help.
Mm-hmm.
, so are you following me here, how the users actually push the decision making
as much as possible into the future.
First they say, Okay, let me just, I'm gonna make the decision when
I go with the cart, and then no, maybe I'm gonna make the decision
when I have the product at home.
This is the word human nature.
We like to push the decision making.
We like to procrastinate.
We like to push the decision making as much as possible in the future.
We like to push the hard decisions as much as possible into the future.
You know, that's the same plan that happens here in the store.
So that's kind of like related to the behavioral part.
We have a lot of options to choose from.
It's called also paradox of choice.
You have so many options to choose from.
You just, you get suffocated from the decision making process.
Yeah.
Matt Edmundson: I've heard about the paradox of choice.
Uh, I'm trying desperately to rack my brains of the book that I came across
it in where they talked about, um, the famous jam experiment where in a
supermarket they put six jams in a, So, do you know the one I'm talking about?
They put six jams in a supermarket island.
They monitored sales, so you could go and you could try them, right?
They put six flavors out, you could try them, and then they monitored sales.
And then they put, I think it was something like 36 different flavors
out and monitored sales after that.
The idea being that the more choice you have, the more likely you are
to find something that you want.
Therefore, sales should increase.
The paradox of choice was actually the more options you gave to
consumers, the more they didn't buy, the more they put off.
Like you say, making that decision with jam.
And I thought it was a fascinating experiment.
And so what you are saying here, actually on your e-commerce
website, a lot of cart abandonment is down to this paradox of choice.
You've got a lot of options.
And what that does is, and I think you're right, you know,
we've all got, what's that phrase?
Decision fatigue.
Yeah.
You know what I mean?
Where we're trying not very hard not to make any more decisions, and
so we just keep putting 'em off and putting 'em off and putting 'em off.
And of course, by not making a decision, you make a decision.
Uh, and I'm laughing because I purchased, um, two coats off a website recently.
Uh, and I purchased two because I wasn't sure which one I wanted, and I
thought, I'll get one and I'll return the one that I don't want, but I need
to see it in person a little bit.
And I wasn't quite sure on the sizing and the fit and all that sort of stuff.
So, um, everything that you have just said, Valon, I am guilty of.
Right.
So
Valon Xhafa: Everyone is guilty.
Yeah.
So it's a human nature.
I mean, I mean, decisions we make and the way we make decisions in, in the real
world outside the online shop, it's the same process in the online shop, right?
So it just, we just get suffocated and it's hard to make a decision.
Matt Edmundson: So we've got the paradox of choice.
Um, we push decisions to the future.
What else sort of affects cart abandonment?
Is, is that the key thing or are there other things that we need to think about?
You said behavioral and technical.
Valon Xhafa: Yeah, so regarding the behavioral part, the non-behavioral
part, everything is, is, I mean, there are different layers of,
of issues that can happen, but everything is tightly related to this.
Having a hard time making a decision, being unsure about making decision,
or making the wrong decision, you know, that can actually lead
to a product return, you know?
So all of this is about like making decision.
And this related to the behavioral part, but then the second category
of reasons why we have cart abandonment are technical issues.
Mm-hmm.
you know, like, uh, you have a bug or you have an issue with a payment,
or you have this or you have that.
You know, So the, the thing here is that there are so many tools out there that can
actually track all these things for you.
You know, the issue though is that resources, technical resource are scarce.
Uh, for online shops, not only online shops in general, but
especially for online shops, right?
So, uh, you won't be able to fix the issues right away.
You, you won't be able to understand, okay, why do I have, you know,
like how big the problem this is, like this technical issue.
And the most important thing is that you, you need to actually prioritize these
issues, you need to understand, okay, um, how big of an issue this technical
bug is so that I can go and fix it.
For example, there are bugs and issues that, or errors that can
happen on the payment page and they only have like 10 times a day.
You know?
But these ten bugs can actually cause you a lot of revenue loss.
Yeah, yeah.
Compared to some other issues that happen, I dunno, like on the product
detail page how that appear like thousand times a day, but they don't
cause you as much issues, you know?
Mm-hmm.
. So there are so many breaking points within the shop that can
happen, and it's really important to understand, okay, how are these
issues tied to cart abandonments?
Because here's the thing, if I add a product to the cart and I go and
wanna make the purchase, And I have issues with the payment and you as a
shop owner, you didn't recognize that I had an issue with the payment and
I abandoned the cart because of that.
You can send me whatever email you want, Matt, with a coupon or something.
I'm not gonna come and buy because it's not, because usually like coupons are
the way to recover cart abandonments, you know, with emails and usually
get a coupon when you abandon a cart.
But I'm not gonna come and buy on your shop because I had a technical
issue I couldn't pay, you know?
Yeah.
So instead of sending me a coupon with an email, why don't you just
contact me and provide me help?
Yeah.
To actually make the payment.
So what I'm wanna summarize here is that, There is no like overview of
what's going on and how are these issues related to cart abandonments because
everything is pretty much related.
Like the latency, like the page or the product image doesn't load.
This is, this can cause a huge issue.
You know, Maybe it's like, oh, maybe just the products not loading.
But yeah, this is interconnected with the user experience and everything, you know?
So yeah, bottom line.
Behavioral issues or reasons and technical issues that actually
lead to cart abandonments.
Matt Edmundson: So actually I'm listening to you talk, Valon, and I'm
going, in my head, I'm thinking I, I get things like the paradox of choice.
I understand that I get things like.
If I don't clearly tell you what my shipping rates are, and you get to a
page and it suddenly surprises you that they're 400 bucks, you're gonna go,
Well, no, I, I get the logic of that and I get the logic of what you are talking
about, but the more you're talking, the bigger and bigger it, the, the web sort
of seems to go, if that makes sense.
Sure.
And so there's cart abandonments are a big issue for e-commerce owners
and the, the, the reasons for cart abandonments can be behavioral.
They can be technical, they can be obvious, they can be really unobvious.
And there's a lot going on here that's gonna require a little bit
of attention, which is where I'm assuming, uh, Valon, if I can sort of
preempt the conversation a little bit.
Um, I'm assuming this is where AI actually can actually make
a really big impact, Right?
If you could apply AI somehow in this to be your, uh, 24/7 eyes
and understand what's going on and reasonings, is that right?
Valon Xhafa: Yeah, so that's, that's actually the, the contribution
of AI here in this space because, uh, AI not only predicts that the
consumer's gonna abandon the cart.
Something that is common, something that is general knowledge, but we have actually
also pushed it a bit further by also building an AI that can explain to us
why is this user gonna abandon the cart.
Yeah.
The why, the reason.
So imagine you, Matt, come to my shop and we predict you're gonna abandon the
cart but then I'm also gonna understand why you're gonna abandon the cart.
You know, like this specific reasons before you even do it.
Mm-hmm.
, you know, and, and through this process I can actually, uh,
intervene while we are still on site.
And help you and support you or whatever you need.
If, if you're gonna abandon the cart because of the technical
issue, then I can assist you.
It's like, Hey, we see that you have, you're having hard time with
payment, or you have, you didn't see this image loaded or something.
Can we help you somehow?
You know?
Or if we see that you have hard time making decisions because of this
product's a choice, or you have so many options, or you're not finding
the right size, and we can predict.
You're gonna abandon it because of these issues, and we can actually reach out to
you while, again, you're still on site and try to provide support according to the
reason why you're gonna abandon the cart.
Right.
So it's all about, It's all about, like the biggest issue is that when
you bring consumer or user to your shop, you already paid a lot of money.
Mm.
You know, so if you leave this consumer to leave your store and try to bring
them back through emails and retalk, this is gonna cost you additional money.
You know, the solution here is like, you need to react why
this user is on site mm-hmm.
, and be able to intervene right away and prevent things before they happen.
You know, like we, we know this concept.
If you prevent something, it's more cost effective than trying to
recover or, or repair or something.
You know, like it's with cars, it's even with health, you know, you can
predict diseases and prevent them.
You can treat them, you know, this will be more efficient, more
successful, and more cost effective than just leaving it go big.
So that's the same concept that we're actually applying here.
Um, predict, prevent, and try to keep the user on site as much as we
can and not like say, Oh, I have, I can send emails and bring back cause
it's gonna cost you additional money.
Especially these days.
The biggest issue of these days is the cost.
The cost of ads have gone so high, That, uh, it's very costly to actually bring, or
it's, it costs you a lot of money to bring consumers or additional traffic to store.
Until a year or two years ago, the only way to grow was to
bring more traffic to your store.
Because it was cheaper.
You could just get more traffic.
You're gotta go now.
Even if you have the money, it's gonna cost you so much money to actually
just bring additional traffic.
So you have to go back to your site and see, okay, what else can I improve here?
So that the traffic that I have right now I can actually increase
my profit and margins, but also increase customer satisfaction
because it's really important.
Matt Edmundson: So how I, it's um, I mean, it sounds wonderful, you know, the sort of
the key problems and, uh, that you have.
So how does AI, if I go back to the sort of list that you gave earlier on,
so let's look at the paradox of choice.
How does AI help a consumer deal with this paradox of choice?
How does that, how does, well have you, how have you figured that out?
Right?
Because that sounds, that sounds like an easy thing to roll off the tongue, right?
Yeah, I can just say that.
But actually how, I'm really curious, how does that, how does it do it?
Valon Xhafa: Yeah, so we can take a very simple example to maybe explain
the paradox of choice, that it's more simplistic for the audience.
So one of the reasons why consumers abandon the carts is because, um, they
just add too many products to the cart.
Which is, which is maybe, um, maybe, uh, a misconception because, uh,
people or brands, uh, think that when consumers add products to the cart,
they have a higher chance of buying.
Yes and no.
Because after a specific number of products in the cart, your
probability of buying goes down mm-hmm.
because you're not gonna buy 20 products or 15 products, you know?
Mm-hmm..
So you're just shortlisting them.
So, um, this is the case when consumers abandon the cart.
And what our AI, for example, does here is that for every product you add to the
cart, we get a probability score, um, of what is your probability of buying.
You know, and if we see that okay, you're probably buying is increasing with more
products and that suddenly starts to decrease, then we know that maybe it's
the right time for you to, to remind you, Matt, that maybe you can have a
look on your cart because, uh, it seems that you're all ready with the cart.
It's like a gentle reminder this kind of like, you know, when you go to the shop.
To a physical store, and you have a shop assistant helping you with
advices and maybe try this or maybe try that, or maybe this would
look good or this or that, Right?
So this is the touch and the feel that you have right now
on online shopping experience.
It's just like one way interaction.
You have to go it self service, pretty much.
You have to go and find the right products and everything.
And what happens here is that most of the consumers, they just lose themselves.
They just like, by trying to find more product and then spending more time.
Instead of like, instead of focus on these three, four products that they
already added to the cart, and see if they can make a decision here.
So what our AI does is that, uh, our AI predicts if the consumer's gonna
add too many products to the cartd, and then they're gonna abandon cart.
And before they do that, we just provide a general reminder to the consumer on site.
You know, like a small note or something.
Hey, Matt, seems that your cart, maybe it's ready or something.
Why don't you have a look on it?
Because we know if you continue doing that process, you're just gonna add
too many products and it's gonna be harder for you to make a decision
and you're just like end up leaving.
And not buying at all.
So that's, for example, one case, but there are a lot of reasons out there.
Matt Edmundson: That's really clever.
I mean, that, it's interesting that, um, I mean, forget all the, the AI, just
the psychology of saying actually now's the ideal time for you to check out bud.
We're gonna help you remember to do that cuz uh, we want you in the shop.
But not too long, I'm kind of reminded of the supermarkets, you know, they,
um, I dunno if they still do, but years ago, Uh, they used to play music at
different speeds depending on how long they wanted you in the shop, right?
So if they wanted you in the shop and out, cause it was busy, they
would pay play faster paced music.
And if they wanted you to hang around the shop for a little while
and just browse and add a few more products to your basket, they
would play slower, calmer music.
Just chill out.
Just take your time.
Um, and it's kind of what you're describing reminds me of that
valon, you know, that actually what you're doing is you're going.
The optimal time for this person buying these types of products is like, I need
them out of the store within four minutes and five seconds, which kind of goes
against the logic, which has actually, we want them to hang around in the site.
We want them to read stuff, we want 'em to get sucked in and drawn in.
Um, actually you are going, well, that's not always the case.
And you're using this sort of AI magic to figure that out.
Right.
Valon Xhafa: Exactly.
So the magic here is that not, not every single customer is the same.
So there is no average consumer.
The average consumer is not, is not real.
It's idealistic, you know, So it doesn't exist.
Mm-hmm.
. So every single consumer has their own timeframe of how much time they
wanna spend or they're gonna spend.
And we can actually predict that, you know?
So if we predict the consumer's gonna spend four minutes.
Then you can just waste your time trying to have them more on your store.
Right.
Or longer in your store.
Which means that, Which means that if you see that the consumers ready to buy,
if you predict that and they don't have enough time then the best possible way
is like to remind them to go to the cart.
And see if they are willing to buy any of these products instead of like trying
to confuse them with like coupons and like showing a popup, asking for their
emails and promotions and this and that.
You know, I don't have a lot of time.
Let's say I'm on a subway going to work and I see an ad and I click
a product and you know, like on Subway you don't have a lot of time.
You have like three, four minutes time spent.
Mm-hmm.
and uh, I add that to the cart and suddenly I get like bombarded with like,
Oh, here's a promotion, here's this.
No, I, I just, I saw an ad, I added this to the cart.
I want to finalize this purchase, so why don't you help me focus
on this instead of you trying to upsell and then lose me completely.
Yeah.
Matt Edmundson: I like, I like the message that you are preaching, valon.
I really do.
I think it's so wise.
I, I wrote down this phrase you used earlier.
And when you said that, when you were talking about how there were too many
options, you called them confusion points.
I don't know if that's a deliberate phrase you use.
Um, uh, but I thought it was a really interesting phrase.
And actually what we're doing is we're on e-commerce at the moment.
We're creating a lot of confusion points, and some of them you've hit
upon like, give me your email address, do this, do that, do the other, And
it's like, I just want to get this product and go, dude, is what I want.
Right.
So what sort of things then does AI look at, So you talk about predicting
the behavior, and so what sort of, does it look at everything?
Like what, whether you're on mobile, uh, you know, whereabouts in the world
you are, what time of day it is, what sort of things would AI look at and sort
of start to take into consideration, Start to calculate behavior?
Valon Xhafa: Yeah, so they almost like behavior based
data, like click stream data.
We don't, in our case, we don't use personal data because we don't, uh,
you know, we not gonna send emails.
We don't, we don't need to know what the user did like
three months ago or something.
It's pretty much completely behavioral data.
Like, um, which ad did you click when you came to the store, or
which, which device are you using?
Which type of device?
And then where did you land on the shop?
Did you land on the product detail page or on the homepage?
Which products did you click?
The sequence of the product clicked and all these differents, so we
use like 200 different variables to actually be able to mm-hmm.
To get a better understanding of the consumer.
And with every click the consumer does, we actually, uh, predict the behavior
and we measure the probability score of buying, you know, because this
is the most important thing here.
Yeah.
So what happens here?
Is that the confusion point that you mentioned?
No, this is something that I'm, I actually mentioned it because, um, we
have seen that, we have seen a lot of cases when the consumer was ready to buy.
High probability of buying, and then the brand showed the popup asking for an
email, and they just lost the consumer.
The, the probability of buying dropped to like 50% or something and they just
left, or, or the result on a big confusion point, which is really upselling.
Upselling is great, you know, upselling something that can
actually make you money, um, increase average order values and everything.
But after a specific point in time when the consumer has
added enough products to cart.
Don't try to upsell too much because you can lose a consumer.
Mm-hmm.
, if I go the cart and then I have three products that I want to buy
and one of them is a T-shirt and then I'm ready to buy, I probably buying.
And then you provide a recommendation on the bottom saying like, Hey, this,
these are also some other T-shirts.
So this is a confusion point because maybe now I like one of the other T-shirts
which are being recommended to me.
So now I'm gonna change my mind's like, damn, which one should I pick?
Should I go with the one that I was ready to buy or now with this
one that I didn't see before?
And then what happens, like maybe you add this new T-shirt and then you just like,
no, I don't know which decision to make.
And then like, two options you gotta do here.
Um, the first is that you're gonna leave the store without buying, or
you're gonna buy both t-shirts and then return one of them, which is not good.
Returns are not good.
You should not do tradeoffs between returns because, uh, one
percentage increase or percentage or an increase in return is not a
one percentage decrease in revenue.
It's four or five because you have all the costs associated to returns.
Yeah, so upselling is a confusion point is popups like asking for emails and
everything, are confusion point, like, uh, user are different and you have to
be able to actually target them better.
If the user is willing to buy.
Why are you asking for an email?
You're gonna get the email.
Either way, you know?
Yeah.
So
Matt Edmundson: that's clever.
It's interesting because what you are doing with ai or what I'm understanding,
valon, let me put it that way, rather than tell you what you're doing,
here's what I understand, um, what I'm understanding is you are using ai, uh,
and AI is using hard real data, right?
It's using 200, 250 data points to try and predict what is going on here and.
I'm assuming the AI is very, um, specific to that store, the behaviors
for that store, because it's gonna be different to the store, you know,
further down the road, isn't it?
It's gonna have a different set of behaviors attached to it, and you are
using data to figure that out rather than,
What tends to happen is I will go to a website, someone's written
a blog post, which says, You must have three upsells per product to
maximize average order value, right?
And so in my head I go, I must have three products to upsell.
And actually what you are using is data, which says, No, actually for
your website, you should have a maximum of two based on this data.
And actually the, the products which you upsell, they really matter as well.
It's not just the fact that you upsell, it's what you upsell.
And if you upsell that, the knock on effect is this.
And I see here and I listen to the t-shirt example and I go, Well, from
a logical point of view, everything that you have just said makes sense.
But I would never have thought about that, um, in a million years, which
is why I guess when I go to the t-shirt websites, they're trying
to upsell me yet another t-shirt.
Um, and so I, I like the smartness of this, and I like
how you are using the, the data.
Can I ask you a question?
How much data do you need?
As in, should I be plugging in, um, your AI if I'm just starting out?
Or should I be plugging in your ai if I've got more than 200 product SKUs,
should I be plugging in your ai if I've got more than 10,000 customers?
I'm really curious how much data you need to start building
this kind of predictive model.
Valon Xhafa: Uh, we do have, this is actually a really, really good question.
Like, um, we're agile in this way because we have our own pre-built
models and AI systems and everything.
So it's not that when we go live with your shop or when we install our AI in
your shop, we gonna start from zero.
Now we already have knowledge collected from different sort, from different
categories and different products because the most important thing here
is that you said that maybe for for your shop two products, upsell is good.
You.
Which is partially true because again, the thing here is that
there is no average score.
There is no average user.
Average user doesn't exist, you know, in most of the cases.
Mm-hmm.
, every user is different and the most important thing is that, okay, let's say
for example, that you, uh, two products to upsell is good for your shop, but
this can also change within seasons.
Mm-hmm.
, for example, when you're like winter season, you won't be
able to sell to upsell to two products cuz the price is high.
You have high jackets and, and everything else which are expensive.
So if you can upsell one product in winter season, that's it's good.
But for example, during summer, you can, maybe you need to upsell three products,
let's say, if we talk about the average.
But again, the average is not, it's not a good, it's not a good way to, to measure
how many products you have to upsell to, to the consumers and everything else.
Um, so like going back to, to the topic of like, How much data?
It's usually like as soon as we install Behamics, um, we can
actually make predictions right away within one, two hours of data.
Mm-hmm.
, we just need to reiterate and readapt everything because we
already have like baseline systems capable of making predictions.
Matt Edmundson: Mm-hmm.
And I guess Behamics is always learning, isn't it?
It's always.
Kind of adapting to what's going on.
And that's the beautiful thing about AI is this constant learning machine, isn't it?
Um, can I ask you, you say you've collated all this data from websites that
you've been on, and obviously you've, you've seen stuff as a result of this.
Um, what have been some of the most surprising things for you?
As in, you've done this on the cart abandonment, you've looked at the
stats, you've looked at the data coming out of stores with your ai.
What have, what have been some of the most surprising findings?
I'm, I'm curious.
Valon Xhafa: Uh, I, I mean like the, I mean, the findings could be like,
Interesting findings or fun findings.
Mm-hmm, which are findings that maybe they don't have a huge impact.
It can be also findings that actually they're not that interesting,
but they have a huge impact.
Mm-hmm.
So I can start with fun finding, One of the findings that I, we found is that,
um, users, these days actually, they also sometimes abandon the carts on purpose.
Because they know they'll receive an email for a coupon.
Yeah.
And we're able to actually, we were actually able to, to, to see this pattern.
We saw the consumer, they abandon the cart even if they had a high chance
of buy, because our AI can predict that they abandoned it on purpose.
And then after a few hours, We saw them coming back and adding the coupon
that they got from the email and coming back from email, you know, so this is
a pattern that we actually saw because recovery emails with coupons were great
in the beginning, but now actually consumers also sometimes abandon cart on
purpose, and hopefully to gather coupon.
Yeah, so this was, this was, this was a really, really
funny finding that we found.
Uh, but the one that is interesting or that can have huge revenue loss
are findings where brands, they know they have issues within their shop.
I mean, we talking like about like brands that are like a hundred million
dollar in annual revenue or something.
They know they have an issue, it's visible and they can go and fix.
Which is, which is like you have, for example, filtering options
are not working or images are not loading or something because they,
they know that this is an issue.
The challenge here is that they don't know how big of this issue is, so
this is why it kind of like led us to think maybe we should tell them, Hey,
this image that is not being loaded is costing you 20 grand a month.
Mm-hmm.
so that maybe they can actually go and fix.
And this was, this is something that is actually across most of
the clients we work with and that we have seen in the industry.
There are issues that are clear they are not being fixed issues
that can be actually fixed easily and can actually make them more
money and generate more money.
And because this issues can actually lead to cart abandonment directly.
Mm.
If the filtering option is not working, then this is another
reason for me to lead the store.
Matt Edmundson: So is this, uh, before we hit the record button, we were
talking about a, a new development that you guys have got called
Flow, which you're excited about.
Is this, is this what you're talking about here?
The ability to.
Uh, put a financial value on inaction for want of a better expression,
uh, on your website, the stuff which is wrong and not working.
It's costing you 20 grand, 30 grand.
The image not loading.
Yeah.
Um, is that what flow does your new, your new product?
Valon Xhafa: Yes.
So we, we took the findings from the psychology part, so we're like, Price
is something that can have a huge impact on everyone, you know, And
it's the same sort also for brands.
So if brands see that, okay, this ad is happening 200 times a day.
That really, it's not really urgent, you know?
But if they see that this error is causing them like 20 grand a day, this is urgent.
You know, like price is really important and if you really wanna prioritize
things, you really have to kind of like be able to see and prioritize and rank
them based on the revenue that these issues are costing so that you can go and
fix them and even escalate the issues.
So that's what we kind of like did here.
We applied AI again, as always, to be able to explain things are happening within the
store and help us attribute every single issue, um, to a revenue loss, which means
that if you fix that issue, you're gonna correct that revenue loss right away.
Pretty much, and we run the tool where actually we launch it with our
existing clients and everything, and we see like astronomical values in
terms of loss because of small issues.
We see like 50 grand a day.
which is like 15% of the whole revenue.
Wow.
You know, like 20 grand a day because of small issues.
And these issues are either like on the payment or they're like on
product list or images or whatever.
These are issues that can be fixed.
But the thing here is not just fixing the issues because you can fix the
issues and then you're gonna go, which is not true because the whole idea
of this tool is to actually prevent these 50 grand in the future because
whenever you push new things and you're change new things to your store,
you constantly add new stuff and the chances of issues appearing are high.
So let me give you an example.
So we had, we have this client who told us that, Look, we added
additional payments options.
And, uh, it actually, we lost revenue, losing revenue.
How is that possible?
And then they told me that they, it took them a month to figure out that some
of the payment options were not working all the time for some devices for some
of this, you know, So pretty much it backfired on them and it backfired because
adding more payment options works or increases conversion rate or whatever.
Backfired because they didn't have the solution in place to actually tell
them, Hey, you're losing money because of this, and this is a new issue.
So if they would have, or tool in place our tool would be able to tell them
right away, Hey look, you have a revenue loss because of this issue, and then
they would be able to go and fix it.
So that's kind of like the, the direction.
Mm-hmm.
Matt Edmundson: That's, I mean, that's incredible, that's
a product in its own right.
I see why It's a standalone problem.
Yeah.
And that's a, that's a beautiful piece of technology.
You know, it's like a constant monitor of your website.
This is wrong.
It's costing you this much money.
Fix it.
I mean, that's, yeah.
I said, for anybody that's from an e-commerce store, I'm like, that's,
that's a beautiful thing, right?
Where do I sign up?
Because I mean, that just sounds great.
So you've got this technology which monitors that.
You've got this ai, which you know, figures out when the best
time to check out is, and a few of the bits and, you know, helps
with the behavioral side of things.
Can AI also help customers make the right choice about a product
so that they don't return it.
So that, you know, it's not like me going, Oh, I'm not sure about this coat
or this coat, and then returning one.
It helps me make the right choice in the first place.
Can, can AI be that clever?
Valon Xhafa: Yeah, so we, uh, we apply, we apply AI for like two, two specific,
let's say goals versus to predict if the consumer's gonna abandon the cart.
Because, um, if they just abandon the cart then there won't be any returns.
Mm-hmm.
, you know, because they won't buy.
Um, but if they are gonna buy a product and if we predict that they're
gonna buy, purchase the product, then we also predict and try to explain
if the user is gonna return that product, individual product, and why.
Before they even buy it.
So pretty much, pretty much, we can actually, with 95-96% of accuracy, we
can predict for every individual product if they're gonna be returned in few
weeks or not as soon as they're bought.
Wow, that's quite a high percentage.
It's a high percentage, yeah, because it really shows that there
are patterns, because otherwise if there are, if there are no patterns,
you won't be able to predict.
This just shows that there are a lot of patterns out there that you can actually
take into account to do these predictions.
But the cool part here is that we can predict if the user is gonna return
the product before they even buy the product, just by having them on the cart.
And then we can explain, okay, why is the consumer gonna return this?
For example, one of the reasons why consumers return product is that,
um, they really want to have a size, a specific size of that product.
Um, but that size is not available right now.
So what they do, they go and add, um, the closest size than that, like a
smaller or a larger size, which is not good because again, it's not the right
size and we can actually predict this.
And in that case, we would try to recommend them to swap this
product with a similar product that has this size available.
Because again, it's really important that we prevent returns before
they actually are purchased, before actually they are returned.
Because that sounds like the most important thing.
But there also many other cases, like for example, um, usually they just go
and add like three, four different or.
Three, four different products of the same category or something so that
they can make the decision back at home because they're not conscious
of the whole environmental mass and the shipping costs and everything
that can actually lead to that.
Um, so we kind of like gently remind to them that, hey, if you add like four
products and we predict that you're return three of them, you know, we
sometimes add a gentle reminder that these products will be returned and this
can cause environmental damages, you know, because of CO2 and everything else
is related to that, you know, So these are some kind of like gentle reminders.
Everything is gently, uh, suggested, like reminded to the consumer that,
hey, your decision has a consequence.
You know, it's not like recommend them directly, Hey, you should buy
this product because three other users bought this product too.
You know, that's kinda like, like too much to push it here, you know?
But we're trying to tackle it from like psychological perspective.
Matt Edmundson: Yeah, I like that.
It's a bit like, um, when I go to a hotel room and they
put on the towels, don't they?
You know, if you want us to wash these, put them here.
Otherwise we'll leave them alone because it's gonna save the planet.
Uh, and it's that sort of gentle reminder.
It gives the consumer a bit more choice.
Yes.
Which seems to work very well from a psychological point of view because you
don't come across as preachy, but you do remind the consumer of what's important
to them, which I think is really clever.
You know what, Valon, I'm aware of time, right?
And I'm, I feel like as I say this to, if you're a regular to the show, I'm really
sorry for the amount of times I say I feel like I'm just scratching the surface.
Uh, it is a genuine thing.
Um, I, I feel like we're just getting into the conversation here.
Um, question for you.
Right?
Uh, bit bit sort of left field.
Um, as you know, the show is sponsored by the e-commerce cohort, right?
Which is just, it's like a, a mastermind group.
So imagine you're in a hotel somewhere and everybody from cohort is there.
You've just delivered your keynote speech.
You've talked about how AI is gonna change their world and rock their lives,
and they're like, Yeah, it's amazing.
Best speech.
Wow.
Wow Valon.
Go, go, go.
And you come on, you take a bow and you take that opportunity to
say thank you to various people.
You know, I wouldn't be here without dot, dot, dot.
I'm curious, who do you thank?
Is it, uh, what, what people, what books, what podcasts, you know, what,
who's kind of impacted your life and, and got you to where you are at?
Valon Xhafa: It's, uh, it's actually at the end of the day, it's more like
just a hands on experience, to be honest, because you just said there
are like so, so, so many articles out there that just provide misconceptions.
You know, like three products are the best one, two products
upsells are the best one.
I think at the end of the day, it's all about, it comes down to like,
just going into the trenches yourself.
And seeing things and measuring things and doing a lot of things because I'm,
I come from an a science background.
Mm-hmm.
and I, even to this day, because we build a lot of new things in
terms of AI and technology within Behamics, we read a lot of articles.
We constantly see what's out there and, you know, I would try to combine different
things and you rarely can, um, Uh, let's say get the results that you see out there
for your own, uh, experiments or tests or something, you know, so everything is kind
of like bubbled up pretty much, you know, like to have the best results, greatest
results, you know, like to provide this traction, to provide this, this buzz
words, you know, as you said, like two product upsell is the best one, you know?
But at the end of the day, the most important thing is the experience,
you just have to go into the trenches yourself, test things yourself,
measure things for yourself, and be able to, um, test things pretty much.
So that's, that's something that I, I think is really
important even to this day.
Uh, we read a lot of stuff out there, um, but not everything is like, Completely
true and or completely false, you know?
So you have to take everything with a grain of salt here.
Matt Edmundson: Yeah, no, fair enough.
Fair enough.
That's very true.
Uh, you have to, you do have to get into the trenches.
I like that phrase.
I do like that phrase.
Um, valon, how do people reach you?
How do they connect with you?
If they want to find out more about Behamics, if they've got questions for you
about ai, what's the best way to do that?
Valon Xhafa: Um, they can definitely, they can definitely, uh, go to our
landing page, um, behamics.com.
They can get more information onto the behamics.com.
Um, or if they, they wanna reach out to me directly, they can just
connect with me on, on LinkedIn.
Um, and yeah, I'd love to talk and see what we can build and how we can
actually apply AI more into eCommerce.
Not just say like, Yeah, AI's gonna change the world, but like actually
applying AI in eCommerce, which is a completely different story.
Matt Edmundson: Yeah.
Yeah.
I like that.
I don't want AI to change the world.
I just want it to make my store better.
Valon Xhafa: Yeah, because you have to start somewhere.
You know?
You do, you do and everything.
Let's start step by step.
You.
Let's just start first, like to implement AI a bit because we cannot
go to like, gonna change the world without actually even starting and then
Matt Edmundson: Yeah.
No, I like that.
It's brilliant.
It's absolutely brilliant.
Yeah.
Sod the world.
Fix my store.
Uh, that's.
That's a good one.
Valon Xhafa: Yeah, that's, yeah.
Yeah, yeah.
Yeah.
Sod the world.
Fix my store.
Maybe I should use that.
Matt Edmundson: See it on the Behamics website.
Valon Xhafa: If I have the copyrights for it.
Matt Edmundson: Yeah, yeah, yeah.
Sure.
that's brilliant.
Absolutely brilliant.
Well, Valon, thank you so much, uh, for joining us.
Really appreciate you being with us.
Genuinely great conversation.
I'm super excited about your products.
I'm really excited to see where they go and what uh,
benefits they bring to people.
So do stay in touch with us and, and bring us the case studies
genuinely really, really curious.
Thank you.
Thank you so much.
Valon Xhafa: Hey Matt.
Thanks a lot.
Thanks for having me.
Um, Take care.
Matt Edmundson: Absolutely.
Well, there you go.
We will of course, link to Valon's info in the show notes, which you
can get for free, along with the transcript at ecommercepodcast.net
or direct to your inbox if you have signed up for our newsletter.
Uh, so thank you so much for joining me.
A huge thanks again to Valon.
Uh, and a big shout out again to today's show sponsor ecommercecohort.com
head over to ecommercecohort.com for more information about this
new type of community that you can and probably should join.
Uh, be sure to follow the eCommerce podcast wherever you get your
podcast from because we have even more great conversations lined up.
Uh, like today's with Valon, I'm talking about all kinds of stuff, which is
gonna help you deliver e-commerce.
Wow.
And I don't want you to miss any of them.
Oh, and just in case no one has told you yet today, dear listener, you are awesome.
Yes you are.
It's just a burden you've got to bear.
I've got the same problem.
Valon's got the same problem.
We just have to bear awesomeness.
Oh well.
Uh, the E-Commerce podcast is produced by Aurion Media.
Uh, you can find our entire archive of episodes on your favorite podcast app.
The team that makes this show possible is Sadaf Beynon, Josh Catchpole.
Estella Robin and Tim Johnson.
Our theme song was written by Josh Edmundson and My Good Self.
And as mentioned, if you'd like to read the transcript or show notes, head over
to our website, ecommercepodcast.net.
As I said, you can also sign up for our weekly newsletter and get all
of this good stuff directly to your inbox totally free, which is amazing.
So that's it for me.
That's it from Valon.
Thank you so much for joining us.
Uh, have a fantastic week wherever you are.
I'll see you next time.
Bye for now.