1 00:00:00,130 --> 00:00:03,630 Hello and welcome to our post NRF Interviews. 2 00:00:03,680 --> 00:00:09,330 So let me introduce Paul Windsor who is Head of Retail and 3 00:00:09,340 --> 00:00:14,610 Industry at Snowflake. So Paul, welcome to the podcast. 4 00:00:14,900 --> 00:00:18,310 For those who don't know, why don't you tell us a little bit 5 00:00:18,320 --> 00:00:22,110 about yourself and then a little bit about the company that you 6 00:00:22,120 --> 00:00:24,040 work for, Snowflake and and what they. 7 00:00:24,050 --> 00:00:26,720 Do, yeah. Thank you, Alex, and thanks for 8 00:00:26,730 --> 00:00:28,510 having me today. Yes. 9 00:00:28,520 --> 00:00:30,230 So a bit of background about myself. 10 00:00:31,010 --> 00:00:35,720 My career in retail can be split split perfectly down the middle. 11 00:00:35,990 --> 00:00:41,700 I spent the 1st 19 years of my career at Sainsbury's, where I 12 00:00:41,710 --> 00:00:45,020 think in those nineteen years I've got an incredible 13 00:00:45,030 --> 00:00:50,340 understanding of the retail business and it was a great 19 14 00:00:50,350 --> 00:00:53,660 years in the company. But my last job I had at 15 00:00:53,670 --> 00:00:57,730 Sainsbury's, My last role was all about the data component and 16 00:00:57,740 --> 00:01:03,270 how data is shared and collaborated on with large CPG 17 00:01:03,280 --> 00:01:07,250 companies. Now that may into my next 19 18 00:01:07,260 --> 00:01:10,230 years of my career, which is always it's been in the data and 19 00:01:10,240 --> 00:01:13,890 analytics space. So I spent 19 years pretty much 20 00:01:14,080 --> 00:01:18,490 working for companies that are helping retailers solve their 21 00:01:18,500 --> 00:01:22,650 data challenges, whether that was with data warehousing in the 22 00:01:22,660 --> 00:01:26,450 kind of first generation of when data warehouses became something 23 00:01:26,460 --> 00:01:30,030 that retailers invested in. I've spent a number of years in 24 00:01:30,040 --> 00:01:35,100 the whole BI space and then recently in the AI space around 25 00:01:35,110 --> 00:01:38,450 automated machine learning. And two years ago I got the 26 00:01:38,460 --> 00:01:42,980 opportunity to join Snowflake, which is the leading data 27 00:01:42,990 --> 00:01:47,860 platform that was born in the cloud and snowflakes. 28 00:01:47,870 --> 00:01:51,680 Mission today is to mobilise the world's data. 29 00:01:51,810 --> 00:01:57,710 So this opportunity to give access to retailers for the data 30 00:01:57,720 --> 00:02:01,530 they need with the data consumers that need to consume 31 00:02:01,540 --> 00:02:06,340 that data in a way that makes it easy and and approachable in the 32 00:02:06,350 --> 00:02:10,169 way that they they tackle their their data challenges. 33 00:02:10,220 --> 00:02:14,090 So, so, yeah, so been with Snowflake for just over 2 years. 34 00:02:14,100 --> 00:02:17,590 I lead our industry go to market for Snowflake in EMEA. 35 00:02:18,400 --> 00:02:22,430 We have a number of retail customers today that are 36 00:02:22,440 --> 00:02:26,390 publicly, publicly I can talk about it's using Snowflake. 37 00:02:26,640 --> 00:02:30,070 One of them happens to be, if I can point to it correctly Alex, 38 00:02:30,120 --> 00:02:33,030 Under Armour which are obviously I've got my Under Armour and 39 00:02:33,040 --> 00:02:37,550 Snake like talk on today, that's all about how they use Snowflake 40 00:02:37,560 --> 00:02:39,610 from their supply chain perspective. 41 00:02:39,760 --> 00:02:43,010 And then my old, my old company that I work for, Sainsbury's is 42 00:02:43,020 --> 00:02:48,010 also a customer of Snowflake as well as John Lewis and River 43 00:02:48,020 --> 00:02:51,530 Island if we kind of localise this down to kind of the UK 44 00:02:51,540 --> 00:02:54,150 market. So that's about me and a little 45 00:02:54,160 --> 00:02:56,710 bit about about Snowflake, but I'd love to get on to the 46 00:02:56,720 --> 00:03:02,820 conversation of NRS as well. Yeah so I just want to this is 47 00:03:02,830 --> 00:03:06,580 in the spirit of you know what my audience is thinking cause 48 00:03:06,710 --> 00:03:11,480 every talk at NRF that had anything with AI in in the title 49 00:03:11,690 --> 00:03:16,760 irrespective of what technology, vendor or partner it was from. 50 00:03:17,010 --> 00:03:20,760 You can imagine all or all of the the the major players had AI 51 00:03:20,770 --> 00:03:24,160 in their title. Everyone you know everyone was 52 00:03:24,170 --> 00:03:26,070 super interested. So I just want to be, you know, 53 00:03:26,290 --> 00:03:30,520 cut to the chase and in in my mind and I'd love your opinion 54 00:03:30,530 --> 00:03:36,450 on this, how much of AI that we talk about is true AI as opposed 55 00:03:36,460 --> 00:03:41,550 to machine learning plus, right. So for those who are data 56 00:03:41,560 --> 00:03:45,050 specialists, they'll be like well that's just that's machine 57 00:03:45,060 --> 00:03:46,830 learning. We've been doing that for years. 58 00:03:46,900 --> 00:03:51,190 That has nothing to do with AI or generative AI. 59 00:03:52,300 --> 00:03:55,790 And and I'm just thinking from a from a strategy perspective 60 00:03:56,820 --> 00:03:59,450 before we sort of sort of go deeper down and down. 61 00:03:59,510 --> 00:04:03,590 If we just keep it at that high level, what are your thoughts in 62 00:04:03,660 --> 00:04:06,440 actual AI outcomes? And if you want, why don't you 63 00:04:06,450 --> 00:04:12,110 define what AI means to you and Snowflake And then we can sort 64 00:04:12,120 --> 00:04:15,170 of distinguish because that was the biggest frustrating thing 65 00:04:15,180 --> 00:04:17,190 for me that people would just like talking about machine 66 00:04:17,200 --> 00:04:19,950 learning as it was a I and hoping that no one would ask 67 00:04:19,959 --> 00:04:23,090 that question and like hang on a minute, that's just machine 68 00:04:23,100 --> 00:04:24,560 learning. That's like something that you 69 00:04:24,570 --> 00:04:27,660 could put in an Excel spreadsheet and extract better 70 00:04:27,710 --> 00:04:30,280 outcomes from. So go, go ahead, tell me. 71 00:04:30,770 --> 00:04:33,550 Well, I mean it's it's it's a great point to kick off with, 72 00:04:33,560 --> 00:04:36,370 right? So without a doubt, those three 73 00:04:36,380 --> 00:04:42,340 days in NRS, the number one hype and the topic and the theme was 74 00:04:42,350 --> 00:04:44,700 Jenna in large language models without a doubt. 75 00:04:44,750 --> 00:04:47,780 But I agree with you, not many of those people that were in 76 00:04:47,790 --> 00:04:52,110 those speaking sessions went much deeper than just sort of 77 00:04:52,190 --> 00:04:54,580 reflecting on the fact that this is something that they'll be 78 00:04:54,590 --> 00:04:56,880 looking to try and experiment with. 79 00:04:57,620 --> 00:05:02,440 Yeah, from snowflakes perspective and perhaps it's 80 00:05:02,450 --> 00:05:06,690 sometimes does get missed off here is that you can't have an 81 00:05:06,700 --> 00:05:10,230 AI strategy without a data strategy. 82 00:05:10,280 --> 00:05:13,210 Absolutely. Now, sometimes this gets lost. 83 00:05:13,220 --> 00:05:15,670 Now this is something that we're trying to kind of. 84 00:05:16,420 --> 00:05:20,940 Interrupt you there, Yeah. Is that is the reason because 85 00:05:20,950 --> 00:05:22,590 there's a separation between the two? 86 00:05:22,820 --> 00:05:26,870 Because the people talking about the subject are not from a day 87 00:05:26,880 --> 00:05:30,000 like yourself? From a data background is what? 88 00:05:30,010 --> 00:05:32,480 What's the reason for the disconnect between the two? 89 00:05:32,730 --> 00:05:37,920 Well, I think, I think we've now come into a new era of how AI 90 00:05:37,930 --> 00:05:42,500 can be used because generative AI is something that can now be 91 00:05:42,510 --> 00:05:44,690 used. Large language models is 92 00:05:44,700 --> 00:05:47,100 something that can be used and deployed. 93 00:05:47,430 --> 00:05:51,500 But if you think about it, it can't be used without the data. 94 00:05:51,550 --> 00:05:56,580 So if you don't have a data strategy, and by a data strategy 95 00:05:56,590 --> 00:06:01,770 we mean you to be able to truly get the best value out of 96 00:06:01,780 --> 00:06:05,810 building a Gen AI model or a large language model, you are 97 00:06:05,820 --> 00:06:10,690 going to need to have access to the data that you need to build 98 00:06:10,700 --> 00:06:13,770 that model. Now this has been a challenge 99 00:06:13,780 --> 00:06:15,440 for a number of companies for years. 100 00:06:15,450 --> 00:06:18,850 This is how Snowflake has grown so fast as a business is that 101 00:06:18,860 --> 00:06:21,790 lots of data sits in multiple silos. 102 00:06:22,480 --> 00:06:26,980 You can't just connect a large language model when data sits in 103 00:06:27,020 --> 00:06:29,220 multiple silos. You need to have that data 104 00:06:29,230 --> 00:06:31,550 unified. You need to have that data in 105 00:06:31,560 --> 00:06:33,820 one place. You need to have it governed. 106 00:06:34,110 --> 00:06:36,940 And also Alex, what's really important here is you need to 107 00:06:36,950 --> 00:06:40,290 have the kind of framework behind it so that when you start 108 00:06:40,300 --> 00:06:44,340 to build these large language models, it's contained inside 109 00:06:44,350 --> 00:06:48,120 your own platform for security purposes. 110 00:06:48,250 --> 00:06:51,300 But the first thing that any company needs to do when we 111 00:06:51,310 --> 00:06:54,300 start to hear from our customers, they're teens hear 112 00:06:54,310 --> 00:06:56,400 about Jenna and large language models. 113 00:06:56,490 --> 00:07:00,050 We always ask the question, you know where are you in terms of 114 00:07:00,060 --> 00:07:03,630 your data strategy? Do you have the data today ready 115 00:07:03,680 --> 00:07:06,930 to start to take advantage of those large language models in 116 00:07:06,940 --> 00:07:09,650 general, I and I don't believe I heard that in NRF. 117 00:07:09,660 --> 00:07:14,850 I heard exciting. I heard market signals increase 118 00:07:14,860 --> 00:07:17,110 the efficiency of the supply chain, but it didn't hear 119 00:07:17,120 --> 00:07:19,430 anything about the fact that first and foremost you need to 120 00:07:19,440 --> 00:07:22,190 have a really good unified data strategy. 121 00:07:23,590 --> 00:07:26,180 So I'm I'm not going crazy thinking that, right? 122 00:07:26,490 --> 00:07:29,590 Why not at all? That that is like there would 123 00:07:29,600 --> 00:07:33,190 cause, I was thinking but just maybe there's an an NLM that 124 00:07:33,200 --> 00:07:36,260 you've not thought about Ali that's actually magically 125 00:07:36,330 --> 00:07:41,000 presenting the data into these platforms. 126 00:07:41,010 --> 00:07:44,540 No, because all the platform providers that's their, you know 127 00:07:44,550 --> 00:07:46,820 come come with us on the journey and we'll take care of. 128 00:07:46,830 --> 00:07:50,920 I'm thinking if your data is not ready and as we know most of the 129 00:07:50,930 --> 00:07:55,010 industry, yeah is not ready because everything sits in 130 00:07:55,020 --> 00:07:58,430 silos. So let let's just talk about the 131 00:07:58,440 --> 00:08:02,210 the, if you like the silos and breaking that down as well. 132 00:08:02,260 --> 00:08:06,510 So does does my finance data need to sit? 133 00:08:06,960 --> 00:08:09,410 Like how do I manage that? My finance take the market 134 00:08:09,420 --> 00:08:11,930 marketing data? My ERP data? 135 00:08:12,480 --> 00:08:14,570 Where? Where do I start with, with my 136 00:08:14,580 --> 00:08:17,070 silos? Within my business or my systems 137 00:08:17,080 --> 00:08:18,400 to be fair? Yeah. 138 00:08:18,410 --> 00:08:21,010 Well, this is one of the, this is one of the key strategies 139 00:08:21,020 --> 00:08:22,790 that Snowflake is helping to solve for. 140 00:08:22,800 --> 00:08:26,370 Our retail customers of course have the source systems where 141 00:08:26,380 --> 00:08:30,480 the data actually is generated and curated, but that's not 142 00:08:30,490 --> 00:08:35,100 where you would take advantage of building large language 143 00:08:35,110 --> 00:08:39,179 models, Gen AI, machine learning models, analysing that data to 144 00:08:39,190 --> 00:08:43,200 understand business performance, looking at past historical sales 145 00:08:43,210 --> 00:08:45,240 etcetera. We all know that that's just 146 00:08:45,250 --> 00:08:48,040 where the data is sourced and generated from. 147 00:08:48,050 --> 00:08:52,020 You need to bring that into a platform which is where the data 148 00:08:52,030 --> 00:08:55,280 can be consumed. So where Snowflakes been helping 149 00:08:55,290 --> 00:08:58,420 retailers for a number of years is to bring exactly those 150 00:08:58,430 --> 00:09:01,520 functional data sets that you just mentioned here. 151 00:09:01,590 --> 00:09:04,740 You need to have your finance data alongside your marketing 152 00:09:04,750 --> 00:09:07,050 data alongside your supply chain data. 153 00:09:07,100 --> 00:09:11,730 If you're thinking about running a campaign that is a promotional 154 00:09:11,740 --> 00:09:16,190 campaign to your consumers, you need to understand their 155 00:09:16,200 --> 00:09:18,010 behaviour. So that's all of the customer 156 00:09:18,020 --> 00:09:22,410 data residing in the same platform as your inventory data. 157 00:09:22,420 --> 00:09:24,670 Because if you're going to be promoting stock, you need to 158 00:09:24,680 --> 00:09:29,290 understand if you actually have the stock available to actually 159 00:09:29,300 --> 00:09:31,110 promote those brands. And you need to have your 160 00:09:31,120 --> 00:09:35,130 finance data available to understand the costs of running 161 00:09:35,140 --> 00:09:38,090 these promotions. So bringing all of that data 162 00:09:38,100 --> 00:09:41,290 together is an absolute necessity and this is what we've 163 00:09:41,300 --> 00:09:45,320 been helping our retail customers in the first instance. 164 00:09:45,330 --> 00:09:48,340 Now Gen AI and large language models is just taking that onto 165 00:09:48,350 --> 00:09:51,780 another level. But again, this is just another 166 00:09:51,790 --> 00:09:55,320 capability to start driving those outcomes and those 167 00:09:55,330 --> 00:09:58,900 predictions. But the underlying part, which 168 00:09:58,910 --> 00:10:02,460 unfortunately is not the interesting exciting parts of 169 00:10:02,470 --> 00:10:06,600 many people, Alex, is you need to have that governed unified 170 00:10:06,650 --> 00:10:10,110 data platform way you can consume that data from. 171 00:10:10,200 --> 00:10:13,130 And then I'll go one step further as well, which is 172 00:10:13,180 --> 00:10:17,470 something that many retailers are not necessarily truly taking 173 00:10:17,540 --> 00:10:23,550 advantage of to a wide degree is in order to kind of build real 174 00:10:23,920 --> 00:10:27,310 good predictions on future behaviour or detections of 175 00:10:27,320 --> 00:10:31,910 market signals, you need to access third party data as well. 176 00:10:31,920 --> 00:10:34,750 So think about all those third party datasets, Alex, that some 177 00:10:34,760 --> 00:10:39,110 of the biggest data providers in the world are monetizing today. 178 00:10:39,180 --> 00:10:42,050 Now everybody draws their attention straight away to 179 00:10:42,060 --> 00:10:44,390 weather data because it's been something that's been talked 180 00:10:44,400 --> 00:10:46,700 about and the retail industry for 20 years. 181 00:10:46,800 --> 00:10:50,490 But we're also talking about demographic data, economic data, 182 00:10:50,560 --> 00:10:54,490 ESG data. We've got all of these types of 183 00:10:54,500 --> 00:11:00,710 datasets that could also be enriched alongside your own data 184 00:11:00,880 --> 00:11:05,110 to then run those kind of AI and those predictions as well. 185 00:11:05,120 --> 00:11:07,920 So you need access to that third party data. 186 00:11:08,200 --> 00:11:12,270 Now another little tiny plug for Snowflake here Alex, it's it's 187 00:11:12,280 --> 00:11:17,180 Snowflake has the largest marketplace on its platform, the 188 00:11:17,190 --> 00:11:21,740 third party datasets. It has over 2200 third party 189 00:11:21,750 --> 00:11:24,520 datasets sitting on the Snowflake platform. 190 00:11:24,610 --> 00:11:27,940 So if you are a Snowflake customer and you'll you'll find 191 00:11:27,950 --> 00:11:31,420 all of your own first party and all your own data unified 192 00:11:31,430 --> 00:11:34,240 together to start to really understand your business. 193 00:11:34,470 --> 00:11:37,360 But you feel that there might be a competitive advantage to 194 00:11:37,370 --> 00:11:41,690 access third party data. That data is available to you as 195 00:11:41,700 --> 00:11:44,610 well as part of the platform for you to enrich it. 196 00:11:44,680 --> 00:11:48,570 Bring that together and now you're really scaling the way 197 00:11:48,580 --> 00:11:51,690 that you can do predictions on things like AI. 198 00:11:52,080 --> 00:11:56,680 So if we take sort of the data economy in action and from your 199 00:11:56,690 --> 00:12:01,660 experience from whichever you know whether it's Sainsbury's 200 00:12:01,670 --> 00:12:05,320 Underarm or one of the the retailers that you, you you you 201 00:12:05,330 --> 00:12:09,600 mentioned and understanding that they must have had some data 202 00:12:09,610 --> 00:12:13,640 strategies that you know they they already set and and now 203 00:12:13,650 --> 00:12:19,060 leveraging your platform. Can you share any sort of I I 204 00:12:19,070 --> 00:12:23,580 don't know if any of them were in customer data or the loyalty 205 00:12:23,590 --> 00:12:28,860 programme area but but I'm what I'm curious is what the use case 206 00:12:28,870 --> 00:12:31,870 is and then what the intelligence is that's being 207 00:12:31,880 --> 00:12:35,660 drawn out to make actionable business outcome. 208 00:12:36,010 --> 00:12:38,690 So can you have you got any examples? 209 00:12:38,700 --> 00:12:42,510 For us, yeah, I do. So I I think if you look at 210 00:12:42,520 --> 00:12:49,030 snowflakes, opportunity to drive success for our retail customers 211 00:12:49,040 --> 00:12:52,820 today, the typical conversations that we're having, So we've had 212 00:12:52,830 --> 00:12:55,530 those data conversations. You need to unify your data, you 213 00:12:55,540 --> 00:12:57,890 need to bring it together, you need to be able to give it 214 00:12:57,900 --> 00:13:01,330 seamless access to that data of all your data consumers. 215 00:13:01,340 --> 00:13:06,650 That's the first part of a road map of a data strategy journey, 216 00:13:06,660 --> 00:13:11,070 bring that data together. We are now having conversations 217 00:13:11,080 --> 00:13:15,280 with our retail customers today which is around surprise Alex, 218 00:13:15,290 --> 00:13:17,660 this is, this is no kind of surprise for you. 219 00:13:17,670 --> 00:13:23,200 And also what is now a hot theme as well, Customer 360 does not 220 00:13:23,210 --> 00:13:27,600 go away and it continues and it will never be sold. 221 00:13:27,610 --> 00:13:30,280 Customer 360 will never be sold. We know that. 222 00:13:30,350 --> 00:13:33,230 But it's about now this opportunity to bring all your 223 00:13:33,240 --> 00:13:35,240 data together from a customer perspective. 224 00:13:35,250 --> 00:13:38,940 I don't know if you picked up on this at NRF, but one of the 225 00:13:38,950 --> 00:13:45,250 sessions I think I went to and I think it was for an Abercrombie 226 00:13:45,260 --> 00:13:48,270 and Fitch I think was speaking at that intersection and they 227 00:13:48,280 --> 00:13:53,170 were talking about well moving now from personalization to 228 00:13:53,180 --> 00:13:56,210 individualization. So this idea now the 229 00:13:56,220 --> 00:13:59,970 personalization is we want to personalise as much as we can 230 00:13:59,980 --> 00:14:02,350 our relationship with the consumers. 231 00:14:02,540 --> 00:14:05,270 Now we're getting down to that level of granularity with the 232 00:14:05,280 --> 00:14:07,130 data. The data is all unified 233 00:14:07,140 --> 00:14:10,150 together. You've got the capabilities with 234 00:14:10,160 --> 00:14:13,880 modelling in AI and this potential around large language 235 00:14:13,890 --> 00:14:15,800 models. Can we get to the point where we 236 00:14:15,810 --> 00:14:19,720 can start to individualise those relationships with our customers 237 00:14:19,730 --> 00:14:21,840 as well? And you can see that happening 238 00:14:21,930 --> 00:14:26,620 just here with Sainsbury's. Sainsbury's are now offering ten 239 00:14:26,690 --> 00:14:31,340 products every seven days to their Nectar card members that 240 00:14:31,350 --> 00:14:35,680 is absolutely personalised and individualised to those shopping 241 00:14:35,690 --> 00:14:38,320 behaviour. We'll be able to bring that data 242 00:14:38,330 --> 00:14:40,800 together. If you're a network card holder 243 00:14:40,810 --> 00:14:46,250 today, every Monday you'll get those and new offers that are 244 00:14:46,340 --> 00:14:49,990 absolutely individualised and personalised to you to be able 245 00:14:50,000 --> 00:14:52,690 to do that. That's really understanding 246 00:14:52,700 --> 00:14:55,600 previous customer shopping behaviour and doing that really, 247 00:14:55,610 --> 00:14:56,940 really well. And you can see from their 248 00:14:56,950 --> 00:15:00,790 results and they talk very positively about how Nectar 249 00:15:00,860 --> 00:15:03,640 pricing is driving revenue from that sector. 250 00:15:03,650 --> 00:15:06,750 It's a customer. 360 is a big, big factor. 251 00:15:06,940 --> 00:15:09,180 Yeah. I've got, I've got another two 252 00:15:09,190 --> 00:15:13,270 I'd love to just touch on. I'll be the second one is no 253 00:15:13,280 --> 00:15:15,550 surprise again is that supply chain. 254 00:15:15,560 --> 00:15:19,800 So you know Under Armour talk publicly about the fact that 255 00:15:19,810 --> 00:15:24,050 they use Snowflake for the importance around sharing data, 256 00:15:24,060 --> 00:15:27,610 Alex, to make the supply chain more efficient. 257 00:15:27,620 --> 00:15:31,710 We know that there are so many situations in the supply chain 258 00:15:31,720 --> 00:15:34,430 where that flow of goods can break down. 259 00:15:34,760 --> 00:15:37,730 The idea that you can support that with data sharing is 260 00:15:37,740 --> 00:15:40,330 really, really important. And where Snowflake does this 261 00:15:40,340 --> 00:15:46,300 exceptionally well is we can help to share data between two 262 00:15:46,310 --> 00:15:49,160 companies without moving or copying the data. 263 00:15:49,430 --> 00:15:53,740 So this is a really good example where in supply chain you need 264 00:15:53,750 --> 00:15:59,480 to share data with sales of inventory of demand and what you 265 00:15:59,490 --> 00:16:03,360 don't want to do is turn that into a supply chain of data 266 00:16:03,370 --> 00:16:07,420 having to move Where Snowflake does this exceptionally well is 267 00:16:07,430 --> 00:16:10,520 around that sharing of data between two companies. 268 00:16:10,590 --> 00:16:13,800 So that's supply chain is another key strategy for a lot 269 00:16:13,810 --> 00:16:16,680 of our retail customers. And then the final one that I 270 00:16:16,690 --> 00:16:19,200 want to sort of touch on is I I don't know if you heard it as 271 00:16:19,210 --> 00:16:22,830 well, but retail media, I mean retail media is where the net I. 272 00:16:22,840 --> 00:16:26,080 Was just about to that was gonna be my final question for you 273 00:16:26,140 --> 00:16:30,610 because later media is absolutely how do I combat the 274 00:16:30,650 --> 00:16:34,260 battering I'm getting from inflation by generating new 275 00:16:34,270 --> 00:16:37,870 revenue from other means. So yeah, absolutely far away on. 276 00:16:37,880 --> 00:16:41,360 That well, well look you know I, I again I captured a tonne of 277 00:16:41,370 --> 00:16:45,120 notes here from last week. This was US numbers, Alex, but 278 00:16:45,130 --> 00:16:49,530 for those listening in today, you've got Retail Media 279 00:16:49,540 --> 00:16:53,220 generating $60 billion of revenue in 2023. 280 00:16:53,230 --> 00:16:58,610 That's expected to increase to $100 billion in 2028. 281 00:16:58,620 --> 00:17:01,190 So four years away from that increasing to $100 billion, 282 00:17:01,870 --> 00:17:06,780 They're talking again very, very positively NRF about the next 283 00:17:06,829 --> 00:17:10,220 era of we're in the golden generation of retail media. 284 00:17:10,230 --> 00:17:14,900 And this is because now we've got this opportunity physically 285 00:17:14,910 --> 00:17:19,000 in store to start generating those advertising and those 286 00:17:19,010 --> 00:17:22,579 promotions through those digital screens that you can use and 287 00:17:22,589 --> 00:17:25,319 place around the store. And then you've got that 288 00:17:25,369 --> 00:17:29,420 absolute fantastic instant response to whether or not that 289 00:17:29,490 --> 00:17:33,390 advert, that advertising and that product has resulted in the 290 00:17:33,400 --> 00:17:37,190 product being purchased and then the closing that loop to how 291 00:17:37,200 --> 00:17:40,250 much was that return on investment for that advertising. 292 00:17:40,260 --> 00:17:43,790 So we're seeing a lot of our retail customers now wanting to 293 00:17:43,800 --> 00:17:48,350 get really, really solid on their first party customer data 294 00:17:48,540 --> 00:17:51,770 because they can use that to really decide which is the best 295 00:17:51,780 --> 00:17:54,650 times to promote which items through their stores. 296 00:17:54,740 --> 00:17:57,430 And then the other part of retail media Alex, which is 297 00:17:57,440 --> 00:18:00,880 again where Snowflake really comes in is we're now seeing 298 00:18:00,890 --> 00:18:04,140 this growth of off site media advertising. 299 00:18:04,150 --> 00:18:07,540 And again the numbers, the numbers were staggering. 300 00:18:07,550 --> 00:18:12,300 So again at NRS last week they talked about currently offsite 301 00:18:12,310 --> 00:18:17,880 media revenue, it's about $6.7 billion in 2023. 302 00:18:17,890 --> 00:18:23,600 That's taking what you can advertise of goods into 303 00:18:23,610 --> 00:18:27,400 advertising and media channels which is away from your on site 304 00:18:27,410 --> 00:18:30,420 channels. That growth is set to go from 305 00:18:30,430 --> 00:18:35,920 6.7 to 24 billion in the next three years and that's because 306 00:18:35,930 --> 00:18:39,410 you can take that data now and you can start to monetize it 307 00:18:39,420 --> 00:18:43,210 through off-site channels. But it's Paul, just interrupt 308 00:18:43,220 --> 00:18:47,270 you on that. But I see that as so I get the 309 00:18:47,280 --> 00:18:53,010 outcome that if you're delivering adverts in store then 310 00:18:53,140 --> 00:18:57,390 delivering those adverts off site somewhere else. 311 00:18:57,400 --> 00:19:00,490 Yeah. But again because I've sort of 312 00:19:00,500 --> 00:19:04,500 grown up in this industry you know the martech that that you 313 00:19:04,510 --> 00:19:08,360 need to to to develop something like that, I don't know, I don't 314 00:19:08,370 --> 00:19:12,900 I don't see it as easy as so. So the I guess the possibility 315 00:19:12,910 --> 00:19:18,500 and the numbers are there to say look if you can do it, it is but 316 00:19:18,510 --> 00:19:22,880 I don't know if I took the UK grocer market bar two of them, I 317 00:19:22,890 --> 00:19:24,740 don't think the others could do that, right. 318 00:19:24,750 --> 00:19:28,140 I don't know if you've got experience in the UK market and 319 00:19:28,150 --> 00:19:30,570 obviously one of the grocers you you're working with. 320 00:19:30,650 --> 00:19:33,460 Yeah. But I don't see how they could 321 00:19:34,560 --> 00:19:37,780 because obviously then you're you're you you're just looking 322 00:19:37,790 --> 00:19:42,420 at the integration with sort of expanding that problem of data 323 00:19:42,430 --> 00:19:47,240 sets out across other other assets that might be owned by 324 00:19:47,250 --> 00:19:49,760 other people. Well, it's interesting the 325 00:19:49,770 --> 00:19:52,200 integration passed the really important words that you just 326 00:19:52,210 --> 00:19:55,620 reflected on here. So of course the grocers are 327 00:19:55,630 --> 00:19:58,550 leading the charge in terms of the way that they can do this. 328 00:19:58,560 --> 00:20:03,680 They can take that marketing budget and they can invest that 329 00:20:03,730 --> 00:20:07,350 in offside channels. This is where the risk is at 330 00:20:07,420 --> 00:20:11,570 right now is obviously protecting personal identifiable 331 00:20:11,580 --> 00:20:16,130 information is EI customer data, your data and my data. 332 00:20:16,200 --> 00:20:18,550 And what you don't want to do is once you've brought that 333 00:20:18,560 --> 00:20:22,430 together inside a data platform like Snowflake, and you've 334 00:20:22,440 --> 00:20:25,470 actually governed it and you've got it secure and now you're 335 00:20:25,480 --> 00:20:29,040 protecting that data and you're using it for your own insights 336 00:20:29,050 --> 00:20:31,450 and your understanding of your customers and your business. 337 00:20:31,560 --> 00:20:34,790 And now you're thinking about a media strategy where data needs 338 00:20:34,800 --> 00:20:39,640 to actually be shared outside of your platform and with 339 00:20:39,650 --> 00:20:42,260 advertisers and media agencies, et cetera. 340 00:20:42,420 --> 00:20:45,880 Now we're into the real era of data cleanrooms. 341 00:20:45,930 --> 00:20:48,350 Now this is where the conversation may get a little 342 00:20:48,360 --> 00:20:52,400 bit more too technical, but for those listening in today, data 343 00:20:52,410 --> 00:20:58,160 clean rooms is a fantastic way of being able to share data but 344 00:20:58,170 --> 00:21:01,660 not expose the data. It's a beautiful way of sharing 345 00:21:01,670 --> 00:21:04,980 data and insights without moving the data. 346 00:21:04,990 --> 00:21:09,330 And so we're starting to see capability baked into platforms 347 00:21:09,340 --> 00:21:14,030 like Snowflake to allow you to be able to share data without 348 00:21:14,040 --> 00:21:15,710 moving or copying or exposing it. 349 00:21:15,720 --> 00:21:18,790 And I think that's where we're going to see the platform 350 00:21:18,800 --> 00:21:22,610 capability enabling that growth of that retail media. 351 00:21:22,620 --> 00:21:26,790 So that advertising can happen off site, but the data stays 352 00:21:26,800 --> 00:21:29,600 governed and secure. So that's where we're, we're 353 00:21:29,610 --> 00:21:32,450 talking to most of our customers today. 354 00:21:33,020 --> 00:21:35,970 Brilliant, Paul. This was going to be a quick 355 00:21:36,010 --> 00:21:40,540 fireside chat, which I think has given the audience a lot of food 356 00:21:40,550 --> 00:21:44,820 for thought. Any sort of where do you, where 357 00:21:44,830 --> 00:21:47,400 do you think will be at next year's NRF? 358 00:21:47,510 --> 00:21:50,990 Well, because it's like almost. Do you have any views of where 359 00:21:51,000 --> 00:21:54,340 this is all going? Will it be AI .2 dot O or 360 00:21:54,350 --> 00:21:58,010 something? I thought you might ask this 361 00:21:58,020 --> 00:22:01,310 question so, so when I when I went 12, I went 12 months ago. 362 00:22:01,320 --> 00:22:03,630 I think you were saying at the start before we started this 363 00:22:03,640 --> 00:22:05,130 podcast. You've been for the last 13 364 00:22:05,140 --> 00:22:08,690 years. Last year I took away three key 365 00:22:08,700 --> 00:22:11,920 themes. The first one was how do we, how 366 00:22:11,930 --> 00:22:16,350 do we help serve the consumers that are dealing with the cost 367 00:22:16,360 --> 00:22:18,860 of living crisis and inflationary pressures. 368 00:22:18,870 --> 00:22:22,540 So it was, it was very much about dealing with with those 369 00:22:22,550 --> 00:22:25,790 kind of consumers. The second one was how do we 370 00:22:26,220 --> 00:22:31,050 move from promising to proving that we are delivering an ESG 371 00:22:31,060 --> 00:22:34,050 sustainability strategy. And the third one was how do we 372 00:22:34,060 --> 00:22:38,720 optimise our assortment and our prices again connected to the 373 00:22:38,730 --> 00:22:42,770 cost of living crisis that was very much in focus 12 months 374 00:22:42,780 --> 00:22:46,040 ago. That felt quite a challenging 375 00:22:46,050 --> 00:22:50,020 time and very much in media. If you look today, the three key 376 00:22:50,030 --> 00:22:55,540 themes was Gen AI, it was retail media. 377 00:22:56,130 --> 00:22:59,970 And the third one was where did I get the third one, please 378 00:22:59,980 --> 00:23:01,520 don't let me go. Yeah. 379 00:23:01,530 --> 00:23:05,470 The third one is connected consumer, Alex, with the three 380 00:23:05,480 --> 00:23:09,470 that were absolutely focus this time that felt more optimistic. 381 00:23:09,640 --> 00:23:12,750 If you asked me in 12 months time, I think we may be at the 382 00:23:12,760 --> 00:23:18,290 point where the NRF is going to be asking retailers to prove out 383 00:23:18,480 --> 00:23:22,670 how they've adopted Jenai large language models, how they've 384 00:23:22,680 --> 00:23:25,350 been able to get to the point of individualising those 385 00:23:25,360 --> 00:23:27,730 relationships with customers. I think we're going to start to 386 00:23:27,740 --> 00:23:31,650 see more proof now in just concept talking. 387 00:23:32,040 --> 00:23:34,630 No, that's wonderful. Paul, thank you so much for 388 00:23:34,640 --> 00:23:39,680 giving up your time in the day and I look forward to seeing 389 00:23:39,690 --> 00:23:43,350 what you guys carry on doing to support retailers go through 390 00:23:43,360 --> 00:23:47,660 this journey of transformation. Well, listen, I'm really 391 00:23:47,670 --> 00:23:49,120 appreciate you inviting me on here, Alex. 392 00:23:49,130 --> 00:23:51,960 It's been a pleasure and great. Thanks so much. 393 00:23:52,030 --> 00:23:52,540 Thank you.