1
00:00:06,870 --> 00:00:09,210
Matt Edmundson: Welcome to
the e-Commerce podcast with

2
00:00:09,210 --> 00:00:10,920
me, your host, Matt Edmundson.

3
00:00:11,250 --> 00:00:16,079
The E-Commerce podcast is all about
helping you deliver e-commerce.

4
00:00:16,079 --> 00:00:16,590
Wow.

5
00:00:16,860 --> 00:00:22,080
And to help us do just that, I am
chatting with today's very special

6
00:00:22,085 --> 00:00:28,724
guest, Valon Xhafa from Behamics
about how AI and consumer behavior can

7
00:00:28,724 --> 00:00:31,875
reduce, uh, shopping cart abandonment.

8
00:00:31,875 --> 00:00:34,155
Yes, we are getting into
all of that good stuff.

9
00:00:34,245 --> 00:00:38,504
AI shopping, cart abandonment, What
is not to like, but before Valon and

10
00:00:38,510 --> 00:00:43,035
I jump into that, let me suggest a few
of the e-commerce podcast episodes that

11
00:00:43,035 --> 00:00:45,435
I think you will enjoy listening to.

12
00:00:45,855 --> 00:00:50,395
Check out why you should be using AI in
your e-commerce business with Shanif.

13
00:00:50,415 --> 00:00:56,084
That was a great conversation, uh, with
him and about how AI is changing shopping

14
00:00:56,385 --> 00:01:02,205
product recommendations, specifically
with Oliver Edholm, who was just, he

15
00:01:02,209 --> 00:01:04,845
blew my mind, uh, with that conversation.

16
00:01:04,905 --> 00:01:06,435
So do check those out.

17
00:01:06,765 --> 00:01:10,965
You can find these and our entire
archive of episodes on our website

18
00:01:10,965 --> 00:01:14,905
for free ecommercepodcast.net.

19
00:01:14,934 --> 00:01:15,540
Yes, you can.

20
00:01:15,540 --> 00:01:20,130
You can also sign up for our newsletter,
and each week we will email you

21
00:01:20,130 --> 00:01:24,240
these links along with the notes and
the links from today's conversation

22
00:01:24,245 --> 00:01:26,520
with Valon directly to your inbox.

23
00:01:26,520 --> 00:01:27,540
Totally free.

24
00:01:27,900 --> 00:01:29,790
It's all amazing.

25
00:01:30,180 --> 00:01:33,300
Now, this episode is brought to
you by the e-commerce cohort,

26
00:01:33,300 --> 00:01:35,009
which helps you deliver e-commerce.

27
00:01:35,009 --> 00:01:37,890
Wow to your customers.

28
00:01:37,905 --> 00:01:38,429
Yes, it does.

29
00:01:38,429 --> 00:01:38,789
Valon.

30
00:01:38,789 --> 00:01:42,899
I'm sure you've come across a whole
bunch of folks stuck with their

31
00:01:42,899 --> 00:01:46,830
eCommerce websites, or they've just
got siloed into working into just

32
00:01:46,830 --> 00:01:51,720
one or two areas of their business
and miss the big picture, well enter

33
00:01:51,725 --> 00:01:54,300
eCommerce cohort to solve this problem.

34
00:01:54,509 --> 00:01:59,259
It's a lightweight membership group with
guided monthly sprints that cycle through

35
00:01:59,259 --> 00:02:04,259
all the key areas of e-commerce, the
sole purpose of which is to provide you

36
00:02:04,259 --> 00:02:09,539
with clear, actionable jobs to be done
so you will know what to work on, uh, and

37
00:02:09,539 --> 00:02:11,130
get the support you need to get it done.

38
00:02:11,130 --> 00:02:14,550
So whether you're just starting out
in e-commerce or if like me, you are

39
00:02:14,550 --> 00:02:19,140
a well established e-commercer, I will
definitely encourage you to check it out.

40
00:02:19,140 --> 00:02:25,530
Head over to www.ecommercecohort.com
for more information.

41
00:02:25,769 --> 00:02:29,970
Or you can email me directly,
matt@ecommercepodcast.net with

42
00:02:29,995 --> 00:02:33,195
any questions and I'll try
my level best to answer them.

43
00:02:33,899 --> 00:02:35,650
Head over to ecommercecohort.com.

44
00:02:36,209 --> 00:02:37,649
Honestly, you're gonna wanna check it out.

45
00:02:37,649 --> 00:02:41,850
So Valon is a top bloke.

46
00:02:41,850 --> 00:02:42,450
Yes, he is.

47
00:02:42,454 --> 00:02:47,070
He has previously worked as an AI
scientist at Google and other research

48
00:02:47,075 --> 00:02:51,570
institutes developing sophisticated
AI techniques and algorithms.

49
00:02:51,950 --> 00:02:55,730
Uh, Valon is always looking for
innovative methods to use artificial

50
00:02:55,730 --> 00:03:00,869
intelligence to improve the online
shopping experience at Behamics.

51
00:03:01,130 --> 00:03:05,270
Valon, it is great to have
you on the show, zooming in

52
00:03:05,270 --> 00:03:06,709
all the way from New York.

53
00:03:06,980 --> 00:03:08,150
Uh, thanks for being with us.

54
00:03:08,150 --> 00:03:08,870
Great to have you.

55
00:03:10,160 --> 00:03:10,670
Valon Xhafa: Hey, Matt.

56
00:03:10,670 --> 00:03:12,310
Um, thanks for having.

57
00:03:13,005 --> 00:03:13,485
Matt Edmundson: Oh no.

58
00:03:13,545 --> 00:03:14,535
Brilliant, brilliant, brilliant.

59
00:03:14,865 --> 00:03:19,035
So Valon, you have an impressive
resume, uh, or CV as we

60
00:03:19,035 --> 00:03:20,565
like to say here in the uk.

61
00:03:20,985 --> 00:03:25,815
Uh, an AI scientist that would make
a really interesting business card.

62
00:03:25,995 --> 00:03:28,605
Do you know, what I mean, where
you've got your name, I'm an AI

63
00:03:28,610 --> 00:03:31,965
scientist, and the conversations that
that would create at dinner parties.

64
00:03:32,445 --> 00:03:34,515
Um, so you've obviously
done some interesting stuff.

65
00:03:34,515 --> 00:03:36,465
You find yourself now at Behamics.

66
00:03:36,785 --> 00:03:38,155
So what is Behamics?

67
00:03:38,175 --> 00:03:38,930
What does it do?

68
00:03:40,450 --> 00:03:45,225
Valon Xhafa: Yeah, I saw Behamics, or
I found Behamics right after I left

69
00:03:45,230 --> 00:03:49,005
Google as a way to apply AI in eCommerce.

70
00:03:49,005 --> 00:03:53,715
So what I saw is that, um, AI is
widely applied in all industries, like

71
00:03:53,720 --> 00:03:59,565
I know driving healthcare, finance,
uh, but not a lot in eCommerce.

72
00:03:59,924 --> 00:04:03,230
And I expected that AI could make
a huge impact in eCommerce too.

73
00:04:03,230 --> 00:04:07,940
So I saw Behamics as a way to
apply AI in eCommerce to reduce

74
00:04:07,970 --> 00:04:10,679
shopping car abandonments, you know,
like shopping cart abandonments.

75
00:04:10,700 --> 00:04:11,810
It's a, it's a big deal.

76
00:04:12,109 --> 00:04:15,530
Um, and it's like 70%
of cards are abandoned.

77
00:04:15,950 --> 00:04:21,560
Um, and it, there is no like feedback
or, or clear facts or statistics.

78
00:04:21,890 --> 00:04:24,140
Why do we have shopping car abandonments?

79
00:04:24,140 --> 00:04:24,230
Mm-hmm.

80
00:04:24,659 --> 00:04:26,030
So this was as a, as a challenge.

81
00:04:26,760 --> 00:04:30,360
And that's why I started beginning
to understand why do we have

82
00:04:30,420 --> 00:04:31,440
shopping cart abandonments?

83
00:04:31,470 --> 00:04:33,780
How can we prevent
shopping cart abandonments?

84
00:04:33,780 --> 00:04:38,011
Not just like recover them through
emails and all these retargetings and

85
00:04:38,016 --> 00:04:42,510
everything that you usually do, but also
be able to understand before they happen

86
00:04:42,630 --> 00:04:45,720
or predict before a cart is abandoned.

87
00:04:47,325 --> 00:04:49,215
Matt Edmundson: So you
wanna predict shopping cart

88
00:04:49,215 --> 00:04:50,955
abandonment before it happens.

89
00:04:51,315 --> 00:04:56,115
Um, which is, uh, if I can put it
bluntly, sounds almost a little

90
00:04:56,115 --> 00:04:57,615
bit like witchcraft, right?

91
00:04:57,795 --> 00:05:03,615
It's that kind of, uh, it's this sort
of, it's this black hole, uh, of stuff

92
00:05:03,615 --> 00:05:06,975
where I think people like me to sort of
get lost in our thinking a little bit.

93
00:05:06,975 --> 00:05:10,155
But AI has made some interesting
advancements, but before

94
00:05:10,155 --> 00:05:12,094
we get into that uh, Valon.

95
00:05:12,465 --> 00:05:17,025
Let's just, for those that are new to
e-commerce, what are cart abandonments?

96
00:05:17,115 --> 00:05:20,035
Um, what do you mean when
you say cart abandonments,

97
00:05:20,175 --> 00:05:21,845
there's 70% cart abandonments?

98
00:05:22,815 --> 00:05:25,065
Valon Xhafa: Yeah, the, there
is a, there is a kind of like a

99
00:05:25,065 --> 00:05:27,615
definition of the cart abandonment.

100
00:05:27,615 --> 00:05:32,414
A simple one, which is like when a user
visits your shop and they, they add

101
00:05:32,414 --> 00:05:36,375
a product to the cart and then they
leave without buying that product.

102
00:05:36,380 --> 00:05:39,284
So that's usually, it's
called cart abandonment.

103
00:05:39,289 --> 00:05:43,495
And because of that you have like 70%
of users, they abandon their carts.

104
00:05:43,515 --> 00:05:43,724
Mm-hmm..

105
00:05:43,965 --> 00:05:48,245
So we're talking about like 70% of
users who added something to the cart.

106
00:05:48,495 --> 00:05:51,105
You know, and not 70%
of your whole traffic.

107
00:05:51,135 --> 00:05:51,375
Right?

108
00:05:51,375 --> 00:05:54,465
Because there are a lot of users
who just come because they just

109
00:05:54,469 --> 00:05:58,215
like, yeah, just wanna kill some
time and just like enjoy themselves.

110
00:05:58,215 --> 00:06:01,395
Like just looking at products and
everything, you know, like everyone,

111
00:06:01,905 --> 00:06:03,495
all of us pretty much we do that.

112
00:06:03,705 --> 00:06:09,075
Uh, but cart abandonments is like this
group of consumers or users who add

113
00:06:09,075 --> 00:06:12,585
something to the cart and then they don't
end up buying them, but they just leave.

114
00:06:12,675 --> 00:06:14,465
So that's the cart abandonments.

115
00:06:15,885 --> 00:06:18,645
Matt Edmundson: So people have
added stuff to their shopping cart,

116
00:06:18,645 --> 00:06:19,905
but they've just not purchased.

117
00:06:19,905 --> 00:06:22,965
And this is, um, this is
a big problem, isn't it?

118
00:06:22,965 --> 00:06:26,235
I say it's one of these things that
I've heard talked about off and on

119
00:06:26,685 --> 00:06:29,595
for a long time now, you know, it's,
it's one of the sort of the key

120
00:06:29,595 --> 00:06:31,245
areas that people like to focus on.

121
00:06:31,245 --> 00:06:35,715
It's like, what is stopping the
people once they've added it to cut

122
00:06:35,925 --> 00:06:38,415
what stops them paying me money?

123
00:06:38,565 --> 00:06:40,425
And I want to understand that process.

124
00:06:40,430 --> 00:06:46,190
So why, why is this, um, And, and
the other thing that I've noticed,

125
00:06:46,190 --> 00:06:49,880
and Valon, correct me if I'm wrong
here, is the percentage of cart

126
00:06:49,880 --> 00:06:52,910
abandonment seems to be going up.

127
00:06:53,090 --> 00:06:55,040
It doesn't seem to be generally falling.

128
00:06:55,040 --> 00:06:58,130
It seems to be, it's becoming
more and more common.

129
00:06:58,130 --> 00:07:02,360
Have I, Is that a fair
reflection or, or have I got that

130
00:07:02,360 --> 00:07:03,830
entirely the wrong way around?

131
00:07:04,850 --> 00:07:05,300
Valon Xhafa: It is.

132
00:07:05,300 --> 00:07:07,670
Its a, its a, it is a statistic.

133
00:07:07,760 --> 00:07:08,510
It is a trend.

134
00:07:08,690 --> 00:07:10,500
Uh, so cart abandonments  are going up.

135
00:07:10,965 --> 00:07:14,715
And connected to that also product
returns, because these two concepts

136
00:07:14,715 --> 00:07:16,755
are like tightly related to each other.

137
00:07:16,965 --> 00:07:21,795
Um, so these are like, these are
like statistics, uh, or KPIs that

138
00:07:21,795 --> 00:07:27,315
are unfortunately like going up, um,
because of a lot of issues, a lot of

139
00:07:27,315 --> 00:07:31,275
reasons out there that we are gonna
explore them during this podcast.

140
00:07:31,575 --> 00:07:33,765
Um, but yeah, that's, that's the reality.

141
00:07:35,085 --> 00:07:38,025
Matt Edmundson: It's interesting
actually, that you've linked there, uh,

142
00:07:38,054 --> 00:07:40,395
product cart abandonment and returns.

143
00:07:40,484 --> 00:07:42,465
Uh, something that I,
I want to come back to.

144
00:07:42,854 --> 00:07:44,445
Um, so I've, I've jotted it down.

145
00:07:44,594 --> 00:07:46,484
Uh, whenever I look
down, I'm making notes.

146
00:07:46,484 --> 00:07:46,815
By the way.

147
00:07:46,815 --> 00:07:47,864
I'm not checking my email.

148
00:07:49,484 --> 00:07:50,145
Just being clear.

149
00:07:53,445 --> 00:07:55,184
Checking the football
score or playing Sudoku.

150
00:07:58,400 --> 00:08:01,770
So what are, what are some of
the main reasons that you have

151
00:08:01,775 --> 00:08:04,700
found, uh, for cart abandonment?

152
00:08:04,730 --> 00:08:07,370
Why do, why do we still partake in this?

153
00:08:07,430 --> 00:08:09,400
And let me be frank, right?

154
00:08:09,400 --> 00:08:13,190
I catch myself doing it, uh, adding
stuff to cart and then not buying.

155
00:08:13,190 --> 00:08:14,960
Why do, what are some
of the reasons we do it?

156
00:08:16,300 --> 00:08:19,580
Valon Xhafa: Uh, so there are like,
uh, external and internal factors.

157
00:08:19,700 --> 00:08:23,060
Um, and we're talking about like the
soar external factors regarding the

158
00:08:23,060 --> 00:08:28,560
soar, And internal factors, at Behamics,
we, we mainly focus ourself on internal

159
00:08:28,560 --> 00:08:32,130
factors because these are like factors
that we can actually impact and we can

160
00:08:32,549 --> 00:08:35,150
improve and prevent cart abandonments.

161
00:08:35,340 --> 00:08:38,819
And these factors usually account
for the majority of the reasons.

162
00:08:39,059 --> 00:08:44,400
So if we're talking about some of the main
reasons, they are like, we group them into

163
00:08:44,460 --> 00:08:50,400
two categories, like behavioral issues
or behavioral reasons and technical.

164
00:08:51,330 --> 00:08:53,130
Technical issues within your shop, right?

165
00:08:53,400 --> 00:08:53,490
Yeah.

166
00:08:53,520 --> 00:08:57,030
Um, so behavioral issues are simple.

167
00:08:57,030 --> 00:09:02,640
Put like users as we as humans, we
have hard time making decisions when we

168
00:09:02,640 --> 00:09:04,439
have a lot of options to choose from.

169
00:09:04,860 --> 00:09:09,990
Imagine, for example, for you or for
me, or for anyone else who had a lot

170
00:09:09,990 --> 00:09:14,040
of options to go to good universities,
you know, or got a lot of offers.

171
00:09:14,070 --> 00:09:14,370
Mm-hmm.

172
00:09:14,760 --> 00:09:18,910
, it was a really hard decision to make,
you know, But when you have only two

173
00:09:19,260 --> 00:09:21,165
offers, it's easier to make a decision.

174
00:09:21,225 --> 00:09:22,725
It's the same story everywhere, right?

175
00:09:22,725 --> 00:09:26,805
If you wanna buy a car or jeans or
t-shirts or whatever you wanna buy,

176
00:09:27,255 --> 00:09:31,635
the more options you have to choose
from the hard it is to make a decision.

177
00:09:32,325 --> 00:09:37,635
And going back to the shops, usually
within a shop for a t-shirt, you have like

178
00:09:37,755 --> 00:09:42,345
20 different T-shirts within the page,
you know, which looked pretty similar.

179
00:09:42,465 --> 00:09:44,685
Mm-hmm . And you only
wanna buy one t-shirt.

180
00:09:45,150 --> 00:09:47,430
So you're gonna be like, Okay,
which one should I pick here?

181
00:09:47,640 --> 00:09:48,690
Because there are a lot of them.

182
00:09:48,690 --> 00:09:50,280
Why is it different from this?

183
00:09:50,280 --> 00:09:51,420
Why is the price changing?

184
00:09:51,510 --> 00:09:55,710
I mean, this looks a bit better, but the
price is higher, or the price is lower.

185
00:09:55,740 --> 00:09:57,360
You know, all these
different confusion points.

186
00:09:57,990 --> 00:10:02,820
So what happens here is that what
usually users do is that they

187
00:10:03,030 --> 00:10:04,950
shortlist some of these T-shirts.

188
00:10:05,010 --> 00:10:06,300
Let's say out 20.

189
00:10:06,300 --> 00:10:08,960
They pick like two or three,
they add them to the cart.

190
00:10:09,750 --> 00:10:10,650
But here's the catch.

191
00:10:11,640 --> 00:10:15,750
Most of the brands, most of the
people think that the user has the

192
00:10:15,750 --> 00:10:19,900
intention to buy this product just
because they added these three T-shirts

193
00:10:19,900 --> 00:10:21,720
to the cart, which is not true.

194
00:10:21,939 --> 00:10:27,390
Mm-hmm., because users actually, they
think the cart as a wishlist, you know,

195
00:10:27,395 --> 00:10:31,560
the wishlist that usually have that you
don't usually use, and most users they

196
00:10:31,560 --> 00:10:33,990
don't use, you know why they don't use it?

197
00:10:33,990 --> 00:10:36,870
Because the cart itself,
it's actual wish list.

198
00:10:37,140 --> 00:10:37,200
Yeah.

199
00:10:37,205 --> 00:10:39,420
It's a short list for the users..

200
00:10:40,050 --> 00:10:44,880
So when a user adds three T-shirts to
the to the cart, they're not adding

201
00:10:44,880 --> 00:10:46,170
because they wanna buy all of them.

202
00:10:46,200 --> 00:10:50,490
They, they're adding because then
they have a shortlist out of which

203
00:10:50,490 --> 00:10:51,990
they can make an easier decision.

204
00:10:52,110 --> 00:10:52,720
Mm-hmm.

205
00:10:53,670 --> 00:10:56,610
And then when they go the cart, they
have three different t-shirts, and then

206
00:10:56,610 --> 00:11:02,580
they can think about it, and here's
where returns are related, because if

207
00:11:02,800 --> 00:11:07,230
the user cannot make a decision, To
pick one T-shirt out of these three

208
00:11:07,230 --> 00:11:12,930
different t-shirts, they will say, Okay,
maybe I can pick two and then buy them

209
00:11:12,930 --> 00:11:14,670
and then make the decision at help.

210
00:11:15,000 --> 00:11:15,090
Mm-hmm.

211
00:11:15,420 --> 00:11:20,490
, so are you following me here, how the
users actually push the decision making

212
00:11:20,550 --> 00:11:22,680
as much as possible into the future.

213
00:11:23,040 --> 00:11:26,970
First they say, Okay, let me just,
I'm gonna make the decision when

214
00:11:26,970 --> 00:11:29,670
I go with the cart, and then no,
maybe I'm gonna make the decision

215
00:11:29,670 --> 00:11:31,620
when I have the product at home.

216
00:11:32,250 --> 00:11:34,170
This is the word human nature.

217
00:11:34,560 --> 00:11:36,720
We like to push the decision making.

218
00:11:36,720 --> 00:11:38,070
We like to procrastinate.

219
00:11:38,130 --> 00:11:41,130
We like to push the decision making
as much as possible in the future.

220
00:11:41,580 --> 00:11:45,900
We like to push the hard decisions
as much as possible into the future.

221
00:11:45,900 --> 00:11:48,540
You know, that's the same plan
that happens here in the store.

222
00:11:49,260 --> 00:11:52,170
So that's kind of like related
to the behavioral part.

223
00:11:52,170 --> 00:11:53,730
We have a lot of options to choose from.

224
00:11:53,730 --> 00:11:55,410
It's called also paradox of choice.

225
00:11:55,710 --> 00:11:57,270
You have so many options to choose from.

226
00:11:57,330 --> 00:12:00,630
You just, you get suffocated
from the decision making process.

227
00:12:00,630 --> 00:12:01,140
Yeah.

228
00:12:03,255 --> 00:12:05,444
Matt Edmundson: I've heard
about the paradox of choice.

229
00:12:05,655 --> 00:12:09,765
Uh, I'm trying desperately to rack my
brains of the book that I came across

230
00:12:09,765 --> 00:12:15,765
it in where they talked about, um,
the famous jam experiment where in a

231
00:12:15,765 --> 00:12:19,574
supermarket they put six jams in a, So,
do you know the one I'm talking about?

232
00:12:19,579 --> 00:12:21,405
They put six jams in a supermarket island.

233
00:12:21,405 --> 00:12:24,975
They monitored sales, so you could
go and you could try them, right?

234
00:12:24,975 --> 00:12:27,805
They put six flavors out, you could try
them, and then they monitored sales.

235
00:12:28,385 --> 00:12:31,185
And then they put, I think it was
something like 36 different flavors

236
00:12:31,185 --> 00:12:33,225
out and monitored sales after that.

237
00:12:33,230 --> 00:12:36,135
The idea being that the more choice
you have, the more likely you are

238
00:12:36,140 --> 00:12:37,575
to find something that you want.

239
00:12:37,905 --> 00:12:39,425
Therefore, sales should increase.

240
00:12:40,125 --> 00:12:43,365
The paradox of choice was actually
the more options you gave to

241
00:12:43,365 --> 00:12:46,545
consumers, the more they didn't
buy, the more they put off.

242
00:12:46,605 --> 00:12:49,585
Like you say, making
that decision with jam.

243
00:12:49,610 --> 00:12:52,455
And I thought it was a
fascinating experiment.

244
00:12:52,815 --> 00:12:55,125
And so what you are saying here,
actually on your e-commerce

245
00:12:55,125 --> 00:12:58,845
website, a lot of cart abandonment
is down to this paradox of choice.

246
00:12:58,850 --> 00:13:00,435
You've got a lot of options.

247
00:13:02,140 --> 00:13:05,380
And what that does is, and I
think you're right, you know,

248
00:13:05,380 --> 00:13:06,819
we've all got, what's that phrase?

249
00:13:06,819 --> 00:13:07,990
Decision fatigue.

250
00:13:08,770 --> 00:13:09,260
Yeah.

251
00:13:09,550 --> 00:13:09,969
You know what I mean?

252
00:13:09,969 --> 00:13:13,209
Where we're trying not very hard
not to make any more decisions, and

253
00:13:13,209 --> 00:13:15,280
so we just keep putting 'em off and
putting 'em off and putting 'em off.

254
00:13:15,280 --> 00:13:17,439
And of course, by not making a
decision, you make a decision.

255
00:13:18,160 --> 00:13:23,939
Uh, and I'm laughing because I purchased,
um, two coats off a website recently.

256
00:13:24,329 --> 00:13:28,170
Uh, and I purchased two because I
wasn't sure which one I wanted, and I

257
00:13:28,170 --> 00:13:30,930
thought, I'll get one and I'll return
the one that I don't want, but I need

258
00:13:30,930 --> 00:13:32,189
to see it in person a little bit.

259
00:13:32,520 --> 00:13:35,280
And I wasn't quite sure on the sizing
and the fit and all that sort of stuff.

260
00:13:35,280 --> 00:13:39,479
So, um, everything that you have
just said, Valon, I am guilty of.

261
00:13:39,569 --> 00:13:39,930
Right.

262
00:13:39,930 --> 00:13:40,260
So

263
00:13:41,750 --> 00:13:42,640
Valon Xhafa: Everyone is guilty.

264
00:13:42,990 --> 00:13:43,230
Yeah.

265
00:13:43,230 --> 00:13:45,270
So it's a human nature.

266
00:13:45,270 --> 00:13:50,385
I mean, I mean, decisions we make and
the way we make decisions in, in the real

267
00:13:50,385 --> 00:13:54,105
world outside the online shop, it's the
same process in the online shop, right?

268
00:13:54,110 --> 00:13:59,275
So it just, we just get suffocated
and it's hard to make a decision.

269
00:14:00,445 --> 00:14:02,595
Matt Edmundson: So we've
got the paradox of choice.

270
00:14:02,775 --> 00:14:06,225
Um, we push decisions to the future.

271
00:14:06,225 --> 00:14:09,105
What else sort of
affects cart abandonment?

272
00:14:09,135 --> 00:14:12,075
Is, is that the key thing or are there
other things that we need to think about?

273
00:14:12,075 --> 00:14:13,575
You said behavioral and technical.

274
00:14:14,490 --> 00:14:17,199
Valon Xhafa: Yeah, so regarding the
behavioral part, the non-behavioral

275
00:14:17,219 --> 00:14:21,060
part, everything is, is, I mean,
there are different layers of,

276
00:14:21,060 --> 00:14:24,719
of issues that can happen, but
everything is tightly related to this.

277
00:14:25,140 --> 00:14:28,410
Having a hard time making a decision,
being unsure about making decision,

278
00:14:28,410 --> 00:14:31,079
or making the wrong decision,
you know, that can actually lead

279
00:14:31,079 --> 00:14:32,880
to a product return, you know?

280
00:14:33,180 --> 00:14:35,480
So all of this is about
like making decision.

281
00:14:35,645 --> 00:14:39,875
And this related to the behavioral
part, but then the second category

282
00:14:39,965 --> 00:14:43,745
of reasons why we have cart
abandonment are technical issues.

283
00:14:43,750 --> 00:14:44,165
Mm-hmm.

284
00:14:44,245 --> 00:14:47,765
you know, like, uh, you have a bug
or you have an issue with a payment,

285
00:14:47,765 --> 00:14:49,415
or you have this or you have that.

286
00:14:49,415 --> 00:14:53,824
You know, So the, the thing here is that
there are so many tools out there that can

287
00:14:53,824 --> 00:14:55,235
actually track all these things for you.

288
00:14:55,324 --> 00:15:01,545
You know, the issue though is that
resources, technical resource are scarce.

289
00:15:02,040 --> 00:15:05,280
Uh, for online shops, not only
online shops in general, but

290
00:15:05,280 --> 00:15:06,630
especially for online shops, right?

291
00:15:06,630 --> 00:15:09,839
So, uh, you won't be able to
fix the issues right away.

292
00:15:09,839 --> 00:15:13,469
You, you won't be able to understand,
okay, why do I have, you know,

293
00:15:13,469 --> 00:15:17,130
like how big the problem this
is, like this technical issue.

294
00:15:17,579 --> 00:15:21,555
And the most important thing is that you,
you need to actually prioritize these

295
00:15:21,555 --> 00:15:26,355
issues, you need to understand, okay,
um, how big of an issue this technical

296
00:15:26,355 --> 00:15:28,275
bug is so that I can go and fix it.

297
00:15:28,635 --> 00:15:31,725
For example, there are bugs and
issues that, or errors that can

298
00:15:31,725 --> 00:15:34,755
happen on the payment page and
they only have like 10 times a day.

299
00:15:35,565 --> 00:15:36,345
You know?

300
00:15:36,405 --> 00:15:39,755
But these ten bugs can actually
cause you a lot of revenue loss.

301
00:15:40,035 --> 00:15:40,516
Yeah, yeah.

302
00:15:40,875 --> 00:15:44,045
Compared to some other issues that
happen, I dunno, like on the product

303
00:15:44,115 --> 00:15:48,645
detail page how that appear like
thousand times a day, but they don't

304
00:15:48,645 --> 00:15:50,355
cause you as much issues, you know?

305
00:15:50,395 --> 00:15:50,715
Mm-hmm.

306
00:15:50,795 --> 00:15:54,495
. So there are so many breaking
points within the shop that can

307
00:15:54,495 --> 00:15:58,155
happen, and it's really important
to understand, okay, how are these

308
00:15:58,155 --> 00:16:00,055
issues tied to cart abandonments?

309
00:16:00,075 --> 00:16:04,155
Because here's the thing, if I add
a product to the cart and I go and

310
00:16:04,155 --> 00:16:08,610
wanna make the purchase, And I have
issues with the payment and you as a

311
00:16:08,610 --> 00:16:12,840
shop owner, you didn't recognize that
I had an issue with the payment and

312
00:16:12,840 --> 00:16:14,460
I abandoned the cart because of that.

313
00:16:15,000 --> 00:16:19,199
You can send me whatever email you
want, Matt, with a coupon or something.

314
00:16:19,740 --> 00:16:25,710
I'm not gonna come and buy because it's
not, because usually like coupons are

315
00:16:25,710 --> 00:16:30,449
the way to recover cart abandonments,
you know, with emails and usually

316
00:16:30,449 --> 00:16:32,670
get a coupon when you abandon a cart.

317
00:16:33,285 --> 00:16:36,435
But I'm not gonna come and buy on
your shop because I had a technical

318
00:16:36,435 --> 00:16:38,685
issue I couldn't pay, you know?

319
00:16:38,685 --> 00:16:38,745
Yeah.

320
00:16:38,805 --> 00:16:42,225
So instead of sending me a coupon
with an email, why don't you just

321
00:16:42,230 --> 00:16:44,805
contact me and provide me help?

322
00:16:45,375 --> 00:16:45,435
Yeah.

323
00:16:45,440 --> 00:16:46,995
To actually make the payment.

324
00:16:47,235 --> 00:16:53,640
So what I'm wanna summarize here is
that, There is no like overview of

325
00:16:53,640 --> 00:16:57,930
what's going on and how are these issues
related to cart abandonments because

326
00:16:57,930 --> 00:16:59,160
everything is pretty much related.

327
00:16:59,190 --> 00:17:04,620
Like the latency, like the page
or the product image doesn't load.

328
00:17:04,830 --> 00:17:06,960
This is, this can cause a huge issue.

329
00:17:06,960 --> 00:17:09,490
You know, Maybe it's like, oh,
maybe just the products not loading.

330
00:17:09,510 --> 00:17:13,500
But yeah, this is interconnected with the
user experience and everything, you know?

331
00:17:13,770 --> 00:17:15,300
So yeah, bottom line.

332
00:17:15,510 --> 00:17:19,750
Behavioral issues or reasons and
technical issues that actually

333
00:17:19,754 --> 00:17:21,800
lead to cart abandonments.

334
00:17:22,349 --> 00:17:24,880
Matt Edmundson: So actually I'm
listening to you talk, Valon, and I'm

335
00:17:24,905 --> 00:17:29,040
going, in my head, I'm thinking I, I
get things like the paradox of choice.

336
00:17:29,040 --> 00:17:30,960
I understand that I get things like.

337
00:17:31,379 --> 00:17:35,730
If I don't clearly tell you what my
shipping rates are, and you get to a

338
00:17:35,730 --> 00:17:39,780
page and it suddenly surprises you that
they're 400 bucks, you're gonna go,

339
00:17:39,780 --> 00:17:44,170
Well, no, I, I get the logic of that and
I get the logic of what you are talking

340
00:17:44,190 --> 00:17:48,450
about, but the more you're talking, the
bigger and bigger it, the, the web sort

341
00:17:48,450 --> 00:17:49,950
of seems to go, if that makes sense.

342
00:17:49,980 --> 00:17:50,250
Sure.

343
00:17:50,670 --> 00:17:55,065
And so there's cart abandonments are
a big issue for e-commerce owners

344
00:17:55,485 --> 00:17:58,845
and the, the, the reasons for cart
abandonments can be behavioral.

345
00:17:58,845 --> 00:18:03,245
They can be technical, they can be
obvious, they can be really unobvious.

346
00:18:03,555 --> 00:18:08,505
And there's a lot going on here
that's gonna require a little bit

347
00:18:08,505 --> 00:18:12,835
of attention, which is where I'm
assuming, uh, Valon, if I can sort of

348
00:18:12,840 --> 00:18:14,385
preempt the conversation a little bit.

349
00:18:14,775 --> 00:18:18,645
Um, I'm assuming this is where
AI actually can actually make

350
00:18:18,645 --> 00:18:21,870
a really big impact, Right?

351
00:18:21,870 --> 00:18:27,840
If you could apply AI somehow in
this to be your, uh, 24/7 eyes

352
00:18:27,840 --> 00:18:31,635
and understand what's going on
and reasonings, is that right?

353
00:18:32,640 --> 00:18:35,100
Valon Xhafa: Yeah, so that's, that's
actually the, the contribution

354
00:18:35,100 --> 00:18:40,920
of AI here in this space because,
uh, AI not only predicts that the

355
00:18:41,310 --> 00:18:42,600
consumer's gonna abandon the cart.

356
00:18:43,004 --> 00:18:49,125
Something that is common, something that
is general knowledge, but we have actually

357
00:18:49,129 --> 00:18:54,315
also pushed it a bit further by also
building an AI that can explain to us

358
00:18:54,315 --> 00:18:56,855
why is this user gonna abandon the cart.

359
00:18:57,005 --> 00:18:57,495
Yeah.

360
00:18:57,495 --> 00:18:58,785
The why, the reason.

361
00:18:59,145 --> 00:19:05,435
So imagine you, Matt, come to my shop
and we predict you're gonna abandon the

362
00:19:05,435 --> 00:19:08,885
cart but then I'm also gonna understand
why you're gonna abandon the cart.

363
00:19:09,075 --> 00:19:12,135
You know, like this specific
reasons before you even do it.

364
00:19:12,215 --> 00:19:12,695
Mm-hmm.

365
00:19:12,795 --> 00:19:17,025
, you know, and, and through this
process I can actually, uh,

366
00:19:17,055 --> 00:19:20,220
intervene while we are still on site.

367
00:19:20,880 --> 00:19:23,669
And help you and support
you or whatever you need.

368
00:19:23,669 --> 00:19:26,040
If, if you're gonna abandon the
cart because of the technical

369
00:19:26,040 --> 00:19:27,870
issue, then I can assist you.

370
00:19:27,870 --> 00:19:30,719
It's like, Hey, we see that you
have, you're having hard time with

371
00:19:30,725 --> 00:19:34,949
payment, or you have, you didn't
see this image loaded or something.

372
00:19:34,949 --> 00:19:35,879
Can we help you somehow?

373
00:19:36,169 --> 00:19:36,610
You know?

374
00:19:37,080 --> 00:19:41,459
Or if we see that you have hard time
making decisions because of this

375
00:19:41,459 --> 00:19:44,550
product's a choice, or you have so
many options, or you're not finding

376
00:19:44,580 --> 00:19:46,860
the right size, and we can predict.

377
00:19:47,125 --> 00:19:51,655
You're gonna abandon it because of these
issues, and we can actually reach out to

378
00:19:51,655 --> 00:19:56,695
you while, again, you're still on site and
try to provide support according to the

379
00:19:56,695 --> 00:19:58,355
reason why you're gonna abandon the cart.

380
00:19:58,375 --> 00:19:58,675
Right.

381
00:19:58,915 --> 00:20:03,865
So it's all about, It's all about,
like the biggest issue is that when

382
00:20:03,865 --> 00:20:08,935
you bring consumer or user to your
shop, you already paid a lot of money.

383
00:20:09,455 --> 00:20:09,805
Mm.

384
00:20:10,230 --> 00:20:15,870
You know, so if you leave this consumer
to leave your store and try to bring

385
00:20:15,870 --> 00:20:19,500
them back through emails and retalk,
this is gonna cost you additional money.

386
00:20:20,250 --> 00:20:24,360
You know, the solution here
is like, you need to react why

387
00:20:24,419 --> 00:20:26,460
this user is on site mm-hmm.

388
00:20:26,700 --> 00:20:30,540
, and be able to intervene right away
and prevent things before they happen.

389
00:20:30,659 --> 00:20:33,000
You know, like we, we know this concept.

390
00:20:33,179 --> 00:20:36,450
If you prevent something, it's
more cost effective than trying to

391
00:20:36,455 --> 00:20:39,780
recover or, or repair or something.

392
00:20:39,780 --> 00:20:43,879
You know, like it's with cars, it's
even with health, you know, you can

393
00:20:43,885 --> 00:20:45,720
predict diseases and prevent them.

394
00:20:46,500 --> 00:20:50,250
You can treat them, you know,
this will be more efficient, more

395
00:20:50,250 --> 00:20:55,440
successful, and more cost effective
than just leaving it go big.

396
00:20:55,440 --> 00:20:58,050
So that's the same concept that
we're actually applying here.

397
00:20:58,320 --> 00:21:03,270
Um, predict, prevent, and try to
keep the user on site as much as we

398
00:21:03,275 --> 00:21:08,010
can and not like say, Oh, I have, I
can send emails and bring back cause

399
00:21:08,010 --> 00:21:09,180
it's gonna cost you additional money.

400
00:21:09,600 --> 00:21:10,680
Especially these days.

401
00:21:11,010 --> 00:21:13,230
The biggest issue of
these days is the cost.

402
00:21:13,530 --> 00:21:22,260
The cost of ads have gone so high, That,
uh, it's very costly to actually bring, or

403
00:21:22,260 --> 00:21:27,090
it's, it costs you a lot of money to bring
consumers or additional traffic to store.

404
00:21:27,240 --> 00:21:31,510
Until a year or two years ago,
the only way to grow was to

405
00:21:31,530 --> 00:21:33,300
bring more traffic to your store.

406
00:21:33,885 --> 00:21:35,235
Because it was cheaper.

407
00:21:35,445 --> 00:21:36,585
You could just get more traffic.

408
00:21:36,585 --> 00:21:38,265
You're gotta go now.

409
00:21:38,265 --> 00:21:40,935
Even if you have the money, it's gonna
cost you so much money to actually

410
00:21:40,935 --> 00:21:42,215
just bring additional traffic.

411
00:21:42,355 --> 00:21:47,685
So you have to go back to your site and
see, okay, what else can I improve here?

412
00:21:47,690 --> 00:21:51,915
So that the traffic that I have
right now I can actually increase

413
00:21:51,920 --> 00:21:55,215
my profit and margins, but also
increase customer satisfaction

414
00:21:55,245 --> 00:21:56,535
because it's really important.

415
00:21:57,899 --> 00:22:02,980
Matt Edmundson: So how I, it's um, I mean,
it sounds wonderful, you know, the sort of

416
00:22:03,210 --> 00:22:05,730
the key problems and, uh, that you have.

417
00:22:05,730 --> 00:22:10,290
So how does AI, if I go back to the
sort of list that you gave earlier on,

418
00:22:10,295 --> 00:22:12,030
so let's look at the paradox of choice.

419
00:22:12,600 --> 00:22:18,190
How does AI help a consumer deal
with this paradox of choice?

420
00:22:18,190 --> 00:22:22,030
How does that, how does, well have
you, how have you figured that out?

421
00:22:22,030 --> 00:22:22,270
Right?

422
00:22:22,270 --> 00:22:26,170
Because that sounds, that sounds like an
easy thing to roll off the tongue, right?

423
00:22:26,200 --> 00:22:27,310
Yeah, I can just say that.

424
00:22:27,430 --> 00:22:30,100
But actually how, I'm really curious,
how does that, how does it do it?

425
00:22:31,515 --> 00:22:35,835
Valon Xhafa: Yeah, so we can take a
very simple example to maybe explain

426
00:22:35,865 --> 00:22:38,675
the paradox of choice, that it's
more simplistic for the audience.

427
00:22:38,895 --> 00:22:43,845
So one of the reasons why consumers
abandon the carts is because, um, they

428
00:22:43,850 --> 00:22:46,065
just add too many products to the cart.

429
00:22:46,469 --> 00:22:51,899
Which is, which is maybe, um, maybe,
uh, a misconception because, uh,

430
00:22:51,929 --> 00:22:57,149
people or brands, uh, think that when
consumers add products to the cart,

431
00:22:57,330 --> 00:22:58,949
they have a higher chance of buying.

432
00:22:59,250 --> 00:23:00,449
Yes and no.

433
00:23:00,780 --> 00:23:04,350
Because after a specific number
of products in the cart, your

434
00:23:04,350 --> 00:23:07,570
probability  of buying goes down mm-hmm.

435
00:23:07,655 --> 00:23:10,590
because you're not gonna buy 20
products or 15 products, you know?

436
00:23:10,770 --> 00:23:11,050
Mm-hmm..

437
00:23:11,135 --> 00:23:12,449
So you're just shortlisting them.

438
00:23:12,870 --> 00:23:16,779
So, um, this is the case when
consumers abandon the cart.

439
00:23:16,800 --> 00:23:21,179
And what our AI, for example, does here
is that for every product you add to the

440
00:23:21,360 --> 00:23:26,010
cart, we get a probability score, um,
of what is your probability of buying.

441
00:23:26,715 --> 00:23:30,405
You know, and if we see that okay, you're
probably buying is increasing with more

442
00:23:30,405 --> 00:23:34,365
products and that suddenly starts to
decrease, then we know that maybe it's

443
00:23:34,645 --> 00:23:38,385
the right time for you to, to remind
you, Matt, that maybe you can have a

444
00:23:38,385 --> 00:23:43,225
look on your cart because, uh, it seems
that you're all ready with the cart.

445
00:23:43,225 --> 00:23:47,584
It's like a gentle reminder this kind of
like, you know, when you go to the shop.

446
00:23:48,004 --> 00:23:51,815
To a physical store, and you have
a shop assistant helping you with

447
00:23:52,085 --> 00:23:55,445
advices and maybe try this or
maybe try that, or maybe this would

448
00:23:55,445 --> 00:23:57,095
look good or this or that, Right?

449
00:23:57,455 --> 00:24:00,305
So this is the touch and the
feel that you have right now

450
00:24:00,305 --> 00:24:02,525
on online shopping experience.

451
00:24:02,645 --> 00:24:04,025
It's just like one way interaction.

452
00:24:04,025 --> 00:24:06,605
You have to go it self
service, pretty much.

453
00:24:06,605 --> 00:24:08,645
You have to go and find the
right products and everything.

454
00:24:08,915 --> 00:24:12,755
And what happens here is that most of
the consumers, they just lose themselves.

455
00:24:12,845 --> 00:24:16,955
They just like, by trying to find more
product and then spending more time.

456
00:24:17,650 --> 00:24:20,890
Instead of like, instead of focus on
these three, four products that they

457
00:24:20,890 --> 00:24:23,950
already added to the cart, and see
if they can make a decision here.

458
00:24:24,010 --> 00:24:28,810
So what our AI does is that, uh, our
AI predicts if the consumer's gonna

459
00:24:28,815 --> 00:24:31,970
add too many products to the cartd,
and then  they're gonna abandon cart.

460
00:24:32,410 --> 00:24:37,120
And before they do that, we just provide a
general reminder to the consumer on site.

461
00:24:37,120 --> 00:24:38,980
You know, like a small note or something.

462
00:24:39,250 --> 00:24:43,360
Hey, Matt, seems that your cart,
maybe it's ready or something.

463
00:24:43,480 --> 00:24:44,760
Why don't you have a look on it?

464
00:24:45,260 --> 00:24:49,020
Because we know if you continue doing
that process, you're just gonna add

465
00:24:49,020 --> 00:24:52,080
too many products and it's gonna be
harder for you to make a decision

466
00:24:52,080 --> 00:24:53,810
and you're just like end up leaving.

467
00:24:54,315 --> 00:24:55,575
And not buying at all.

468
00:24:55,575 --> 00:24:59,325
So that's, for example, one case, but
there are a lot of reasons out there.

469
00:25:00,195 --> 00:25:01,275
Matt Edmundson: That's really clever.

470
00:25:01,425 --> 00:25:07,275
I mean, that, it's interesting that,
um, I mean, forget all the, the AI, just

471
00:25:07,275 --> 00:25:12,635
the psychology of saying actually now's
the ideal time for you to check out bud.

472
00:25:12,650 --> 00:25:16,635
We're gonna help you remember to do
that cuz uh, we want you in the shop.

473
00:25:16,635 --> 00:25:19,935
But not too long, I'm kind of reminded
of the supermarkets, you know, they,

474
00:25:20,445 --> 00:25:25,640
um, I dunno if they still do, but years
ago, Uh, they used to play music at

475
00:25:25,640 --> 00:25:29,180
different speeds depending on how long
they wanted you in the shop, right?

476
00:25:29,180 --> 00:25:32,450
So if they wanted you in the shop
and out, cause it was busy, they

477
00:25:32,450 --> 00:25:34,460
would pay play faster paced music.

478
00:25:34,460 --> 00:25:36,830
And if they wanted you to hang
around the shop for a little while

479
00:25:36,830 --> 00:25:39,530
and just browse and add a few
more products to your basket, they

480
00:25:39,530 --> 00:25:41,630
would play slower, calmer music.

481
00:25:41,660 --> 00:25:42,200
Just chill out.

482
00:25:42,205 --> 00:25:42,770
Just take your time.

483
00:25:43,430 --> 00:25:46,400
Um, and it's kind of what you're
describing reminds me of that

484
00:25:46,400 --> 00:25:48,770
valon, you know, that actually
what you're doing is you're going.

485
00:25:49,480 --> 00:25:55,030
The optimal time for this person buying
these types of products is like, I need

486
00:25:55,030 --> 00:25:58,780
them out of the store within four minutes
and five seconds, which kind of goes

487
00:25:58,785 --> 00:26:02,800
against the logic, which has actually,
we want them to hang around in the site.

488
00:26:02,800 --> 00:26:05,890
We want them to read stuff, we want
'em to get sucked in and drawn in.

489
00:26:06,280 --> 00:26:09,040
Um, actually you are going,
well, that's not always the case.

490
00:26:09,040 --> 00:26:11,680
And you're using this sort of
AI magic to figure that out.

491
00:26:11,680 --> 00:26:12,010
Right.

492
00:26:13,679 --> 00:26:13,995
Valon Xhafa: Exactly.

493
00:26:13,995 --> 00:26:18,764
So the magic here is that not, not
every single customer is the same.

494
00:26:18,764 --> 00:26:20,415
So there is no average consumer.

495
00:26:20,415 --> 00:26:23,264
The average consumer is not, is not real.

496
00:26:23,445 --> 00:26:25,574
It's idealistic, you
know, So it doesn't exist.

497
00:26:25,605 --> 00:26:25,695
Mm-hmm.

498
00:26:25,935 --> 00:26:29,865
. So every single consumer has their
own timeframe of how much time they

499
00:26:29,865 --> 00:26:31,574
wanna spend or they're gonna spend.

500
00:26:31,635 --> 00:26:33,885
And we can actually
predict that, you know?

501
00:26:33,945 --> 00:26:36,751
So if we predict the consumer's
gonna spend four minutes.

502
00:26:36,951 --> 00:26:42,255
Then you can just waste your time
trying to have them more on your store.

503
00:26:42,315 --> 00:26:42,585
Right.

504
00:26:42,585 --> 00:26:43,695
Or longer in your store.

505
00:26:44,025 --> 00:26:48,045
Which means that, Which means that if
you see that the consumers ready to buy,

506
00:26:48,135 --> 00:26:52,945
if you predict that and they don't have
enough time then the best possible way

507
00:26:52,945 --> 00:26:54,635
is like to remind them to go to the cart.

508
00:26:54,970 --> 00:26:59,500
And see if they are willing to buy any
of these products instead of like trying

509
00:26:59,500 --> 00:27:04,659
to confuse them with like coupons and
like showing a popup, asking for their

510
00:27:04,690 --> 00:27:08,020
emails and promotions and this and that.

511
00:27:08,020 --> 00:27:10,000
You know, I don't have a lot of time.

512
00:27:10,000 --> 00:27:15,430
Let's say I'm on a subway going to
work and I see an ad and I click

513
00:27:15,430 --> 00:27:18,310
a product and you know, like on
Subway you don't have a lot of time.

514
00:27:18,310 --> 00:27:19,870
You have like three,
four minutes time spent.

515
00:27:20,030 --> 00:27:20,629
Mm-hmm.

516
00:27:20,930 --> 00:27:25,830
and uh, I add that to the cart and
suddenly I get like bombarded with like,

517
00:27:25,860 --> 00:27:27,900
Oh, here's a promotion, here's this.

518
00:27:27,900 --> 00:27:31,780
No, I, I just, I saw an ad,
I added this to the cart.

519
00:27:31,800 --> 00:27:35,490
I want to finalize this purchase,
so why don't you help me focus

520
00:27:35,495 --> 00:27:40,050
on this instead of you trying to
upsell and then lose me completely.

521
00:27:40,320 --> 00:27:40,800
Yeah.

522
00:27:41,430 --> 00:27:44,520
Matt Edmundson: I like, I like the
message that you are preaching, valon.

523
00:27:44,550 --> 00:27:45,270
I really do.

524
00:27:45,270 --> 00:27:46,470
I think it's so wise.

525
00:27:46,740 --> 00:27:48,870
I, I wrote down this
phrase you used earlier.

526
00:27:50,145 --> 00:27:52,274
And when you said that, when you were
talking about how there were too many

527
00:27:52,274 --> 00:27:54,405
options, you called them confusion points.

528
00:27:54,405 --> 00:27:56,475
I don't know if that's a
deliberate phrase you use.

529
00:27:56,985 --> 00:28:00,284
Um, uh, but I thought it was
a really interesting phrase.

530
00:28:00,284 --> 00:28:03,435
And actually what we're doing is
we're on e-commerce at the moment.

531
00:28:03,435 --> 00:28:06,405
We're creating a lot of confusion
points, and some of them you've hit

532
00:28:06,405 --> 00:28:09,825
upon like, give me your email address,
do this, do that, do the other, And

533
00:28:09,825 --> 00:28:13,185
it's like, I just want to get this
product and go, dude, is what I want.

534
00:28:13,185 --> 00:28:13,514
Right.

535
00:28:13,514 --> 00:28:19,889
So what sort of things then does AI
look at, So you talk about predicting

536
00:28:19,889 --> 00:28:25,379
the behavior, and so what sort
of, does it look at everything?

537
00:28:25,385 --> 00:28:29,970
Like what, whether you're on mobile,
uh, you know, whereabouts in the world

538
00:28:29,970 --> 00:28:34,379
you are, what time of day it is, what
sort of things would AI look at and sort

539
00:28:34,379 --> 00:28:38,439
of start to take into consideration,
Start to calculate behavior?

540
00:28:39,520 --> 00:28:42,870
Valon Xhafa: Yeah, so they
almost like behavior based

541
00:28:42,870 --> 00:28:44,610
data, like click stream data.

542
00:28:44,880 --> 00:28:48,840
We don't, in our case, we don't use
personal data because we don't, uh,

543
00:28:49,080 --> 00:28:50,610
you know, we not gonna send emails.

544
00:28:50,640 --> 00:28:53,520
We don't, we don't need to
know what the user did like

545
00:28:53,520 --> 00:28:54,660
three months ago or something.

546
00:28:54,660 --> 00:28:56,460
It's pretty much
completely behavioral data.

547
00:28:56,465 --> 00:29:01,700
Like, um, which ad did you click
when you came to the store, or

548
00:29:01,700 --> 00:29:04,040
which, which device are you using?

549
00:29:04,040 --> 00:29:05,899
Which type of device?

550
00:29:05,899 --> 00:29:09,290
And then where did you land on the shop?

551
00:29:09,294 --> 00:29:12,080
Did you land on the product
detail page or on the homepage?

552
00:29:12,085 --> 00:29:13,310
Which products did you click?

553
00:29:13,310 --> 00:29:17,000
The sequence of the product clicked
and all these differents, so we

554
00:29:17,000 --> 00:29:21,870
use like 200 different variables
to actually be able to mm-hmm.

555
00:29:22,465 --> 00:29:25,055
To get a better understanding
of the consumer.

556
00:29:25,145 --> 00:29:30,065
And with every click the consumer does,
we actually, uh, predict the behavior

557
00:29:30,065 --> 00:29:33,544
and we measure the probability score
of buying, you know, because this

558
00:29:33,544 --> 00:29:35,044
is the most important thing here.

559
00:29:35,315 --> 00:29:35,375
Yeah.

560
00:29:35,375 --> 00:29:36,485
So what happens here?

561
00:29:36,490 --> 00:29:38,794
Is that the confusion
point that you mentioned?

562
00:29:38,794 --> 00:29:43,445
No, this is something that I'm, I
actually mentioned it because, um, we

563
00:29:43,445 --> 00:29:47,375
have seen that, we have seen a lot of
cases when the consumer was ready to buy.

564
00:29:47,969 --> 00:29:52,500
High probability of buying, and then
the brand showed the popup asking for an

565
00:29:52,500 --> 00:29:55,139
email, and they just lost the consumer.

566
00:29:55,139 --> 00:29:59,520
The, the probability of buying dropped
to like 50% or something and they just

567
00:29:59,520 --> 00:30:04,530
left, or, or the result on a big confusion
point, which is really upselling.

568
00:30:05,040 --> 00:30:08,340
Upselling is great, you know,
upselling something that can

569
00:30:08,689 --> 00:30:11,639
actually make you money, um, increase
average order values and everything.

570
00:30:12,240 --> 00:30:15,270
But after a specific point
in time when the consumer has

571
00:30:15,270 --> 00:30:16,930
added enough products to cart.

572
00:30:17,540 --> 00:30:20,745
Don't try to upsell too much
because you can lose a consumer.

573
00:30:20,985 --> 00:30:21,074
Mm-hmm.

574
00:30:21,375 --> 00:30:25,905
, if I go the cart and then I have
three products that I want to buy

575
00:30:26,475 --> 00:30:30,945
and one of them is a T-shirt and then
I'm ready to buy, I probably buying.

576
00:30:31,425 --> 00:30:36,165
And then you provide a recommendation
on the bottom saying like, Hey, this,

577
00:30:36,195 --> 00:30:38,084
these are also some other T-shirts.

578
00:30:38,715 --> 00:30:43,965
So this is a confusion point because
maybe now I like one of the other T-shirts

579
00:30:43,965 --> 00:30:45,504
which are being recommended to me.

580
00:30:46,485 --> 00:30:51,315
So now I'm gonna change my mind's
like, damn, which one should I pick?

581
00:30:51,315 --> 00:30:54,885
Should I go with the one that I
was ready to buy or now with this

582
00:30:54,885 --> 00:30:56,505
one that I didn't see before?

583
00:30:57,645 --> 00:31:02,115
And then what happens, like maybe you add
this new T-shirt and then you just like,

584
00:31:02,385 --> 00:31:04,245
no, I don't know which decision to make.

585
00:31:04,545 --> 00:31:06,555
And then like, two
options you gotta do here.

586
00:31:06,855 --> 00:31:09,885
Um, the first is that you're gonna
leave the store without buying, or

587
00:31:09,885 --> 00:31:13,135
you're gonna buy both t-shirts and then
return one of them, which is not good.

588
00:31:13,605 --> 00:31:14,625
Returns are not good.

589
00:31:14,715 --> 00:31:18,615
You should not do tradeoffs
between returns because, uh, one

590
00:31:19,754 --> 00:31:24,225
percentage increase or percentage
or an increase in return is not a

591
00:31:24,225 --> 00:31:26,264
one percentage decrease in revenue.

592
00:31:26,324 --> 00:31:30,475
It's four or five because you have
all the costs associated to returns.

593
00:31:30,695 --> 00:31:36,000
Yeah, so upselling is a confusion point
is popups like asking for emails and

594
00:31:36,000 --> 00:31:39,960
everything, are confusion point, like,
uh, user are different and you have to

595
00:31:39,960 --> 00:31:42,060
be able to actually target them better.

596
00:31:42,120 --> 00:31:44,460
If the user is willing to buy.

597
00:31:44,520 --> 00:31:45,870
Why are you asking for an email?

598
00:31:45,870 --> 00:31:46,550
You're gonna get the email.

599
00:31:47,445 --> 00:31:48,515
Either way, you know?

600
00:31:48,585 --> 00:31:48,855
Yeah.

601
00:31:48,860 --> 00:31:48,995
Yeah.

602
00:31:49,085 --> 00:31:49,435
So

603
00:31:49,905 --> 00:31:50,445
Matt Edmundson: that's clever.

604
00:31:51,015 --> 00:31:55,425
It's interesting because what you are
doing with ai or what I'm understanding,

605
00:31:55,425 --> 00:31:58,425
valon, let me put it that way, rather
than tell you what you're doing,

606
00:31:58,845 --> 00:32:04,695
here's what I understand, um, what I'm
understanding is you are using ai, uh,

607
00:32:04,695 --> 00:32:08,655
and AI is using hard real data, right?

608
00:32:08,655 --> 00:32:15,075
It's using 200, 250 data points to try
and predict what is going on here and.

609
00:32:15,750 --> 00:32:22,590
I'm assuming the AI is very, um,
specific to that store, the behaviors

610
00:32:22,595 --> 00:32:25,650
for that store, because it's gonna
be different to the store, you know,

611
00:32:25,650 --> 00:32:26,700
further down the road, isn't it?

612
00:32:26,700 --> 00:32:29,880
It's gonna have a different set of
behaviors attached to it, and you are

613
00:32:29,880 --> 00:32:33,090
using data to figure that out rather than,

614
00:32:34,425 --> 00:32:37,875
What tends to happen is I will go
to a website, someone's written

615
00:32:37,875 --> 00:32:41,625
a blog post, which says, You must
have three upsells per product to

616
00:32:42,064 --> 00:32:43,784
maximize average order value, right?

617
00:32:43,935 --> 00:32:47,895
And so in my head I go, I must
have three products to upsell.

618
00:32:47,955 --> 00:32:50,955
And actually what you are using is
data, which says, No, actually for

619
00:32:50,955 --> 00:32:54,465
your website, you should have a
maximum of two based on this data.

620
00:32:54,645 --> 00:32:59,175
And actually the, the products which
you upsell, they really matter as well.

621
00:32:59,690 --> 00:33:02,970
It's not just the fact that you
upsell, it's what you upsell.

622
00:33:02,970 --> 00:33:05,570
And if you upsell that, the
knock on effect is this.

623
00:33:05,870 --> 00:33:09,290
And I see here and I listen to the
t-shirt example and I go, Well, from

624
00:33:09,295 --> 00:33:12,500
a logical point of view, everything
that you have just said makes sense.

625
00:33:12,530 --> 00:33:17,600
But I would never have thought about
that, um, in a million years, which

626
00:33:17,600 --> 00:33:20,270
is why I guess when I go to the
t-shirt websites, they're trying

627
00:33:20,270 --> 00:33:22,010
to upsell me yet another t-shirt.

628
00:33:22,580 --> 00:33:25,700
Um, and so I, I like the
smartness of this, and I like

629
00:33:25,705 --> 00:33:27,410
how you are using the, the data.

630
00:33:27,415 --> 00:33:28,280
Can I ask you a question?

631
00:33:29,445 --> 00:33:32,415
How much data do you need?

632
00:33:32,415 --> 00:33:36,945
As in, should I be plugging in, um,
your AI if I'm just starting out?

633
00:33:36,945 --> 00:33:41,965
Or should I be plugging in your ai if
I've got more than 200 product SKUs,

634
00:33:41,985 --> 00:33:44,895
should I be plugging in your ai if
I've got more than 10,000 customers?

635
00:33:45,225 --> 00:33:48,165
I'm really curious how much
data you need to start building

636
00:33:48,165 --> 00:33:49,625
this kind of predictive model.

637
00:33:50,775 --> 00:33:54,090
Valon Xhafa: Uh, we do have, this is
actually a really, really good question.

638
00:33:54,090 --> 00:33:59,780
Like, um, we're agile in this way
because we have our own pre-built

639
00:34:00,090 --> 00:34:02,370
models and AI systems and everything.

640
00:34:02,670 --> 00:34:07,920
So it's not that when we go live with
your shop or when we install our AI in

641
00:34:07,920 --> 00:34:09,389
your shop, we gonna start from zero.

642
00:34:09,395 --> 00:34:13,110
Now we already have knowledge collected
from different sort, from different

643
00:34:13,110 --> 00:34:16,920
categories and different products
because the most important thing here

644
00:34:16,920 --> 00:34:22,380
is that you said that maybe for for
your shop two products, upsell is good.

645
00:34:22,380 --> 00:34:22,469
You.

646
00:34:23,185 --> 00:34:27,985
Which is partially true because
again, the thing here is that

647
00:34:28,135 --> 00:34:29,755
there is no average score.

648
00:34:29,875 --> 00:34:31,255
There is no average user.

649
00:34:31,735 --> 00:34:35,905
Average user doesn't exist,
you know, in most of the cases.

650
00:34:35,995 --> 00:34:36,145
Mm-hmm.

651
00:34:36,385 --> 00:34:41,095
, every user is different and the most
important thing is that, okay, let's say

652
00:34:41,100 --> 00:34:45,775
for example, that you, uh, two products
to upsell is good for your shop, but

653
00:34:45,775 --> 00:34:48,235
this can also change within seasons.

654
00:34:48,385 --> 00:34:48,745
Mm-hmm.

655
00:34:48,990 --> 00:34:52,440
, for example, when you're like
winter season,  you won't be

656
00:34:52,440 --> 00:34:55,800
able to sell to upsell to two
products cuz the price is high.

657
00:34:55,800 --> 00:34:59,100
You have high jackets and, and
everything else which are expensive.

658
00:34:59,550 --> 00:35:04,260
So if you can upsell one product
in winter season, that's it's good.

659
00:35:05,205 --> 00:35:08,924
But for example, during summer, you can,
maybe you need to upsell three products,

660
00:35:08,955 --> 00:35:10,875
let's say, if we talk about the average.

661
00:35:11,535 --> 00:35:15,735
But again, the average is not, it's not a
good, it's not a good way to, to measure

662
00:35:16,035 --> 00:35:19,605
how many products you have to upsell to,
to the consumers and everything else.

663
00:35:19,935 --> 00:35:23,815
Um, so like going back to, to
the topic of like, How much data?

664
00:35:23,815 --> 00:35:27,085
It's usually like as soon as
we install Behamics, um, we can

665
00:35:27,435 --> 00:35:30,565
actually make predictions right
away within one, two hours of data.

666
00:35:30,714 --> 00:35:31,194
Mm-hmm.

667
00:35:31,274 --> 00:35:34,165
, we just need to reiterate and
readapt everything because we

668
00:35:34,165 --> 00:35:38,095
already have like baseline systems
capable of making predictions.

669
00:35:38,125 --> 00:35:38,214
Matt Edmundson: Mm-hmm.

670
00:35:38,935 --> 00:35:42,595
And I guess Behamics is
always learning, isn't it?

671
00:35:42,600 --> 00:35:42,915
It's always.

672
00:35:43,355 --> 00:35:45,035
Kind of adapting to what's going on.

673
00:35:45,035 --> 00:35:48,425
And that's the beautiful thing about AI is
this constant learning machine, isn't it?

674
00:35:49,175 --> 00:35:53,015
Um, can I ask you, you say you've
collated all this data from websites that

675
00:35:53,020 --> 00:35:56,195
you've been on, and obviously you've,
you've seen stuff as a result of this.

676
00:35:56,315 --> 00:36:02,735
Um, what have been some of the
most surprising things for you?

677
00:36:02,855 --> 00:36:05,585
As in, you've done this on the cart
abandonment, you've looked at the

678
00:36:05,585 --> 00:36:08,465
stats, you've looked at the data
coming out of stores with your ai.

679
00:36:09,885 --> 00:36:12,375
What have, what have been some
of the most surprising findings?

680
00:36:12,375 --> 00:36:13,365
I'm, I'm curious.

681
00:36:14,815 --> 00:36:18,345
Valon Xhafa: Uh, I, I mean like the,
I mean, the findings could be like,

682
00:36:19,995 --> 00:36:21,859
Interesting findings or fun findings.

683
00:36:21,884 --> 00:36:24,855
Mm-hmm, which are findings that
maybe they don't have a huge impact.

684
00:36:25,335 --> 00:36:29,075
It can be also findings that actually
they're not that interesting,

685
00:36:29,080 --> 00:36:30,345
but they have a huge impact.

686
00:36:30,645 --> 00:36:30,735
Mm-hmm.

687
00:36:30,735 --> 00:36:34,965
So I can start with fun finding, One of
the findings that I, we found is that,

688
00:36:35,535 --> 00:36:40,565
um, users, these days actually, they also
sometimes abandon the carts on purpose.

689
00:36:40,980 --> 00:36:44,040
Because they know they'll
receive an email for a coupon.

690
00:36:44,580 --> 00:36:44,850
Yeah.

691
00:36:44,940 --> 00:36:49,860
And we're able to actually, we were
actually able to, to, to see this pattern.

692
00:36:49,890 --> 00:36:52,830
We saw the consumer, they abandon the
cart even if they had a high chance

693
00:36:52,830 --> 00:36:57,060
of buy, because our AI can predict
that they abandoned it on purpose.

694
00:36:57,065 --> 00:37:02,160
And then after a few hours, We saw
them coming back and adding the coupon

695
00:37:02,250 --> 00:37:06,270
that they got from the email and coming
back from email, you know, so this is

696
00:37:06,270 --> 00:37:10,890
a pattern that we actually saw because
recovery emails with coupons were great

697
00:37:10,890 --> 00:37:14,520
in the beginning, but now actually
consumers also sometimes abandon cart on

698
00:37:14,520 --> 00:37:16,860
purpose, and hopefully to gather coupon.

699
00:37:17,290 --> 00:37:21,630
Yeah, so this was, this was,
this was a really, really

700
00:37:21,630 --> 00:37:23,759
funny finding that we found.

701
00:37:24,299 --> 00:37:28,890
Uh, but the one that is interesting
or that can have huge revenue loss

702
00:37:29,370 --> 00:37:33,750
are findings where brands, they know
they have issues within their shop.

703
00:37:34,200 --> 00:37:38,490
I mean, we talking like about like
brands that are like a hundred million

704
00:37:38,490 --> 00:37:40,110
dollar in annual revenue or something.

705
00:37:40,115 --> 00:37:44,339
They know they have an issue, it's
visible and they can go and fix.

706
00:37:45,195 --> 00:37:49,185
Which is, which is like you have,
for example, filtering options

707
00:37:49,185 --> 00:37:54,675
are not working or images are not
loading or something because they,

708
00:37:54,675 --> 00:37:56,235
they know that this is an issue.

709
00:37:56,415 --> 00:38:00,585
The challenge here is that they don't
know how big of this issue is, so

710
00:38:00,585 --> 00:38:04,755
this is why it kind of like led us to
think maybe we should tell them, Hey,

711
00:38:05,205 --> 00:38:11,025
this image that is not being loaded
is costing you 20 grand a month.

712
00:38:11,115 --> 00:38:11,205
Mm-hmm.

713
00:38:11,640 --> 00:38:13,875
so that maybe they can
actually go and fix.

714
00:38:14,640 --> 00:38:17,760
And this was, this is something
that is actually across most of

715
00:38:17,760 --> 00:38:20,940
the clients we work with and that
we have seen in the industry.

716
00:38:21,000 --> 00:38:25,320
There are issues that are clear
they are not being fixed issues

717
00:38:25,320 --> 00:38:29,580
that can be actually fixed easily
and can actually make them more

718
00:38:29,580 --> 00:38:31,110
money and generate more money.

719
00:38:31,260 --> 00:38:34,020
And because this issues can actually
lead to cart abandonment directly.

720
00:38:34,200 --> 00:38:34,550
Mm.

721
00:38:34,555 --> 00:38:37,380
If the filtering option is not
working, then this is another

722
00:38:37,380 --> 00:38:38,820
reason for me to lead the store.

723
00:38:40,274 --> 00:38:42,825
Matt Edmundson: So is this, uh, before
we hit the record button, we were

724
00:38:42,825 --> 00:38:45,464
talking about a, a new development
that you guys have got called

725
00:38:45,464 --> 00:38:46,935
Flow, which you're excited about.

726
00:38:46,935 --> 00:38:48,944
Is this, is this what
you're talking about here?

727
00:38:48,944 --> 00:38:50,384
The ability to.

728
00:38:51,780 --> 00:38:56,370
Uh, put a financial value on inaction
for want of a better expression,

729
00:38:56,400 --> 00:38:58,710
uh, on your website, the stuff
which is wrong and not working.

730
00:38:58,710 --> 00:39:00,600
It's costing you 20 grand, 30 grand.

731
00:39:00,605 --> 00:39:01,290
The image not loading.

732
00:39:01,290 --> 00:39:01,410
Yeah.

733
00:39:02,070 --> 00:39:04,770
Um, is that what flow does
your new, your new product?

734
00:39:05,400 --> 00:39:05,880
Valon Xhafa: Yes.

735
00:39:05,880 --> 00:39:10,610
So we, we took the findings from the
psychology part, so we're like, Price

736
00:39:10,610 --> 00:39:15,560
is something that can have a huge
impact on everyone, you know, And

737
00:39:15,560 --> 00:39:17,450
it's the same sort also for brands.

738
00:39:17,660 --> 00:39:22,210
So if brands see that, okay, this
ad is happening 200 times a day.

739
00:39:22,740 --> 00:39:26,040
That really, it's not
really urgent, you know?

740
00:39:26,220 --> 00:39:31,650
But if they see that this error is causing
them like 20 grand a day, this is urgent.

741
00:39:31,650 --> 00:39:35,490
You know, like price is really important
and if you really wanna prioritize

742
00:39:35,495 --> 00:39:39,399
things, you really have to kind of like
be able to see and prioritize and rank

743
00:39:39,399 --> 00:39:43,575
them based on the revenue that these
issues are costing so that you can go and

744
00:39:43,575 --> 00:39:46,035
fix them and even escalate the issues.

745
00:39:46,335 --> 00:39:48,345
So that's what we kind of like did here.

746
00:39:48,505 --> 00:39:53,565
We applied AI again, as always, to be able
to explain things are happening within the

747
00:39:53,565 --> 00:40:00,255
store and help us attribute every single
issue, um, to a revenue loss, which means

748
00:40:00,260 --> 00:40:04,335
that if you fix that issue, you're gonna
correct that revenue loss right away.

749
00:40:04,710 --> 00:40:08,850
Pretty much, and we run the tool
where actually we launch it with our

750
00:40:08,850 --> 00:40:13,379
existing clients and everything, and
we see like astronomical values in

751
00:40:13,379 --> 00:40:15,810
terms of loss because of small issues.

752
00:40:16,170 --> 00:40:17,850
We see like 50 grand a day.

753
00:40:19,195 --> 00:40:21,810
which is like 15% of the whole revenue.

754
00:40:22,170 --> 00:40:22,590
Wow.

755
00:40:22,595 --> 00:40:26,250
You know, like 20 grand a
day because of small issues.

756
00:40:26,640 --> 00:40:30,180
And these issues are either like
on the payment or they're like on

757
00:40:30,390 --> 00:40:32,100
product list or images or whatever.

758
00:40:32,280 --> 00:40:33,570
These are issues that can be fixed.

759
00:40:33,840 --> 00:40:38,790
But the thing here is not just fixing
the issues because you can fix the

760
00:40:38,790 --> 00:40:43,800
issues and then you're gonna go, which
is not true because the whole idea

761
00:40:43,800 --> 00:40:48,885
of this tool is to actually prevent
these 50 grand in the future because

762
00:40:49,575 --> 00:40:52,725
whenever you push new things and
you're change new things to your store,

763
00:40:52,845 --> 00:40:56,835
you constantly add new stuff and the
chances of issues appearing are high.

764
00:40:57,405 --> 00:40:59,805
So let me give you an example.

765
00:40:59,805 --> 00:41:03,795
So we had, we have this client
who told us that, Look, we added

766
00:41:03,915 --> 00:41:05,495
additional payments options.

767
00:41:06,435 --> 00:41:11,685
And, uh, it actually, we
lost revenue, losing revenue.

768
00:41:11,715 --> 00:41:12,884
How is that possible?

769
00:41:13,275 --> 00:41:17,865
And then they told me that they, it took
them a month to figure out that some

770
00:41:17,865 --> 00:41:21,765
of the payment options were not working
all the time for some devices for some

771
00:41:21,765 --> 00:41:26,874
of this, you know, So pretty much it
backfired on them and it backfired because

772
00:41:26,895 --> 00:41:31,045
adding more payment options works or
increases conversion rate or whatever.

773
00:41:31,475 --> 00:41:34,509
Backfired because they didn't have
the solution in place to actually tell

774
00:41:34,509 --> 00:41:38,290
them, Hey, you're losing money because
of this, and this is a new issue.

775
00:41:38,860 --> 00:41:42,610
So if they would have, or tool in place
our tool would be able to tell them

776
00:41:42,640 --> 00:41:47,320
right away, Hey look, you have a revenue
loss because of this issue, and then

777
00:41:47,320 --> 00:41:48,460
they would be able to go and fix it.

778
00:41:49,205 --> 00:41:51,635
So that's kind of like the, the direction.

779
00:41:51,665 --> 00:41:52,275
Mm-hmm.

780
00:41:52,280 --> 00:41:53,615
Matt Edmundson: That's, I
mean, that's incredible, that's

781
00:41:53,615 --> 00:41:54,825
a product in its own right.

782
00:41:54,825 --> 00:41:56,235
I see why It's a standalone problem.

783
00:41:56,235 --> 00:41:56,295
Yeah.

784
00:41:56,295 --> 00:41:58,875
And that's a, that's a
beautiful piece of technology.

785
00:41:58,875 --> 00:42:01,515
You know, it's like a constant
monitor of your website.

786
00:42:01,515 --> 00:42:02,205
This is wrong.

787
00:42:02,205 --> 00:42:03,525
It's costing you this much money.

788
00:42:03,530 --> 00:42:04,065
Fix it.

789
00:42:04,095 --> 00:42:04,995
I mean, that's, yeah.

790
00:42:05,610 --> 00:42:09,330
I said, for anybody that's from an
e-commerce store, I'm like, that's,

791
00:42:09,330 --> 00:42:10,710
that's a beautiful thing, right?

792
00:42:10,710 --> 00:42:11,550
Where do I sign up?

793
00:42:11,550 --> 00:42:13,050
Because I mean, that just sounds great.

794
00:42:13,050 --> 00:42:15,960
So you've got this technology
which monitors that.

795
00:42:15,960 --> 00:42:19,200
You've got this ai, which you
know, figures out when the best

796
00:42:19,200 --> 00:42:21,690
time to check out is, and a few
of the bits and, you know, helps

797
00:42:21,690 --> 00:42:22,890
with the behavioral side of things.

798
00:42:23,430 --> 00:42:29,325
Can AI also help customers make
the right choice about a product

799
00:42:29,325 --> 00:42:31,214
so that they don't return it.

800
00:42:31,334 --> 00:42:33,975
So that, you know, it's not like me
going, Oh, I'm not sure about this coat

801
00:42:33,975 --> 00:42:35,595
or this coat, and then returning one.

802
00:42:35,955 --> 00:42:38,234
It helps me make the right
choice in the first place.

803
00:42:38,240 --> 00:42:39,855
Can, can AI be that clever?

804
00:42:41,105 --> 00:42:46,915
Valon Xhafa: Yeah, so we, uh, we apply,
we apply AI for like two, two specific,

805
00:42:47,535 --> 00:42:51,654
let's say goals versus to predict if
the consumer's gonna abandon the cart.

806
00:42:52,470 --> 00:42:57,900
Because, um, if they just abandon the
cart then there won't be any returns.

807
00:42:58,110 --> 00:42:58,200
Mm-hmm.

808
00:42:58,445 --> 00:42:59,490
, you know, because they won't buy.

809
00:42:59,880 --> 00:43:04,650
Um, but if they are gonna buy a
product and if we predict that they're

810
00:43:04,650 --> 00:43:09,060
gonna buy, purchase the product, then
we also predict and try to explain

811
00:43:09,060 --> 00:43:12,720
if the user is gonna return that
product, individual product, and why.

812
00:43:13,500 --> 00:43:14,835
Before they even buy it.

813
00:43:14,835 --> 00:43:21,705
So pretty much, pretty much, we can
actually, with 95-96% of accuracy, we

814
00:43:21,705 --> 00:43:25,575
can predict for every individual product
if they're gonna be returned in few

815
00:43:25,575 --> 00:43:27,985
weeks or not as soon as they're bought.

816
00:43:28,365 --> 00:43:30,075
Wow, that's quite a high percentage.

817
00:43:30,885 --> 00:43:33,885
It's a high percentage, yeah,
because it really shows that there

818
00:43:33,885 --> 00:43:38,835
are patterns, because otherwise if
there are, if there are no patterns,

819
00:43:38,835 --> 00:43:39,945
you won't be able to predict.

820
00:43:40,485 --> 00:43:43,275
This just shows that there are a lot of
patterns out there that you can actually

821
00:43:43,275 --> 00:43:45,345
take into account to do these predictions.

822
00:43:45,705 --> 00:43:49,545
But the cool part here is that we can
predict if the user is gonna return

823
00:43:49,545 --> 00:43:53,845
the product before they even buy the
product, just by having them on the cart.

824
00:43:54,360 --> 00:43:58,140
And then we can explain, okay, why
is the consumer gonna return this?

825
00:43:59,490 --> 00:44:02,580
For example, one of the reasons why
consumers return product is that,

826
00:44:02,940 --> 00:44:08,640
um, they really want to have a size,
a specific size of that product.

827
00:44:09,090 --> 00:44:10,980
Um, but that size is
not available right now.

828
00:44:11,490 --> 00:44:16,380
So what they do, they go and add, um,
the closest size than that, like a

829
00:44:16,380 --> 00:44:21,481
smaller or a larger size, which is not
good because again, it's not the right

830
00:44:21,486 --> 00:44:23,280
size and we can actually predict this.

831
00:44:23,820 --> 00:44:28,455
And in that case, we would try
to recommend them to swap this

832
00:44:28,455 --> 00:44:34,365
product with a similar product
that has this size available.

833
00:44:36,000 --> 00:44:40,470
Because again, it's really important
that we prevent returns before

834
00:44:40,470 --> 00:44:43,680
they actually are purchased,
before actually they are returned.

835
00:44:43,950 --> 00:44:45,480
Because that sounds like
the most important thing.

836
00:44:45,960 --> 00:44:49,980
But there also many other cases, like
for example, um, usually they just go

837
00:44:49,985 --> 00:44:51,950
and add like three, four different or.

838
00:44:52,310 --> 00:44:55,520
Three, four different products of the
same category or something so that

839
00:44:55,520 --> 00:44:58,549
they can make the decision back at
home because they're not conscious

840
00:44:58,549 --> 00:45:02,210
of the whole environmental mass and
the shipping costs and everything

841
00:45:02,210 --> 00:45:03,560
that can actually lead to that.

842
00:45:04,009 --> 00:45:09,560
Um, so we kind of like gently remind
to them that, hey, if you add like four

843
00:45:09,560 --> 00:45:15,020
products and we predict that you're
return three of them, you know, we

844
00:45:15,080 --> 00:45:20,100
sometimes add a gentle reminder that
these products will be returned and this

845
00:45:20,100 --> 00:45:24,689
can cause environmental damages, you
know, because of CO2 and everything else

846
00:45:24,750 --> 00:45:28,599
is related to that, you know, So these
are some kind of like gentle reminders.

847
00:45:28,620 --> 00:45:33,120
Everything is gently, uh, suggested,
like reminded to the consumer that,

848
00:45:33,120 --> 00:45:35,410
hey, your decision has a consequence.

849
00:45:35,540 --> 00:45:39,015
You know, it's not like recommend
them directly, Hey, you should buy

850
00:45:39,015 --> 00:45:42,615
this product because three other
users bought this product too.

851
00:45:42,615 --> 00:45:46,335
You know, that's kinda like, like
too much to push it here, you know?

852
00:45:46,335 --> 00:45:50,535
But we're trying to tackle it from
like psychological perspective.

853
00:45:51,075 --> 00:45:51,915
Matt Edmundson: Yeah, I like that.

854
00:45:52,125 --> 00:45:55,515
It's a bit like, um, when I
go to a hotel room and they

855
00:45:55,515 --> 00:45:56,984
put on the towels, don't they?

856
00:45:56,984 --> 00:46:00,314
You know, if you want us to
wash these, put them here.

857
00:46:00,319 --> 00:46:02,984
Otherwise we'll leave them alone
because it's gonna save the planet.

858
00:46:03,404 --> 00:46:05,595
Uh, and it's that sort of gentle reminder.

859
00:46:05,595 --> 00:46:07,274
It gives the consumer a bit more choice.

860
00:46:07,279 --> 00:46:07,314
Yes.

861
00:46:07,785 --> 00:46:11,174
Which seems to work very well from a
psychological point of view because you

862
00:46:11,234 --> 00:46:15,254
don't come across as preachy, but you do
remind the consumer of what's important

863
00:46:15,254 --> 00:46:16,754
to them, which I think is really clever.

864
00:46:19,080 --> 00:46:21,660
You know what, Valon,
I'm aware of time, right?

865
00:46:21,720 --> 00:46:25,620
And I'm, I feel like as I say this to, if
you're a regular to the show, I'm really

866
00:46:25,620 --> 00:46:29,220
sorry for the amount of times I say I
feel like I'm just scratching the surface.

867
00:46:29,610 --> 00:46:30,750
Uh, it is a genuine thing.

868
00:46:30,810 --> 00:46:33,450
Um, I, I feel like we're just
getting into the conversation here.

869
00:46:33,990 --> 00:46:37,230
Um, question for you.

870
00:46:37,350 --> 00:46:37,710
Right?

871
00:46:37,890 --> 00:46:39,630
Uh, bit bit sort of left field.

872
00:46:41,025 --> 00:46:44,085
Um, as you know, the show is sponsored
by the e-commerce cohort, right?

873
00:46:44,085 --> 00:46:46,125
Which is just, it's like
a, a mastermind group.

874
00:46:46,125 --> 00:46:51,075
So imagine you're in a hotel somewhere
and everybody from cohort is there.

875
00:46:51,075 --> 00:46:52,995
You've just delivered your keynote speech.

876
00:46:52,995 --> 00:46:56,595
You've talked about how AI is gonna
change their world and rock their lives,

877
00:46:56,595 --> 00:46:57,825
and they're like, Yeah, it's amazing.

878
00:46:57,830 --> 00:46:58,335
Best speech.

879
00:46:58,340 --> 00:46:58,575
Wow.

880
00:46:59,075 --> 00:46:59,634
Wow Valon.

881
00:46:59,655 --> 00:47:00,195
Go, go, go.

882
00:47:01,850 --> 00:47:05,510
And you come on, you take a bow
and you take that opportunity to

883
00:47:05,510 --> 00:47:08,270
say thank you to various people.

884
00:47:08,330 --> 00:47:10,130
You know, I wouldn't be
here without dot, dot, dot.

885
00:47:10,700 --> 00:47:12,750
I'm curious, who do you thank?

886
00:47:12,830 --> 00:47:17,360
Is it, uh, what, what people, what
books, what podcasts, you know, what,

887
00:47:18,650 --> 00:47:22,010
who's kind of impacted your life
and, and got you to where you are at?

888
00:47:23,280 --> 00:47:27,360
Valon Xhafa: It's, uh, it's actually
at the end of the day, it's more like

889
00:47:27,360 --> 00:47:32,460
just a hands on experience, to be
honest, because you just said there

890
00:47:32,465 --> 00:47:37,350
are like so, so, so many articles out
there that just provide misconceptions.

891
00:47:37,710 --> 00:47:40,230
You know, like three products
are the best one, two products

892
00:47:40,230 --> 00:47:41,370
upsells are the best one.

893
00:47:41,940 --> 00:47:44,580
I think at the end of the day, it's
all about, it comes down to like,

894
00:47:44,580 --> 00:47:47,470
just going into the trenches yourself.

895
00:47:47,830 --> 00:47:53,879
And seeing things and measuring things
and doing a lot of things because I'm,

896
00:47:54,089 --> 00:47:56,160
I come from an a science background.

897
00:47:56,549 --> 00:47:56,700
Mm-hmm.

898
00:47:56,939 --> 00:48:01,020
and I, even to this day, because
we build a lot of new things in

899
00:48:01,020 --> 00:48:05,250
terms of AI and technology within
Behamics, we read a lot of articles.

900
00:48:05,250 --> 00:48:08,729
We constantly see what's out there and,
you know, I would try to combine different

901
00:48:08,729 --> 00:48:16,005
things and you rarely can, um, Uh, let's
say get the results that you see out there

902
00:48:16,035 --> 00:48:20,655
for your own, uh, experiments or tests or
something, you know, so everything is kind

903
00:48:20,655 --> 00:48:25,485
of like bubbled up pretty much, you know,
like to have the best results, greatest

904
00:48:25,485 --> 00:48:29,685
results, you know, like to provide this
traction, to provide this, this buzz

905
00:48:29,690 --> 00:48:33,285
words, you know, as you said, like two
product upsell is the best one, you know?

906
00:48:33,525 --> 00:48:37,015
But at the end of the day, the most
important thing is the experience,

907
00:48:37,109 --> 00:48:40,379
you just have to go into the trenches
yourself, test things yourself,

908
00:48:40,439 --> 00:48:46,350
measure things for yourself, and be
able to, um, test things pretty much.

909
00:48:46,350 --> 00:48:48,419
So that's, that's something
that I, I think is really

910
00:48:48,419 --> 00:48:49,950
important even to this day.

911
00:48:50,160 --> 00:48:56,115
Uh, we read a lot of stuff out there, um,
but not everything is like, Completely

912
00:48:56,115 --> 00:48:58,695
true and or completely false, you know?

913
00:48:58,695 --> 00:49:01,995
So you have to take everything
with a grain of salt here.

914
00:49:02,295 --> 00:49:03,675
Matt Edmundson: Yeah, no, fair enough.

915
00:49:03,885 --> 00:49:04,335
Fair enough.

916
00:49:04,340 --> 00:49:05,115
That's very true.

917
00:49:05,595 --> 00:49:07,935
Uh, you have to, you do have
to get into the trenches.

918
00:49:07,935 --> 00:49:08,685
I like that phrase.

919
00:49:08,685 --> 00:49:09,525
I do like that phrase.

920
00:49:10,215 --> 00:49:14,115
Um, valon, how do people reach you?

921
00:49:14,115 --> 00:49:15,105
How do they connect with you?

922
00:49:15,105 --> 00:49:18,435
If they want to find out more about
Behamics, if they've got questions for you

923
00:49:18,435 --> 00:49:21,585
about ai, what's the best way to do that?

924
00:49:23,040 --> 00:49:26,430
Valon Xhafa: Um, they can definitely,
they can definitely, uh, go to our

925
00:49:26,490 --> 00:49:29,110
landing page, um, behamics.com.

926
00:49:29,250 --> 00:49:32,300
They can get more information
onto the behamics.com.

927
00:49:32,730 --> 00:49:36,690
Um, or if they, they wanna reach
out to me directly, they can just

928
00:49:36,780 --> 00:49:38,460
connect with me on, on LinkedIn.

929
00:49:39,029 --> 00:49:45,779
Um, and yeah, I'd love to talk and
see what we can build and how we can

930
00:49:45,779 --> 00:49:48,000
actually apply AI more into eCommerce.

931
00:49:48,000 --> 00:49:52,140
Not just say like, Yeah, AI's gonna
change the world, but like actually

932
00:49:52,145 --> 00:49:55,799
applying AI in eCommerce, which
is a completely different story.

933
00:49:56,210 --> 00:49:56,700
Matt Edmundson: Yeah.

934
00:49:56,700 --> 00:49:56,859
Yeah.

935
00:49:56,865 --> 00:49:57,420
I like that.

936
00:49:57,660 --> 00:49:59,009
I don't want AI to change the world.

937
00:49:59,009 --> 00:50:00,450
I just want it to make my store better.

938
00:50:01,920 --> 00:50:04,109
Valon Xhafa: Yeah, because
you have to start somewhere.

939
00:50:04,109 --> 00:50:04,410
You know?

940
00:50:04,420 --> 00:50:06,750
You do, you do and everything.

941
00:50:07,440 --> 00:50:09,339
Let's start step by step.

942
00:50:09,339 --> 00:50:09,580
You.

943
00:50:12,390 --> 00:50:17,340
Let's just start first, like to
implement AI a bit because we cannot

944
00:50:17,340 --> 00:50:21,350
go to like, gonna change the world
without actually even starting and then

945
00:50:21,350 --> 00:50:21,790
Matt Edmundson: Yeah.

946
00:50:22,200 --> 00:50:23,110
No, I like that.

947
00:50:24,940 --> 00:50:25,630
It's brilliant.

948
00:50:25,630 --> 00:50:26,510
It's absolutely brilliant.

949
00:50:26,910 --> 00:50:27,150
Yeah.

950
00:50:27,180 --> 00:50:27,760
Sod the world.

951
00:50:27,760 --> 00:50:28,470
Fix my store.

952
00:50:28,800 --> 00:50:29,370
Uh, that's.

953
00:50:30,220 --> 00:50:31,430
That's a good one.

954
00:50:31,780 --> 00:50:33,870
Valon Xhafa: Yeah, that's, yeah.

955
00:50:33,870 --> 00:50:34,350
Yeah, yeah.

956
00:50:34,350 --> 00:50:34,510
Yeah.

957
00:50:34,840 --> 00:50:36,410
Sod the world.

958
00:50:36,770 --> 00:50:37,870
Fix my store.

959
00:50:38,055 --> 00:50:38,985
Maybe I should use that.

960
00:50:40,965 --> 00:50:42,375
Matt Edmundson: See it
on the Behamics website.

961
00:50:43,345 --> 00:50:44,975
Valon Xhafa: If I have
the copyrights for it.

962
00:50:44,975 --> 00:50:45,815
Matt Edmundson: Yeah, yeah, yeah.

963
00:50:45,820 --> 00:50:45,945
Sure.

964
00:50:47,625 --> 00:50:48,195
that's brilliant.

965
00:50:48,465 --> 00:50:49,245
Absolutely brilliant.

966
00:50:50,025 --> 00:50:52,965
Well, Valon, thank you so
much, uh, for joining us.

967
00:50:52,965 --> 00:50:55,005
Really appreciate you being with us.

968
00:50:55,005 --> 00:50:56,505
Genuinely great conversation.

969
00:50:56,505 --> 00:50:58,125
I'm super excited about your products.

970
00:50:58,125 --> 00:51:00,899
I'm really excited to see
where they go and what uh,

971
00:51:00,899 --> 00:51:02,279
benefits they bring to people.

972
00:51:02,279 --> 00:51:05,019
So do stay in touch with us and,
and bring us the case studies

973
00:51:05,189 --> 00:51:06,810
genuinely really, really curious.

974
00:51:06,810 --> 00:51:07,140
Thank you.

975
00:51:07,140 --> 00:51:08,009
Thank you so much.

976
00:51:09,029 --> 00:51:09,450
Valon Xhafa: Hey Matt.

977
00:51:09,450 --> 00:51:10,109
Thanks a lot.

978
00:51:10,109 --> 00:51:11,009
Thanks for having me.

979
00:51:11,189 --> 00:51:13,169
Um, Take care.

980
00:51:13,440 --> 00:51:14,100
Matt Edmundson: Absolutely.

981
00:51:14,940 --> 00:51:16,020
Well, there you go.

982
00:51:16,020 --> 00:51:19,620
We will of course, link to Valon's
info in the show notes, which you

983
00:51:19,899 --> 00:51:23,869
can get for free, along with the
transcript at ecommercepodcast.net

984
00:51:24,120 --> 00:51:27,690
or direct to your inbox if you
have signed up for our newsletter.

985
00:51:28,109 --> 00:51:30,060
Uh, so thank you so much for joining me.

986
00:51:30,065 --> 00:51:31,529
A huge thanks again to Valon.

987
00:51:31,859 --> 00:51:37,299
Uh, and a big shout out again to
today's show sponsor ecommercecohort.com

988
00:51:37,590 --> 00:51:40,470
head over to ecommercecohort.com
for more information about this

989
00:51:40,470 --> 00:51:45,360
new type of community that you
can and probably should join.

990
00:51:45,720 --> 00:51:48,600
Uh, be sure to follow the eCommerce
podcast wherever you get your

991
00:51:48,600 --> 00:51:52,980
podcast from because we have even
more great conversations lined up.

992
00:51:53,190 --> 00:51:55,800
Uh, like today's with Valon, I'm talking
about all kinds of stuff, which is

993
00:51:55,800 --> 00:51:57,660
gonna help you deliver e-commerce.

994
00:51:57,660 --> 00:51:58,080
Wow.

995
00:51:58,260 --> 00:52:01,040
And I don't want you to miss any of them.

996
00:52:01,314 --> 00:52:07,375
Oh, and just in case no one has told you
yet today, dear listener, you are awesome.

997
00:52:07,435 --> 00:52:08,064
Yes you are.

998
00:52:08,424 --> 00:52:10,734
It's just a burden you've got to bear.

999
00:52:10,825 --> 00:52:11,785
I've got the same problem.

1000
00:52:11,865 --> 00:52:13,015
Valon's got the same problem.

1001
00:52:13,254 --> 00:52:14,904
We just have to bear awesomeness.

1002
00:52:15,714 --> 00:52:16,044
Oh well.

1003
00:52:16,590 --> 00:52:19,980
Uh, the E-Commerce podcast
is produced by Aurion Media.

1004
00:52:20,220 --> 00:52:24,390
Uh, you can find our entire archive of
episodes on your favorite podcast app.

1005
00:52:24,720 --> 00:52:28,400
The team that makes this show possible
is Sadaf  Beynon, Josh Catchpole.

1006
00:52:28,790 --> 00:52:30,210
Estella Robin and Tim Johnson.

1007
00:52:30,560 --> 00:52:34,560
Our theme song was written by
Josh Edmundson and My Good Self.

1008
00:52:34,590 --> 00:52:37,800
And as mentioned, if you'd like to read
the transcript or show notes, head over

1009
00:52:37,800 --> 00:52:41,580
to our website, ecommercepodcast.net.

1010
00:52:42,090 --> 00:52:46,260
As I said, you can also sign up for
our weekly newsletter and get all

1011
00:52:46,260 --> 00:52:51,660
of this good stuff directly to your
inbox totally free, which is amazing.

1012
00:52:51,930 --> 00:52:52,950
So that's it for me.

1013
00:52:52,950 --> 00:52:53,820
That's it from Valon.

1014
00:52:53,820 --> 00:52:55,740
Thank you so much for joining us.

1015
00:52:56,040 --> 00:52:58,260
Uh, have a fantastic
week wherever you are.

1016
00:52:58,500 --> 00:52:59,640
I'll see you next time.

1017
00:53:00,120 --> 00:53:00,600
Bye for now.