Speaker 1: This is Masters in Business with very Ridholts on Bloomberg Radio.
Speaker 1: This week on the podcast, I have Fred Fox. He
Speaker 1: is the founder and CEO of Planalytics, which is really,
Speaker 1: I'm gonna say it's the most interesting firm you've probably
Speaker 1: never heard of. They specialize in integrating weather data into
Speaker 1: the planning of various companies from retail to insurance to transportation. Uh.
Speaker 1: What they try and do is find ways of removing
Speaker 1: the impact of weather from a company's data so they
Speaker 1: could create a baseline to look forward and do future planning.
Speaker 1: And when you turns out that when you do that,
Speaker 1: there are some really fascinating things you learn. You learn
Speaker 1: who is impacted by whether it's not necessarily who you
Speaker 1: might think. When when you look at the economy in
Speaker 1: the US is about se consumer spending, and you think
Speaker 1: how large retail is as a part of that, whether
Speaker 1: it really has an outsized impact on the overall economy. Now, granted,
Speaker 1: you may give up sales in February and recapture them
Speaker 1: in in March, but there are consequences to that, whether
Speaker 1: it's in the same quarter, whether you're getting the same price,
Speaker 1: the same margin, etcetera. It really has a very very
Speaker 1: significant discount and the impact of weather on online retailers
Speaker 1: like Amazon is absolutely stunning. You will be shocked when
Speaker 1: when you hear this. So, with no further ado, here
Speaker 1: is my interview with Fred Fox. My guest today is
Speaker 1: Fred Fox. He is the founder and CEO of of Planalytics.
Speaker 1: Fred has pioneered the development and commercialization of weather analytics
Speaker 1: and how this impacts the global supply chain. He advises
Speaker 1: many of the world's largest corporations UH Previously, he is
Speaker 1: the inventor of a patent which I think we could
Speaker 1: call whether nom mix. Is that a short description. He
Speaker 1: received a BS degree from the Georgia Institute of Technology.
Speaker 1: This is really a fascinating subject because, as as we
Speaker 1: see and have seen over the past couple of years,
Speaker 1: whether has an enormous impact on the economy, on the
Speaker 1: basically where people live, how they live, how companies can operate.
Speaker 1: What first got you interested in this topic? Well, it's
Speaker 1: great to be here. I left out Welcome to Bloomberg,
Speaker 1: he apologized, I'm like racing right into this because I'm
Speaker 1: jazzed about this subject. Well, look, you know you start
Speaker 1: with several key facts. One, whether he is the UH
Speaker 1: on the velope that we all live in and have
Speaker 1: lived in since mankind first walked upright on the planet,
Speaker 1: and even before I would say, even be four. So
Speaker 1: if you look at two days economy, sixty of the
Speaker 1: world's g MP gross national product it's from consumer spending,
Speaker 1: and sixty of consumer spending is highly affected by weather.
Speaker 1: So when you take a step back, we'll explain that
Speaker 1: what do you mean six of consumer spending is affected
Speaker 1: by weather, meaning the ability of we hear it every season.
Speaker 1: Retailers quarterly numbers, they come out and said, well, winter
Speaker 1: started late, it's a cold winter, it's a warm winter.
Speaker 1: It's do you mean that sort of effect or actually
Speaker 1: moving goods around the world now, actually just just affects,
Speaker 1: affecting consumer purchasing decisions in a meaningful way. Weather has
Speaker 1: that effect still today. And so what I mean is
Speaker 1: that weather drives need not once. So when we drives
Speaker 1: needs not once. Okay, So the summer people by swim gear,
Speaker 1: and the winter they buy heavy coats if they're in
Speaker 1: that part exactly. So everything that we wear as clothing,
Speaker 1: most of it has a weather impact, unless it's high fashion.
Speaker 1: Everything we put on our body, whether it be suntan lotion,
Speaker 1: anything to do with the eyes, ears, nos face, skin,
Speaker 1: all is weather affected. Any any anything we do outdoors, lawn, garden,
Speaker 1: outdoor hiking activities, all all of those things are highly
Speaker 1: affected by weather. And so when you look at the
Speaker 1: impact that weather has on when we buy, how much
Speaker 1: we buy it, what price we buy it at, weather
Speaker 1: is involved in all of those purchasing decisions. On an
Speaker 1: unconscious level. You have me thinking about the things I purchase,
Speaker 1: be it be its suntan lotion or the fact that
Speaker 1: I have a jeep because I live in part of
Speaker 1: the world where when it snows and you have a
Speaker 1: regular car, you sometimes ain't going anywhere without. And I'm
Speaker 1: not a truck guy, but let me tell you, I
Speaker 1: don't even think twice about snow anymore. All Right, it's
Speaker 1: gonna slow everything down, but at least I know I
Speaker 1: could get to where I have to go. Those sorts
Speaker 1: of decisions absolutely. And so we know there are seasons.
Speaker 1: You know, there's fall, uh, spring, winter, summer, but the
Speaker 1: timing of when those seasons start, where they start affects
Speaker 1: enormously when and where and how much people buy. So,
Speaker 1: for instance, uh, this February in the northeast was fairly mild.
Speaker 1: We had an early start to the spring season, and
Speaker 1: then March came and March was downright cold, freeze, wet, nasty,
Speaker 1: and and so spending on normal spring summer stuff stopped
Speaker 1: for almost four to five weeks. We had the same
Speaker 1: thing in the summer. It was a surprisingly cool May
Speaker 1: and a ridiculously hot June, right, and so April came,
Speaker 1: April was warm and and and nice, and so consumer
Speaker 1: spending shot up. And then May came and it was
Speaker 1: cold and wet again, and so you know what took
Speaker 1: place in June. Even though June was a nice month
Speaker 1: to sell, everything was selling at off price because by
Speaker 1: that point everyone needs to get rid of merchandise or
Speaker 1: too late in the season. So while retailers may have
Speaker 1: sold out of their inventory, they sold a much lower
Speaker 1: price level than they would have had a sold in March.
Speaker 1: And so weather has an enormous effect on all of
Speaker 1: these things. How do you define business weather intelligence? So
Speaker 1: what we mean by that is really putting weather into
Speaker 1: a con text that businesses can use and action to
Speaker 1: make different types of decisions. And that's what really weather
Speaker 1: analytics and the world of analytics allows us to do
Speaker 1: today on a mess scale an affordable basis is to
Speaker 1: take all this rich data that comes out of the
Speaker 1: retail and retail supplier industries, mounds of data, and match
Speaker 1: it with weather so that we no longer have to
Speaker 1: guess at what our weather impact is today. There's we
Speaker 1: can know exactly when, where, and how much weather is
Speaker 1: driving consumers to buy or not buy. Let's talk a
Speaker 1: little bit about some of the ways the weather impacts business.
Speaker 1: I keep hearing these crazy numbers. The eclipse that took
Speaker 1: place earlier this year was seven millions to a billion
Speaker 1: dollars in US business. Is that remotely true? It certainly
Speaker 1: could be true if you assume that all of us
Speaker 1: took off a few hours of work to watch it.
Speaker 1: I guess, as I guess it could be the impact
Speaker 1: of weather, though tends not to be the big storms
Speaker 1: that we know. So something like a sandy, yeah, I mean,
Speaker 1: I mean it's something like a sandy definitely had an impact,
Speaker 1: but it was very localized and and and it was
Speaker 1: very short term. In those localized areas, the large impacts
Speaker 1: come from temperature and precipitation, which are much more spread
Speaker 1: out over broader areas. So this past March and May
Speaker 1: when it was cold and wet, it was cold and
Speaker 1: wet over the eastern half of the US. That's a
Speaker 1: pretty big area for it to drive versus a big
Speaker 1: storm like uh, like a sandy. So so that's surprising
Speaker 1: to hear. I think of big storms, and I think
Speaker 1: of sandy. In the New York area, we were out,
Speaker 1: didn't have power for thirteen days. You see Katrina which
Speaker 1: reached havoc on a city like New Orleans. But all
Speaker 1: those things were fairly localized. You're saying, the day to
Speaker 1: day changes in whether the unexpected or at least drier
Speaker 1: than or hotter than, are those the big impacts? Yes,
Speaker 1: So March probably had about a three billion dollar negative
Speaker 1: impact on just the d i y industry, meaning people
Speaker 1: who who do their own home building, their own repairs,
Speaker 1: their own gardening. Is that that because of all the
Speaker 1: rain and cold, it really the the laid what people
Speaker 1: did Now they made a bunch of that back up
Speaker 1: in April, but not all of it. That's fascinating. What
Speaker 1: what are other ways that weather impacts business and the
Speaker 1: economy that that I think most people don't realize, well,
Speaker 1: whether in impacts t affic. So the choice to go
Speaker 1: to a restaurant, it's based on how nice it is
Speaker 1: outside as well as anything. So rain and lots of
Speaker 1: rain really dampens no pun intend but but it dampens
Speaker 1: restaurant traffic. And so the more lower price the restaurant,
Speaker 1: more in the QSR, the more the impact, meaning that
Speaker 1: the higher priced again need versus once. If we're going
Speaker 1: to a high price restaurant, we may go out during
Speaker 1: rain exactly where if a little rain should keep you
Speaker 1: up right, if it's a McDonald's or a sub subway,
Speaker 1: we probably won't go if it's raining. Uh. Little things
Speaker 1: like restaurants that have a drive through are less weather
Speaker 1: impacted than those that don't have a drive through, meaning
Speaker 1: people don't have to get out of the cars. They'll
Speaker 1: drive through it, they'll pick it up there. Exactly about
Speaker 1: the weather exactly what what about things like rain is easy?
Speaker 1: What about snow which makes it a will dice you
Speaker 1: to drive outside? Now the caveat to that as you
Speaker 1: go to Canton in the middle of January and they're
Speaker 1: obrevious to it. Exactly. So whether affects people differently based
Speaker 1: on where they live. So, for instance, a few flakes
Speaker 1: of snow will shut down Atlanta, Georgia a few. Remember
Speaker 1: Dallas last year had a brief ice storm. Literally the
Speaker 1: entire city was closed. Kills them where if you go
Speaker 1: up to let's say, uh, Vermont, they're driving in several
Speaker 1: feet of snow. It doesn't even bother them. So uh. People,
Speaker 1: let's say in Minneapolis, will buy thermal underwear only when
Speaker 1: it goes down below let's say degrees and when it
Speaker 1: starts to sell. But in Florida, they'll start buying it
Speaker 1: when it's about sixty Thermal underwear in Florida below, yes,
Speaker 1: because they are used to warmer temperatures, so that slight
Speaker 1: change is as big an impact as it is in
Speaker 1: Minneapolis when it goes below. So let me push back
Speaker 1: on this theory a little bit. Look, the weather goes
Speaker 1: The counter argument is cyclical. It's cold in the winter,
Speaker 1: it's warm in the summer. Aren't people more likely to
Speaker 1: buy things like overcoats or thermal underwear in northern climates
Speaker 1: as we head into winter. How much is that really
Speaker 1: impacted by short time swings in the weather. It's a
Speaker 1: lot more than you think. So most people don't buy
Speaker 1: ahead of a season Most people buy on impulse when
Speaker 1: they start feeling a certain way, So it's an internal
Speaker 1: clock that goes off as soon as we feel warm
Speaker 1: or feel cold. Wet or dry sort of puts off
Speaker 1: that clock. Oh we have to go and buy this
Speaker 1: or buy that. So when when when? Uh, when unfavorable
Speaker 1: weather happens early in the season, it puts off purchases.
Speaker 1: The more you put off purchases, the more people as
Speaker 1: you go through the season are likely to put it
Speaker 1: off for an entire season. So there's a point at
Speaker 1: which they say, you know what, I don't really need
Speaker 1: that overcoade. I'll get through the winter with what I've
Speaker 1: got and I'll wait till next year, or I'll wait
Speaker 1: till the they go on sale in the spring. So
Speaker 1: as far as the way the price game is played, uh,
Speaker 1: you know, the earlier in the season it sells, they
Speaker 1: higher the price you're going to get. The later in
Speaker 1: the season itsels, the lower the price. So it makes
Speaker 1: a big impact on profits and margins. That's quite fascinating.
Speaker 1: So we always hear whether as a go to excuse
Speaker 1: of CEOs during earnings calls, How legitimate is it? I
Speaker 1: mean when I hear a retailer say, hey, our sales
Speaker 1: are soft because it's snowed in the winter. Well, it's
Speaker 1: supposed to a snow in the winter. Why would your
Speaker 1: sales be off? How much of this is excuse making
Speaker 1: and how much of this is a legitimate concern of
Speaker 1: companies like retailers. So we always say that whether is
Speaker 1: a reason for retail performance are Our main message to
Speaker 1: the industry is that though that you can't manage to
Speaker 1: what you've never measured. And in a world of big
Speaker 1: data analytics where you can really analyze any sized data
Speaker 1: set for very little money, there's no excuse why why
Speaker 1: companies today can't know and tell the street and investors
Speaker 1: precisely how weathers driving them. How do you guys use
Speaker 1: big data to help companies create those sort of analytics.
Speaker 1: So it used to be up until a few years ago,
Speaker 1: that a chief executive could get away with just blaming
Speaker 1: weather and not be punished for it. And today blaming
Speaker 1: weather is just not enough. Our message is that you
Speaker 1: can now take all of your historical data, sales data,
Speaker 1: measure it against weather, and come up with a precise
Speaker 1: model that every day, week, month, quarter, any given time
Speaker 1: you can know exactly how weather is driving your business.
Speaker 1: We had one client I won't name a d I
Speaker 1: Y group that put out to the street in this
Speaker 1: that weather really hurt him in the spring, but they
Speaker 1: didn't explain exactly how it hurt him, and so everyone
Speaker 1: assumed it was an excuse and their stock dropped mightily
Speaker 1: as a result of it, and there's still recovering from it. Uh.
Speaker 1: Those companies that can put out not only the weather
Speaker 1: drove them, but explain it drove us down by five
Speaker 1: due to weather in the northeast that hit us here
Speaker 1: and at our sales are up here. That can that
Speaker 1: can really be transparent about it, are going to be
Speaker 1: rewarded by investors. Uh. Weather can hide as well good
Speaker 1: fighting natural results. So in in this company's UH example,
Speaker 1: when you stripped out the impact of weather, their sales
Speaker 1: are actually way up over last year, but they were
Speaker 1: against the headwind of climate and weather, so it was
Speaker 1: really it was really covering up how good that they
Speaker 1: were doing. So that sort of information sounds like it
Speaker 1: would be very helpful for hedge funds trader as anybody
Speaker 1: who's looking to capture a short term advantage in short
Speaker 1: term market movements. Raises the question who are your clients
Speaker 1: aside from some of these big retailers and others. Are
Speaker 1: you selling this research, these analytics to Wall Street? So uh,
Speaker 1: we sell obviously to our to all the large retailers
Speaker 1: manufacturers globally that they that's our main client base. We
Speaker 1: do a little bit in in agriculture, and we also
Speaker 1: sell some of the benchmark research to FI financial firms
Speaker 1: who are using it for you the other own research,
Speaker 1: any hedge funds, anybody who's really specifically looking for show
Speaker 1: me where the data is better but it's masked by
Speaker 1: by weather. Is that is that a potential client base?
Speaker 1: It is is a potential client base, It's not currently
Speaker 1: a big client base of ours. So we're talking about retail.
Speaker 1: You have Amazon as the eight pound gulla and gorilla
Speaker 1: in the room. And when I'm sitting in the comfort
Speaker 1: of my living room with the fireplace burning, the weather
Speaker 1: outside almost doesn't matter. How has Amazon impacted your business
Speaker 1: and is whether significant to them in their seals. I
Speaker 1: I love the US this point because here's one of
Speaker 1: the most fascinating things. Amazon and online sales are actually
Speaker 1: more impacted by weather than store based sales. I would
Speaker 1: never have guessed that and I wouldn't have either until
Speaker 1: we started to look at the data several years ago.
Speaker 1: And this is this is this is the reason. Again,
Speaker 1: weather drives need. And when it's a certain type of
Speaker 1: weather and you suddenly want to buy a certain type
Speaker 1: of product because of the weather, you still have to
Speaker 1: get into a car to go to a store to
Speaker 1: buy it. So there is a versus picking up your phone,
Speaker 1: punching in a few points to it, and ordering it
Speaker 1: in the palm of your hands. In other words, Uh,
Speaker 1: that need the weather drives can be full filled, quicker, easier,
Speaker 1: uh through a mobile sale than getting into a car,
Speaker 1: which gets you to think, do I really want to
Speaker 1: get into a car to go and buy this? And
Speaker 1: so mobile sales are actually more weather affected. Uh. To
Speaker 1: the upside though, it's yes, like so it's bad weather
Speaker 1: works to Amazon's advantage. I just saw a data point today.
Speaker 1: Amazon is now something like of all online sales, some
Speaker 1: insane number like that bad weather means people are less
Speaker 1: likely to get into the car and therefore might be
Speaker 1: more likely to go online and purchase it. Or just
Speaker 1: good weather. So let's say in February it's warm outside
Speaker 1: and it's dry, I no one wants to go to
Speaker 1: a store if you get a spade of good weather.
Speaker 1: And so I wanted to plant a orchard in my
Speaker 1: backyard this year, and my original goal was to go
Speaker 1: to a dscaper, but I was busy, and so in
Speaker 1: order to order it because it was warm in February,
Speaker 1: I went on my phone. I went to an online
Speaker 1: service and I ordered my orchard online, which then came
Speaker 1: in March when was cold and wet. That's a that's
Speaker 1: that's fascinating. Let's talk a little bit about global warming
Speaker 1: because clearly that's going to have a big impact on
Speaker 1: various businesses. How are companies thinking about climate change and
Speaker 1: what sort of impact does that have on their businesses? So,
Speaker 1: you know, climate change happens over very long cycles thirty
Speaker 1: fifty years. Most chief executives are in their job for
Speaker 1: five years, so it doesn't have a direct in the pact.
Speaker 1: I think the biggest thing with the climate change that
Speaker 1: that we always push is is is really knowing how
Speaker 1: climate is impacting your companies. Most companies today can't measure
Speaker 1: and don't know exactly what climate means to them. Really,
Speaker 1: that is that is shocking to hear one would imagine
Speaker 1: that especially for companies like transport or insurers, or or
Speaker 1: anybody who's build building real estate in southern Florida. These
Speaker 1: things would be significant, they know in general terms, but
Speaker 1: they've really not modeled. And again our message to market
Speaker 1: is that you can't uh manage to what you've never measured.
Speaker 1: So dealing with climate change, what we say is why
Speaker 1: we don't know really what that change is going to be.
Speaker 1: We should know how climate affects our companies as a
Speaker 1: first stage. And so what we've built it is an
Speaker 1: analytical cloud based engine that does just that, that takes
Speaker 1: an all the data, mixes it with weather and measures
Speaker 1: it and comes up with an ongoing model that they
Speaker 1: can know any given time exactly and precisely how weather
Speaker 1: is impacting their their their group from a financial standpoint, revenue, profits, etcetera.
Speaker 1: That's quite fascinating. So what percentage of companies are actively
Speaker 1: engaged in thinking about climate change, in building it into
Speaker 1: some of their long term planning. Very few are actually
Speaker 1: doing it. Some of them are our clients, some of
Speaker 1: them are not, but very few. A lot of people
Speaker 1: are talking about it, thinking about it. And you you
Speaker 1: have to separate the words sustainability from really taking into
Speaker 1: account how climate impacts you. So most big companies today
Speaker 1: do do some type of sustainability, which is great for
Speaker 1: saving resources, money, clean environment, but very few are actually
Speaker 1: analytically using their data say O, hey, how how are
Speaker 1: we really affected by by the weather and they're there before,
Speaker 1: How can we act differently as a result of it.
Speaker 1: So we talked earlier about Katrina and Sandy and some
Speaker 1: of the other pretty substantial um disasters weather disasters. How
Speaker 1: do these and remember everybody just has to flash back
Speaker 1: to this for a few minutes. It was Walt's Well
Speaker 1: television coverage you couldn't get away from it, at least
Speaker 1: if you were in the path of of either of
Speaker 1: those Katrina, even here in New York because the I
Speaker 1: guess the political component. Uh, you couldn't get away from that.
Speaker 1: For weeks, there was NonStop coverage of that. What is
Speaker 1: the actual impact of these large weather events? Well remembered that. Uh,
Speaker 1: you know, in the case of Sandy, it hit it
Speaker 1: hit the New York areas so and hit the hub
Speaker 1: of media. Uh. These these impacts are large on a
Speaker 1: very local basis. So obviously New Orleans was devastated, uh
Speaker 1: by that storm. Uh, New York City and the Jersey
Speaker 1: Shore was devastated by sandy. But those are fairly small
Speaker 1: geographical areas. When you look at a national company, uh
Speaker 1: that is spanning a place like the US that has
Speaker 1: on ninety different climate zones. So the maybe the bigger
Speaker 1: question is when you have these big localized events, does
Speaker 1: it have any sort of impact on the rest of
Speaker 1: the country other than a five minute television spot. Does
Speaker 1: everybody shrug it off and go on? I mean, I
Speaker 1: know that when when Katrina hit, I didn't stop going
Speaker 1: out to restaurants, So I'm assuming the rest of the
Speaker 1: country is the same way. Well, it really depends when
Speaker 1: it where your businesses are located. So if their spread
Speaker 1: out nationally, it has a very large impact. If you
Speaker 1: if you're concentrated in the Northeast as far as where
Speaker 1: your revenue comes from when the sandy hit, yes, that
Speaker 1: has a much larger impact. When you look at companies
Speaker 1: in Canada, Uh, they are spread out on a national basis,
Speaker 1: but really it's two cities that matter there the weather
Speaker 1: in the Terronto area and the weather up in the
Speaker 1: mont Montal area. Out I would think Vancouver is just
Speaker 1: as big as either of those But that's much nicer weather,
Speaker 1: isn't it. It's much nicer and it's a much smaller
Speaker 1: number of people. Really, So when you look at a
Speaker 1: national company, that's I think the third or fourth largest.
Speaker 1: Calgary is another one. So it really comes down to
Speaker 1: the weather in those two main markets drive most businesses
Speaker 1: in UH Canada. So what can companies do to mitigate
Speaker 1: weather related business risk? The first thing they have to
Speaker 1: do is to measure it and really understand how is
Speaker 1: whether driving us UH. Then they have to strip whether
Speaker 1: out of their data to normalize it. That normalization reduces volatility,
Speaker 1: increases forecast accuracy, so it's probably removing weather from last
Speaker 1: year's data which is messing up their planning. We can
Speaker 1: increase forecast accuracy anywhere from two to eight on a
Speaker 1: national enterprise basis, which is huge. And then from an
Speaker 1: in season standpoint, looking at a week, they have to
Speaker 1: insert weather into their data so that they can action
Speaker 1: according to where and when consumer demon is going to
Speaker 1: be next week driven by weather. So it seems like
Speaker 1: we've been making fun of the weather man for for forever.
Speaker 1: Is forecasting weather getting any better? And I know from
Speaker 1: my personal experience using the app on the phone. UM,
Speaker 1: perfect example, I have a concert. We're recording this. I
Speaker 1: have an outdoor concert tonight. For the past four days,
Speaker 1: it's supposed to be raining tonight. Only by the time
Speaker 1: we got till Tuesday night, it poured Tuesday night, and
Speaker 1: now the sun's out and it's supposed to be really
Speaker 1: nice tonight. So how good is weather forecasting? How much
Speaker 1: better has or worse has it gotten over the years?
Speaker 1: And can we really look out much beyond a couple
Speaker 1: of days and make an intelligent forecast. So weather forecasting
Speaker 1: has improved dramatically over the last three to four decades.
Speaker 1: Satellites have to help, satellite being able to crunch more data.
Speaker 1: When I was a kid, Uh, we could forecast out
Speaker 1: about a day or two. Today we can get a
Speaker 1: good five day forecast. It's fairly accurate. You can get
Speaker 1: seven to ten days out. That gives your pretty good
Speaker 1: trending that didn't happen even uh twenty years ago. Uh.
Speaker 1: I think the biggest thing to ask is, now that
Speaker 1: we have access to so much computing power through the cloud,
Speaker 1: will that advance weather forecasting out more time? And that's
Speaker 1: really the work that's being done out there. It's certainly
Speaker 1: helping to get more more local, so to get more
Speaker 1: accurate local forecast in one town versus another. There's no
Speaker 1: doubt that that is taking place. Whether it gets us
Speaker 1: out a week or a month or more, I think
Speaker 1: is yet to be seen. Do we overlook the fact
Speaker 1: that weather forecasters give us the forecast in terms of probability,
Speaker 1: and when the probability, hey, the chance of rain turns up?
Speaker 1: Are we wrong for blaming them for that low probabilistic
Speaker 1: outcome actually taking place? Well, I think that the profession
Speaker 1: hurts themselves by giving probabilities, you know I want. I
Speaker 1: once worked with a man named Irving Crick in in
Speaker 1: my youth. Irving crick was Eisenhower's meteorologist chief information officer
Speaker 1: during the Second World War, and he always taught me
Speaker 1: never to give probabilities. He said, just tell them what's
Speaker 1: going to happen and stick by it. You're really going
Speaker 1: to be right or wrong, he said. Probabilities just give
Speaker 1: you a cushion, but doesn't really help the user to
Speaker 1: know what's going to happen. And so from then on
Speaker 1: and and in fact, in up planelytics, we don't give
Speaker 1: any probabilities. We just tell them what's going to happen,
Speaker 1: tell them how it's going to impact their sales, and
Speaker 1: we stick by it. What happens when you're looking out
Speaker 1: and there's certain things that are fifty fifty that there
Speaker 1: isn't a clear uh likely outcome, Well, in those cases
Speaker 1: you have to be driven by the data and of
Speaker 1: course the plane lytics. We don't make our own forecasts.
Speaker 1: We buy the weather forecast data just like anyone else, uh,
Speaker 1: and uh what we do is we use that data
Speaker 1: to go into our models to forecast sales. Are they
Speaker 1: going to be up or down? And generally are Our
Speaker 1: sales models are much more accurate than let's say, just
Speaker 1: the weather, because we are applying different types of analytics
Speaker 1: to it to give a client exactly what they need
Speaker 1: to know, which is how much is weather going to
Speaker 1: drive us? So you're much more concerned with more extreme
Speaker 1: events than that sort of partly cloudy sort of day, right,
Speaker 1: I mean, the part partly cloudy rainy on a day
Speaker 1: day basis is not so much driving sales. Is what's
Speaker 1: happening in the warming or colding or cold versus last
Speaker 1: year or normal. Those are the things that are really
Speaker 1: driving people to buy or to not buy. We have
Speaker 1: been speaking with Fred Fox. He is the chief executive
Speaker 1: officer and founder of weather analytics company planalytic Us. We
Speaker 1: love your comments, feedback and suggestions right to me at
Speaker 1: m IB podcast at Bloomberg dot net. Be sure and
Speaker 1: check out my daily column on Bloomberg View dot com.
Speaker 1: You can follow me on Twitter at rid Holts. I'm
Speaker 1: Barry rid Holts. You're listening to Masters in Business on
Speaker 1: Bloomberg Radio. Welcome to the podcast, Fred, Thank you so
Speaker 1: much for doing this. This is really fascinating stuff for me. Um,
Speaker 1: Let's let's talk a little bit about about some things
Speaker 1: we missed that I really wanted to get to. You
Speaker 1: founded the company in n so you're you're twenty plus
Speaker 1: years old. What was the original vision? What was the plan?
Speaker 1: Because I can't imagine in the mid ninety nineties, with
Speaker 1: technology booming and the dot coms blowing up, who stopped
Speaker 1: and says I have an idea, let's do a weather
Speaker 1: analytics company for data services for businesses. What what motivated
Speaker 1: that and what was the original vision behind this? Well,
Speaker 1: we were doing some work at a group called Charming
Speaker 1: Shops outside of Philly, their big chain. Are they still
Speaker 1: around they're not, but they were discount women's fashion, and uh,
Speaker 1: the ahead of that company, David Wax, who was a
Speaker 1: good family friend, said, look at our numbers here because
Speaker 1: they're all up and down. And I'm wondering if it's weather.
Speaker 1: And I asked everyone around the organization. No one knew,
Speaker 1: and someone said, why don't you speak to such and
Speaker 1: such who is a merchant who has been here for
Speaker 1: thirty years. And I went to speak to this person
Speaker 1: and and I was a young guy, and he answered me.
Speaker 1: He didn't say it, but he said it like the
Speaker 1: stupid it's the weather. And I got some weather data
Speaker 1: and we looked at some of their numbers and was
Speaker 1: just shocked at how how the weather matched their sales,
Speaker 1: almost one to one. And it gave me an idea
Speaker 1: that if this is such a big deal and no
Speaker 1: one's looking at it, this is this is something that
Speaker 1: these businesses need to know. And so the the original
Speaker 1: mission that we started with really hasn't changed, which is
Speaker 1: to help businesses measure and manage the impact of weather
Speaker 1: to get to improve their their sales and profits. Did
Speaker 1: you have any background in weather or meteorology or anything
Speaker 1: like that. No, No, I went to an engineering school,
Speaker 1: like graduated from a College of Architecture at at Georgia Tech.
Speaker 1: I was analytically minded, but it wasn't what I had
Speaker 1: set out to do in life. Did you get any
Speaker 1: push back from friends or family business associates when you said,
Speaker 1: here's my idea, I want to open up a weather
Speaker 1: analytics company to help businesses deal with weather. Did anybody
Speaker 1: look at you like, dude, what are you talking abouh?
Speaker 1: Lots of people. In fact, a close family friend who
Speaker 1: who who was very successful in industry, pulled me aside
Speaker 1: and said, Fred, just remember that when you have your
Speaker 1: first failure, make sure to get up off your feet
Speaker 1: and try again. And I remember saying to him, David,
Speaker 1: what do you mean you you I think when I fail?
Speaker 1: He said, no, no, I'm I'm I'm not saying that.
Speaker 1: I'm just saying that when you have your first failure. Uh.
Speaker 1: So that was very loud and clear. Note that he
Speaker 1: didn't think much of what I thought. He was just
Speaker 1: doing to encourage you. Hey, listen, everybody slips and falls.
Speaker 1: You have to get up and keep trying. That's interesting,
Speaker 1: And so what was the original vision when when you
Speaker 1: first started mapping this out. How did you see this development?
Speaker 1: So our original vision hasn't changed, the means by which
Speaker 1: we're doing it has changed radically, and and and so
Speaker 1: our vision has been to help businesses uncover how weathers
Speaker 1: affecting them and to measure it and then provide them
Speaker 1: the information that they can action and do things differently
Speaker 1: as a result of knowing how weather is going to
Speaker 1: affect them next week, next month, next season. What's changed is,
Speaker 1: of course, our means of doing it. When we started
Speaker 1: out in nineties six with Planalytics, the most advanced technology
Speaker 1: we had was UH Excel that was our analytical platform,
Speaker 1: as well as UH email. So just a simple spreadsheet,
Speaker 1: not any big database exactly. We would get the data,
Speaker 1: we would match it up, we would provide them results
Speaker 1: in power Point, which was sort of the newest, uh
Speaker 1: latest thing around. Then UH we slowly evolve that we
Speaker 1: raised a lot of money UH in and two oh
Speaker 1: one to really build out a whole web based platform
Speaker 1: and tool for presenting the data. UH We we moved
Speaker 1: from a consulting shot more to a software is a
Speaker 1: service model in around two oh five and six. And
Speaker 1: software is a service, so your clients is it a
Speaker 1: subscription model, so they're subscribing to their service and they
Speaker 1: can run their data through your engine and deal with
Speaker 1: the outright themselves. And then what has really changed our
Speaker 1: world more than anything else has been me a cloud.
Speaker 1: So the cloud has allowed us to measure all of
Speaker 1: the company's data all at once, going as far back
Speaker 1: as they have, and come up with an enterprise model
Speaker 1: to how weather affects every bit of their enterprise. We
Speaker 1: were never able to do that a few years ago.
Speaker 1: I mean it would have shut down our whole operation,
Speaker 1: and all of a sudden, the cloud allows you to
Speaker 1: do that. So for the first time, we can really
Speaker 1: fulfill our mission that we started with twenty years ago
Speaker 1: to now tell companies exactly how they are weather affected
Speaker 1: and to transform the way that they operate, plan allocate
Speaker 1: everything due to climate. So let's talk about that transformation,
Speaker 1: because there's I want to ask you about different sectors.
Speaker 1: But before I get to that, I'm a retailer and
Speaker 1: I live in a part of the I operate stores
Speaker 1: in the part of the country that is subject to
Speaker 1: hot and cold and wet and snow and what have you.
Speaker 1: What what is it that I'm going to do? Two
Speaker 1: plan around weather events. So there are several things, and
Speaker 1: that's both preseason, in season and postseason preseason. What we
Speaker 1: can do is to remove weather from last year's data.
Speaker 1: Why is that important because the weather only reoccurs about
Speaker 1: a time and they've given place and time. So by
Speaker 1: leaving weather in their data, they're actually having chance that
Speaker 1: last year won't happen. So by removing it and normalizing it,
Speaker 1: they can plan from a weather neutral basis, which improves
Speaker 1: their accuracy tremendously on specific product categories. That can be
Speaker 1: as much as twenty forecast improvement just by removing weather vaults. So,
Speaker 1: in other words, before the season starts, you can say,
Speaker 1: this is what our sales were like minus the impact
Speaker 1: of weather last year, and we'll just extrapolate s sent
Speaker 1: gain into this year exactly. There's your baseline, right. And
Speaker 1: then in season, as we start to get the data
Speaker 1: in from from all the weather forecast models, we didn't
Speaker 1: put that into our business model that we've built that
Speaker 1: tells them exactly how much and where weather we'll drive
Speaker 1: them in terms of dollars and units next few days,
Speaker 1: next next week, and so that affects how they replenish,
Speaker 1: how they market how they order. And then postseason, which
Speaker 1: is looking at from a reporting standpoint, how to weather
Speaker 1: really affect us last week? Was it the weather? Was
Speaker 1: it our marketing? Was it something else? So being able
Speaker 1: to separate out weather pro provides enormous worth in helping
Speaker 1: them to know how they are really performing, so whether
Speaker 1: affects them in all three time zones. And the last
Speaker 1: thing is because we can do interrupt because we can
Speaker 1: now do UH enterprise modeling, we now are integrating those
Speaker 1: analytics directly into their existing software and RP systems. So
Speaker 1: we have relationships with a j d A or call
Speaker 1: all of those firms and we can put that data
Speaker 1: right into their existing systems. So it's now automatic what
Speaker 1: we call algorithmic retailing that this can now take place.
Speaker 1: So retail is pretty obvious. Or one would imagine what
Speaker 1: other sectors are significantly affected by UM whether I would
Speaker 1: have to imagine transportation, UM, insurance, what what else is
Speaker 1: there an impact? Well, so anyone, uh even so anyone
Speaker 1: making selling servicing consumer goods, So a manufacturers, retailer's rest
Speaker 1: aren't small's UH, online or stores all very weather affected
Speaker 1: when you go outside of the consumer sectors, UH, one
Speaker 1: of the things that we are working on is a
Speaker 1: new business your business called I graw Metrics, which is
Speaker 1: instead of measuring the impact of weather on consumer demand,
Speaker 1: it is looking at the impact of weather on the
Speaker 1: crops globally and really how that affects all the different
Speaker 1: businesses servicing the agricultural chains. And I have to come
Speaker 1: back to you just mentioned online and offline. I have
Speaker 1: to come back to the whole issue of e commerce
Speaker 1: and weather. What does this mean for for retailers going forward?
Speaker 1: If e commerce is not only weather resistant but good weather,
Speaker 1: people don't go to the stores bad weather, they owner online. Uh.
Speaker 1: Is the impact of weather just driving a further adoption
Speaker 1: of of e commerce. Well, Uh, I would say that
Speaker 1: weather is affecting online sales even more than it affects
Speaker 1: store based sales, and so as consumers go more online.
Speaker 1: For for us as a weather analytics company, that's really
Speaker 1: good news because it means that more and more types
Speaker 1: of shopping behavior has a higher level of weather impact
Speaker 1: than the stores. So it's even more important for for
Speaker 1: these companies to really understand how much weather is driving it.
Speaker 1: I'll say it another way too, that when you look
Speaker 1: at the residual meaning after they've measured everything else, what
Speaker 1: is the part left they can measure? So what are
Speaker 1: these groups know now? Well, they can measure rams, they
Speaker 1: can measure hot products, uh, and they can measure what
Speaker 1: types of consumers and how advertising works. But the residual
Speaker 1: comes down to really two things. Price and weather. Those
Speaker 1: are the two levels that they have. And yet every
Speaker 1: company out there looks at price, but almost no one's
Speaker 1: looking at weather. And yet weather can have a higher
Speaker 1: impact on their sales than even priced us. So an
Speaker 1: optimum let's let's imagine an optimum company that is fully
Speaker 1: adapted to dealing with weather. What are they doing as
Speaker 1: the weather changes in order to address that, not just
Speaker 1: the reporting of it, but getting things to consumers and
Speaker 1: capturing their attention in response to weather related events. Well,
Speaker 1: how our clients use it and how others should be
Speaker 1: using it is to use it again preseason to set
Speaker 1: what the plan is to allocate to to pre allocate
Speaker 1: to the stores based on where the demand is going
Speaker 1: to be. So if you can allocate right the first time,
Speaker 1: that saves a lot on inventory uh. And then once
Speaker 1: you get into the season, being able to replenish the
Speaker 1: stores ahead to where and when consumer DMan, especially weather
Speaker 1: driven can consumer demand is give us an example of
Speaker 1: weather driven consumer demand. So so right now, if you
Speaker 1: are a retailer or even a manufacturer going to out
Speaker 1: of d C, you are only looking backwards if you're
Speaker 1: not using weather, meaning that you know that you're running
Speaker 1: out of a certain good and so you want to
Speaker 1: ship products to your d C s or stores based
Speaker 1: to make sure that you have stuff on the shelf.
Speaker 1: But if you use weather analytics, all of a sudden
Speaker 1: you have a predictive tool to know not only where
Speaker 1: your inventory is low, but where the consumer demand is
Speaker 1: going to be for these products, so you can ship
Speaker 1: ahead of it to know that, hey, we're going to
Speaker 1: ship to the Northeast right away, because next week we
Speaker 1: are showing whether driven demand is going to be up
Speaker 1: over last year and we're out of products, versus the
Speaker 1: Southeast where we're out of products too, but whether driven
Speaker 1: demand is going to be down cent so we may
Speaker 1: not be chipping there as fast we we need to
Speaker 1: run to the stores. Another example is really marketing into
Speaker 1: waves of weather driven demand. So we know from all
Speaker 1: of our experience that when you markt into upcoming weather
Speaker 1: driven demand, you do get greater lift you you get
Speaker 1: a greater response, so you can actually drive more people
Speaker 1: to your site, to your stores. I'm making them aware
Speaker 1: of the right product at at the right time. So
Speaker 1: I want to get to our favorite standard questions. Before
Speaker 1: I do that, I have to ask you've mentioned several
Speaker 1: things that I just never imagined about. Whether tell me
Speaker 1: one one more to give me one more example of
Speaker 1: things that most people, including businesses that should know better,
Speaker 1: are just holy oblivious to about how weather impacts the economy,
Speaker 1: different businesses, etcetera. Well, you know, uh, things that you
Speaker 1: would think are not whether affected are weather. Fact. One
Speaker 1: of the products we start analyze a lot a few
Speaker 1: years ago is a pet food. Pet food tends to
Speaker 1: be whether affected. See I would imagine pet food is
Speaker 1: my pet is I have a couple of big dogs.
Speaker 1: They eat a lot of food. I order that on Amazon,
Speaker 1: and I do it pretty regularly. I know, all right,
Speaker 1: I'm two thirds of the way through the bag or
Speaker 1: another bag. Why would that be affected by weather? It's
Speaker 1: because people are buying other things that are affects by weather.
Speaker 1: So they're going to a site, They're going into the
Speaker 1: stores to buy more seasonal foods. Seasonal items UH. And
Speaker 1: what we're seeing is is that there's that there's an
Speaker 1: impact on weather. It's not as big as let's say,
Speaker 1: UH shorts or fleece or boots, but we're seeing a
Speaker 1: one to two percent UH lift or drag due to weather,
Speaker 1: which for pet foods it doesn't have a lot of drivers.
Speaker 1: That's a big one. Give me, give me one more.
Speaker 1: That's that's quite fascinating. Never would have guessed that, Well,
Speaker 1: anything to do with restaurant traffic is very weather effected.
Speaker 1: That the people don't really think through that. You know,
Speaker 1: we just don't go to to buy food out if
Speaker 1: it's raining, or if it's very hot, or or what
Speaker 1: do you do in places like Seattle or London where
Speaker 1: it seems to rain much of the time. How does
Speaker 1: that impact the restaurants or is it more regional? Well,
Speaker 1: here is the funny thing that when you live in
Speaker 1: a climate that's always wet, people shop when it's wet
Speaker 1: and shop less when it's sunny because they they want
Speaker 1: to be outside. It's it's why the weather impacts on
Speaker 1: Tampa and Miami, Florida are so different for the same
Speaker 1: weather and you have to say why they're both in Florida.
Speaker 1: They're they're both in South Florida. Well, No, Tampa and
Speaker 1: Miami have very different demographics. Tampa is a much older
Speaker 1: group of people in it than Miami, which is younger.
Speaker 1: So when the weather gets nice in Tampa, shopping goes
Speaker 1: up because older people are shopping when it's nice. When
Speaker 1: the weather gets nice in Miami, it's it's it's much
Speaker 1: younger people. They aren't shopping, they're outdoing outdoor activities. So
Speaker 1: it really varies by where and when you live. So
Speaker 1: you're you're you would say, a wetter area like Seattle.
Speaker 1: People in the shop when it rains, They'll go to
Speaker 1: restaurants when it rains. It shouldn't really make any exactly.
Speaker 1: I have a friend who uh whose company was bought
Speaker 1: by Yahoo in the nine nineties, and when I had
Speaker 1: asked him about that um some years later, he said,
Speaker 1: you know how you adapting to California as a lifelong
Speaker 1: New Yorker And his answer was, it took us two
Speaker 1: years to unpack. Why did it take you so long
Speaker 1: to unpack? Well, we plan on unpacking each weekend, and
Speaker 1: each weekend it's nice out, so we would run outside
Speaker 1: and do outdoor activities. It took us about two years
Speaker 1: to figure out, Oh, it's always nice out, so we're
Speaker 1: not gonna miss anything by unpacking. That's the fear. You
Speaker 1: unpacking on a Saturday, it rains on a Sunday, you
Speaker 1: ruin your whole your whole weekend. So that that was
Speaker 1: jeff story was literally took him two years to to unpack. Um,
Speaker 1: let's get to some of my favorite questions that we
Speaker 1: ask AOL our guests tell us, tell us the most
Speaker 1: important thing that people don't know about your background. Well,
Speaker 1: I think one of the things that people may or
Speaker 1: may not know is that I am a a stutterer
Speaker 1: and that uh and and when I was younger, I
Speaker 1: pretty much couldn't talk still how old till my mid
Speaker 1: late teens, and even through college, I had a lot
Speaker 1: of difficulties. Uh So I had to overcome that in
Speaker 1: really everything that I do. And I think that when
Speaker 1: you have a disability, when you're younger and you are
Speaker 1: over and that you are able to overcome that disability,
Speaker 1: and it really set you up differently than others. It
Speaker 1: gives you a certain fortitude, It gives you certain empathy
Speaker 1: maybe towards others. So you're a CEO. You're obviously doing
Speaker 1: sales with different companies. How does that presentation factor impact you?
Speaker 1: I mean, you you have to be communicating constantly with people.
Speaker 1: I have to speak all the time, I have to
Speaker 1: speak in front of audiences, and uh, you know, I
Speaker 1: just don't I just don't really let it get to me.
Speaker 1: So if I think my speech is going off, then
Speaker 1: I'll take a deep breath and I'll start again. And
Speaker 1: you just get You just get used to plowing through
Speaker 1: and doing the best you can. And I do like sales,
Speaker 1: so it's not that I just have to do sales,
Speaker 1: but I love selling. So I'm a stutterer that loves selling.
Speaker 1: Well that that is quite quite fascinating to tell us
Speaker 1: about some of your early mentors. You know, I've had
Speaker 1: a lot of great people. I've been blessed to know
Speaker 1: great people, starting with my mother and father, who are
Speaker 1: really still great mentors. My my, my, my, my grandmother, uncle,
Speaker 1: and aunt all taught me a lot of things about people, ethics,
Speaker 1: had to deal with people. I mean, I grew up
Speaker 1: in a household of five, so we were always fighting
Speaker 1: for attention and food, uh at a table, and and
Speaker 1: in work. I've I've worked with just a lot of
Speaker 1: tremendous people. Irving Crisk, who my first company was was with.
Speaker 1: I mean working with a World War two guy that
Speaker 1: knew Eisenhower and worked with him when I was in
Speaker 1: my twenties. Was was really neat. I've had a lot
Speaker 1: of people that I've worked with. The who I've hired
Speaker 1: have become my mentors, So I I like to think
Speaker 1: that I learned from those who I work with as well.
Speaker 1: Let's talk about um other and I'm normally if I'm
Speaker 1: speaking to an investor, I asked about other investors. But
Speaker 1: you're a little difficult to pigeonhole. So what sort of
Speaker 1: data analytics people and or weather people influence your approach
Speaker 1: to to your business? You know, one of my the
Speaker 1: person who who who helped me to get Planets going
Speaker 1: in nineties six and raise money is uh Howard Morgan,
Speaker 1: who is the co founder of first Round Capital and
Speaker 1: has been involved with the Internet really since it's just
Speaker 1: since in the seventies, so he's sort of become a legend,
Speaker 1: and I was very lucky to have him as a
Speaker 1: person that helped me get the company going. Has been
Speaker 1: on my board for many years up until a first
Speaker 1: round capital and he's still still a guy go to
Speaker 1: for advice and just watching people like that, how they
Speaker 1: deal with investors, how they raise money, their expectations has
Speaker 1: been a great guide to me about how to work
Speaker 1: with in in investors. So this is everybody's favorite question.
Speaker 1: Tell us about some of your favorite books. They could
Speaker 1: be analytics or whether related or not, fiction, non fiction?
Speaker 1: What are you reading? What do you like? You know,
Speaker 1: the books has sort of evolved since my youth, so
Speaker 1: if you you know, if you look at my youth,
Speaker 1: I loved Iron Ran the fountain Head Atlas Shrug. I
Speaker 1: love reading about history. The People Disdiscovery of Freedom by
Speaker 1: or by rose A. Wild. It was a great book
Speaker 1: I read early on. You know, recently, I just read
Speaker 1: a great book by Peters and On on Accidental Superpower,
Speaker 1: and it talks about the US and why why we
Speaker 1: are the way we are. So history people is really
Speaker 1: a love of mine to read. And I think that
Speaker 1: goes into the to the climate area too, because climate
Speaker 1: is part of what makes history warming, cooling, drought. I
Speaker 1: mean that is what has driven mankind and civilization and
Speaker 1: war is very much to do with how climate drives people.
Speaker 1: There's a reason why there's there's a lot of shiites
Speaker 1: in a South Iraq, it's because of the drought cycle
Speaker 1: over thousands of years. When there's drought up in the
Speaker 1: the northern plains of Persia Iran, you know, it drives
Speaker 1: people down to the lower delta of the euphrates where
Speaker 1: there's lots of order and food. So just little things
Speaker 1: like that. Understanding climate allows you to answer part of
Speaker 1: the why of civilization, people and the nations. That's quite interesting.
Speaker 1: Um So since you launched planets over twenty years ago,
Speaker 1: what's changed in the industry. What's the key element that
Speaker 1: has altered how you approach this business. Well, there are
Speaker 1: several key changes. Uh. When I started, whether data had
Speaker 1: a lot of of worth. Today it has very little
Speaker 1: UH value because it's become a comodity product, meaning that
Speaker 1: anyone can get as much weather data as they want.
Speaker 1: UH that very easily. In the In the States, weather
Speaker 1: data is a free it's paid for by the taxpayers.
Speaker 1: So our ability to access that has become very easy. UH. Analytics,
Speaker 1: the strides and the ability to analyze huge amounts of
Speaker 1: data have been revolutionary in just the last twenty years.
Speaker 1: And and that's why for me. I'm as excited, if
Speaker 1: not more excited than when we started playing analytics twenty
Speaker 1: years ago, because the analytics really allows us to full
Speaker 1: fulfill the mission that we started with in ways that
Speaker 1: we couldn't even dream of even a several years ago.
Speaker 1: So that really leads into the next question, what what
Speaker 1: are the next major shifts? Is it all technology or
Speaker 1: what what is going to change going forward? Well, for us, uh,
Speaker 1: the the certainly the technology cloud computing provides us the
Speaker 1: ability to what I call weather eyes the entire business world.
Speaker 1: So we now have the world as our oyster of
Speaker 1: which to play in, which we didn't a few years ago.
Speaker 1: So our ability to grow at scale with a single
Speaker 1: analytics engine and analyze any company anywhere is now upon us.
Speaker 1: So we have an incredible opportunity to not only change
Speaker 1: our company, but to change the entire world how it
Speaker 1: uses and brings in weather and climate. So that's pretty
Speaker 1: exciting because you don't get those opportunities to make that
Speaker 1: big of an impact as we now can do. Uh,
Speaker 1: There's there's certainly lots of other changes that take place
Speaker 1: out outside of our little part of the world, but
Speaker 1: for us, that's as big as it gets. Well to
Speaker 1: whether it is all of business for sure, Um tell
Speaker 1: us about a time you failed and what you learned
Speaker 1: from the experience. Well, uh, you know, we all failed
Speaker 1: many times, and it's uh, you know, the key is
Speaker 1: to get up off the ground, shake it off, and
Speaker 1: just keep walking ahead. I I remember, uh, after Night eleven,
Speaker 1: we had raised almost a little less than a year before,
Speaker 1: a lot of venture money, and we had hired a
Speaker 1: lot of people, and we were gearing up to really
Speaker 1: break open. And then and Not eleven happened and the
Speaker 1: world stopped. And it's sort of it was pretty obvious
Speaker 1: that the spend that we had was not going to
Speaker 1: match our our our growth, and that at some point
Speaker 1: down the road, about a year down the road, that
Speaker 1: we would run the money. So I went back to
Speaker 1: my board and I said, we need to really cut
Speaker 1: our spend a lot, and we need to look at
Speaker 1: a different world than when we started a year ago.
Speaker 1: Part of my board so that's great, And part of
Speaker 1: my board wanted to fire me because they said, we
Speaker 1: didn't put money into Planalytics for you to cut spending.
Speaker 1: We expect you to spend. And it was a real
Speaker 1: fight and a real lesson, and I basically said, look,
Speaker 1: if you want to fire me, then fire me. But
Speaker 1: this is what I think and this is what I'm recommending,
Speaker 1: and more of my board agreed, many agreed with me
Speaker 1: than didn't, and we ended up taking the company down
Speaker 1: to about half of our spend, so we put major
Speaker 1: cuts and were able to really make it through the
Speaker 1: downturn that lasted a few years. What do you do
Speaker 1: to keep mentally and or physically fit outside of the office.
Speaker 1: What do you do for relaxation or enjoyment? Well, I
Speaker 1: have an exercise routine that can travel, so anything I
Speaker 1: can do on the floor to exercise works for me.
Speaker 1: So just push up, sit up, stretches, uh, anything like that,
Speaker 1: walking stairs, I found it. If I have to go
Speaker 1: to a gym that I don't go. So I have
Speaker 1: a routine I do. As far as relaxation, I had
Speaker 1: a spending time in my life my kids, especially my
Speaker 1: five year old daughter, which you know, nothing is more
Speaker 1: fun than a five year old girl. Um. What sort
Speaker 1: of advice would you give to millennials or someone just
Speaker 1: starting out their career who want to go into some
Speaker 1: form of data analytics, consulting, or anything involving weather. Well,
Speaker 1: I would thinks if if I could just go broader
Speaker 1: a little bit. Uh, you know, the first advice that
Speaker 1: I would give them is make sure you love what
Speaker 1: you're doing. You know, life is short, and uh, whatever
Speaker 1: you do, you better love it. And it doesn't matter
Speaker 1: what you're paid. Yes, it helps to be paid a lot,
Speaker 1: but the more important thing is to be happy. Uh.
Speaker 1: You know. Another thing that I would say is is
Speaker 1: to uh, you know, treat treat others as you want
Speaker 1: to be treated. So as you know it says in
Speaker 1: the good book Love Love Viticus, you know, love your
Speaker 1: fellow as yourself. So you know, treat others well and
Speaker 1: expect to be treated well, and to try to do
Speaker 1: things that are going to help help the people. Uh
Speaker 1: you know. I I also think that I also think
Speaker 1: that when you're looking at uh what you want to
Speaker 1: do and where you want to do it, the important
Speaker 1: thing is to surround yourself with a happy, positive people.
Speaker 1: So and it's not that we shouldn't try to help
Speaker 1: those who are down in our luck, but there are
Speaker 1: people that are just not positive. And I find that, uh,
Speaker 1: I'm much better off if I hire positive people. If
Speaker 1: you know, if I'm with positive people, because they're always
Speaker 1: a lift. I like that answer, Um, and tell me
Speaker 1: for our final question, what is it that you know
Speaker 1: about whether analytics as a business today that you wish
Speaker 1: you knew twenty years ago when you were starting out. Well,
Speaker 1: I'm glad I didn't know how hard it would be
Speaker 1: to make a market because market making is definitely hard.
Speaker 1: It's also the most most exciting thing that one can
Speaker 1: do to to educate people, change the way they think,
Speaker 1: and to provide huge value. So it's not so much
Speaker 1: what I wish I knew it. It's it's more about
Speaker 1: what I've learned, Because if I knew how hard it
Speaker 1: would be, you would you do what I have still
Speaker 1: done it? I think I would, but I have to
Speaker 1: measure that against the benefits I've gotten from really learning
Speaker 1: and all the great people that I've met in doing it,
Speaker 1: and more importantly, taking something as a morphous is weather
Speaker 1: and suddenly putting it into a major company systems that
Speaker 1: they're using it every day, planning with every day. That's
Speaker 1: what really gets our juice is going quite quite fascinating.
Speaker 1: We have been speaking to Fred Fox. He is the
Speaker 1: founder and CEO of Planalytics. If you enjoy this conversation.
Speaker 1: Be sure to look up or down a few inches
Speaker 1: on Apple iTunes, where you can see any of the
Speaker 1: prior hundred and fifty or so previous podcasts. I would
Speaker 1: be remiss if I did not thank all the people
Speaker 1: who helped put this podcast together. Medina Partwana is our
Speaker 1: recording and audio engineer, who makes me sound far better
Speaker 1: than I actually am in real life. Taylor Riggs is
Speaker 1: our producer and booker. Michael bat Nick is the head
Speaker 1: of research. We love your comments, feedback, end suggestions right
Speaker 1: to us at m IB podcast at Bloomberg dot net.
Speaker 1: I'm Barry Ritults. You've been listening to Masters in Business
Speaker 1: on Bloomberg Radio