1 00:00:04,920 --> 00:00:09,150 Welcome to the eCommerce podcast with me, your host, Matt Edmundson, and 2 00:00:09,150 --> 00:00:14,190 the eCommerce podcast is all about helping you deliver eCommerce wow. 3 00:00:14,460 --> 00:00:20,730 And this week to help us do just that, I am chatting, uh, with Oliver Edholm 4 00:00:20,730 --> 00:00:28,410 from Depict AI about how AI is changing shopping product recommendations. 5 00:00:28,410 --> 00:00:28,800 That's right. 6 00:00:28,800 --> 00:00:33,810 We are talking about all things AI, why it's such a big deal, why 7 00:00:33,815 --> 00:00:34,980 you want to get involved in it. 8 00:00:35,220 --> 00:00:36,300 You're not gonna wanna miss it. 9 00:00:36,390 --> 00:00:40,950 But before we jump into that, let me suggest a few other eCommerce 10 00:00:40,950 --> 00:00:45,045 podcast episodes to listen to that I think you will enjoy. 11 00:00:45,315 --> 00:00:46,095 Oh, yes. 12 00:00:46,275 --> 00:00:51,135 The first one, uh, is my conversation with Shanif Dhanani about why you 13 00:00:51,135 --> 00:00:56,355 should be using AI in your eCommerce business, another AI conversation, 14 00:00:56,715 --> 00:01:00,495 uh, and also check out my conversation with Tim Jordan about how to choose 15 00:01:00,495 --> 00:01:02,475 a winning product every time. 16 00:01:03,339 --> 00:01:05,830 Quite quickly becoming one of our most popular episodes. 17 00:01:05,920 --> 00:01:08,140 So do check it out and you'll find out why. 18 00:01:08,200 --> 00:01:13,520 And you can find them on our website for free at ecommercepodcast.net. 19 00:01:15,000 --> 00:01:21,870 Now this episode is brought to you by the fantabulous eCommerce cohort, 20 00:01:22,080 --> 00:01:26,820 which is gonna help you deliver eCommerce wow to your customers 21 00:01:26,820 --> 00:01:29,820 in very real and practical ways. 22 00:01:30,130 --> 00:01:34,320 If you are a regular to the show, you will know, for the past few weeks, we have been 23 00:01:34,320 --> 00:01:40,265 waxing lyrical about the eCommerce cohort, uh, and there are many, many reasons as 24 00:01:40,265 --> 00:01:44,795 to why, uh, if you're not sure what it is, it's like a, the best way to describe 25 00:01:44,795 --> 00:01:47,465 it is like an online mastermind group. 26 00:01:47,465 --> 00:01:51,995 It's a membership group, basically all to do with eCommerce where you and a 27 00:01:51,995 --> 00:01:55,955 whole bunch of other folks, uh, are gonna build your eCommerce business. 28 00:01:55,960 --> 00:01:57,035 Gonna learn what it takes. 29 00:01:57,040 --> 00:02:00,685 You're gonna get some expert coaching, but fundamentally, you guys do the work. 30 00:02:00,685 --> 00:02:05,335 So it's not just like an online course that you sit, watch the first half of and 31 00:02:05,335 --> 00:02:09,265 then never do anything with, because what, they just don't work anymore, do they? 32 00:02:09,445 --> 00:02:10,795 So it's very lightweight. 33 00:02:10,975 --> 00:02:12,685 Uh, you can dip in, dip out. 34 00:02:12,690 --> 00:02:14,905 It's not gonna be too onerous on your schedule. 35 00:02:14,905 --> 00:02:18,060 But let me tell you, if you get in there on a regular basis, it's 36 00:02:18,060 --> 00:02:21,570 gonna help you grow your eCommerce business like nothing else. 37 00:02:21,840 --> 00:02:26,310 So if like me, you're a well established eCommerce, or even if you're just 38 00:02:26,310 --> 00:02:29,490 starting out, if you're doing a startup, you're gonna want to check it out. 39 00:02:29,910 --> 00:02:33,270 Strongly recommend that you do. 40 00:02:33,450 --> 00:02:35,580 Honestly, it's gonna be great for your online business. 41 00:02:35,580 --> 00:02:40,445 You can find more information at ecommercecohort.com. 42 00:02:40,860 --> 00:02:41,990 Uh, do check it out. 43 00:02:42,860 --> 00:02:46,005 Uh, or you can email me directly if you've got any questions, and I'll try 44 00:02:46,005 --> 00:02:48,075 my level best to answer them for you. 45 00:02:48,345 --> 00:02:51,855 Uh, you can reach me at matt@ecommercepodcast.net. 46 00:02:52,725 --> 00:02:53,804 That's my email. 47 00:02:54,015 --> 00:02:55,065 Yes it is. 48 00:02:55,065 --> 00:02:58,605 Or like I say, eCommercecohort.com is the website. 49 00:02:58,605 --> 00:02:59,625 Do check them out. 50 00:02:59,654 --> 00:03:00,075 Now. 51 00:03:01,800 --> 00:03:05,970 Without further ado, because you're not gonna want to miss it. 52 00:03:06,030 --> 00:03:06,660 No, no, no. 53 00:03:06,960 --> 00:03:08,010 Here is my 54 00:03:08,040 --> 00:03:10,800 conversation with the incredible Oliver. 55 00:03:10,890 --> 00:03:11,520 Check it out. 56 00:03:12,300 --> 00:03:18,330 Well, I am here with Oliver, a 15, well, well, he is not 15 now, but when he 57 00:03:18,335 --> 00:03:20,610 was 15 he dropped out of high school. 58 00:03:20,670 --> 00:03:23,280 Uh, not quite sure what his parents would've made of that 59 00:03:23,280 --> 00:03:25,140 actually, maybe one day we them. 60 00:03:25,350 --> 00:03:28,290 Uh, but at 16 he seemed what he seems to have done right for himself. 61 00:03:28,290 --> 00:03:32,430 He moved across the world and hustled his way into a machine 62 00:03:32,430 --> 00:03:36,540 learning research position at the National University of Singapore. 63 00:03:37,200 --> 00:03:37,950 Oh yes. 64 00:03:38,220 --> 00:03:39,359 Uh, 17. 65 00:03:39,420 --> 00:03:41,010 How many of you did that when you were 16? 66 00:03:41,040 --> 00:03:42,630 Just raise your hands please. 67 00:03:42,930 --> 00:03:49,230 Uh, at 17 he founded Depict, uh, which he basically wanted to revolutionize 68 00:03:49,234 --> 00:03:51,210 the way we discover products online. 69 00:03:51,210 --> 00:03:57,300 At 18, his company was valued at $15 million, and now at 19 70 00:03:57,300 --> 00:03:59,869 he leads a team of 35 employees. 71 00:04:00,120 --> 00:04:03,120 Uh, one of the most cutting edge, uh, e-commerce startups in Europe. 72 00:04:03,390 --> 00:04:05,820 And it is all about AI. 73 00:04:05,970 --> 00:04:08,760 It is all about shop recommendations. 74 00:04:08,760 --> 00:04:10,170 It's all about that good stuff. 75 00:04:10,174 --> 00:04:13,380 And I'm honestly, uh, Oliver, welcome to the show. 76 00:04:13,385 --> 00:04:14,790 It's so great to have you here. 77 00:04:15,120 --> 00:04:18,630 You are by far the youngest person uh, we've had as a guest on the show. 78 00:04:18,630 --> 00:04:22,980 As I'm sure you, every podcast you have been on, you will be the youngest person. 79 00:04:22,980 --> 00:04:25,710 So I'm really keen to talk to you actually, uh, because I, 80 00:04:25,710 --> 00:04:26,820 I have to tell you the truth. 81 00:04:27,210 --> 00:04:32,025 At 16, I was not hustling my way into machine learning programs 82 00:04:32,025 --> 00:04:33,255 at the University of Singapore. 83 00:04:33,255 --> 00:04:34,455 How did that come about? 84 00:04:35,295 --> 00:04:35,685 Yeah. 85 00:04:35,784 --> 00:04:38,565 Uh, thank you for the introduction. 86 00:04:38,570 --> 00:04:43,755 So I'll, I guess I'll rewind a little bit from there. 87 00:04:43,815 --> 00:04:52,515 Um, so always been this kind of person who likes technical things, computers, 88 00:04:53,055 --> 00:04:55,395 computer games, all those things. 89 00:04:55,425 --> 00:04:59,924 And then, when I was relatively young, even younger. 90 00:05:00,104 --> 00:05:03,465 I played a lot of Minecraft, uh, the computer game. 91 00:05:03,469 --> 00:05:03,895 Mm-hmm. 92 00:05:03,979 --> 00:05:10,184 . And it turns out that if you get bored with Minecraft, they've been clever and 93 00:05:10,184 --> 00:05:16,015 found a great way to make kids start programming by you being able to change 94 00:05:16,244 --> 00:05:18,224 the programming code behind the game. 95 00:05:18,554 --> 00:05:18,765 Okay. 96 00:05:18,765 --> 00:05:20,655 And make modifications to it. 97 00:05:20,655 --> 00:05:23,625 So that's actually how I got into coding. 98 00:05:23,775 --> 00:05:31,830 And from there, you kinda realize more and more that the adult world is, 99 00:05:31,980 --> 00:05:34,710 uh, more tangible than you thought. 100 00:05:34,830 --> 00:05:35,760 Uh, okay. 101 00:05:35,760 --> 00:05:39,960 Programming you can do more than just change the game of Minecraft. 102 00:05:39,960 --> 00:05:40,440 Mm-hmm. 103 00:05:40,445 --> 00:05:43,380 through programming, you can create smartphone apps. 104 00:05:44,040 --> 00:05:48,450 And, you know, as a, like a 12 year old, it sounds very abstract. 105 00:05:48,510 --> 00:05:51,870 Like, okay, you open this app and it adults do stuff. 106 00:05:51,870 --> 00:05:56,040 And no, I couldn't make my own smartphone app and wow, people pay for it. 107 00:05:56,040 --> 00:06:03,360 And then you get this, I guess, high and it kind of just spirals from there. 108 00:06:03,360 --> 00:06:12,045 So, uh, when I was in, uh, middle school around 13, 14 years old, I came 109 00:06:12,045 --> 00:06:17,594 across a book called Super Intelligence by a professor at OS Oxford called 110 00:06:17,594 --> 00:06:24,765 Nick Bostrom, and I, I see that book as kind of a pivotal moment for me 111 00:06:25,034 --> 00:06:31,995 since in that book he lays out the argument of why artificial intelligence 112 00:06:32,355 --> 00:06:38,235 and its development, which is just exponentially growing year after year, 113 00:06:38,655 --> 00:06:46,095 could be the most, or will be probably the most impactful thing ever for humanity. 114 00:06:46,635 --> 00:06:52,785 Imagine what, what, what would happen if we created Einstein, but a thousand times 115 00:06:53,145 --> 00:06:56,175 smarter, you know, , that kind of thing. 116 00:06:56,175 --> 00:07:02,715 And as a kind of tech interested person, I felt well. 117 00:07:03,690 --> 00:07:06,900 If you're going to have a meaning in life and all that. 118 00:07:06,960 --> 00:07:09,120 I was very much looking for a meaning in life. 119 00:07:09,300 --> 00:07:11,310 You know, when you just entered the teenage years. 120 00:07:11,660 --> 00:07:12,240 Yeah, yeah. 121 00:07:12,300 --> 00:07:17,250 You get into that crisis thing and the then, yeah, that's probably 122 00:07:17,250 --> 00:07:21,720 a good meaning, kind of ensuring that AI and its development 123 00:07:21,720 --> 00:07:23,040 happens in the best possible way. 124 00:07:23,040 --> 00:07:27,104 Since we all know technology is a double edged sword. 125 00:07:27,615 --> 00:07:31,455 It can go horribly wrong as well, so probably if you want 126 00:07:31,455 --> 00:07:33,375 to be a part of it at least. 127 00:07:33,885 --> 00:07:42,044 And uh, from there I just went all in on trying to learn as much 128 00:07:42,044 --> 00:07:46,094 as possible around artificial intelligence and machine learning. 129 00:07:46,455 --> 00:07:51,075 Um, and I skipped lectures in school already then, so 130 00:07:51,075 --> 00:07:53,625 I was very all in one point. 131 00:07:54,615 --> 00:07:55,365 on that. 132 00:07:55,485 --> 00:08:02,414 And then, um, yeah, and then three is building things. 133 00:08:02,414 --> 00:08:06,195 I never kind of went all in on the academic route necessarily. 134 00:08:06,200 --> 00:08:08,235 I was building things constantly. 135 00:08:08,240 --> 00:08:08,615 Mm-hmm. 136 00:08:08,695 --> 00:08:13,335 . So I happened to want to build something which I thought useful 137 00:08:13,335 --> 00:08:14,625 would be useful for myself. 138 00:08:14,985 --> 00:08:18,695 And then through using technology, I learned more and more mm-hmm, 139 00:08:18,780 --> 00:08:25,469 and through that, uh, when high school was approaching, I got in touch through 140 00:08:25,469 --> 00:08:29,520 these projects and being able to show what I could do at a young age. 141 00:08:29,909 --> 00:08:37,860 I got in touch with Klarna, uh, when I was 15, and they were very welcoming in 142 00:08:37,860 --> 00:08:42,659 some sense, and lets me do like a summer internship in their AI research team 143 00:08:42,659 --> 00:08:43,199 there. 144 00:08:43,530 --> 00:08:47,250 Then I guess they got impressed, like, Oh, he does things fast and 145 00:08:47,250 --> 00:08:52,920 he knows much more than his age, so I, I got to stay there after this. 146 00:08:53,010 --> 00:08:59,430 And then from there, you know, that's kinda where the seed for 147 00:08:59,430 --> 00:09:01,650 Depict started to come about. 148 00:09:01,655 --> 00:09:04,980 Also, that's where it started having one foot in e-commerce, having one foot in 149 00:09:05,580 --> 00:09:14,190 artificial intelligence and, um, yeah, then, from there, that's how kind of, 150 00:09:14,250 --> 00:09:19,230 since I was working on things I loved and like I saw myself doing in the future 151 00:09:19,230 --> 00:09:22,950 either way, that's how I came to the decision of dropping out of high school. 152 00:09:23,100 --> 00:09:23,710 Mm-hmm. 153 00:09:23,795 --> 00:09:26,970 , uh, I, I didn't really enjoy it as well. 154 00:09:27,060 --> 00:09:31,080 Uh, the process of learning in the Swedish school system Sure. 155 00:09:31,200 --> 00:09:31,950 And. 156 00:09:32,910 --> 00:09:40,260 And then had like adventurous streak, uh, going to Singapore as you mentioned. 157 00:09:40,650 --> 00:09:50,040 Uh, where that was where I was, I had, uh, some ideas on what I wanted to build to 158 00:09:50,045 --> 00:09:52,860 kind of have positive impact on the world. 159 00:09:52,865 --> 00:09:53,350 Mm-hmm. 160 00:09:53,430 --> 00:09:54,750 through machine learning and AI. 161 00:09:55,305 --> 00:09:57,645 Uh, that wasn't the Depict related. 162 00:09:57,945 --> 00:10:02,175 Uh, so this is like a sidetrack of the story, but it wasn't Depict related. 163 00:10:02,415 --> 00:10:07,365 It was the app for helping blind and vision impaired people browse 164 00:10:07,370 --> 00:10:11,775 websites in a much more accessible manner since, you know, they can't 165 00:10:11,775 --> 00:10:13,415 see the website, so it's much harder. 166 00:10:13,935 --> 00:10:17,625 And through that I, I got this collaboration with the National University 167 00:10:17,625 --> 00:10:20,564 of Singapore who helped out with that. 168 00:10:20,564 --> 00:10:24,135 And, but it was during this, to keep it simple, it was during this 169 00:10:24,135 --> 00:10:31,275 period exploring various ideas I, I, deep down you that I'm a builder. 170 00:10:31,965 --> 00:10:34,725 I want to have, I am have, I have a lot of ambitions. 171 00:10:34,725 --> 00:10:37,725 I want to have good, great impact through the world. 172 00:10:38,460 --> 00:10:43,110 Was exploring various ideas and through this period where I had, 173 00:10:43,620 --> 00:10:48,150 through Klarna, through consulting for various e-commerce sites, that's 174 00:10:48,155 --> 00:10:50,460 where the seed Depict came about. 175 00:10:50,460 --> 00:10:54,450 Where I had one foot in, in artificial intelligence, one foot in e-commerce. 176 00:10:54,710 --> 00:11:02,130 And you could see, for instance, the amazing business cases and 177 00:11:02,130 --> 00:11:04,950 the impact Amazon has applying 178 00:11:05,745 --> 00:11:07,814 practically machine learning on the website. 179 00:11:07,814 --> 00:11:14,385 You, you, if you, if you do research about Jeff Bezos, the founder, he has all 180 00:11:14,385 --> 00:11:20,385 of these Jeffisms they're called, where he's constantly repeating himself with 181 00:11:20,385 --> 00:11:27,359 things he loves and one of his Jeffisms is, uh, how much product recommendations 182 00:11:27,390 --> 00:11:29,579 has impacted Amazon's business. 183 00:11:29,819 --> 00:11:29,939 Mm-hmm. 184 00:11:30,209 --> 00:11:33,959 , these others also who have predicted products with this, they've given 185 00:11:33,959 --> 00:11:38,459 huge impact to their business, being able to help customers find what 186 00:11:38,459 --> 00:11:43,079 they're looking for, upselling, cross-selling, all over the place. 187 00:11:43,280 --> 00:11:51,944 And, uh, let's keep it short here, but, uh, If you look at the rest 188 00:11:51,944 --> 00:11:56,415 of the industry in how they handle specifically product recommendations, 189 00:11:56,925 --> 00:11:59,595 it's nowhere near the level of Amazon's. 190 00:11:59,954 --> 00:12:03,855 Amazon has huge, the rest of the industry don't have the scale 191 00:12:04,155 --> 00:12:07,665 to be able to get Amazon level AI and product recommendations. 192 00:12:07,665 --> 00:12:11,834 So the thought of Depict was how could we create an organization 193 00:12:11,834 --> 00:12:14,175 which democratizes this? 194 00:12:14,295 --> 00:12:17,895 This basically by applying the latest research. 195 00:12:18,360 --> 00:12:24,600 And I can go into what we specifically do to create recommendations 196 00:12:24,600 --> 00:12:26,550 which require less data. 197 00:12:26,555 --> 00:12:26,820 Mm-hmm. 198 00:12:26,900 --> 00:12:29,580 since data is the new oil in artificial intelligence. 199 00:12:29,580 --> 00:12:30,000 Yeah, it is. 200 00:12:30,000 --> 00:12:32,610 And through that, have all this impact. 201 00:12:32,610 --> 00:12:37,020 So now as you mentioned, we have a lot of clients. 202 00:12:37,050 --> 00:12:41,100 We raised over 20 million US dollars, multiple founding 203 00:12:41,100 --> 00:12:44,280 grounds, over 35 employees. 204 00:12:44,340 --> 00:12:49,995 And, uh, yeah, it's, it is been, uh, journey to, to be. 205 00:12:50,245 --> 00:12:50,735 Yeah. 206 00:12:50,735 --> 00:12:53,715 It sounds like it's been one heck of a journey to get from, from 207 00:12:53,715 --> 00:12:55,395 where you were to where you are. 208 00:12:55,545 --> 00:12:56,385 Uh, such a, Yeah. 209 00:12:56,655 --> 00:12:59,505 And I'm sure that people say this to you all the time, and I don't wanna 210 00:12:59,505 --> 00:13:03,525 be patronizing at all, but it's such a young age to achieve such a lot 211 00:13:03,525 --> 00:13:05,175 it's quite extraordinary, I think. 212 00:13:05,955 --> 00:13:11,490 Um, Now I, I have a son who's not too dissimilar to you in age, and I'm 213 00:13:11,490 --> 00:13:16,890 trying to, I'm sitting here Oliver going, How would I feel if my child 214 00:13:16,890 --> 00:13:21,480 at 16 says, Dad, I'm dropping outta school and I'm, I'm going to Singapore. 215 00:13:21,600 --> 00:13:21,960 Right. 216 00:13:22,650 --> 00:13:25,110 Um, how are your parents with all of this? 217 00:13:25,800 --> 00:13:28,560 Yeah, it's a good question. 218 00:13:28,620 --> 00:13:34,740 So my parents are incredibly open minded and supportive. 219 00:13:35,010 --> 00:13:42,615 Um, With that said, of course, it's not like you automatically say yes 220 00:13:42,645 --> 00:13:44,265 when you hear something like that. 221 00:13:44,835 --> 00:13:49,455 Uh, so it was a process, I would say. 222 00:13:49,785 --> 00:13:58,485 Um, but also they've been very supportive that I should do what I kind 223 00:13:58,485 --> 00:14:02,025 of have a passion for and so forth. 224 00:14:02,745 --> 00:14:12,810 And, uh, Through also, like it was, I was, I'm in a thankful industry where there's 225 00:14:12,814 --> 00:14:15,750 more of a, there's always a plan B. 226 00:14:15,755 --> 00:14:19,100 If, let's say you don't, you want to start a company mm-hmm. 227 00:14:19,500 --> 00:14:23,040 and, uh, well, you could always be a software engineer or something like that. 228 00:14:23,040 --> 00:14:23,310 Right? 229 00:14:23,400 --> 00:14:23,670 Yeah. 230 00:14:23,730 --> 00:14:27,660 Whilst in some other industries, it's much less like if you want to 231 00:14:27,660 --> 00:14:29,220 play an actor or somewhat, it's much. 232 00:14:30,480 --> 00:14:31,770 Kind of I'm with you. 233 00:14:32,280 --> 00:14:32,910 Yeah, I'm with you. 234 00:14:33,210 --> 00:14:33,570 Yeah. 235 00:14:34,050 --> 00:14:39,930 Well, your parents sound amazing, uh, and, um, to, to give you that freedom, um, at 236 00:14:39,935 --> 00:14:41,970 such a young age, I, I hats off to them. 237 00:14:41,970 --> 00:14:43,200 I'm, and all power to them. 238 00:14:44,010 --> 00:14:49,170 So you, you are in this whole machine learning world and you, you 239 00:14:49,170 --> 00:14:50,940 made reference to the fact that. 240 00:14:52,365 --> 00:14:58,035 Obviously for Amazon, uh, we, we've all been on Amazon's website buying 241 00:14:58,035 --> 00:15:00,584 something and there's a product recommendation and you kind of 242 00:15:00,590 --> 00:15:02,925 go, Oh, I'll have a look at that. 243 00:15:02,925 --> 00:15:04,694 And before, you know, yeah, you've purchased something that 244 00:15:04,694 --> 00:15:06,105 you never set out to purchase. 245 00:15:06,944 --> 00:15:13,975 Um, and occasionally I, you do sit there and go, How in the world did Amazon know? 246 00:15:14,640 --> 00:15:18,000 That would be a good product to show me what is it? 247 00:15:18,210 --> 00:15:19,980 Um, is it witchcraft? 248 00:15:20,250 --> 00:15:22,800 Is it just, is it just luck? 249 00:15:23,310 --> 00:15:26,580 uh, or was there something a bit more intentional behind it? 250 00:15:26,580 --> 00:15:26,670 Yeah. 251 00:15:26,730 --> 00:15:30,840 So this is where you say they've got these really clever 252 00:15:30,840 --> 00:15:32,310 algorithms with machine learning. 253 00:15:32,310 --> 00:15:32,400 Mm-hmm. 254 00:15:32,640 --> 00:15:32,730 , right? 255 00:15:33,720 --> 00:15:34,140 Yes. 256 00:15:34,200 --> 00:15:40,170 So I can't ex, you know, I, I haven't worked at Amazon, I 257 00:15:40,170 --> 00:15:44,564 haven't looked into intellectual property or anything like that. 258 00:15:44,745 --> 00:15:52,214 But there are ways you, I've heard sources from various places and so 259 00:15:52,214 --> 00:15:58,485 forth, and I kind know what the state of the art in terms of the algorithms 260 00:15:58,875 --> 00:16:02,145 powering, let's say YouTube's recommendation engineering, so forth. 261 00:16:02,714 --> 00:16:09,015 Uh, and I, I think so they have many variants and it's a huge company. 262 00:16:09,750 --> 00:16:14,819 At its core, Amazon's recommendation engine is very simple. 263 00:16:15,150 --> 00:16:15,420 Mm-hmm. 264 00:16:15,750 --> 00:16:20,100 actually in sense, but what they are really good at utilizing is 265 00:16:20,370 --> 00:16:22,920 their huge quantities of data. 266 00:16:23,010 --> 00:16:23,100 Mm-hmm. 267 00:16:23,425 --> 00:16:28,230 User behavior data specifically, and the fact that they have a lot of recurring 268 00:16:28,230 --> 00:16:31,290 users, so they always come back. 269 00:16:31,560 --> 00:16:36,780 So you can then start to see patterns where, okay, this kind of user. 270 00:16:37,320 --> 00:16:41,460 Who bought this product tends to buy these products. 271 00:16:41,580 --> 00:16:47,640 That's that kind of logic is the core of Amazon's recommendation engine. 272 00:16:47,880 --> 00:16:52,650 And then they just extrapolated based on that with their insane 273 00:16:52,680 --> 00:16:54,840 quantities of data they have. 274 00:16:55,590 --> 00:17:02,850 If you're a say no typical merchant, well you don't have products across 275 00:17:03,900 --> 00:17:06,930 every possible category, right? 276 00:17:06,990 --> 00:17:10,619 They have like electronics, fashion furniture, blah, blah, blah, blah, blah. 277 00:17:10,780 --> 00:17:12,550 You're probably a little bit niche. 278 00:17:12,619 --> 00:17:13,230 Mm-hmm. 279 00:17:13,310 --> 00:17:19,859 you probably don't have as recurring users constantly buying something 280 00:17:20,490 --> 00:17:22,109 at least every month, right? 281 00:17:22,139 --> 00:17:22,230 Mm-hmm. 282 00:17:22,530 --> 00:17:26,760 . So, um, and then their, their biggest e-commerce. 283 00:17:27,300 --> 00:17:28,109 Site in the world. 284 00:17:28,109 --> 00:17:33,030 So that's kind of what you're standing up against. 285 00:17:33,360 --> 00:17:38,790 So if you're a normal e-commerce merchant and you want to be on par or closer 286 00:17:38,794 --> 00:17:46,110 to Amazon, you have to find other data sources than only this user behavior data. 287 00:17:46,115 --> 00:17:50,070 People who bought this also bought that, uh, and that's what we've 288 00:17:50,070 --> 00:17:52,139 been really focused on doing. 289 00:17:52,439 --> 00:17:57,270 So what we also incorporate into our recommendation engines we 290 00:17:57,275 --> 00:18:02,370 serve to our customers is for, uh, also the product information. 291 00:18:02,460 --> 00:18:09,180 So, uh, the product information, uh, is of course incredible useful 292 00:18:09,510 --> 00:18:10,530 when you recommend a product. 293 00:18:10,560 --> 00:18:13,500 If you go to a physical retail store and ask someone for advice 294 00:18:13,500 --> 00:18:18,629 there, that's like an essential part of a kind of salesperson. 295 00:18:18,629 --> 00:18:23,640 They're giving you advice, but almost all existing recommender systems ignore. 296 00:18:24,450 --> 00:18:31,800 And what, what we can do is we can apply incredibly smart image recognition 297 00:18:31,860 --> 00:18:37,470 algorithms, understanding subtle patterns, which a few years ago was 298 00:18:37,470 --> 00:18:40,290 impossible, but which is now possible. 299 00:18:40,290 --> 00:18:44,790 So let's say it's, um, furniture. 300 00:18:44,790 --> 00:18:46,710 Well, you can see these subtle patterns. 301 00:18:46,740 --> 00:18:50,940 Oh, this correlates to Scandinavian design, or this is probably a little 302 00:18:50,940 --> 00:18:55,365 bit upper end like this, subtle patterns, and then also understanding 303 00:18:55,575 --> 00:18:57,825 all the text data behind the products. 304 00:18:57,825 --> 00:19:02,385 And when you combine it with the user data they have, well it turns out you 305 00:19:02,385 --> 00:19:07,065 have really good results and we, we are pretty confident in showing this. 306 00:19:07,070 --> 00:19:11,685 So we've always had this approach where the first two months use 307 00:19:11,685 --> 00:19:15,435 Depict, you can always kick us out, you can see that it works live on 308 00:19:15,435 --> 00:19:18,245 the site and then decide from there. 309 00:19:20,125 --> 00:19:20,305 So 310 00:19:20,305 --> 00:19:25,875 you've got this system then that, um, doesn't need the quantities of, 311 00:19:26,115 --> 00:19:28,845 because this has been the problem with machine learning for a long time. 312 00:19:28,845 --> 00:19:31,815 And I think, uh, it's, we've had, we've talked about this a little bit on the 313 00:19:31,815 --> 00:19:38,115 show in the past, that AI machine learning have been inaccessible for a lot of people 314 00:19:38,115 --> 00:19:42,735 because we just don't have the data set. 315 00:19:42,855 --> 00:19:46,515 And my understanding with, um, certainly in the early days was machine 316 00:19:46,515 --> 00:19:49,410 learning needed insane levels of data. 317 00:19:49,410 --> 00:19:52,050 I mean, you needed super computers just to process the, 318 00:19:52,200 --> 00:19:52,770 the data. 319 00:19:52,770 --> 00:19:52,870 Yeah. 320 00:19:53,010 --> 00:19:54,180 There's still the case. 321 00:19:54,360 --> 00:20:03,630 There's still the case is like if you Google, uh, uh, open AI, uh, image 322 00:20:03,630 --> 00:20:09,420 generation for instance, there are this insane, uh, artificial intelligence 323 00:20:09,420 --> 00:20:15,650 models which spit out like boththe realistic images where you can just write 324 00:20:16,170 --> 00:20:22,110 a prompt of, let's say a teddy bear on the moon riding a horse like that 325 00:20:22,110 --> 00:20:25,980 sounds weird and you can literally paint a foot realistic image of that. 326 00:20:25,980 --> 00:20:30,150 So like it's really developing, but it still needs insane 327 00:20:30,150 --> 00:20:33,750 quantities of data and it's like millions of dollars just to train. 328 00:20:34,320 --> 00:20:39,320 I was interrupting you with definitely true still today, I would say 329 00:20:40,110 --> 00:20:40,420 So 330 00:20:40,420 --> 00:20:47,715 I mean, is this where you, um, I, you know, you kind of hear the stories 331 00:20:47,715 --> 00:20:50,835 like say you raised 20 million in funding and you kind of go, where 332 00:20:50,835 --> 00:20:52,095 do you spend that 20 million? 333 00:20:52,095 --> 00:20:54,675 Does a lot of it go into then analyzing data? 334 00:20:54,675 --> 00:20:58,425 It's to sheer just get data in and let's find some patterns in there. 335 00:20:58,425 --> 00:20:58,905 Yeah. 336 00:20:58,905 --> 00:21:01,965 Um, it still goes mostly to head count. 337 00:21:02,025 --> 00:21:02,685 Right now. 338 00:21:03,075 --> 00:21:07,605 We definitely have more server cost than the typical SaaS business 339 00:21:07,845 --> 00:21:14,415 due to kind of having to deal with a lot of data, um, and so forth. 340 00:21:14,820 --> 00:21:24,210 Uh, but, uh, and you're right about the fact that a lot of, a lot of companies 341 00:21:24,215 --> 00:21:26,700 try to be on the forefront, Okay. 342 00:21:26,700 --> 00:21:29,010 AI machine learning is really trending right now. 343 00:21:29,010 --> 00:21:32,460 We, we need to get ourselves some AI, right? 344 00:21:32,550 --> 00:21:35,490 Like in the early times we need to get sales on internet. 345 00:21:35,850 --> 00:21:36,030 Mm-hmm. 346 00:21:36,445 --> 00:21:44,264 I don't know what it does, but should probably get it and, uh, Usually 347 00:21:44,264 --> 00:21:49,965 it doesn't go that well if you're not kind of explicit about it. 348 00:21:49,995 --> 00:21:54,735 Since if you try to use some state of the art model, well, it requires huge 349 00:21:54,735 --> 00:21:56,385 quantities of data, which you don't have. 350 00:21:56,745 --> 00:21:56,834 Mm-hmm. 351 00:21:57,074 --> 00:22:03,014 . Whilst probably for most eCommerce merchants, what's extreme still 352 00:22:03,014 --> 00:22:05,725 extremely high leverage for you. 353 00:22:07,125 --> 00:22:12,015 Being data driven and starting from there, having a core foundation 354 00:22:12,015 --> 00:22:16,995 where you can measure things and have quick feedback loops where I have a 355 00:22:16,995 --> 00:22:20,745 hypothesis, I can actually measure, change something, measure it, and then 356 00:22:21,105 --> 00:22:23,055 go through the cycle pretty quickly. 357 00:22:23,385 --> 00:22:25,875 Uh, that's probably where I would start. 358 00:22:26,145 --> 00:22:30,165 Then there are third party services like Depict where you can like plug and 359 00:22:30,165 --> 00:22:32,565 play and really get impact through that. 360 00:22:32,565 --> 00:22:37,290 But that's, there's so many things you have to handle as e-commerce merchants. 361 00:22:37,290 --> 00:22:39,000 So like I, I would start there. 362 00:22:39,000 --> 00:22:45,510 And then, yeah, there, there are some AI things, machine learning things 363 00:22:45,570 --> 00:22:53,730 which are probably not as complex, but you know, there's some, some simple 364 00:22:53,730 --> 00:22:57,900 algorithms which require less data, which you can still, uh, gets use of. 365 00:22:57,900 --> 00:23:02,430 But I would start with can just, how can we data driven and shorten 366 00:23:02,430 --> 00:23:07,440 the feedback loops between having a hypothesis, measuring it and 367 00:23:07,440 --> 00:23:10,710 make, making a change, and then measuring the impact, measuring the, 368 00:23:12,210 --> 00:23:19,170 and so with, um, something like depict then, um, what I'm picking 369 00:23:19,170 --> 00:23:21,570 up is actually now machine learning. 370 00:23:22,260 --> 00:23:28,070 Um, AI is at a place where if you've got significant quantities of data, great, but 371 00:23:28,070 --> 00:23:30,660 if you don't, we can still work with that. 372 00:23:30,780 --> 00:23:33,180 Am I, am I, is that, am I understanding that right? 373 00:23:33,180 --> 00:23:33,500 Exactly. 374 00:23:33,570 --> 00:23:34,080 Exactly. 375 00:23:34,080 --> 00:23:36,180 And that's especially where Depict comes in. 376 00:23:36,810 --> 00:23:41,370 Uh, if you don't have, let's say, this is a great example. 377 00:23:41,700 --> 00:23:46,910 Let's say you launch a totally new product collection, uh, and you, so 378 00:23:47,700 --> 00:23:52,150 what's traditionally you would do from a product recommendation perspective 379 00:23:52,710 --> 00:23:54,450 in the recommendation related product. 380 00:23:55,320 --> 00:23:59,910 Bar is that you would look at the historical purchases of a product and 381 00:23:59,910 --> 00:24:01,890 say, people bought this, bought that. 382 00:24:02,370 --> 00:24:05,490 Well you, you just launched a new collection. 383 00:24:05,610 --> 00:24:09,720 You have all these campaigns, all these kind marketing, getting a lot 384 00:24:09,725 --> 00:24:15,300 of traffic to these products, but there's no historical data people 385 00:24:15,300 --> 00:24:16,470 bought this also bought that. 386 00:24:16,470 --> 00:24:18,120 Well, no one really bought it. 387 00:24:18,470 --> 00:24:25,005 Yeah, before, so what you're gonna do well with Depict we, we understand 388 00:24:25,005 --> 00:24:30,315 the product as well, so there's a lot, a lot of context that in the 389 00:24:30,315 --> 00:24:35,625 same way a shopper would utilize or recommend, uh, in store clerk would 390 00:24:35,625 --> 00:24:37,305 utilize when recommending products. 391 00:24:37,365 --> 00:24:38,865 We, we, we can still do. 392 00:24:38,955 --> 00:24:44,355 Um, so that's a clear example where kind of lack of data or cold start problem. 393 00:24:45,480 --> 00:24:46,530 Big example. 394 00:24:46,530 --> 00:24:49,590 Another example is let's say you have Black Friday. 395 00:24:49,980 --> 00:24:56,969 Uh, you have a lot of campaigns all over the place, and, uh, the, the user 396 00:24:56,969 --> 00:25:01,230 behavior on your site is drastically different than outside of Black Friday. 397 00:25:01,830 --> 00:25:09,980 Uh, if your recommendation engine learned from that behavior and kind 398 00:25:09,980 --> 00:25:13,889 of tries to copy it, oh, people buy this product and this product a lot. 399 00:25:15,100 --> 00:25:17,160 but actually it's due to the fact that they have like a 400 00:25:17,160 --> 00:25:18,960 90% discount or something. 401 00:25:19,080 --> 00:25:19,170 Mm-hmm. 402 00:25:19,920 --> 00:25:22,440 doing that after Black Friday is a really stupid idea. 403 00:25:23,340 --> 00:25:25,080 . So it comes to this problem again, right? 404 00:25:25,140 --> 00:25:25,940 So, yeah. 405 00:25:26,150 --> 00:25:26,900 Um, yeah. 406 00:25:28,200 --> 00:25:31,650 So how much, I dunno if you can answer this. 407 00:25:31,650 --> 00:25:34,860 How much data do I need to have reasonably to get started? 408 00:25:34,860 --> 00:25:35,790 Like if I was start. 409 00:25:36,659 --> 00:25:38,879 Um, a new product range, that's fine, but mm-hmm. 410 00:25:39,540 --> 00:25:42,600 , I you, there's an assumption there that I've got a website that's already 411 00:25:42,600 --> 00:25:44,010 trading, that has already sold product. 412 00:25:44,580 --> 00:25:48,629 Um, I'm, I'm starting from zero today. 413 00:25:48,635 --> 00:25:50,520 I've got no, no track record. 414 00:25:50,520 --> 00:25:53,260 At what point does AI start to make sense for 415 00:25:53,280 --> 00:25:53,490 me? 416 00:25:53,970 --> 00:25:54,300 Yeah. 417 00:25:54,304 --> 00:26:01,860 So I would start with, Okay, you don't have any data then I would implicitly 418 00:26:01,865 --> 00:26:03,929 assume you don't have that much traffic. 419 00:26:04,050 --> 00:26:04,659 Mm-hmm. 420 00:26:04,740 --> 00:26:05,260 on your site. 421 00:26:05,955 --> 00:26:13,095 Well, if you don't have that much traffic, then you can't really work 422 00:26:13,095 --> 00:26:18,225 as data driven as you would want to since you, you, you need to have 423 00:26:18,525 --> 00:26:22,305 ways to measure your hypothesis and see how it impacts the customer. 424 00:26:22,305 --> 00:26:26,715 You need to, have significant amount of data sufficiently the amount, 425 00:26:26,715 --> 00:26:30,225 sufficient amount of data, so you can see statistically significant 426 00:26:30,735 --> 00:26:32,815 correlations between different behaviors. 427 00:26:33,179 --> 00:26:39,240 So there's some limit there where, where, And if you don't have any traffic, you 428 00:26:39,240 --> 00:26:41,310 should probably solve that issue before. 429 00:26:41,580 --> 00:26:44,459 Maybe there's a marketing solution which uses AI. 430 00:26:44,459 --> 00:26:48,750 But I would look at what problem does this AI solution solve and does it 431 00:26:48,990 --> 00:26:50,610 solve the problem I need to solve? 432 00:26:50,639 --> 00:26:52,889 I wouldn't ask, Are you using AI? 433 00:26:52,919 --> 00:26:55,110 Oh, you say it, then I should use it. 434 00:26:55,115 --> 00:26:57,179 You should ask, what problem are you solving? 435 00:26:57,209 --> 00:26:58,469 How well are you solving the problem? 436 00:26:58,469 --> 00:26:59,820 Is it the problem I want to solve? 437 00:27:00,270 --> 00:27:00,959 And. 438 00:27:01,755 --> 00:27:08,145 And, uh, yeah, if it turns out that you want to increase, you have sufficient 439 00:27:08,145 --> 00:27:10,905 amount of data to have a sense of that. 440 00:27:10,965 --> 00:27:15,255 Well, we want to increase our conversion rate, average order value. 441 00:27:15,525 --> 00:27:21,955 Let's say you have, uh, a lot of products in your warehouse, which aren't 442 00:27:21,975 --> 00:27:25,005 selling, and they're just lying there. 443 00:27:25,965 --> 00:27:31,200 Or you have some specific business objective you want to optimize for 444 00:27:31,200 --> 00:27:36,389 is recommendation engines can help, uh, pushing certain products in 445 00:27:36,395 --> 00:27:39,000 certain categories to, for extent. 446 00:27:39,030 --> 00:27:45,990 Then I would look at, uh, look at applying a recommendation and then, and then see, 447 00:27:46,920 --> 00:27:50,210 see what, what the recommendation engine costs and how much you get out of it. 448 00:27:50,210 --> 00:27:52,470 See the ROI multiple. 449 00:27:53,600 --> 00:27:57,735 Depict is a little bit more of a premium provider today, since we get so many 450 00:27:57,735 --> 00:28:03,284 requests to work with us, we don't have time to work with with everyone. 451 00:28:04,875 --> 00:28:09,524 So that's on the traffic part, it's a little bit of a ramble, but then there 452 00:28:09,585 --> 00:28:15,105 another important aspect is also how many SKU or products you have on your site. 453 00:28:15,110 --> 00:28:15,645 Mm-hmm. 454 00:28:15,725 --> 00:28:28,125 . So, um, If you only have like 30 products, SKUsor whatever, then it's quite easy 455 00:28:28,125 --> 00:28:31,545 as finding the products you want. 456 00:28:31,905 --> 00:28:38,085 But let's say it's over 300, even a thousand, well, suddenly you really need 457 00:28:38,085 --> 00:28:43,245 to aid the customer in getting a sense of what you have in your product collection. 458 00:28:44,415 --> 00:28:44,985 So it's 459 00:28:44,985 --> 00:28:45,195 a. 460 00:28:46,230 --> 00:28:49,560 I guess if you wanna get started out with it, you are looking at both your traffic 461 00:28:49,560 --> 00:28:50,700 and the number of skus that you have. 462 00:28:50,700 --> 00:28:51,060 Exactly. 463 00:28:51,240 --> 00:28:51,690 Yep. 464 00:28:52,020 --> 00:28:55,860 Um, and so that's got to be at a reasonable level before 465 00:28:55,865 --> 00:28:57,960 AI makes sense for you. 466 00:28:58,020 --> 00:28:58,500 Exactly. 467 00:28:58,500 --> 00:29:02,400 And so several hundred skus and probably what, a couple of thousand 468 00:29:02,400 --> 00:29:04,050 people at least visiting your website. 469 00:29:04,050 --> 00:29:09,840 I'm thinking, so, And is it, is it, is it fair to say, Oliver, that the more 470 00:29:09,840 --> 00:29:12,790 data I have, the better my AI will be? 471 00:29:13,970 --> 00:29:20,790 Um, that tends to be the rule of thumb, but Depict really works to ensure 472 00:29:20,790 --> 00:29:27,600 that we can work with more sparse with sparse data sets, which have less data. 473 00:29:28,050 --> 00:29:33,149 Uh, there's, for instance, we, for instance, work with some marketplaces, 474 00:29:33,330 --> 00:29:35,350 they have millions of skus. 475 00:29:35,370 --> 00:29:39,300 I think the marketplace with the most skus has 16.8 million. 476 00:29:40,210 --> 00:29:44,250 Uh, they have a ton of traffic, I assure you that. 477 00:29:44,340 --> 00:29:48,560 But a lot of SKUs have very few interactions. 478 00:29:48,840 --> 00:29:54,570 So there's also the question like, uh, inter amount of interactions per 479 00:29:54,639 --> 00:30:00,300 SKU, so we still in those cases, have to work with low quantities of data. 480 00:30:00,750 --> 00:30:01,139 Uh, y 481 00:30:02,389 --> 00:30:02,940 Yeah, no, 482 00:30:02,940 --> 00:30:03,930 that's fair enough. 483 00:30:03,990 --> 00:30:06,280 1.6 or 16.8 million skus. 484 00:30:06,300 --> 00:30:08,220 I mean, that's gonna be a headache for somebody, right? 485 00:30:10,050 --> 00:30:10,440 Geez. 486 00:30:10,740 --> 00:30:12,300 Someone's gotta put those on the system. 487 00:30:12,389 --> 00:30:12,879 Yeah. 488 00:30:13,110 --> 00:30:15,870 So, Okay. 489 00:30:16,020 --> 00:30:19,170 Uh, I tell you what we're gonna do, We just gonna take quick break, listen 490 00:30:19,170 --> 00:30:22,650 from this week's show sponsors, and then I'm gonna be right back with Oliver to 491 00:30:22,650 --> 00:30:27,480 carry on our conversation, uh, about AI and all things machine learning. 492 00:30:27,480 --> 00:30:28,570 Don't go anywhere. 493 00:30:36,585 --> 00:30:37,155 Hey there. 494 00:30:37,215 --> 00:30:41,295 Are you a business owner ? Here at Aurion Digital we know firsthand 495 00:30:41,385 --> 00:30:44,535 that running an eCommerce business can be really hard work. 496 00:30:45,075 --> 00:30:48,765 As the online space gets more competitive, it is becoming even more 497 00:30:48,765 --> 00:30:50,625 challenging to stay ahead of the curve. 498 00:30:50,805 --> 00:30:52,005 We totally get it. 499 00:30:52,185 --> 00:30:55,545 So we wanna help you succeed by offering a wide range of services. 500 00:30:56,070 --> 00:31:00,540 From fulfillment, marketing, customer service, and even coaching and consulting, 501 00:31:00,960 --> 00:31:03,360 just so that you can do what matters most. 502 00:31:03,630 --> 00:31:07,920 Save yourself the time and the money, and let us handle the day to day tasks. 503 00:31:08,280 --> 00:31:11,970 This way you can run your business without having to worry about the boring stuff. 504 00:31:12,420 --> 00:31:13,320 So what do you say? 505 00:31:13,380 --> 00:31:14,940 Are we a good fit for each other? 506 00:31:15,420 --> 00:31:18,750 Come check us out at auriondigital.com and let us know what you. 507 00:31:24,885 --> 00:31:27,254 So welcome back. 508 00:31:27,375 --> 00:31:33,675 Uh, we are talking about AI machine learning, um, and how it can 509 00:31:33,675 --> 00:31:35,430 make a difference to eCommerce. 510 00:31:35,430 --> 00:31:38,100 So you've talked specifically about recommend what you call 511 00:31:38,100 --> 00:31:41,550 the recommendation engine, which actually sounds like something outta 512 00:31:41,550 --> 00:31:43,080 Star Wars, if I'm honest with you. 513 00:31:43,320 --> 00:31:44,880 This is a recommendation engine. 514 00:31:44,880 --> 00:31:46,980 It drives this spaceship over here. 515 00:31:47,100 --> 00:31:47,190 Mm-hmm. 516 00:31:47,520 --> 00:31:49,260 , Um, it's a, it's a great phrase. 517 00:31:49,380 --> 00:31:55,575 Um, And so I understand that AI can be used to recommend products 518 00:31:55,575 --> 00:31:59,145 based on the traffic, based on behavior and past behavior and the 519 00:31:59,145 --> 00:32:00,435 data sets and so on and so forth. 520 00:32:00,945 --> 00:32:05,535 Where else do you see AI having a big impact, uh, in eCommerce 521 00:32:05,535 --> 00:32:06,345 that we should be thinking 522 00:32:06,350 --> 00:32:06,675 about? 523 00:32:06,825 --> 00:32:07,155 Yeah. 524 00:32:07,155 --> 00:32:13,710 Uh, I would look, so I would look at three categories primarily. 525 00:32:13,800 --> 00:32:16,830 And one is the space we are in, right? 526 00:32:17,310 --> 00:32:20,580 Like pro, some sort of product discovery, personalization, 527 00:32:20,580 --> 00:32:23,210 product discovery, uh, Depict. 528 00:32:23,790 --> 00:32:27,390 Some people associate depict with only being on the PDP 529 00:32:27,450 --> 00:32:29,460 product page for instance. 530 00:32:29,460 --> 00:32:35,220 But no, we were across the whole buying experience, the landing page. 531 00:32:35,950 --> 00:32:38,985 Uh, Product detail page check out. 532 00:32:38,985 --> 00:32:41,925 When you add something to the basket, you helps slow cross off. 533 00:32:41,930 --> 00:32:44,145 So there's a lot of areas we can, you can be there. 534 00:32:44,595 --> 00:32:52,215 Um, and you can use product discovery for impacting not only like this 535 00:32:52,835 --> 00:32:57,975 general conversion rate average shorter values, like talk metrics, 536 00:32:57,975 --> 00:33:03,415 but you can go much more pinpointed as well with saying for instance, 537 00:33:03,725 --> 00:33:08,400 oh, we have these higher margins products we want to push for this 538 00:33:08,400 --> 00:33:10,080 kind of product customer segment. 539 00:33:10,260 --> 00:33:13,440 Well, you can do that through great product discovery. 540 00:33:13,740 --> 00:33:17,430 You can look at, at a lot of things like that. 541 00:33:17,790 --> 00:33:22,890 Then I'm less, I'm, I have worked less within these areas, but I know for sure 542 00:33:22,890 --> 00:33:25,140 that there's a lot of impact there. 543 00:33:25,230 --> 00:33:27,060 Uh, first is logistics. 544 00:33:27,370 --> 00:33:27,860 Okay? 545 00:33:28,710 --> 00:33:32,490 Uh, yes, Google Logistics. 546 00:33:33,720 --> 00:33:39,990 Artificial intelligence, Amazon, or, uh, then, then you, you 547 00:33:39,990 --> 00:33:41,190 will find a lot of things. 548 00:33:41,190 --> 00:33:46,980 It's everything from how you optimize various routes, how you place all 549 00:33:46,980 --> 00:33:49,980 the products within the warehouses. 550 00:33:50,730 --> 00:33:55,920 Um, there's a lot of, lot of optimization problems within logistics where, where 551 00:33:55,920 --> 00:34:00,510 you can apply AI, so to say, uh, and. 552 00:34:01,320 --> 00:34:09,360 Then there's also the aspect of marketing or more quantified parts of the marketing. 553 00:34:09,360 --> 00:34:12,240 So where you have a lot of data where, well, there you 554 00:34:12,240 --> 00:34:14,880 can apply AI to some extent. 555 00:34:14,880 --> 00:34:17,190 Well, of course you have Facebook and Google ads. 556 00:34:17,280 --> 00:34:22,000 Those are extremely sophisticated artificial intelligence engines. 557 00:34:22,380 --> 00:34:26,080 But, um, I, there there's a lot of. 558 00:34:26,845 --> 00:34:32,115 I, I, I'm less aware of the specific use cases there, but there's a lot 559 00:34:32,115 --> 00:34:33,284 of cool stuff you can do there. 560 00:34:33,365 --> 00:34:33,855 Okay. 561 00:34:34,095 --> 00:34:34,514 For sure. 562 00:34:36,645 --> 00:34:37,605 It's interesting, isn't it? 563 00:34:37,634 --> 00:34:38,804 Uh, AI and marketing. 564 00:34:38,804 --> 00:34:44,714 I've seen a lot of things coming up recently on my, uh, on my social media 565 00:34:44,714 --> 00:34:47,714 feed, which is normally where you, you know, you see things in it first. 566 00:34:48,254 --> 00:34:52,665 And, um, I've seen, uh, AI copywriters. 567 00:34:52,985 --> 00:34:55,205 So, you know, the, the computer will write 568 00:34:55,205 --> 00:34:56,074 copy for you. 569 00:34:56,255 --> 00:34:57,395 Those are getting really good. 570 00:34:57,545 --> 00:34:57,845 Yeah. 571 00:34:57,904 --> 00:34:58,295 Yeah. 572 00:34:58,355 --> 00:34:59,825 They all getting really good. 573 00:35:00,545 --> 00:35:01,475 Scary, good. 574 00:35:01,480 --> 00:35:02,045 Actually. 575 00:35:02,045 --> 00:35:05,045 It's quite fascinating to me how that, how that all works. 576 00:35:05,645 --> 00:35:09,845 Um, I was just, before we were on our recording, I was having lunch with 577 00:35:09,845 --> 00:35:11,465 a friend of mine who's a barrister. 578 00:35:11,975 --> 00:35:13,174 Um, so a lawyer here in the. 579 00:35:14,520 --> 00:35:20,100 Contract barrister, but he, uh, like you, uh, has a really good understanding 580 00:35:20,100 --> 00:35:23,220 of machine learning and he's, he's done all kinds of stuff at university in 581 00:35:23,220 --> 00:35:25,200 it and quite how he ended up in law. 582 00:35:25,200 --> 00:35:25,800 I have no idea. 583 00:35:25,800 --> 00:35:29,010 But anyway, he, he's, uh, he's, he's a machine learning guy and he's 584 00:35:29,010 --> 00:35:32,730 got some very expensive computers in one of the rooms in his house 585 00:35:32,730 --> 00:35:37,200 where he's, he's got stuff being churned out and he's, he's doing AI 586 00:35:38,245 --> 00:35:41,940 um, to process, uh, case history and legal case. 587 00:35:41,945 --> 00:35:42,150 Case history. 588 00:35:42,670 --> 00:35:43,160 Yeah. 589 00:35:43,165 --> 00:35:43,260 Yeah. 590 00:35:43,260 --> 00:35:47,940 To be able to fill in stuff, uh, and do a lot of the sort of the, the mundane 591 00:35:47,945 --> 00:35:49,800 work that a barrister has to do. 592 00:35:50,325 --> 00:35:52,665 Where he spends hours of time, this thing will just do it in 593 00:35:52,695 --> 00:35:54,105 obviously fractions of a second. 594 00:35:54,105 --> 00:35:57,555 And so he's, he's having a bit of fun experimenting with that. 595 00:35:57,585 --> 00:35:57,675 Mm-hmm. 596 00:35:58,515 --> 00:36:02,385 . Um, so I, I, I've seen AI in law, I've seen it in copywriting, I've 597 00:36:02,385 --> 00:36:05,955 seen it in marketing, you know, where the claims are you don't even 598 00:36:05,960 --> 00:36:07,425 have to do your own adverts anymore. 599 00:36:07,425 --> 00:36:09,405 This thing will just do the whole thing for you. 600 00:36:09,405 --> 00:36:10,665 subscribe to that service. 601 00:36:11,175 --> 00:36:16,185 Some of it, if I'm honest with you, feels a bit gimmicky, a bit 602 00:36:16,185 --> 00:36:19,515 like someone's just got an idea and they've thrown the phrase machine 603 00:36:19,515 --> 00:36:21,285 learning or artificial intelligence. 604 00:36:21,285 --> 00:36:25,125 It, uh, and really it's to stem with an Excel spreadsheet 605 00:36:25,125 --> 00:36:26,174 somewhere in the background. 606 00:36:26,174 --> 00:36:26,265 Mm-hmm. 607 00:36:26,265 --> 00:36:26,504 Right. 608 00:36:27,105 --> 00:36:32,550 Um, How do I, I guess, how do I avoid the gimmicks? 609 00:36:32,550 --> 00:36:37,150 How do I avoid the hype and the bluster and the, just the sheerer 610 00:36:37,150 --> 00:36:41,100 to nonsense and just focus on what is actually gonna help me in my 611 00:36:41,100 --> 00:36:41,280 business? 612 00:36:41,280 --> 00:36:41,970 That's a great question. 613 00:36:42,140 --> 00:36:42,960 I, I agree. 614 00:36:42,960 --> 00:36:49,560 There's a lot of buzzwords around AI, uh, ton of buzzwords 615 00:36:49,560 --> 00:36:55,470 around ai and many exploit less. 616 00:36:56,565 --> 00:37:02,295 But I'm not asking you to become an AI expert of any sort. 617 00:37:02,625 --> 00:37:09,915 I think being an AI expert and being a great operator of an e-commerce site 618 00:37:10,815 --> 00:37:13,005 doesn't necessarily correlate that much. 619 00:37:13,305 --> 00:37:18,975 So I would actually become really good at asking yourself, what 620 00:37:18,975 --> 00:37:20,655 problems do I need to solve? 621 00:37:21,015 --> 00:37:21,105 Mm-hmm. 622 00:37:21,375 --> 00:37:25,335 . And does this solution actually solve this problem. 623 00:37:25,634 --> 00:37:29,895 You don't have to look at the inside of it necessarily. 624 00:37:30,345 --> 00:37:33,734 You can just look at, can I run an AB test here? 625 00:37:33,765 --> 00:37:38,895 Can I see some sort, you know, tricks around that. 626 00:37:39,044 --> 00:37:42,585 Where does this, what's the track record of this solution? 627 00:37:42,585 --> 00:37:42,645 Yeah. 628 00:37:42,645 --> 00:37:44,174 And solving my actual problem. 629 00:37:44,174 --> 00:37:47,024 And then don't care if it uses AI or not. 630 00:37:47,384 --> 00:37:50,475 Uh, simple solutions tend to be. 631 00:37:51,285 --> 00:37:55,185 Working surprisingly well a lot of times. 632 00:37:55,425 --> 00:37:58,455 So that's, that's how I would think about it. 633 00:37:59,115 --> 00:38:07,215 Then on a like more meta level, I really believe that in, as for 634 00:38:07,215 --> 00:38:14,025 every year that comes, state of art within AI drastically develops 635 00:38:14,025 --> 00:38:15,795 and it's developing exponentially. 636 00:38:15,795 --> 00:38:19,125 So I think in 10 years or much less than. 637 00:38:19,950 --> 00:38:28,470 The average e-commerce buying experience will be drastically evolved to the extent 638 00:38:28,470 --> 00:38:34,140 that, let's say I'm buying, I dunno, groceries, uh, we have some of these 10 639 00:38:34,145 --> 00:38:37,410 minute grocery delivery apps as customers. 640 00:38:37,740 --> 00:38:42,210 Well, I, I believe that at some point I'm just opening my app. 641 00:38:42,615 --> 00:38:44,685 And the basket is already filled in. 642 00:38:44,685 --> 00:38:45,975 He knows what I want. 643 00:38:46,125 --> 00:38:52,875 You know, , I buy, I'm so predictable in my, like grocery purchases. 644 00:38:53,115 --> 00:38:54,675 Oh, it's scroll milk. 645 00:38:54,675 --> 00:38:57,315 Yeah, avocados, toilet paper, whatever. 646 00:38:57,705 --> 00:38:58,425 Looks good. 647 00:38:58,485 --> 00:38:59,475 Buy right. 648 00:38:59,685 --> 00:39:07,075 Then within fashion, let's say, uh, even though, uh, an AI engine 649 00:39:08,435 --> 00:39:09,705 actually knows what you want. 650 00:39:10,500 --> 00:39:14,460 Maybe you shouldn't have the same transactional experience as with 651 00:39:14,460 --> 00:39:18,990 groceries, but in fashion, it'll be very interesting to see how 652 00:39:19,470 --> 00:39:25,780 the user experience will look when the AI knows what you want, but 653 00:39:26,895 --> 00:39:31,904 buy shoppers don't want to have the sense that it already knows what you want. 654 00:39:31,904 --> 00:39:32,295 Right. 655 00:39:34,395 --> 00:39:35,865 it's a real delicate balance. 656 00:39:35,865 --> 00:39:36,075 Right? 657 00:39:36,485 --> 00:39:36,975 Yeah. 658 00:39:37,025 --> 00:39:42,225 Uh, some, some like illusion of choice and, and, and it's scary. 659 00:39:42,225 --> 00:39:42,825 Scary. 660 00:39:42,855 --> 00:39:48,225 You know, it's scary that kind negative side effects of 661 00:39:48,225 --> 00:39:50,625 this on society and I think. 662 00:39:51,345 --> 00:39:55,754 That's, I want to Depict to be a kind of thought leader in that a, as we 663 00:39:55,754 --> 00:40:01,725 grow, I, I believe that just because it has bad people have bad effects 664 00:40:01,725 --> 00:40:02,895 on society, you shouldn't run away. 665 00:40:02,895 --> 00:40:06,734 Well, you should be part of it, ensure that it's done in the best possible 666 00:40:06,734 --> 00:40:09,254 way, uh, since it's going to happen. 667 00:40:09,854 --> 00:40:12,645 So, uh, that's on a more meta level. 668 00:40:12,674 --> 00:40:16,154 But right now, if we want to grow your e-commerce business, I will 669 00:40:16,154 --> 00:40:22,800 focus on the fundamentals and, um, a lot of AI solutions out there, uh, 670 00:40:22,800 --> 00:40:24,990 are much more stupid than you think. 671 00:40:26,340 --> 00:40:26,580 Yeah, 672 00:40:26,910 --> 00:40:28,560 That's What do you mean by that? 673 00:40:28,560 --> 00:40:28,800 Why? 674 00:40:28,800 --> 00:40:31,020 Why would you say they're much more stupid than you think? 675 00:40:31,200 --> 00:40:31,440 Well, 676 00:40:31,440 --> 00:40:36,780 they, they, as you said, they have some Excel, some equivalent of 677 00:40:36,785 --> 00:40:41,670 an Excel spreadsheet somewhere, and they do simple volume 678 00:40:41,720 --> 00:40:45,450 correlations, et cetera, cetera, to. 679 00:40:46,665 --> 00:40:49,305 That that wouldn't feel as much as ai. 680 00:40:50,085 --> 00:40:53,625 It's kind of a subjective term when you call something AI or not. 681 00:40:54,045 --> 00:40:54,585 Uh, yeah. 682 00:40:54,615 --> 00:40:58,275 If it turns out that they use an Excel sheet and it really 683 00:40:58,280 --> 00:41:03,165 solves your problem well, Well, I wouldn't be happy using that. 684 00:41:03,165 --> 00:41:03,225 Yeah. 685 00:41:03,945 --> 00:41:04,185 Yeah. 686 00:41:04,305 --> 00:41:04,605 Yeah. 687 00:41:05,175 --> 00:41:06,135 That's really interesting. 688 00:41:06,375 --> 00:41:07,065 It's really interesting. 689 00:41:07,335 --> 00:41:11,955 So, I mean, that's where you, I suppose you see the future of AI and eCommerce 690 00:41:11,955 --> 00:41:17,040 going is it's gonna be much more I've, you feel like you've got a choice, but 691 00:41:17,040 --> 00:41:18,810 really we know what the choices you're 692 00:41:18,810 --> 00:41:19,200 gonna make. 693 00:41:19,710 --> 00:41:21,270 We're not interesting where that evolves. 694 00:41:21,330 --> 00:41:22,410 Yeah, yeah, for sure. 695 00:41:22,415 --> 00:41:22,600 Yeah. 696 00:41:22,605 --> 00:41:22,950 That's, that's 697 00:41:22,950 --> 00:41:23,700 gonna be clever. 698 00:41:24,330 --> 00:41:28,980 Where do you, where do you see eCommerce generally going in the future? 699 00:41:30,050 --> 00:41:35,790 Uh, okay, so there's multiple timelines here, right? 700 00:41:35,790 --> 00:41:39,630 So, uh, it's. 701 00:41:41,665 --> 00:41:45,825 I would double down on what I previously said around kinda 702 00:41:45,975 --> 00:41:48,134 on the longer term time scale. 703 00:41:48,314 --> 00:41:54,015 I really believe that like at the end of the day, when you build a 704 00:41:54,015 --> 00:42:01,064 core engine, which knows what the customer wants, and you can hopefully 705 00:42:01,904 --> 00:42:05,145 take into account, you know, Okay. 706 00:42:05,145 --> 00:42:08,295 How much does this customer care about sustainability? 707 00:42:08,295 --> 00:42:12,015 How much does this customer care about you know, those things which 708 00:42:12,345 --> 00:42:16,845 aren't necessarily as flagged in, in today's shopping experience, 709 00:42:17,325 --> 00:42:18,585 uh, can be taken account. 710 00:42:18,590 --> 00:42:22,275 I, I think that will be one of the most pivotal things, 711 00:42:22,275 --> 00:42:23,625 the timeline for that time. 712 00:42:24,125 --> 00:42:25,335 Less, less, less. 713 00:42:25,335 --> 00:42:25,654 Sure. 714 00:42:25,660 --> 00:42:35,595 About, um, something I see on a very, very short, short term basis, uh, is that, um, 715 00:42:36,255 --> 00:42:43,725 e-commerce website, e-commerce merchants lack the IT resources for them to really, 716 00:42:44,235 --> 00:42:47,085 uh, you know, move as fast as they should. 717 00:42:47,475 --> 00:42:47,565 Mm-hmm. 718 00:42:47,805 --> 00:42:53,995 . So a lot of, you know, when you want to shame, when you have a hypothesis on how 719 00:42:53,995 --> 00:42:58,245 you want to shade in something, IT is usually involved in some way or another. 720 00:42:58,485 --> 00:43:04,484 And if you don't have the foundation or the expertise in house, to make 721 00:43:04,484 --> 00:43:09,734 those changes really quickly, then you're kind of locked and yeah, you, 722 00:43:09,734 --> 00:43:11,265 you can't move as fast as you want. 723 00:43:11,265 --> 00:43:18,104 So, uh, I hope that e-commerce is moving towards kind of building 724 00:43:18,104 --> 00:43:21,044 a better foundation on, on, on that than in the future. 725 00:43:21,075 --> 00:43:21,495 Yeah, 726 00:43:21,500 --> 00:43:22,694 that's a really fair point. 727 00:43:22,935 --> 00:43:28,709 I kind of see myself the, um, . I, I almost wonder, cuz let's say, 728 00:43:28,709 --> 00:43:31,740 I mean Amazon by far is the biggest e-commerce platform. 729 00:43:31,740 --> 00:43:31,830 Mm-hmm. 730 00:43:32,100 --> 00:43:32,399 , right? 731 00:43:32,430 --> 00:43:36,509 Uh, in terms of transactional, one of the other biggest platforms, 732 00:43:36,569 --> 00:43:38,430 let's say, is Shopify for sure. 733 00:43:38,850 --> 00:43:39,149 Yeah. 734 00:43:39,209 --> 00:43:41,730 And so you've got a lot of sites who use Shopify. 735 00:43:42,270 --> 00:43:46,620 Um, I'm curious to see if Shopify in their development are gonna build 736 00:43:46,620 --> 00:43:48,910 in AI features into that platform. 737 00:43:49,430 --> 00:43:55,245 Um, so that they, So that if I launch a website on Shopify, yeah, it starts 738 00:43:55,245 --> 00:43:59,595 from day one, analyzing data and helping me build pictures as I go along. 739 00:44:00,165 --> 00:44:03,605 Um, I've yet to see an eCommerce plat. 740 00:44:03,605 --> 00:44:07,455 I, I, I see, you know, you've got depict, for example, which plugs into, say, 741 00:44:07,455 --> 00:44:08,985 a Shopify site or to other websites. 742 00:44:09,405 --> 00:44:13,585 Um, I just wonder whether at some point in the future that somebody's 743 00:44:13,585 --> 00:44:18,420 gonna write a really clever uh, platform that understands eCommerce, 744 00:44:18,420 --> 00:44:22,440 that understands ai, that understands, you know, all the different elements 745 00:44:22,440 --> 00:44:25,620 that make up eCommerce and not just one aspect of it or one bit of it. 746 00:44:26,130 --> 00:44:29,790 Um, so everything from shipping to, to whatever, and. 747 00:44:30,825 --> 00:44:35,205 I'm, I'm, I'm really curious, will there become like this super AI platform that 748 00:44:35,205 --> 00:44:38,865 really transforms how eCommerce is done? 749 00:44:38,865 --> 00:44:39,495 I don't know. 750 00:44:39,495 --> 00:44:40,125 It's in effect. 751 00:44:40,125 --> 00:44:42,404 Like Amazon going here, here's my platform. 752 00:44:42,705 --> 00:44:44,024 Set up your website with this, 753 00:44:44,024 --> 00:44:44,325 right? 754 00:44:44,325 --> 00:44:44,384 Yeah. 755 00:44:44,504 --> 00:44:51,015 I, I, I really hope that, that it will happen, uh, doing like being 756 00:44:52,305 --> 00:44:57,855 being great everything is hard , especially when you go into very like 757 00:44:58,245 --> 00:45:03,674 technical niche things, which requires building up an organization and so forth. 758 00:45:03,674 --> 00:45:09,254 So my impression based on the interactions I've had around Shopify 759 00:45:09,259 --> 00:45:17,475 is that they really want to emphasize the ecosystem around Shopify and 760 00:45:17,475 --> 00:45:20,325 the marketplace they have and 761 00:45:21,030 --> 00:45:30,720 create an environment where if you are, if you are an AI researcher, an engineer, 762 00:45:30,720 --> 00:45:37,560 and you have this great hypothesis for this new, new thing, which could work for 763 00:45:37,560 --> 00:45:42,450 eCommerce merchants, instead of having to create all this integrations to all 764 00:45:42,450 --> 00:45:47,070 these different platforms, etcetera, you could just in a very simple manner. 765 00:45:47,834 --> 00:45:52,484 Create this algorithm or simple surveys and through Shopify could 766 00:45:52,484 --> 00:45:58,395 be, could be this multiplier effect where other merchants can see the 767 00:45:58,395 --> 00:46:00,674 track record of that app and so forth. 768 00:46:00,674 --> 00:46:05,205 So, uh, I hope that it, it that will happen. 769 00:46:05,745 --> 00:46:10,665 Uh, and, and my, my, my sense of Shopify strategy right now is they 770 00:46:10,665 --> 00:46:16,455 want to welcome players like depict to kind of help on those things. 771 00:46:17,160 --> 00:46:20,640 They necessarily don't have the focus to really nail down right. 772 00:46:21,480 --> 00:46:21,779 Yeah, 773 00:46:22,080 --> 00:46:23,100 that sounds fascinating. 774 00:46:23,430 --> 00:46:27,150 I am watching and waiting with bated breath because I, I, I was there 775 00:46:27,150 --> 00:46:30,660 when eCommerce was born and I'm, Yeah, it was very, very simple. 776 00:46:31,830 --> 00:46:37,920 , and now it seems very, very, uh, it's, you know, uh, extremely smart young people 777 00:46:37,920 --> 00:46:41,640 like yourself are taking over in ways that no one ever dreamed possible 10 years ago. 778 00:46:41,640 --> 00:46:46,860 So, Um, it's brilliant to see and, and, and brilliant to chat to you Oliver, Thank 779 00:46:46,860 --> 00:46:48,480 you so much for coming onto the podcast. 780 00:46:48,480 --> 00:46:49,590 How do people reach you? 781 00:46:49,590 --> 00:46:52,529 How do they connect with you if they, if they want to do so? 782 00:46:52,529 --> 00:46:52,920 Yeah, thank 783 00:46:52,920 --> 00:46:53,130 you. 784 00:46:53,130 --> 00:46:58,770 So you can reach me at Oliver.Edholm@depict ai. 785 00:46:58,830 --> 00:47:00,330 I think that's the easiest one. 786 00:47:00,600 --> 00:47:09,750 So it's Oliver dot EDHOLM@depict.ai, uh, then I'm on LinkedIn if you 787 00:47:09,750 --> 00:47:14,370 search for Oliver Edholm there uh, we can also chat through there. 788 00:47:14,820 --> 00:47:15,600 I think that's wonderful. 789 00:47:15,600 --> 00:47:15,660 The 790 00:47:15,780 --> 00:47:16,180 easiest one. 791 00:47:16,830 --> 00:47:17,490 No, that's great. 792 00:47:17,490 --> 00:47:20,790 And we will of course put the links to Oliver, his LinkedIn and his email 793 00:47:20,790 --> 00:47:22,320 in the show notes so you can get hold. 794 00:47:22,935 --> 00:47:26,655 Uh, if you are subscribed to the show notes, uh, then we, they'll 795 00:47:26,655 --> 00:47:30,315 all be there and, um, reach out to Oliver with your questions. 796 00:47:30,315 --> 00:47:34,095 Uh, it's gonna, and watch, watch what, uh, Depict does, because I'm 797 00:47:34,095 --> 00:47:37,395 really intrigued by how they're gonna motor forward on this. 798 00:47:37,905 --> 00:47:39,645 Uh, Oliver, thank you so much for joining us. 799 00:47:39,645 --> 00:47:41,235 Really appreciate you being here. 800 00:47:41,475 --> 00:47:43,005 It's been honestly, a real treat. 801 00:47:43,005 --> 00:47:43,395 Thank you. 802 00:47:43,935 --> 00:47:45,345 So there you have it. 803 00:47:47,515 --> 00:47:49,945 I'm still mesmerized by this conversation. 804 00:47:50,335 --> 00:47:52,705 Uh, such an incredible story, isn't it? 805 00:47:52,975 --> 00:47:53,455 Uh, huge. 806 00:47:53,460 --> 00:47:56,185 Thanks again to Oliver for joining me today. 807 00:47:56,455 --> 00:47:59,575 Uh, and also, let me give another big shout out to today's show 808 00:47:59,575 --> 00:48:01,345 sponsor the eCommerce cohort. 809 00:48:01,375 --> 00:48:06,085 Do head over to eCommercecohort.com for more information about this 810 00:48:06,085 --> 00:48:08,755 new type of community that you can. 811 00:48:09,765 --> 00:48:13,365 Be sure to also subscribe wherever you get your podcast from because we've got 812 00:48:13,365 --> 00:48:17,895 some great conversations lined up and I don't want you to miss any of them. 813 00:48:18,194 --> 00:48:25,964 And in case no one has told you today, you my friend are awesome, utterly, awesome. 814 00:48:25,964 --> 00:48:28,095 Its a burden we all have to bear. 815 00:48:29,115 --> 00:48:29,865 It's just the way it is. 816 00:48:30,405 --> 00:48:30,585 Now. 817 00:48:30,585 --> 00:48:34,245 The eCommerce podcast is produced by Orient Media. 818 00:48:34,485 --> 00:48:38,835 You can find our entire archive of episodes on your favorite podcast app. 819 00:48:39,135 --> 00:48:41,945 The team that makes this show possible is Sadaf Beynon, Josh 820 00:48:41,945 --> 00:48:45,335 . Catchpole, Estella Robin and Tim Johnson. 821 00:48:45,945 --> 00:48:50,510 Our theme song has been written by me and my son, Josh Edmundson, 822 00:48:51,020 --> 00:48:52,400 uh, people ask me about this. 823 00:48:52,400 --> 00:48:56,360 To be fair, I wrote a very basic melody and Josh did everything else, so I 824 00:48:56,360 --> 00:48:58,310 should probably give him more credit. 825 00:48:58,640 --> 00:49:02,450 Uh, if you would like to read the transcript, all show notes, head over to 826 00:49:02,450 --> 00:49:07,990 the website, eCommerce podcast.net where you can also sign up for, our newsletter. 827 00:49:08,210 --> 00:49:09,140 That's it for me. 828 00:49:09,140 --> 00:49:13,670 Thank you so much for joining me, and I hope you have a fantastic week. 829 00:49:13,760 --> 00:49:15,110 I will see you next time. 830 00:49:15,740 --> 00:49:16,310 Bye for now.