WEBVTT - Fred Fox: Don’t Just Blame it on the Weather 0:00:02.240 --> 0:00:06.800 This is Masters in Business with very Ridholts on Bloomberg Radio. 0:00:09.480 --> 0:00:12.280 This week on the podcast, I have Fred Fox. He 0:00:12.520 --> 0:00:16.920 is the founder and CEO of Planalytics, which is really, 0:00:17.160 --> 0:00:19.800 I'm gonna say it's the most interesting firm you've probably 0:00:19.960 --> 0:00:25.720 never heard of. They specialize in integrating weather data into 0:00:25.760 --> 0:00:32.919 the planning of various companies from retail to insurance to transportation. Uh. 0:00:32.960 --> 0:00:37.680 What they try and do is find ways of removing 0:00:37.720 --> 0:00:41.559 the impact of weather from a company's data so they 0:00:41.640 --> 0:00:45.559 could create a baseline to look forward and do future planning. 0:00:46.080 --> 0:00:48.519 And when you turns out that when you do that, 0:00:49.040 --> 0:00:52.000 there are some really fascinating things you learn. You learn 0:00:52.720 --> 0:00:57.400 who is impacted by whether it's not necessarily who you 0:00:57.480 --> 0:01:00.080 might think. When when you look at the economy in 0:01:00.120 --> 0:01:03.880 the US is about se consumer spending, and you think 0:01:04.200 --> 0:01:08.200 how large retail is as a part of that, whether 0:01:08.240 --> 0:01:13.920 it really has an outsized impact on the overall economy. Now, granted, 0:01:14.319 --> 0:01:17.119 you may give up sales in February and recapture them 0:01:17.120 --> 0:01:21.000 in in March, but there are consequences to that, whether 0:01:21.040 --> 0:01:23.920 it's in the same quarter, whether you're getting the same price, 0:01:24.040 --> 0:01:27.039 the same margin, etcetera. It really has a very very 0:01:27.040 --> 0:01:32.640 significant discount and the impact of weather on online retailers 0:01:32.680 --> 0:01:36.440 like Amazon is absolutely stunning. You will be shocked when 0:01:36.480 --> 0:01:39.319 when you hear this. So, with no further ado, here 0:01:39.400 --> 0:01:47.080 is my interview with Fred Fox. My guest today is 0:01:47.160 --> 0:01:52.280 Fred Fox. He is the founder and CEO of of Planalytics. 0:01:53.120 --> 0:01:59.720 Fred has pioneered the development and commercialization of weather analytics 0:01:59.760 --> 0:02:04.600 and how this impacts the global supply chain. He advises 0:02:04.680 --> 0:02:09.480 many of the world's largest corporations UH Previously, he is 0:02:09.520 --> 0:02:12.760 the inventor of a patent which I think we could 0:02:12.760 --> 0:02:16.080 call whether nom mix. Is that a short description. He 0:02:16.160 --> 0:02:21.400 received a BS degree from the Georgia Institute of Technology. 0:02:21.720 --> 0:02:26.120 This is really a fascinating subject because, as as we 0:02:26.240 --> 0:02:28.960 see and have seen over the past couple of years, 0:02:29.360 --> 0:02:33.720 whether has an enormous impact on the economy, on the 0:02:34.600 --> 0:02:38.600 basically where people live, how they live, how companies can operate. 0:02:39.280 --> 0:02:43.519 What first got you interested in this topic? Well, it's 0:02:43.520 --> 0:02:46.160 great to be here. I left out Welcome to Bloomberg, 0:02:46.160 --> 0:02:49.079 he apologized, I'm like racing right into this because I'm 0:02:49.200 --> 0:02:53.520 jazzed about this subject. Well, look, you know you start 0:02:53.560 --> 0:02:59.800 with several key facts. One, whether he is the UH 0:03:00.520 --> 0:03:02.920 on the velope that we all live in and have 0:03:03.160 --> 0:03:07.000 lived in since mankind first walked upright on the planet, 0:03:07.760 --> 0:03:11.000 and even before I would say, even be four. So 0:03:11.320 --> 0:03:15.200 if you look at two days economy, sixty of the 0:03:15.240 --> 0:03:20.679 world's g MP gross national product it's from consumer spending, 0:03:21.720 --> 0:03:26.040 and sixty of consumer spending is highly affected by weather. 0:03:27.000 --> 0:03:29.520 So when you take a step back, we'll explain that 0:03:29.560 --> 0:03:32.600 what do you mean six of consumer spending is affected 0:03:32.600 --> 0:03:37.280 by weather, meaning the ability of we hear it every season. 0:03:37.840 --> 0:03:41.320 Retailers quarterly numbers, they come out and said, well, winter 0:03:41.440 --> 0:03:43.720 started late, it's a cold winter, it's a warm winter. 0:03:43.880 --> 0:03:46.640 It's do you mean that sort of effect or actually 0:03:46.640 --> 0:03:51.839 moving goods around the world now, actually just just affects, 0:03:51.880 --> 0:03:58.480 affecting consumer purchasing decisions in a meaningful way. Weather has 0:03:58.560 --> 0:04:02.480 that effect still today. And so what I mean is 0:04:02.520 --> 0:04:07.560 that weather drives need not once. So when we drives 0:04:07.640 --> 0:04:12.440 needs not once. Okay, So the summer people by swim gear, 0:04:12.520 --> 0:04:14.440 and the winter they buy heavy coats if they're in 0:04:14.440 --> 0:04:18.320 that part exactly. So everything that we wear as clothing, 0:04:18.960 --> 0:04:22.120 most of it has a weather impact, unless it's high fashion. 0:04:22.560 --> 0:04:25.760 Everything we put on our body, whether it be suntan lotion, 0:04:25.839 --> 0:04:29.799 anything to do with the eyes, ears, nos face, skin, 0:04:29.960 --> 0:04:34.560 all is weather affected. Any any anything we do outdoors, lawn, garden, 0:04:34.760 --> 0:04:40.239 outdoor hiking activities, all all of those things are highly 0:04:40.279 --> 0:04:44.039 affected by weather. And so when you look at the 0:04:44.080 --> 0:04:47.200 impact that weather has on when we buy, how much 0:04:47.240 --> 0:04:50.480 we buy it, what price we buy it at, weather 0:04:50.839 --> 0:04:54.440 is involved in all of those purchasing decisions. On an 0:04:54.520 --> 0:04:59.920 unconscious level. You have me thinking about the things I purchase, 0:05:00.839 --> 0:05:03.440 be it be its suntan lotion or the fact that 0:05:03.480 --> 0:05:05.880 I have a jeep because I live in part of 0:05:05.880 --> 0:05:09.120 the world where when it snows and you have a 0:05:09.120 --> 0:05:13.360 regular car, you sometimes ain't going anywhere without. And I'm 0:05:13.360 --> 0:05:15.600 not a truck guy, but let me tell you, I 0:05:15.640 --> 0:05:18.480 don't even think twice about snow anymore. All Right, it's 0:05:18.480 --> 0:05:20.800 gonna slow everything down, but at least I know I 0:05:20.800 --> 0:05:23.400 could get to where I have to go. Those sorts 0:05:23.440 --> 0:05:27.560 of decisions absolutely. And so we know there are seasons. 0:05:27.920 --> 0:05:32.280 You know, there's fall, uh, spring, winter, summer, but the 0:05:32.320 --> 0:05:37.280 timing of when those seasons start, where they start affects 0:05:37.480 --> 0:05:42.039 enormously when and where and how much people buy. So, 0:05:42.160 --> 0:05:49.159 for instance, uh, this February in the northeast was fairly mild. 0:05:50.000 --> 0:05:52.440 We had an early start to the spring season, and 0:05:52.440 --> 0:05:57.200 then March came and March was downright cold, freeze, wet, nasty, 0:05:57.440 --> 0:06:03.040 and and so spending on normal spring summer stuff stopped 0:06:03.200 --> 0:06:06.839 for almost four to five weeks. We had the same 0:06:06.880 --> 0:06:10.800 thing in the summer. It was a surprisingly cool May 0:06:10.839 --> 0:06:14.920 and a ridiculously hot June, right, and so April came, 0:06:15.000 --> 0:06:19.280 April was warm and and and nice, and so consumer 0:06:19.360 --> 0:06:21.599 spending shot up. And then May came and it was 0:06:21.680 --> 0:06:25.119 cold and wet again, and so you know what took 0:06:25.120 --> 0:06:28.240 place in June. Even though June was a nice month 0:06:28.320 --> 0:06:33.400 to sell, everything was selling at off price because by 0:06:33.440 --> 0:06:36.000 that point everyone needs to get rid of merchandise or 0:06:36.040 --> 0:06:39.480 too late in the season. So while retailers may have 0:06:39.560 --> 0:06:42.119 sold out of their inventory, they sold a much lower 0:06:42.160 --> 0:06:44.320 price level than they would have had a sold in March. 0:06:44.880 --> 0:06:48.800 And so weather has an enormous effect on all of 0:06:48.839 --> 0:06:54.359 these things. How do you define business weather intelligence? So 0:06:54.880 --> 0:06:59.320 what we mean by that is really putting weather into 0:06:59.360 --> 0:07:04.320 a con text that businesses can use and action to 0:07:04.440 --> 0:07:08.520 make different types of decisions. And that's what really weather 0:07:08.560 --> 0:07:12.240 analytics and the world of analytics allows us to do 0:07:12.360 --> 0:07:16.920 today on a mess scale an affordable basis is to 0:07:18.200 --> 0:07:21.240 take all this rich data that comes out of the 0:07:21.320 --> 0:07:28.200 retail and retail supplier industries, mounds of data, and match 0:07:28.280 --> 0:07:33.800 it with weather so that we no longer have to 0:07:33.880 --> 0:07:38.200 guess at what our weather impact is today. There's we 0:07:38.400 --> 0:07:41.600 can know exactly when, where, and how much weather is 0:07:41.680 --> 0:07:46.880 driving consumers to buy or not buy. Let's talk a 0:07:46.920 --> 0:07:51.160 little bit about some of the ways the weather impacts business. 0:07:51.320 --> 0:07:54.880 I keep hearing these crazy numbers. The eclipse that took 0:07:54.920 --> 0:07:58.960 place earlier this year was seven millions to a billion 0:07:59.000 --> 0:08:05.320 dollars in US business. Is that remotely true? It certainly 0:08:05.440 --> 0:08:07.600 could be true if you assume that all of us 0:08:08.080 --> 0:08:10.760 took off a few hours of work to watch it. 0:08:10.840 --> 0:08:15.280 I guess, as I guess it could be the impact 0:08:15.280 --> 0:08:19.440 of weather, though tends not to be the big storms 0:08:19.480 --> 0:08:22.400 that we know. So something like a sandy, yeah, I mean, 0:08:22.480 --> 0:08:25.000 I mean it's something like a sandy definitely had an impact, 0:08:26.000 --> 0:08:29.640 but it was very localized and and and it was 0:08:29.800 --> 0:08:33.800 very short term. In those localized areas, the large impacts 0:08:33.880 --> 0:08:38.120 come from temperature and precipitation, which are much more spread 0:08:38.160 --> 0:08:43.160 out over broader areas. So this past March and May 0:08:43.559 --> 0:08:45.160 when it was cold and wet, it was cold and 0:08:45.160 --> 0:08:48.400 wet over the eastern half of the US. That's a 0:08:48.400 --> 0:08:51.120 pretty big area for it to drive versus a big 0:08:51.120 --> 0:08:56.000 storm like uh, like a sandy. So so that's surprising 0:08:56.000 --> 0:08:57.880 to hear. I think of big storms, and I think 0:08:57.880 --> 0:09:00.960 of sandy. In the New York area, we were out, 0:09:01.160 --> 0:09:04.640 didn't have power for thirteen days. You see Katrina which 0:09:04.679 --> 0:09:09.360 reached havoc on a city like New Orleans. But all 0:09:09.360 --> 0:09:12.800 those things were fairly localized. You're saying, the day to 0:09:12.920 --> 0:09:17.000 day changes in whether the unexpected or at least drier 0:09:17.040 --> 0:09:20.040 than or hotter than, are those the big impacts? Yes, 0:09:20.200 --> 0:09:25.199 So March probably had about a three billion dollar negative 0:09:25.240 --> 0:09:29.360 impact on just the d i y industry, meaning people 0:09:29.400 --> 0:09:33.679 who who do their own home building, their own repairs, 0:09:33.720 --> 0:09:36.280 their own gardening. Is that that because of all the 0:09:36.400 --> 0:09:40.720 rain and cold, it really the the laid what people 0:09:40.720 --> 0:09:43.000 did Now they made a bunch of that back up 0:09:43.160 --> 0:09:47.920 in April, but not all of it. That's fascinating. What 0:09:47.920 --> 0:09:53.080 what are other ways that weather impacts business and the 0:09:53.160 --> 0:09:57.439 economy that that I think most people don't realize, well, 0:09:57.880 --> 0:10:02.000 whether in impacts t affic. So the choice to go 0:10:02.200 --> 0:10:06.080 to a restaurant, it's based on how nice it is 0:10:06.120 --> 0:10:09.800 outside as well as anything. So rain and lots of 0:10:09.880 --> 0:10:13.240 rain really dampens no pun intend but but it dampens 0:10:13.720 --> 0:10:19.240 restaurant traffic. And so the more lower price the restaurant, 0:10:19.360 --> 0:10:22.559 more in the QSR, the more the impact, meaning that 0:10:23.080 --> 0:10:27.160 the higher priced again need versus once. If we're going 0:10:27.160 --> 0:10:29.400 to a high price restaurant, we may go out during 0:10:29.480 --> 0:10:34.080 rain exactly where if a little rain should keep you 0:10:34.200 --> 0:10:38.920 up right, if it's a McDonald's or a sub subway, 0:10:39.040 --> 0:10:42.559 we probably won't go if it's raining. Uh. Little things 0:10:42.679 --> 0:10:46.679 like restaurants that have a drive through are less weather 0:10:46.880 --> 0:10:50.000 impacted than those that don't have a drive through, meaning 0:10:50.040 --> 0:10:51.640 people don't have to get out of the cars. They'll 0:10:51.720 --> 0:10:54.079 drive through it, they'll pick it up there. Exactly about 0:10:54.080 --> 0:10:58.120 the weather exactly what what about things like rain is easy? 0:10:58.160 --> 0:11:01.200 What about snow which makes it a will dice you 0:11:01.280 --> 0:11:04.640 to drive outside? Now the caveat to that as you 0:11:04.679 --> 0:11:07.000 go to Canton in the middle of January and they're 0:11:07.040 --> 0:11:11.360 obrevious to it. Exactly. So whether affects people differently based 0:11:11.400 --> 0:11:15.480 on where they live. So, for instance, a few flakes 0:11:15.480 --> 0:11:19.920 of snow will shut down Atlanta, Georgia a few. Remember 0:11:20.120 --> 0:11:23.880 Dallas last year had a brief ice storm. Literally the 0:11:24.040 --> 0:11:26.280 entire city was closed. Kills them where if you go 0:11:26.400 --> 0:11:30.880 up to let's say, uh, Vermont, they're driving in several 0:11:30.920 --> 0:11:34.840 feet of snow. It doesn't even bother them. So uh. People, 0:11:35.200 --> 0:11:39.640 let's say in Minneapolis, will buy thermal underwear only when 0:11:39.679 --> 0:11:44.360 it goes down below let's say degrees and when it 0:11:44.440 --> 0:11:48.800 starts to sell. But in Florida, they'll start buying it 0:11:48.880 --> 0:11:53.920 when it's about sixty Thermal underwear in Florida below, yes, 0:11:53.960 --> 0:11:56.840 because they are used to warmer temperatures, so that slight 0:11:56.960 --> 0:12:00.880 change is as big an impact as it is in 0:12:00.920 --> 0:12:05.440 Minneapolis when it goes below. So let me push back 0:12:05.480 --> 0:12:09.400 on this theory a little bit. Look, the weather goes 0:12:09.440 --> 0:12:13.920 The counter argument is cyclical. It's cold in the winter, 0:12:14.040 --> 0:12:17.240 it's warm in the summer. Aren't people more likely to 0:12:17.320 --> 0:12:22.120 buy things like overcoats or thermal underwear in northern climates 0:12:22.120 --> 0:12:25.400 as we head into winter. How much is that really 0:12:25.440 --> 0:12:30.080 impacted by short time swings in the weather. It's a 0:12:30.160 --> 0:12:34.800 lot more than you think. So most people don't buy 0:12:34.840 --> 0:12:40.920 ahead of a season Most people buy on impulse when 0:12:40.920 --> 0:12:45.040 they start feeling a certain way, So it's an internal 0:12:45.160 --> 0:12:47.640 clock that goes off as soon as we feel warm 0:12:47.720 --> 0:12:51.760 or feel cold. Wet or dry sort of puts off 0:12:51.880 --> 0:12:54.439 that clock. Oh we have to go and buy this 0:12:54.640 --> 0:13:02.520 or buy that. So when when when? Uh, when unfavorable 0:13:02.559 --> 0:13:05.880 weather happens early in the season, it puts off purchases. 0:13:05.960 --> 0:13:09.600 The more you put off purchases, the more people as 0:13:09.640 --> 0:13:13.240 you go through the season are likely to put it 0:13:13.280 --> 0:13:15.640 off for an entire season. So there's a point at 0:13:15.640 --> 0:13:17.280 which they say, you know what, I don't really need 0:13:17.280 --> 0:13:19.800 that overcoade. I'll get through the winter with what I've 0:13:19.840 --> 0:13:22.680 got and I'll wait till next year, or I'll wait 0:13:22.720 --> 0:13:26.680 till the they go on sale in the spring. So 0:13:27.559 --> 0:13:31.200 as far as the way the price game is played, uh, 0:13:31.240 --> 0:13:33.840 you know, the earlier in the season it sells, they 0:13:33.920 --> 0:13:35.880 higher the price you're going to get. The later in 0:13:35.880 --> 0:13:38.600 the season itsels, the lower the price. So it makes 0:13:38.600 --> 0:13:42.720 a big impact on profits and margins. That's quite fascinating. 0:13:42.840 --> 0:13:46.360 So we always hear whether as a go to excuse 0:13:46.400 --> 0:13:52.280 of CEOs during earnings calls, How legitimate is it? I 0:13:52.280 --> 0:13:55.120 mean when I hear a retailer say, hey, our sales 0:13:55.160 --> 0:13:59.480 are soft because it's snowed in the winter. Well, it's 0:13:59.480 --> 0:14:01.520 supposed to a snow in the winter. Why would your 0:14:01.520 --> 0:14:04.240 sales be off? How much of this is excuse making 0:14:04.280 --> 0:14:08.319 and how much of this is a legitimate concern of 0:14:08.559 --> 0:14:12.720 companies like retailers. So we always say that whether is 0:14:12.760 --> 0:14:20.160 a reason for retail performance are Our main message to 0:14:20.280 --> 0:14:24.800 the industry is that though that you can't manage to 0:14:24.920 --> 0:14:28.480 what you've never measured. And in a world of big 0:14:28.560 --> 0:14:32.920 data analytics where you can really analyze any sized data 0:14:32.960 --> 0:14:37.800 set for very little money, there's no excuse why why 0:14:37.920 --> 0:14:44.040 companies today can't know and tell the street and investors 0:14:44.360 --> 0:14:48.840 precisely how weathers driving them. How do you guys use 0:14:48.880 --> 0:14:53.680 big data to help companies create those sort of analytics. 0:14:53.720 --> 0:14:56.600 So it used to be up until a few years ago, 0:14:56.800 --> 0:14:59.880 that a chief executive could get away with just blaming 0:15:00.000 --> 0:15:04.880 weather and not be punished for it. And today blaming 0:15:04.880 --> 0:15:09.400 weather is just not enough. Our message is that you 0:15:09.440 --> 0:15:13.560 can now take all of your historical data, sales data, 0:15:14.000 --> 0:15:16.560 measure it against weather, and come up with a precise 0:15:16.640 --> 0:15:20.320 model that every day, week, month, quarter, any given time 0:15:20.400 --> 0:15:23.680 you can know exactly how weather is driving your business. 0:15:24.720 --> 0:15:28.840 We had one client I won't name a d I 0:15:29.000 --> 0:15:32.600 Y group that put out to the street in this 0:15:33.440 --> 0:15:36.240 that weather really hurt him in the spring, but they 0:15:36.240 --> 0:15:40.520 didn't explain exactly how it hurt him, and so everyone 0:15:40.560 --> 0:15:44.440 assumed it was an excuse and their stock dropped mightily 0:15:45.200 --> 0:15:50.680 as a result of it, and there's still recovering from it. Uh. 0:15:50.720 --> 0:15:53.360 Those companies that can put out not only the weather 0:15:53.440 --> 0:15:56.040 drove them, but explain it drove us down by five 0:15:56.680 --> 0:15:59.480 due to weather in the northeast that hit us here 0:15:59.680 --> 0:16:02.880 and at our sales are up here. That can that 0:16:02.920 --> 0:16:05.920 can really be transparent about it, are going to be 0:16:06.360 --> 0:16:12.080 rewarded by investors. Uh. Weather can hide as well good 0:16:12.240 --> 0:16:17.520 fighting natural results. So in in this company's UH example, 0:16:17.720 --> 0:16:20.440 when you stripped out the impact of weather, their sales 0:16:20.480 --> 0:16:24.760 are actually way up over last year, but they were 0:16:24.800 --> 0:16:28.000 against the headwind of climate and weather, so it was 0:16:28.040 --> 0:16:31.760 really it was really covering up how good that they 0:16:31.840 --> 0:16:35.160 were doing. So that sort of information sounds like it 0:16:35.200 --> 0:16:38.840 would be very helpful for hedge funds trader as anybody 0:16:38.840 --> 0:16:43.760 who's looking to capture a short term advantage in short 0:16:43.840 --> 0:16:48.280 term market movements. Raises the question who are your clients 0:16:48.320 --> 0:16:52.440 aside from some of these big retailers and others. Are 0:16:52.480 --> 0:16:58.240 you selling this research, these analytics to Wall Street? So uh, 0:16:58.400 --> 0:17:02.560 we sell obviously to our to all the large retailers 0:17:02.600 --> 0:17:07.159 manufacturers globally that they that's our main client base. We 0:17:07.240 --> 0:17:10.879 do a little bit in in agriculture, and we also 0:17:11.080 --> 0:17:15.600 sell some of the benchmark research to FI financial firms 0:17:15.640 --> 0:17:18.840 who are using it for you the other own research, 0:17:20.680 --> 0:17:24.639 any hedge funds, anybody who's really specifically looking for show 0:17:24.720 --> 0:17:27.960 me where the data is better but it's masked by 0:17:27.960 --> 0:17:31.200 by weather. Is that is that a potential client base? 0:17:31.680 --> 0:17:34.760 It is is a potential client base, It's not currently 0:17:34.800 --> 0:17:38.720 a big client base of ours. So we're talking about retail. 0:17:39.960 --> 0:17:43.240 You have Amazon as the eight pound gulla and gorilla 0:17:43.280 --> 0:17:47.080 in the room. And when I'm sitting in the comfort 0:17:47.080 --> 0:17:50.960 of my living room with the fireplace burning, the weather 0:17:51.000 --> 0:17:56.920 outside almost doesn't matter. How has Amazon impacted your business 0:17:57.160 --> 0:18:03.960 and is whether significant to them in their seals. I 0:18:03.960 --> 0:18:07.920 I love the US this point because here's one of 0:18:07.960 --> 0:18:13.440 the most fascinating things. Amazon and online sales are actually 0:18:13.560 --> 0:18:17.639 more impacted by weather than store based sales. I would 0:18:17.680 --> 0:18:21.119 never have guessed that and I wouldn't have either until 0:18:21.160 --> 0:18:24.560 we started to look at the data several years ago. 0:18:24.800 --> 0:18:29.960 And this is this is this is the reason. Again, 0:18:30.000 --> 0:18:33.520 weather drives need. And when it's a certain type of 0:18:33.560 --> 0:18:37.400 weather and you suddenly want to buy a certain type 0:18:37.400 --> 0:18:41.000 of product because of the weather, you still have to 0:18:41.080 --> 0:18:44.560 get into a car to go to a store to 0:18:44.720 --> 0:18:51.840 buy it. So there is a versus picking up your phone, 0:18:52.640 --> 0:18:56.360 punching in a few points to it, and ordering it 0:18:56.520 --> 0:18:59.879 in the palm of your hands. In other words, Uh, 0:19:00.280 --> 0:19:05.680 that need the weather drives can be full filled, quicker, easier, 0:19:06.440 --> 0:19:10.480 uh through a mobile sale than getting into a car, 0:19:10.560 --> 0:19:12.439 which gets you to think, do I really want to 0:19:12.440 --> 0:19:15.119 get into a car to go and buy this? And 0:19:15.160 --> 0:19:20.639 so mobile sales are actually more weather affected. Uh. To 0:19:20.760 --> 0:19:23.960 the upside though, it's yes, like so it's bad weather 0:19:24.000 --> 0:19:27.359 works to Amazon's advantage. I just saw a data point today. 0:19:27.359 --> 0:19:31.879 Amazon is now something like of all online sales, some 0:19:32.680 --> 0:19:36.400 insane number like that bad weather means people are less 0:19:36.400 --> 0:19:38.520 likely to get into the car and therefore might be 0:19:38.560 --> 0:19:41.040 more likely to go online and purchase it. Or just 0:19:41.240 --> 0:19:46.240 good weather. So let's say in February it's warm outside 0:19:46.640 --> 0:19:48.520 and it's dry, I no one wants to go to 0:19:48.560 --> 0:19:50.480 a store if you get a spade of good weather. 0:19:50.560 --> 0:19:55.359 And so I wanted to plant a orchard in my 0:19:55.440 --> 0:19:59.320 backyard this year, and my original goal was to go 0:19:59.440 --> 0:20:03.359 to a dscaper, but I was busy, and so in 0:20:03.440 --> 0:20:05.879 order to order it because it was warm in February, 0:20:05.920 --> 0:20:08.160 I went on my phone. I went to an online 0:20:08.680 --> 0:20:13.880 service and I ordered my orchard online, which then came 0:20:13.920 --> 0:20:18.120 in March when was cold and wet. That's a that's 0:20:18.160 --> 0:20:21.439 that's fascinating. Let's talk a little bit about global warming 0:20:21.480 --> 0:20:25.600 because clearly that's going to have a big impact on 0:20:25.920 --> 0:20:32.119 various businesses. How are companies thinking about climate change and 0:20:32.720 --> 0:20:36.800 what sort of impact does that have on their businesses? So, 0:20:37.280 --> 0:20:40.679 you know, climate change happens over very long cycles thirty 0:20:41.320 --> 0:20:44.639 fifty years. Most chief executives are in their job for 0:20:44.800 --> 0:20:48.800 five years, so it doesn't have a direct in the pact. 0:20:50.000 --> 0:20:53.240 I think the biggest thing with the climate change that 0:20:53.240 --> 0:20:58.359 that we always push is is is really knowing how 0:20:58.440 --> 0:21:04.080 climate is impacting your companies. Most companies today can't measure 0:21:04.359 --> 0:21:09.280 and don't know exactly what climate means to them. Really, 0:21:10.040 --> 0:21:12.959 that is that is shocking to hear one would imagine 0:21:13.680 --> 0:21:19.439 that especially for companies like transport or insurers, or or 0:21:19.480 --> 0:21:23.120 anybody who's build building real estate in southern Florida. These 0:21:23.119 --> 0:21:26.840 things would be significant, they know in general terms, but 0:21:27.000 --> 0:21:30.840 they've really not modeled. And again our message to market 0:21:31.240 --> 0:21:35.760 is that you can't uh manage to what you've never measured. 0:21:35.880 --> 0:21:39.200 So dealing with climate change, what we say is why 0:21:39.280 --> 0:21:41.760 we don't know really what that change is going to be. 0:21:42.800 --> 0:21:46.520 We should know how climate affects our companies as a 0:21:46.600 --> 0:21:49.480 first stage. And so what we've built it is an 0:21:49.480 --> 0:21:53.359 analytical cloud based engine that does just that, that takes 0:21:53.359 --> 0:21:57.040 an all the data, mixes it with weather and measures 0:21:57.040 --> 0:21:59.560 it and comes up with an ongoing model that they 0:21:59.600 --> 0:22:03.280 can know any given time exactly and precisely how weather 0:22:03.960 --> 0:22:12.199 is impacting their their their group from a financial standpoint, revenue, profits, etcetera. 0:22:12.760 --> 0:22:17.280 That's quite fascinating. So what percentage of companies are actively 0:22:17.400 --> 0:22:23.359 engaged in thinking about climate change, in building it into 0:22:23.440 --> 0:22:28.960 some of their long term planning. Very few are actually 0:22:28.960 --> 0:22:31.320 doing it. Some of them are our clients, some of 0:22:31.320 --> 0:22:35.000 them are not, but very few. A lot of people 0:22:35.040 --> 0:22:39.280 are talking about it, thinking about it. And you you 0:22:39.320 --> 0:22:45.679 have to separate the words sustainability from really taking into 0:22:45.720 --> 0:22:50.240 account how climate impacts you. So most big companies today 0:22:50.480 --> 0:22:54.720 do do some type of sustainability, which is great for 0:22:54.840 --> 0:23:02.320 saving resources, money, clean environment, but very few are actually 0:23:02.359 --> 0:23:05.040 analytically using their data say O, hey, how how are 0:23:05.040 --> 0:23:11.399 we really affected by by the weather and they're there before, 0:23:11.440 --> 0:23:14.480 How can we act differently as a result of it. 0:23:14.720 --> 0:23:18.480 So we talked earlier about Katrina and Sandy and some 0:23:18.600 --> 0:23:25.000 of the other pretty substantial um disasters weather disasters. How 0:23:25.160 --> 0:23:28.760 do these and remember everybody just has to flash back 0:23:28.800 --> 0:23:31.320 to this for a few minutes. It was Walt's Well 0:23:31.359 --> 0:23:34.399 television coverage you couldn't get away from it, at least 0:23:34.480 --> 0:23:37.080 if you were in the path of of either of 0:23:37.119 --> 0:23:40.679 those Katrina, even here in New York because the I 0:23:40.680 --> 0:23:44.359 guess the political component. Uh, you couldn't get away from that. 0:23:44.440 --> 0:23:47.399 For weeks, there was NonStop coverage of that. What is 0:23:47.440 --> 0:23:54.720 the actual impact of these large weather events? Well remembered that. Uh, 0:23:54.800 --> 0:23:57.679 you know, in the case of Sandy, it hit it 0:23:57.840 --> 0:24:01.120 hit the New York areas so and hit the hub 0:24:01.160 --> 0:24:05.840 of media. Uh. These these impacts are large on a 0:24:05.960 --> 0:24:10.840 very local basis. So obviously New Orleans was devastated, uh 0:24:11.440 --> 0:24:14.520 by that storm. Uh, New York City and the Jersey 0:24:14.560 --> 0:24:18.560 Shore was devastated by sandy. But those are fairly small 0:24:19.280 --> 0:24:24.080 geographical areas. When you look at a national company, uh 0:24:24.160 --> 0:24:27.880 that is spanning a place like the US that has 0:24:28.240 --> 0:24:32.560 on ninety different climate zones. So the maybe the bigger 0:24:32.640 --> 0:24:37.600 question is when you have these big localized events, does 0:24:37.640 --> 0:24:39.399 it have any sort of impact on the rest of 0:24:39.400 --> 0:24:42.960 the country other than a five minute television spot. Does 0:24:43.000 --> 0:24:46.040 everybody shrug it off and go on? I mean, I 0:24:46.119 --> 0:24:49.600 know that when when Katrina hit, I didn't stop going 0:24:49.640 --> 0:24:52.280 out to restaurants, So I'm assuming the rest of the 0:24:52.280 --> 0:24:55.800 country is the same way. Well, it really depends when 0:24:55.880 --> 0:25:00.280 it where your businesses are located. So if their spread 0:25:00.280 --> 0:25:03.000 out nationally, it has a very large impact. If you 0:25:03.400 --> 0:25:07.200 if you're concentrated in the Northeast as far as where 0:25:07.200 --> 0:25:10.200 your revenue comes from when the sandy hit, yes, that 0:25:10.359 --> 0:25:13.240 has a much larger impact. When you look at companies 0:25:13.320 --> 0:25:18.560 in Canada, Uh, they are spread out on a national basis, 0:25:18.600 --> 0:25:21.200 but really it's two cities that matter there the weather 0:25:21.400 --> 0:25:25.720 in the Terronto area and the weather up in the 0:25:25.800 --> 0:25:30.359 mont Montal area. Out I would think Vancouver is just 0:25:30.400 --> 0:25:32.880 as big as either of those But that's much nicer weather, 0:25:32.960 --> 0:25:35.480 isn't it. It's much nicer and it's a much smaller 0:25:35.600 --> 0:25:38.119 number of people. Really, So when you look at a 0:25:38.240 --> 0:25:41.840 national company, that's I think the third or fourth largest. 0:25:41.840 --> 0:25:44.439 Calgary is another one. So it really comes down to 0:25:44.640 --> 0:25:48.320 the weather in those two main markets drive most businesses 0:25:48.920 --> 0:25:53.720 in UH Canada. So what can companies do to mitigate 0:25:54.359 --> 0:25:57.960 weather related business risk? The first thing they have to 0:25:58.040 --> 0:26:01.720 do is to measure it and really understand how is 0:26:01.800 --> 0:26:05.720 whether driving us UH. Then they have to strip whether 0:26:05.800 --> 0:26:10.800 out of their data to normalize it. That normalization reduces volatility, 0:26:10.840 --> 0:26:15.240 increases forecast accuracy, so it's probably removing weather from last 0:26:15.320 --> 0:26:19.320 year's data which is messing up their planning. We can 0:26:19.359 --> 0:26:23.280 increase forecast accuracy anywhere from two to eight on a 0:26:23.400 --> 0:26:27.600 national enterprise basis, which is huge. And then from an 0:26:27.600 --> 0:26:30.199 in season standpoint, looking at a week, they have to 0:26:30.320 --> 0:26:34.520 insert weather into their data so that they can action 0:26:34.600 --> 0:26:37.680 according to where and when consumer demon is going to 0:26:37.760 --> 0:26:40.720 be next week driven by weather. So it seems like 0:26:40.760 --> 0:26:43.560 we've been making fun of the weather man for for forever. 0:26:44.440 --> 0:26:49.720 Is forecasting weather getting any better? And I know from 0:26:49.720 --> 0:26:54.080 my personal experience using the app on the phone. UM, 0:26:54.440 --> 0:26:58.040 perfect example, I have a concert. We're recording this. I 0:26:58.080 --> 0:27:01.119 have an outdoor concert tonight. For the past four days, 0:27:01.320 --> 0:27:04.800 it's supposed to be raining tonight. Only by the time 0:27:04.800 --> 0:27:08.720 we got till Tuesday night, it poured Tuesday night, and 0:27:08.760 --> 0:27:10.479 now the sun's out and it's supposed to be really 0:27:10.600 --> 0:27:17.080