September 10, 2025 • 35 mins
Weather drives nearly $1 trillion in annual spending-pattern shifts — boosting long john sales in Miami on a 60-degree day while slowing them in Minnesota. In this episode of ESG Currents, Bloomberg Intelligence senior climate analyst Andrew Stevenson speaks with Planalytics CEO Fred Fox about how companies such as Lowe’s are using weather insights to improve bottom-line results.
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Speaker 1 (00:09):
ESG is constantly evolving. Over the years. It is shifted
from socially responsible investing to impact to sustainable finance. While
the terminology continues to change, what hasn't changed are the
underlying science, market pressures, and tangible physical and financial impacts
of the Kimic crisis. We aim to filter out the
noise by speaking with industry experts to identify what is
(00:32):
really driving value. Welcome to ESG Kurrentz. I am Andy Stevenson,
Senior climate analyst and your host for today's episode. Today
we are talking about using weather patterns to forecast consumption
trends with Fred Fox, who is CEO of Planalytics, will
discuss how shifts and temperature, rain and snow can lead
(00:53):
consumers to make different choices going forward. Thanks for joining Fred,
Thank you, and I had to be here with you
guys great. Can you explain what Planet Linx does for us,
what types of customers you engage with, that kind of
thing that would be really helpful.
Speaker 2 (01:12):
Sure. So, I think I have one of the most
fun jobs on the planet because we get to help
businesses understand how weather influences consumer demand and purchasing be
behavior all around weather. So if you start and ue
(01:34):
with weather is one of the largest external drivers of
consumer purchasing decisions. So it affects everything that we decide
as consumers the win, where, what, and how much to buy. Yeah,
it's the least understood, the least measured, and really the
least utilized a factor that's out there. Weather is driving
(01:58):
consumers to purchases goods and services. So what we do
with Planalytics is we've built a capability, a technology that
inculs all types of client data and creates a climate
model for the business on the demands side exactly how
weather's driving consumer purchases at the level of purchase, which
(02:21):
is the store or on the e commerce side that
they can you know the when, where, and how much
the weather is driving them?
Speaker 1 (02:32):
Gotcha? Can you give us a sense of the scale
of what you're talking about here? Obviously we have a
US economy is absolutely massive when it comes to consumer spending,
many trillions of dollars. Is this how much of the
needle is moving with respect to climate? Can you just
give us a weather patterns? Can you just give us
a sense of the scale of it? Sure?
Speaker 2 (02:53):
So weather drives about three percent of retail sales globally.
So in retailing is the world's largest industry and it
drives three percent of that. So if if you think
of it, that that equates to about a trillion dollars
(03:15):
any given time of weather based sales swings on a
annual basis. So whether can change consumer purchasing enough that
you're going to get about a trillion dollars of swings
in purchasing year to year due to weather.
Speaker 1 (03:34):
That's a phenomenally large amount of money that, as you say,
is very under recognized as a driving force in terms
of sales patterns and shifts. Do you want to, like,
maybe give us an example of where you of one
of these patterns and how it kind of played out
in terms of you know, what the consumer ended up
(03:54):
not buying or buying. And I know that your data
is very regional or even city specific, maybe highlight how
that plays relative to another city, you know, just to
keep just to make give some comparison.
Speaker 2 (04:08):
Sure, So we are engulfing data from some of the
world's largest retailers and consumer packaged goods businesses, and we're
doing it every day, trillions and trillions of bits of
data on purchasing exactly everything they're selling in every location,
in every day, so we have a view on how
(04:32):
weather's affecting the entire assume economy. So when you think
of weather, weather affects people differently based on several key factors.
One where you are, I mean, sixty degrees let's say
in Miami is going to drive consumer purchasing different than
(04:53):
sixty degrees in Minneapolis. You know, if you take a
product like long underwear, which is a very warm weather product,
they sell it in Minneapolis, but temperatures have to get
down to sort of the low twenties before people start
(05:13):
to feel the need to buy it. In Miami, it
gets below sixty and you'll start to see long underwear
Staye'll start to spike, you know, So people get used
to the weather of where they are, and something like
cold is an example time of year place part two.
So if you look at like, sixty degrees in October
(05:36):
is different than sixty degrees in February. In October it's
starting to feel a built chili. In February it's feeling warm,
and so you know, it depends on when those temperatures
hit the I would say too that the specific products matter.
So if you think of something like ice cream for instance,
(06:00):
you know ice ice cream can sell and the NOVELI,
which is the handheld, it can sell in the pine,
it can sell in a gallon. In a place like Europe,
they sell a lot more novelies extremely weather affected, like
fifteen eighteen percent swings week to week in sales due
to weather. Pints are affected about maybe eight to twelve
(06:25):
percent swings and sales due to weather. Gallons which we
buy in dates and we've now spread to other parts
of the world. We put in our freezer there about
three to four percent weather affected because it's more of
a staple product, So that matters. If you look at
things like you like, you know, water, I mean a
(06:50):
six pack is different than a single bottle where their
soul matters. Convenience stores are much more weather effected than
a mess merchant because a convenience store we are going
in there. Age matters. You know, older people are influenced
differently than the younger people to weather, so older people
(07:12):
may be more sensitive to weather than let's say a
younger person. Pricing and fashion matters. You know, weather drives
needs versus one When you think of it, weather is
driving our need to buy a product, not our want
to buy a product. As you go up in price scale,
(07:32):
you move from the need to more than one. So
high fashion boots you don't need to buy high fashion boots.
You want to buy high fashion boots and may cost
one thousand dollars, but those one hundred and fifty dollars
pair of boots, it's more on a need basis, can
be much more weather effected. Gender matters men versus women's
(07:54):
So I can go on and on on this.
Speaker 1 (07:56):
No I want you to go on and on. I
just want to break up a little bit. And you
made me think of because I just came from living
in Florida and when it was sixty degrees, the ugs
came out, like you can't believe. So you had all
these you know, women dressed in kind of the summer
kind of where but they are wearing ugs as a compliment.
So it just obviously very winter friendly and well, a
(08:21):
lot of what you're pointing to sounds like logistics is
a huge component of what you're describing as well, right,
I mean you're talking about not only are how people
are changing their consumption functions, but but the wear is
very very important. So if you are a retailer for
in particular, you need to get those goods to where
(08:44):
they need where they're going to be sold, right, I mean,
that's that seems like one of the functions that you
probably are one of the most successful things your company does,
is allow for better logistics. And how that kind of
plays maybe if you talk a little bit about that,
because let's say air conditioners for example, you know, like
(09:05):
the demand for air conditioners, you want more where they're needed, right,
So I'm just curious if that plays a role at all.
Speaker 2 (09:13):
Yes, So companies use us for logistics, inventory management, planning, marketing, effectiveness,
labor as well as just financial reporting and planning. So
whether affects every part of a retailer or consumer packaged
(09:34):
goods business because it affects the consumer, so it flows
throughout how they use weather analytics, so in each part
is very different. So a grosser, we can model anywhere
from twenty to seventy thousand items a day that goes
into their replenishment system, adjust their system according to what
(09:58):
goods are going to be selling a over the next week,
and they're shipping into that the man where there is
weather driven d man and they're not shipping as much
into those stores that there's not going to be that demand,
So that drives things like waste savings because they're not
(10:20):
do you waste as much goods on shelf availability. There's
all types of metrics as well as labor, and that
adds up to huge, huge dollar savings for these businesses
that all can be measured other businesses uses for marketing.
So those same analytics we may build for a inventory
(10:42):
model can also be used to drive a marketing model
because you want to fish where the fish are if
you're into fishing, and what that means is that you
want to market your product when and where the consumer
is mentally in the mood to buy those goods based
(11:03):
on whether so they're going to buy those goods and
you want them to go into your funnel versus your
competitors tunnel.
Speaker 1 (11:10):
Wow, And I just wanted to go back to what
you mentioned on labor because that's a very interesting component
that I think is harder for people to get their
heads around because you think about, Okay, I understand that
there's going to be different actual purchases, but can you
talk a little bit about the labor implications of that,
because that's a pretty interesting thread. Sure.
Speaker 2 (11:30):
So you know, labor is very much based on the
amount of anticipated demand into a store. Most labor systems
today just look at the payass sales and do some
average of the type of foot traffic you would expect
(11:52):
in a certain part of the day as a certain
day day of the week, and then adjust and you
make sure you have enough people there. What it doesn't
account for so well is really how much weather is
affecting store traffic. So when we model data, we know
exactly how much traffic you're going to get all based
(12:14):
on weather. That use for labor scheduling allows these companies
to do a much more effect a job of both the
front end of the store as well as well as
to the back end having enough people or having less
people to really support that type of traffic.
Speaker 1 (12:32):
Can you just get a sense of, like how much
the impact is on labor. So let's say it's gonna
let's say there's a storm coming, like a major storm
or something along those lines, or you know, giant a freeze.
It doesn't really matter how much of a swing in
labor is. Can that be.
Speaker 2 (12:50):
Well, a storm, whether it's a hurricane or let's say
a snowstorm, you know, can both add huge opportunity and
risk for a retill how the opportunity is of course
before that storm hits, because you have a weather driven
traffic in anticipation of that storm. So knowing how much
(13:13):
they need to ship into those areas is part of
what we model based on how these storms will drive traffic.
And because we are analyzing years and years of their
sales data against similar types of weather, we know exactly
how many sales are going to have based on an
incoming storm. But then there's the adverse effect, what is
(13:36):
a lag factor after the storm. Because the lag of
these storms can also drive traffic, it can also depressed
traffic because people have bought a head of the storm.
The best type of storm that our retailers love is
one of the forecasts but never hits because you get
all the sales and none of none of the cleanup
(13:57):
mets here.
Speaker 1 (13:58):
That makes sense, no downside. They just keep on spending.
But you think about I mean, obviously retail and restaurants
and things like that, people will either you know, they'll
they'll pick up that pace and then you can't replace
those days. Right if the storm closes your street or
something along those lines. That's sort of the end of
it for the label.
Speaker 2 (14:17):
Well, I mean live factors in retail due to weather
are a big part of what we model now. Certain
parts of retail, such as a restaurant, there is no makeup.
I mean, you're not going to go out to a
restaurant and double down two three times a day to
make up for the fact that you didn't go the
day before because it was heavy rain or snow. So
(14:38):
lost sales do the weather is really lost sales. If
you look at a parel yes, there's time to make
it up. So in this last season is really a
great case. I had a pretty rough May in apparel,
and I would say that it was much clothing last year,
and about the eastern half of the nation sales were down.
(15:01):
You had some economic headwinds as well. It was a
pretty good storm. By mid June the weather totally changed.
Summer came in. We sort of missed spring and we
saw a big sales job. The same was true in
the DIY sector. A lot of consumers were putting off
(15:21):
purchases due to unfavable weather for many outdoor products, and
some of that was made up in the latter part
of the June and July period, so you know you
can make these up if the weather turns, you never
make up all of it. The other thing that I'll
mention too, is that making up sales later in the season,
(15:44):
you may be making up top line sales, but you're
not making up margin because you know the highest margin
is always going to be at the start of the season.
That's why we're here in labor day. It's feeling like fall.
It's great selling weather, especially for the soft line retail
just because they will sell a lot more products at
full price, which is their best way to sell. If
(16:07):
those things sit on the shelves and don't sell and
they have to start to discount prices, as all these
retailers do, then they can sell them later in the season,
but they can sell at a much tighter margin.
Speaker 1 (16:18):
And would you say this, so, is this an average
year or this is an atypical year just out of curiosity.
Speaker 2 (16:24):
Well, in weather, there's no thing as normal, even though
there is normal. So the weather's always warmer, colder, wet,
or dryer. That's really the beauty of it for us
is that it's always changing. And therefore companies that don't
incorporate that knowledge into the way they operate, are really
(16:47):
going out into every season a little bit blind. They've
done everything else right, they've planned it, they've spent multiple
amounts of time on the right products, the right fashion,
the right pricing. But if they're not incorporating weather, then
they're leaving a big part of what they do to chance.
(17:10):
If you look at some of the quotes from this
last spring, you look at Low's, you look at oh Riley,
you had a really rough May, and Marvin Ellis from
Low's said that weather is their number one one of
the number one drivers, and their business aligns heavily with weather,
(17:33):
and that was to explain that they were having a rough,
fairly start to spring. Now they had a great second
half of spring weather eyes, and they were able to
make a lot of that up. It's hard to say
how much was left on the table because they weren't
really planning ahead and anticipating that type of weather. And versely,
(17:54):
the same wet cold weather that was hurting Low's helped
oh'illy auto sales because when you get cold, wet weather
in the spring, it means more accidents, more breakdowns, more
traffic into to get to their stores.
Speaker 1 (18:10):
It's just a revolving ricocheted bullet rickcheting bullets. That's where
you're discovering, yes, and I mean obviously from a trading perspective,
just to go into the financial you know, case of this,
like knowing that they had a soft and acknowledge soft
early part of the season, but seeing the weather kind
(18:30):
of pick up, knowing that there is scope for them
to make that up right, because there's there's sort of
people kind of always promised to make it up, but
you know, like the weather needs to help you make
that up right. So the weather needs to behave in
a way that allows that to happen. So that's also
kind of an interesting insight that you're able to glean
from some of your work, is that not only are
(18:51):
you seeing what's going on as it's happening, but the
kind of downness of the market relative you know, because
of statements like that you just described from the CEO
of Lows, there is sort of a ray of hope there,
you know, like that's that's not necessarily the end of
the size. You say, the spring didn't end. You know,
(19:12):
they were able to make that up. So that's a
pretty interesting insight. I think, you know, from a financial
standard standpoint.
Speaker 2 (19:19):
Yeah, they were able to make it up from from
an operational standpoint. But if they're not incorporating weather analytics,
how much are they leaving during on the table? And
numbers are quite large. So now weather can hurt and
help and all these companies are operationally set up to
deal with that. But whether and whether analytics present an
(19:42):
opportunity into day's world of a cloud based scalable analytics
incorporating AI, there's the ability to model mask of amounts
of data and to not guess, but to know. So
(20:02):
so you know, these companies should know what's going to
be hitting them and they should know how to act
because they do not even know what it's hitting them.
But what is going to come around the corner. That's massive,
massive knowledge. So at least we talk to the market about.
Now we've been doing this a number of years, I
(20:23):
am still surprised in twenty twenty five how few companies
incorporate these types of weather analytics. Very very few. It's
it's rare we show up and someone is doing something
about it. There's a lot of people now that are
throw weather into an AI system. But you know, raw
(20:47):
weather data into a AI system doesn't really give give
the answers, so you know, it's it's it's one of
those things that what we do is we put our
pre engineered numbers into these systems, and it's like a turbocharger.
Speaker 1 (21:05):
Gotcha? And how far out are you when you're describing
whether is this a month forward? What's the forward? How
far out do you look when you look at for
these patterns, like where your confidence level you feel comfortable making,
you know, assessments because the weather does shift.
Speaker 2 (21:22):
Well, weather forecasting, the state of the the state of
the science today, the state of the art is really
about two weeks out. I mean that's that's how much
weather modelor is out. But we can strip whether out
of last year's data and allow companies to to plan
(21:43):
from a more normalized, clean base base one. So by
by doing that from an analytical point of view, you
automatically raise their ability to hit plan because you're not
planning off last year. If you're not taking last year's
weather out of the data, you're assuming last year's whether
(22:04):
what would occur next year, which may happen twenty some
percent of the time. So by taking it out, you're
really upping your odds quite a bit. You're flipping those odds.
Speaker 1 (22:17):
Yeah. The only the industry that comes to mind that
actually does that is the utility sector. Like they actually
publish every quarter. I mean, you could probably get it
even daily if you want it, if you have that
much time on your hands. But they report you know,
weather related impacts, right, because they try to make sure
(22:37):
that that is separated out as not a factor of
growth or you know, it does not reflect positively or
negatively because it obviously swings both ways on you know,
customer demand, right, because that's what they're trying to at
least from an investors standpoint. They're trying to highlight the
piece that doesn't include the weather patterns, right, right.
Speaker 2 (23:00):
Like you get that, we all get that in our bills,
are right, it's exactly what those normalized us are. So
that's a sense we're doing for a very complex industry,
which you know, thousands and thousands of different to products
categories sold through different locations. We take that complexity and
(23:21):
we work through it and provide it back in very
useful ways to to our clients.
Speaker 1 (23:27):
That's great, And you mentioned that you do this globally.
Is that that's correct, we do it globally, and would
you say the US because from a damages standpoint, and
particularly this year, I mean not that this is necessarily
the norm, but ninety percent of the insured losses in
the first half the year. We're in the US as
an example for a client from a climate perspective, and
(23:47):
you know, obviously we've had great floods this year. Severe
storms were a feature of the summer. Well, not necessary
like never before, but certainly unusually high relative to the history.
Does the US stand out as being the more one
of the most dynamic users of you know, I'm just
(24:08):
saying from an application standpoint, clients in the US, are
they like they really should be looking at what you're
talking about? Or is it or is it similar in
other places?
Speaker 2 (24:20):
Well, weather affects consumers globally, in every place on the planet,
even in the San Diego which doesn't really have weather.
I like to joke with my friends that live there
it's always seventy. But one of the really fascinating things
about the US that we have one of the most violent,
one of the most violent weather on the planet, the
(24:42):
most changeable, and most people here don't realize that. We
just take it for granted that we had weather that
swings so much. That's not true in the rest of
the world. And the reason is is because we're almost
like an island nation. We have this warm pool of
water sitting at our south called the Golf and we have,
(25:03):
of course the polar to the north, and our mountain
ranges run north to south. So unlike Europe which has
the Alps, because the US have a warm body, where
do they have the Mediterranean as how, polar air coming down,
but there the Apps sort of stops those patterns from
(25:24):
coming together. A lot. We don't have that. I mean
the Rockies and really really the Appalachians run north south.
We have the planes in the middle, and so you
have this cold air and the warm tropical air are
always fighting each other and so they're always moving up
and down and that creates tremendous volatility that most places
(25:46):
on this planet don't really have.
Speaker 1 (25:49):
Yeah, I can believe that. And imagine is Canada close
or is Canada not even close? Like from a just
from a volatility perspective.
Speaker 2 (26:00):
I mean they have some you know, the southern part
of Canada, you have the changeable weather, uh quite a lot.
I mean if you look at the you know, if
you look at the southern part, which is really along
the US border, that that changed as much as our
cities like Detroit, UH Andicago.
Speaker 1 (26:21):
Sure, Minneapolis and places like that. That makes sense. I
wanted to spend a little time on supply chains because
I think this is obviously when you were talking about forecasting,
and you know, you're looking at history, you're getting some
sense of what the impacts could be. But just from
a goods production standpoint, is this is are you seeing
(26:43):
shifts in because of the work that you do? You know, like,
are you seeing manufacturing actually being affected in places like lows?
You know, are they more reserved about having, you know,
leaf blowers in July or something like that because of
what they've been seeing historically, or is it doesn't reach
(27:03):
that far back.
Speaker 2 (27:04):
Well, it really depends on the use. We have five
different use products. I mean, we have the financial reporting
and planning, which is from a finance point of view,
it's a top down view. We have merchandise planning and allocation.
(27:25):
We also have marketing, and we have operations which is
extreme weather. And then we have inventory. So when companies
are using us for inventory and we've modeled all their
weather affected products, which can be anywhere. It's that said
twenty thirty forty thousand items a day, it's going into
(27:47):
their systems. That's in adjusting their systems on a regular
basis to ship into their stores from their warehouse. So
in those cases, we're having a very meaningful bottom line effect.
It's we're making sure using that old added so that
they're having the right product the right place at the
right time. When we affect the planning side, most companies
(28:11):
aren't using us for the buy because that's a whole
different area, but they are using us to plan their
overall sales. They're using us to what parts of but
the States are going to load load up more than
others where we think they're going to have a better
opportunity from a preseason standpoint. And then the marketing area
(28:33):
is is to a very measurable area. I mean, we
were generating about thirty percent increased row ads due to
aligning their marketing activities with greater lift. So you know,
it really depends where and how they're using us, gotcha.
Speaker 1 (28:50):
Yeah, And the marketing side sounds like a pretty fascinating
area because you are you kind of have for knowledge,
you know, like you're as try everything kind of is
built on some kind of in and this is this
is the instinct that we don't know we have, you know,
which is that these weather patterns are actually making informing
us as we go along about what we think we
(29:11):
need on a kind of constant basis, shall we say, Right,
So there's there's certainly that. Well we're kind of winding
down towards the end here. What I did want to
give you some time to talk about where you see
your business kind of growing and maybe you know, like
you opportunities you think for Planeltics going forward, Like what
excites you about I mean obviously you're you're in a
(29:31):
space where not many people are playing, you know, and
in such an impactful way, And just was curious if
what what was sort of next for Planelytics.
Speaker 2 (29:43):
Well, you know, our our mission has never changed, but
our means of achieving that just has changed radically. And
so it's it's for us, it's always fresh and it's
always using new technology. To get to the bottom line
is how is weather d driving the consumer and how
can these companies apply that knowledge to make better the decisions,
(30:06):
whether the decisions are through their four kiss engines, whether
they're through a management team sitting in a Monday morning meeting,
all can be used. So what a psyche sets is
the ability to help more and more companies at tree
mendous scale. And we are always finding new ways. I
(30:28):
total client the other day in Europe that if they
have questions that we can't answer, or if we come
across something that we haven't yet looked at, we just
automatically analyze that. We don't charge it for it because
we want to get to the bottom of it. We
(30:49):
want to see how is weather driving a consumer And
it gets complex with these multi national businesses. How they operate,
how they plan, it depends on their data. So you know,
it's really sifting through that and it's become in some
ways much easier to do because we've built this engine
(31:10):
that can take any type of data. So we're always
pushing the bounds of what we can do to look
at these impacts. You know, if I look at what's
on our exciting lists right now. Other than building new
automation through our AI tools, you know, we're just building
(31:32):
these lag factors after a major storm passes, so you know,
what will consumers buy after the storm? Some clients asked
us that we'd have an answer, and we're harder. We're
getting it, but you know, it takes a lot of
modeling to make that happen, and then we have to
put that into a galible engine. Yeah.
Speaker 1 (31:53):
Yeah, yeah, we're at Blomberg. We're looking at smoke days,
you know, the wildfire effects on consumption and labor at
the moment and putting out a piece next week or
putting out a piece related to how the twenty twenty
three events, which were kind of a factor of five
in terms of smoke days relative to history, you know,
because of the Canadian wildfires, was very impactful to wages
(32:18):
period like it had you talk about three trillion in
the kind of front facing sectors which includes retail, but
wholesale and others. And you find that, you know, some
of these impacts were up to five to fifteen percent
of the wage bill was being negatively impacted by this.
So it's several hundred billions of dollars and if you're
(32:39):
looking at in one year, historically it's been very very
small relative to that. But that obviously also means people
aren't going to restaurants and people aren't doing you know,
I'm saying there's a whole cascading effect that affects retail
and wholesale and other areas that are kind of on
the front lines construction Obviously, construction sale are difficult. In
(33:00):
construction building, it slows down, even cement slows down, which
is so it's a there's a yeah, it's a lot
to absorb, and you need very very good data modelers
to come up with the actual smoke, you know, pulling
the smoke out of that. We have pollution period, right,
(33:22):
you know, air pollution is a thing hasn't gone away.
It's pretty good. You know, the air is pretty good,
but we're kind of crawling the other direction because of
the smoke, and it's having a pretty interesting impact on
you know, the economy period. Which because we've used to
be very localized on the West coast, we're seeing this
(33:42):
really significant drift east of these impacts. And so what
was kind of a California problem, you know that maybe
or Oregon problem, is now a problem Minnesota as you
just mentioned Minnesota. But you know, the the biggest impacts
were in like Tennessee and North Carolina, surprisingly relative to history.
So just just as if you're looking for something to do.
(34:06):
You can you can look at that one as well
if you want to throw that in the in the mix.
Speaker 2 (34:10):
But I'll well, I can't wait to read your article.
But that that that sounds really a fascinating and anything
you'd like us to look at.
Speaker 1 (34:20):
Okay, okay, yeah, we're I mean I this, as you know,
this is not a very well pursued area of expertise,
so unfortunately, I would argue for for people's consciousness of
what actually is going on. But anyway, but thank you,
thank you very very much. This was great, Fred. I
learned a ton. I hope this was helpful to others
(34:41):
really understanding the power of what you're talking about here.
And these are you know, three percent on thirty three
trillion is a is a trillion, you know, so that's
a that's a massive amount of money, right, So there's
no no denying that this is this is already well
established as a as a driver of consumer preference, and
(35:02):
obviously from a financial perspective that makes a lot of difference.
But thank you again for your time. This was great.
Speaker 2 (35:09):
Thanks for having me.
Speaker 1 (35:11):
You can find more information on topics like climate damages
and impacts by going to the Blomberg function BI Carbon
and then the Climate Damages tracker on the Bloomberg terminal.
If you have an ESG quandary or burning question you
would like to ask bi's expert analysts, please send us
an email at A Stevenson seventy eight at bloomberg dot
(35:35):
com or ESG Currents at Bloomberg dot net
ESG is constantly evolving. Over the years. It is shifted
from socially responsible investing to impact to sustainable finance. While
the terminology continues to change, what hasn't changed are the
underlying science, market pressures, and tangible physical and financial impacts
of the Kimic crisis. We aim to filter out the
noise by speaking with industry experts to identify what is
(00:32):
really driving value. Welcome to ESG Kurrentz. I am Andy Stevenson,
Senior climate analyst and your host for today's episode. Today
we are talking about using weather patterns to forecast consumption
trends with Fred Fox, who is CEO of Planalytics, will
discuss how shifts and temperature, rain and snow can lead
(00:53):
consumers to make different choices going forward. Thanks for joining Fred,
Thank you, and I had to be here with you
guys great. Can you explain what Planet Linx does for us,
what types of customers you engage with, that kind of
thing that would be really helpful.
Speaker 2 (01:12):
Sure. So, I think I have one of the most
fun jobs on the planet because we get to help
businesses understand how weather influences consumer demand and purchasing be
behavior all around weather. So if you start and ue
(01:34):
with weather is one of the largest external drivers of
consumer purchasing decisions. So it affects everything that we decide
as consumers the win, where, what, and how much to buy. Yeah,
it's the least understood, the least measured, and really the
least utilized a factor that's out there. Weather is driving
(01:58):
consumers to purchases goods and services. So what we do
with Planalytics is we've built a capability, a technology that
inculs all types of client data and creates a climate
model for the business on the demands side exactly how
weather's driving consumer purchases at the level of purchase, which
(02:21):
is the store or on the e commerce side that
they can you know the when, where, and how much
the weather is driving them?
Speaker 1 (02:32):
Gotcha? Can you give us a sense of the scale
of what you're talking about here? Obviously we have a
US economy is absolutely massive when it comes to consumer spending,
many trillions of dollars. Is this how much of the
needle is moving with respect to climate? Can you just
give us a weather patterns? Can you just give us
a sense of the scale of it? Sure?
Speaker 2 (02:53):
So weather drives about three percent of retail sales globally.
So in retailing is the world's largest industry and it
drives three percent of that. So if if you think
of it, that that equates to about a trillion dollars
(03:15):
any given time of weather based sales swings on a
annual basis. So whether can change consumer purchasing enough that
you're going to get about a trillion dollars of swings
in purchasing year to year due to weather.
Speaker 1 (03:34):
That's a phenomenally large amount of money that, as you say,
is very under recognized as a driving force in terms
of sales patterns and shifts. Do you want to, like,
maybe give us an example of where you of one
of these patterns and how it kind of played out
in terms of you know, what the consumer ended up
(03:54):
not buying or buying. And I know that your data
is very regional or even city specific, maybe highlight how
that plays relative to another city, you know, just to
keep just to make give some comparison.
Speaker 2 (04:08):
Sure, So we are engulfing data from some of the
world's largest retailers and consumer packaged goods businesses, and we're
doing it every day, trillions and trillions of bits of
data on purchasing exactly everything they're selling in every location,
in every day, so we have a view on how
(04:32):
weather's affecting the entire assume economy. So when you think
of weather, weather affects people differently based on several key factors.
One where you are, I mean, sixty degrees let's say
in Miami is going to drive consumer purchasing different than
(04:53):
sixty degrees in Minneapolis. You know, if you take a
product like long underwear, which is a very warm weather product,
they sell it in Minneapolis, but temperatures have to get
down to sort of the low twenties before people start
(05:13):
to feel the need to buy it. In Miami, it
gets below sixty and you'll start to see long underwear
Staye'll start to spike, you know, So people get used
to the weather of where they are, and something like
cold is an example time of year place part two.
So if you look at like, sixty degrees in October
(05:36):
is different than sixty degrees in February. In October it's
starting to feel a built chili. In February it's feeling warm,
and so you know, it depends on when those temperatures
hit the I would say too that the specific products matter.
So if you think of something like ice cream for instance,
(06:00):
you know ice ice cream can sell and the NOVELI,
which is the handheld, it can sell in the pine,
it can sell in a gallon. In a place like Europe,
they sell a lot more novelies extremely weather affected, like
fifteen eighteen percent swings week to week in sales due
to weather. Pints are affected about maybe eight to twelve
(06:25):
percent swings and sales due to weather. Gallons which we
buy in dates and we've now spread to other parts
of the world. We put in our freezer there about
three to four percent weather affected because it's more of
a staple product, So that matters. If you look at
things like you like, you know, water, I mean a
(06:50):
six pack is different than a single bottle where their
soul matters. Convenience stores are much more weather effected than
a mess merchant because a convenience store we are going
in there. Age matters. You know, older people are influenced
differently than the younger people to weather, so older people
(07:12):
may be more sensitive to weather than let's say a
younger person. Pricing and fashion matters. You know, weather drives
needs versus one When you think of it, weather is
driving our need to buy a product, not our want
to buy a product. As you go up in price scale,
(07:32):
you move from the need to more than one. So
high fashion boots you don't need to buy high fashion boots.
You want to buy high fashion boots and may cost
one thousand dollars, but those one hundred and fifty dollars
pair of boots, it's more on a need basis, can
be much more weather effected. Gender matters men versus women's
(07:54):
So I can go on and on on this.
Speaker 1 (07:56):
No I want you to go on and on. I
just want to break up a little bit. And you
made me think of because I just came from living
in Florida and when it was sixty degrees, the ugs
came out, like you can't believe. So you had all
these you know, women dressed in kind of the summer
kind of where but they are wearing ugs as a compliment.
So it just obviously very winter friendly and well, a
(08:21):
lot of what you're pointing to sounds like logistics is
a huge component of what you're describing as well, right,
I mean you're talking about not only are how people
are changing their consumption functions, but but the wear is
very very important. So if you are a retailer for
in particular, you need to get those goods to where
(08:44):
they need where they're going to be sold, right, I mean,
that's that seems like one of the functions that you
probably are one of the most successful things your company does,
is allow for better logistics. And how that kind of
plays maybe if you talk a little bit about that,
because let's say air conditioners for example, you know, like
(09:05):
the demand for air conditioners, you want more where they're needed, right,
So I'm just curious if that plays a role at all.
Speaker 2 (09:13):
Yes, So companies use us for logistics, inventory management, planning, marketing, effectiveness,
labor as well as just financial reporting and planning. So
whether affects every part of a retailer or consumer packaged
(09:34):
goods business because it affects the consumer, so it flows
throughout how they use weather analytics, so in each part
is very different. So a grosser, we can model anywhere
from twenty to seventy thousand items a day that goes
into their replenishment system, adjust their system according to what
(09:58):
goods are going to be selling a over the next week,
and they're shipping into that the man where there is
weather driven d man and they're not shipping as much
into those stores that there's not going to be that demand,
So that drives things like waste savings because they're not
(10:20):
do you waste as much goods on shelf availability. There's
all types of metrics as well as labor, and that
adds up to huge, huge dollar savings for these businesses
that all can be measured other businesses uses for marketing.
So those same analytics we may build for a inventory
(10:42):
model can also be used to drive a marketing model
because you want to fish where the fish are if
you're into fishing, and what that means is that you
want to market your product when and where the consumer
is mentally in the mood to buy those goods based
(11:03):
on whether so they're going to buy those goods and
you want them to go into your funnel versus your
competitors tunnel.
Speaker 1 (11:10):
Wow, And I just wanted to go back to what
you mentioned on labor because that's a very interesting component
that I think is harder for people to get their
heads around because you think about, Okay, I understand that
there's going to be different actual purchases, but can you
talk a little bit about the labor implications of that,
because that's a pretty interesting thread. Sure.
Speaker 2 (11:30):
So you know, labor is very much based on the
amount of anticipated demand into a store. Most labor systems
today just look at the payass sales and do some
average of the type of foot traffic you would expect
(11:52):
in a certain part of the day as a certain
day day of the week, and then adjust and you
make sure you have enough people there. What it doesn't
account for so well is really how much weather is
affecting store traffic. So when we model data, we know
exactly how much traffic you're going to get all based
(12:14):
on weather. That use for labor scheduling allows these companies
to do a much more effect a job of both the
front end of the store as well as well as
to the back end having enough people or having less
people to really support that type of traffic.
Speaker 1 (12:32):
Can you just get a sense of, like how much
the impact is on labor. So let's say it's gonna
let's say there's a storm coming, like a major storm
or something along those lines, or you know, giant a freeze.
It doesn't really matter how much of a swing in
labor is. Can that be.
Speaker 2 (12:50):
Well, a storm, whether it's a hurricane or let's say
a snowstorm, you know, can both add huge opportunity and
risk for a retill how the opportunity is of course
before that storm hits, because you have a weather driven
traffic in anticipation of that storm. So knowing how much
(13:13):
they need to ship into those areas is part of
what we model based on how these storms will drive traffic.
And because we are analyzing years and years of their
sales data against similar types of weather, we know exactly
how many sales are going to have based on an
incoming storm. But then there's the adverse effect, what is
(13:36):
a lag factor after the storm. Because the lag of
these storms can also drive traffic, it can also depressed
traffic because people have bought a head of the storm.
The best type of storm that our retailers love is
one of the forecasts but never hits because you get
all the sales and none of none of the cleanup
(13:57):
mets here.
Speaker 1 (13:58):
That makes sense, no downside. They just keep on spending.
But you think about I mean, obviously retail and restaurants
and things like that, people will either you know, they'll
they'll pick up that pace and then you can't replace
those days. Right if the storm closes your street or
something along those lines. That's sort of the end of
it for the label.
Speaker 2 (14:17):
Well, I mean live factors in retail due to weather
are a big part of what we model now. Certain
parts of retail, such as a restaurant, there is no makeup.
I mean, you're not going to go out to a
restaurant and double down two three times a day to
make up for the fact that you didn't go the
day before because it was heavy rain or snow. So
(14:38):
lost sales do the weather is really lost sales. If
you look at a parel yes, there's time to make
it up. So in this last season is really a
great case. I had a pretty rough May in apparel,
and I would say that it was much clothing last year,
and about the eastern half of the nation sales were down.
(15:01):
You had some economic headwinds as well. It was a
pretty good storm. By mid June the weather totally changed.
Summer came in. We sort of missed spring and we
saw a big sales job. The same was true in
the DIY sector. A lot of consumers were putting off
(15:21):
purchases due to unfavable weather for many outdoor products, and
some of that was made up in the latter part
of the June and July period, so you know you
can make these up if the weather turns, you never
make up all of it. The other thing that I'll
mention too, is that making up sales later in the season,
(15:44):
you may be making up top line sales, but you're
not making up margin because you know the highest margin
is always going to be at the start of the season.
That's why we're here in labor day. It's feeling like fall.
It's great selling weather, especially for the soft line retail
just because they will sell a lot more products at
full price, which is their best way to sell. If
(16:07):
those things sit on the shelves and don't sell and
they have to start to discount prices, as all these
retailers do, then they can sell them later in the season,
but they can sell at a much tighter margin.
Speaker 1 (16:18):
And would you say this, so, is this an average
year or this is an atypical year just out of curiosity.
Speaker 2 (16:24):
Well, in weather, there's no thing as normal, even though
there is normal. So the weather's always warmer, colder, wet,
or dryer. That's really the beauty of it for us
is that it's always changing. And therefore companies that don't
incorporate that knowledge into the way they operate, are really
(16:47):
going out into every season a little bit blind. They've
done everything else right, they've planned it, they've spent multiple
amounts of time on the right products, the right fashion,
the right pricing. But if they're not incorporating weather, then
they're leaving a big part of what they do to chance.
(17:10):
If you look at some of the quotes from this
last spring, you look at Low's, you look at oh Riley,
you had a really rough May, and Marvin Ellis from
Low's said that weather is their number one one of
the number one drivers, and their business aligns heavily with weather,
(17:33):
and that was to explain that they were having a rough,
fairly start to spring. Now they had a great second
half of spring weather eyes, and they were able to
make a lot of that up. It's hard to say
how much was left on the table because they weren't
really planning ahead and anticipating that type of weather. And versely,
(17:54):
the same wet cold weather that was hurting Low's helped
oh'illy auto sales because when you get cold, wet weather
in the spring, it means more accidents, more breakdowns, more
traffic into to get to their stores.
Speaker 1 (18:10):
It's just a revolving ricocheted bullet rickcheting bullets. That's where
you're discovering, yes, and I mean obviously from a trading perspective,
just to go into the financial you know, case of this,
like knowing that they had a soft and acknowledge soft
early part of the season, but seeing the weather kind
(18:30):
of pick up, knowing that there is scope for them
to make that up right, because there's there's sort of
people kind of always promised to make it up, but
you know, like the weather needs to help you make
that up right. So the weather needs to behave in
a way that allows that to happen. So that's also
kind of an interesting insight that you're able to glean
from some of your work, is that not only are
(18:51):
you seeing what's going on as it's happening, but the
kind of downness of the market relative you know, because
of statements like that you just described from the CEO
of Lows, there is sort of a ray of hope there,
you know, like that's that's not necessarily the end of
the size. You say, the spring didn't end. You know,
(19:12):
they were able to make that up. So that's a
pretty interesting insight. I think, you know, from a financial
standard standpoint.
Speaker 2 (19:19):
Yeah, they were able to make it up from from
an operational standpoint. But if they're not incorporating weather analytics,
how much are they leaving during on the table? And
numbers are quite large. So now weather can hurt and
help and all these companies are operationally set up to
deal with that. But whether and whether analytics present an
(19:42):
opportunity into day's world of a cloud based scalable analytics
incorporating AI, there's the ability to model mask of amounts
of data and to not guess, but to know. So
(20:02):
so you know, these companies should know what's going to
be hitting them and they should know how to act
because they do not even know what it's hitting them.
But what is going to come around the corner. That's massive,
massive knowledge. So at least we talk to the market about.
Now we've been doing this a number of years, I
(20:23):
am still surprised in twenty twenty five how few companies
incorporate these types of weather analytics. Very very few. It's
it's rare we show up and someone is doing something
about it. There's a lot of people now that are
throw weather into an AI system. But you know, raw
(20:47):
weather data into a AI system doesn't really give give
the answers, so you know, it's it's it's one of
those things that what we do is we put our
pre engineered numbers into these systems, and it's like a turbocharger.
Speaker 1 (21:05):
Gotcha? And how far out are you when you're describing
whether is this a month forward? What's the forward? How
far out do you look when you look at for
these patterns, like where your confidence level you feel comfortable making,
you know, assessments because the weather does shift.
Speaker 2 (21:22):
Well, weather forecasting, the state of the the state of
the science today, the state of the art is really
about two weeks out. I mean that's that's how much
weather modelor is out. But we can strip whether out
of last year's data and allow companies to to plan
(21:43):
from a more normalized, clean base base one. So by
by doing that from an analytical point of view, you
automatically raise their ability to hit plan because you're not
planning off last year. If you're not taking last year's
weather out of the data, you're assuming last year's whether
(22:04):
what would occur next year, which may happen twenty some
percent of the time. So by taking it out, you're
really upping your odds quite a bit. You're flipping those odds.
Speaker 1 (22:17):
Yeah. The only the industry that comes to mind that
actually does that is the utility sector. Like they actually
publish every quarter. I mean, you could probably get it
even daily if you want it, if you have that
much time on your hands. But they report you know,
weather related impacts, right, because they try to make sure
(22:37):
that that is separated out as not a factor of
growth or you know, it does not reflect positively or
negatively because it obviously swings both ways on you know,
customer demand, right, because that's what they're trying to at
least from an investors standpoint. They're trying to highlight the
piece that doesn't include the weather patterns, right, right.
Speaker 2 (23:00):
Like you get that, we all get that in our bills,
are right, it's exactly what those normalized us are. So
that's a sense we're doing for a very complex industry,
which you know, thousands and thousands of different to products
categories sold through different locations. We take that complexity and
(23:21):
we work through it and provide it back in very
useful ways to to our clients.
Speaker 1 (23:27):
That's great, And you mentioned that you do this globally.
Is that that's correct, we do it globally, and would
you say the US because from a damages standpoint, and
particularly this year, I mean not that this is necessarily
the norm, but ninety percent of the insured losses in
the first half the year. We're in the US as
an example for a client from a climate perspective, and
(23:47):
you know, obviously we've had great floods this year. Severe
storms were a feature of the summer. Well, not necessary
like never before, but certainly unusually high relative to the history.
Does the US stand out as being the more one
of the most dynamic users of you know, I'm just
(24:08):
saying from an application standpoint, clients in the US, are
they like they really should be looking at what you're
talking about? Or is it or is it similar in
other places?
Speaker 2 (24:20):
Well, weather affects consumers globally, in every place on the planet,
even in the San Diego which doesn't really have weather.
I like to joke with my friends that live there
it's always seventy. But one of the really fascinating things
about the US that we have one of the most violent,
one of the most violent weather on the planet, the
(24:42):
most changeable, and most people here don't realize that. We
just take it for granted that we had weather that
swings so much. That's not true in the rest of
the world. And the reason is is because we're almost
like an island nation. We have this warm pool of
water sitting at our south called the Golf and we have,
(25:03):
of course the polar to the north, and our mountain
ranges run north to south. So unlike Europe which has
the Alps, because the US have a warm body, where
do they have the Mediterranean as how, polar air coming down,
but there the Apps sort of stops those patterns from
(25:24):
coming together. A lot. We don't have that. I mean
the Rockies and really really the Appalachians run north south.
We have the planes in the middle, and so you
have this cold air and the warm tropical air are
always fighting each other and so they're always moving up
and down and that creates tremendous volatility that most places
(25:46):
on this planet don't really have.
Speaker 1 (25:49):
Yeah, I can believe that. And imagine is Canada close
or is Canada not even close? Like from a just
from a volatility perspective.
Speaker 2 (26:00):
I mean they have some you know, the southern part
of Canada, you have the changeable weather, uh quite a lot.
I mean if you look at the you know, if
you look at the southern part, which is really along
the US border, that that changed as much as our
cities like Detroit, UH Andicago.
Speaker 1 (26:21):
Sure, Minneapolis and places like that. That makes sense. I
wanted to spend a little time on supply chains because
I think this is obviously when you were talking about forecasting,
and you know, you're looking at history, you're getting some
sense of what the impacts could be. But just from
a goods production standpoint, is this is are you seeing
(26:43):
shifts in because of the work that you do? You know, like,
are you seeing manufacturing actually being affected in places like lows?
You know, are they more reserved about having, you know,
leaf blowers in July or something like that because of
what they've been seeing historically, or is it doesn't reach
(27:03):
that far back.
Speaker 2 (27:04):
Well, it really depends on the use. We have five
different use products. I mean, we have the financial reporting
and planning, which is from a finance point of view,
it's a top down view. We have merchandise planning and allocation.
(27:25):
We also have marketing, and we have operations which is
extreme weather. And then we have inventory. So when companies
are using us for inventory and we've modeled all their
weather affected products, which can be anywhere. It's that said
twenty thirty forty thousand items a day, it's going into
(27:47):
their systems. That's in adjusting their systems on a regular
basis to ship into their stores from their warehouse. So
in those cases, we're having a very meaningful bottom line effect.
It's we're making sure using that old added so that
they're having the right product the right place at the
right time. When we affect the planning side, most companies
(28:11):
aren't using us for the buy because that's a whole
different area, but they are using us to plan their
overall sales. They're using us to what parts of but
the States are going to load load up more than
others where we think they're going to have a better
opportunity from a preseason standpoint. And then the marketing area
(28:33):
is is to a very measurable area. I mean, we
were generating about thirty percent increased row ads due to
aligning their marketing activities with greater lift. So you know,
it really depends where and how they're using us, gotcha.
Speaker 1 (28:50):
Yeah, And the marketing side sounds like a pretty fascinating
area because you are you kind of have for knowledge,
you know, like you're as try everything kind of is
built on some kind of in and this is this
is the instinct that we don't know we have, you know,
which is that these weather patterns are actually making informing
us as we go along about what we think we
(29:11):
need on a kind of constant basis, shall we say, Right,
So there's there's certainly that. Well we're kind of winding
down towards the end here. What I did want to
give you some time to talk about where you see
your business kind of growing and maybe you know, like
you opportunities you think for Planeltics going forward, Like what
excites you about I mean obviously you're you're in a
(29:31):
space where not many people are playing, you know, and
in such an impactful way, And just was curious if
what what was sort of next for Planelytics.
Speaker 2 (29:43):
Well, you know, our our mission has never changed, but
our means of achieving that just has changed radically. And
so it's it's for us, it's always fresh and it's
always using new technology. To get to the bottom line
is how is weather d driving the consumer and how
can these companies apply that knowledge to make better the decisions,
(30:06):
whether the decisions are through their four kiss engines, whether
they're through a management team sitting in a Monday morning meeting,
all can be used. So what a psyche sets is
the ability to help more and more companies at tree
mendous scale. And we are always finding new ways. I
(30:28):
total client the other day in Europe that if they
have questions that we can't answer, or if we come
across something that we haven't yet looked at, we just
automatically analyze that. We don't charge it for it because
we want to get to the bottom of it. We
(30:49):
want to see how is weather driving a consumer And
it gets complex with these multi national businesses. How they operate,
how they plan, it depends on their data. So you know,
it's really sifting through that and it's become in some
ways much easier to do because we've built this engine
(31:10):
that can take any type of data. So we're always
pushing the bounds of what we can do to look
at these impacts. You know, if I look at what's
on our exciting lists right now. Other than building new
automation through our AI tools, you know, we're just building
(31:32):
these lag factors after a major storm passes, so you know,
what will consumers buy after the storm? Some clients asked
us that we'd have an answer, and we're harder. We're
getting it, but you know, it takes a lot of
modeling to make that happen, and then we have to
put that into a galible engine. Yeah.
Speaker 1 (31:53):
Yeah, yeah, we're at Blomberg. We're looking at smoke days,
you know, the wildfire effects on consumption and labor at
the moment and putting out a piece next week or
putting out a piece related to how the twenty twenty
three events, which were kind of a factor of five
in terms of smoke days relative to history, you know,
because of the Canadian wildfires, was very impactful to wages
(32:18):
period like it had you talk about three trillion in
the kind of front facing sectors which includes retail, but
wholesale and others. And you find that, you know, some
of these impacts were up to five to fifteen percent
of the wage bill was being negatively impacted by this.
So it's several hundred billions of dollars and if you're
(32:39):
looking at in one year, historically it's been very very
small relative to that. But that obviously also means people
aren't going to restaurants and people aren't doing you know,
I'm saying there's a whole cascading effect that affects retail
and wholesale and other areas that are kind of on
the front lines construction Obviously, construction sale are difficult. In
(33:00):
construction building, it slows down, even cement slows down, which
is so it's a there's a yeah, it's a lot
to absorb, and you need very very good data modelers
to come up with the actual smoke, you know, pulling
the smoke out of that. We have pollution period, right,
(33:22):
you know, air pollution is a thing hasn't gone away.
It's pretty good. You know, the air is pretty good,
but we're kind of crawling the other direction because of
the smoke, and it's having a pretty interesting impact on
you know, the economy period. Which because we've used to
be very localized on the West coast, we're seeing this
(33:42):
really significant drift east of these impacts. And so what
was kind of a California problem, you know that maybe
or Oregon problem, is now a problem Minnesota as you
just mentioned Minnesota. But you know, the the biggest impacts
were in like Tennessee and North Carolina, surprisingly relative to history.
So just just as if you're looking for something to do.
(34:06):
You can you can look at that one as well
if you want to throw that in the in the mix.
Speaker 2 (34:10):
But I'll well, I can't wait to read your article.
But that that that sounds really a fascinating and anything
you'd like us to look at.
Speaker 1 (34:20):
Okay, okay, yeah, we're I mean I this, as you know,
this is not a very well pursued area of expertise,
so unfortunately, I would argue for for people's consciousness of
what actually is going on. But anyway, but thank you,
thank you very very much. This was great, Fred. I
learned a ton. I hope this was helpful to others
(34:41):
really understanding the power of what you're talking about here.
And these are you know, three percent on thirty three
trillion is a is a trillion, you know, so that's
a that's a massive amount of money, right, So there's
no no denying that this is this is already well
established as a as a driver of consumer preference, and
(35:02):
obviously from a financial perspective that makes a lot of difference.
But thank you again for your time. This was great.
Speaker 2 (35:09):
Thanks for having me.
Speaker 1 (35:11):
You can find more information on topics like climate damages
and impacts by going to the Blomberg function BI Carbon
and then the Climate Damages tracker on the Bloomberg terminal.
If you have an ESG quandary or burning question you
would like to ask bi's expert analysts, please send us
an email at A Stevenson seventy eight at bloomberg dot
(35:35):
com or ESG Currents at Bloomberg dot net
Host
Eric Kane
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