September 2, 2024 • 51 mins
Unlock the secrets to mastering data in the auto care industry with our special guest, Daniel Zenko, Director of Data Innovation at the Auto Care Association. In this episode of Auto Care ON AIR's "Indicators," Daniel and host, Mike Chung, dive deep into the realm of data delivery and consumption. We explore the vital role of benchmarking data for sales performance and dissect the historical challenges that have plagued the industry. Daniel demystifies the terms "front-of-the-counter" and "behind-the-counter" data, shedding light on how program groups collaborate to ensure timely part deliveries.
Ever wondered how fragmented data sources impact the automotive aftermarket industry? We tackle this head-on by examining the strengths and limitations of US government data and alternative sources like Visa, MasterCard, IMR, and MPD. Our conversation underscores the importance of triangulating data from multiple sources to gain a holistic market understanding. Plus, we introduce you to the TrendLens platform, a revolutionary tool offering nearly 50 economic and industry indicators that can significantly enhance your market analysis prowess.
Looking ahead, we delve into the future of data platforms and AI in the auto care industry. They discuss the potential for expanding data coverage to include e-commerce and heavy-duty markets while maintaining high website performance and user experience. We consider AI's role in generating insightful narratives, albeit with necessary human oversight. To round off, we reflect on how economic indicators influence business confidence, stressing the importance of long-term trends over short-term fluctuations. Don't miss this rich episode filled with invaluable insights for industry professionals!
To learn more about the Auto Care Association visit autocare.org.
To learn more about our show and suggest future topics and guests, visit autocare.org/podcast
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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.
This is Mike Chung, and I amreally delighted to have Daniel
Zenko, director of DataInnovation at AutoCare
Association, joined me as aguest today.
So welcome, daniel.
Thank you for having me.
(00:48):
So, daniel, tell us a littlebit about what you do at
AutoCare.
Daniel Zenko (00:54):
Hi, my title is Director of Data Innovation,
which really means that I'mdealing with kind of new ways to
disperse and disseminate datafor our users and our members.
That's pretty much.
Our goal is to kind of improvethe way that data is consumed by
our users.
Mike Chung (01:14):
So why is it called data innovation?
Daniel Zenko (01:17):
Well, we try to find innovation, but mostly in a
way the data is delivered.
There's not much innovationabout the way the data is
collected or what data we areusing.
It's pretty much what industrywas using all along.
It's just a matter ofefficiency and urgency, of the
ability to consume the data in aproper, timely manner.
(01:39):
That was kind of missing before.
So we are trying to improve onthat and, of course, to find new
data sources that might beuseful for our members and
community.
Mike Chung (01:52):
Sure, so we'll dive into some of those subtopics as
we go on here, but before we dothat, tell me a little bit about
your journey to AutoCare.
What brought you here and howdid you get here?
Daniel Zenko (02:02):
Yeah, I'm not an auto care lifer at all.
I spend my career in differentindustries, but all of my work
was related to the marketintelligence sales intelligence
to find useful data to be usedby different industry insiders
(02:24):
to be able to sell theirproducts better.
So in that context, my previousexperience was actually pretty
relevant to what we're doinghere, and, of course, each
industry has its own quirks andidiosyncrasies, but overall it's
pretty similar, I would say.
But overall it's pretty similar, I would say.
Mike Chung (02:45):
Sure.
Daniel Zenko (02:57):
So, when we think about the automotive aftermarket
and recognizing that not all ofour listeners may necessarily
be in our industry, what kind ofdata are you referring to help
understand the market trends, toput their own performance in a
context, to be able tounderstand better what's really
going on with their sales?
Because your sales might be up10% last month but without
(03:17):
having a context, knowing thatoverall industry is maybe up 50%
or maybe just 5%, it's reallyhard to gauge how well you're
really doing and should be happywith your sales efforts, or
maybe just 5%.
it's really hard to gauge howwell you're really doing and
should be, happy with your salesefforts, or maybe you should be
worried about what's going on.
Mike Chung (03:32):
So some of what you're talking about now sounds
like benchmarking data, so salesperformance data of a company
relative to the rest of theindustry, for example.
Daniel Zenko (03:40):
Correct, correct and the industry for a while
struggled to have a usefulbenchmark that will really be
indicative of what's reallygoing on.
Mike Chung (03:49):
So, before you joined AutoCare, what were some
of those benchmarks that peoplein the industry were monitoring?
Daniel Zenko (03:57):
Well, there wasn't much when it comes to that like
a cumulative industry effort.
There are some isolated datafeeds from separate retailers or
program groups that our memberswould be able to access, but
that wasn't really much, andalso there was some of the
(04:21):
government data that can be used, but all in all it was mostly
on a yearly basis or just in aspecific slice of the market
that was covered, especiallywhen it comes to the hard parts
data, meaning data for the partsthat are sold behind the
counter.
For the front of the counterdata, there was pretty
significant data out there thatusers could use.
Mike Chung (04:46):
Sure.
So one of the things youmentioned was hard parts data.
So if I'm not in the automotiveindustry and I'm a consumer, I
have my own car.
Are you referring to, say I goand get a part changed, say I
get my brakes done at a?
Would that be considered behindthe counter or hard parts?
Daniel Zenko (05:07):
Well, it depends.
An easier way to explain thisis when you go to the AutoZone
store, let's say, and you wantto buy some part, if that part
is displayed out there, you canjust pick it up and bring it to
the counter that's front of thecounter item like wipers or a
refresh or something like that.
If you have to talk with a,with a person there and he goes
(05:29):
back into the warehouse to bringyou actual part, that's behind
the, behind the counterpart, andthere'll be, like you know,
chassis parts or like brake padsor stuff, like you know, stuff
of the most significant, likevolume and weight, and that that
usually goes into the integralpart of the car and that's would
certainly be very model makespecific.
(05:50):
It sounds like that uh, yeah, Imean, depends, you know, some,
some parts are very specific tomake a model and some are
relatively general.
It's really, you know, dependson the type of part that makes
sense.
Mike Chung (06:02):
And then something else you mentioned before that
may be a new term for some ofour listeners program group.
Can you just briefly tell uswhat a program group is?
Daniel Zenko (06:10):
A program group is basically association or some
kind of joint effort by thewholesale distributors, which
are the companies that provideparts to our shops.
For example, shops don't keepall of their necessary inventory
(06:31):
at the shop.
So when you come there with aproblem, very often they don't
have that part available rightthere to put it in your car.
So they call those distributorsto bring this part to them from
their warehouses they have allover the country and timeliness
is essential here, so that partoften makes it to the shop in
(06:54):
like half an hour or less, whichis really amazing.
And so these distributors, theykeep all the parts in their
warehouses and have the disposalto the shops as soon as needed.
And to gain pricing power andto streamline some of the
operations these wholesalersorganize into these program
(07:15):
groups where they join forces.
And for several reasons that'sa good approach to give them
more leverage in the market.
Mike Chung (07:23):
That makes a lot of sense.
Thanks for giving us thatbackground.
One of the other things youtouched on was government data
and thinking about theautomotive aftermarket.
My understanding is that it isnot necessarily a US census
defined entity, right?
So I think that adds to thechallenge of getting that
indicator data that can beuseful for people across the
(07:46):
industry.
Is that kind of in line withwhat you were talking about?
Daniel Zenko (07:49):
Yeah, yeah, I mean there is a category called,
like, aftermarket retail stores,which is relatively easily
defined and understandable, butthere is a slew of different
other providers of the parts andservices in the industry that
are not necessarily easilydefined and also there's a good
(08:11):
portion of our sales in ourindustry is happening through
the street orders like Walmart,who have a bay that you can
bring your car to do some basicrepairs and change parts and
stuff like that.
So it's really hard to use thegovernment data to get a
(08:33):
wholesale idea of what's reallygoing on overall in the industry
.
Mike Chung (08:39):
So what I'm hearing is the government data can be
very rich and informative.
However, since the automotiveaftermarket is so spread out
across so many retail outlets,it could be a grocery store, it
could be a big box store likeCostco, where you could have
service done you can get frontof the counter parts, for
(09:00):
instance.
Daniel Zenko (09:01):
But the stat that you highlighted is a specific,
specific NAICS code for autoparts, accessories, entire store
sales and therefore doesn'tcapture everything right, and
there's also anothercomplication with the online
retailers like like Amazon oreBay that have a pretty healthy
you know car parts businessthere and some of it it's their
(09:24):
own sales.
So it's like just like a market.
They provide marketplace to theindependent salesmen and those
are really not classifiedanywhere as such.
So it gets kind of murky outthere when you want to kind of
grasp what's the really fullmarket out there for parts.
Mike Chung (09:42):
Sure.
So pretty broad, open-endedquestion.
But what data sources shouldpeople in the industry be
following?
Daniel Zenko (09:52):
I mean still the government is the best source
out there for this general data.
There's also a significantamount of data that comes out of
the companies like Visa orMasterCard, when you can monitor
the swipes of the cards at theretail places and get some kind
(10:13):
of information from there.
Also, the Autograph Associationprovides a lot of information
that's useful for industry, aswell as some other industry
specialist companies thatspecialize in providing data in
the industry, like IMR and MPDand some others.
Mike Chung (10:35):
So going back to the government data first.
What makes the US governmentdata such a rich resource?
What are some of the advantagesof the US government data?
Daniel Zenko (10:46):
Well it's.
You know, it's a well organizedin and, again, it's limited in
its core but what's available iswell organized and will
maintain and there's like a lotof restatements when it's
necessary.
So it's quite dependable andyou know, and there's no really
I should put it fudge in thedata that will be created by a
(11:12):
special interest of somebodywho's providing the data.
So it's objective.
That's really really good.
Mike Chung (11:21):
So what I'm hearing is regular process, objective,
quote-unquote, third party ifyou will, and they do seasonal
adjustments and otherstatistical processing to make
sure it's representative.
Is that fair to say?
Daniel Zenko (11:35):
Yeah, yeah, they're quite good at that.
And also it comes with aregular schedule.
So if some indicator ispublished, usually like second
Tuesday in a month, you cancount it.
It's going to happen that way.
So there's regular cadence ofthe data.
That's again it's a basis forany kind of serious analysis,
but still not probably enough toreally satisfy all the needs
(11:57):
that you have to monitor what'sgoing on in the industry.
Mike Chung (12:00):
The second one you mentioned was MasterCard, visa.
Can you explain a little bitmore about what you meant by
that?
Daniel Zenko (12:07):
Yeah, I mean when you swipe your card at a grocery
store or any kind of store,mastercard, visa retains that
information and resell it.
You know they are not allowedto resell who actually swiped it
, your identity or anything, butthey have liberty to resell.
You know what was bought whenyou know, and so that data is
(12:30):
anonymized and you know and sold, you know, in some kind of
packages to the specificindustry players.
You know that might beinterested in this particular
type of data.
So that gives us a pretty goodinsight about you know, dynamics
of the sales and you know whatkind of items people buy, let's
say together in kind of the samebasket and stuff like that.
(12:52):
So you know it's pretty useful.
It's very wide, you knowthere's almost too much of it,
you know but you know, with theproper analysis you know there's
some insight that can be takenfrom there.
Mike Chung (13:06):
So that's what I wanted to clarify.
When you said MasterCard andVisa, I thought about data
that's available for purchaseand then that could require
further analysis to drill down,categorize the data, and I would
expect that to be similar in inconcept to, say, some of the
(13:27):
commercial providers youhighlighted towards the end of
that answer.
You mentioned npd groups orkana, other other data sources
where a subscriber couldpurchase data and then use it
for analytical purposes, tounderstand the consumer,
understand consumer behaviorbetter.
Daniel Zenko (13:44):
Yeah, and also there are specialist players who
, let's say, focus on exactlywhat's happening with Amazon so
which parts sold well in Amazon,which companies sold well there
and trends there Monitor,internet searches and stuff like
that there's a lot ofinformation out there that can
be used, but it's best used astriangulation.
(14:05):
It compares you with somethingelse.
That gives you kind of morebroad understanding of what's
going on.
Mike Chung (14:12):
Sure, and to that point.
I think that's not to make thissound like a commercial for the
data platforms that AutoCarenecessarily provides, but one of
the things that you architectedfor the TrendLens platform was
economic and industry indicators.
I think there are nearly 50data points that people can look
(14:33):
at.
Daniel Zenko (14:33):
Yeah, that product was originally brainchild of
the Market IntelligenceCommittee and Association, but
we actually put it in a digitalform association but we actually
put it in a digital form.
Basically, we we've selectedmaybe like around 50 economic
industry indicators that we feelthe most important for our
(14:55):
industry participants to monitorand follow and and we put them
together in in this package thatwe publish on our website for
free for all our users, and thatpackage is updated as the new
data comes along which is notpublished on the, about sales of
the cars, sales of gasoline,unemployment rates, inflation,
(15:28):
all kinds of stuff that areuseful to understand better in a
context of the industry.
Mike Chung (15:37):
And some of those other indicators are disposable
income, consumer debt, inflation, unemployment, prime interest
rate and when we think aboutparticipants in the industry,
our quote-unquote audience, it'swide as far as well as broad,
and what I mean by that is typeof company, from parts
(16:02):
manufacturers to distributors,to retailers, to service
providers, and then roles withinthose companies, from sales and
marketing category management,executive, c, c-suite there's a
whole range of individuals thatare monitoring different touch
points, if you will, to keep apulse on the aftermarket yeah, I
(16:23):
.
Daniel Zenko (16:23):
I mean, we provide a lot of information about that
and I'm sure that all ourindustry participants can figure
out which broad indicators arekind of related to their own
sales process, either be like aweather or a disposable income
(16:44):
or maybe price of gasoline, andyou know.
So they're always alreadytracking them.
But we provide this technologytool to follow them all at the
same time and to get some kindof insight of interaction of
these different indicators togive them better understanding
of what's the big picture, howthe market is really behaving
(17:08):
overall.
Mike Chung (17:09):
And when you put together this platform, how did
you decide on data sources?
Tell us a little bit about whatyou were looking for in data
sources.
Daniel Zenko (17:21):
Well, number one requirement was the data is
published monthly, because allthese indicators are refreshed
every month, so we have to havea data point that actually is
published monthly so we can useit.
Also, we're trying to findindicators that have national
(17:41):
level data but also regionallevel if possible, so we can get
some more insight.
And again, some of them arefairly obvious, like you know,
miles driven, prices of gasolineand stuff like that.
But some others are maybe notthat obvious but definitely are
important to understand well theoverall health of US economy,
(18:05):
like housing starts anddisposable income, and you know
the consumer confidence, forexample.
Those are very important onesto really understand what's
going on the level ofindebtedness of the consumers.
There's several onesunemployment rate.
Those are all very importantfactors that have a significant
(18:27):
impact, not just our industrybut all industries in the United
States.
But definitely important tounderstand what's going on with
ours too is the United States,but definitely important to
understand what's going on withours too.
Mike Chung (18:39):
So another aspect of , or another part of, the
TrendLens platform is, of course, demand index, which is the
anonymized aggregatedpoint-of-sale data.
When you're building consensusto create the demand index, can
you tell us about that process?
Was there anything that made itmore?
Daniel Zenko (18:57):
challenging, for example.
Well, our industry,specifically category management
committee in our associationand its members, you know,
recognize the lack of adequatedata to monitor what's going on
with sales, especially hardparts in our industry.
So they joined forces anddecided to share their sales
(19:21):
data in a way that can be usefulfor industries.
So while constructing thisparticular data product, we
consulted with both retailersand problem groups who produce
this data and the manufacturerswho consume this data on other
side, in order to find the levelof detail that's useful but
(19:44):
also not overwhelming, in thesense that it's really hard to
actually produce the data ofnecessary quality.
This is one of the situationsthat good enough, the perfect is
enemy if enemy of good enough.
You try not to make it toocomplicated as a product in the
first place, so the datadelivery may be delayed or not
(20:06):
possible.
On the other hand, you have tomake it detailed enough to be
useful for users.
It was a gradual process.
We started with 15 differentcategories or segments to follow
.
Now we are up to 130-something,and it's constantly growing,
growing not just in width, butalso in depth.
(20:26):
We are getting more and more indetail, which makes the data
more actionable and more usefulfor different kinds of users.
Mike Chung (20:32):
Well, that's fascinating More than eight-fold
growth from 15 to more than 130categories, the consensus
building and, like you said, thedepth there.
Are there other aspects youmentioned.
Daniel Zenko (20:43):
You talked about national to regional for in
dollars, sales in units and allother kind of potential data and
we are constantly expanding itbased on the interest of users.
What part of the market isDatafill?
(21:10):
It's served regarding the datacoverage, so we're always trying
to get better and deepercoverage, but not too much
strain to the data contributorswho actually help us with
sending the data for theseparticular categories.
Mike Chung (21:29):
And to circle back on a topic you touched on
earlier, with regard to thefidelity and usefulness of that
data, there's also that elementof confidentiality and making
sure that, uh, somebody can'treverse, engineer and figure out
, say, a competitor's marketshare, for instance.
(21:51):
Yeah, I'm balancing it withthat aspect.
Daniel Zenko (21:53):
We have a big number of data contributors and
it's hard to say exactly, but weprobably cover like 80% of the
full market in any category.
Transparency is not an issuethat much.
Every other data is well hiddenin a mass, so it's hard to
(22:15):
really decipher what's going on.
Plus, we don't go in that muchof a detail.
You know we focus on like ninegeographic regions and United
States.
That's as deep as it goes, so Idon't see that as a problem.
But the fact is that we do havethe biggest coverage of any
kind of other providers of thiskind of information, and I think
(22:40):
it's a pretty big distancebetween us and the second
largest provider of such data.
Mike Chung (22:47):
Sure, and now that this subscription product has
been available for several years, it has momentum.
Do you feel like it's easier tohave new participants in terms
of adding more to that 80coverage, if you will?
Yeah absolutely.
Daniel Zenko (23:07):
I mean we are working with several other
retailers and and program groupsto join the panel.
Although again, we havecoverage where it's very
commanding, like 80%, we canstill do better and try to get
as close as we can to the 100%.
(23:28):
I mean this product, unless wehave 100% of the market which
we'll never have, obviously willalways be approximation you
have to take a lot for theadjustment for the part that's
missing and try to figure outokay which part is actually
missing for a particular part.
But again, it's still by farthe best and most comprehensive
(23:49):
indicator of that sort out there.
Mike Chung (23:52):
And to circle back on something you mentioned, the
US government will do datarestatements.
So if you were to add morecontributors, then certainly
that would necessitate a kind ofa restatement, recalibration of
sorts.
Is that fair to say?
Daniel Zenko (24:16):
our representation .
Even though it's not 100% ofthe market, it's still
relatively representative,meaning that these new
contributors we might sign onwill not make big difference in
overall indices.
You know, which is veryunlikely, that will happen
anyway because, again, we coveralready all the major retailers
and program groups.
So you know, you know therewill be some, you know, on
(24:38):
margins, some small changes, butwe don't anticipate any kind of
big change anytime soon.
Mike Chung (24:45):
When you were constructing the demand index, I
asked you about challenges.
How about things that wereperhaps easier and glided
through a little more easilythan you may have expected?
Was there anything that wasremarkable that way?
Daniel Zenko (25:00):
well, it's kind of interesting with this kind of
type of data is that um and thehigher some of the up is up in a
in a company you know,leadership, that we discuss this
.
It's easier for them tounderstand what you're trying to
do.
This because this is not aproduct that focuses on
individual SKUs or anything likethis.
(25:20):
We just cover, let's say,breakpads that's what we do
which is pretty wide and for alot of category managers maybe
not detailed enough to reallyspark their interest.
But if you hire executives ofthese companies to understand
what's going on overall with thebrake pads, that's quite useful
information.
(25:41):
So to get a bind from the uppermanagement of the industry was
relatively simple because theyunderstood the strategic
importance of having thisinformation.
But again, when you talk withthe people whose job is to just
cover ceramic breakpads, havinga number for overall breakpads
(26:01):
is not necessarily that useful.
So on that side there's alittle challenge to get to have
information detailed enough tobe useful for even ranking file
or category managers In somecategories.
It might be a little tricky butwe're getting there.
A lot of categories that wecover are now split into
relatively small chunks, smallpieces.
(26:22):
So, we're definitely going inthe right direction.
Ted Hughes (26:25):
Hi, I'm Ted Hughes, executive Director of AWDA and
Senior Director of CommunityEngagement for the Auto Care
Association.
We provide our members withnumerous avenues for connection
and collaboration through ourdiverse range of committees and
communities.
Whether you're interested inadvancing your career through
the Women in Auto Care programor our vibrant Under 40 group,
(26:45):
or simply wish to network andglean insights from fellow
distributors, shops andmanufacturers, we have dedicated
committees and communitieseager to connect with you.
Learn more at autocareorg slashcommunities.
Mike Chung (27:00):
If you were to give advice to somebody who's
building a data platform in anyindustry, whether it's a free
resource or a subscriptionresource, what pieces of advice
might you give to that person?
Daniel Zenko (27:13):
Well, first, I would say, focus on on selling
the vision, meaning you know,getting buy in regarding what
goal you're trying to achieveand worry about details later.
You know, because if you startlet's say, in our case, if you
started with three or 15 productlines or categories, it's not
(27:34):
necessarily a big difference,you know, it's not necessarily a
big difference.
It's important to get a buy-infrom all important players in
the industry and to have aconsensus about what we're
trying to do with this so we cancontinue developing the product
in the right direction.
Last thing you want to do is tobuild something and then in the
(27:54):
middle, realize, oh, wait aminute, this is not what.
You know what's needed.
You know this is more like, uh,you know, solution is a short
problem, you know, instead ofother way around.
So, you know, make sure thatyou start from the, from the,
from the correct starting point.
You know that.
You know that you're not, uh,barking wrong tree.
Mike Chung (28:16):
I would say so, to replace some of that, what I'm
hearing is communicating theconcept to your audience, making
sure that they understand itand you're aligned on that,
rather than worrying about someof the details.
You gave the example of threeversus 15 product categories, so
I can see there where theprocess is going to be very
(28:39):
similar whether it's three, 15,or 150, but really getting
buy-in on the concept.
And then, to harken back tosomething you said earlier,
don't let perfect be the enemyof good enough.
Daniel Zenko (28:50):
Correct, you know, because with data there's
always, you know, there's alwayssomething going on, some kind
of restatement going on.
Something was misclassified, sothere are going to be changes,
those little restatement changes, but those changes are on the
margin usually, so you don'tneed to worry too much about
(29:10):
that, because that's just theway it goes.
Mike Chung (29:13):
The process will take care of those things.
Yeah, yeah, yeah.
Daniel Zenko (29:16):
But if you have everybody aligned in what we're
trying to achieve with thateverything's fine.
Mike Chung (29:22):
So, thinking about, say, demand index, what does the
future hold?
Daniel Zenko (29:29):
Well, we can go even deeper and in more detail
with the stuff we have, butthere's a limited leeway, but
there's a limited playing fieldthat we can still expand to.
So we are looking into differentareas.
We're looking into indexes thatwill be more specific to
(29:49):
particular types of sales, let'ssay e-commerce, maybe to have
indicators that will coverexactly what's going on with the
sales over the internet.
Then there's an interestingconcept about maybe expanding
this to the HD field, likeheavy-duty vehicles, but just
(30:10):
for the heavy-duty parts.
We're looking into it andeventually we might get to that.
Also, we are working with someservice providers, companies
that provide car repair servicesand maintenance services to
capture data on that level, likebrake jobs, all changes,
(30:34):
changes of batteries to provideinformation on that level.
Because right now of batteriesto provide information on that
level, because right now what wedo is provide information on
the level of point of sales whenpart is sold, but we don't
really cover how many.
All changes were happening inthe United States in the last
month which we can anddefinitely we'll give some kind
(30:54):
of insight to our users.
That would be interested.
And stuff like that.
There are other parts of ourindustry that are lacking the
necessary data and we are tryingour best to figure out.
Is it feasible to actuallycollect the data and disseminate
(31:15):
it in a proper way?
Mike Chung (31:21):
So what I'm hearing is you've highlighted about 80%
of market coverage with ourcurrent demand index and that's
going to be US light duty or, Iguess, automotive parts, and so
now we're looking further afieldto different quote-unquote
markets and platforms orchannels, shall we say whether
it's e-commerce or heavy duty.
Daniel Zenko (31:38):
Or maybe like a tool and equipment sales.
There's another aftermarket,adjacent industries that are
heavy players in our industrybut they're not necessarily
covered.
Or maybe something about tires,specifically that kind of stuff
.
So yeah there's definitely roomto expand, you know, in the
(31:59):
right direction, andunfortunately it seems that our
industry is lacking likenecessary data in a lot of
aspects.
So there's plenty of ground tocover.
It's just a matter of findingsomething that's feasible and
can be pulled off in arelatively quick manner.
Mike Chung (32:15):
This is not my field of expertise, but I think about
all the computing horsepower,all the storage.
Can you touch on any of thoseinfrastructure concerns or is
that another department?
Daniel Zenko (32:26):
Well, so far it's not particularly bad regarding
that, because, yes, we'retalking about millions of lines
of data, but in the grand schemeof things, that's not huge.
We can definitely handle itEven further.
(32:48):
The concerns are mainly on theperformance of the website.
Because more data is loaded onit, it takes slower to actually
render the data, which makesworse consumer user experience.
So that's something we need toparticularly worry about to make
sure that we don't upload somuch data in a system that it
(33:14):
can't handle it with the properspeed.
That's our primary concern atthis point to make sure that the
user experience is adequate.
Mike Chung (33:23):
Makes a lot of sense , and thinking about data
platforms in general, what isthe future of data platforms?
We've seen a lot of datavisualization over the past
decade and a lot of ways tocustomize your view and do
analysis from there.
But looking into your crystalball five years, 10 years into
(33:43):
the future, what could a dataplatform like Demand Index or
otherwise look like in thefuture?
What might we seetechnologically develop?
Daniel Zenko (33:53):
Well, it's more about customization.
Really, in the future, what Ithink is going to happen is that
people will be able to choosetheir individual experience of
using the data, because rightnow, it's like a buffet table
Saturday in a restaurantEverything is out there and for
(34:17):
a lot of users, a lot of thatdata is immaterial because it
doesn't really touch what theydo.
So maybe we can organize it ina way that you get better
experience of using it byremoving everything that's not
really in your you know, in yourballpark and something that you
(34:37):
want to use and also providethe you know ability to render
the data on your cell phones andother platforms.
Right now it's pretty much onlyon a computer, but you know we
will definitely look into, youknow, expanding it on other data
platforms look into expandingother data platforms and
(34:59):
thinking not just about demandindex, but not just about demand
index, but data platforms ingeneral.
Mike Chung (35:03):
We hear about AI right, so are we getting to a
point where we can use AI in adata platform to answer
insightful research questions?
Daniel Zenko (35:18):
Well, yes and no, as this artificial intelligence
tools are improving, there'sstill a lot of questions about
(35:43):
their ability to criticallyassess what's right data what's
not.
But if you provide this toollet's say these five charts and
ask them to analyze those chartsand create some kind of insight
out of it, I believe I can do apretty good job with it because
the scope is defined.
They don't need to search theinternet for different potential
data sources that may not beproperly vetted.
You know there's no confusionin their artificial mind, in you
(36:04):
know what's the data that theyshould focus on.
They have this, you know,limited set of data inputs that
they have to put in some kind ofcontext and relationship.
I believe they can help withthat, you know, to basically to
create some kind of commentaryor write ups or you know stuff
like some basic analysis, youknow.
(36:25):
So, yeah, that that I can seeon that narrative side.
Definitely they can.
They can help on you knownumber crunching side.
It's just, you know, youbasically have to check the math
all the time, which then killsthe efficiency.
You have to spend more timeverifying that everything that
(36:48):
was included was supposed to beincluded, you know or not.
So I'm a little reserved aboutthat.
But definitely on the outputside, on the kind of
presentation of the data side,artificial intelligence can help
.
Mike Chung (37:06):
So it sounds like to get some words on a blank sheet
of paper to get a narrativestarted.
That seems facilitable.
As for the numerical analysis,the statistical, the forecasting
analysis, that will certainlyrequire a bit of human
intervention, if you will, tomake sure that the analysis is
(37:28):
appropriate.
Daniel Zenko (37:29):
Yeah, and at this point there's not much problem
regarding number crunching.
There are plenty of tools thatare very good at you know
playing with numbers, you know.
So there's really no need foranother layer of you know
technology to help with that.
The problem starts really when,okay, you have all this data,
(37:51):
and data is verified and it'scorrect, and now how to create a
story around it.
So I'm not saying thatartificial intelligence will be
able to give you a perfect pitch, perfect story about you know
what's going on based on theseparticular inputs, but it will
give you some proper guidelinesthat you can kind of okay, so
(38:14):
this is how it looks like andthen you can you can maybe a bit
more deeper into it, or maybe,you know, adjust a little bit
what output is from artificialintelligence and go with that
you know.
So that interpretation of thedata is a little tricky, you
know, because you know glasshalf full, half empty.
It's really, you know, dependson your personality really, how
(38:36):
you interpret it's really, youknow, depends on your
personality really, how youinterpret certain numbers you
know.
So maybe we can count on AI tobe kind of impartial about, you
know, especially if you havesome kind of political views.
You know you can alwaysinterpret numbers that convey
your narrative that thisparticular administration is bad
for economy or is particularlygood for economy.
(38:59):
Something like AI, again, ifyou give them limited sources to
play with, can probably giveyou an objective story without
any kind of bias.
Mike Chung (39:11):
Oh, that's helpful and I think the context that
somebody like you or I mighthave being in the industry, and
we can add that truthing elementto the narrative that an AI, a
generative AI tool, may provide.
Daniel Zenko (39:29):
Yeah, yeah, absolutely no, there's a,
there's a, there's a.
You know, there's no way Ithink that these tools can
replace human input.
But they can provide some help,definitely again, but it has to
be, has to be done in a waythat it's, you know, controlled
and uh, and reasonable, you knowso yeah definitely there's.
(39:52):
There's something there, butmaybe not as much as some people
hope for and also not as muchas some people are afraid of.
Mike Chung (40:02):
Yeah, it's certainly an interesting development that
we'll keep watching.
Just in our last few minutes, Iwant to touch on a couple of
things that you and I havetalked about from an economics
perspective and for our audience.
Daniel has a lot of backgroundin economics and we've had some
good, healthy discussions onsome of these topics and perhaps
we could pursue these inanother conversation on this
(40:24):
podcast.
But one of the things thatwe've talked about is US debt.
Okay, so concerns of the UnitedStates being over leveraged.
We're at about $36 trillion ofdebt right now.
Nominal GDP last year was about$29 trillion.
Us debt was recently downgradedto AA plus, I believe, and
(40:49):
we're in an era of sustainedinterest rates being high, so is
this a big deal?
What's your interpretation ofthese circumstances?
Daniel Zenko (41:03):
Well, when thinking about death, I think
the best way to refer to it isto understand how much of the
burden for the government isservicing the debt.
Some governments haverelatively small debt but nobody
(41:26):
wants to borrow their money, sothey have to pay huge interest
rates to get those funds, andeven small debt for those
countries is a really big deal.
Countries like the US have alot of leeway there because
everybody wants to lend themmoney and give them cheap
interest rates for that now hasto pay around 3% of the national
(41:55):
GDP every year for servicingthe debt, which is slightly
higher than in the last decadeor so, but significantly still
lower than, let's say, duringthe Reagan years.
So, yeah, it definitely is goingup.
The price of the servicing, thedebt, is definitely going up,
but compared to the long-termaverage it's not that bad.
(42:19):
And also compared to the otherdeveloped countries like Japan
or the.
United Kingdom or something it'sstill much, much better.
It's still much, much better.
So, yeah, doesn't seem storydoesn't really check out as the
(42:41):
debt is like a big problem allof a sudden that you know
creditors will decide, oh no, usis not country that we can, you
know, let money to.
You know we have to be carefulwith that.
It's just not not something atthis point that that seems of
serious concern, especially inthe context of other economic
indicators.
It's that inflation rate is,let's say, average.
(43:06):
If you look at the nocturnalterm it's falling and the US
economy doesn't show signs ofgoing into either depression or
overheating at this point.
So there's really no concern atthis point of international
creditors or domestic creditorsto really worry about the
creditworthiness of the UnitedStates government.
(43:27):
So the overall story seems likea non-story at this point.
You know, again, things canchange, but nothing really there
, you know, to worry about asmuch as some people would like
to.
Mike Chung (43:45):
I appreciate that perspective and you touched on.
Another topic and I think we'llclose with this is inflation.
We've had high inflation.
It was a little above 9% in2022.
It's currently at about 3%.
We hear about the goal beingabout 2%.
What are your thoughts on a 2%goal?
(44:07):
Is it appropriate, thinkingabout our industry, consumers in
our industry?
What are your thoughts about 2%inflation goal?
Daniel Zenko (44:17):
Well, that kind of that goal is a little bit
moving of the goalpost.
You know, for the long, longtime of federal you know,
federal US Fed had a goal tohave an inflation band between
2% and 4%.
That was an acceptable rangeand somehow all of a sudden that
(44:39):
2% lower band became a goal,which is not necessarily true,
because cutting interest rate ispretty much the only tool that
Ferdinand Garland has to propthe economy.
The only tool that FerdinandGarland has to prop the economy.
If interest rate is alreadyvery low and inflation is very
(45:05):
low, then simply there's notmuch lever there.
They can't do much to furtherprop up the economy.
In the case of an emergency, 2%is okay as maybe a goal, but
it's not really the goal.
The goal is to keep it somewherebetween 2% and 4% and long-term
average, I think, is somethinglike 3.4% in the United States.
So current inflation rate of 3%is well beneath that level.
(45:27):
So I don't believe that federalagencies would mind that it
stays just like it is, providingthe economic growth is solid.
Now there are some talks thatthere will be need relatively
soon to cut interest rates,which will again heat up the
(45:50):
economy and bring up maybe someinflation you know together, but
still you know there's a plentyof room there at this point to
move.
You know inflation a little bitup if necessary or down if
that's the goal, you know.
So we are in a good spot inthis point.
And uh, again two percent as agoal.
I think it's better tounderstand that there's a lower
(46:11):
band, lower band of thebandwidth that's acceptable for
the inflation.
I know for some people it'smaybe hard to accept that zero
inflation is not a good idea,but it's really not.
Having a small 2-3% inflationis perfectly fine and actually
desirable.
Mike Chung (46:30):
That's helpful to clarify that band, as you talked
about, and perhaps one of thethings that's been hard for
consumers is this is following aperiod of sustained high
inflation, so it's like a littlebit of salt into an open wound
and perhaps over time thingswill equilibrate in terms of our
adaptation to sustain interestrates.
Daniel Zenko (46:51):
It's a recent bias because current generation of
of grownups.
They grew up in an era of very,very low inflation Since the
last 20 years.
It was exceptionally lowinflation rate for the United
States that pretty much neverbefore existed, or maybe rarely,
so they were kind ofconditioned to expect inflation
(47:13):
rates and interest rates to bereally, really low.
But those low interest rateswere just a product of the
government trying to prop USeconomy after the dot-com boom
and bust, after the real estateboom and bust and also mortgage
crisis.
So those low interest rateswere not normal.
(47:35):
It was just a specific targetto actually help boost the US
economy, which helped, but it'snot something that is good
long-term for a way to do it.
So I would say current interestrates and current inflation
rates are pretty much averageand pretty much what you want.
So again, in specificsituations you want to adjust it
(47:57):
a little bit, but unless goodreasons come to actually do that
, I think we are in the properband.
Regarding both inflation rateand unemployment rate and
interest rate, it's all aboutaround the long-term average
right now.
Mike Chung (48:15):
Oh, that's helpful.
I love the recency bias and itmade me think about if you grew
up in Boston before the Red Soxwon the World Series in 2004 and
the Patriots did really wellfor a long time.
Kids who grew up in Bostonsince that era are used to the
Boston teams winning all thetime, but we know that it was 86
years between uh before theywon it again in 2004.
(48:38):
So I think that's a that'sthat's.
Daniel Zenko (48:41):
No matter how hard you have to try to explain that
to somebody, they will neverunderstand because that's not
their experience.
They weren't there.
That's what then?
That's all they know.
Mike Chung (48:49):
Well, as we close up , Daniel, has been really a
great pleasure to talk with youon this edition of Indicators.
Is there any last thoughts thatyou had as we were talking that
you might want to share withour audience?
Daniel Zenko (48:59):
Yeah, yeah.
One of the indicators that wefollow is the survey that we do
among the executives in ourindustry about the expectations
about economy in general and ourindustry.
Mike Chung (49:12):
Our business confidence index and economic
confidence index, andunfortunately, the results of
these surveys are prettydiscouraging.
Daniel Zenko (49:20):
I mean our executives are not really good
at this point to forecast what'sgoing to happen.
Tell me what you mean by that Imean that it would really be
good idea to for them to to toask somebody on their team to
spend more time looking atspecifically our indicators in
(49:44):
our fact book, to kind of spendmore time with the data to get a
better insight and hopefullybetter predict what's going to
happen in the future, becausethere's really no other way to
do it but to actually dig deepin the data and get a better
grasp of really what's going onso what I'm hearing is, when our
executives answer this survey,they might be thinking things
are going well, but in factthey're not.
Mike Chung (50:05):
Am I hearing you correct?
Daniel Zenko (50:06):
all the way around .
Again, it's a little bit mayberecency bias and this and that
you know, but clearly when youlook at the, the chart of their
expectations and chart was whathappened it just doesn't match.
They don't align doesn't alignand you know, I know they're
busy, but I'm sure they can findsomebody, that team, to kind of
give them like briefings hereand there what was really going
on, to kind of just get a morerealistic perspective, you know,
(50:28):
of the of the additionalbenefit.
You know them and theircompanies, you know interesting.
So unless we have something, itis a relatively small sample
size, we maybe get perspectiveof the benefit them and their
companies Interesting.
Mike Chung (50:34):
So unless we have something it is a relatively
small sample size we maybe get50 responses.
So perhaps we're with theshining stars whose
organizations are justoutperforming.
But you heard the challengeeverybody out there who takes
this survey Engage your team,study the fact books, study
trend lines and let's watchthose predictions and indicators
get even stronger.
(50:55):
So thank you, daniel, forjoining us today.
Really, really illuminatingconversation.
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 Auto Care
(51:16):
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.
This is Mike Chung, and I amreally delighted to have Daniel
Zenko, director of DataInnovation at AutoCare
Association, joined me as aguest today.
So welcome, daniel.
Thank you for having me.
(00:48):
So, daniel, tell us a littlebit about what you do at
AutoCare.
Daniel Zenko (00:54):
Hi, my title is Director of Data Innovation,
which really means that I'mdealing with kind of new ways to
disperse and disseminate datafor our users and our members.
That's pretty much.
Our goal is to kind of improvethe way that data is consumed by
our users.
Mike Chung (01:14):
So why is it called data innovation?
Daniel Zenko (01:17):
Well, we try to find innovation, but mostly in a
way the data is delivered.
There's not much innovationabout the way the data is
collected or what data we areusing.
It's pretty much what industrywas using all along.
It's just a matter ofefficiency and urgency, of the
ability to consume the data in aproper, timely manner.
(01:39):
That was kind of missing before.
So we are trying to improve onthat and, of course, to find new
data sources that might beuseful for our members and
community.
Mike Chung (01:52):
Sure, so we'll dive into some of those subtopics as
we go on here, but before we dothat, tell me a little bit about
your journey to AutoCare.
What brought you here and howdid you get here?
Daniel Zenko (02:02):
Yeah, I'm not an auto care lifer at all.
I spend my career in differentindustries, but all of my work
was related to the marketintelligence sales intelligence
to find useful data to be usedby different industry insiders
(02:24):
to be able to sell theirproducts better.
So in that context, my previousexperience was actually pretty
relevant to what we're doinghere, and, of course, each
industry has its own quirks andidiosyncrasies, but overall it's
pretty similar, I would say.
But overall it's pretty similar, I would say.
Mike Chung (02:45):
Sure.
Daniel Zenko (02:57):
So, when we think about the automotive aftermarket
and recognizing that not all ofour listeners may necessarily
be in our industry, what kind ofdata are you referring to help
understand the market trends, toput their own performance in a
context, to be able tounderstand better what's really
going on with their sales?
Because your sales might be up10% last month but without
(03:17):
having a context, knowing thatoverall industry is maybe up 50%
or maybe just 5%, it's reallyhard to gauge how well you're
really doing and should be happywith your sales efforts, or
maybe just 5%.
it's really hard to gauge howwell you're really doing and
should be, happy with your salesefforts, or maybe you should be
worried about what's going on.
Mike Chung (03:32):
So some of what you're talking about now sounds
like benchmarking data, so salesperformance data of a company
relative to the rest of theindustry, for example.
Daniel Zenko (03:40):
Correct, correct and the industry for a while
struggled to have a usefulbenchmark that will really be
indicative of what's reallygoing on.
Mike Chung (03:49):
So, before you joined AutoCare, what were some
of those benchmarks that peoplein the industry were monitoring?
Daniel Zenko (03:57):
Well, there wasn't much when it comes to that like
a cumulative industry effort.
There are some isolated datafeeds from separate retailers or
program groups that our memberswould be able to access, but
that wasn't really much, andalso there was some of the
(04:21):
government data that can be used, but all in all it was mostly
on a yearly basis or just in aspecific slice of the market
that was covered, especiallywhen it comes to the hard parts
data, meaning data for the partsthat are sold behind the
counter.
For the front of the counterdata, there was pretty
significant data out there thatusers could use.
Mike Chung (04:46):
Sure.
So one of the things youmentioned was hard parts data.
So if I'm not in the automotiveindustry and I'm a consumer, I
have my own car.
Are you referring to, say I goand get a part changed, say I
get my brakes done at a?
Would that be considered behindthe counter or hard parts?
Daniel Zenko (05:07):
Well, it depends.
An easier way to explain thisis when you go to the AutoZone
store, let's say, and you wantto buy some part, if that part
is displayed out there, you canjust pick it up and bring it to
the counter that's front of thecounter item like wipers or a
refresh or something like that.
If you have to talk with a,with a person there and he goes
(05:29):
back into the warehouse to bringyou actual part, that's behind
the, behind the counterpart, andthere'll be, like you know,
chassis parts or like brake padsor stuff, like you know, stuff
of the most significant, likevolume and weight, and that that
usually goes into the integralpart of the car and that's would
certainly be very model makespecific.
(05:50):
It sounds like that uh, yeah, Imean, depends, you know, some,
some parts are very specific tomake a model and some are
relatively general.
It's really, you know, dependson the type of part that makes
sense.
Mike Chung (06:02):
And then something else you mentioned before that
may be a new term for some ofour listeners program group.
Can you just briefly tell uswhat a program group is?
Daniel Zenko (06:10):
A program group is basically association or some
kind of joint effort by thewholesale distributors, which
are the companies that provideparts to our shops.
For example, shops don't keepall of their necessary inventory
(06:31):
at the shop.
So when you come there with aproblem, very often they don't
have that part available rightthere to put it in your car.
So they call those distributorsto bring this part to them from
their warehouses they have allover the country and timeliness
is essential here, so that partoften makes it to the shop in
(06:54):
like half an hour or less, whichis really amazing.
And so these distributors, theykeep all the parts in their
warehouses and have the disposalto the shops as soon as needed.
And to gain pricing power andto streamline some of the
operations these wholesalersorganize into these program
(07:15):
groups where they join forces.
And for several reasons that'sa good approach to give them
more leverage in the market.
Mike Chung (07:23):
That makes a lot of sense.
Thanks for giving us thatbackground.
One of the other things youtouched on was government data
and thinking about theautomotive aftermarket.
My understanding is that it isnot necessarily a US census
defined entity, right?
So I think that adds to thechallenge of getting that
indicator data that can beuseful for people across the
(07:46):
industry.
Is that kind of in line withwhat you were talking about?
Daniel Zenko (07:49):
Yeah, yeah, I mean there is a category called,
like, aftermarket retail stores,which is relatively easily
defined and understandable, butthere is a slew of different
other providers of the parts andservices in the industry that
are not necessarily easilydefined and also there's a good
(08:11):
portion of our sales in ourindustry is happening through
the street orders like Walmart,who have a bay that you can
bring your car to do some basicrepairs and change parts and
stuff like that.
So it's really hard to use thegovernment data to get a
(08:33):
wholesale idea of what's reallygoing on overall in the industry
.
Mike Chung (08:39):
So what I'm hearing is the government data can be
very rich and informative.
However, since the automotiveaftermarket is so spread out
across so many retail outlets,it could be a grocery store, it
could be a big box store likeCostco, where you could have
service done you can get frontof the counter parts, for
(09:00):
instance.
Daniel Zenko (09:01):
But the stat that you highlighted is a specific,
specific NAICS code for autoparts, accessories, entire store
sales and therefore doesn'tcapture everything right, and
there's also anothercomplication with the online
retailers like like Amazon oreBay that have a pretty healthy
you know car parts businessthere and some of it it's their
(09:24):
own sales.
So it's like just like a market.
They provide marketplace to theindependent salesmen and those
are really not classifiedanywhere as such.
So it gets kind of murky outthere when you want to kind of
grasp what's the really fullmarket out there for parts.
Mike Chung (09:42):
Sure.
So pretty broad, open-endedquestion.
But what data sources shouldpeople in the industry be
following?
Daniel Zenko (09:52):
I mean still the government is the best source
out there for this general data.
There's also a significantamount of data that comes out of
the companies like Visa orMasterCard, when you can monitor
the swipes of the cards at theretail places and get some kind
(10:13):
of information from there.
Also, the Autograph Associationprovides a lot of information
that's useful for industry, aswell as some other industry
specialist companies thatspecialize in providing data in
the industry, like IMR and MPDand some others.
Mike Chung (10:35):
So going back to the government data first.
What makes the US governmentdata such a rich resource?
What are some of the advantagesof the US government data?
Daniel Zenko (10:46):
Well it's.
You know, it's a well organizedin and, again, it's limited in
its core but what's available iswell organized and will
maintain and there's like a lotof restatements when it's
necessary.
So it's quite dependable andyou know, and there's no really
I should put it fudge in thedata that will be created by a
(11:12):
special interest of somebodywho's providing the data.
So it's objective.
That's really really good.
Mike Chung (11:21):
So what I'm hearing is regular process, objective,
quote-unquote, third party ifyou will, and they do seasonal
adjustments and otherstatistical processing to make
sure it's representative.
Is that fair to say?
Daniel Zenko (11:35):
Yeah, yeah, they're quite good at that.
And also it comes with aregular schedule.
So if some indicator ispublished, usually like second
Tuesday in a month, you cancount it.
It's going to happen that way.
So there's regular cadence ofthe data.
That's again it's a basis forany kind of serious analysis,
but still not probably enough toreally satisfy all the needs
(11:57):
that you have to monitor what'sgoing on in the industry.
Mike Chung (12:00):
The second one you mentioned was MasterCard, visa.
Can you explain a little bitmore about what you meant by
that?
Daniel Zenko (12:07):
Yeah, I mean when you swipe your card at a grocery
store or any kind of store,mastercard, visa retains that
information and resell it.
You know they are not allowedto resell who actually swiped it
, your identity or anything, butthey have liberty to resell.
You know what was bought whenyou know, and so that data is
(12:30):
anonymized and you know and sold, you know, in some kind of
packages to the specificindustry players.
You know that might beinterested in this particular
type of data.
So that gives us a pretty goodinsight about you know, dynamics
of the sales and you know whatkind of items people buy, let's
say together in kind of the samebasket and stuff like that.
(12:52):
So you know it's pretty useful.
It's very wide, you knowthere's almost too much of it,
you know but you know, with theproper analysis you know there's
some insight that can be takenfrom there.
Mike Chung (13:06):
So that's what I wanted to clarify.
When you said MasterCard andVisa, I thought about data
that's available for purchaseand then that could require
further analysis to drill down,categorize the data, and I would
expect that to be similar in inconcept to, say, some of the
(13:27):
commercial providers youhighlighted towards the end of
that answer.
You mentioned npd groups orkana, other other data sources
where a subscriber couldpurchase data and then use it
for analytical purposes, tounderstand the consumer,
understand consumer behaviorbetter.
Daniel Zenko (13:44):
Yeah, and also there are specialist players who
, let's say, focus on exactlywhat's happening with Amazon so
which parts sold well in Amazon,which companies sold well there
and trends there Monitor,internet searches and stuff like
that there's a lot ofinformation out there that can
be used, but it's best used astriangulation.
(14:05):
It compares you with somethingelse.
That gives you kind of morebroad understanding of what's
going on.
Mike Chung (14:12):
Sure, and to that point.
I think that's not to make thissound like a commercial for the
data platforms that AutoCarenecessarily provides, but one of
the things that you architectedfor the TrendLens platform was
economic and industry indicators.
I think there are nearly 50data points that people can look
(14:33):
at.
Daniel Zenko (14:33):
Yeah, that product was originally brainchild of
the Market IntelligenceCommittee and Association, but
we actually put it in a digitalform association but we actually
put it in a digital form.
Basically, we we've selectedmaybe like around 50 economic
industry indicators that we feelthe most important for our
(14:55):
industry participants to monitorand follow and and we put them
together in in this package thatwe publish on our website for
free for all our users, and thatpackage is updated as the new
data comes along which is notpublished on the, about sales of
the cars, sales of gasoline,unemployment rates, inflation,
(15:28):
all kinds of stuff that areuseful to understand better in a
context of the industry.
Mike Chung (15:37):
And some of those other indicators are disposable
income, consumer debt, inflation, unemployment, prime interest
rate and when we think aboutparticipants in the industry,
our quote-unquote audience, it'swide as far as well as broad,
and what I mean by that is typeof company, from parts
(16:02):
manufacturers to distributors,to retailers, to service
providers, and then roles withinthose companies, from sales and
marketing category management,executive, c, c-suite there's a
whole range of individuals thatare monitoring different touch
points, if you will, to keep apulse on the aftermarket yeah, I
(16:23):
.
Daniel Zenko (16:23):
I mean, we provide a lot of information about that
and I'm sure that all ourindustry participants can figure
out which broad indicators arekind of related to their own
sales process, either be like aweather or a disposable income
(16:44):
or maybe price of gasoline, andyou know.
So they're always alreadytracking them.
But we provide this technologytool to follow them all at the
same time and to get some kindof insight of interaction of
these different indicators togive them better understanding
of what's the big picture, howthe market is really behaving
(17:08):
overall.
Mike Chung (17:09):
And when you put together this platform, how did
you decide on data sources?
Tell us a little bit about whatyou were looking for in data
sources.
Daniel Zenko (17:21):
Well, number one requirement was the data is
published monthly, because allthese indicators are refreshed
every month, so we have to havea data point that actually is
published monthly so we can useit.
Also, we're trying to findindicators that have national
(17:41):
level data but also regionallevel if possible, so we can get
some more insight.
And again, some of them arefairly obvious, like you know,
miles driven, prices of gasolineand stuff like that.
But some others are maybe notthat obvious but definitely are
important to understand well theoverall health of US economy,
(18:05):
like housing starts anddisposable income, and you know
the consumer confidence, forexample.
Those are very important onesto really understand what's
going on the level ofindebtedness of the consumers.
There's several onesunemployment rate.
Those are all very importantfactors that have a significant
(18:27):
impact, not just our industrybut all industries in the United
States.
But definitely important tounderstand what's going on with
ours too is the United States,but definitely important to
understand what's going on withours too.
Mike Chung (18:39):
So another aspect of , or another part of, the
TrendLens platform is, of course, demand index, which is the
anonymized aggregatedpoint-of-sale data.
When you're building consensusto create the demand index, can
you tell us about that process?
Was there anything that made itmore?
Daniel Zenko (18:57):
challenging, for example.
Well, our industry,specifically category management
committee in our associationand its members, you know,
recognize the lack of adequatedata to monitor what's going on
with sales, especially hardparts in our industry.
So they joined forces anddecided to share their sales
(19:21):
data in a way that can be usefulfor industries.
So while constructing thisparticular data product, we
consulted with both retailersand problem groups who produce
this data and the manufacturerswho consume this data on other
side, in order to find the levelof detail that's useful but
(19:44):
also not overwhelming, in thesense that it's really hard to
actually produce the data ofnecessary quality.
This is one of the situationsthat good enough, the perfect is
enemy if enemy of good enough.
You try not to make it toocomplicated as a product in the
first place, so the datadelivery may be delayed or not
(20:06):
possible.
On the other hand, you have tomake it detailed enough to be
useful for users.
It was a gradual process.
We started with 15 differentcategories or segments to follow
.
Now we are up to 130-something,and it's constantly growing,
growing not just in width, butalso in depth.
(20:26):
We are getting more and more indetail, which makes the data
more actionable and more usefulfor different kinds of users.
Mike Chung (20:32):
Well, that's fascinating More than eight-fold
growth from 15 to more than 130categories, the consensus
building and, like you said, thedepth there.
Are there other aspects youmentioned.
Daniel Zenko (20:43):
You talked about national to regional for in
dollars, sales in units and allother kind of potential data and
we are constantly expanding itbased on the interest of users.
What part of the market isDatafill?
(21:10):
It's served regarding the datacoverage, so we're always trying
to get better and deepercoverage, but not too much
strain to the data contributorswho actually help us with
sending the data for theseparticular categories.
Mike Chung (21:29):
And to circle back on a topic you touched on
earlier, with regard to thefidelity and usefulness of that
data, there's also that elementof confidentiality and making
sure that, uh, somebody can'treverse, engineer and figure out
, say, a competitor's marketshare, for instance.
(21:51):
Yeah, I'm balancing it withthat aspect.
Daniel Zenko (21:53):
We have a big number of data contributors and
it's hard to say exactly, but weprobably cover like 80% of the
full market in any category.
Transparency is not an issuethat much.
Every other data is well hiddenin a mass, so it's hard to
(22:15):
really decipher what's going on.
Plus, we don't go in that muchof a detail.
You know we focus on like ninegeographic regions and United
States.
That's as deep as it goes, so Idon't see that as a problem.
But the fact is that we do havethe biggest coverage of any
kind of other providers of thiskind of information, and I think
(22:40):
it's a pretty big distancebetween us and the second
largest provider of such data.
Mike Chung (22:47):
Sure, and now that this subscription product has
been available for several years, it has momentum.
Do you feel like it's easier tohave new participants in terms
of adding more to that 80coverage, if you will?
Yeah absolutely.
Daniel Zenko (23:07):
I mean we are working with several other
retailers and and program groupsto join the panel.
Although again, we havecoverage where it's very
commanding, like 80%, we canstill do better and try to get
as close as we can to the 100%.
(23:28):
I mean this product, unless wehave 100% of the market which
we'll never have, obviously willalways be approximation you
have to take a lot for theadjustment for the part that's
missing and try to figure outokay which part is actually
missing for a particular part.
But again, it's still by farthe best and most comprehensive
(23:49):
indicator of that sort out there.
Mike Chung (23:52):
And to circle back on something you mentioned, the
US government will do datarestatements.
So if you were to add morecontributors, then certainly
that would necessitate a kind ofa restatement, recalibration of
sorts.
Is that fair to say?
Daniel Zenko (24:16):
our representation .
Even though it's not 100% ofthe market, it's still
relatively representative,meaning that these new
contributors we might sign onwill not make big difference in
overall indices.
You know, which is veryunlikely, that will happen
anyway because, again, we coveralready all the major retailers
and program groups.
So you know, you know therewill be some, you know, on
(24:38):
margins, some small changes, butwe don't anticipate any kind of
big change anytime soon.
Mike Chung (24:45):
When you were constructing the demand index, I
asked you about challenges.
How about things that wereperhaps easier and glided
through a little more easilythan you may have expected?
Was there anything that wasremarkable that way?
Daniel Zenko (25:00):
well, it's kind of interesting with this kind of
type of data is that um and thehigher some of the up is up in a
in a company you know,leadership, that we discuss this
.
It's easier for them tounderstand what you're trying to
do.
This because this is not aproduct that focuses on
individual SKUs or anything likethis.
(25:20):
We just cover, let's say,breakpads that's what we do
which is pretty wide and for alot of category managers maybe
not detailed enough to reallyspark their interest.
But if you hire executives ofthese companies to understand
what's going on overall with thebrake pads, that's quite useful
information.
(25:41):
So to get a bind from the uppermanagement of the industry was
relatively simple because theyunderstood the strategic
importance of having thisinformation.
But again, when you talk withthe people whose job is to just
cover ceramic breakpads, havinga number for overall breakpads
(26:01):
is not necessarily that useful.
So on that side there's alittle challenge to get to have
information detailed enough tobe useful for even ranking file
or category managers In somecategories.
It might be a little tricky butwe're getting there.
A lot of categories that wecover are now split into
relatively small chunks, smallpieces.
(26:22):
So, we're definitely going inthe right direction.
Ted Hughes (26:25):
Hi, I'm Ted Hughes, executive Director of AWDA and
Senior Director of CommunityEngagement for the Auto Care
Association.
We provide our members withnumerous avenues for connection
and collaboration through ourdiverse range of committees and
communities.
Whether you're interested inadvancing your career through
the Women in Auto Care programor our vibrant Under 40 group,
(26:45):
or simply wish to network andglean insights from fellow
distributors, shops andmanufacturers, we have dedicated
committees and communitieseager to connect with you.
Learn more at autocareorg slashcommunities.
Mike Chung (27:00):
If you were to give advice to somebody who's
building a data platform in anyindustry, whether it's a free
resource or a subscriptionresource, what pieces of advice
might you give to that person?
Daniel Zenko (27:13):
Well, first, I would say, focus on on selling
the vision, meaning you know,getting buy in regarding what
goal you're trying to achieveand worry about details later.
You know, because if you startlet's say, in our case, if you
started with three or 15 productlines or categories, it's not
(27:34):
necessarily a big difference,you know, it's not necessarily a
big difference.
It's important to get a buy-infrom all important players in
the industry and to have aconsensus about what we're
trying to do with this so we cancontinue developing the product
in the right direction.
Last thing you want to do is tobuild something and then in the
(27:54):
middle, realize, oh, wait aminute, this is not what.
You know what's needed.
You know this is more like, uh,you know, solution is a short
problem, you know, instead ofother way around.
So, you know, make sure thatyou start from the, from the,
from the correct starting point.
You know that.
You know that you're not, uh,barking wrong tree.
Mike Chung (28:16):
I would say so, to replace some of that, what I'm
hearing is communicating theconcept to your audience, making
sure that they understand itand you're aligned on that,
rather than worrying about someof the details.
You gave the example of threeversus 15 product categories, so
I can see there where theprocess is going to be very
(28:39):
similar whether it's three, 15,or 150, but really getting
buy-in on the concept.
And then, to harken back tosomething you said earlier,
don't let perfect be the enemyof good enough.
Daniel Zenko (28:50):
Correct, you know, because with data there's
always, you know, there's alwayssomething going on, some kind
of restatement going on.
Something was misclassified, sothere are going to be changes,
those little restatement changes, but those changes are on the
margin usually, so you don'tneed to worry too much about
(29:10):
that, because that's just theway it goes.
Mike Chung (29:13):
The process will take care of those things.
Yeah, yeah, yeah.
Daniel Zenko (29:16):
But if you have everybody aligned in what we're
trying to achieve with thateverything's fine.
Mike Chung (29:22):
So, thinking about, say, demand index, what does the
future hold?
Daniel Zenko (29:29):
Well, we can go even deeper and in more detail
with the stuff we have, butthere's a limited leeway, but
there's a limited playing fieldthat we can still expand to.
So we are looking into differentareas.
We're looking into indexes thatwill be more specific to
(29:49):
particular types of sales, let'ssay e-commerce, maybe to have
indicators that will coverexactly what's going on with the
sales over the internet.
Then there's an interestingconcept about maybe expanding
this to the HD field, likeheavy-duty vehicles, but just
(30:10):
for the heavy-duty parts.
We're looking into it andeventually we might get to that.
Also, we are working with someservice providers, companies
that provide car repair servicesand maintenance services to
capture data on that level, likebrake jobs, all changes,
(30:34):
changes of batteries to provideinformation on that level.
Because right now of batteriesto provide information on that
level, because right now what wedo is provide information on
the level of point of sales whenpart is sold, but we don't
really cover how many.
All changes were happening inthe United States in the last
month which we can anddefinitely we'll give some kind
(30:54):
of insight to our users.
That would be interested.
And stuff like that.
There are other parts of ourindustry that are lacking the
necessary data and we are tryingour best to figure out.
Is it feasible to actuallycollect the data and disseminate
(31:15):
it in a proper way?
Mike Chung (31:21):
So what I'm hearing is you've highlighted about 80%
of market coverage with ourcurrent demand index and that's
going to be US light duty or, Iguess, automotive parts, and so
now we're looking further afieldto different quote-unquote
markets and platforms orchannels, shall we say whether
it's e-commerce or heavy duty.
Daniel Zenko (31:38):
Or maybe like a tool and equipment sales.
There's another aftermarket,adjacent industries that are
heavy players in our industrybut they're not necessarily
covered.
Or maybe something about tires,specifically that kind of stuff
.
So yeah there's definitely roomto expand, you know, in the
(31:59):
right direction, andunfortunately it seems that our
industry is lacking likenecessary data in a lot of
aspects.
So there's plenty of ground tocover.
It's just a matter of findingsomething that's feasible and
can be pulled off in arelatively quick manner.
Mike Chung (32:15):
This is not my field of expertise, but I think about
all the computing horsepower,all the storage.
Can you touch on any of thoseinfrastructure concerns or is
that another department?
Daniel Zenko (32:26):
Well, so far it's not particularly bad regarding
that, because, yes, we'retalking about millions of lines
of data, but in the grand schemeof things, that's not huge.
We can definitely handle itEven further.
(32:48):
The concerns are mainly on theperformance of the website.
Because more data is loaded onit, it takes slower to actually
render the data, which makesworse consumer user experience.
So that's something we need toparticularly worry about to make
sure that we don't upload somuch data in a system that it
(33:14):
can't handle it with the properspeed.
That's our primary concern atthis point to make sure that the
user experience is adequate.
Mike Chung (33:23):
Makes a lot of sense , and thinking about data
platforms in general, what isthe future of data platforms?
We've seen a lot of datavisualization over the past
decade and a lot of ways tocustomize your view and do
analysis from there.
But looking into your crystalball five years, 10 years into
(33:43):
the future, what could a dataplatform like Demand Index or
otherwise look like in thefuture?
What might we seetechnologically develop?
Daniel Zenko (33:53):
Well, it's more about customization.
Really, in the future, what Ithink is going to happen is that
people will be able to choosetheir individual experience of
using the data, because rightnow, it's like a buffet table
Saturday in a restaurantEverything is out there and for
(34:17):
a lot of users, a lot of thatdata is immaterial because it
doesn't really touch what theydo.
So maybe we can organize it ina way that you get better
experience of using it byremoving everything that's not
really in your you know, in yourballpark and something that you
(34:37):
want to use and also providethe you know ability to render
the data on your cell phones andother platforms.
Right now it's pretty much onlyon a computer, but you know we
will definitely look into, youknow, expanding it on other data
platforms look into expandingother data platforms and
(34:59):
thinking not just about demandindex, but not just about demand
index, but data platforms ingeneral.
Mike Chung (35:03):
We hear about AI right, so are we getting to a
point where we can use AI in adata platform to answer
insightful research questions?
Daniel Zenko (35:18):
Well, yes and no, as this artificial intelligence
tools are improving, there'sstill a lot of questions about
(35:43):
their ability to criticallyassess what's right data what's
not.
But if you provide this toollet's say these five charts and
ask them to analyze those chartsand create some kind of insight
out of it, I believe I can do apretty good job with it because
the scope is defined.
They don't need to search theinternet for different potential
data sources that may not beproperly vetted.
You know there's no confusionin their artificial mind, in you
(36:04):
know what's the data that theyshould focus on.
They have this, you know,limited set of data inputs that
they have to put in some kind ofcontext and relationship.
I believe they can help withthat, you know, to basically to
create some kind of commentaryor write ups or you know stuff
like some basic analysis, youknow.
(36:25):
So, yeah, that that I can seeon that narrative side.
Definitely they can.
They can help on you knownumber crunching side.
It's just, you know, youbasically have to check the math
all the time, which then killsthe efficiency.
You have to spend more timeverifying that everything that
(36:48):
was included was supposed to beincluded, you know or not.
So I'm a little reserved aboutthat.
But definitely on the outputside, on the kind of
presentation of the data side,artificial intelligence can help
.
Mike Chung (37:06):
So it sounds like to get some words on a blank sheet
of paper to get a narrativestarted.
That seems facilitable.
As for the numerical analysis,the statistical, the forecasting
analysis, that will certainlyrequire a bit of human
intervention, if you will, tomake sure that the analysis is
(37:28):
appropriate.
Daniel Zenko (37:29):
Yeah, and at this point there's not much problem
regarding number crunching.
There are plenty of tools thatare very good at you know
playing with numbers, you know.
So there's really no need foranother layer of you know
technology to help with that.
The problem starts really when,okay, you have all this data,
(37:51):
and data is verified and it'scorrect, and now how to create a
story around it.
So I'm not saying thatartificial intelligence will be
able to give you a perfect pitch, perfect story about you know
what's going on based on theseparticular inputs, but it will
give you some proper guidelinesthat you can kind of okay, so
(38:14):
this is how it looks like andthen you can you can maybe a bit
more deeper into it, or maybe,you know, adjust a little bit
what output is from artificialintelligence and go with that
you know.
So that interpretation of thedata is a little tricky, you
know, because you know glasshalf full, half empty.
It's really, you know, dependson your personality really, how
(38:36):
you interpret it's really, youknow, depends on your
personality really, how youinterpret certain numbers you
know.
So maybe we can count on AI tobe kind of impartial about, you
know, especially if you havesome kind of political views.
You know you can alwaysinterpret numbers that convey
your narrative that thisparticular administration is bad
for economy or is particularlygood for economy.
(38:59):
Something like AI, again, ifyou give them limited sources to
play with, can probably giveyou an objective story without
any kind of bias.
Mike Chung (39:11):
Oh, that's helpful and I think the context that
somebody like you or I mighthave being in the industry, and
we can add that truthing elementto the narrative that an AI, a
generative AI tool, may provide.
Daniel Zenko (39:29):
Yeah, yeah, absolutely no, there's a,
there's a, there's a.
You know, there's no way Ithink that these tools can
replace human input.
But they can provide some help,definitely again, but it has to
be, has to be done in a waythat it's, you know, controlled
and uh, and reasonable, you knowso yeah definitely there's.
(39:52):
There's something there, butmaybe not as much as some people
hope for and also not as muchas some people are afraid of.
Mike Chung (40:02):
Yeah, it's certainly an interesting development that
we'll keep watching.
Just in our last few minutes, Iwant to touch on a couple of
things that you and I havetalked about from an economics
perspective and for our audience.
Daniel has a lot of backgroundin economics and we've had some
good, healthy discussions onsome of these topics and perhaps
we could pursue these inanother conversation on this
(40:24):
podcast.
But one of the things thatwe've talked about is US debt.
Okay, so concerns of the UnitedStates being over leveraged.
We're at about $36 trillion ofdebt right now.
Nominal GDP last year was about$29 trillion.
Us debt was recently downgradedto AA plus, I believe, and
(40:49):
we're in an era of sustainedinterest rates being high, so is
this a big deal?
What's your interpretation ofthese circumstances?
Daniel Zenko (41:03):
Well, when thinking about death, I think
the best way to refer to it isto understand how much of the
burden for the government isservicing the debt.
Some governments haverelatively small debt but nobody
(41:26):
wants to borrow their money, sothey have to pay huge interest
rates to get those funds, andeven small debt for those
countries is a really big deal.
Countries like the US have alot of leeway there because
everybody wants to lend themmoney and give them cheap
interest rates for that now hasto pay around 3% of the national
(41:55):
GDP every year for servicingthe debt, which is slightly
higher than in the last decadeor so, but significantly still
lower than, let's say, duringthe Reagan years.
So, yeah, it definitely is goingup.
The price of the servicing, thedebt, is definitely going up,
but compared to the long-termaverage it's not that bad.
(42:19):
And also compared to the otherdeveloped countries like Japan
or the.
United Kingdom or something it'sstill much, much better.
It's still much, much better.
So, yeah, doesn't seem storydoesn't really check out as the
(42:41):
debt is like a big problem allof a sudden that you know
creditors will decide, oh no, usis not country that we can, you
know, let money to.
You know we have to be carefulwith that.
It's just not not something atthis point that that seems of
serious concern, especially inthe context of other economic
indicators.
It's that inflation rate is,let's say, average.
(43:06):
If you look at the nocturnalterm it's falling and the US
economy doesn't show signs ofgoing into either depression or
overheating at this point.
So there's really no concern atthis point of international
creditors or domestic creditorsto really worry about the
creditworthiness of the UnitedStates government.
(43:27):
So the overall story seems likea non-story at this point.
You know, again, things canchange, but nothing really there
, you know, to worry about asmuch as some people would like
to.
Mike Chung (43:45):
I appreciate that perspective and you touched on.
Another topic and I think we'llclose with this is inflation.
We've had high inflation.
It was a little above 9% in2022.
It's currently at about 3%.
We hear about the goal beingabout 2%.
What are your thoughts on a 2%goal?
(44:07):
Is it appropriate, thinkingabout our industry, consumers in
our industry?
What are your thoughts about 2%inflation goal?
Daniel Zenko (44:17):
Well, that kind of that goal is a little bit
moving of the goalpost.
You know, for the long, longtime of federal you know,
federal US Fed had a goal tohave an inflation band between
2% and 4%.
That was an acceptable rangeand somehow all of a sudden that
(44:39):
2% lower band became a goal,which is not necessarily true,
because cutting interest rate ispretty much the only tool that
Ferdinand Garland has to propthe economy.
The only tool that FerdinandGarland has to prop the economy.
If interest rate is alreadyvery low and inflation is very
(45:05):
low, then simply there's notmuch lever there.
They can't do much to furtherprop up the economy.
In the case of an emergency, 2%is okay as maybe a goal, but
it's not really the goal.
The goal is to keep it somewherebetween 2% and 4% and long-term
average, I think, is somethinglike 3.4% in the United States.
So current inflation rate of 3%is well beneath that level.
(45:27):
So I don't believe that federalagencies would mind that it
stays just like it is, providingthe economic growth is solid.
Now there are some talks thatthere will be need relatively
soon to cut interest rates,which will again heat up the
(45:50):
economy and bring up maybe someinflation you know together, but
still you know there's a plentyof room there at this point to
move.
You know inflation a little bitup if necessary or down if
that's the goal, you know.
So we are in a good spot inthis point.
And uh, again two percent as agoal.
I think it's better tounderstand that there's a lower
(46:11):
band, lower band of thebandwidth that's acceptable for
the inflation.
I know for some people it'smaybe hard to accept that zero
inflation is not a good idea,but it's really not.
Having a small 2-3% inflationis perfectly fine and actually
desirable.
Mike Chung (46:30):
That's helpful to clarify that band, as you talked
about, and perhaps one of thethings that's been hard for
consumers is this is following aperiod of sustained high
inflation, so it's like a littlebit of salt into an open wound
and perhaps over time thingswill equilibrate in terms of our
adaptation to sustain interestrates.
Daniel Zenko (46:51):
It's a recent bias because current generation of
of grownups.
They grew up in an era of very,very low inflation Since the
last 20 years.
It was exceptionally lowinflation rate for the United
States that pretty much neverbefore existed, or maybe rarely,
so they were kind ofconditioned to expect inflation
(47:13):
rates and interest rates to bereally, really low.
But those low interest rateswere just a product of the
government trying to prop USeconomy after the dot-com boom
and bust, after the real estateboom and bust and also mortgage
crisis.
So those low interest rateswere not normal.
(47:35):
It was just a specific targetto actually help boost the US
economy, which helped, but it'snot something that is good
long-term for a way to do it.
So I would say current interestrates and current inflation
rates are pretty much averageand pretty much what you want.
So again, in specificsituations you want to adjust it
(47:57):
a little bit, but unless goodreasons come to actually do that
, I think we are in the properband.
Regarding both inflation rateand unemployment rate and
interest rate, it's all aboutaround the long-term average
right now.
Mike Chung (48:15):
Oh, that's helpful.
I love the recency bias and itmade me think about if you grew
up in Boston before the Red Soxwon the World Series in 2004 and
the Patriots did really wellfor a long time.
Kids who grew up in Bostonsince that era are used to the
Boston teams winning all thetime, but we know that it was 86
years between uh before theywon it again in 2004.
(48:38):
So I think that's a that'sthat's.
Daniel Zenko (48:41):
No matter how hard you have to try to explain that
to somebody, they will neverunderstand because that's not
their experience.
They weren't there.
That's what then?
That's all they know.
Mike Chung (48:49):
Well, as we close up , Daniel, has been really a
great pleasure to talk with youon this edition of Indicators.
Is there any last thoughts thatyou had as we were talking that
you might want to share withour audience?
Daniel Zenko (48:59):
Yeah, yeah.
One of the indicators that wefollow is the survey that we do
among the executives in ourindustry about the expectations
about economy in general and ourindustry.
Mike Chung (49:12):
Our business confidence index and economic
confidence index, andunfortunately, the results of
these surveys are prettydiscouraging.
Daniel Zenko (49:20):
I mean our executives are not really good
at this point to forecast what'sgoing to happen.
Tell me what you mean by that Imean that it would really be
good idea to for them to to toask somebody on their team to
spend more time looking atspecifically our indicators in
(49:44):
our fact book, to kind of spendmore time with the data to get a
better insight and hopefullybetter predict what's going to
happen in the future, becausethere's really no other way to
do it but to actually dig deepin the data and get a better
grasp of really what's going onso what I'm hearing is, when our
executives answer this survey,they might be thinking things
are going well, but in factthey're not.
Mike Chung (50:05):
Am I hearing you correct?
Daniel Zenko (50:06):
all the way around .
Again, it's a little bit mayberecency bias and this and that
you know, but clearly when youlook at the, the chart of their
expectations and chart was whathappened it just doesn't match.
They don't align doesn't alignand you know, I know they're
busy, but I'm sure they can findsomebody, that team, to kind of
give them like briefings hereand there what was really going
on, to kind of just get a morerealistic perspective, you know,
(50:28):
of the of the additionalbenefit.
You know them and theircompanies, you know interesting.
So unless we have something, itis a relatively small sample
size, we maybe get perspectiveof the benefit them and their
companies Interesting.
Mike Chung (50:34):
So unless we have something it is a relatively
small sample size we maybe get50 responses.
So perhaps we're with theshining stars whose
organizations are justoutperforming.
But you heard the challengeeverybody out there who takes
this survey Engage your team,study the fact books, study
trend lines and let's watchthose predictions and indicators
get even stronger.
(50:55):
So thank you, daniel, forjoining us today.
Really, really illuminatingconversation.
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 Auto Care
(51:16):
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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