August 5, 2024 • 48 mins
Unlock the secrets of consumer behavior in the automotive aftermarket with insights from Nathan Shipley, Executive Director at Circana. Discover how Circana's comprehensive data collection helps retailers and manufacturers comprehend purchasing patterns across industries and the unique nuances that drive consumer behavior in the automotive sector.
Ever wondered how rising food prices and macroeconomic shifts influence your spending habits, especially when it comes to essential purchases like tires? Nathan provides a deep dive into how inflation, credit card debt, and declining savings rates are reshaping consumer priorities. Learn about the broader implications of these economic changes, from increased housing costs to the adaptations consumers are making in their shopping choices. This conversation shines a light on the real-world challenges consumers face and the strategies they're employing to navigate them.
Peek into the future of consumer behavior tracking and data analysis as Nathan discusses advancements in data collection methodologies and the transformative power of AI in the retail industry. He emphasizes the importance of having clear objectives when analyzing data and ensuring that the data is representative of the target audience. From the evolving landscape of e-commerce to the critical role of accurate data in making informed decisions, this episode is a treasure trove of insights for anyone interested in market intelligence and the ever-changing dynamics of consumer behavior in the automotive aftermarket.
To learn more about the Auto Care Association visit autocare.org.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Mike Chung (00:08):
Welcome to AutoCare OnAir, a candid podcast for a
curious industry.
I'm Mike Chung, Senior Directorof Market Intelligence at the
AutoCare Association, and thisis Indicators, where we identify
and explore data that will helpyou monitor and forecast
industry performance.
This includes global economicdata, industry indicators and
new data that will help youmonitor and forecast industry
performance.
(00:28):
This includes global economicdata, industry indicators and
new data sources.
Happy to welcome Nathan Shipley, Executive Director at Circana.
So, Nathan, welcome to the show.
Good to see you, Mike.
Thanks for having me on theshow.
(00:48):
Glad to be here, Absolutely.
Nathan Shipley (00:51):
So tell us a little bit about what you do for
Cercana.
Sure thing, mike, I've workedwith you and the association for
a long time and in my role atCercana my job is it's an
industry analyst role and sowhat our company does very high
level.
I will probably get into that alittle more detail, but we
track a variety of differentretail industries, and the
automotive aftermarket is one ofthose, and so my job is to work
(01:14):
with retailers, manufacturers,associations such as the Auto
Care Association, on just kindof what we're seeing through the
eyes of the consumer in termsof what we track and all that in
terms of what we're seeingthrough the eyes of the consumer
in terms of what we track andall that in terms of what we're
seeing what's selling and someof the whys behind it.
My job is to kind ofcommunicate that out to the
folks that we work with.
Mike Chung (01:33):
Oh well, thanks for that introduction, and tell us a
little bit about how you got toyour role at Cercana.
Nathan Shipley (01:40):
You know, mike, you gave me this as a kind of a
pre-question.
I thought a lot about it, but Iwill give you a quick history
lesson.
The gentleman that used to runthe automotive business at what
was the NPD Group, named LarryMoore, was a neighbor of mine
growing up in the Houston Texasarea.
Junior year of college I waswashing my car among all things
Very fitting and said, hey,would you want an internship?
(02:12):
And I had no clue what he did.
But I said sure, and so Istarted working for this company
that was at the time called theMPD Group and I was making a
lot of copies and shipping out alot of paper reports at the
time, and it was an internshipthat turned into a full-time gig
a couple of years later andI've been here for most of my
career.
I started out doing some clientmanagement work.
I actually left the company andworked to work for Nielsen on a
(02:35):
CPG brand, which gave me areally interesting perspective
on consumer behavior and whattype of information is available
in a category or industry thatis very fast moving, whereas,
you know, obviously automotivecategories aren't quite as fast
moving.
But ended up coming back acouple of years later, and again
.
I've been here most of mycareer and have been in this
(02:57):
role now for about seven years.
The industry analyst role.
Mike Chung (03:02):
Oh, thanks for sharing that overview.
If you don't mind me asking,what did you study in college?
Did you have visions of workingin this line of business?
Nathan Shipley (03:16):
No, I was probably the guy.
I've got a business degree.
I focused on marketing, butdidn't have a specific plan in
mind as to what I was going todo.
I thought I'd go into a salesrole of some sort, but this
company, which again was NPD andthen we'll probably talk about
it, but has turned into Sarkana,through a variety of steps,
what we do is it's prettyinteresting.
(03:36):
I find consumer behavior to befascinating.
We're all different in terms ofhow we approach things, and so
being able to work in a kind ofa consultative type role where
we get to go in and work withcompanies and help them
understand their customerswhether that be the end consumer
, or help them understand theircustomers which for a lot of
(03:58):
manufacturers, is the retailersthat are selling the products
understanding why people aredoing what they're doing, what
they're buying, what they're notbuying it's really interesting.
So, no, I didn't see myselfgoing into a role like this, but
I've really enjoyed doing whatI do and I'm a car guy, so that
helps too.
Mike Chung (04:18):
Yeah, you mentioned there are different categories
that your company covers.
I think it's really fascinatinghow, as you shared, you're
washing your car, you gravitatetowards the automotive, you have
an interest in it and you'recovering that area.
So just tell me a little bitabout what type of consumer data
you're collecting and viewingin your role.
Nathan Shipley (04:40):
Sure, yeah, and I guess this is probably a good
time to introduce the idea ofSarkana.
So my career was primarilyspent at a company called the
NPD Group, and the NPD Grouptracked behavior and purchasing
patterns in what we call generalmerchandise industries.
So the automotive aftermarketis one of those industries, but
(05:01):
you could be talking aboutconsumer electronics or
cosmetics or apparel, anythingthat's not food, like groceries,
or consumer packaged goods liketoilet paper, paper, towels,
shampoo, etc.
So NPD was focused on thegeneral merchandise side of a
store or of categories orindustries.
There's a company out therethat's been around forever, it's
(05:23):
called IRI.
There's a company out therethat's been around forever.
It's called IRI.
Iri tracked and does track ordid track grocery and consumer
packaged goods and didn't reallyfocus on the general
merchandise industries that NPDtracked.
And so over the last two plusyears or so, through a series of
(05:49):
private equity moves in termsof NPD and IRI being purchased
by private equity, those twofirms were merged and the
combined firm has a new namewhich, as of a little over a
year ago, was Circana.
And so Circana is tracking thecomplete consumer.
We are tracking everything thata consumer could buy, whether
it's general merchandisecategories or food or CPG, and
so I am still focused on theautomotive aftermarket.
(06:11):
But what's really cool about myrole is that I have the ability
to see spending or purchasebehavior across everything that
a consumer might buy, and howwhat's happening at, maybe, a
grocery store is impactingwhat's happening in the
automotive aftermarket, as anexample.
And so you know.
In terms of the data sources youknow there's there's two
(06:33):
primary sources.
You know most industries notall, but most.
We have partnerships withretailers, and those retailers
send us their sales information.
For most, all the stores in theaftermarket, it's every store
that's under their umbrella,every store, every item, every
(06:53):
week, and there's some caveatsto that in terms of what
categories, et cetera.
So we're able to see, you knowcomparatively, what is selling
and what's not, and so there's alot of data security in terms
of us not exposing individualretailers.
So we're not able to seeindividual retailers, but
combined, we're able to say, hey, this is what's happening.
(07:15):
The other side of what we sourceinformation from is receipts,
and so we work directly withconsumers, and those consumers
provide us their actual receiptsfrom stores or online, if they
shop online, and so we know,through the panels that we have,
who those people are where theywent, what they bought, what
(07:35):
they paid, where else they went.
You know, it's reallyfascinating to watch a consumer
maybe hop around betweendifferent auto parts stores in
the same month.
It's like, well, if you'regoing to one, why are you going
to this other?
And was it an out-of-stockissue?
Was there a price issue?
There's some fascinatinglearning behind that type of
(07:56):
information.
So, again, two main sources ofdata that's coming in point of
sale, and then receipts, andthen in terms of what we can
actually do with it, it's awhole other conversation because
there's a lot of analytics thatcan be layered on top of that
to do some pretty powerful stuff.
Mike Chung (08:11):
That's really fascinating.
Thanks for that overview.
So I'm going to play a littlebit of that back and dive into
it, because there are two thingsthat kind of made me think of
some follow-up questions here.
So, just as an example, thegrocery stores, right.
So if your organization haspartnerships with, let's just
say, safeway, giant, let's justeven say Walmart, right, you're
(08:34):
able to.
They send you their sales dataand you can kind of bucket it,
categorize it at the store level, then roll it up to a regional
level.
You can look at it for fruitsand vegetables or I don't know,
like cereal, various categories,and you can slice and dice the
(08:55):
data in different ways.
And it's through thosepartnerships that you're able to
get comparatives, I guess, likeyou say, between those grocery
stores.
And like you said, there aresome data sharing agreements in
terms of the proprietary natureof how that data can be shared
out.
Am I getting that correct,Nathan?
Nathan Shipley (09:14):
That's right.
Yeah, I mean in terms of pointof sale data, like what you're
talking about, retailer privacyis paramount.
Data security is paramount,meaning we can't in a business
model like ours, we cannotexpose individual retailers.
That doesn't work.
So how does the model work?
We aggregate this data together.
So if we're talking about thefood industry as an example,
(09:36):
let's talk about the automotiveaftermarket we're not reporting.
When we report out what'shappening, we don't report about
individual retailers.
It's the aggregate of thoseretailers that we're reporting,
with the goal not, again, wecan't expose retailers.
Uh, that wouldn't work.
Um, and so you know a lot offolks.
Well, why would retailers dothat?
(09:56):
Or, you know, in any industry,why would?
What's the what's the benefitto them?
And the benefit to thoseretailers is that we report back
to those retailers.
You mentioned Walmart as anexample.
Walmart's able to see theirperformance, walmart's
performance relative to theaggregate of every other
retailer that we're tracking,and so it's performance.
(10:19):
But it's what's happening inindividual markets, as you
mentioned, certain parts of thecountry, certain cities in the
country, and you can kind of godown from there, you can drill
in deeper.
You know, are they performingwell in certain categories in a
store but not in othercategories in a store, and if
there's opportunities to thendive in to say, all right, why
aren't we performing well inthis category that we're talking
(10:41):
about this?
You know whatever category thiswe're talking about.
So that's the nature of themodel, is it's insights into
what's happening in thecategories in which they play.
And the most basic explanation Ihave and it seems to resonate
pretty well is you know, let'ssay I'm a retailer, you know, I
(11:05):
personally live in Houston,texas, and my goal this year,
internally, is to grow 5%.
And you know what?
In the Houston Texas market,I'm growing 5%.
I'm right in line with mypersonal goal of growing 5%.
But then I look at data from acompany like Cercana that's
tracking every retailer in theHouston market and that data
(11:26):
tells me that the Houston market, on average, is growing 9%.
And so, yeah, I'm hitting mypersonal goals of five.
But this information tells meI'm losing market share and my
competitors are growing fasterthan me for some reason, and I
need to figure out why that is.
What am I doing wrong?
What are they doing better?
What's going on?
(11:47):
And that's where it starts at avery high level, and then we
start drilling in a prettyintricate level of detail to
figure out what's going on andhow to fix it.
Mike Chung (11:55):
So benchmarking is certainly one of the advantages
that anybody who is subscribingcould glean from this and
monitor their performance versusthe rest of the markets.
That makes a lot of sense?
Nathan Shipley (12:09):
Yeah, that's definitely it.
But you start getting intoheavy analytics around promotion
strategy what's working, what'snot?
Pricing strategies by marketswhat price points are working?
Are you overpriced, underpricedwhen you promote?
Are you promoting too high, notenough?
Are you promoting too much?
Are you giving product away?
(12:30):
Are you not promoting enough?
The analytics that can be done.
It's very, very powerfulanalytics on what's happening at
a very granular level.
When you start digging into it,it's pretty neat at a very
granular level.
Mike Chung (12:44):
When you start digging into it, it's pretty
neat, sure, and two things thatI was thinking about.
One is in terms of the metatrends.
When you aggregate all thisdata, sometimes I get this
question how are private labelsdoing?
That's something that you wouldbe able to see across the
market for any category, whetherit's cereal or motor oil or
filters or what have you Indeed?
Nathan Shipley (13:05):
That's right, yeah, and so you know, again,
talking about retailerconfidentiality, you know
private label.
You know we're not going to betalking about an individual
retailer's private label brand,but an aggregate.
Yes, we're aggregating.
You know, it's motor oil.
We're going to put all theprivate labels together and call
it private label and we can seethis private label taking share
from brands, yes or no?
(13:26):
And that is a question that'scoming up a lot these days, it
seems.
Mike Chung (13:30):
Mike by the way, and to that end, when you talk
about the analytics that arepossible, I can see merging of
that data with things likeinflation consumer confidence.
To say what?
Inflation consumer confidence?
To say what?
Like a driver's analysis rightTo say if private label is
increasing, sales of privatelabel products are increasing.
(13:52):
Perhaps.
What can we attribute that to?
Is that fair to say?
Nathan Shipley (13:55):
It's very fair to say.
And so I mean, among otherthings, we do a lot of
forecasting work and so themodels they take a lot of those
macro factors, like you justmentioned.
You know inflation, what'shappening with inflation, what's
happening with food prices,what's?
You know there's all these,there's all these inputs that go
in, and then the model can lookback and say, all right, what
(14:16):
we can see, sales, you knowdemand, and then we can have all
these macro inputs and likewhat is most correlated with
demand from a macro standpointand like what is most correlated
with demand from a macrostandpoint, what's not
correlated with demand.
And so, as an example, one ofthe demand drivers.
So let me back up, we can see,through our point of sale data,
(14:36):
what's happening with foodprices at the grocery store.
You hear about it in the media,you feel it personally when you
go to the grocery store.
We all feel it personally.
And you go to the grocery store, we all feel it.
And so we can see how foodprices correlate with demand in
certain categories and, as anexample, the tires category.
(14:56):
It's one that we track.
The highest correlated macrofactor that we're seeing right
now is food prices and that wecan go back over time and it
kind of makes it's like okay,okay, that's, that's.
That's not a big shocker, butnot all categories are like that
.
Not all categories have a highcorrelation with food prices,
but tires in particular do um,and let's see.
(15:20):
No, I'm not going to go downthat rabbit hole per se, but yes
, you can start diving intomacro factors and macro economic
factors and kind of theircorrelation.
But even outside of the macroeconomic, within the four walls
(15:42):
of Sarkana, increasing foodprices again as an example,
right, wages have gone up Xpercent but food prices are
going up Y, which means that hastaken spending power away from
a lot of consumers becausethey're spending more of their
income on food as a percentageof their total wallet.
Where is that money coming from?
(16:03):
Where are they cutting back ifthey're cutting back?
And so it's interesting to lookat it that way too.
And so, kind of tying it backto my role, my job is kind of
look at the macro, like what'shappening big picture with
consumers in our space and someof the whys behind it.
(16:24):
If you were to ask me what'shappening with so-and-so item
and so-and-so category, Iprobably wouldn't be able to
answer the question off the cuff, I need to go port-a-port or
something.
Yeah, the informationinternally.
It's fascinating the amount ofinformation that that Sarkana
(16:44):
has compiled.
Mike Chung (16:46):
Yeah, so I'm going to revisit some of those
questions.
But if we back out a little bit, what general consumer trends
are you seeing?
You mentioned inflation andfeel free to talk about just
overall general consumerbehavior.
Is it perhaps spending lessbecause of inflation?
(17:07):
Is it going from a premiumbrand to a more mainline brand
or economy brand?
Is it delaying of purchase?
Can you tell me a little bitabout that?
And then, what's happening inthe automotive aftermarket?
Nathan Shipley (17:22):
Sure, yeah, it's the last.
It's been four or five yearsnow since COVID became a thing,
but observing consumer behaviorand kind of the macro economy
over the last several years hasbeen fascinating.
Not all for good reasons,obviously, but present day, yes,
(17:43):
inflation is hurting theconsumer.
We are seeing credit card debtper household at relatively high
levels.
Savings rates have declined.
Savings amounts have declinedper household.
Delinquencies are up a littlebit.
Housing-related expenses arereally chipping away at
(18:07):
discretionary spending power.
Are really chipping away atdiscretionary spending power.
So I guess big theme one is thisconcept of destruction of
discretionary spending power.
You go back three or four yearsago or five years ago and the
economy looked different.
Interest rates were a lot lower, spending on a lot of things
stopped because we all werelocked down, so we weren't
(18:30):
spending money on travel andsports for our kids and whatever
else.
Oh and, by the way, there wasstimulus money being pumped into
the economy and so all of asudden there was very high
levels of discretionary spendingpower at that time and so
that's changed.
You know, back then we sawcredit card debt coming down and
savings rates going up.
(18:50):
Back then we saw credit carddebt coming down and saving
(19:14):
treats going up and from a bigpicture, at least financially,
not so much from a public healthstandpoint housing and food as
two examples.
Utilities those make up afairly significant amount of
their weekly or monthly budget,and so when their income hasn't
kept up with the rate ofincrease of those big items,
their discretionary spendingpower is affected by it.
So, from a pricing standpoint,we are seeing actually all this
talk about prices and higherprices and everything's more
(19:36):
expensive, and now what you'restarting to hear, what you are
hearing, is prices are.
Things are calming down withprice, and the rate of change
definitely has slowed, and insome industries it's turned
negative.
We're seeing price deflation insome categories, but food is an
example, while the rate ofchange isn't what it was meaning
(19:59):
.
The rate of change has sloweddown.
Prices are still way higherthan they were two, three, four
years ago, and so that's stilleating into discretionary
spending power of the consumer.
So we are seeing trade down insome regards.
You know, I think you need tothink about trade down both.
From a, I'm standing at theshelf in a store that I always
shop at and am I trading downfrom brand A to brand B or brand
(20:22):
B, a private label as anexample, or am I trading down in
terms of where I shop?
Am I typically shopping in aspecialty store and now I'm
going to a mass retailer?
Or I'm typically shopping at amass retailer and now I'm going
to a dollar store?
So we're seeing some of that aswell A little bit bigger
picture than all of that.
There's some things thathappened during and because of
(20:44):
COVID that we're talking aboutand watching, things like where
people are living right now.
There are certain states thathave seen a reduction in their
population and certain statesthat have seen an increase in
their population, and this wholework from home economy, work
from home market, has reallyenabled a lot of that.
(21:05):
As I always use the example ofI was working a technology job
and I was required to be in SanFrancisco for that job and I
moved there for the job and nowall of a sudden, I can work from
anywhere and that's not my home.
Why would I continue to live ina city or state where the cost
of living is so high?
I'm going to move somewhereelse, and so I'm going to start
(21:25):
trying to tie some of this tothe automotive aftermarket,
because that example I just used, maybe that person living in a
tech-focused city didn't need avehicle because those cities
have mass transit etc very goodmass transit and now that person
has moved to a city where maybemass transit's not quite as
(21:45):
available and so they had to gobuy a car, right, and so there's
a little tie to the automotiveaftermarket.
I'm sure we'll talk about thatin more detail.
But there's macro.
I think what you're hearingwith retail in general is a
cautious outlook.
There's just general concernsfrom a macro standpoint about
(22:06):
the ability of the consumer tocontinue to spend with where
prices are, interest rates are,et cetera, and I'll pause there.
Mike Chung (22:15):
Yeah, I'll make one comment and then circle back to
another question.
The real estate is a greattopic.
I don't think we'll dive intoit now, but it will be
interesting to see if thatmovement of residences the
example you just gave Californiato another city perhaps that
will slow down as interest ratesremain high, as prices of homes
(22:39):
remain high and as homeownerswith low mortgage rates have a
little bit of a disincentive tosell their home and move right.
Nathan Shipley (22:52):
I'm one of those that happened to buy a home
when interest rates were two anda half percent, right, you know
.
And so, like the running jokeis like, that's your retirement
home, nathan, because you'renever going to walk away from
that two and a half percent youknow, you really have to sweeten
the pot to make it attractivefor you.
Generalities, of course.
But yeah, and then on the flipside, folks that would like to
(23:15):
get into a home.
Rates are where rates are todaycompared to where they were
several years ago.
It just makes it much harder toget into a home for that same
person.
So, yeah, that's not helping,and obviously with inflation I
mean, I live in Texas withproperty taxes and all that you
know if you own investment homesand you have renters, well,
(23:37):
your costs of those homes aregoing up, so you're driving rent
prices up too, you know.
And so it's just this conundrumfor consumers in terms of what
they can afford because ofwhat's happening with the
economy today.
DTP (23:51):
Consumers in terms of what they can afford because of
what's happening with theeconomy today.
This is DTP IT Director andSustainability Committee Staff
Liaison at Auto Care Association.
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Mike Chung (24:38):
One other note about inflation is I know auto
insurance rates have gone up,maybe up to 40% in some cases
year over year, and that's justanother component of any
consumer's budget.
You mentioned shelter, housing.
If you have a car payment,certainly that can be a
substantial portion ofsomebody's income payments.
Certainly that can be asubstantial portion of
somebody's income, but anothermonthly expense right In terms
of if your car insurance ishigher, I can see how that would
have an impact on the familybudget.
(25:00):
To perhaps, as you mentioned,shop for, change brands or
change locales.
And tying to that, earlier yousaid something interesting in
terms of looking at the consumerdata that you have and being
able to say that, oh, thisconsumer bought at store X and
also went to store Y, or theyvisited different competitors.
(25:24):
So I'd like to dig into that alittle bit more.
So I was thinking about you'reable to see consumers purchase
patterns, but are you also ableto see if they go to a store and
not purchase something there?
Nathan Shipley (25:38):
That's through traditional data collection
methodology that we have.
The answer to that question isno.
We track what people buy period.
So if they walked into a storeand didn't buy anything, we're
not going to know that.
Mike Chung (25:53):
So that's the answer to that question yeah, Okay,
that's helpful because I thinkwhen you think about the future,
right, what do you see down theroad no pun intended in terms
of companies like yours who areable to amplify that database?
Right, Because you have pointof sale data, you have consumer
(26:15):
data.
Is there going to be anotherlayer of data to perhaps change
those forecasts?
Nathan Shipley (26:24):
Ooh, so are you talking at kind of using the
example of that consumer thatwalked into a store and didn't
buy and walked back out?
Exactly?
You know I, in terms of whatwe're doing.
You know, mike, I don't know ifI'll go down that path deep.
I mean, we are very focused onconsumer behavior and will we
(26:50):
ever be able to talk to?
I mean, we do a lot of in-storework, depending on the industry
.
I think, just kind of backingup and thinking about the
question.
Think about the power that ane-commerce retailer has, because
you use the example of aconsumer physically walking into
a store and not buying anything, right, and they walk back out,
(27:10):
okay, so what do you do withthat?
Think about an e-commerceretailer that can track what I'm
searching for.
It's the same idea, right?
I went to let's use Amazon,right?
I went to amazoncom and I typedin tennis shoes.
I didn't make a purchase, butthey know I'm looking and so the
fascinating.
(27:31):
And what do you do with thatdata?
So this guy came to our siteand he typed in tennis shoes and
he poked around a little bitand he left.
So which path do you go down?
He's in the market for tennisshoes, so we should start
lobbing ads at him on his phone.
Or do you now know that I'mAmazon?
(27:52):
That, okay, let's look atconsumer search behavior for
this category, and 80% ofconsumers come to our site and
they don't actually type in abrand.
They just type in tennis shoes.
They're not worried about thebrand.
Or is it the other way around?
80% are coming to the site andwhen they're searching for shoes
(28:13):
, they're typing in Nike, and so, wow, nike has a pretty strong
following the amount ofinformation that can be gleaned
off of search behavior, andthat's not even purchase
behavior.
This is just searches.
Again, this is not suggestingat all that we're going down
that path.
I'm talking in general.
This has nothing to do withSarkana.
(28:34):
But what retailers do with this, you know, because most, most
retailers have obviouslye-commerce side of their
business too, and so there'sthis whole being able to know
what's selling, et cetera, butit's also what's not selling,
and so tracking people going inand out of stores is one thing,
but also tracking what'shappening online, I think, is
(28:55):
another fascinating input.
In terms of talking about thedata lake and how much
information there's out, thereis just another one to throw at
you.
Mike Chung (29:04):
I think that's a great example and you could go
in a lot of different directionswith this right, because it
might be somebody getting somecomparison shopping right.
I look online, get a feel forwhat the prices are for certain
products, certain models, size,availability, but then I go to a
brick and mortar store topurchase it right.
So there could be those usecases and I think putting
(29:26):
together that complete pictureright Is that possible with
point of sale data?
Perhaps there's the onlinesurvey type of data, the
in-depth interview, the focusgroup.
But you raise a great questionor topic in terms of I'm looking
for my phone, but all the datathat's on my phone, in terms of
(29:49):
tracking behavior or in person,if I'm carrying my phone, how
can that location data perhapsbe used to make a composite
image of Mike Chung's purchase,shopping, et cetera, behavior?
Nathan Shipley (30:04):
Well, and I think, because there's company
that doesn't do that, I thinkwe're probably both thinking of
one in particular, right thatdoes just that.
Right, they track movement datawith phones, and I think
there's a couple of general bestpractice and not to start
preaching, but there's so muchinformation coming at all of us
(30:26):
from all different areas.
One is like the cell phonemovement data, the search data I
just talked about informationlike what Circona has, which is
actual purchase behavior, fromreceipts or from point of sale.
There's so much information, abest practice that I try to take
what are we trying tounderstand?
(30:48):
What is the actual questionwe're trying to answer?
And then let's back into it andfigure out which sources will
help with that.
So if I'm a retailer, as anexample, and one of my questions
is I'm trying to understand ifout of stock you know I'm a
brick and mortar retailer Areout of stocks affecting me, yes
(31:10):
or no?
Mortar retailer are out ofstocks affecting me, yes or no?
Well, one way we could figurethat out is we could that
example you gave a minute agowith actual purchase behavior.
We could go talk to a group ofconsumers that shopped at three
different auto parts retailersin the same week and we could
follow up with them and say whydid?
Did you go from A to B and B toC?
(31:30):
Right, because we're trying tofigure out, was an out of stock
the issue?
And maybe they'll tell us that.
They tell us that we're tryingto answer a specific question,
so we're kind of curating ourresearch to just that.
If we had the ability to go backand talk to people that walked
into stores and walked back out,why did they do that Right?
(31:51):
And so, if we're trying toanswer the question, was there
an out-of-stock issue?
Maybe then that group of peoplethat we know walked into that
store.
We could go back and ask themthat question.
You know, but it's, there's somuch information available from
so many different sources.
I think a lot of us are likewhat do we do with all this
information resources?
(32:12):
I think a lot of us are likewhat, what do we do with all
this information?
There's so much.
And so, trying to start withthe end in mind, not to sound
cliche, but there are veryrelevant and specific business
questions and issues that can beaddressed by these things.
Mike Chung (32:24):
you have to know what you're trying to figure out
first that's really helpfuladvice, because I can see where
some of the challenge is.
As you say, what is thequestion we're trying to solve?
Are we looking at the rightdata sources?
So can you tell me some of thechallenges that you've either
seen or do see with regard to,if you're working with a group
(32:47):
of analysts, they have a set ofdata they're trying to solve, a
advice that you might give tothose groups or if it's from a
technological perspective right,because I think now we live in
an age where there's much morecomputing horsepower than there
was perhaps when you and I werein college, right?
(33:08):
So is that a concern that youand your team have?
Nathan Shipley (33:13):
Don't try to age me.
I just graduated college, likethree years ago, Mike.
Mike Chung (33:18):
Don't try to age me.
I described when youaccomplished, like three years
ago Mike, come on, you'relooking really good for 29,.
Nathan, let me see.
Nathan Shipley (33:22):
Yeah, I know, I don't know if it's the gray, I
don't know if it's going to begoing to be a live video or just
the audio.
But you know a couple ofthoughts you know.
One is yeah, I kind of hit onthat point what is the end goal?
What are you trying to answer?
If you're just diving into adata set with no specific
(33:45):
question that you're trying toanswer, it's going to be very
hard to do something with it.
Two is question your datasource.
Make sure you understand thatit's representative of whatever
audience you're trying toanalyze.
I think very important is don'tassume this is the beauty of
(34:05):
data.
Don't assume that how thingsare in your own world are how
things are in the world.
Meaning I live in Houston Texas, right, meaning I live in
Houston Texas, right.
A lot of people drive full-sizetrucks and full-size SUVs like
Chevrolet Suburbans and F-250sand that's just the market where
(34:27):
I live.
And what you don't see is a lotof Subarus as an example.
And so if I just looked at kindof my own world, I'd say, ah,
subaru has no market share.
You don't see it right.
But then you go to colorado asan example of what you don't see
a lot of full-size trucks.
You don't see a lot.
You know.
What you do see is a lot ofmid-size trucks and a lot of
jeeps and a lot of four-wheelersand a lot of subarus, right.
And so in that world, like, oh,subaru is like they're crushing
(34:50):
it, so you can't look at yourown world and think it's what's
really happening, and so youhave to look at information
that's representative ofwhatever it is you're trying to
analyze, and so don't besurprised when some trend pops
up that in your mind you're likethat can't be true.
(35:12):
Well, because that's just yourown perspective of the world.
And that's where you getinformation.
You start talking to consumersand looking at point of sale
data.
I'm like, wow, people actuallybuy this world.
And that's where you getinformation.
You start talking to consumersand looking at point of sale
data.
I'm like, wow, people actuallybuy this stuff and it's a thing
so obviously your source.
Don't put blinders on to theworld and then think about what
the end goal was.
I think those three things canreally help focus any kind of
(35:34):
work you're trying to do interms of looking at information.
Mike Chung (35:37):
Really helpful advice.
And I want to go back to one ofyour earlier examples.
So you mentioned that tiresales.
There's some correlation tofood.
Sure, and probablyoversimplifying, and I just want
to not necessarily get intothat specific example.
But when you make a revelationlike that, some of the things
(35:59):
that come to mind, for me atleast, are we've talked about
monthly expenses, right, tiresis typically not a monthly
expense, but how do youreconcile, say, a regularly
occurring spend, whether rent,versus something that is not a
(36:20):
regularly occurring event or anemergency type of repair?
And then the second part ofthat is I always think about
modeling and what pool of datayou're looking at.
What are the?
Is it the dependent variables?
Are you including the rightdependent variables, because
perhaps there's something thatwe as an analyst team may have
(36:42):
not considered and are notincluding in that.
Can you tell us a little bitabout your thoughts on those
matters?
Nathan Shipley (36:49):
Yeah.
So a couple of thoughts.
You hit the nail on the headwith timing.
A set of tires, I meanabsolutely.
You got to flat to replacesomething whatever.
A set of tires for a typicalconsumer is what three years,
four years, maybe five, you knowto buy a set of tires?
Um, and so if I find a consumerthat walks into a tire store
(37:11):
today to buy the same set oftires that I bought four or five
years ago, on average I'mlooking at a price point that's
40% to 50% higher.
That's based on point-of-saledata that we have.
And so there is absolutesticker shock, and we have all
rode this wave of food prices.
We go to the grocery storeevery week, and it's been a
(37:32):
little bit every week.
And here we are a few yearslater and it's like whoa,
they're 30%, 40% higher thanthey were, but something like
tires or an automotive batteryor something like that.
If you are a major repair, youjust don't do it that often.
You walk in and oh, by the way,you're already feeling squeezed
by something we talked abouthigher food prices, higher
(37:52):
utilities, higher insurance,whatever it is and then you walk
in and that set of tires thatwas eight800 last time you
bought it.
Now it's compared to quick math, that was a thousand or 1100.
There's a shock there when thatprice point gets hit.
And so, in an economy like this, what we do see is okay,
(38:12):
there's trade down taking placeright.
It's this instant decisionbecause you're already feeling
so squeezed, and every otherpart of your financial life, I
just can't do this.
I'm going to trade down.
So I don't know if that gets atthe answer to your question
directly.
Mike Chung (38:30):
Oh, it's very helpful and I think it aligns
with the things that we've beentalking about the trade down,
contending with inflation,higher cost of living and I
don't have the stats at myfingertips, but if I remember
correctly, there are four tiersof tires and how the lower tiers
have been selling more, partlybecause of the things you've
(38:51):
talked about and partly becausethe quality has gone up over the
years.
Nathan Shipley (38:55):
So that's a good start.
Yeah, and that's.
I mean I don't want to talk toodeep on the tires industry in
particular, but yeah, that is agreat example of one where we
are seeing very clear data thatthere's trade now taking place
and so that concept if you lookat other major purchase
(39:15):
categories, it's happening.
And one thing and we'reprobably getting close to time,
but one thing we haven't reallytalked about if we talk about
the industry as a whole, is thishigher income consumer that,
like me, I don't know about you,I know Jackie working from home
, and I didn't work from homeprior to COVID, but I do now.
(39:36):
So my driving patterns havechanged, my ability to work from
anywhere has changed, and sodriving patterns have changed.
And this higher income grouphas, oddly enough, become more
engaged with the aftermarket.
And you start trying to dig insome of the whys behind that.
Well, you can start looking atwhat's happened with vehicle
(39:57):
prices over the last severalyears, and that's a moving
target today.
But what happened with new caravailability and all these
things?
And so it kind of pushed theaverage age older, and so now
average of cars, and so nowthat's pushed that consumer more
into our sweet spot and theyhave some more time on their
hands, maybe now they're doingsome more DIY instead of having
work done for them, and sothat's benefiting the industry.
(40:19):
And so, kind of tying it backto data analysis, you can't just
look at a category and say, wow, the category is growing,
that's fantastic.
And when you start digging inand you start breaking that
apart, it's like, well, wait asecond.
The core consumer of thiscategory, which traditionally is
a lower income consumer andthey typically drive much older
(40:41):
cars and they're DIYers they'reactually suffering, they're
actually deferring maintenanceand they're trading down and
there's a lot going on here inthis bucket.
But over here in this highincome bucket, that maybe isn't
traditionally thought of as acore DIY consumer and not
someone we're thinking aboutmarketing to.
But now, all of a sudden,they've decided to keep their
(41:01):
car longer and they're doingmore DIY because they have the
time, because they're workingfrom home five days a week.
They're driving the sales ofbrand and items in certain
categories and so when you groupit all together it's like, ah,
category is doing pretty good,brands are doing okay, you know,
but in reality there's two verydifferent stories going on with
(41:22):
two different consumer groups,and you start to see retailers
that recognize that or brandsthat recognize that, and they
start changing strategies around.
We need to market and try tosell to these groups differently
, because their needs and theirissues right now are very, very
different.
But if you look at it just inaggregate, things are doing okay
.
It's not quite the story that'sreally going on.
Mike Chung (41:44):
That's a great example and it makes me think of
, as you've talked about before,what's the story within the
story, right?
So I guess, like two questionsthat I have as we go to close
here, one, looking into thefuture, five years, 15 years,
what are your thoughts in termsof the challenges that lie ahead
(42:05):
for data analysts in thestories that we're trying to
extract from the data?
And I'll just harken back tosomething you talked about
earlier e-commerce.
Right, that was a game changerthroughout the 90s, into the
2000s.
And how do you, as a datacompany for lack of a better
(42:26):
word factor in new kind ofchannels of purchase or new data
sources?
So, thinking about the future,what challenges, what
opportunities do you see withregard to new data sources, the
volume of data, perhaps, thetechnical aspects, the
infrastructure of managing thatdata, the accuracy of that data?
Nathan Shipley (42:49):
Yeah, I mean the new channels.
That's something.
A company like e-commerce is agreat example.
We've evolved, you know, andthat's like all right, that's a
channel we need to be tracking,and so that's what we do, right,
we work with pure playe-commerce retailers like Amazon
, as an example, or it's Walmartthat we've worked with for a
long time the dot-com side oftheir business, right, and then
you can get into a first partyversus third party, and there's
all these different things.
(43:10):
So when we see something wherebecause, again, what our company
is focused on is what arepeople buying and so when a
channel like e-commerce becomeswhat it has become, if we're not
tracking that, that's a problem, that's a blind spot to what's
happening.
So we do track it.
As you look down the road andthink about challenges, a
(43:32):
variety come to mind.
One is consumer privacy.
That's a big one.
It has been and will continueto be a big one, not to get down
to the whole, data privacy,what we're dealing with with the
Auto Care Association in termsof who owns the vehicle data,
all these big topics that arehappening in our industry.
(43:52):
But it's no different than thedata being pulled from a phone,
right?
They're tracking my location orwhat I'm buying as a consumer
and how that's being tracked.
It's all the same.
It's consumer data, and so doesdata privacy.
What happens with that?
You know, how does that evolve?
That's one thing I'll bethinking about.
And, as other sources continueto come online, filtering out
(44:16):
the noise, that's a big one.
Don't make it too complicated,because, at the end of the day,
we're just simple people thatare just out buying stuff and so
just how do we track that?
So where does it go from heredown the road?
I think, big picture, withretail, it's going to be
fascinating to watch.
I think when e-commerce reallystarted to be taking people are
(44:39):
taking notice of it, it's likeman.
Five or six years ago we wereliterally in a presentation,
let's say, at Apex.
I would be talking about thenumber of retail doors that
closed in the last 12 months.
I was like 12,000 stores in theUS across all of it just closed
.
And it's because this wholee-com story that's not something
(45:00):
we're talking about today, andthe reason why is because brick
and mortar retailers have gottenvery competitive and where are
they strong and so verycompetitive against pure-fly
e-comm guys.
And so what's going to happenwith online?
What's going to happen withbrick and mortar and how does
that evolve?
I think it'll be fascinating towatch.
From a data collectionstandpoint, obviously, the
(45:26):
faster we can get informationout the better.
So data speed is something thatI know we're always focused on.
But yeah, I mean as an analyst,I think those big things, some
of the things we've alreadytalked about in terms of just
filtering out the noise willcontinue to be important.
Mike Chung (45:42):
Sure.
So I think you may have alreadyanswered this last question
that I have for you, but ifyou're talking to a senior in
college, maybe a master'sstudent, who's getting ready to
graduate and enter the so-calledreal world, what advice might
you have for people as theythink about careers?
How data is collected, how datais analyzed and how it's used.
Nathan Shipley (46:03):
Yeah, I think this is one I've used for a
while.
But if you're coming into a newrole, don't assume that
whatever company you're comingto work for, whatever role
you're getting into, let's saythat company you're working for
they don't have it all figuredout.
In general, whether it's dataand whatever it is, they don't
(46:24):
have it all figured out.
If everything was figured out,a lot of us wouldn't be working
right.
There's a reason why companieshave as many people as they do
because every single day,there's a new challenge in terms
of what, how they're sellingwhat, the products they're
making, the information comingin their way, and so, again,
don't assume it's all figuredout and look at whatever role
you're in or you're going intoas tremendous opportunity to
(46:46):
make a difference in whatever itis that you're doing and that
can be done through informationand something.
Even on our side we'll have newanalysts that will start and
they'll start digging into acategory that maybe I've kind of
personally written off.
I new analysts that will startand they'll start digging into a
category that maybe I've kindof personally written off.
I'm like, ah, I don't want tolook at that.
And I'm like, hey, did you knowthis is going on in this
category?
And I'm like what?
I had no idea, right?
(47:06):
So it's, I think, be curious.
Don't assume all the answersare out there because they're
not.
Have fun with it.
Mike Chung (47:14):
And I think you know .
Just a quick corollary to thatis you shared that from your
perspectives.
What advice would you give toexecutives across industries
when they think about data?
We didn't go into AI, butcertainly that's.
That could be another topic foranother day but as executives
think about the proliferation,availability, the ability to
process and, you know, usegenerative AI.
(47:36):
What might you say to theexecutive crowd?
Nathan Shipley (47:41):
Yeah, I think same thing.
I've kind of already hit on.
Ai is to your point, mike,that's something we didn't touch
on, but you think about thefuture.
I mean, it's here, right, it'scoming hot and heavy, but it's
no different than anything elsefiltering out the noise.
And I have to imagine that ifyou're an executive at one of
(48:06):
these major retailers, you havesources of information coming at
you daily, some of which youknow what they are and some of
which you have no clue, and youmight have two sources that tell
you the exact opposite thing interms of what's happening in
the market, and so coming upwith trusted advisors,
information you know trustbecause of sound methodology and
kind of staying focused on that.
But also it goes back to whatare you trying to answer, what
(48:26):
are you trying to know aboutyour business that maybe that
information can help with, ormaybe it can't, and you need to
look somewhere else becausethere's just so much information
swirling around.
It's important to filter outthe noise.
Mike Chung (48:38):
Well, really appreciate your insights and
taking the time to talk with ushere.
Thanks for tuning in to anotherepisode of Auto Care On Air.
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It helps others discover ourshow.
Auto Care On Air is proud to bea production of the AutoCare
(48:58):
Association, dedicated toadvancing the auto care industry
and supporting professionalslike you.
To learn more about theassociation and its initiatives,
visit autocareorg.
Welcome to AutoCare OnAir, a candid podcast for a
curious industry.
I'm Mike Chung, Senior Directorof Market Intelligence at the
AutoCare Association, and thisis Indicators, where we identify
and explore data that will helpyou monitor and forecast
industry performance.
This includes global economicdata, industry indicators and
new data that will help youmonitor and forecast industry
performance.
(00:28):
This includes global economicdata, industry indicators and
new data sources.
Happy to welcome Nathan Shipley, Executive Director at Circana.
So, Nathan, welcome to the show.
Good to see you, Mike.
Thanks for having me on theshow.
(00:48):
Glad to be here, Absolutely.
Nathan Shipley (00:51):
So tell us a little bit about what you do for
Cercana.
Sure thing, mike, I've workedwith you and the association for
a long time and in my role atCercana my job is it's an
industry analyst role and sowhat our company does very high
level.
I will probably get into that alittle more detail, but we
track a variety of differentretail industries, and the
automotive aftermarket is one ofthose, and so my job is to work
(01:14):
with retailers, manufacturers,associations such as the Auto
Care Association, on just kindof what we're seeing through the
eyes of the consumer in termsof what we track and all that in
terms of what we're seeingthrough the eyes of the consumer
in terms of what we track andall that in terms of what we're
seeing what's selling and someof the whys behind it.
My job is to kind ofcommunicate that out to the
folks that we work with.
Mike Chung (01:33):
Oh well, thanks for that introduction, and tell us a
little bit about how you got toyour role at Cercana.
Nathan Shipley (01:40):
You know, mike, you gave me this as a kind of a
pre-question.
I thought a lot about it, but Iwill give you a quick history
lesson.
The gentleman that used to runthe automotive business at what
was the NPD Group, named LarryMoore, was a neighbor of mine
growing up in the Houston Texasarea.
Junior year of college I waswashing my car among all things
Very fitting and said, hey,would you want an internship?
(02:12):
And I had no clue what he did.
But I said sure, and so Istarted working for this company
that was at the time called theMPD Group and I was making a
lot of copies and shipping out alot of paper reports at the
time, and it was an internshipthat turned into a full-time gig
a couple of years later andI've been here for most of my
career.
I started out doing some clientmanagement work.
I actually left the company andworked to work for Nielsen on a
(02:35):
CPG brand, which gave me areally interesting perspective
on consumer behavior and whattype of information is available
in a category or industry thatis very fast moving, whereas,
you know, obviously automotivecategories aren't quite as fast
moving.
But ended up coming back acouple of years later, and again
.
I've been here most of mycareer and have been in this
(02:57):
role now for about seven years.
The industry analyst role.
Mike Chung (03:02):
Oh, thanks for sharing that overview.
If you don't mind me asking,what did you study in college?
Did you have visions of workingin this line of business?
Nathan Shipley (03:16):
No, I was probably the guy.
I've got a business degree.
I focused on marketing, butdidn't have a specific plan in
mind as to what I was going todo.
I thought I'd go into a salesrole of some sort, but this
company, which again was NPD andthen we'll probably talk about
it, but has turned into Sarkana,through a variety of steps,
what we do is it's prettyinteresting.
(03:36):
I find consumer behavior to befascinating.
We're all different in terms ofhow we approach things, and so
being able to work in a kind ofa consultative type role where
we get to go in and work withcompanies and help them
understand their customerswhether that be the end consumer
, or help them understand theircustomers which for a lot of
(03:58):
manufacturers, is the retailersthat are selling the products
understanding why people aredoing what they're doing, what
they're buying, what they're notbuying it's really interesting.
So, no, I didn't see myselfgoing into a role like this, but
I've really enjoyed doing whatI do and I'm a car guy, so that
helps too.
Mike Chung (04:18):
Yeah, you mentioned there are different categories
that your company covers.
I think it's really fascinatinghow, as you shared, you're
washing your car, you gravitatetowards the automotive, you have
an interest in it and you'recovering that area.
So just tell me a little bitabout what type of consumer data
you're collecting and viewingin your role.
Nathan Shipley (04:40):
Sure, yeah, and I guess this is probably a good
time to introduce the idea ofSarkana.
So my career was primarilyspent at a company called the
NPD Group, and the NPD Grouptracked behavior and purchasing
patterns in what we call generalmerchandise industries.
So the automotive aftermarketis one of those industries, but
(05:01):
you could be talking aboutconsumer electronics or
cosmetics or apparel, anythingthat's not food, like groceries,
or consumer packaged goods liketoilet paper, paper, towels,
shampoo, etc.
So NPD was focused on thegeneral merchandise side of a
store or of categories orindustries.
There's a company out therethat's been around forever, it's
(05:23):
called IRI.
There's a company out therethat's been around forever.
It's called IRI.
Iri tracked and does track ordid track grocery and consumer
packaged goods and didn't reallyfocus on the general
merchandise industries that NPDtracked.
And so over the last two plusyears or so, through a series of
(05:49):
private equity moves in termsof NPD and IRI being purchased
by private equity, those twofirms were merged and the
combined firm has a new namewhich, as of a little over a
year ago, was Circana.
And so Circana is tracking thecomplete consumer.
We are tracking everything thata consumer could buy, whether
it's general merchandisecategories or food or CPG, and
so I am still focused on theautomotive aftermarket.
(06:11):
But what's really cool about myrole is that I have the ability
to see spending or purchasebehavior across everything that
a consumer might buy, and howwhat's happening at, maybe, a
grocery store is impactingwhat's happening in the
automotive aftermarket, as anexample.
And so you know.
In terms of the data sources youknow there's there's two
(06:33):
primary sources.
You know most industries notall, but most.
We have partnerships withretailers, and those retailers
send us their sales information.
For most, all the stores in theaftermarket, it's every store
that's under their umbrella,every store, every item, every
(06:53):
week, and there's some caveatsto that in terms of what
categories, et cetera.
So we're able to see, you knowcomparatively, what is selling
and what's not, and so there's alot of data security in terms
of us not exposing individualretailers.
So we're not able to seeindividual retailers, but
combined, we're able to say, hey, this is what's happening.
(07:15):
The other side of what we sourceinformation from is receipts,
and so we work directly withconsumers, and those consumers
provide us their actual receiptsfrom stores or online, if they
shop online, and so we know,through the panels that we have,
who those people are where theywent, what they bought, what
(07:35):
they paid, where else they went.
You know, it's reallyfascinating to watch a consumer
maybe hop around betweendifferent auto parts stores in
the same month.
It's like, well, if you'regoing to one, why are you going
to this other?
And was it an out-of-stockissue?
Was there a price issue?
There's some fascinatinglearning behind that type of
(07:56):
information.
So, again, two main sources ofdata that's coming in point of
sale, and then receipts, andthen in terms of what we can
actually do with it, it's awhole other conversation because
there's a lot of analytics thatcan be layered on top of that
to do some pretty powerful stuff.
Mike Chung (08:11):
That's really fascinating.
Thanks for that overview.
So I'm going to play a littlebit of that back and dive into
it, because there are two thingsthat kind of made me think of
some follow-up questions here.
So, just as an example, thegrocery stores, right.
So if your organization haspartnerships with, let's just
say, safeway, giant, let's justeven say Walmart, right, you're
(08:34):
able to.
They send you their sales dataand you can kind of bucket it,
categorize it at the store level, then roll it up to a regional
level.
You can look at it for fruitsand vegetables or I don't know,
like cereal, various categories,and you can slice and dice the
(08:55):
data in different ways.
And it's through thosepartnerships that you're able to
get comparatives, I guess, likeyou say, between those grocery
stores.
And like you said, there aresome data sharing agreements in
terms of the proprietary natureof how that data can be shared
out.
Am I getting that correct,Nathan?
Nathan Shipley (09:14):
That's right.
Yeah, I mean in terms of pointof sale data, like what you're
talking about, retailer privacyis paramount.
Data security is paramount,meaning we can't in a business
model like ours, we cannotexpose individual retailers.
That doesn't work.
So how does the model work?
We aggregate this data together.
So if we're talking about thefood industry as an example,
(09:36):
let's talk about the automotiveaftermarket we're not reporting.
When we report out what'shappening, we don't report about
individual retailers.
It's the aggregate of thoseretailers that we're reporting,
with the goal not, again, wecan't expose retailers.
Uh, that wouldn't work.
Um, and so you know a lot offolks.
Well, why would retailers dothat?
(09:56):
Or, you know, in any industry,why would?
What's the what's the benefitto them?
And the benefit to thoseretailers is that we report back
to those retailers.
You mentioned Walmart as anexample.
Walmart's able to see theirperformance, walmart's
performance relative to theaggregate of every other
retailer that we're tracking,and so it's performance.
(10:19):
But it's what's happening inindividual markets, as you
mentioned, certain parts of thecountry, certain cities in the
country, and you can kind of godown from there, you can drill
in deeper.
You know, are they performingwell in certain categories in a
store but not in othercategories in a store, and if
there's opportunities to thendive in to say, all right, why
aren't we performing well inthis category that we're talking
(10:41):
about this?
You know whatever category thiswe're talking about.
So that's the nature of themodel, is it's insights into
what's happening in thecategories in which they play.
And the most basic explanation Ihave and it seems to resonate
pretty well is you know, let'ssay I'm a retailer, you know, I
(11:05):
personally live in Houston,texas, and my goal this year,
internally, is to grow 5%.
And you know what?
In the Houston Texas market,I'm growing 5%.
I'm right in line with mypersonal goal of growing 5%.
But then I look at data from acompany like Cercana that's
tracking every retailer in theHouston market and that data
(11:26):
tells me that the Houston market, on average, is growing 9%.
And so, yeah, I'm hitting mypersonal goals of five.
But this information tells meI'm losing market share and my
competitors are growing fasterthan me for some reason, and I
need to figure out why that is.
What am I doing wrong?
What are they doing better?
What's going on?
(11:47):
And that's where it starts at avery high level, and then we
start drilling in a prettyintricate level of detail to
figure out what's going on andhow to fix it.
Mike Chung (11:55):
So benchmarking is certainly one of the advantages
that anybody who is subscribingcould glean from this and
monitor their performance versusthe rest of the markets.
That makes a lot of sense?
Nathan Shipley (12:09):
Yeah, that's definitely it.
But you start getting intoheavy analytics around promotion
strategy what's working, what'snot?
Pricing strategies by marketswhat price points are working?
Are you overpriced, underpricedwhen you promote?
Are you promoting too high, notenough?
Are you promoting too much?
Are you giving product away?
(12:30):
Are you not promoting enough?
The analytics that can be done.
It's very, very powerfulanalytics on what's happening at
a very granular level.
When you start digging into it,it's pretty neat at a very
granular level.
Mike Chung (12:44):
When you start digging into it, it's pretty
neat, sure, and two things thatI was thinking about.
One is in terms of the metatrends.
When you aggregate all thisdata, sometimes I get this
question how are private labelsdoing?
That's something that you wouldbe able to see across the
market for any category, whetherit's cereal or motor oil or
filters or what have you Indeed?
Nathan Shipley (13:05):
That's right, yeah, and so you know, again,
talking about retailerconfidentiality, you know
private label.
You know we're not going to betalking about an individual
retailer's private label brand,but an aggregate.
Yes, we're aggregating.
You know, it's motor oil.
We're going to put all theprivate labels together and call
it private label and we can seethis private label taking share
from brands, yes or no?
(13:26):
And that is a question that'scoming up a lot these days, it
seems.
Mike Chung (13:30):
Mike by the way, and to that end, when you talk
about the analytics that arepossible, I can see merging of
that data with things likeinflation consumer confidence.
To say what?
Inflation consumer confidence?
To say what?
Like a driver's analysis rightTo say if private label is
increasing, sales of privatelabel products are increasing.
(13:52):
Perhaps.
What can we attribute that to?
Is that fair to say?
Nathan Shipley (13:55):
It's very fair to say.
And so I mean, among otherthings, we do a lot of
forecasting work and so themodels they take a lot of those
macro factors, like you justmentioned.
You know inflation, what'shappening with inflation, what's
happening with food prices,what's?
You know there's all these,there's all these inputs that go
in, and then the model can lookback and say, all right, what
(14:16):
we can see, sales, you knowdemand, and then we can have all
these macro inputs and likewhat is most correlated with
demand from a macro standpointand like what is most correlated
with demand from a macrostandpoint, what's not
correlated with demand.
And so, as an example, one ofthe demand drivers.
So let me back up, we can see,through our point of sale data,
(14:36):
what's happening with foodprices at the grocery store.
You hear about it in the media,you feel it personally when you
go to the grocery store.
We all feel it personally.
And you go to the grocery store, we all feel it.
And so we can see how foodprices correlate with demand in
certain categories and, as anexample, the tires category.
(14:56):
It's one that we track.
The highest correlated macrofactor that we're seeing right
now is food prices and that wecan go back over time and it
kind of makes it's like okay,okay, that's, that's.
That's not a big shocker, butnot all categories are like that
.
Not all categories have a highcorrelation with food prices,
but tires in particular do um,and let's see.
(15:20):
No, I'm not going to go downthat rabbit hole per se, but yes
, you can start diving intomacro factors and macro economic
factors and kind of theircorrelation.
But even outside of the macroeconomic, within the four walls
(15:42):
of Sarkana, increasing foodprices again as an example,
right, wages have gone up Xpercent but food prices are
going up Y, which means that hastaken spending power away from
a lot of consumers becausethey're spending more of their
income on food as a percentageof their total wallet.
Where is that money coming from?
(16:03):
Where are they cutting back ifthey're cutting back?
And so it's interesting to lookat it that way too.
And so, kind of tying it backto my role, my job is kind of
look at the macro, like what'shappening big picture with
consumers in our space and someof the whys behind it.
(16:24):
If you were to ask me what'shappening with so-and-so item
and so-and-so category, Iprobably wouldn't be able to
answer the question off the cuff, I need to go port-a-port or
something.
Yeah, the informationinternally.
It's fascinating the amount ofinformation that that Sarkana
(16:44):
has compiled.
Mike Chung (16:46):
Yeah, so I'm going to revisit some of those
questions.
But if we back out a little bit, what general consumer trends
are you seeing?
You mentioned inflation andfeel free to talk about just
overall general consumerbehavior.
Is it perhaps spending lessbecause of inflation?
(17:07):
Is it going from a premiumbrand to a more mainline brand
or economy brand?
Is it delaying of purchase?
Can you tell me a little bitabout that?
And then, what's happening inthe automotive aftermarket?
Nathan Shipley (17:22):
Sure, yeah, it's the last.
It's been four or five yearsnow since COVID became a thing,
but observing consumer behaviorand kind of the macro economy
over the last several years hasbeen fascinating.
Not all for good reasons,obviously, but present day, yes,
(17:43):
inflation is hurting theconsumer.
We are seeing credit card debtper household at relatively high
levels.
Savings rates have declined.
Savings amounts have declinedper household.
Delinquencies are up a littlebit.
Housing-related expenses arereally chipping away at
(18:07):
discretionary spending power.
Are really chipping away atdiscretionary spending power.
So I guess big theme one is thisconcept of destruction of
discretionary spending power.
You go back three or four yearsago or five years ago and the
economy looked different.
Interest rates were a lot lower, spending on a lot of things
stopped because we all werelocked down, so we weren't
(18:30):
spending money on travel andsports for our kids and whatever
else.
Oh and, by the way, there wasstimulus money being pumped into
the economy and so all of asudden there was very high
levels of discretionary spendingpower at that time and so
that's changed.
You know, back then we sawcredit card debt coming down and
savings rates going up.
(18:50):
Back then we saw credit carddebt coming down and saving
(19:14):
treats going up and from a bigpicture, at least financially,
not so much from a public healthstandpoint housing and food as
two examples.
Utilities those make up afairly significant amount of
their weekly or monthly budget,and so when their income hasn't
kept up with the rate ofincrease of those big items,
their discretionary spendingpower is affected by it.
So, from a pricing standpoint,we are seeing actually all this
talk about prices and higherprices and everything's more
(19:36):
expensive, and now what you'restarting to hear, what you are
hearing, is prices are.
Things are calming down withprice, and the rate of change
definitely has slowed, and insome industries it's turned
negative.
We're seeing price deflation insome categories, but food is an
example, while the rate ofchange isn't what it was meaning
(19:59):
.
The rate of change has sloweddown.
Prices are still way higherthan they were two, three, four
years ago, and so that's stilleating into discretionary
spending power of the consumer.
So we are seeing trade down insome regards.
You know, I think you need tothink about trade down both.
From a, I'm standing at theshelf in a store that I always
shop at and am I trading downfrom brand A to brand B or brand
(20:22):
B, a private label as anexample, or am I trading down in
terms of where I shop?
Am I typically shopping in aspecialty store and now I'm
going to a mass retailer?
Or I'm typically shopping at amass retailer and now I'm going
to a dollar store?
So we're seeing some of that aswell A little bit bigger
picture than all of that.
There's some things thathappened during and because of
(20:44):
COVID that we're talking aboutand watching, things like where
people are living right now.
There are certain states thathave seen a reduction in their
population and certain statesthat have seen an increase in
their population, and this wholework from home economy, work
from home market, has reallyenabled a lot of that.
(21:05):
As I always use the example ofI was working a technology job
and I was required to be in SanFrancisco for that job and I
moved there for the job and nowall of a sudden, I can work from
anywhere and that's not my home.
Why would I continue to live ina city or state where the cost
of living is so high?
I'm going to move somewhereelse, and so I'm going to start
(21:25):
trying to tie some of this tothe automotive aftermarket,
because that example I just used, maybe that person living in a
tech-focused city didn't need avehicle because those cities
have mass transit etc very goodmass transit and now that person
has moved to a city where maybemass transit's not quite as
(21:45):
available and so they had to gobuy a car, right, and so there's
a little tie to the automotiveaftermarket.
I'm sure we'll talk about thatin more detail.
But there's macro.
I think what you're hearingwith retail in general is a
cautious outlook.
There's just general concernsfrom a macro standpoint about
(22:06):
the ability of the consumer tocontinue to spend with where
prices are, interest rates are,et cetera, and I'll pause there.
Mike Chung (22:15):
Yeah, I'll make one comment and then circle back to
another question.
The real estate is a greattopic.
I don't think we'll dive intoit now, but it will be
interesting to see if thatmovement of residences the
example you just gave Californiato another city perhaps that
will slow down as interest ratesremain high, as prices of homes
(22:39):
remain high and as homeownerswith low mortgage rates have a
little bit of a disincentive tosell their home and move right.
Nathan Shipley (22:52):
I'm one of those that happened to buy a home
when interest rates were two anda half percent, right, you know
.
And so, like the running jokeis like, that's your retirement
home, nathan, because you'renever going to walk away from
that two and a half percent youknow, you really have to sweeten
the pot to make it attractivefor you.
Generalities, of course.
But yeah, and then on the flipside, folks that would like to
(23:15):
get into a home.
Rates are where rates are todaycompared to where they were
several years ago.
It just makes it much harder toget into a home for that same
person.
So, yeah, that's not helping,and obviously with inflation I
mean, I live in Texas withproperty taxes and all that you
know if you own investment homesand you have renters, well,
(23:37):
your costs of those homes aregoing up, so you're driving rent
prices up too, you know.
And so it's just this conundrumfor consumers in terms of what
they can afford because ofwhat's happening with the
economy today.
DTP (23:51):
Consumers in terms of what they can afford because of
what's happening with theeconomy today.
This is DTP IT Director andSustainability Committee Staff
Liaison at Auto Care Association.
Are you passionate aboutshaping your industry's future?
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real impact as a volunteer.
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(24:13):
chain.
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committees.
Mike Chung (24:38):
One other note about inflation is I know auto
insurance rates have gone up,maybe up to 40% in some cases
year over year, and that's justanother component of any
consumer's budget.
You mentioned shelter, housing.
If you have a car payment,certainly that can be a
substantial portion ofsomebody's income payments.
Certainly that can be asubstantial portion of
somebody's income, but anothermonthly expense right In terms
of if your car insurance ishigher, I can see how that would
have an impact on the familybudget.
(25:00):
To perhaps, as you mentioned,shop for, change brands or
change locales.
And tying to that, earlier yousaid something interesting in
terms of looking at the consumerdata that you have and being
able to say that, oh, thisconsumer bought at store X and
also went to store Y, or theyvisited different competitors.
(25:24):
So I'd like to dig into that alittle bit more.
So I was thinking about you'reable to see consumers purchase
patterns, but are you also ableto see if they go to a store and
not purchase something there?
Nathan Shipley (25:38):
That's through traditional data collection
methodology that we have.
The answer to that question isno.
We track what people buy period.
So if they walked into a storeand didn't buy anything, we're
not going to know that.
Mike Chung (25:53):
So that's the answer to that question yeah, Okay,
that's helpful because I thinkwhen you think about the future,
right, what do you see down theroad no pun intended in terms
of companies like yours who areable to amplify that database?
Right, Because you have pointof sale data, you have consumer
(26:15):
data.
Is there going to be anotherlayer of data to perhaps change
those forecasts?
Nathan Shipley (26:24):
Ooh, so are you talking at kind of using the
example of that consumer thatwalked into a store and didn't
buy and walked back out?
Exactly?
You know I, in terms of whatwe're doing.
You know, mike, I don't know ifI'll go down that path deep.
I mean, we are very focused onconsumer behavior and will we
(26:50):
ever be able to talk to?
I mean, we do a lot of in-storework, depending on the industry
.
I think, just kind of backingup and thinking about the
question.
Think about the power that ane-commerce retailer has, because
you use the example of aconsumer physically walking into
a store and not buying anything, right, and they walk back out,
(27:10):
okay, so what do you do withthat?
Think about an e-commerceretailer that can track what I'm
searching for.
It's the same idea, right?
I went to let's use Amazon,right?
I went to amazoncom and I typedin tennis shoes.
I didn't make a purchase, butthey know I'm looking and so the
fascinating.
(27:31):
And what do you do with thatdata?
So this guy came to our siteand he typed in tennis shoes and
he poked around a little bitand he left.
So which path do you go down?
He's in the market for tennisshoes, so we should start
lobbing ads at him on his phone.
Or do you now know that I'mAmazon?
(27:52):
That, okay, let's look atconsumer search behavior for
this category, and 80% ofconsumers come to our site and
they don't actually type in abrand.
They just type in tennis shoes.
They're not worried about thebrand.
Or is it the other way around?
80% are coming to the site andwhen they're searching for shoes
(28:13):
, they're typing in Nike, and so, wow, nike has a pretty strong
following the amount ofinformation that can be gleaned
off of search behavior, andthat's not even purchase
behavior.
This is just searches.
Again, this is not suggestingat all that we're going down
that path.
I'm talking in general.
This has nothing to do withSarkana.
(28:34):
But what retailers do with this, you know, because most, most
retailers have obviouslye-commerce side of their
business too, and so there'sthis whole being able to know
what's selling, et cetera, butit's also what's not selling,
and so tracking people going inand out of stores is one thing,
but also tracking what'shappening online, I think, is
(28:55):
another fascinating input.
In terms of talking about thedata lake and how much
information there's out, thereis just another one to throw at
you.
Mike Chung (29:04):
I think that's a great example and you could go
in a lot of different directionswith this right, because it
might be somebody getting somecomparison shopping right.
I look online, get a feel forwhat the prices are for certain
products, certain models, size,availability, but then I go to a
brick and mortar store topurchase it right.
So there could be those usecases and I think putting
(29:26):
together that complete pictureright Is that possible with
point of sale data?
Perhaps there's the onlinesurvey type of data, the
in-depth interview, the focusgroup.
But you raise a great questionor topic in terms of I'm looking
for my phone, but all the datathat's on my phone, in terms of
(29:49):
tracking behavior or in person,if I'm carrying my phone, how
can that location data perhapsbe used to make a composite
image of Mike Chung's purchase,shopping, et cetera, behavior?
Nathan Shipley (30:04):
Well, and I think, because there's company
that doesn't do that, I thinkwe're probably both thinking of
one in particular, right thatdoes just that.
Right, they track movement datawith phones, and I think
there's a couple of general bestpractice and not to start
preaching, but there's so muchinformation coming at all of us
(30:26):
from all different areas.
One is like the cell phonemovement data, the search data I
just talked about informationlike what Circona has, which is
actual purchase behavior, fromreceipts or from point of sale.
There's so much information, abest practice that I try to take
what are we trying tounderstand?
(30:48):
What is the actual questionwe're trying to answer?
And then let's back into it andfigure out which sources will
help with that.
So if I'm a retailer, as anexample, and one of my questions
is I'm trying to understand ifout of stock you know I'm a
brick and mortar retailer Areout of stocks affecting me, yes
(31:10):
or no?
Mortar retailer are out ofstocks affecting me, yes or no?
Well, one way we could figurethat out is we could that
example you gave a minute agowith actual purchase behavior.
We could go talk to a group ofconsumers that shopped at three
different auto parts retailersin the same week and we could
follow up with them and say whydid?
Did you go from A to B and B toC?
(31:30):
Right, because we're trying tofigure out, was an out of stock
the issue?
And maybe they'll tell us that.
They tell us that we're tryingto answer a specific question,
so we're kind of curating ourresearch to just that.
If we had the ability to go backand talk to people that walked
into stores and walked back out,why did they do that Right?
(31:51):
And so, if we're trying toanswer the question, was there
an out-of-stock issue?
Maybe then that group of peoplethat we know walked into that
store.
We could go back and ask themthat question.
You know, but it's, there's somuch information available from
so many different sources.
I think a lot of us are likewhat do we do with all this
information resources?
(32:12):
I think a lot of us are likewhat, what do we do with all
this information?
There's so much.
And so, trying to start withthe end in mind, not to sound
cliche, but there are veryrelevant and specific business
questions and issues that can beaddressed by these things.
Mike Chung (32:24):
you have to know what you're trying to figure out
first that's really helpfuladvice, because I can see where
some of the challenge is.
As you say, what is thequestion we're trying to solve?
Are we looking at the rightdata sources?
So can you tell me some of thechallenges that you've either
seen or do see with regard to,if you're working with a group
(32:47):
of analysts, they have a set ofdata they're trying to solve, a
advice that you might give tothose groups or if it's from a
technological perspective right,because I think now we live in
an age where there's much morecomputing horsepower than there
was perhaps when you and I werein college, right?
(33:08):
So is that a concern that youand your team have?
Nathan Shipley (33:13):
Don't try to age me.
I just graduated college, likethree years ago, Mike.
Mike Chung (33:18):
Don't try to age me.
I described when youaccomplished, like three years
ago Mike, come on, you'relooking really good for 29,.
Nathan, let me see.
Nathan Shipley (33:22):
Yeah, I know, I don't know if it's the gray, I
don't know if it's going to begoing to be a live video or just
the audio.
But you know a couple ofthoughts you know.
One is yeah, I kind of hit onthat point what is the end goal?
What are you trying to answer?
If you're just diving into adata set with no specific
(33:45):
question that you're trying toanswer, it's going to be very
hard to do something with it.
Two is question your datasource.
Make sure you understand thatit's representative of whatever
audience you're trying toanalyze.
I think very important is don'tassume this is the beauty of
(34:05):
data.
Don't assume that how thingsare in your own world are how
things are in the world.
Meaning I live in Houston Texas, right, meaning I live in
Houston Texas, right.
A lot of people drive full-sizetrucks and full-size SUVs like
Chevrolet Suburbans and F-250sand that's just the market where
(34:27):
I live.
And what you don't see is a lotof Subarus as an example.
And so if I just looked at kindof my own world, I'd say, ah,
subaru has no market share.
You don't see it right.
But then you go to colorado asan example of what you don't see
a lot of full-size trucks.
You don't see a lot.
You know.
What you do see is a lot ofmid-size trucks and a lot of
jeeps and a lot of four-wheelersand a lot of subarus, right.
And so in that world, like, oh,subaru is like they're crushing
(34:50):
it, so you can't look at yourown world and think it's what's
really happening, and so youhave to look at information
that's representative ofwhatever it is you're trying to
analyze, and so don't besurprised when some trend pops
up that in your mind you're likethat can't be true.
(35:12):
Well, because that's just yourown perspective of the world.
And that's where you getinformation.
You start talking to consumersand looking at point of sale
data.
I'm like, wow, people actuallybuy this world.
And that's where you getinformation.
You start talking to consumersand looking at point of sale
data.
I'm like, wow, people actuallybuy this stuff and it's a thing
so obviously your source.
Don't put blinders on to theworld and then think about what
the end goal was.
I think those three things canreally help focus any kind of
(35:34):
work you're trying to do interms of looking at information.
Mike Chung (35:37):
Really helpful advice.
And I want to go back to one ofyour earlier examples.
So you mentioned that tiresales.
There's some correlation tofood.
Sure, and probablyoversimplifying, and I just want
to not necessarily get intothat specific example.
But when you make a revelationlike that, some of the things
(35:59):
that come to mind, for me atleast, are we've talked about
monthly expenses, right, tiresis typically not a monthly
expense, but how do youreconcile, say, a regularly
occurring spend, whether rent,versus something that is not a
(36:20):
regularly occurring event or anemergency type of repair?
And then the second part ofthat is I always think about
modeling and what pool of datayou're looking at.
What are the?
Is it the dependent variables?
Are you including the rightdependent variables, because
perhaps there's something thatwe as an analyst team may have
(36:42):
not considered and are notincluding in that.
Can you tell us a little bitabout your thoughts on those
matters?
Nathan Shipley (36:49):
Yeah.
So a couple of thoughts.
You hit the nail on the headwith timing.
A set of tires, I meanabsolutely.
You got to flat to replacesomething whatever.
A set of tires for a typicalconsumer is what three years,
four years, maybe five, you knowto buy a set of tires?
Um, and so if I find a consumerthat walks into a tire store
(37:11):
today to buy the same set oftires that I bought four or five
years ago, on average I'mlooking at a price point that's
40% to 50% higher.
That's based on point-of-saledata that we have.
And so there is absolutesticker shock, and we have all
rode this wave of food prices.
We go to the grocery storeevery week, and it's been a
(37:32):
little bit every week.
And here we are a few yearslater and it's like whoa,
they're 30%, 40% higher thanthey were, but something like
tires or an automotive batteryor something like that.
If you are a major repair, youjust don't do it that often.
You walk in and oh, by the way,you're already feeling squeezed
by something we talked abouthigher food prices, higher
(37:52):
utilities, higher insurance,whatever it is and then you walk
in and that set of tires thatwas eight800 last time you
bought it.
Now it's compared to quick math, that was a thousand or 1100.
There's a shock there when thatprice point gets hit.
And so, in an economy like this, what we do see is okay,
(38:12):
there's trade down taking placeright.
It's this instant decisionbecause you're already feeling
so squeezed, and every otherpart of your financial life, I
just can't do this.
I'm going to trade down.
So I don't know if that gets atthe answer to your question
directly.
Mike Chung (38:30):
Oh, it's very helpful and I think it aligns
with the things that we've beentalking about the trade down,
contending with inflation,higher cost of living and I
don't have the stats at myfingertips, but if I remember
correctly, there are four tiersof tires and how the lower tiers
have been selling more, partlybecause of the things you've
(38:51):
talked about and partly becausethe quality has gone up over the
years.
Nathan Shipley (38:55):
So that's a good start.
Yeah, and that's.
I mean I don't want to talk toodeep on the tires industry in
particular, but yeah, that is agreat example of one where we
are seeing very clear data thatthere's trade now taking place
and so that concept if you lookat other major purchase
(39:15):
categories, it's happening.
And one thing and we'reprobably getting close to time,
but one thing we haven't reallytalked about if we talk about
the industry as a whole, is thishigher income consumer that,
like me, I don't know about you,I know Jackie working from home
, and I didn't work from homeprior to COVID, but I do now.
(39:36):
So my driving patterns havechanged, my ability to work from
anywhere has changed, and sodriving patterns have changed.
And this higher income grouphas, oddly enough, become more
engaged with the aftermarket.
And you start trying to dig insome of the whys behind that.
Well, you can start looking atwhat's happened with vehicle
(39:57):
prices over the last severalyears, and that's a moving
target today.
But what happened with new caravailability and all these
things?
And so it kind of pushed theaverage age older, and so now
average of cars, and so nowthat's pushed that consumer more
into our sweet spot and theyhave some more time on their
hands, maybe now they're doingsome more DIY instead of having
work done for them, and sothat's benefiting the industry.
(40:19):
And so, kind of tying it backto data analysis, you can't just
look at a category and say, wow, the category is growing,
that's fantastic.
And when you start digging inand you start breaking that
apart, it's like, well, wait asecond.
The core consumer of thiscategory, which traditionally is
a lower income consumer andthey typically drive much older
(40:41):
cars and they're DIYers they'reactually suffering, they're
actually deferring maintenanceand they're trading down and
there's a lot going on here inthis bucket.
But over here in this highincome bucket, that maybe isn't
traditionally thought of as acore DIY consumer and not
someone we're thinking aboutmarketing to.
But now, all of a sudden,they've decided to keep their
(41:01):
car longer and they're doingmore DIY because they have the
time, because they're workingfrom home five days a week.
They're driving the sales ofbrand and items in certain
categories and so when you groupit all together it's like, ah,
category is doing pretty good,brands are doing okay, you know,
but in reality there's two verydifferent stories going on with
(41:22):
two different consumer groups,and you start to see retailers
that recognize that or brandsthat recognize that, and they
start changing strategies around.
We need to market and try tosell to these groups differently
, because their needs and theirissues right now are very, very
different.
But if you look at it just inaggregate, things are doing okay
.
It's not quite the story that'sreally going on.
Mike Chung (41:44):
That's a great example and it makes me think of
, as you've talked about before,what's the story within the
story, right?
So I guess, like two questionsthat I have as we go to close
here, one, looking into thefuture, five years, 15 years,
what are your thoughts in termsof the challenges that lie ahead
(42:05):
for data analysts in thestories that we're trying to
extract from the data?
And I'll just harken back tosomething you talked about
earlier e-commerce.
Right, that was a game changerthroughout the 90s, into the
2000s.
And how do you, as a datacompany for lack of a better
(42:26):
word factor in new kind ofchannels of purchase or new data
sources?
So, thinking about the future,what challenges, what
opportunities do you see withregard to new data sources, the
volume of data, perhaps, thetechnical aspects, the
infrastructure of managing thatdata, the accuracy of that data?
Nathan Shipley (42:49):
Yeah, I mean the new channels.
That's something.
A company like e-commerce is agreat example.
We've evolved, you know, andthat's like all right, that's a
channel we need to be tracking,and so that's what we do, right,
we work with pure playe-commerce retailers like Amazon
, as an example, or it's Walmartthat we've worked with for a
long time the dot-com side oftheir business, right, and then
you can get into a first partyversus third party, and there's
all these different things.
(43:10):
So when we see something wherebecause, again, what our company
is focused on is what arepeople buying and so when a
channel like e-commerce becomeswhat it has become, if we're not
tracking that, that's a problem, that's a blind spot to what's
happening.
So we do track it.
As you look down the road andthink about challenges, a
(43:32):
variety come to mind.
One is consumer privacy.
That's a big one.
It has been and will continueto be a big one, not to get down
to the whole, data privacy,what we're dealing with with the
Auto Care Association in termsof who owns the vehicle data,
all these big topics that arehappening in our industry.
(43:52):
But it's no different than thedata being pulled from a phone,
right?
They're tracking my location orwhat I'm buying as a consumer
and how that's being tracked.
It's all the same.
It's consumer data, and so doesdata privacy.
What happens with that?
You know, how does that evolve?
That's one thing I'll bethinking about.
And, as other sources continueto come online, filtering out
(44:16):
the noise, that's a big one.
Don't make it too complicated,because, at the end of the day,
we're just simple people thatare just out buying stuff and so
just how do we track that?
So where does it go from heredown the road?
I think, big picture, withretail, it's going to be
fascinating to watch.
I think when e-commerce reallystarted to be taking people are
(44:39):
taking notice of it, it's likeman.
Five or six years ago we wereliterally in a presentation,
let's say, at Apex.
I would be talking about thenumber of retail doors that
closed in the last 12 months.
I was like 12,000 stores in theUS across all of it just closed
.
And it's because this wholee-com story that's not something
(45:00):
we're talking about today, andthe reason why is because brick
and mortar retailers have gottenvery competitive and where are
they strong and so verycompetitive against pure-fly
e-comm guys.
And so what's going to happenwith online?
What's going to happen withbrick and mortar and how does
that evolve?
I think it'll be fascinating towatch.
From a data collectionstandpoint, obviously, the
(45:26):
faster we can get informationout the better.
So data speed is something thatI know we're always focused on.
But yeah, I mean as an analyst,I think those big things, some
of the things we've alreadytalked about in terms of just
filtering out the noise willcontinue to be important.
Mike Chung (45:42):
Sure.
So I think you may have alreadyanswered this last question
that I have for you, but ifyou're talking to a senior in
college, maybe a master'sstudent, who's getting ready to
graduate and enter the so-calledreal world, what advice might
you have for people as theythink about careers?
How data is collected, how datais analyzed and how it's used.
Nathan Shipley (46:03):
Yeah, I think this is one I've used for a
while.
But if you're coming into a newrole, don't assume that
whatever company you're comingto work for, whatever role
you're getting into, let's saythat company you're working for
they don't have it all figuredout.
In general, whether it's dataand whatever it is, they don't
(46:24):
have it all figured out.
If everything was figured out,a lot of us wouldn't be working
right.
There's a reason why companieshave as many people as they do
because every single day,there's a new challenge in terms
of what, how they're sellingwhat, the products they're
making, the information comingin their way, and so, again,
don't assume it's all figuredout and look at whatever role
you're in or you're going intoas tremendous opportunity to
(46:46):
make a difference in whatever itis that you're doing and that
can be done through informationand something.
Even on our side we'll have newanalysts that will start and
they'll start digging into acategory that maybe I've kind of
personally written off.
I new analysts that will startand they'll start digging into a
category that maybe I've kindof personally written off.
I'm like, ah, I don't want tolook at that.
And I'm like, hey, did you knowthis is going on in this
category?
And I'm like what?
I had no idea, right?
(47:06):
So it's, I think, be curious.
Don't assume all the answersare out there because they're
not.
Have fun with it.
Mike Chung (47:14):
And I think you know .
Just a quick corollary to thatis you shared that from your
perspectives.
What advice would you give toexecutives across industries
when they think about data?
We didn't go into AI, butcertainly that's.
That could be another topic foranother day but as executives
think about the proliferation,availability, the ability to
process and, you know, usegenerative AI.
(47:36):
What might you say to theexecutive crowd?
Nathan Shipley (47:41):
Yeah, I think same thing.
I've kind of already hit on.
Ai is to your point, mike,that's something we didn't touch
on, but you think about thefuture.
I mean, it's here, right, it'scoming hot and heavy, but it's
no different than anything elsefiltering out the noise.
And I have to imagine that ifyou're an executive at one of
(48:06):
these major retailers, you havesources of information coming at
you daily, some of which youknow what they are and some of
which you have no clue, and youmight have two sources that tell
you the exact opposite thing interms of what's happening in
the market, and so coming upwith trusted advisors,
information you know trustbecause of sound methodology and
kind of staying focused on that.
But also it goes back to whatare you trying to answer, what
(48:26):
are you trying to know aboutyour business that maybe that
information can help with, ormaybe it can't, and you need to
look somewhere else becausethere's just so much information
swirling around.
It's important to filter outthe noise.
Mike Chung (48:38):
Well, really appreciate your insights and
taking the time to talk with ushere.
Thanks for tuning in to anotherepisode of Auto Care On Air.
Make sure to subscribe to ourpodcast so that you never miss
an episode.
Don't forget to leave us arating and review.
It helps others discover ourshow.
Auto Care On Air is proud to bea production of the AutoCare
(48:58):
Association, dedicated toadvancing the auto care industry
and supporting professionalslike you.
To learn more about theassociation and its initiatives,
visit autocareorg.
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