1 00:00:02,240 --> 00:00:06,800 Speaker 1: This is Masters in Business with very Ridholts on Bloomberg Radio. 2 00:00:09,480 --> 00:00:12,280 Speaker 1: This week on the podcast, I have Fred Fox. He 3 00:00:12,520 --> 00:00:16,920 Speaker 1: is the founder and CEO of Planalytics, which is really, 4 00:00:17,160 --> 00:00:19,800 Speaker 1: I'm gonna say it's the most interesting firm you've probably 5 00:00:19,960 --> 00:00:25,720 Speaker 1: never heard of. They specialize in integrating weather data into 6 00:00:25,760 --> 00:00:32,919 Speaker 1: the planning of various companies from retail to insurance to transportation. Uh. 7 00:00:32,960 --> 00:00:37,680 Speaker 1: What they try and do is find ways of removing 8 00:00:37,720 --> 00:00:41,559 Speaker 1: the impact of weather from a company's data so they 9 00:00:41,640 --> 00:00:45,559 Speaker 1: could create a baseline to look forward and do future planning. 10 00:00:46,080 --> 00:00:48,519 Speaker 1: And when you turns out that when you do that, 11 00:00:49,040 --> 00:00:52,000 Speaker 1: there are some really fascinating things you learn. You learn 12 00:00:52,720 --> 00:00:57,400 Speaker 1: who is impacted by whether it's not necessarily who you 13 00:00:57,480 --> 00:01:00,080 Speaker 1: might think. When when you look at the economy in 14 00:01:00,120 --> 00:01:03,880 Speaker 1: the US is about se consumer spending, and you think 15 00:01:04,200 --> 00:01:08,200 Speaker 1: how large retail is as a part of that, whether 16 00:01:08,240 --> 00:01:13,920 Speaker 1: it really has an outsized impact on the overall economy. Now, granted, 17 00:01:14,319 --> 00:01:17,119 Speaker 1: you may give up sales in February and recapture them 18 00:01:17,120 --> 00:01:21,000 Speaker 1: in in March, but there are consequences to that, whether 19 00:01:21,040 --> 00:01:23,920 Speaker 1: it's in the same quarter, whether you're getting the same price, 20 00:01:24,040 --> 00:01:27,039 Speaker 1: the same margin, etcetera. It really has a very very 21 00:01:27,040 --> 00:01:32,640 Speaker 1: significant discount and the impact of weather on online retailers 22 00:01:32,680 --> 00:01:36,440 Speaker 1: like Amazon is absolutely stunning. You will be shocked when 23 00:01:36,480 --> 00:01:39,319 Speaker 1: when you hear this. So, with no further ado, here 24 00:01:39,400 --> 00:01:47,080 Speaker 1: is my interview with Fred Fox. My guest today is 25 00:01:47,160 --> 00:01:52,280 Speaker 1: Fred Fox. He is the founder and CEO of of Planalytics. 26 00:01:53,120 --> 00:01:59,720 Speaker 1: Fred has pioneered the development and commercialization of weather analytics 27 00:01:59,760 --> 00:02:04,600 Speaker 1: and how this impacts the global supply chain. He advises 28 00:02:04,680 --> 00:02:09,480 Speaker 1: many of the world's largest corporations UH Previously, he is 29 00:02:09,520 --> 00:02:12,760 Speaker 1: the inventor of a patent which I think we could 30 00:02:12,760 --> 00:02:16,080 Speaker 1: call whether nom mix. Is that a short description. He 31 00:02:16,160 --> 00:02:21,400 Speaker 1: received a BS degree from the Georgia Institute of Technology. 32 00:02:21,720 --> 00:02:26,120 Speaker 1: This is really a fascinating subject because, as as we 33 00:02:26,240 --> 00:02:28,960 Speaker 1: see and have seen over the past couple of years, 34 00:02:29,360 --> 00:02:33,720 Speaker 1: whether has an enormous impact on the economy, on the 35 00:02:34,600 --> 00:02:38,600 Speaker 1: basically where people live, how they live, how companies can operate. 36 00:02:39,280 --> 00:02:43,519 Speaker 1: What first got you interested in this topic? Well, it's 37 00:02:43,520 --> 00:02:46,160 Speaker 1: great to be here. I left out Welcome to Bloomberg, 38 00:02:46,160 --> 00:02:49,079 Speaker 1: he apologized, I'm like racing right into this because I'm 39 00:02:49,200 --> 00:02:53,520 Speaker 1: jazzed about this subject. Well, look, you know you start 40 00:02:53,560 --> 00:02:59,800 Speaker 1: with several key facts. One, whether he is the UH 41 00:03:00,520 --> 00:03:02,920 Speaker 1: on the velope that we all live in and have 42 00:03:03,160 --> 00:03:07,000 Speaker 1: lived in since mankind first walked upright on the planet, 43 00:03:07,760 --> 00:03:11,000 Speaker 1: and even before I would say, even be four. So 44 00:03:11,320 --> 00:03:15,200 Speaker 1: if you look at two days economy, sixty of the 45 00:03:15,240 --> 00:03:20,679 Speaker 1: world's g MP gross national product it's from consumer spending, 46 00:03:21,720 --> 00:03:26,040 Speaker 1: and sixty of consumer spending is highly affected by weather. 47 00:03:27,000 --> 00:03:29,520 Speaker 1: So when you take a step back, we'll explain that 48 00:03:29,560 --> 00:03:32,600 Speaker 1: what do you mean six of consumer spending is affected 49 00:03:32,600 --> 00:03:37,280 Speaker 1: by weather, meaning the ability of we hear it every season. 50 00:03:37,840 --> 00:03:41,320 Speaker 1: Retailers quarterly numbers, they come out and said, well, winter 51 00:03:41,440 --> 00:03:43,720 Speaker 1: started late, it's a cold winter, it's a warm winter. 52 00:03:43,880 --> 00:03:46,640 Speaker 1: It's do you mean that sort of effect or actually 53 00:03:46,640 --> 00:03:51,839 Speaker 1: moving goods around the world now, actually just just affects, 54 00:03:51,880 --> 00:03:58,480 Speaker 1: affecting consumer purchasing decisions in a meaningful way. Weather has 55 00:03:58,560 --> 00:04:02,480 Speaker 1: that effect still today. And so what I mean is 56 00:04:02,520 --> 00:04:07,560 Speaker 1: that weather drives need not once. So when we drives 57 00:04:07,640 --> 00:04:12,440 Speaker 1: needs not once. Okay, So the summer people by swim gear, 58 00:04:12,520 --> 00:04:14,440 Speaker 1: and the winter they buy heavy coats if they're in 59 00:04:14,440 --> 00:04:18,320 Speaker 1: that part exactly. So everything that we wear as clothing, 60 00:04:18,960 --> 00:04:22,120 Speaker 1: most of it has a weather impact, unless it's high fashion. 61 00:04:22,560 --> 00:04:25,760 Speaker 1: Everything we put on our body, whether it be suntan lotion, 62 00:04:25,839 --> 00:04:29,799 Speaker 1: anything to do with the eyes, ears, nos face, skin, 63 00:04:29,960 --> 00:04:34,560 Speaker 1: all is weather affected. Any any anything we do outdoors, lawn, garden, 64 00:04:34,760 --> 00:04:40,239 Speaker 1: outdoor hiking activities, all all of those things are highly 65 00:04:40,279 --> 00:04:44,039 Speaker 1: affected by weather. And so when you look at the 66 00:04:44,080 --> 00:04:47,200 Speaker 1: impact that weather has on when we buy, how much 67 00:04:47,240 --> 00:04:50,480 Speaker 1: we buy it, what price we buy it at, weather 68 00:04:50,839 --> 00:04:54,440 Speaker 1: is involved in all of those purchasing decisions. On an 69 00:04:54,520 --> 00:04:59,920 Speaker 1: unconscious level. You have me thinking about the things I purchase, 70 00:05:00,839 --> 00:05:03,440 Speaker 1: be it be its suntan lotion or the fact that 71 00:05:03,480 --> 00:05:05,880 Speaker 1: I have a jeep because I live in part of 72 00:05:05,880 --> 00:05:09,120 Speaker 1: the world where when it snows and you have a 73 00:05:09,120 --> 00:05:13,360 Speaker 1: regular car, you sometimes ain't going anywhere without. And I'm 74 00:05:13,360 --> 00:05:15,600 Speaker 1: not a truck guy, but let me tell you, I 75 00:05:15,640 --> 00:05:18,480 Speaker 1: don't even think twice about snow anymore. All Right, it's 76 00:05:18,480 --> 00:05:20,800 Speaker 1: gonna slow everything down, but at least I know I 77 00:05:20,800 --> 00:05:23,400 Speaker 1: could get to where I have to go. Those sorts 78 00:05:23,440 --> 00:05:27,560 Speaker 1: of decisions absolutely. And so we know there are seasons. 79 00:05:27,920 --> 00:05:32,280 Speaker 1: You know, there's fall, uh, spring, winter, summer, but the 80 00:05:32,320 --> 00:05:37,280 Speaker 1: timing of when those seasons start, where they start affects 81 00:05:37,480 --> 00:05:42,039 Speaker 1: enormously when and where and how much people buy. So, 82 00:05:42,160 --> 00:05:49,159 Speaker 1: for instance, uh, this February in the northeast was fairly mild. 83 00:05:50,000 --> 00:05:52,440 Speaker 1: We had an early start to the spring season, and 84 00:05:52,440 --> 00:05:57,200 Speaker 1: then March came and March was downright cold, freeze, wet, nasty, 85 00:05:57,440 --> 00:06:03,040 Speaker 1: and and so spending on normal spring summer stuff stopped 86 00:06:03,200 --> 00:06:06,839 Speaker 1: for almost four to five weeks. We had the same 87 00:06:06,880 --> 00:06:10,800 Speaker 1: thing in the summer. It was a surprisingly cool May 88 00:06:10,839 --> 00:06:14,920 Speaker 1: and a ridiculously hot June, right, and so April came, 89 00:06:15,000 --> 00:06:19,280 Speaker 1: April was warm and and and nice, and so consumer 90 00:06:19,360 --> 00:06:21,599 Speaker 1: spending shot up. And then May came and it was 91 00:06:21,680 --> 00:06:25,119 Speaker 1: cold and wet again, and so you know what took 92 00:06:25,120 --> 00:06:28,240 Speaker 1: place in June. Even though June was a nice month 93 00:06:28,320 --> 00:06:33,400 Speaker 1: to sell, everything was selling at off price because by 94 00:06:33,440 --> 00:06:36,000 Speaker 1: that point everyone needs to get rid of merchandise or 95 00:06:36,040 --> 00:06:39,480 Speaker 1: too late in the season. So while retailers may have 96 00:06:39,560 --> 00:06:42,119 Speaker 1: sold out of their inventory, they sold a much lower 97 00:06:42,160 --> 00:06:44,320 Speaker 1: price level than they would have had a sold in March. 98 00:06:44,880 --> 00:06:48,800 Speaker 1: And so weather has an enormous effect on all of 99 00:06:48,839 --> 00:06:54,359 Speaker 1: these things. How do you define business weather intelligence? So 100 00:06:54,880 --> 00:06:59,320 Speaker 1: what we mean by that is really putting weather into 101 00:06:59,360 --> 00:07:04,320 Speaker 1: a con text that businesses can use and action to 102 00:07:04,440 --> 00:07:08,520 Speaker 1: make different types of decisions. And that's what really weather 103 00:07:08,560 --> 00:07:12,240 Speaker 1: analytics and the world of analytics allows us to do 104 00:07:12,360 --> 00:07:16,920 Speaker 1: today on a mess scale an affordable basis is to 105 00:07:18,200 --> 00:07:21,240 Speaker 1: take all this rich data that comes out of the 106 00:07:21,320 --> 00:07:28,200 Speaker 1: retail and retail supplier industries, mounds of data, and match 107 00:07:28,280 --> 00:07:33,800 Speaker 1: it with weather so that we no longer have to 108 00:07:33,880 --> 00:07:38,200 Speaker 1: guess at what our weather impact is today. There's we 109 00:07:38,400 --> 00:07:41,600 Speaker 1: can know exactly when, where, and how much weather is 110 00:07:41,680 --> 00:07:46,880 Speaker 1: driving consumers to buy or not buy. Let's talk a 111 00:07:46,920 --> 00:07:51,160 Speaker 1: little bit about some of the ways the weather impacts business. 112 00:07:51,320 --> 00:07:54,880 Speaker 1: I keep hearing these crazy numbers. The eclipse that took 113 00:07:54,920 --> 00:07:58,960 Speaker 1: place earlier this year was seven millions to a billion 114 00:07:59,000 --> 00:08:05,320 Speaker 1: dollars in US business. Is that remotely true? It certainly 115 00:08:05,440 --> 00:08:07,600 Speaker 1: could be true if you assume that all of us 116 00:08:08,080 --> 00:08:10,760 Speaker 1: took off a few hours of work to watch it. 117 00:08:10,840 --> 00:08:15,280 Speaker 1: I guess, as I guess it could be the impact 118 00:08:15,280 --> 00:08:19,440 Speaker 1: of weather, though tends not to be the big storms 119 00:08:19,480 --> 00:08:22,400 Speaker 1: that we know. So something like a sandy, yeah, I mean, 120 00:08:22,480 --> 00:08:25,000 Speaker 1: I mean it's something like a sandy definitely had an impact, 121 00:08:26,000 --> 00:08:29,640 Speaker 1: but it was very localized and and and it was 122 00:08:29,800 --> 00:08:33,800 Speaker 1: very short term. In those localized areas, the large impacts 123 00:08:33,880 --> 00:08:38,120 Speaker 1: come from temperature and precipitation, which are much more spread 124 00:08:38,160 --> 00:08:43,160 Speaker 1: out over broader areas. So this past March and May 125 00:08:43,559 --> 00:08:45,160 Speaker 1: when it was cold and wet, it was cold and 126 00:08:45,160 --> 00:08:48,400 Speaker 1: wet over the eastern half of the US. That's a 127 00:08:48,400 --> 00:08:51,120 Speaker 1: pretty big area for it to drive versus a big 128 00:08:51,120 --> 00:08:56,000 Speaker 1: storm like uh, like a sandy. So so that's surprising 129 00:08:56,000 --> 00:08:57,880 Speaker 1: to hear. I think of big storms, and I think 130 00:08:57,880 --> 00:09:00,960 Speaker 1: of sandy. In the New York area, we were out, 131 00:09:01,160 --> 00:09:04,640 Speaker 1: didn't have power for thirteen days. You see Katrina which 132 00:09:04,679 --> 00:09:09,360 Speaker 1: reached havoc on a city like New Orleans. But all 133 00:09:09,360 --> 00:09:12,800 Speaker 1: those things were fairly localized. You're saying, the day to 134 00:09:12,920 --> 00:09:17,000 Speaker 1: day changes in whether the unexpected or at least drier 135 00:09:17,040 --> 00:09:20,040 Speaker 1: than or hotter than, are those the big impacts? Yes, 136 00:09:20,200 --> 00:09:25,199 Speaker 1: So March probably had about a three billion dollar negative 137 00:09:25,240 --> 00:09:29,360 Speaker 1: impact on just the d i y industry, meaning people 138 00:09:29,400 --> 00:09:33,679 Speaker 1: who who do their own home building, their own repairs, 139 00:09:33,720 --> 00:09:36,280 Speaker 1: their own gardening. Is that that because of all the 140 00:09:36,400 --> 00:09:40,720 Speaker 1: rain and cold, it really the the laid what people 141 00:09:40,720 --> 00:09:43,000 Speaker 1: did Now they made a bunch of that back up 142 00:09:43,160 --> 00:09:47,920 Speaker 1: in April, but not all of it. That's fascinating. What 143 00:09:47,920 --> 00:09:53,080 Speaker 1: what are other ways that weather impacts business and the 144 00:09:53,160 --> 00:09:57,439 Speaker 1: economy that that I think most people don't realize, well, 145 00:09:57,880 --> 00:10:02,000 Speaker 1: whether in impacts t affic. So the choice to go 146 00:10:02,200 --> 00:10:06,080 Speaker 1: to a restaurant, it's based on how nice it is 147 00:10:06,120 --> 00:10:09,800 Speaker 1: outside as well as anything. So rain and lots of 148 00:10:09,880 --> 00:10:13,240 Speaker 1: rain really dampens no pun intend but but it dampens 149 00:10:13,720 --> 00:10:19,240 Speaker 1: restaurant traffic. And so the more lower price the restaurant, 150 00:10:19,360 --> 00:10:22,559 Speaker 1: more in the QSR, the more the impact, meaning that 151 00:10:23,080 --> 00:10:27,160 Speaker 1: the higher priced again need versus once. If we're going 152 00:10:27,160 --> 00:10:29,400 Speaker 1: to a high price restaurant, we may go out during 153 00:10:29,480 --> 00:10:34,080 Speaker 1: rain exactly where if a little rain should keep you 154 00:10:34,200 --> 00:10:38,920 Speaker 1: up right, if it's a McDonald's or a sub subway, 155 00:10:39,040 --> 00:10:42,559 Speaker 1: we probably won't go if it's raining. Uh. Little things 156 00:10:42,679 --> 00:10:46,679 Speaker 1: like restaurants that have a drive through are less weather 157 00:10:46,880 --> 00:10:50,000 Speaker 1: impacted than those that don't have a drive through, meaning 158 00:10:50,040 --> 00:10:51,640 Speaker 1: people don't have to get out of the cars. They'll 159 00:10:51,720 --> 00:10:54,079 Speaker 1: drive through it, they'll pick it up there. Exactly about 160 00:10:54,080 --> 00:10:58,120 Speaker 1: the weather exactly what what about things like rain is easy? 161 00:10:58,160 --> 00:11:01,200 Speaker 1: What about snow which makes it a will dice you 162 00:11:01,280 --> 00:11:04,640 Speaker 1: to drive outside? Now the caveat to that as you 163 00:11:04,679 --> 00:11:07,000 Speaker 1: go to Canton in the middle of January and they're 164 00:11:07,040 --> 00:11:11,360 Speaker 1: obrevious to it. Exactly. So whether affects people differently based 165 00:11:11,400 --> 00:11:15,480 Speaker 1: on where they live. So, for instance, a few flakes 166 00:11:15,480 --> 00:11:19,920 Speaker 1: of snow will shut down Atlanta, Georgia a few. Remember 167 00:11:20,120 --> 00:11:23,880 Speaker 1: Dallas last year had a brief ice storm. Literally the 168 00:11:24,040 --> 00:11:26,280 Speaker 1: entire city was closed. Kills them where if you go 169 00:11:26,400 --> 00:11:30,880 Speaker 1: up to let's say, uh, Vermont, they're driving in several 170 00:11:30,920 --> 00:11:34,840 Speaker 1: feet of snow. It doesn't even bother them. So uh. People, 171 00:11:35,200 --> 00:11:39,640 Speaker 1: let's say in Minneapolis, will buy thermal underwear only when 172 00:11:39,679 --> 00:11:44,360 Speaker 1: it goes down below let's say degrees and when it 173 00:11:44,440 --> 00:11:48,800 Speaker 1: starts to sell. But in Florida, they'll start buying it 174 00:11:48,880 --> 00:11:53,920 Speaker 1: when it's about sixty Thermal underwear in Florida below, yes, 175 00:11:53,960 --> 00:11:56,840 Speaker 1: because they are used to warmer temperatures, so that slight 176 00:11:56,960 --> 00:12:00,880 Speaker 1: change is as big an impact as it is in 177 00:12:00,920 --> 00:12:05,440 Speaker 1: Minneapolis when it goes below. So let me push back 178 00:12:05,480 --> 00:12:09,400 Speaker 1: on this theory a little bit. Look, the weather goes 179 00:12:09,440 --> 00:12:13,920 Speaker 1: The counter argument is cyclical. It's cold in the winter, 180 00:12:14,040 --> 00:12:17,240 Speaker 1: it's warm in the summer. Aren't people more likely to 181 00:12:17,320 --> 00:12:22,120 Speaker 1: buy things like overcoats or thermal underwear in northern climates 182 00:12:22,120 --> 00:12:25,400 Speaker 1: as we head into winter. How much is that really 183 00:12:25,440 --> 00:12:30,080 Speaker 1: impacted by short time swings in the weather. It's a 184 00:12:30,160 --> 00:12:34,800 Speaker 1: lot more than you think. So most people don't buy 185 00:12:34,840 --> 00:12:40,920 Speaker 1: ahead of a season Most people buy on impulse when 186 00:12:40,920 --> 00:12:45,040 Speaker 1: they start feeling a certain way, So it's an internal 187 00:12:45,160 --> 00:12:47,640 Speaker 1: clock that goes off as soon as we feel warm 188 00:12:47,720 --> 00:12:51,760 Speaker 1: or feel cold. Wet or dry sort of puts off 189 00:12:51,880 --> 00:12:54,439 Speaker 1: that clock. Oh we have to go and buy this 190 00:12:54,640 --> 00:13:02,520 Speaker 1: or buy that. So when when when? Uh, when unfavorable 191 00:13:02,559 --> 00:13:05,880 Speaker 1: weather happens early in the season, it puts off purchases. 192 00:13:05,960 --> 00:13:09,600 Speaker 1: The more you put off purchases, the more people as 193 00:13:09,640 --> 00:13:13,240 Speaker 1: you go through the season are likely to put it 194 00:13:13,280 --> 00:13:15,640 Speaker 1: off for an entire season. So there's a point at 195 00:13:15,640 --> 00:13:17,280 Speaker 1: which they say, you know what, I don't really need 196 00:13:17,280 --> 00:13:19,800 Speaker 1: that overcoade. I'll get through the winter with what I've 197 00:13:19,840 --> 00:13:22,680 Speaker 1: got and I'll wait till next year, or I'll wait 198 00:13:22,720 --> 00:13:26,680 Speaker 1: till the they go on sale in the spring. So 199 00:13:27,559 --> 00:13:31,200 Speaker 1: as far as the way the price game is played, uh, 200 00:13:31,240 --> 00:13:33,840 Speaker 1: you know, the earlier in the season it sells, they 201 00:13:33,920 --> 00:13:35,880 Speaker 1: higher the price you're going to get. The later in 202 00:13:35,880 --> 00:13:38,600 Speaker 1: the season itsels, the lower the price. So it makes 203 00:13:38,600 --> 00:13:42,720 Speaker 1: a big impact on profits and margins. That's quite fascinating. 204 00:13:42,840 --> 00:13:46,360 Speaker 1: So we always hear whether as a go to excuse 205 00:13:46,400 --> 00:13:52,280 Speaker 1: of CEOs during earnings calls, How legitimate is it? I 206 00:13:52,280 --> 00:13:55,120 Speaker 1: mean when I hear a retailer say, hey, our sales 207 00:13:55,160 --> 00:13:59,480 Speaker 1: are soft because it's snowed in the winter. Well, it's 208 00:13:59,480 --> 00:14:01,520 Speaker 1: supposed to a snow in the winter. Why would your 209 00:14:01,520 --> 00:14:04,240 Speaker 1: sales be off? How much of this is excuse making 210 00:14:04,280 --> 00:14:08,319 Speaker 1: and how much of this is a legitimate concern of 211 00:14:08,559 --> 00:14:12,720 Speaker 1: companies like retailers. So we always say that whether is 212 00:14:12,760 --> 00:14:20,160 Speaker 1: a reason for retail performance are Our main message to 213 00:14:20,280 --> 00:14:24,800 Speaker 1: the industry is that though that you can't manage to 214 00:14:24,920 --> 00:14:28,480 Speaker 1: what you've never measured. And in a world of big 215 00:14:28,560 --> 00:14:32,920 Speaker 1: data analytics where you can really analyze any sized data 216 00:14:32,960 --> 00:14:37,800 Speaker 1: set for very little money, there's no excuse why why 217 00:14:37,920 --> 00:14:44,040 Speaker 1: companies today can't know and tell the street and investors 218 00:14:44,360 --> 00:14:48,840 Speaker 1: precisely how weathers driving them. How do you guys use 219 00:14:48,880 --> 00:14:53,680 Speaker 1: big data to help companies create those sort of analytics. 220 00:14:53,720 --> 00:14:56,600 Speaker 1: So it used to be up until a few years ago, 221 00:14:56,800 --> 00:14:59,880 Speaker 1: that a chief executive could get away with just blaming 222 00:15:00,000 --> 00:15:04,880 Speaker 1: weather and not be punished for it. And today blaming 223 00:15:04,880 --> 00:15:09,400 Speaker 1: weather is just not enough. Our message is that you 224 00:15:09,440 --> 00:15:13,560 Speaker 1: can now take all of your historical data, sales data, 225 00:15:14,000 --> 00:15:16,560 Speaker 1: measure it against weather, and come up with a precise 226 00:15:16,640 --> 00:15:20,320 Speaker 1: model that every day, week, month, quarter, any given time 227 00:15:20,400 --> 00:15:23,680 Speaker 1: you can know exactly how weather is driving your business. 228 00:15:24,720 --> 00:15:28,840 Speaker 1: We had one client I won't name a d I 229 00:15:29,000 --> 00:15:32,600 Speaker 1: Y group that put out to the street in this 230 00:15:33,440 --> 00:15:36,240 Speaker 1: that weather really hurt him in the spring, but they 231 00:15:36,240 --> 00:15:40,520 Speaker 1: didn't explain exactly how it hurt him, and so everyone 232 00:15:40,560 --> 00:15:44,440 Speaker 1: assumed it was an excuse and their stock dropped mightily 233 00:15:45,200 --> 00:15:50,680 Speaker 1: as a result of it, and there's still recovering from it. Uh. 234 00:15:50,720 --> 00:15:53,360 Speaker 1: Those companies that can put out not only the weather 235 00:15:53,440 --> 00:15:56,040 Speaker 1: drove them, but explain it drove us down by five 236 00:15:56,680 --> 00:15:59,480 Speaker 1: due to weather in the northeast that hit us here 237 00:15:59,680 --> 00:16:02,880 Speaker 1: and at our sales are up here. That can that 238 00:16:02,920 --> 00:16:05,920 Speaker 1: can really be transparent about it, are going to be 239 00:16:06,360 --> 00:16:12,080 Speaker 1: rewarded by investors. Uh. Weather can hide as well good 240 00:16:12,240 --> 00:16:17,520 Speaker 1: fighting natural results. So in in this company's UH example, 241 00:16:17,720 --> 00:16:20,440 Speaker 1: when you stripped out the impact of weather, their sales 242 00:16:20,480 --> 00:16:24,760 Speaker 1: are actually way up over last year, but they were 243 00:16:24,800 --> 00:16:28,000 Speaker 1: against the headwind of climate and weather, so it was 244 00:16:28,040 --> 00:16:31,760 Speaker 1: really it was really covering up how good that they 245 00:16:31,840 --> 00:16:35,160 Speaker 1: were doing. So that sort of information sounds like it 246 00:16:35,200 --> 00:16:38,840 Speaker 1: would be very helpful for hedge funds trader as anybody 247 00:16:38,840 --> 00:16:43,760 Speaker 1: who's looking to capture a short term advantage in short 248 00:16:43,840 --> 00:16:48,280 Speaker 1: term market movements. Raises the question who are your clients 249 00:16:48,320 --> 00:16:52,440 Speaker 1: aside from some of these big retailers and others. Are 250 00:16:52,480 --> 00:16:58,240 Speaker 1: you selling this research, these analytics to Wall Street? So uh, 251 00:16:58,400 --> 00:17:02,560 Speaker 1: we sell obviously to our to all the large retailers 252 00:17:02,600 --> 00:17:07,159 Speaker 1: manufacturers globally that they that's our main client base. We 253 00:17:07,240 --> 00:17:10,879 Speaker 1: do a little bit in in agriculture, and we also 254 00:17:11,080 --> 00:17:15,600 Speaker 1: sell some of the benchmark research to FI financial firms 255 00:17:15,640 --> 00:17:18,840 Speaker 1: who are using it for you the other own research, 256 00:17:20,680 --> 00:17:24,639 Speaker 1: any hedge funds, anybody who's really specifically looking for show 257 00:17:24,720 --> 00:17:27,960 Speaker 1: me where the data is better but it's masked by 258 00:17:27,960 --> 00:17:31,200 Speaker 1: by weather. Is that is that a potential client base? 259 00:17:31,680 --> 00:17:34,760 Speaker 1: It is is a potential client base, It's not currently 260 00:17:34,800 --> 00:17:38,720 Speaker 1: a big client base of ours. So we're talking about retail. 261 00:17:39,960 --> 00:17:43,240 Speaker 1: You have Amazon as the eight pound gulla and gorilla 262 00:17:43,280 --> 00:17:47,080 Speaker 1: in the room. And when I'm sitting in the comfort 263 00:17:47,080 --> 00:17:50,960 Speaker 1: of my living room with the fireplace burning, the weather 264 00:17:51,000 --> 00:17:56,920 Speaker 1: outside almost doesn't matter. How has Amazon impacted your business 265 00:17:57,160 --> 00:18:03,960 Speaker 1: and is whether significant to them in their seals. I 266 00:18:03,960 --> 00:18:07,920 Speaker 1: I love the US this point because here's one of 267 00:18:07,960 --> 00:18:13,440 Speaker 1: the most fascinating things. Amazon and online sales are actually 268 00:18:13,560 --> 00:18:17,639 Speaker 1: more impacted by weather than store based sales. I would 269 00:18:17,680 --> 00:18:21,119 Speaker 1: never have guessed that and I wouldn't have either until 270 00:18:21,160 --> 00:18:24,560 Speaker 1: we started to look at the data several years ago. 271 00:18:24,800 --> 00:18:29,960 Speaker 1: And this is this is this is the reason. Again, 272 00:18:30,000 --> 00:18:33,520 Speaker 1: weather drives need. And when it's a certain type of 273 00:18:33,560 --> 00:18:37,400 Speaker 1: weather and you suddenly want to buy a certain type 274 00:18:37,400 --> 00:18:41,000 Speaker 1: of product because of the weather, you still have to 275 00:18:41,080 --> 00:18:44,560 Speaker 1: get into a car to go to a store to 276 00:18:44,720 --> 00:18:51,840 Speaker 1: buy it. So there is a versus picking up your phone, 277 00:18:52,640 --> 00:18:56,360 Speaker 1: punching in a few points to it, and ordering it 278 00:18:56,520 --> 00:18:59,879 Speaker 1: in the palm of your hands. In other words, Uh, 279 00:19:00,280 --> 00:19:05,680 Speaker 1: that need the weather drives can be full filled, quicker, easier, 280 00:19:06,440 --> 00:19:10,480 Speaker 1: uh through a mobile sale than getting into a car, 281 00:19:10,560 --> 00:19:12,439 Speaker 1: which gets you to think, do I really want to 282 00:19:12,440 --> 00:19:15,119 Speaker 1: get into a car to go and buy this? And 283 00:19:15,160 --> 00:19:20,639 Speaker 1: so mobile sales are actually more weather affected. Uh. To 284 00:19:20,760 --> 00:19:23,960 Speaker 1: the upside though, it's yes, like so it's bad weather 285 00:19:24,000 --> 00:19:27,359 Speaker 1: works to Amazon's advantage. I just saw a data point today. 286 00:19:27,359 --> 00:19:31,879 Speaker 1: Amazon is now something like of all online sales, some 287 00:19:32,680 --> 00:19:36,400 Speaker 1: insane number like that bad weather means people are less 288 00:19:36,400 --> 00:19:38,520 Speaker 1: likely to get into the car and therefore might be 289 00:19:38,560 --> 00:19:41,040 Speaker 1: more likely to go online and purchase it. Or just 290 00:19:41,240 --> 00:19:46,240 Speaker 1: good weather. So let's say in February it's warm outside 291 00:19:46,640 --> 00:19:48,520 Speaker 1: and it's dry, I no one wants to go to 292 00:19:48,560 --> 00:19:50,480 Speaker 1: a store if you get a spade of good weather. 293 00:19:50,560 --> 00:19:55,359 Speaker 1: And so I wanted to plant a orchard in my 294 00:19:55,440 --> 00:19:59,320 Speaker 1: backyard this year, and my original goal was to go 295 00:19:59,440 --> 00:20:03,359 Speaker 1: to a dscaper, but I was busy, and so in 296 00:20:03,440 --> 00:20:05,879 Speaker 1: order to order it because it was warm in February, 297 00:20:05,920 --> 00:20:08,160 Speaker 1: I went on my phone. I went to an online 298 00:20:08,680 --> 00:20:13,880 Speaker 1: service and I ordered my orchard online, which then came 299 00:20:13,920 --> 00:20:18,120 Speaker 1: in March when was cold and wet. That's a that's 300 00:20:18,160 --> 00:20:21,439 Speaker 1: that's fascinating. Let's talk a little bit about global warming 301 00:20:21,480 --> 00:20:25,600 Speaker 1: because clearly that's going to have a big impact on 302 00:20:25,920 --> 00:20:32,119 Speaker 1: various businesses. How are companies thinking about climate change and 303 00:20:32,720 --> 00:20:36,800 Speaker 1: what sort of impact does that have on their businesses? So, 304 00:20:37,280 --> 00:20:40,679 Speaker 1: you know, climate change happens over very long cycles thirty 305 00:20:41,320 --> 00:20:44,639 Speaker 1: fifty years. Most chief executives are in their job for 306 00:20:44,800 --> 00:20:48,800 Speaker 1: five years, so it doesn't have a direct in the pact. 307 00:20:50,000 --> 00:20:53,240 Speaker 1: I think the biggest thing with the climate change that 308 00:20:53,240 --> 00:20:58,359 Speaker 1: that we always push is is is really knowing how 309 00:20:58,440 --> 00:21:04,080 Speaker 1: climate is impacting your companies. Most companies today can't measure 310 00:21:04,359 --> 00:21:09,280 Speaker 1: and don't know exactly what climate means to them. Really, 311 00:21:10,040 --> 00:21:12,959 Speaker 1: that is that is shocking to hear one would imagine 312 00:21:13,680 --> 00:21:19,439 Speaker 1: that especially for companies like transport or insurers, or or 313 00:21:19,480 --> 00:21:23,120 Speaker 1: anybody who's build building real estate in southern Florida. These 314 00:21:23,119 --> 00:21:26,840 Speaker 1: things would be significant, they know in general terms, but 315 00:21:27,000 --> 00:21:30,840 Speaker 1: they've really not modeled. And again our message to market 316 00:21:31,240 --> 00:21:35,760 Speaker 1: is that you can't uh manage to what you've never measured. 317 00:21:35,880 --> 00:21:39,200 Speaker 1: So dealing with climate change, what we say is why 318 00:21:39,280 --> 00:21:41,760 Speaker 1: we don't know really what that change is going to be. 319 00:21:42,800 --> 00:21:46,520 Speaker 1: We should know how climate affects our companies as a 320 00:21:46,600 --> 00:21:49,480 Speaker 1: first stage. And so what we've built it is an 321 00:21:49,480 --> 00:21:53,359 Speaker 1: analytical cloud based engine that does just that, that takes 322 00:21:53,359 --> 00:21:57,040 Speaker 1: an all the data, mixes it with weather and measures 323 00:21:57,040 --> 00:21:59,560 Speaker 1: it and comes up with an ongoing model that they 324 00:21:59,600 --> 00:22:03,280 Speaker 1: can know any given time exactly and precisely how weather 325 00:22:03,960 --> 00:22:12,199 Speaker 1: is impacting their their their group from a financial standpoint, revenue, profits, etcetera. 326 00:22:12,760 --> 00:22:17,280 Speaker 1: That's quite fascinating. So what percentage of companies are actively 327 00:22:17,400 --> 00:22:23,359 Speaker 1: engaged in thinking about climate change, in building it into 328 00:22:23,440 --> 00:22:28,960 Speaker 1: some of their long term planning. Very few are actually 329 00:22:28,960 --> 00:22:31,320 Speaker 1: doing it. Some of them are our clients, some of 330 00:22:31,320 --> 00:22:35,000 Speaker 1: them are not, but very few. A lot of people 331 00:22:35,040 --> 00:22:39,280 Speaker 1: are talking about it, thinking about it. And you you 332 00:22:39,320 --> 00:22:45,679 Speaker 1: have to separate the words sustainability from really taking into 333 00:22:45,720 --> 00:22:50,240 Speaker 1: account how climate impacts you. So most big companies today 334 00:22:50,480 --> 00:22:54,720 Speaker 1: do do some type of sustainability, which is great for 335 00:22:54,840 --> 00:23:02,320 Speaker 1: saving resources, money, clean environment, but very few are actually 336 00:23:02,359 --> 00:23:05,040 Speaker 1: analytically using their data say O, hey, how how are 337 00:23:05,040 --> 00:23:11,399 Speaker 1: we really affected by by the weather and they're there before, 338 00:23:11,440 --> 00:23:14,480 Speaker 1: How can we act differently as a result of it. 339 00:23:14,720 --> 00:23:18,480 Speaker 1: So we talked earlier about Katrina and Sandy and some 340 00:23:18,600 --> 00:23:25,000 Speaker 1: of the other pretty substantial um disasters weather disasters. How 341 00:23:25,160 --> 00:23:28,760 Speaker 1: do these and remember everybody just has to flash back 342 00:23:28,800 --> 00:23:31,320 Speaker 1: to this for a few minutes. It was Walt's Well 343 00:23:31,359 --> 00:23:34,399 Speaker 1: television coverage you couldn't get away from it, at least 344 00:23:34,480 --> 00:23:37,080 Speaker 1: if you were in the path of of either of 345 00:23:37,119 --> 00:23:40,679 Speaker 1: those Katrina, even here in New York because the I 346 00:23:40,680 --> 00:23:44,359 Speaker 1: guess the political component. Uh, you couldn't get away from that. 347 00:23:44,440 --> 00:23:47,399 Speaker 1: For weeks, there was NonStop coverage of that. What is 348 00:23:47,440 --> 00:23:54,720 Speaker 1: the actual impact of these large weather events? Well remembered that. Uh, 349 00:23:54,800 --> 00:23:57,679 Speaker 1: you know, in the case of Sandy, it hit it 350 00:23:57,840 --> 00:24:01,120 Speaker 1: hit the New York areas so and hit the hub 351 00:24:01,160 --> 00:24:05,840 Speaker 1: of media. Uh. These these impacts are large on a 352 00:24:05,960 --> 00:24:10,840 Speaker 1: very local basis. So obviously New Orleans was devastated, uh 353 00:24:11,440 --> 00:24:14,520 Speaker 1: by that storm. Uh, New York City and the Jersey 354 00:24:14,560 --> 00:24:18,560 Speaker 1: Shore was devastated by sandy. But those are fairly small 355 00:24:19,280 --> 00:24:24,080 Speaker 1: geographical areas. When you look at a national company, uh 356 00:24:24,160 --> 00:24:27,880 Speaker 1: that is spanning a place like the US that has 357 00:24:28,240 --> 00:24:32,560 Speaker 1: on ninety different climate zones. So the maybe the bigger 358 00:24:32,640 --> 00:24:37,600 Speaker 1: question is when you have these big localized events, does 359 00:24:37,640 --> 00:24:39,399 Speaker 1: it have any sort of impact on the rest of 360 00:24:39,400 --> 00:24:42,960 Speaker 1: the country other than a five minute television spot. Does 361 00:24:43,000 --> 00:24:46,040 Speaker 1: everybody shrug it off and go on? I mean, I 362 00:24:46,119 --> 00:24:49,600 Speaker 1: know that when when Katrina hit, I didn't stop going 363 00:24:49,640 --> 00:24:52,280 Speaker 1: out to restaurants, So I'm assuming the rest of the 364 00:24:52,280 --> 00:24:55,800 Speaker 1: country is the same way. Well, it really depends when 365 00:24:55,880 --> 00:25:00,280 Speaker 1: it where your businesses are located. So if their spread 366 00:25:00,280 --> 00:25:03,000 Speaker 1: out nationally, it has a very large impact. If you 367 00:25:03,400 --> 00:25:07,200 Speaker 1: if you're concentrated in the Northeast as far as where 368 00:25:07,200 --> 00:25:10,200 Speaker 1: your revenue comes from when the sandy hit, yes, that 369 00:25:10,359 --> 00:25:13,240 Speaker 1: has a much larger impact. When you look at companies 370 00:25:13,320 --> 00:25:18,560 Speaker 1: in Canada, Uh, they are spread out on a national basis, 371 00:25:18,600 --> 00:25:21,200 Speaker 1: but really it's two cities that matter there the weather 372 00:25:21,400 --> 00:25:25,720 Speaker 1: in the Terronto area and the weather up in the 373 00:25:25,800 --> 00:25:30,359 Speaker 1: mont Montal area. Out I would think Vancouver is just 374 00:25:30,400 --> 00:25:32,880 Speaker 1: as big as either of those But that's much nicer weather, 375 00:25:32,960 --> 00:25:35,480 Speaker 1: isn't it. It's much nicer and it's a much smaller 376 00:25:35,600 --> 00:25:38,119 Speaker 1: number of people. Really, So when you look at a 377 00:25:38,240 --> 00:25:41,840 Speaker 1: national company, that's I think the third or fourth largest. 378 00:25:41,840 --> 00:25:44,439 Speaker 1: Calgary is another one. So it really comes down to 379 00:25:44,640 --> 00:25:48,320 Speaker 1: the weather in those two main markets drive most businesses 380 00:25:48,920 --> 00:25:53,720 Speaker 1: in UH Canada. So what can companies do to mitigate 381 00:25:54,359 --> 00:25:57,960 Speaker 1: weather related business risk? The first thing they have to 382 00:25:58,040 --> 00:26:01,720 Speaker 1: do is to measure it and really understand how is 383 00:26:01,800 --> 00:26:05,720 Speaker 1: whether driving us UH. Then they have to strip whether 384 00:26:05,800 --> 00:26:10,800 Speaker 1: out of their data to normalize it. That normalization reduces volatility, 385 00:26:10,840 --> 00:26:15,240 Speaker 1: increases forecast accuracy, so it's probably removing weather from last 386 00:26:15,320 --> 00:26:19,320 Speaker 1: year's data which is messing up their planning. We can 387 00:26:19,359 --> 00:26:23,280 Speaker 1: increase forecast accuracy anywhere from two to eight on a 388 00:26:23,400 --> 00:26:27,600 Speaker 1: national enterprise basis, which is huge. And then from an 389 00:26:27,600 --> 00:26:30,199 Speaker 1: in season standpoint, looking at a week, they have to 390 00:26:30,320 --> 00:26:34,520 Speaker 1: insert weather into their data so that they can action 391 00:26:34,600 --> 00:26:37,680 Speaker 1: according to where and when consumer demon is going to 392 00:26:37,760 --> 00:26:40,720 Speaker 1: be next week driven by weather. So it seems like 393 00:26:40,760 --> 00:26:43,560 Speaker 1: we've been making fun of the weather man for for forever. 394 00:26:44,440 --> 00:26:49,720 Speaker 1: Is forecasting weather getting any better? And I know from 395 00:26:49,720 --> 00:26:54,080 Speaker 1: my personal experience using the app on the phone. UM, 396 00:26:54,440 --> 00:26:58,040 Speaker 1: perfect example, I have a concert. We're recording this. I 397 00:26:58,080 --> 00:27:01,119 Speaker 1: have an outdoor concert tonight. For the past four days, 398 00:27:01,320 --> 00:27:04,800 Speaker 1: it's supposed to be raining tonight. Only by the time 399 00:27:04,800 --> 00:27:08,720 Speaker 1: we got till Tuesday night, it poured Tuesday night, and 400 00:27:08,760 --> 00:27:10,479 Speaker 1: now the sun's out and it's supposed to be really 401 00:27:10,600 --> 00:27:17,080 Speaker 1: nice tonight. So how good is weather forecasting? How much 402 00:27:17,240 --> 00:27:19,800 Speaker 1: better has or worse has it gotten over the years? 403 00:27:19,840 --> 00:27:22,919 Speaker 1: And can we really look out much beyond a couple 404 00:27:22,920 --> 00:27:28,920 Speaker 1: of days and make an intelligent forecast. So weather forecasting 405 00:27:29,000 --> 00:27:34,520 Speaker 1: has improved dramatically over the last three to four decades. 406 00:27:34,880 --> 00:27:39,280 Speaker 1: Satellites have to help, satellite being able to crunch more data. 407 00:27:39,560 --> 00:27:43,520 Speaker 1: When I was a kid, Uh, we could forecast out 408 00:27:43,520 --> 00:27:46,240 Speaker 1: about a day or two. Today we can get a 409 00:27:46,280 --> 00:27:51,600 Speaker 1: good five day forecast. It's fairly accurate. You can get 410 00:27:51,640 --> 00:27:54,840 Speaker 1: seven to ten days out. That gives your pretty good 411 00:27:55,040 --> 00:28:00,000 Speaker 1: trending that didn't happen even uh twenty years ago. Uh. 412 00:28:00,200 --> 00:28:03,840 Speaker 1: I think the biggest thing to ask is, now that 413 00:28:03,880 --> 00:28:09,720 Speaker 1: we have access to so much computing power through the cloud, 414 00:28:10,359 --> 00:28:15,600 Speaker 1: will that advance weather forecasting out more time? And that's 415 00:28:15,760 --> 00:28:19,080 Speaker 1: really the work that's being done out there. It's certainly 416 00:28:19,080 --> 00:28:22,200 Speaker 1: helping to get more more local, so to get more 417 00:28:22,240 --> 00:28:26,680 Speaker 1: accurate local forecast in one town versus another. There's no 418 00:28:26,760 --> 00:28:29,760 Speaker 1: doubt that that is taking place. Whether it gets us 419 00:28:29,760 --> 00:28:32,440 Speaker 1: out a week or a month or more, I think 420 00:28:32,600 --> 00:28:35,120 Speaker 1: is yet to be seen. Do we overlook the fact 421 00:28:35,160 --> 00:28:39,680 Speaker 1: that weather forecasters give us the forecast in terms of probability, 422 00:28:39,800 --> 00:28:43,239 Speaker 1: and when the probability, hey, the chance of rain turns up? 423 00:28:43,680 --> 00:28:47,800 Speaker 1: Are we wrong for blaming them for that low probabilistic 424 00:28:47,800 --> 00:28:53,520 Speaker 1: outcome actually taking place? Well, I think that the profession 425 00:28:54,080 --> 00:28:59,280 Speaker 1: hurts themselves by giving probabilities, you know I want. I 426 00:28:59,480 --> 00:29:04,160 Speaker 1: once worked with a man named Irving Crick in in 427 00:29:04,440 --> 00:29:09,200 Speaker 1: my youth. Irving crick was Eisenhower's meteorologist chief information officer 428 00:29:09,440 --> 00:29:13,239 Speaker 1: during the Second World War, and he always taught me 429 00:29:13,600 --> 00:29:17,120 Speaker 1: never to give probabilities. He said, just tell them what's 430 00:29:17,160 --> 00:29:19,920 Speaker 1: going to happen and stick by it. You're really going 431 00:29:19,960 --> 00:29:22,760 Speaker 1: to be right or wrong, he said. Probabilities just give 432 00:29:22,800 --> 00:29:26,320 Speaker 1: you a cushion, but doesn't really help the user to 433 00:29:26,400 --> 00:29:28,840 Speaker 1: know what's going to happen. And so from then on 434 00:29:29,080 --> 00:29:32,520 Speaker 1: and and in fact, in up planelytics, we don't give 435 00:29:32,560 --> 00:29:36,600 Speaker 1: any probabilities. We just tell them what's going to happen, 436 00:29:36,720 --> 00:29:41,120 Speaker 1: tell them how it's going to impact their sales, and 437 00:29:41,240 --> 00:29:45,120 Speaker 1: we stick by it. What happens when you're looking out 438 00:29:45,240 --> 00:29:48,680 Speaker 1: and there's certain things that are fifty fifty that there 439 00:29:48,760 --> 00:29:55,080 Speaker 1: isn't a clear uh likely outcome, Well, in those cases 440 00:29:55,160 --> 00:29:57,479 Speaker 1: you have to be driven by the data and of 441 00:29:57,480 --> 00:30:00,480 Speaker 1: course the plane lytics. We don't make our own forecasts. 442 00:30:00,600 --> 00:30:05,400 Speaker 1: We buy the weather forecast data just like anyone else, uh, 443 00:30:05,440 --> 00:30:08,480 Speaker 1: and uh what we do is we use that data 444 00:30:08,520 --> 00:30:12,160 Speaker 1: to go into our models to forecast sales. Are they 445 00:30:12,200 --> 00:30:15,720 Speaker 1: going to be up or down? And generally are Our 446 00:30:15,760 --> 00:30:18,280 Speaker 1: sales models are much more accurate than let's say, just 447 00:30:18,560 --> 00:30:21,840 Speaker 1: the weather, because we are applying different types of analytics 448 00:30:21,840 --> 00:30:25,719 Speaker 1: to it to give a client exactly what they need 449 00:30:25,760 --> 00:30:27,880 Speaker 1: to know, which is how much is weather going to 450 00:30:27,960 --> 00:30:32,160 Speaker 1: drive us? So you're much more concerned with more extreme 451 00:30:32,240 --> 00:30:36,560 Speaker 1: events than that sort of partly cloudy sort of day, right, 452 00:30:36,640 --> 00:30:40,280 Speaker 1: I mean, the part partly cloudy rainy on a day 453 00:30:40,320 --> 00:30:43,280 Speaker 1: day basis is not so much driving sales. Is what's 454 00:30:43,320 --> 00:30:47,360 Speaker 1: happening in the warming or colding or cold versus last 455 00:30:47,480 --> 00:30:49,440 Speaker 1: year or normal. Those are the things that are really 456 00:30:49,520 --> 00:30:52,680 Speaker 1: driving people to buy or to not buy. We have 457 00:30:52,760 --> 00:30:55,600 Speaker 1: been speaking with Fred Fox. He is the chief executive 458 00:30:55,640 --> 00:31:00,800 Speaker 1: officer and founder of weather analytics company planalytic Us. We 459 00:31:00,920 --> 00:31:04,920 Speaker 1: love your comments, feedback and suggestions right to me at 460 00:31:05,360 --> 00:31:08,640 Speaker 1: m IB podcast at Bloomberg dot net. Be sure and 461 00:31:08,720 --> 00:31:11,760 Speaker 1: check out my daily column on Bloomberg View dot com. 462 00:31:11,840 --> 00:31:15,080 Speaker 1: You can follow me on Twitter at rid Holts. I'm 463 00:31:15,160 --> 00:31:18,200 Speaker 1: Barry rid Holts. You're listening to Masters in Business on 464 00:31:18,360 --> 00:31:36,480 Speaker 1: Bloomberg Radio. Welcome to the podcast, Fred, Thank you so 465 00:31:36,560 --> 00:31:41,080 Speaker 1: much for doing this. This is really fascinating stuff for me. Um, 466 00:31:41,200 --> 00:31:43,760 Speaker 1: Let's let's talk a little bit about about some things 467 00:31:43,760 --> 00:31:46,160 Speaker 1: we missed that I really wanted to get to. You 468 00:31:46,360 --> 00:31:52,280 Speaker 1: founded the company in n so you're you're twenty plus 469 00:31:52,440 --> 00:31:56,280 Speaker 1: years old. What was the original vision? What was the plan? 470 00:31:56,360 --> 00:31:59,800 Speaker 1: Because I can't imagine in the mid ninety nineties, with 471 00:32:00,120 --> 00:32:04,920 Speaker 1: technology booming and the dot coms blowing up, who stopped 472 00:32:04,960 --> 00:32:08,000 Speaker 1: and says I have an idea, let's do a weather 473 00:32:08,040 --> 00:32:13,160 Speaker 1: analytics company for data services for businesses. What what motivated 474 00:32:13,240 --> 00:32:18,280 Speaker 1: that and what was the original vision behind this? Well, 475 00:32:20,120 --> 00:32:24,800 Speaker 1: we were doing some work at a group called Charming 476 00:32:24,840 --> 00:32:29,160 Speaker 1: Shops outside of Philly, their big chain. Are they still 477 00:32:29,200 --> 00:32:35,160 Speaker 1: around they're not, but they were discount women's fashion, and uh, 478 00:32:35,320 --> 00:32:38,200 Speaker 1: the ahead of that company, David Wax, who was a 479 00:32:38,240 --> 00:32:43,680 Speaker 1: good family friend, said, look at our numbers here because 480 00:32:43,680 --> 00:32:47,240 Speaker 1: they're all up and down. And I'm wondering if it's weather. 481 00:32:47,960 --> 00:32:52,760 Speaker 1: And I asked everyone around the organization. No one knew, 482 00:32:53,720 --> 00:32:56,520 Speaker 1: and someone said, why don't you speak to such and 483 00:32:56,560 --> 00:32:58,920 Speaker 1: such who is a merchant who has been here for 484 00:32:58,960 --> 00:33:01,440 Speaker 1: thirty years. And I went to speak to this person 485 00:33:01,720 --> 00:33:05,560 Speaker 1: and and I was a young guy, and he answered me. 486 00:33:05,720 --> 00:33:07,440 Speaker 1: He didn't say it, but he said it like the 487 00:33:07,600 --> 00:33:13,320 Speaker 1: stupid it's the weather. And I got some weather data 488 00:33:13,440 --> 00:33:15,880 Speaker 1: and we looked at some of their numbers and was 489 00:33:15,960 --> 00:33:20,920 Speaker 1: just shocked at how how the weather matched their sales, 490 00:33:21,120 --> 00:33:24,800 Speaker 1: almost one to one. And it gave me an idea 491 00:33:24,960 --> 00:33:27,440 Speaker 1: that if this is such a big deal and no 492 00:33:27,480 --> 00:33:30,040 Speaker 1: one's looking at it, this is this is something that 493 00:33:30,080 --> 00:33:33,520 Speaker 1: these businesses need to know. And so the the original 494 00:33:33,600 --> 00:33:37,360 Speaker 1: mission that we started with really hasn't changed, which is 495 00:33:37,400 --> 00:33:41,959 Speaker 1: to help businesses measure and manage the impact of weather 496 00:33:42,240 --> 00:33:47,760 Speaker 1: to get to improve their their sales and profits. Did 497 00:33:47,800 --> 00:33:51,080 Speaker 1: you have any background in weather or meteorology or anything 498 00:33:51,120 --> 00:33:54,600 Speaker 1: like that. No, No, I went to an engineering school, 499 00:33:54,680 --> 00:33:59,640 Speaker 1: like graduated from a College of Architecture at at Georgia Tech. 500 00:34:00,160 --> 00:34:03,920 Speaker 1: I was analytically minded, but it wasn't what I had 501 00:34:03,920 --> 00:34:06,640 Speaker 1: set out to do in life. Did you get any 502 00:34:06,720 --> 00:34:10,720 Speaker 1: push back from friends or family business associates when you said, 503 00:34:11,360 --> 00:34:13,359 Speaker 1: here's my idea, I want to open up a weather 504 00:34:13,400 --> 00:34:18,720 Speaker 1: analytics company to help businesses deal with weather. Did anybody 505 00:34:18,719 --> 00:34:21,120 Speaker 1: look at you like, dude, what are you talking abouh? 506 00:34:21,600 --> 00:34:26,440 Speaker 1: Lots of people. In fact, a close family friend who 507 00:34:26,520 --> 00:34:32,080 Speaker 1: who who was very successful in industry, pulled me aside 508 00:34:32,080 --> 00:34:37,960 Speaker 1: and said, Fred, just remember that when you have your 509 00:34:38,000 --> 00:34:41,720 Speaker 1: first failure, make sure to get up off your feet 510 00:34:41,719 --> 00:34:45,920 Speaker 1: and try again. And I remember saying to him, David, 511 00:34:46,000 --> 00:34:48,399 Speaker 1: what do you mean you you I think when I fail? 512 00:34:48,440 --> 00:34:50,960 Speaker 1: He said, no, no, I'm I'm I'm not saying that. 513 00:34:51,080 --> 00:34:55,279 Speaker 1: I'm just saying that when you have your first failure. Uh. 514 00:34:55,320 --> 00:34:58,320 Speaker 1: So that was very loud and clear. Note that he 515 00:34:59,239 --> 00:35:02,040 Speaker 1: didn't think much of what I thought. He was just 516 00:35:02,160 --> 00:35:05,040 Speaker 1: doing to encourage you. Hey, listen, everybody slips and falls. 517 00:35:05,040 --> 00:35:08,000 Speaker 1: You have to get up and keep trying. That's interesting, 518 00:35:08,080 --> 00:35:10,840 Speaker 1: And so what was the original vision when when you 519 00:35:10,920 --> 00:35:14,960 Speaker 1: first started mapping this out. How did you see this development? 520 00:35:15,480 --> 00:35:19,919 Speaker 1: So our original vision hasn't changed, the means by which 521 00:35:19,960 --> 00:35:22,719 Speaker 1: we're doing it has changed radically, and and and so 522 00:35:22,760 --> 00:35:27,080 Speaker 1: our vision has been to help businesses uncover how weathers 523 00:35:27,640 --> 00:35:31,319 Speaker 1: affecting them and to measure it and then provide them 524 00:35:31,360 --> 00:35:35,719 Speaker 1: the information that they can action and do things differently 525 00:35:35,960 --> 00:35:38,080 Speaker 1: as a result of knowing how weather is going to 526 00:35:38,160 --> 00:35:43,640 Speaker 1: affect them next week, next month, next season. What's changed is, 527 00:35:43,719 --> 00:35:47,040 Speaker 1: of course, our means of doing it. When we started 528 00:35:47,080 --> 00:35:52,160 Speaker 1: out in nineties six with Planalytics, the most advanced technology 529 00:35:52,280 --> 00:35:58,000 Speaker 1: we had was UH Excel that was our analytical platform, 530 00:35:58,040 --> 00:36:03,120 Speaker 1: as well as UH email. So just a simple spreadsheet, 531 00:36:03,280 --> 00:36:06,560 Speaker 1: not any big database exactly. We would get the data, 532 00:36:06,680 --> 00:36:09,680 Speaker 1: we would match it up, we would provide them results 533 00:36:09,719 --> 00:36:12,160 Speaker 1: in power Point, which was sort of the newest, uh 534 00:36:12,800 --> 00:36:17,600 Speaker 1: latest thing around. Then UH we slowly evolve that we 535 00:36:17,719 --> 00:36:22,319 Speaker 1: raised a lot of money UH in and two oh 536 00:36:22,320 --> 00:36:25,160 Speaker 1: one to really build out a whole web based platform 537 00:36:25,360 --> 00:36:31,240 Speaker 1: and tool for presenting the data. UH We we moved 538 00:36:31,239 --> 00:36:34,560 Speaker 1: from a consulting shot more to a software is a 539 00:36:34,640 --> 00:36:38,560 Speaker 1: service model in around two oh five and six. And 540 00:36:38,600 --> 00:36:42,279 Speaker 1: software is a service, so your clients is it a 541 00:36:42,320 --> 00:36:46,080 Speaker 1: subscription model, so they're subscribing to their service and they 542 00:36:46,080 --> 00:36:49,920 Speaker 1: can run their data through your engine and deal with 543 00:36:49,960 --> 00:36:54,040 Speaker 1: the outright themselves. And then what has really changed our 544 00:36:54,080 --> 00:36:56,640 Speaker 1: world more than anything else has been me a cloud. 545 00:36:57,200 --> 00:37:03,000 Speaker 1: So the cloud has allowed us to measure all of 546 00:37:03,000 --> 00:37:06,400 Speaker 1: the company's data all at once, going as far back 547 00:37:06,440 --> 00:37:09,360 Speaker 1: as they have, and come up with an enterprise model 548 00:37:09,880 --> 00:37:14,719 Speaker 1: to how weather affects every bit of their enterprise. We 549 00:37:14,719 --> 00:37:17,439 Speaker 1: were never able to do that a few years ago. 550 00:37:17,480 --> 00:37:19,879 Speaker 1: I mean it would have shut down our whole operation, 551 00:37:19,960 --> 00:37:21,960 Speaker 1: and all of a sudden, the cloud allows you to 552 00:37:22,040 --> 00:37:24,280 Speaker 1: do that. So for the first time, we can really 553 00:37:25,040 --> 00:37:27,879 Speaker 1: fulfill our mission that we started with twenty years ago 554 00:37:28,040 --> 00:37:32,360 Speaker 1: to now tell companies exactly how they are weather affected 555 00:37:33,040 --> 00:37:37,360 Speaker 1: and to transform the way that they operate, plan allocate 556 00:37:37,640 --> 00:37:41,720 Speaker 1: everything due to climate. So let's talk about that transformation, 557 00:37:41,760 --> 00:37:44,600 Speaker 1: because there's I want to ask you about different sectors. 558 00:37:44,640 --> 00:37:48,520 Speaker 1: But before I get to that, I'm a retailer and 559 00:37:48,560 --> 00:37:51,680 Speaker 1: I live in a part of the I operate stores 560 00:37:51,719 --> 00:37:54,239 Speaker 1: in the part of the country that is subject to 561 00:37:54,320 --> 00:37:56,799 Speaker 1: hot and cold and wet and snow and what have you. 562 00:37:57,560 --> 00:38:01,560 Speaker 1: What what is it that I'm going to do? Two 563 00:38:02,080 --> 00:38:08,239 Speaker 1: plan around weather events. So there are several things, and 564 00:38:08,280 --> 00:38:14,200 Speaker 1: that's both preseason, in season and postseason preseason. What we 565 00:38:14,280 --> 00:38:17,839 Speaker 1: can do is to remove weather from last year's data. 566 00:38:18,000 --> 00:38:21,240 Speaker 1: Why is that important because the weather only reoccurs about 567 00:38:21,960 --> 00:38:24,960 Speaker 1: a time and they've given place and time. So by 568 00:38:25,040 --> 00:38:29,960 Speaker 1: leaving weather in their data, they're actually having chance that 569 00:38:30,120 --> 00:38:34,239 Speaker 1: last year won't happen. So by removing it and normalizing it, 570 00:38:34,840 --> 00:38:39,200 Speaker 1: they can plan from a weather neutral basis, which improves 571 00:38:39,239 --> 00:38:44,040 Speaker 1: their accuracy tremendously on specific product categories. That can be 572 00:38:44,080 --> 00:38:49,400 Speaker 1: as much as twenty forecast improvement just by removing weather vaults. So, 573 00:38:49,440 --> 00:38:52,760 Speaker 1: in other words, before the season starts, you can say, 574 00:38:52,840 --> 00:38:56,160 Speaker 1: this is what our sales were like minus the impact 575 00:38:56,200 --> 00:39:00,200 Speaker 1: of weather last year, and we'll just extrapolate s sent 576 00:39:00,320 --> 00:39:03,920 Speaker 1: gain into this year exactly. There's your baseline, right. And 577 00:39:03,960 --> 00:39:06,680 Speaker 1: then in season, as we start to get the data 578 00:39:06,760 --> 00:39:11,399 Speaker 1: in from from all the weather forecast models, we didn't 579 00:39:11,400 --> 00:39:14,520 Speaker 1: put that into our business model that we've built that 580 00:39:14,560 --> 00:39:18,279 Speaker 1: tells them exactly how much and where weather we'll drive 581 00:39:18,320 --> 00:39:21,839 Speaker 1: them in terms of dollars and units next few days, 582 00:39:21,920 --> 00:39:26,319 Speaker 1: next next week, and so that affects how they replenish, 583 00:39:26,520 --> 00:39:31,160 Speaker 1: how they market how they order. And then postseason, which 584 00:39:31,239 --> 00:39:35,600 Speaker 1: is looking at from a reporting standpoint, how to weather 585 00:39:35,680 --> 00:39:38,160 Speaker 1: really affect us last week? Was it the weather? Was 586 00:39:38,200 --> 00:39:40,239 Speaker 1: it our marketing? Was it something else? So being able 587 00:39:40,280 --> 00:39:47,320 Speaker 1: to separate out weather pro provides enormous worth in helping 588 00:39:47,360 --> 00:39:51,479 Speaker 1: them to know how they are really performing, so whether 589 00:39:51,560 --> 00:39:55,360 Speaker 1: affects them in all three time zones. And the last 590 00:39:55,360 --> 00:39:58,759 Speaker 1: thing is because we can do interrupt because we can 591 00:39:58,800 --> 00:40:04,799 Speaker 1: now do UH enterprise modeling, we now are integrating those 592 00:40:04,840 --> 00:40:10,480 Speaker 1: analytics directly into their existing software and RP systems. So 593 00:40:10,520 --> 00:40:13,920 Speaker 1: we have relationships with a j d A or call 594 00:40:13,960 --> 00:40:17,000 Speaker 1: all of those firms and we can put that data 595 00:40:17,120 --> 00:40:22,480 Speaker 1: right into their existing systems. So it's now automatic what 596 00:40:22,560 --> 00:40:28,320 Speaker 1: we call algorithmic retailing that this can now take place. 597 00:40:28,840 --> 00:40:34,000 Speaker 1: So retail is pretty obvious. Or one would imagine what 598 00:40:34,080 --> 00:40:41,560 Speaker 1: other sectors are significantly affected by UM whether I would 599 00:40:41,600 --> 00:40:46,719 Speaker 1: have to imagine transportation, UM, insurance, what what else is 600 00:40:46,719 --> 00:40:53,960 Speaker 1: there an impact? Well, so anyone, uh even so anyone 601 00:40:54,040 --> 00:40:59,919 Speaker 1: making selling servicing consumer goods, So a manufacturers, retailer's rest 602 00:41:00,000 --> 00:41:06,800 Speaker 1: aren't small's UH, online or stores all very weather affected 603 00:41:06,840 --> 00:41:11,959 Speaker 1: when you go outside of the consumer sectors, UH, one 604 00:41:12,000 --> 00:41:16,000 Speaker 1: of the things that we are working on is a 605 00:41:16,080 --> 00:41:21,360 Speaker 1: new business your business called I graw Metrics, which is 606 00:41:21,920 --> 00:41:25,560 Speaker 1: instead of measuring the impact of weather on consumer demand, 607 00:41:25,640 --> 00:41:27,880 Speaker 1: it is looking at the impact of weather on the 608 00:41:27,960 --> 00:41:32,600 Speaker 1: crops globally and really how that affects all the different 609 00:41:32,640 --> 00:41:38,640 Speaker 1: businesses servicing the agricultural chains. And I have to come 610 00:41:38,680 --> 00:41:42,560 Speaker 1: back to you just mentioned online and offline. I have 611 00:41:42,800 --> 00:41:46,880 Speaker 1: to come back to the whole issue of e commerce 612 00:41:46,960 --> 00:41:51,440 Speaker 1: and weather. What does this mean for for retailers going forward? 613 00:41:52,040 --> 00:41:56,480 Speaker 1: If e commerce is not only weather resistant but good weather, 614 00:41:56,560 --> 00:42:00,840 Speaker 1: people don't go to the stores bad weather, they owner online. Uh. 615 00:42:01,000 --> 00:42:04,560 Speaker 1: Is the impact of weather just driving a further adoption 616 00:42:04,880 --> 00:42:12,040 Speaker 1: of of e commerce. Well, Uh, I would say that 617 00:42:12,200 --> 00:42:17,719 Speaker 1: weather is affecting online sales even more than it affects 618 00:42:18,160 --> 00:42:23,239 Speaker 1: store based sales, and so as consumers go more online. 619 00:42:23,560 --> 00:42:27,239 Speaker 1: For for us as a weather analytics company, that's really 620 00:42:27,280 --> 00:42:30,200 Speaker 1: good news because it means that more and more types 621 00:42:30,200 --> 00:42:34,600 Speaker 1: of shopping behavior has a higher level of weather impact 622 00:42:34,920 --> 00:42:39,400 Speaker 1: than the stores. So it's even more important for for 623 00:42:39,440 --> 00:42:43,640 Speaker 1: these companies to really understand how much weather is driving it. 624 00:42:44,800 --> 00:42:49,160 Speaker 1: I'll say it another way too, that when you look 625 00:42:49,200 --> 00:42:54,040 Speaker 1: at the residual meaning after they've measured everything else, what 626 00:42:54,239 --> 00:42:57,480 Speaker 1: is the part left they can measure? So what are 627 00:42:57,520 --> 00:43:00,640 Speaker 1: these groups know now? Well, they can measure rams, they 628 00:43:00,640 --> 00:43:06,360 Speaker 1: can measure hot products, uh, and they can measure what 629 00:43:06,480 --> 00:43:11,279 Speaker 1: types of consumers and how advertising works. But the residual 630 00:43:11,360 --> 00:43:15,520 Speaker 1: comes down to really two things. Price and weather. Those 631 00:43:15,560 --> 00:43:19,840 Speaker 1: are the two levels that they have. And yet every 632 00:43:20,120 --> 00:43:22,919 Speaker 1: company out there looks at price, but almost no one's 633 00:43:22,960 --> 00:43:26,440 Speaker 1: looking at weather. And yet weather can have a higher 634 00:43:26,640 --> 00:43:32,080 Speaker 1: impact on their sales than even priced us. So an 635 00:43:32,200 --> 00:43:36,040 Speaker 1: optimum let's let's imagine an optimum company that is fully 636 00:43:36,080 --> 00:43:43,200 Speaker 1: adapted to dealing with weather. What are they doing as 637 00:43:43,280 --> 00:43:48,640 Speaker 1: the weather changes in order to address that, not just 638 00:43:48,719 --> 00:43:52,640 Speaker 1: the reporting of it, but getting things to consumers and 639 00:43:52,680 --> 00:43:58,000 Speaker 1: capturing their attention in response to weather related events. Well, 640 00:43:58,040 --> 00:44:00,880 Speaker 1: how our clients use it and how others should be 641 00:44:01,000 --> 00:44:04,759 Speaker 1: using it is to use it again preseason to set 642 00:44:04,800 --> 00:44:08,040 Speaker 1: what the plan is to allocate to to pre allocate 643 00:44:08,080 --> 00:44:10,400 Speaker 1: to the stores based on where the demand is going 644 00:44:10,440 --> 00:44:13,120 Speaker 1: to be. So if you can allocate right the first time, 645 00:44:13,200 --> 00:44:18,600 Speaker 1: that saves a lot on inventory uh. And then once 646 00:44:18,640 --> 00:44:22,840 Speaker 1: you get into the season, being able to replenish the 647 00:44:22,920 --> 00:44:27,000 Speaker 1: stores ahead to where and when consumer DMan, especially weather 648 00:44:27,080 --> 00:44:30,520 Speaker 1: driven can consumer demand is give us an example of 649 00:44:30,560 --> 00:44:35,359 Speaker 1: weather driven consumer demand. So so right now, if you 650 00:44:35,400 --> 00:44:39,600 Speaker 1: are a retailer or even a manufacturer going to out 651 00:44:39,640 --> 00:44:43,799 Speaker 1: of d C, you are only looking backwards if you're 652 00:44:43,840 --> 00:44:47,200 Speaker 1: not using weather, meaning that you know that you're running 653 00:44:47,200 --> 00:44:49,520 Speaker 1: out of a certain good and so you want to 654 00:44:50,800 --> 00:44:53,760 Speaker 1: ship products to your d C s or stores based 655 00:44:53,840 --> 00:44:56,960 Speaker 1: to make sure that you have stuff on the shelf. 656 00:44:57,400 --> 00:44:59,760 Speaker 1: But if you use weather analytics, all of a sudden 657 00:44:59,760 --> 00:45:04,560 Speaker 1: you have a predictive tool to know not only where 658 00:45:04,600 --> 00:45:09,680 Speaker 1: your inventory is low, but where the consumer demand is 659 00:45:09,680 --> 00:45:11,760 Speaker 1: going to be for these products, so you can ship 660 00:45:11,800 --> 00:45:15,400 Speaker 1: ahead of it to know that, hey, we're going to 661 00:45:15,480 --> 00:45:18,360 Speaker 1: ship to the Northeast right away, because next week we 662 00:45:18,440 --> 00:45:21,319 Speaker 1: are showing whether driven demand is going to be up 663 00:45:22,520 --> 00:45:26,120 Speaker 1: over last year and we're out of products, versus the 664 00:45:26,120 --> 00:45:30,319 Speaker 1: Southeast where we're out of products too, but whether driven 665 00:45:30,320 --> 00:45:33,239 Speaker 1: demand is going to be down cent so we may 666 00:45:33,239 --> 00:45:36,800 Speaker 1: not be chipping there as fast we we need to 667 00:45:36,880 --> 00:45:41,680 Speaker 1: run to the stores. Another example is really marketing into 668 00:45:42,520 --> 00:45:47,600 Speaker 1: waves of weather driven demand. So we know from all 669 00:45:47,600 --> 00:45:50,439 Speaker 1: of our experience that when you markt into upcoming weather 670 00:45:50,520 --> 00:45:53,480 Speaker 1: driven demand, you do get greater lift you you get 671 00:45:53,480 --> 00:45:57,360 Speaker 1: a greater response, so you can actually drive more people 672 00:45:57,440 --> 00:46:01,520 Speaker 1: to your site, to your stores. I'm making them aware 673 00:46:01,600 --> 00:46:05,040 Speaker 1: of the right product at at the right time. So 674 00:46:05,120 --> 00:46:08,320 Speaker 1: I want to get to our favorite standard questions. Before 675 00:46:08,360 --> 00:46:13,080 Speaker 1: I do that, I have to ask you've mentioned several 676 00:46:13,120 --> 00:46:16,279 Speaker 1: things that I just never imagined about. Whether tell me 677 00:46:16,320 --> 00:46:18,640 Speaker 1: one one more to give me one more example of 678 00:46:18,680 --> 00:46:23,360 Speaker 1: things that most people, including businesses that should know better, 679 00:46:23,880 --> 00:46:28,480 Speaker 1: are just holy oblivious to about how weather impacts the economy, 680 00:46:28,640 --> 00:46:34,440 Speaker 1: different businesses, etcetera. Well, you know, uh, things that you 681 00:46:34,440 --> 00:46:37,640 Speaker 1: would think are not whether affected are weather. Fact. One 682 00:46:37,640 --> 00:46:41,279 Speaker 1: of the products we start analyze a lot a few 683 00:46:41,320 --> 00:46:45,080 Speaker 1: years ago is a pet food. Pet food tends to 684 00:46:45,160 --> 00:46:48,560 Speaker 1: be whether affected. See I would imagine pet food is 685 00:46:49,120 --> 00:46:51,240 Speaker 1: my pet is I have a couple of big dogs. 686 00:46:51,360 --> 00:46:54,920 Speaker 1: They eat a lot of food. I order that on Amazon, 687 00:46:55,120 --> 00:46:58,040 Speaker 1: and I do it pretty regularly. I know, all right, 688 00:46:58,120 --> 00:46:59,840 Speaker 1: I'm two thirds of the way through the bag or 689 00:47:00,000 --> 00:47:03,759 Speaker 1: another bag. Why would that be affected by weather? It's 690 00:47:03,920 --> 00:47:08,399 Speaker 1: because people are buying other things that are affects by weather. 691 00:47:08,520 --> 00:47:11,560 Speaker 1: So they're going to a site, They're going into the 692 00:47:11,840 --> 00:47:17,719 Speaker 1: stores to buy more seasonal foods. Seasonal items UH. And 693 00:47:17,800 --> 00:47:22,359 Speaker 1: what we're seeing is is that there's that there's an 694 00:47:22,360 --> 00:47:24,920 Speaker 1: impact on weather. It's not as big as let's say, 695 00:47:25,800 --> 00:47:30,319 Speaker 1: UH shorts or fleece or boots, but we're seeing a 696 00:47:30,400 --> 00:47:35,320 Speaker 1: one to two percent UH lift or drag due to weather, 697 00:47:35,400 --> 00:47:38,239 Speaker 1: which for pet foods it doesn't have a lot of drivers. 698 00:47:38,920 --> 00:47:41,799 Speaker 1: That's a big one. Give me, give me one more. 699 00:47:41,840 --> 00:47:44,920 Speaker 1: That's that's quite fascinating. Never would have guessed that, Well, 700 00:47:45,480 --> 00:47:52,080 Speaker 1: anything to do with restaurant traffic is very weather effected. 701 00:47:52,120 --> 00:47:56,120 Speaker 1: That the people don't really think through that. You know, 702 00:47:56,640 --> 00:48:02,719 Speaker 1: we just don't go to to buy food out if 703 00:48:02,760 --> 00:48:06,040 Speaker 1: it's raining, or if it's very hot, or or what 704 00:48:06,080 --> 00:48:10,040 Speaker 1: do you do in places like Seattle or London where 705 00:48:09,760 --> 00:48:13,560 Speaker 1: it seems to rain much of the time. How does 706 00:48:13,600 --> 00:48:17,360 Speaker 1: that impact the restaurants or is it more regional? Well, 707 00:48:18,120 --> 00:48:20,840 Speaker 1: here is the funny thing that when you live in 708 00:48:21,000 --> 00:48:25,840 Speaker 1: a climate that's always wet, people shop when it's wet 709 00:48:25,920 --> 00:48:30,640 Speaker 1: and shop less when it's sunny because they they want 710 00:48:30,680 --> 00:48:33,719 Speaker 1: to be outside. It's it's why the weather impacts on 711 00:48:33,880 --> 00:48:38,799 Speaker 1: Tampa and Miami, Florida are so different for the same 712 00:48:38,840 --> 00:48:41,840 Speaker 1: weather and you have to say why they're both in Florida. 713 00:48:42,080 --> 00:48:47,239 Speaker 1: They're they're both in South Florida. Well, No, Tampa and 714 00:48:47,480 --> 00:48:53,040 Speaker 1: Miami have very different demographics. Tampa is a much older 715 00:48:53,960 --> 00:48:56,720 Speaker 1: group of people in it than Miami, which is younger. 716 00:48:56,840 --> 00:49:00,719 Speaker 1: So when the weather gets nice in Tampa, shopping goes 717 00:49:00,840 --> 00:49:04,759 Speaker 1: up because older people are shopping when it's nice. When 718 00:49:04,760 --> 00:49:09,200 Speaker 1: the weather gets nice in Miami, it's it's it's much 719 00:49:09,239 --> 00:49:13,759 Speaker 1: younger people. They aren't shopping, they're outdoing outdoor activities. So 720 00:49:13,800 --> 00:49:16,719 Speaker 1: it really varies by where and when you live. So 721 00:49:16,840 --> 00:49:20,360 Speaker 1: you're you're you would say, a wetter area like Seattle. 722 00:49:21,600 --> 00:49:23,960 Speaker 1: People in the shop when it rains, They'll go to 723 00:49:24,000 --> 00:49:26,960 Speaker 1: restaurants when it rains. It shouldn't really make any exactly. 724 00:49:27,960 --> 00:49:30,439 Speaker 1: I have a friend who uh whose company was bought 725 00:49:30,480 --> 00:49:33,160 Speaker 1: by Yahoo in the nine nineties, and when I had 726 00:49:33,239 --> 00:49:37,160 Speaker 1: asked him about that um some years later, he said, 727 00:49:37,280 --> 00:49:40,360 Speaker 1: you know how you adapting to California as a lifelong 728 00:49:40,480 --> 00:49:43,920 Speaker 1: New Yorker And his answer was, it took us two 729 00:49:44,040 --> 00:49:46,960 Speaker 1: years to unpack. Why did it take you so long 730 00:49:47,000 --> 00:49:49,640 Speaker 1: to unpack? Well, we plan on unpacking each weekend, and 731 00:49:49,680 --> 00:49:52,880 Speaker 1: each weekend it's nice out, so we would run outside 732 00:49:52,920 --> 00:49:55,680 Speaker 1: and do outdoor activities. It took us about two years 733 00:49:55,680 --> 00:49:58,399 Speaker 1: to figure out, Oh, it's always nice out, so we're 734 00:49:58,400 --> 00:50:01,680 Speaker 1: not gonna miss anything by unpacking. That's the fear. You 735 00:50:01,760 --> 00:50:03,680 Speaker 1: unpacking on a Saturday, it rains on a Sunday, you 736 00:50:04,000 --> 00:50:06,960 Speaker 1: ruin your whole your whole weekend. So that that was 737 00:50:07,320 --> 00:50:12,080 Speaker 1: jeff story was literally took him two years to to unpack. Um, 738 00:50:12,160 --> 00:50:15,239 Speaker 1: let's get to some of my favorite questions that we 739 00:50:15,320 --> 00:50:19,360 Speaker 1: ask AOL our guests tell us, tell us the most 740 00:50:19,400 --> 00:50:25,399 Speaker 1: important thing that people don't know about your background. Well, 741 00:50:25,440 --> 00:50:28,120 Speaker 1: I think one of the things that people may or 742 00:50:28,120 --> 00:50:30,960 Speaker 1: may not know is that I am a a stutterer 743 00:50:31,360 --> 00:50:35,000 Speaker 1: and that uh and and when I was younger, I 744 00:50:35,040 --> 00:50:40,680 Speaker 1: pretty much couldn't talk still how old till my mid 745 00:50:40,800 --> 00:50:43,680 Speaker 1: late teens, and even through college, I had a lot 746 00:50:43,719 --> 00:50:49,160 Speaker 1: of difficulties. Uh So I had to overcome that in 747 00:50:49,239 --> 00:50:53,200 Speaker 1: really everything that I do. And I think that when 748 00:50:53,200 --> 00:50:58,319 Speaker 1: you have a disability, when you're younger and you are 749 00:50:58,400 --> 00:51:02,600 Speaker 1: over and that you are able to overcome that disability, 750 00:51:02,760 --> 00:51:07,160 Speaker 1: and it really set you up differently than others. It 751 00:51:07,200 --> 00:51:10,280 Speaker 1: gives you a certain fortitude, It gives you certain empathy 752 00:51:10,320 --> 00:51:15,640 Speaker 1: maybe towards others. So you're a CEO. You're obviously doing 753 00:51:15,680 --> 00:51:23,319 Speaker 1: sales with different companies. How does that presentation factor impact you? 754 00:51:23,360 --> 00:51:27,400 Speaker 1: I mean, you you have to be communicating constantly with people. 755 00:51:27,719 --> 00:51:29,560 Speaker 1: I have to speak all the time, I have to 756 00:51:29,600 --> 00:51:33,919 Speaker 1: speak in front of audiences, and uh, you know, I 757 00:51:34,000 --> 00:51:38,480 Speaker 1: just don't I just don't really let it get to me. 758 00:51:39,000 --> 00:51:43,839 Speaker 1: So if I think my speech is going off, then 759 00:51:43,880 --> 00:51:48,520 Speaker 1: I'll take a deep breath and I'll start again. And 760 00:51:49,960 --> 00:51:53,440 Speaker 1: you just get You just get used to plowing through 761 00:51:53,800 --> 00:51:57,880 Speaker 1: and doing the best you can. And I do like sales, 762 00:51:57,880 --> 00:51:59,680 Speaker 1: so it's not that I just have to do sales, 763 00:51:59,719 --> 00:52:03,480 Speaker 1: but I love selling. So I'm a stutterer that loves selling. 764 00:52:04,960 --> 00:52:08,160 Speaker 1: Well that that is quite quite fascinating to tell us 765 00:52:08,160 --> 00:52:11,759 Speaker 1: about some of your early mentors. You know, I've had 766 00:52:11,840 --> 00:52:14,840 Speaker 1: a lot of great people. I've been blessed to know 767 00:52:14,960 --> 00:52:18,240 Speaker 1: great people, starting with my mother and father, who are 768 00:52:18,760 --> 00:52:27,920 Speaker 1: really still great mentors. My my, my, my, my grandmother, uncle, 769 00:52:28,000 --> 00:52:32,960 Speaker 1: and aunt all taught me a lot of things about people, ethics, 770 00:52:33,280 --> 00:52:35,120 Speaker 1: had to deal with people. I mean, I grew up 771 00:52:35,120 --> 00:52:37,440 Speaker 1: in a household of five, so we were always fighting 772 00:52:37,440 --> 00:52:43,239 Speaker 1: for attention and food, uh at a table, and and 773 00:52:43,320 --> 00:52:46,080 Speaker 1: in work. I've I've worked with just a lot of 774 00:52:46,320 --> 00:52:51,960 Speaker 1: tremendous people. Irving Crisk, who my first company was was with. 775 00:52:52,080 --> 00:52:54,640 Speaker 1: I mean working with a World War two guy that 776 00:52:54,719 --> 00:52:57,279 Speaker 1: knew Eisenhower and worked with him when I was in 777 00:52:57,360 --> 00:53:01,560 Speaker 1: my twenties. Was was really neat. I've had a lot 778 00:53:01,600 --> 00:53:03,760 Speaker 1: of people that I've worked with. The who I've hired 779 00:53:03,800 --> 00:53:07,799 Speaker 1: have become my mentors, So I I like to think 780 00:53:07,840 --> 00:53:10,960 Speaker 1: that I learned from those who I work with as well. 781 00:53:12,160 --> 00:53:17,279 Speaker 1: Let's talk about um other and I'm normally if I'm 782 00:53:17,320 --> 00:53:20,920 Speaker 1: speaking to an investor, I asked about other investors. But 783 00:53:21,080 --> 00:53:24,879 Speaker 1: you're a little difficult to pigeonhole. So what sort of 784 00:53:26,040 --> 00:53:31,360 Speaker 1: data analytics people and or weather people influence your approach 785 00:53:31,440 --> 00:53:35,120 Speaker 1: to to your business? You know, one of my the 786 00:53:35,480 --> 00:53:40,239 Speaker 1: person who who who helped me to get Planets going 787 00:53:40,239 --> 00:53:44,560 Speaker 1: in nineties six and raise money is uh Howard Morgan, 788 00:53:44,760 --> 00:53:48,319 Speaker 1: who is the co founder of first Round Capital and 789 00:53:48,440 --> 00:53:52,080 Speaker 1: has been involved with the Internet really since it's just 790 00:53:52,160 --> 00:53:55,280 Speaker 1: since in the seventies, so he's sort of become a legend, 791 00:53:55,320 --> 00:53:57,440 Speaker 1: and I was very lucky to have him as a 792 00:53:57,480 --> 00:54:00,399 Speaker 1: person that helped me get the company going. Has been 793 00:54:00,400 --> 00:54:06,480 Speaker 1: on my board for many years up until a first 794 00:54:06,680 --> 00:54:12,120 Speaker 1: round capital and he's still still a guy go to 795 00:54:13,680 --> 00:54:16,440 Speaker 1: for advice and just watching people like that, how they 796 00:54:16,480 --> 00:54:20,480 Speaker 1: deal with investors, how they raise money, their expectations has 797 00:54:20,560 --> 00:54:26,200 Speaker 1: been a great guide to me about how to work 798 00:54:26,280 --> 00:54:31,840 Speaker 1: with in in investors. So this is everybody's favorite question. 799 00:54:32,040 --> 00:54:35,279 Speaker 1: Tell us about some of your favorite books. They could 800 00:54:35,320 --> 00:54:38,760 Speaker 1: be analytics or whether related or not, fiction, non fiction? 801 00:54:39,320 --> 00:54:41,640 Speaker 1: What are you reading? What do you like? You know, 802 00:54:42,800 --> 00:54:45,680 Speaker 1: the books has sort of evolved since my youth, so 803 00:54:45,760 --> 00:54:48,440 Speaker 1: if you you know, if you look at my youth, 804 00:54:48,640 --> 00:54:54,799 Speaker 1: I loved Iron Ran the fountain Head Atlas Shrug. I 805 00:54:54,920 --> 00:55:02,520 Speaker 1: love reading about history. The People Disdiscovery of Freedom by 806 00:55:02,680 --> 00:55:06,879 Speaker 1: or by rose A. Wild. It was a great book 807 00:55:06,960 --> 00:55:12,200 Speaker 1: I read early on. You know, recently, I just read 808 00:55:12,200 --> 00:55:18,120 Speaker 1: a great book by Peters and On on Accidental Superpower, 809 00:55:18,480 --> 00:55:21,360 Speaker 1: and it talks about the US and why why we 810 00:55:21,400 --> 00:55:24,399 Speaker 1: are the way we are. So history people is really 811 00:55:24,400 --> 00:55:27,200 Speaker 1: a love of mine to read. And I think that 812 00:55:27,239 --> 00:55:30,400 Speaker 1: goes into the to the climate area too, because climate 813 00:55:31,040 --> 00:55:36,120 Speaker 1: is part of what makes history warming, cooling, drought. I 814 00:55:36,160 --> 00:55:39,960 Speaker 1: mean that is what has driven mankind and civilization and 815 00:55:40,040 --> 00:55:46,600 Speaker 1: war is very much to do with how climate drives people. 816 00:55:48,040 --> 00:55:50,799 Speaker 1: There's a reason why there's there's a lot of shiites 817 00:55:51,200 --> 00:55:55,840 Speaker 1: in a South Iraq, it's because of the drought cycle 818 00:55:57,040 --> 00:56:00,640 Speaker 1: over thousands of years. When there's drought up in the 819 00:56:00,360 --> 00:56:04,880 Speaker 1: the northern plains of Persia Iran, you know, it drives 820 00:56:04,920 --> 00:56:09,000 Speaker 1: people down to the lower delta of the euphrates where 821 00:56:09,000 --> 00:56:13,000 Speaker 1: there's lots of order and food. So just little things 822 00:56:13,040 --> 00:56:17,440 Speaker 1: like that. Understanding climate allows you to answer part of 823 00:56:17,480 --> 00:56:23,760 Speaker 1: the why of civilization, people and the nations. That's quite interesting. 824 00:56:24,440 --> 00:56:28,399 Speaker 1: Um So since you launched planets over twenty years ago, 825 00:56:29,640 --> 00:56:34,439 Speaker 1: what's changed in the industry. What's the key element that 826 00:56:34,560 --> 00:56:38,640 Speaker 1: has altered how you approach this business. Well, there are 827 00:56:38,640 --> 00:56:44,000 Speaker 1: several key changes. Uh. When I started, whether data had 828 00:56:44,040 --> 00:56:49,520 Speaker 1: a lot of of worth. Today it has very little 829 00:56:50,840 --> 00:56:55,880 Speaker 1: UH value because it's become a comodity product, meaning that 830 00:56:56,239 --> 00:57:00,279 Speaker 1: anyone can get as much weather data as they want. 831 00:57:00,560 --> 00:57:06,600 Speaker 1: UH that very easily. In the In the States, weather 832 00:57:06,719 --> 00:57:09,640 Speaker 1: data is a free it's paid for by the taxpayers. 833 00:57:09,719 --> 00:57:16,040 Speaker 1: So our ability to access that has become very easy. UH. Analytics, 834 00:57:16,720 --> 00:57:20,240 Speaker 1: the strides and the ability to analyze huge amounts of 835 00:57:20,320 --> 00:57:24,760 Speaker 1: data have been revolutionary in just the last twenty years. 836 00:57:24,800 --> 00:57:28,480 Speaker 1: And and that's why for me. I'm as excited, if 837 00:57:28,520 --> 00:57:31,160 Speaker 1: not more excited than when we started playing analytics twenty 838 00:57:31,240 --> 00:57:37,760 Speaker 1: years ago, because the analytics really allows us to full 839 00:57:37,800 --> 00:57:41,120 Speaker 1: fulfill the mission that we started with in ways that 840 00:57:41,120 --> 00:57:44,200 Speaker 1: we couldn't even dream of even a several years ago. 841 00:57:44,800 --> 00:57:47,720 Speaker 1: So that really leads into the next question, what what 842 00:57:47,880 --> 00:57:52,120 Speaker 1: are the next major shifts? Is it all technology or 843 00:57:52,400 --> 00:57:58,760 Speaker 1: what what is going to change going forward? Well, for us, uh, 844 00:57:59,320 --> 00:58:06,480 Speaker 1: the the certainly the technology cloud computing provides us the 845 00:58:06,560 --> 00:58:10,360 Speaker 1: ability to what I call weather eyes the entire business world. 846 00:58:10,560 --> 00:58:14,920 Speaker 1: So we now have the world as our oyster of 847 00:58:15,000 --> 00:58:18,160 Speaker 1: which to play in, which we didn't a few years ago. 848 00:58:18,280 --> 00:58:24,120 Speaker 1: So our ability to grow at scale with a single 849 00:58:24,840 --> 00:58:31,000 Speaker 1: analytics engine and analyze any company anywhere is now upon us. 850 00:58:31,000 --> 00:58:34,800 Speaker 1: So we have an incredible opportunity to not only change 851 00:58:34,840 --> 00:58:38,440 Speaker 1: our company, but to change the entire world how it 852 00:58:38,560 --> 00:58:43,320 Speaker 1: uses and brings in weather and climate. So that's pretty 853 00:58:43,360 --> 00:58:46,760 Speaker 1: exciting because you don't get those opportunities to make that 854 00:58:46,800 --> 00:58:52,880 Speaker 1: big of an impact as we now can do. Uh, 855 00:58:52,920 --> 00:58:55,960 Speaker 1: There's there's certainly lots of other changes that take place 856 00:58:56,160 --> 00:58:58,560 Speaker 1: out outside of our little part of the world, but 857 00:58:58,720 --> 00:59:01,840 Speaker 1: for us, that's as big as it gets. Well to 858 00:59:01,960 --> 00:59:04,960 Speaker 1: whether it is all of business for sure, Um tell 859 00:59:05,040 --> 00:59:07,520 Speaker 1: us about a time you failed and what you learned 860 00:59:07,520 --> 00:59:13,440 Speaker 1: from the experience. Well, uh, you know, we all failed 861 00:59:13,440 --> 00:59:16,160 Speaker 1: many times, and it's uh, you know, the key is 862 00:59:16,200 --> 00:59:19,200 Speaker 1: to get up off the ground, shake it off, and 863 00:59:19,240 --> 00:59:25,080 Speaker 1: just keep walking ahead. I I remember, uh, after Night eleven, 864 00:59:25,520 --> 00:59:29,840 Speaker 1: we had raised almost a little less than a year before, 865 00:59:30,560 --> 00:59:33,360 Speaker 1: a lot of venture money, and we had hired a 866 00:59:33,360 --> 00:59:37,840 Speaker 1: lot of people, and we were gearing up to really 867 00:59:38,200 --> 00:59:44,240 Speaker 1: break open. And then and Not eleven happened and the 868 00:59:44,240 --> 00:59:50,000 Speaker 1: world stopped. And it's sort of it was pretty obvious 869 00:59:51,000 --> 00:59:54,240 Speaker 1: that the spend that we had was not going to 870 00:59:54,400 --> 00:59:59,040 Speaker 1: match our our our growth, and that at some point 871 00:59:59,080 --> 01:00:00,840 Speaker 1: down the road, about a year down the road, that 872 01:00:00,840 --> 01:00:02,640 Speaker 1: we would run the money. So I went back to 873 01:00:02,720 --> 01:00:07,160 Speaker 1: my board and I said, we need to really cut 874 01:00:07,160 --> 01:00:11,480 Speaker 1: our spend a lot, and we need to look at 875 01:00:11,520 --> 01:00:14,760 Speaker 1: a different world than when we started a year ago. 876 01:00:15,840 --> 01:00:18,040 Speaker 1: Part of my board so that's great, And part of 877 01:00:18,040 --> 01:00:22,080 Speaker 1: my board wanted to fire me because they said, we 878 01:00:22,120 --> 01:00:27,440 Speaker 1: didn't put money into Planalytics for you to cut spending. 879 01:00:27,480 --> 01:00:29,919 Speaker 1: We expect you to spend. And it was a real 880 01:00:30,960 --> 01:00:35,000 Speaker 1: fight and a real lesson, and I basically said, look, 881 01:00:35,200 --> 01:00:38,040 Speaker 1: if you want to fire me, then fire me. But 882 01:00:38,120 --> 01:00:41,720 Speaker 1: this is what I think and this is what I'm recommending, 883 01:00:41,960 --> 01:00:46,920 Speaker 1: and more of my board agreed, many agreed with me 884 01:00:46,960 --> 01:00:50,640 Speaker 1: than didn't, and we ended up taking the company down 885 01:00:50,760 --> 01:00:53,360 Speaker 1: to about half of our spend, so we put major 886 01:00:53,440 --> 01:00:57,320 Speaker 1: cuts and were able to really make it through the 887 01:00:57,760 --> 01:01:02,760 Speaker 1: downturn that lasted a few years. What do you do 888 01:01:02,840 --> 01:01:07,320 Speaker 1: to keep mentally and or physically fit outside of the office. 889 01:01:07,320 --> 01:01:11,400 Speaker 1: What do you do for relaxation or enjoyment? Well, I 890 01:01:11,640 --> 01:01:16,240 Speaker 1: have an exercise routine that can travel, so anything I 891 01:01:16,240 --> 01:01:18,800 Speaker 1: can do on the floor to exercise works for me. 892 01:01:19,000 --> 01:01:23,720 Speaker 1: So just push up, sit up, stretches, uh, anything like that, 893 01:01:23,920 --> 01:01:27,360 Speaker 1: walking stairs, I found it. If I have to go 894 01:01:27,440 --> 01:01:30,400 Speaker 1: to a gym that I don't go. So I have 895 01:01:30,560 --> 01:01:34,760 Speaker 1: a routine I do. As far as relaxation, I had 896 01:01:34,800 --> 01:01:39,720 Speaker 1: a spending time in my life my kids, especially my 897 01:01:40,000 --> 01:01:42,760 Speaker 1: five year old daughter, which you know, nothing is more 898 01:01:42,800 --> 01:01:47,880 Speaker 1: fun than a five year old girl. Um. What sort 899 01:01:47,920 --> 01:01:50,760 Speaker 1: of advice would you give to millennials or someone just 900 01:01:50,800 --> 01:01:54,120 Speaker 1: starting out their career who want to go into some 901 01:01:54,240 --> 01:02:01,280 Speaker 1: form of data analytics, consulting, or anything involving weather. Well, 902 01:02:03,320 --> 01:02:06,280 Speaker 1: I would thinks if if I could just go broader 903 01:02:06,960 --> 01:02:11,280 Speaker 1: a little bit. Uh, you know, the first advice that 904 01:02:11,360 --> 01:02:13,960 Speaker 1: I would give them is make sure you love what 905 01:02:14,000 --> 01:02:19,240 Speaker 1: you're doing. You know, life is short, and uh, whatever 906 01:02:19,280 --> 01:02:22,280 Speaker 1: you do, you better love it. And it doesn't matter 907 01:02:22,360 --> 01:02:24,479 Speaker 1: what you're paid. Yes, it helps to be paid a lot, 908 01:02:25,000 --> 01:02:29,080 Speaker 1: but the more important thing is to be happy. Uh. 909 01:02:29,160 --> 01:02:34,560 Speaker 1: You know. Another thing that I would say is is 910 01:02:34,640 --> 01:02:39,000 Speaker 1: to uh, you know, treat treat others as you want 911 01:02:39,040 --> 01:02:42,280 Speaker 1: to be treated. So as you know it says in 912 01:02:42,440 --> 01:02:48,920 Speaker 1: the good book Love Love Viticus, you know, love your 913 01:02:48,960 --> 01:02:53,360 Speaker 1: fellow as yourself. So you know, treat others well and 914 01:02:53,560 --> 01:02:58,160 Speaker 1: expect to be treated well, and to try to do 915 01:02:58,320 --> 01:03:02,480 Speaker 1: things that are going to help help the people. Uh 916 01:03:02,520 --> 01:03:08,080 Speaker 1: you know. I I also think that I also think 917 01:03:08,160 --> 01:03:12,920 Speaker 1: that when you're looking at uh what you want to 918 01:03:12,960 --> 01:03:17,960 Speaker 1: do and where you want to do it, the important 919 01:03:18,000 --> 01:03:23,560 Speaker 1: thing is to surround yourself with a happy, positive people. 920 01:03:24,720 --> 01:03:27,360 Speaker 1: So and it's not that we shouldn't try to help 921 01:03:27,400 --> 01:03:28,960 Speaker 1: those who are down in our luck, but there are 922 01:03:29,000 --> 01:03:34,200 Speaker 1: people that are just not positive. And I find that, uh, 923 01:03:34,280 --> 01:03:38,360 Speaker 1: I'm much better off if I hire positive people. If 924 01:03:38,400 --> 01:03:40,880 Speaker 1: you know, if I'm with positive people, because they're always 925 01:03:40,880 --> 01:03:44,840 Speaker 1: a lift. I like that answer, Um, and tell me 926 01:03:45,320 --> 01:03:48,320 Speaker 1: for our final question, what is it that you know 927 01:03:48,600 --> 01:03:53,240 Speaker 1: about whether analytics as a business today that you wish 928 01:03:53,280 --> 01:03:57,840 Speaker 1: you knew twenty years ago when you were starting out. Well, 929 01:03:58,720 --> 01:04:00,880 Speaker 1: I'm glad I didn't know how hard it would be 930 01:04:00,960 --> 01:04:06,320 Speaker 1: to make a market because market making is definitely hard. 931 01:04:06,760 --> 01:04:10,040 Speaker 1: It's also the most most exciting thing that one can 932 01:04:10,040 --> 01:04:14,880 Speaker 1: do to to educate people, change the way they think, 933 01:04:15,080 --> 01:04:19,120 Speaker 1: and to provide huge value. So it's not so much 934 01:04:19,200 --> 01:04:23,120 Speaker 1: what I wish I knew it. It's it's more about 935 01:04:23,160 --> 01:04:25,600 Speaker 1: what I've learned, Because if I knew how hard it 936 01:04:25,640 --> 01:04:29,560 Speaker 1: would be, you would you do what I have still 937 01:04:29,560 --> 01:04:35,320 Speaker 1: done it? I think I would, but I have to 938 01:04:35,360 --> 01:04:40,200 Speaker 1: measure that against the benefits I've gotten from really learning 939 01:04:40,880 --> 01:04:44,200 Speaker 1: and all the great people that I've met in doing it, 940 01:04:44,280 --> 01:04:49,840 Speaker 1: and more importantly, taking something as a morphous is weather 941 01:04:50,720 --> 01:04:54,520 Speaker 1: and suddenly putting it into a major company systems that 942 01:04:54,560 --> 01:04:57,520 Speaker 1: they're using it every day, planning with every day. That's 943 01:04:57,600 --> 01:05:02,000 Speaker 1: what really gets our juice is going quite quite fascinating. 944 01:05:02,600 --> 01:05:05,200 Speaker 1: We have been speaking to Fred Fox. He is the 945 01:05:05,200 --> 01:05:10,480 Speaker 1: founder and CEO of Planalytics. If you enjoy this conversation. 946 01:05:10,720 --> 01:05:13,120 Speaker 1: Be sure to look up or down a few inches 947 01:05:13,200 --> 01:05:16,280 Speaker 1: on Apple iTunes, where you can see any of the 948 01:05:16,360 --> 01:05:21,120 Speaker 1: prior hundred and fifty or so previous podcasts. I would 949 01:05:21,120 --> 01:05:23,360 Speaker 1: be remiss if I did not thank all the people 950 01:05:23,480 --> 01:05:27,840 Speaker 1: who helped put this podcast together. Medina Partwana is our 951 01:05:28,080 --> 01:05:31,880 Speaker 1: recording and audio engineer, who makes me sound far better 952 01:05:31,920 --> 01:05:34,840 Speaker 1: than I actually am in real life. Taylor Riggs is 953 01:05:34,840 --> 01:05:38,280 Speaker 1: our producer and booker. Michael bat Nick is the head 954 01:05:38,320 --> 01:05:42,920 Speaker 1: of research. We love your comments, feedback, end suggestions right 955 01:05:43,000 --> 01:05:46,439 Speaker 1: to us at m IB podcast at Bloomberg dot net. 956 01:05:47,040 --> 01:05:50,200 Speaker 1: I'm Barry Ritults. You've been listening to Masters in Business 957 01:05:50,600 --> 01:05:51,760 Speaker 1: on Bloomberg Radio