1 00:00:00,280 --> 00:00:02,320 Well, hello and thank you all again for tuning into another 2 00:00:02,320 --> 00:00:05,000 episode of the Professional Pricing Society podcast. 3 00:00:05,720 --> 00:00:09,040 My name is Terrence. In today's discussion, we have a 4 00:00:09,040 --> 00:00:12,600 very special guest with us. His name is Kieran Gontgay and 5 00:00:12,600 --> 00:00:17,160 he is here to discuss with us his new online course with PPS 6 00:00:17,520 --> 00:00:21,000 titled Using Analytics for AI for Pricing. 7 00:00:21,440 --> 00:00:23,960 Kieran is the Director and founder of global launch base 8 00:00:23,960 --> 00:00:28,240 and Inter nationalized consulting firm founded in 2021, 9 00:00:28,720 --> 00:00:31,800 which helps European companies with international go to market 10 00:00:31,800 --> 00:00:34,960 strategies and market launches. Kieran also is the CEO and 11 00:00:34,960 --> 00:00:39,120 Founder of Rapid Pricer, which is an AI based company in 12 00:00:39,120 --> 00:00:43,320 Amsterdam that specializes in reducing food waste through real 13 00:00:43,320 --> 00:00:46,960 time pricing for retailers. Kieran, how are we doing today? 14 00:00:47,560 --> 00:00:49,560 Very good, very good. Thank you for the introduction, 15 00:00:49,560 --> 00:00:51,640 Terence. Absolutely. 16 00:00:52,160 --> 00:00:55,240 Now you have a new course with us and we're super excited to 17 00:00:55,240 --> 00:00:58,560 kind of start promoting it and kind of getting more people to 18 00:00:58,560 --> 00:01:01,440 talk about it, which is the using analytics and AI for 19 00:01:01,440 --> 00:01:05,560 pricing. And you know, you are what some 20 00:01:05,560 --> 00:01:08,240 may consider an expert in the AI industry. 21 00:01:08,240 --> 00:01:10,120 And so we're super glad to have you. 22 00:01:10,480 --> 00:01:13,200 I want to jump into this conversation and ask you a few 23 00:01:13,200 --> 00:01:16,040 questions to kind of get give people a better grasp as to what 24 00:01:16,040 --> 00:01:19,440 they can expect with this new course and what are some of your 25 00:01:19,440 --> 00:01:22,280 favorite things about it? But first foremost, how is 26 00:01:22,280 --> 00:01:26,920 analytics in AI currently being used in pricing today and how do 27 00:01:26,920 --> 00:01:29,720 you think it's changed in recent times? 28 00:01:29,720 --> 00:01:32,680 And this can probably provide a better context of what your 29 00:01:32,680 --> 00:01:35,080 course is kind of made-up of. Yeah. 30 00:01:35,080 --> 00:01:39,560 So analytics and AI, just to take it a few years ago, 31 00:01:39,560 --> 00:01:43,120 Terence, this was a very highly specialized skill. 32 00:01:43,520 --> 00:01:46,720 You would have to get people with the particular skill sets 33 00:01:46,720 --> 00:01:51,760 to do it for you. Now with more tools available 34 00:01:51,760 --> 00:01:56,720 and people are able to use analytics and AI with the 35 00:01:56,720 --> 00:01:59,880 business knowledge, more of a business knowledge and less of a 36 00:01:59,880 --> 00:02:02,520 technical knowledge. Earlier it was the other way 37 00:02:02,520 --> 00:02:04,720 around. You needed more technical and 38 00:02:04,720 --> 00:02:08,360 little business skills to get something done to build a 39 00:02:08,360 --> 00:02:11,240 dashboard say for example, or build an algorithm which can 40 00:02:11,240 --> 00:02:14,760 give you pricing or to find out what is the right price based on 41 00:02:14,760 --> 00:02:18,080 all the changing conditions. Now you could use many 42 00:02:18,080 --> 00:02:21,760 functionalities of tools in a do it yourself fashion. 43 00:02:22,400 --> 00:02:25,960 So it's important to know what is possible and what tools to 44 00:02:25,960 --> 00:02:28,720 use. But it's no longer necessary to 45 00:02:28,720 --> 00:02:33,800 know coding or you know, development or or writing all of 46 00:02:33,800 --> 00:02:36,640 your analytical algorithms. All of that is done 47 00:02:36,640 --> 00:02:39,520 automatically through algorithms which are pre built. 48 00:02:40,040 --> 00:02:42,520 So that's the big difference literally happening over the 49 00:02:42,520 --> 00:02:47,720 last couple of years and then pricing as well as many other 50 00:02:47,720 --> 00:02:50,520 industries as well. Understanding basically how to 51 00:02:50,520 --> 00:02:55,840 utilize the tool of AI compared to in previous times where you 52 00:02:55,840 --> 00:02:59,440 have to go through all the coding, you know, jargon, if you 53 00:02:59,440 --> 00:03:02,400 will, and, and that skill set, but that's cool. 54 00:03:02,840 --> 00:03:05,840 And as it pertains to pricing, I'm sure there's a plethora of 55 00:03:05,880 --> 00:03:08,480 opportunities where AI can be utilized. 56 00:03:08,480 --> 00:03:11,040 And you know, we hear a lot of terms in industry like 57 00:03:11,040 --> 00:03:16,280 artificial intelligence, Gen. AILOM and and how relevant are 58 00:03:16,280 --> 00:03:19,600 they for the industry in the realm of pricing? 59 00:03:20,360 --> 00:03:24,640 So, so it's very important to understand where we stand in 60 00:03:24,640 --> 00:03:29,840 the, in the ladder of using analytics and AI, right? 61 00:03:30,200 --> 00:03:33,840 So say for example, I have been to many a situation where the 62 00:03:33,840 --> 00:03:39,520 clients want to use the whatever the latest buzzword is, OK, the 63 00:03:39,560 --> 00:03:42,040 boardroom says we need to do something with Gen. 64 00:03:42,040 --> 00:03:44,640 AI. I'm like, OK, what have you 65 00:03:44,640 --> 00:03:46,800 started doing your rules based pricing? 66 00:03:47,120 --> 00:03:49,600 Do you know where your competition stands right now, 67 00:03:50,000 --> 00:03:51,760 right. Have you done the basic 68 00:03:52,520 --> 00:03:55,280 infrastructure needed to start implementing all of the 69 00:03:55,280 --> 00:03:58,960 analytics and AI? So, so it is possible that the 70 00:03:58,960 --> 00:04:02,920 many, many say for example, when it comes to retail, the many 71 00:04:02,920 --> 00:04:06,360 innovative leading edge retailers like Target and Best 72 00:04:06,360 --> 00:04:10,840 Buy or Fantastics or even in Europe, the very small 73 00:04:10,840 --> 00:04:14,520 retailers, highly specialized, highly automated systems and 74 00:04:14,520 --> 00:04:16,920 they're putting to good use all of the latest technologies 75 00:04:16,920 --> 00:04:20,240 available. But it's also important to know 76 00:04:20,839 --> 00:04:23,840 you might use the latest technologies to do simple tasks 77 00:04:23,840 --> 00:04:26,040 also. Let's say for example, you could 78 00:04:26,040 --> 00:04:31,280 feed multiple sources of data and say to Jane AI, if you build 79 00:04:31,280 --> 00:04:34,040 it right, what are the latest trends you see? 80 00:04:34,040 --> 00:04:36,920 Or give me the best set of prices and products I can use to 81 00:04:36,920 --> 00:04:38,640 promote for the upcoming Christmas season. 82 00:04:38,640 --> 00:04:41,600 Given what's happened in the same situation last year, you 83 00:04:41,600 --> 00:04:46,680 could simply write a query now and bring out some very simple 84 00:04:47,120 --> 00:04:50,400 recommendations based on the latest technologies. 85 00:04:51,040 --> 00:04:54,880 But still, you're not going to have to do something like fully 86 00:04:54,880 --> 00:04:58,200 automate my prices based on the freshness of the produce kind of 87 00:04:58,200 --> 00:05:02,480 a situation yet. So very basic steps can use the 88 00:05:02,480 --> 00:05:05,600 most complex of AI if you have the right infrastructure in 89 00:05:05,600 --> 00:05:07,880 place. But at the same time, you can't 90 00:05:07,880 --> 00:05:13,200 just say bring me Jane AI if you haven't done the basics of Excel 91 00:05:13,200 --> 00:05:15,920 and analytical infrastructure first, so. 92 00:05:18,160 --> 00:05:20,920 It sounds like there's tears to this and that levels, if you 93 00:05:20,920 --> 00:05:25,200 will, the the basics of the infrastructure of AI is 94 00:05:25,200 --> 00:05:28,520 essentially the foundation. It then once that's established, 95 00:05:29,840 --> 00:05:32,200 then we can start talking about other things like G and AI, 96 00:05:32,760 --> 00:05:33,960 things of that nature. Is that correct? 97 00:05:34,600 --> 00:05:37,280 Yeah, I, I, I wouldn't say talk about G and AI yet. 98 00:05:37,800 --> 00:05:41,320 Let it have a background, see see what can be done with the 99 00:05:41,320 --> 00:05:44,720 tools you have already available to answer the business questions 100 00:05:44,720 --> 00:05:49,000 first. I thought, do we know what roles 101 00:05:49,000 --> 00:05:52,200 each categories are playing? Do we know which ones are my 102 00:05:52,200 --> 00:05:53,840 profit drivers? Right? 103 00:05:54,320 --> 00:05:57,720 Do we know which items are contributing to my share in 104 00:05:57,720 --> 00:06:01,600 market share growth or decline? Say, for example, where are my 105 00:06:01,600 --> 00:06:04,440 customers most sensitive to price changes? 106 00:06:04,960 --> 00:06:08,080 Don't worry so much about whether it is Gen. 107 00:06:08,080 --> 00:06:13,200 AI or Excel in the background. Business needs to answer these 108 00:06:13,200 --> 00:06:16,600 questions first and then go on making it more and more 109 00:06:16,600 --> 00:06:19,360 efficient with more technology in the future. 110 00:06:20,320 --> 00:06:22,640 Got you. And staying obviously in the 111 00:06:22,760 --> 00:06:26,840 realm of pricing as it pertains to understanding the business 112 00:06:26,840 --> 00:06:31,880 behind it and using AI, can you walk us through how how the 113 00:06:31,880 --> 00:06:36,400 science works behind a typical price optimization solution? 114 00:06:37,680 --> 00:06:41,520 This could be done using Excel, it could be done using like 115 00:06:41,520 --> 00:06:45,360 regression on on like something like SAS or something, or it 116 00:06:45,360 --> 00:06:48,920 could be done using AI, right? But the basic is you need to 117 00:06:48,920 --> 00:06:54,640 have the relevant input data going into your data lake or 118 00:06:54,640 --> 00:06:57,640 wherever you put it. It could be as simple as clean 119 00:06:57,640 --> 00:07:01,760 point of sale data, inventory information, sales information, 120 00:07:01,760 --> 00:07:05,480 holiday calendar, weather, demographics, economic 121 00:07:05,480 --> 00:07:07,480 conditions, whatever is relevant for you. 122 00:07:07,840 --> 00:07:09,320 Let's start with the basics first. 123 00:07:09,840 --> 00:07:13,600 Once you have this data in place, we need to align this 124 00:07:13,600 --> 00:07:17,080 with what is the business question we're looking to solve 125 00:07:17,240 --> 00:07:20,280 with this available data, right? It's, it's good to have this 126 00:07:20,280 --> 00:07:23,040 cool data and you can easily build lots of visualization. 127 00:07:23,040 --> 00:07:25,800 What happened last year, last month where we're going up and 128 00:07:25,800 --> 00:07:31,320 down, but now we can put together algorithms and systems 129 00:07:31,320 --> 00:07:34,320 in place to make use of the data in real time. 130 00:07:34,440 --> 00:07:38,080 Say, for example, if you're automatically overstocked on a 131 00:07:38,080 --> 00:07:41,520 certain product and there isn't enough time to sell it before 132 00:07:41,520 --> 00:07:45,600 the end of the season, right? That should automatically in 133 00:07:45,600 --> 00:07:48,440 your process of pricing come back and say, OK, we need to 134 00:07:48,440 --> 00:07:52,080 sell it at this price to make sure we sell this through, 135 00:07:52,560 --> 00:07:54,960 right? So build the right input 136 00:07:55,080 --> 00:07:59,520 infrastructure, have the right business rules in place to make 137 00:07:59,520 --> 00:08:01,040 sure you're making the right decision. 138 00:08:01,040 --> 00:08:03,840 And then when you come out with an output, you can make the 139 00:08:03,840 --> 00:08:06,640 whole process into something which is streamlined, which 140 00:08:06,640 --> 00:08:09,120 happens automatically on an ongoing basis. 141 00:08:10,240 --> 00:08:14,400 So, so this course I have explained what could be some of 142 00:08:14,400 --> 00:08:17,680 the sources of data. And again, you could start doing 143 00:08:17,680 --> 00:08:20,600 this with small tables in Excel, but you could do it a lot 144 00:08:20,600 --> 00:08:24,120 faster, clean up data a lot faster using AI, say, for 145 00:08:24,120 --> 00:08:27,360 example, or you could aggregate data or connect different 146 00:08:27,360 --> 00:08:31,080 sources of data using AI. But the basic framework needs to 147 00:08:31,080 --> 00:08:35,280 be understood from the scratch. What exactly is happening? 148 00:08:35,280 --> 00:08:39,360 You know, to give you a quick side example, that one of the 149 00:08:39,360 --> 00:08:43,480 best bosses I've ever had was the CEO of Fry's Electronics. 150 00:08:45,000 --> 00:08:48,480 And he once told me that Kiran, go get me an algorithm built to 151 00:08:48,480 --> 00:08:51,920 reduce my inventory. OK, So I went and bought all 152 00:08:51,920 --> 00:08:54,520 these fancy books, and here's the algorithm. 153 00:08:54,520 --> 00:08:56,520 Give you an equation. It's going to save you $50 154 00:08:56,520 --> 00:08:57,400 million. OK. 155 00:08:57,720 --> 00:09:01,440 Now, why is this a square root? Why is this not, you know, a 156 00:09:01,440 --> 00:09:03,760 square? Why are we doing the 157 00:09:03,760 --> 00:09:06,960 differentiation here? So he wanted to know every 158 00:09:06,960 --> 00:09:10,640 component of the equation, what it did, why we had it in there 159 00:09:10,800 --> 00:09:13,120 before we made it into an algorithm like that. 160 00:09:14,000 --> 00:09:17,400 How we do it can come later. What are we doing? 161 00:09:17,840 --> 00:09:21,080 What are we trying to solve? What does each component of the 162 00:09:21,080 --> 00:09:23,320 data do for you? That needs to be understood 163 00:09:24,520 --> 00:09:27,000 without all of the technology, right? 164 00:09:27,280 --> 00:09:30,040 What is possible needs to be clear. 165 00:09:30,160 --> 00:09:31,880 And then we make it faster, right? 166 00:09:31,880 --> 00:09:35,400 Let's first go walking, and then let's take a bicycle, then a 167 00:09:35,400 --> 00:09:37,800 motorcycle, then a speedboat, whatever you want. 168 00:09:38,840 --> 00:09:40,600 That happens with technology later. 169 00:09:40,600 --> 00:09:44,080 But this course explains what are the fundamentals you need to 170 00:09:44,080 --> 00:09:46,840 understand before you bring all of the technology on top of 171 00:09:46,880 --> 00:09:48,960 this. That's good. 172 00:09:49,640 --> 00:09:50,800 That's a, that's a, that's a great. 173 00:09:50,800 --> 00:09:54,120 And you know, that's a great point as well, because you want 174 00:09:54,120 --> 00:09:57,040 to understand everything as best you possibly can so that your 175 00:09:57,040 --> 00:10:00,720 inputs, what your your infrastructure is for this 176 00:10:00,720 --> 00:10:04,400 technology can be correct as far as it, you know, as it pertains 177 00:10:04,400 --> 00:10:07,760 to whatever the goal is the company is moving toward. 178 00:10:08,920 --> 00:10:11,440 And so I'm glad you kind of, you know, broke it down and put it 179 00:10:11,440 --> 00:10:14,400 that way, because a lot of folks may just want to jump straight 180 00:10:14,400 --> 00:10:19,120 into the technology without having the, the true grasp of 181 00:10:19,560 --> 00:10:22,000 what their company needs, where the company is going and you 182 00:10:22,000 --> 00:10:26,320 know, their, their their company goals using tools like, you 183 00:10:26,320 --> 00:10:29,360 know, Excel or AI. So that's cool. 184 00:10:30,280 --> 00:10:32,520 Now let me ask you this as well. What are some techniques and 185 00:10:32,520 --> 00:10:37,280 processes that can be put in place to help with pricing using 186 00:10:37,280 --> 00:10:38,920 analytics and AI? You kind of alluded to a little 187 00:10:38,920 --> 00:10:41,800 bit before, but if you don't mind going a bit in depth with 188 00:10:41,800 --> 00:10:45,400 that. So, so first thing is to make 189 00:10:45,400 --> 00:10:49,800 sure all of the data is in a centralized place and it is able 190 00:10:49,800 --> 00:10:53,000 to talk to each other, right? So say, for example, if the 191 00:10:53,000 --> 00:10:56,280 promotions team operate differently, the inventories 192 00:10:56,320 --> 00:10:58,440 coming from the warehouse, right? 193 00:10:58,440 --> 00:11:02,080 The product assortment is with the category managers, the, the 194 00:11:02,080 --> 00:11:05,360 data needs to be in a place where we can connect all of the 195 00:11:05,360 --> 00:11:06,760 different streams of data, right? 196 00:11:06,760 --> 00:11:10,480 So, so once this data is available, I, I like to use this 197 00:11:10,480 --> 00:11:14,360 example of vegetables being chopped up before a chef can 198 00:11:14,360 --> 00:11:17,360 come in, right? We have all the ingredients 199 00:11:17,360 --> 00:11:21,120 lined up, we have all the meats and vegetables ready to go. 200 00:11:21,480 --> 00:11:24,400 Then you can think about how best are you going to use this 201 00:11:24,880 --> 00:11:26,440 to, to make your business decision. 202 00:11:26,440 --> 00:11:28,920 So the infrastructure, the technology infrastructure, 203 00:11:29,320 --> 00:11:32,080 again, it's a lot easier these days and many, many 204 00:11:32,080 --> 00:11:34,680 organizations pretty much already have it in place. 205 00:11:35,040 --> 00:11:38,560 But we might want to specify that, hey, I need to have POS 206 00:11:38,600 --> 00:11:42,480 data connected to inventory, to the promotions data, say, for 207 00:11:42,480 --> 00:11:44,800 example, or to the store opening data. 208 00:11:45,360 --> 00:11:48,480 And then you'll have to go through answering your business 209 00:11:48,480 --> 00:11:52,600 questions one step at a time. OK, how are we doing against 210 00:11:52,600 --> 00:11:55,200 competition? A simple elasticity analysis, 211 00:11:55,200 --> 00:11:57,960 say, for example, you could do it yourself or get it done. 212 00:11:58,400 --> 00:12:02,240 You can use those numbers of elasticity to find out what 213 00:12:02,240 --> 00:12:05,640 roles each products are playing or what role each category is 214 00:12:05,640 --> 00:12:08,800 playing, right? And then try to see which 215 00:12:08,800 --> 00:12:11,280 competitor matters to you by how much. 216 00:12:12,000 --> 00:12:15,040 You might just think Walmart, say, for example, might assume 217 00:12:15,040 --> 00:12:18,280 Target is the biggest competitor, but most likely it's 218 00:12:18,280 --> 00:12:21,640 it's going to be a store right across the street from Walmart. 219 00:12:21,920 --> 00:12:24,720 It could be enabled open mom and pop store. 220 00:12:25,960 --> 00:12:29,640 So that shows up in the analysis once you have it tied up. 221 00:12:29,640 --> 00:12:32,240 And we ask the right question the right way, right? 222 00:12:32,600 --> 00:12:39,560 So the good news is there's always a good next step in this 223 00:12:39,560 --> 00:12:42,160 journey of analytics. You cannot say that I have 224 00:12:42,160 --> 00:12:45,720 figured out everything there is in this industry right You you 225 00:12:45,720 --> 00:12:50,240 could start as much as a simple Excel pivot table. 226 00:12:50,720 --> 00:12:55,320 Go all the way up to using JNAI to automatically price your 227 00:12:55,320 --> 00:12:59,160 products based on whatever is happening around the store and 228 00:12:59,240 --> 00:13:03,400 inside the store. And you know, you are someone 229 00:13:03,400 --> 00:13:05,840 who is well versed in the market. 230 00:13:06,040 --> 00:13:10,080 What are some successful cases of using analytics and AI that 231 00:13:10,080 --> 00:13:15,360 you've seen in the market? So often times if you go to a 232 00:13:15,360 --> 00:13:17,360 really large solution provider, right? 233 00:13:17,360 --> 00:13:22,400 So, so they have evolved their solutions so much and it has 234 00:13:22,400 --> 00:13:27,960 become so robust, it is actually detrimental that it, it loses 235 00:13:27,960 --> 00:13:31,920 the flexibility to change with the new needs of the market to 236 00:13:31,920 --> 00:13:34,120 the new data sources available, right? 237 00:13:34,640 --> 00:13:37,680 So, So what I have seen in the most successful case studies are 238 00:13:37,680 --> 00:13:43,080 almost always re engineering of a new algorithm based on the 239 00:13:43,080 --> 00:13:46,880 needs of the business. And once these businesses figure 240 00:13:46,880 --> 00:13:49,640 out, OK, now we're going to start using, let's say for 241 00:13:49,640 --> 00:13:53,360 example, we're going to start using what kind of a traffic is 242 00:13:53,360 --> 00:13:56,360 parked in the parking lot to determine what kind of 243 00:13:56,360 --> 00:13:58,200 promotions I'm going to run inside the store. 244 00:13:58,240 --> 00:14:00,120 You could use JNAI to do that today. 245 00:14:00,600 --> 00:14:04,440 It can analyse what models of car are there, how big are the 246 00:14:04,440 --> 00:14:07,720 cars and who's likely to walk into the store and how many of 247 00:14:07,720 --> 00:14:11,480 them, and then tie it with all of the other factors to 248 00:14:11,480 --> 00:14:14,880 determine the promotions, right? So, so I don't think I can give 249 00:14:14,880 --> 00:14:19,080 you direct names of my clients without permission. 250 00:14:19,080 --> 00:14:22,880 But if you see examples of what Best Buy is doing, what Target 251 00:14:22,880 --> 00:14:27,640 is doing in the US from a retail standpoint, you'll see many 252 00:14:27,640 --> 00:14:31,920 examples of pricing decisions or Amazon, Everybody's familiar 253 00:14:31,920 --> 00:14:36,040 with Amazon, right? So Amazon changes its prices of 254 00:14:36,120 --> 00:14:40,320 it's every single product on average changes its price in 10 255 00:14:40,320 --> 00:14:43,480 minutes. And no person is making this 256 00:14:43,480 --> 00:14:46,200 decision, right? They're basing it based on so 257 00:14:46,200 --> 00:14:50,120 many conditions and they are at a stage where nobody knows why 258 00:14:50,120 --> 00:14:51,920 these price changes are happening anymore. 259 00:14:52,080 --> 00:14:55,960 It is coming out of a black box, but it is what the results are 260 00:14:55,960 --> 00:14:57,840 getting demonstrated automatically. 261 00:14:59,440 --> 00:15:03,080 These are some end price use cases you would see. 262 00:15:04,240 --> 00:15:07,440 You'll also see an edge. You'll see companies like what I 263 00:15:07,440 --> 00:15:10,400 do at Rapid Pricer. There is also our competitors 264 00:15:10,880 --> 00:15:14,800 who look at the condition of fresh produce and change the 265 00:15:14,800 --> 00:15:18,040 prices based on the how many days of life is left on it. 266 00:15:18,360 --> 00:15:21,280 And this is all using AI because you're you're recognizing those 267 00:15:21,280 --> 00:15:23,440 images and predicting demand in real time. 268 00:15:24,120 --> 00:15:26,840 And then in some cases, we're able to reflect these prices 269 00:15:26,840 --> 00:15:30,520 back immediately when retailers have electronic shelf labels. 270 00:15:31,040 --> 00:15:35,040 And it reminds me of AI. Can't remember what the quote is 271 00:15:35,040 --> 00:15:39,080 or who said it, but the gist of that quote was if you don't 272 00:15:39,080 --> 00:15:41,840 evolve, you'll get left behind. And the companies that tend to 273 00:15:41,840 --> 00:15:46,040 be the most successful are the ones that evolve as time 274 00:15:46,040 --> 00:15:49,000 progresses, as the trends happen, as you know, especially 275 00:15:49,000 --> 00:15:53,600 as it pertains to their specific market, their specific company, 276 00:15:53,880 --> 00:15:56,560 the clients and customers that they cater to, you know, 277 00:15:56,880 --> 00:16:01,520 tracking those very minute details, the type of car that 278 00:16:01,520 --> 00:16:05,040 typically comes into the parking lot, that that is information 279 00:16:05,040 --> 00:16:09,000 that is, I mean, extremely advanced compared to previous 280 00:16:09,000 --> 00:16:12,280 years, because that way you can kind of decipher, you know, who 281 00:16:12,280 --> 00:16:13,680 should we start to market to a little bit better? 282 00:16:13,680 --> 00:16:17,600 And so that's, that's a high level marketing that a lot of 283 00:16:17,600 --> 00:16:21,520 these AI tools are, are really moving toward to better suit 284 00:16:21,520 --> 00:16:24,040 their customers. I think of other companies like 285 00:16:24,120 --> 00:16:27,040 bigger name companies like Netflix, you know, if you're, if 286 00:16:27,040 --> 00:16:31,320 you watch television or if you watch movies online, they are 287 00:16:31,320 --> 00:16:34,600 really good at promoting and marketing specific things that 288 00:16:34,600 --> 00:16:37,160 you've watched that you tend to like because you've watched 289 00:16:37,160 --> 00:16:41,800 other things before, you know, and that's a huge market 290 00:16:41,800 --> 00:16:43,160 strategy, which is awesome to me. 291 00:16:43,440 --> 00:16:45,480 Agree, agree. See see one one more thing I 292 00:16:45,480 --> 00:16:48,680 want to put caution to hear Terrance. 293 00:16:48,680 --> 00:16:52,480 It's, it's, it's very important to be innovative and to evolve, 294 00:16:53,320 --> 00:16:56,880 but easiest. And the first thing we should do 295 00:16:56,880 --> 00:16:59,760 is we should already learn from what other people have 296 00:17:00,280 --> 00:17:04,560 discovered in the market, right? Similar, which is why I love PPS 297 00:17:04,920 --> 00:17:08,319 and many courses on PPS. We should talk about how other 298 00:17:08,319 --> 00:17:12,000 people have evolved already. And then once, once you reach 299 00:17:12,000 --> 00:17:14,560 that level of maturity and then there's so much to learn from 300 00:17:14,560 --> 00:17:17,760 everybody else and then we can think about pushing the 301 00:17:17,760 --> 00:17:20,160 boundaries. I like to use another quote is 302 00:17:20,160 --> 00:17:24,040 don't be the bleeding edge of technology, but try and be at 303 00:17:24,040 --> 00:17:26,720 the leading edge of technology. Don't innovate so much that 304 00:17:26,720 --> 00:17:28,720 you're experimenting with a lot of things. 305 00:17:29,400 --> 00:17:32,600 There's much to use which has already been experimented, and 306 00:17:32,600 --> 00:17:35,000 you can do a little bit more experimentation on top of it 307 00:17:35,000 --> 00:17:36,840 also. That's good. 308 00:17:37,280 --> 00:17:41,000 Now, let me ask you this final question for those pricers who 309 00:17:41,000 --> 00:17:45,160 may be on the fence about taking your course or you know, not 310 00:17:45,160 --> 00:17:47,320 sure if it's not sure if it's worth their time, what would you 311 00:17:47,960 --> 00:17:50,000 say to those individuals that may be on the fence? 312 00:17:51,680 --> 00:17:56,960 I would say that it's a good idea for for, for you to check 313 00:17:56,960 --> 00:17:59,800 the book which I've written, which is called the expert guide 314 00:17:59,800 --> 00:18:03,600 to retail pricing load of the concepts from the book. 315 00:18:03,640 --> 00:18:07,280 It's, it's in fact a support material for this course will be 316 00:18:07,280 --> 00:18:09,920 very relevant to what is being taught on the course. 317 00:18:10,240 --> 00:18:14,120 So that will give them a feel of what is going to be taught. 318 00:18:14,400 --> 00:18:17,160 And and if it does make sense, if it is, say for example, not 319 00:18:17,160 --> 00:18:20,880 everybody's in the same level of learning. 320 00:18:20,960 --> 00:18:23,680 Somebody might be a little bit too at the beginning, somebody 321 00:18:23,680 --> 00:18:26,400 might be looking at to learn advanced machine learning 322 00:18:26,400 --> 00:18:28,960 algorithms. So you'll be able to find out 323 00:18:28,960 --> 00:18:32,560 where you fit and if this course is indeed the right fit for you 324 00:18:32,560 --> 00:18:35,280 based on the information you'll find on this book. 325 00:18:35,280 --> 00:18:39,280 It's available on Amazon or other places that could give 326 00:18:39,280 --> 00:18:41,840 them the edge. And then this podcast hopefully 327 00:18:41,840 --> 00:18:43,840 will give you some more information to make this 328 00:18:43,840 --> 00:18:46,360 decision also. So thank you for that, Terence. 329 00:18:47,400 --> 00:18:50,240 Absolutely not, not a problem. Thank you so much again for your 330 00:18:50,240 --> 00:18:50,920 time. Kieran. 331 00:18:51,080 --> 00:18:53,320 Where can those who are interested in learning more 332 00:18:53,320 --> 00:18:56,960 about you and your company and what you stand for find out that 333 00:18:56,960 --> 00:19:00,320 information? The easiest, if you remember my 334 00:19:00,320 --> 00:19:04,280 name, it's Kiran gangway.com and you have a website. 335 00:19:04,280 --> 00:19:08,120 Otherwise my company is Rapid pricer.com. 336 00:19:09,080 --> 00:19:10,440 So these two places will be good. 337 00:19:10,440 --> 00:19:13,760 And then of course, we have our PPS material available. 338 00:19:13,760 --> 00:19:15,120 You're going to post these links too. 339 00:19:16,000 --> 00:19:17,760 Yes, absolutely. All right. 340 00:19:18,040 --> 00:19:20,440 Well, thanks so much again for your time, Karen. 341 00:19:20,440 --> 00:19:22,520 Until next time. We'll see you all later. 342 00:19:22,640 --> 00:19:23,280 Have a good one. Bye. 343 00:19:23,280 --> 00:19:24,720 Bye. Thank you, Terence.