September 16, 2024 • 50 mins
Unlock the secrets to mastering the automotive aftermarket with Nate Chenenko from Ducker Carlisle on this episode of Indicators! We promise you'll gain invaluable insights into the complex world of OEMs, tier suppliers, and service organizations as Nate, with his 12 years of experience, breaks down pricing studies, supply chain strategies, and growth planning. Learn how dealer relationships and strategic foresight are essential to anticipating industry trends and preparing for the next decade.
Discover how antitrust considerations are crucial in shaping strategies within automotive and heavy equipment industries. Nate illuminates how data transparency and benchmarking help clients navigate these waters, ensuring compliance while extracting valuable insights.
Rethink traditional metrics with us as we dive into the complexities of measuring dealer and service retention. Nate will guide you through the intricacies of data segmentation by vehicle age and type, offering a more accurate picture of service retention. We also explore the shift towards aftermarket service providers and the evolving landscape of dealer service retention.
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 Auto Care ON AIR, a candid podcast for a
curious industry.
I'm Mike Chung, Senior Directorof Market Intelligence at the
Auto Care 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.
(00:34):
Hello everybody, I'm very happyto introduce my good friend and
colleague, Nate Chenenko ofDucker Carlisle.
So, Nate, welcome to the show.
Nate Chenenko (00:44):
Thanks, Mike, happy to be here.
Thanks for inviting me.
Absolutely, so maybe tell us alittle bit about your company
and their involvement in theaftermarket.
Sure, I work for a strategyconsulting company called Ducker
Carlisle.
We're US based, with offices inbasically every continent that
you could think of.
(01:05):
We do work primarily in theautomotive aftermarket at least
my group within the DuckerCarlisle Umbrella Organization.
We do a lot of work with OEMs,plenty of work with tier one
suppliers, service organizations, parts retailers, et cetera.
I personally have been here for12 years, spent the entirety of
(01:27):
that 12 years in the automotiveaftermarket, again with a
primary focus on OEMs and theirrelationship to the aftermarket,
and have not even come close tomastering the aftermarket.
And it continues to engage meregularly.
So I'm still here.
Mike Chung (01:45):
Oh, that's great and it is a complex market and you
mentioned your company beinginvolved with OEMs.
Do you also have clients in theI guess in the parts
manufacturing distributor space,if I may ask?
Nate Chenenko (01:58):
Yep, we do.
We work with pretty much theentire value chain, you might
call it, ranging all the wayfrom mostly tier one suppliers
but occasionally tier two, tierthree suppliers.
We have a research arm as wellthat does research into some of
the raw materials.
Particularly like aluminum is aspecialty of ours and that
(02:21):
research gets consumed by a lotof tier suppliers.
We do work with lightweighting.
So that's all on the partssupply side, even like more of
the raw materials side, and alot of that research is consumed
directly by, like I said,primarily tier one suppliers.
And then our work with OEMs.
(02:42):
They're buying from tier onesuppliers, pricing those things,
distributing those things.
We do a lot of work with OEMs.
They're buying from tier onesuppliers, pricing those things,
distributing those things.
We do a lot of work with partssupply chain, parts pricing and
then some growth strategy workfor aftermarket companies as
well.
Mike Chung (02:57):
Oh, that's exciting and that gives a little bit more
color into the strategyconsulting.
So some of the things I heardthere are pricing studies,
research from materials and howdoes that affect the cost of a
good that an OEM might sell?
And you mentioned research.
So it could be research ofcustomers, it could be research
with surveys with suppliers,things like that.
Nate Chenenko (03:20):
Yeah.
And then because we do quite abit of work with OEMs and they
yeah.
And then because we do quite abit of work with OEMs and they
in many cases, especially in theAmerican market, can't sell or
don't sell directly to consumers.
We also have to be very attunedto how all of that information
plays with dealers.
So the OEM sets a price, thenthe dealer can change that price
(03:41):
.
The OEM sets a strategy andthat strategy is deeply reliant
on the dealers buying in andhelping execute that strategy.
So that adds an additionallayer of complexity.
I think the complexity you saidearlier that it's a very
complex market.
I think that complexity is whatkeeps me interested here.
Mike Chung (03:58):
Yeah, and you said you've been there for 12 years.
So tell me a little bit aboutthe journey to Ducker Carlisle,
what brought you there, and tellme a little bit about your
background.
Nate Chenenko (04:08):
I had the misfortune of graduating from
university during the globalfinancial crisis, so I ended up
finding my way into strategywork in sort of a roundabout
fashion, by starting with somegovernment contract consulting
work, which was the only thingthat had any money back then.
Luckily, it didn't take me toolong to get into the private
(04:29):
sector and ended up at Carlyleand Company, which later merged
with Ducker to become DuckerCarlyle and hit the automotive
aftermarket, like I said, in2012.
Then worked on quite a bit ofsupply chain work related to
aftermarket and after salessupply chains for OEMs and other
(04:51):
companies, but also was exposedto a lot of our work in pricing
.
And then, over the years, asI've become more and more
interested and started to readmore and engage with content
from a variety of sources likethe Auto Care Association, among
others, ended up leading a lotof our thought leadership
activities over that period oftime, and so now I spend about
(05:16):
half of my time doing some morestrategic research rather than
trying to answer one specificquestion, more so trying to
figure out hey, where is thisindustry going to be in 5, 10,
15 years?
What are the things we need todo now in order to prepare for
that.
How are the differentstakeholders going to react to
(05:37):
those things that we have to do?
How can we challenge somepre-existing maybe assumptions
that exist?
And then also, one of myfavorite topics is what is
everybody paying attention toright now?
That is important.
And what is everybody payingattention to right now?
That may not be as important aswe think it is, and I'm often
(05:57):
wrong on those things, butluckily I'm able to be right a
little bit more than I'm wrong,so people are still interested
in taking a look at what wecreate.
Mike Chung (06:09):
Oh, that's exciting and I think I appreciate that
description and the overview,because when we think about
trend spotting, trend tracking,we have our finger on a number
of different things right, andwhat I'm hearing is you're
looking at the market, you'relooking at qualitative
indicators, you're probablylooking at some quantitative
(06:30):
metrics and indices as well asyour kind of knowledge of being
in the space.
So I guess, with that in mind,what are some of the
quantitative metrics that youand your team might look at to
think about informing that fiveto 10 year horizon.
Nate Chenenko (06:45):
Yeah, you really so accurately characterized that
.
It is a mix of qualitative andquantitative, and oftentimes
we'll start digging into aquantitative metric after
hearing somebody tell us thatthey have a qualitative problem.
So I over the years, boththrough my personal life and
professionally, have developedsome friendly relationships with
(07:08):
people that work at autodealerships, for example, and
I'll just give you a specificexample Earlier this year, like
in February or March, a localfriend I'm from Rochester, new
York, and a local friend of minewho works at a dealership said
hey, we've been working like 60%time since the beginning of
January.
(07:28):
Things are super slow here andI'm not sure what's going on now
.
He works for one dealership outof the like 15,000 plus
dealerships in the country.
So his experience may not berepresentative at all, is not
representative at all.
But that little seed we plantthat and then we can collect
(07:48):
information from, especiallyfrom OEMs which, as I've
mentioned, is the bulk of ourclientele to understand whether
that's a real thing or not.
And so we use that to set up a.
We do some monthly metricsgathering from OEMs.
It's a product that we havecalled Metric Watch.
(08:10):
It's available just to OEMs inthe after sales space and we
collect basic information fromthem, like sales information,
and we communicate thisinformation in generally an
anonymized fashion so that we'reabove board on all regulations
and other regulatory issues.
But sales information lines,shipped fill rates from a supply
(08:36):
chain perspective, whatpercentage of the parts are
actually in stock at the timethat the person orders it and
then some warehouse metrics aswell.
And we can often validate thequalitative input that we're
getting with the quantitativedata and that, in this
particular example, did not holdtrue.
So if I had just listened to myfriend who works at this local
(08:59):
dealership, we would have hadbig, big concerns about the
health of the service industry.
Now it just so happened thathis brand and two or three
others were down on volume, butindustry-wide there was not
enough of a degradation inservice volume for us to be
worried about it.
It was a bit slow, but it musthave been slower locally than we
(09:22):
realized, but it must have beenslower locally than we realized
.
So having the data, or beingable to get the data, is a great
way to know whether whatsomebody is telling you is
really representative of theentire market or there may be a
local issue going on.
So that's a fairly specificexample, but there's a lot of
different ways that we try toaccomplish that across the
(09:43):
various pieces of the industrywe touch parts and service both
ways that we try to accomplishthat across the various pieces
of the industry.
Mike Chung (09:48):
We touch parts and service both.
Yeah, let me dial into that alittle bit more, because in this
case you were able to conductprimary research to get data
firsthand from the audience andthe market of interest, and when
you have a big pool of 15,000plus dealerships, that's a
pretty sizable data source right.
And you mentioned somethinginteresting in there and it's
(10:08):
kind of came from that antitrustperspective of aggregation and
anonymization.
So just to expand on that alittle bit, the first things
that came to mind for me are onebrand's data, like Toyota, for
example.
They may not be able to seeHonda's, but you can kind of
look at it in the aggregate andsay, well, maybe there are cuts
(10:31):
that are available to everybody,and it's foreign makes versus
domestic makes or body styles,let's just say light trucks
versus passenger cars.
So are those some of thecontours that go along with the
legalities that you mentioned?
Nate Chenenko (10:51):
Yeah, we have a really good antitrust attorney
that we work with and antitrustdrives pretty much everything
that we do.
To be candid, we're workingwith, especially in the
automotive industry, 18different companies who are
retailing a substantial volumeof parts in the market and the
(11:12):
ability to enter the market isincredibly small.
The OEM market specifically,like the manufacturer market
Tier ones I'll hold for aseparate conversation.
So antitrust is deeplyimportant to us.
We also work with heavyequipment companies and on that
(11:33):
side of the business antitrustis even more of an issue because
there are a lot of very, verysmall players but a few very
large players who do control aneven bigger portion of the
market than the biggest of theauto manufacturers.
So everything that we releaseto our clients that has data
from more than one company wereview from an antitrust
(11:57):
perspective.
A lot of information we areable to share attributed to the
company that submits it to us,so something like warehouse
quality, for example.
I'm sure you've had an incidentwhere you've ordered product A
from an e-commerce vendor, let'ssay, and they ship you product
B, or you order two of product Aand they only ship you one, and
(12:18):
that's frustrating as acustomer and that happens in
supply chains all the timehappens in supply chains all the
time.
It's not an antitrust risk foranybody to know how everybody
else's warehouse qualitycompares to one another.
So we know and we can tell ourclients you know company A, you
(12:38):
did 500 errors per million linesshipped.
That's good industryperformance.
Per million lines shipped,that's good industry performance
, not best in class, but goodCompany B 3,000.
You should do these things thatcompany A does to improve their
performance.
(12:59):
So we use that benchmark dataand then we develop case studies
around it and that's whatallows us to that sort of feeds
a lot of our strategy consultingwork from especially the supply
chain and service perspective,because it's all quantifiable
and most of the information thatwe collect can be shared.
That way, where we can't shareany information, we don't do any
price benchmarking and if wedid we wouldn't be able to
(13:21):
attribute that information toone another.
But we don't collect anyinformation on prices from our
clients and anything related tosales has to be or is generally
anonymized.
But we're really careful aboutthat because if we cause a
problem with that, then not onlydoes our entire business
evaporate and we cease to exist,we've really hurt a lot of our
(13:43):
clients.
So in many cases we'reovercautious and we probably
could push the limits a littlebit more.
It's just not a smart decisionfor the long run for us.
Mike Chung (13:51):
Oh, that's, that's really helpful to understand.
Thanks, and just kind of onelast question about the, the,
the study with the, theindividual you know in the
Rochester area, was that like aone-time study or was that did
that get established into like arecurring study?
Nate Chenenko (14:07):
So the after effect of that our monthly
metric watch product has beenrecurring.
So every month we collect basicsupply chain metrics from a
variety of OEMs and couldcertainly do it for more
companies if there was broaderinterest and we collect all of
that information and it's onlythe core, most important pieces
(14:28):
that really fluctuate quite abit month over month.
So something like inventorylevels, which again are not at
dollar cost level, they're atlike a months of supplier and
inventory turns level, some ofthe common inventory management
metrics that the supply chainprofessionals use, and so we
compare those every month andthen once a month there's a 45
(14:50):
minute webinar that I lead.
Hey, you're up, can you explainwhy?
This has been reallyinteresting also because, like,
seeing the data is one thing,but of course you see somebody,
you see an OEMs number changemonth over month and you want to
know why, and so we always makesure to combine our industry
(15:11):
benchmark work and our datacollection work with a at least
digital meeting to discuss theresults and understand why
things are happening the waythat they were happening.
So, mike, you probably recalllast summer there were quite a
few, or late last summer I guessAugust, september there were
multiple OEMs had strikes acrossthe production environment and
(15:36):
some of those companies sawthose strikes echo into the
after-sales environment as welland they affected their
aftermarket warehouses and theiraftermarket distribution.
So of course that has a hugeimpact on the metrics that we
see your lines shipped and thedollars that you ship drop
precipitously.
And so we saw that in the data,which was not a surprise.
(16:05):
Everybody knew why, but it wasreally interesting to see the
recovery.
What recovered first?
Which warehouses?
How did it come back?
How did inventory follow on?
How was warehouse performanceaffected when there were some
temp staff, some headquartersstaff, some full-time staff
doing the work that typicallyunion staff would have done
during that time?
(16:25):
So it's an excellent way for usto understand what's going on.
And then we've developed goodrelationships around the
industry, so the companies thatparticipate in this research are
quite willing to talk to oneanother about what's going on.
And then again back to yourantitrust point.
We facilitate all of thosediscussions and we've got really
strict rules about what can andcan't be discussed, and people
(16:48):
understand the rules.
We rarely have an issue, but ifwe do ever stray close to the
boundaries one of my teammatesor myself I facilitate a lot of
them, but not all of them.
We just make sure to put up anappropriate boundary and push
things back into the safeterritory so we don't have any
issues there.
So it's a great learningopportunity and it allows us to
(17:09):
understand why the numbers aremoving the way that they're
moving, because, like, just anumber often just spawns a lot
of very good questions and thatallows us to get the answers
often just spawns a lot of verygood questions and that allows
us to get the answers Right.
Mike Chung (17:27):
And what I'm hearing is just that having the
discussion allows for thatadditional color and kind of
validation, as well as to tellme more from people in the
industry, and I don't know ifthis will come off funny, but I
thought about the example of thefill rates when you had the
managerial staff going in andtrying to load things and I can
see that perhaps being some funbetween the regular labor staff
(17:48):
and the managerial staff sayinghey yeah fill rates, leave it to
us.
Nate Chenenko (17:54):
Exactly yeah.
The numbers are a lot betterwhen the union staff come to do
it.
Mike Chung (18:02):
They're there for a reason, yeah.
Nate Chenenko (18:04):
Partly because they do it all day, every day,
and partly because there aremore of them, and but yes, I
that that definitely is one ofthe things that we take a look
at, and also the whole thing.
It's trust building.
If somebody just gives you anumber and you don't know them,
you're inclined to disbelievethe number.
(18:27):
And when you know the personand they've explained to you why
the number looks the way thatit looks, and you believe them.
And you've seen many, manyother numbers that they've
submitted, all of which areright and you believe.
The whole thing builds trustfor the entire endeavor, which
is helpful.
And then we also get veryvaluable data to use to work
(18:51):
with everybody to improve theirbusinesses as well.
So it's foundational for ourapproach.
Mike Chung (18:56):
Yeah, thanks for sharing that.
And one last question before Ipivot over to the dealer
retention rate.
And I feel like this could be anice segue, because when I
think about the type of studywhere it's a monthly metric, I
think about the data you'relooking for, but also the ease
of people on the receiving endto provide that data.
Say for that monthly metricthat you're that data.
(19:22):
Say for that monthly metricthat you're referring to, how is
it for the people taking thesurvey I would imagine they're
kind of industry standards.
There's some data points thatthey should be able to access
fairly easily and I guess asubset of that question could be
.
If it turns out it's adifficult question to answer.
How does that affect you from adata collector standpoint?
Do you alter the question?
Do you go to something else?
Nate Chenenko (19:43):
Yeah, I'm going to try not to give you too long
of an answer because I couldtalk about this all day and I've
been doing this for a while.
But benchmarking is.
The foundation of benchmarkingare good data definitions.
So if I ask you somethingsimple like how tall are you
definitions?
So if I ask you somethingsimple like how tall are you,
you could answer that questionbecause society has defined what
is a foot, what is an inch,what is a meter, all of the
(20:06):
basic building blocks formeasuring things.
But the supply chain is verycomplicated and the service
business, which we'll get to ina moment when we start talking
about retention, as youmentioned, is even more
complicated.
So trying to create a metricwithout a standard definition is
almost worse than useless.
Like if I simply ask somebodywhat's your fill rate, that is
(20:31):
worse than not knowing, in myopinion, because one company may
say 88 and another company maysay 96, and they could both be
right.
But they're talking about twodifferent things.
The metaphor that everybodyuses, or the idiom, I suppose,
because it's so popular, isapples to oranges comparisons,
and it's really our job to makesure that that doesn't happen.
(20:52):
So long before I joined thecompany or got into this
industry.
Other smarter people created aset of industry standard
definitions especially for thesecore metrics that are, like the
most important ones to measurethe supply chain and measure the
service business which we'llget to, and those definitions,
(21:16):
because we work with so manycompanies around the industry,
have become the industrystandard.
So we still, of course, have adetailed, like.
When I say facing fill, the 10people on the phone know what
that means.
Case of FacingFill like 33years ago somebody wrote a 90
(21:42):
word definition that everybodyhas just intuited and put into
their systems and that's the waythat the systems generate this
information.
So in many cases gathering thedata now is easy because they
push a button and then it spitsout these 10 numbers and then
they just put them into oursystem.
So it takes a few minutes whenwe try to create a new
definition.
Like, for example, we'velaunched an electric vehicle
(22:03):
after sales benchmark.
Because electric vehicles are.
They consume parts differently,they consume service
differently, they require adifferent set of capabilities.
It's hard to warehouse thebatteries, it's hard to train
dealers on how to service thosevehicles.
So we've got a new benchmarkwhich requires an adapted set of
definitions and the definitionof even what makes up an
(22:25):
electric vehicle is like 80words, because some people call
an electric vehicle like aplug-in hybrid.
They may consider an electricvehicle or even a regular hybrid
like a not plug-in hybrid, forexample a traditional Prius.
So setting those definitions istricky and the earlier we are
in the process, the harder it isto standardize on those
(22:45):
definitions.
But as time goes on, thedefinition generally expands to
clarify some inconsistenciesthat may have happened in year
one or year two and, with thebenefit of having access to
historical data, we can betterunderstand whether companies are
accurately interpreting thedefinition.
(23:07):
So we do a big validationprocess whenever we collect this
information to make sure it'saccurate, because if we're
communicating benchmarkinformation that's not truly
comparable, again I considerthat worse than not having any
information.
Mike Chung (23:22):
Right, it can create confusion because the 88 to 96,
.
It could be something like isthat units, is that percent?
Is that overall for aparticular category?
What's the time span we'relooking at?
So what I'm hearing isdefinitions are critical, and I
would imagine just going to thesame person or making sure that
he or she can loop in theappropriate person.
(23:43):
Those are some of thefoundations for having
consistent, reliable data.
Nate Chenenko (23:47):
Yes, yeah, and we also keep.
We've had relationships for along time as well.
But we take notes too.
So when a company switches over, you know people rotate around
companies all the time.
Either they have some positiveattrition and they get promoted,
or they go to a different jobwithin the company, or maybe
they leave and go to a differentcompany, and so we take notes
(24:09):
as well for each of theparticipants.
In many cases we'll know hey,you get this information from
this system and I can't tell youexactly what buttons to push,
but I can tell you what systemit comes from.
We've got the historicalinformation, and if it's
somebody new to the role, weoften help them out early on in
the process, which helps ouraccuracy, and then it's helpful.
(24:31):
It saves them a lot of time andstress as well.
Mark Bogdansky (24:35):
Hi, I'm Mark Bogdanski, vice President of
Trade Shows and CommunityEngagement at the Auto Care
Association, and I'm thrilled toinvite you to join me in Las
Vegas at Apex, the aftermarket'spremiere show, this November
5th to the 7th.
I guarantee you will have anamazing week full of unmatched
networking opportunities, accessto unparalleled educational
(24:56):
sessions, including our enhancedEV stage, our new ADAS and
sustainability stages, and ouraftermarket shop training.
You'll also see a show floorwith over 2,700 companies
featuring the latest products,services and experiences.
This truly is the aftermarket'shomecoming and you do not want
to miss it.
Register now at apexshow.
(25:16):
com and I hope to see you there.
Mike Chung (25:21):
For the benefit of our listeners, can you give us
just a little thumbnail on whatthe dealer retention rate is, a
little bit about its history andwhat it means for the industry?
Nate.
Nate Chenenko (25:31):
Yeah.
So when assessing a market andin this case we're talking about
the service market I've talkedquite a lot about supply chain
metrics, but we're going topivot over to talk about service
and, again, for purposes ofthis discussion, we'll focus
specifically on service that'sperformed in the dealer service
(25:51):
lane, or in the dealer channel,as I will refer to it.
So this is taking your car to aFord dealer or Toyota dealer, as
many of your listeners probablydo, and whenever we assess a
market like the dealer servicemarket, I really care about two
things.
Primarily, I want to know howbig is the market, which the
(26:13):
Auto Care Factbook does a verygood job of communicating, and
then I also want to know what ismy share of the market, what is
the company's share of themarket and the Auto Care
Factbook does a good job ofcommunicating.
And then I also want to knowwhat is my share of the market,
what is the company's share ofthe market and the Auto Care
Factbook does a good job ofcommunicating that as well, at a
very, very high level.
But each individual companythat we work with also wants to
know what their share of themarket is, and it is
(26:35):
surprisingly hard to calculate,and it is surprisingly hard to
calculate share of a servicemarket because it is hard to get
all of the data, particularlybecause a lot of this data
historically lived with dealersand was not easily accessible to
the OEM up until like the mid2000s so again prior to my
(27:01):
joining the company, we createda metric called dealer service
retention and the definitionwhich we talked about is deeply
important is the percentage ofcustomers who have come to the
dealer for customer pay serviceat least once in a calendar year
.
So not warranty service notwarranty service, and the
(27:22):
definition is much longer thanthat In practice.
It excludes no cost repairorders.
It excludes internal repairorders, like a dealer working on
VIN 1, 2, 3, 4, 5, 6 to refurbit for the used car sales.
Those are excluded.
It has to be like a quote,unquote real customer who owns a
(27:42):
real vehicle and goes to thedealer for service.
We also exclude fleet vehiclesand fleet service from that
metric.
So sounds easy, right, just goask all the dealers how many
customers came back for customerpay service.
But you have to define it soprecisely.
So I talked about what weexclude, but we will include
(28:04):
somebody who goes once.
That counts.
We'll also include somebody whogoes five times.
That counts too.
And so that's nice because thatgives you the numerator, like I
might know.
Okay, I had exactly 1 million,just to make the numbers easy.
I had exactly 1 million, just tomake the numbers easy.
I had exactly 1 millioncustomers come back to me in the
(28:25):
calendar year for service.
Well, that's nice to know andthat's great if you have a
million customers on the road,but it's a lot less good if
you're like General Motors andyou've got tens of millions of
customers on the road, and so weneed to divide that number by
something and we divide it bythe units in operation which we
(28:46):
buy from the vehicleregistration data that I suspect
a excuse me who've returned toa dealer for customer pay
service at least once in thecalendar year, and divide that
(29:09):
by the total number of customersor the total number of vehicles
on the road for that brand, andthat gives us the dealer
service retention metric.
And there's a lot of reasonsthat metric is not great, which
I suspect we will get to.
There's a lot of reasons thatmetric is not great, which I
(29:36):
suspect we will get to, but thatis better than what we, the
industry, knew prior to 2008,when we created the metric,
which was just a top line numberof customers who had returned,
and a lot of companies werecalculating their own version of
service retention but wereunable to compare to one another
accurately.
Mike Chung (29:45):
Apples and oranges phenomenon.
Nate Chenenko (29:47):
Exactly.
Mike Chung (29:47):
Referring to earlier .
So if I think about it from adealer perspective, an OEM might
say nationwide the numerator,the denominator you described as
a, perhaps a benchmark, andthen applying that across the
country to say, okay, dealer X,dealer Y, dealer 243 in
California, dealer 596 in DesMoines, iowa.
(30:10):
This is sort of the metric andwe're going to set the bar here
and I would imagine, with thedata sources you're referring to
, you're able to delineate anddrill down to the state, the
local level.
I would think.
Nate Chenenko (30:27):
Yeah, the calculation methodology is used
by most of the OEMs as well.
So when they calculate theirinternal like for all of their
dealerships, they do exactlywhat you were just mentioning
and I just had a conversationwith an OEM about this on Friday
where they calculate thatnationwide number as you were
(30:48):
mentioning.
Then they calculate a statewidenumber, but when you get down
to the metro area, it can betricky because there could be
one dealer in the metro area orthere could be four dealers in
the metro area, depending on thesize.
There could be 10 in really bigcities or more.
And so there's, even when youget down to that, like the more
granularity you want to go into,the more complexity it adds.
(31:10):
And that's again, if the themeof the conversation is
complexity, like that's whatkeeps us coming back and trying
to build more detail in withoutcreating too much complexity so
that you're giving somebody 200numbers and they look at none of
them.
One number is often not enough,but 200 numbers is too many and
(31:30):
we have to find the rightbalance of how many metrics can
people actually keep in theirheads and strive for versus,
which may not be perfectlycomprehensive.
They are accurate, but they maynot be comprehensive, and so
(31:52):
that drives a lot of what we do,as well as trying to say, okay,
here's the best way we can dothis to balance the amount of
different data points andmetrics that we have to
communicate with our ability todrive action, because ultimately
, the point here is to enablepeople to change their behavior,
to sell more, do more, providebetter customer service, provide
a better value and thenultimately increase their
(32:13):
revenues and profits.
It's not just to give people agiant list of numbers.
Sure.
Mike Chung (32:18):
And so, like the complexities that you mentioned
in terms of whether it's a datagap or availability of
information going to that dealerretention rate, what are some
of the things that you and yourteam have sort of climbed up the
mountain towards?
Nate Chenenko (32:38):
So there's a problem with dealer service
retention, which is probablyfairly obvious.
When I talked about when Iintroduced the concept, I talked
about market size, which isalways in dollars, and then I
talked about market share, whichis a percentage.
But typically when people thinkabout market share, they think
about market share as apercentage of the dollars, not
(33:02):
as a percentage of the peoplethat go somewhere.
And because of the complexityof the industry, the best the
industry has been able to dountil fairly recently has been
the service retention metricthat I've described, the
percentage of people that go toa dealer.
The problem with that metric isthat I maybe I'm a personally
(33:27):
good example of this.
I went to a dealer for customerpay service last year and I
also went to an aftermarket likea chain shop by my house
because I needed a stateinspection, which counts as
customer pay work, and I alsowent to the advanced auto parts
down the street from where Ilive and I bought wiper blades
and I bought washer fluid and Ibought a cabin air filter for my
(33:50):
other car.
But according to our and theindustry's service retention
metric, I still count asretained, but I would argue I'm
not fully retained because Iwent elsewhere.
This is sort of like McDonald'ssaying Mike bought a
cheeseburger from us on January1st, therefore he's our customer
(34:13):
.
Well, you might've bought one,but perhaps you go to Wendy's
like every Thursday, and soyou're retained by McDonald's,
but McDonald's might have 1% ofthe amount of money that you
spend.
Sure, that's the biggestproblem with the service
retention metric.
There are some secondary issues.
Service retention is still veryinformative.
(34:35):
It's relatively easy tocalculate and it is certainly
better than what the industryhad before.
Some of the other problems withservice retention are and these
are solvable, and I'll explainhow we've solved them.
Service retention differs agreat deal based on the age of
the vehicle.
So a customer of a brand newlet's say, one-year-old,
(34:58):
two-year-old premium vehicle BMW, lexus, mercedes, et cetera
about 80% of them, 85% of themreturn to the dealer for service
at least once a year.
By the time you're looking at a10-year-old non-premium vehicle
Chevy, hyundai, toyota, etcetera it's more like 20%.
(35:20):
So this is where we can go allthe way back to what you asked
earlier, where we segment theindustry.
So we'll take a look at premiumbrands.
There's zero to three yearretention, or zero to four, zero
to seven year retention, sevento 10.
We break those age categoriesdown pretty narrowly and we can
compare across those agecategories and then also we look
(35:45):
at the slope of that line.
So you can envision that linegoes all the way from starting
at 85% all the way down to maybe30% for a premium brand, 20%
for a non-premium brand.
The slope of that line reallymatters because that shows how
good an OEM and its dealers areat retaining customers as the
vehicle ages.
And as the vehicle ages theownership transitions.
(36:06):
The car leaves lease after 36months.
Generally that may be 50, 55,60% of the vehicles on the road.
For a premium brand it maychange hands again year seven or
year eight.
Now you're into a third owner.
You've got less connection tothem.
It's probably not going back tothe original dealer who
retailed the vehicle when it wasnew.
So it gets incrediblychallenging for dealers to
(36:29):
retain those customers as timegoes on.
And so we can address that.
We can solve that problem bylooking at specific narrow age
segments.
So that's a solvable problem.
But that does not address theissue of you buying one
cheeseburger at McDonald's andthen 99 of them at Wendy's and
(36:50):
then McDonald's being happy thatyou're a customer.
So the industry needs to gainsome sophistication in that area
.
Mike Chung (36:57):
Right, and I think from a quote unquote share of
wallet perspective too right.
Let's say I have aseven-year-old vehicle.
What is my total spend on thatvehicle?
You, have some great examples ofthe local parts shop, the
inspection station, thedealership, as well as maybe you
(37:18):
purchase something on ane-commerce outlet and you do it
yourself.
So I think, looking out interms of all the data that's
available, what's in the realmof reasonable and how much can
we get for a particular vehicleand, kind of like you said, what
data makes sense for yourpurchasing audience, if you will
(37:42):
right, because I think aboutthe number of people, the number
of vehicles, what percentage ofthose vehicles are going back
and, to the extent that you'reable to segment it, like you
mentioned, by age of vehicle,type of vehicle, whether premium
or non-premium, and it could beinteresting too Are you able to
do any sort of?
Here is an approximation of thedollars share that the dealer
(38:08):
is getting for spend on that car.
Nate Chenenko (38:11):
Yeah, we finally started doing that this year at
an industry aggregated basis, sowe're not yet able to do it in
as much accuracy as I'mcomfortable with on the brand
level for individual OEMs, butat the industry level we were
able to pull it together thisyear.
So we took the serviceretention data we've been
(38:33):
talking about and just as apoint of reference.
If you exclude all of thevehicle age segments that we're
looking at here, dealer serviceretention is about 50%, so about
half of vehicles go to a dealerfor customer pay work at least
once a year.
That seems really good, butthat doesn't again communicate
(38:54):
any of the dollars and we can'tuse that information to get a
share of wallet.
So I finally got a little bitfed up with being the data
arbiter for the industry and notactually having a good answer
to this question, and we boughta data set that allowed us to
(39:14):
calculate with a reasonabledegree of accuracy it is
definitely not perfect andprobably will never be perfect
the share of wallet for dealers.
So their service retention is50%.
That sounds quite good.
Their share of wallet is only43%, which is still good, but
it's not 50%.
(39:35):
So customers that dealersperceive as loyal must be going
somewhere else for service.
And that's really where you see, in particular, the independent
aftermarket.
We call them independent repairfacilities or IRFs, and they
have also 43% share of wallet.
(39:56):
So dealers with all of theinfrastructure, all of the
branding, all of the built-incustomer base.
I bought a car from a dealer.
The easiest thing for me to do,as the customer, should be to
go back to that dealer.
I know they exist, I know wherethey are, I'm in the CRM system
.
They have all of my information.
(40:18):
I have some degree of trustwith them.
Presumably In many cases thatshould be good.
Some cases, of course, it's not.
That should be the default andit is the default.
But what we find is that theindependent aftermarket with low
to no marketing budget, minimalonline booking technology, no
(40:39):
business development center toanswer the phone when people
call, unlikely to have Saturdayhours, don't sell genuine parts,
don't have loaner vehicles,don't offer Uber or Lyft as a
standard part of the serviceexperience to their customers,
don't serve coffee in thewaiting room, may not even have
a waiting room.
They're getting 43% of thedollars in the market and that's
(41:04):
a pretty striking differencewhen you look at the costs that
dealers put into their servicebusiness versus the costs that
the independent aftermarket putsinto their service business.
Mike Chung (41:18):
Right, yeah, really fascinating and just a testament
to the strength of theindependent repair facilities in
terms of the ability to retaincustomers as well as presumably
offer a compelling valueproposition.
And I think we've got aboutfive minutes left here, and one
of the things that I'm kind ofthinking about here, too, is you
(41:42):
mentioned purchasing a new dataset.
Tell us a little bit about whatthat looks like from a process.
What types of things are youlooking at from a data provider
when you make that type ofdecision?
Nate Chenenko (41:54):
Yeah, I'll give my personal take here and then
I'll layer on some other.
Of course we have plenty ofcompany policies as well that
I'll try to skip past.
That's the more boring piece.
When we're buying data, I wantthe most granular information
that I can possibly get my handson.
The more aggregation that'sperformed in whatever I'm buying
(42:16):
, the less useful it is for me.
So what we bought in this casewas a giant batch of credit card
data with 28.7 billiontransaction records in it.
From that was over the courseof three years, so it's not
quite that many on an annualbasis.
And there are companies whowould have sold us the answer to
(42:40):
the question, which is what isthe share of wallet for dealers
for?
For the independent aftermarket, for chains in the aftermarket,
for parts retailers in theaftermarket.
But I want the individualpieces because I want to know
and have 100% trust in what I'vegot and I want to be able to
understand the limitations ofwhat we have as well, because in
(43:03):
order to provide a service thatI feel good about that I think
our clients can really trust, weneed to lean into the problems
with the methodology that wehave, and if I don't have all
the granularity, then I can'tunderstand the problems and then
I can't communicate theproblems and that puts me in a
(43:23):
very awkward could put me, if welet this happen, in a very
awkward position where somebodyasks a question and I don't have
the answer.
That's like the worst casescenario for me.
I'm fine to say I don't know,let me find out and get back to
you.
But if my answer is, if I'msaying something and somebody
can give me a compellingargument why I'm wrong and I
(43:44):
can't at least address thatargument, that's a huge failure
for our business.
It's a trust killer.
I can't imagine trying to run abusiness that way, especially a
consulting business that way.
(44:06):
I also want to buy that datafrom somebody who has a similar
attitude to my attitude toconsuming the data.
I want somebody who's going totell me what I can do and also
what I can't do.
It's sort of like a goodattorney.
A bad attorney will tell youwhat you can't do.
A good attorney will tell youwhat you can't do, but then also
what you can do, and I thinkthe opposite may be true for
data that we buy.
I want to know both of thosethings.
What are the limitations?
(44:28):
And for the credit card dataspecifically, the big limitation
is that I don't get to see anyPII, any of the names of the
people whose credit cards it is,and I also don't get to see
what type of vehicle they own.
And I want to buy from somebodywho's going to own that and
make that very clear to me earlyon in the process.
Mike Chung (44:49):
That's really helpful and, I think, going
along with some of the otherthings we talked about with
regard to what are thelimitations of my data, what
questions can I answer and can'tI answer.
Because if I buy something fromxyzcom and install it myself,
you don't necessarily see inthat credit card transaction my
VIN number, and there's only somuch you can conclude or infer
(45:14):
from my transaction and how itmight apply to a particular
brand, make, year, etc.
Cetera.
And perhaps similarly, if Itake my vehicle to an
independent repair facility,that VIN number may or may not
be included in the credit carddata.
(45:35):
So being able for you as acollector and analyzer of data,
analyst of data, to be able tosay what can and can't we answer
, so really appreciate thoseperspectives.
Nate Chenenko (45:43):
Yeah, that's critical for us just knowing
what and also being able to saythis is what we can't do,
because otherwise people havedoubts and then they are not
interested in working with usand I can't argue with them.
I wouldn't want to work with useither if we weren't upfront
about it.
Sure, sure.
Mike Chung (46:00):
Yeah, so I guess one last topical question here is,
in the next, say, five, tenyears, the dealer service
retention.
Do you see any significantchanges in terms of being able
to be able to say now we can addthis type of analysis because
XYZ data is available, or we'reable to incorporate a new
(46:22):
analytical methodology to tellmore?
Nate Chenenko (46:27):
I'll answer this question.
I'll try to touch on the dataaspect and then the non-data
aspect.
Dealer service retention hasbeen slipping very slowly for
like a decade plus, and it movesslowly because the units in
operation, like the body of carson the road, changes very
slowly, especially as peoplekeep their cars for longer.
(46:47):
So I'm 40% concerned withgetting better data and
understanding that more.
I'm 60% concerned withunderstanding exactly why that's
happening and trying to helpall of our clients improve their
share of wallet.
So from a data standpoint, Ithink we will get better at
(47:10):
calculating share of wallet.
The more data sources we get,the better we can triangulate on
this information.
At the brand level, we can geta lot of data on specific
service points or serviceproviders and see what they're
doing well and what they may notbe doing well.
We can see in that data whethera tire chain, for example, is
(47:34):
growing more rapidly than adealer group.
We can see whether a partsretailer is growing faster than
another parts retailer, and wecan get that information before
they release their quarterlyresults.
Now we're not investors, wedon't invest on that information
, but it's nice to have it earlybecause it gives us an
indication of where things aregoing At an aggregate level,
(47:55):
like beyond the data.
Dealers are increasing theirprices at a higher rate than the
aftermarket.
Either chains or independentshops are increasing their
prices and the dealers start outat a higher rate than the
aftermarket.
Either chains or independentshops are increasing their
prices and the dealers start outat a higher point.
So increasing a $500 price tagby 10% is a lot more than
increasing a $300 price tag by5%, and the wider that price gap
(48:20):
gets, the harder it's going tobe for dealers to keep their
retention high and keep theirshare of wallet high.
They are trending towardsgetting an ever-increasing
amount of their revenue from anever-shrinking population of
people, and that is for anon-luxury business.
We're not talking about LouisVuitton.
(48:41):
We're not talking about Hermeshere, where that's a viable
strategy.
We're not talking about LouisVuitton.
We're not talking about Hermeshere, where that's a viable
strategy.
We're talking about a massmarket business.
That's a risky strategy that Ithink has to reverse course at
some point.
Otherwise service retention andshare of wallet for dealers
will continue to fall.
Mike Chung (48:57):
I'd love to keep the conversation going, perhaps in
a future episode more on creditcard data and considerations
there, as well as new trends incustomer behavior and provider
offerings, but I'll just endwith this last non-topical
related question.
(49:17):
You mentioned having familyget-togethers recently.
Let's say you have anotherget-together this weekend.
It's at your house.
You and your wife are servingfood.
What kind of foods might youprepare for the gathering?
Nate Chenenko (49:38):
Mike, I am a very simple person and I would
happily eat the same food everyday for the rest of my life, if
I'm one of those people.
But we are going to mybrother-in-law's house this
weekend and I am certain thatthey live in Beverly,
massachusetts, like up on theNorth shore, and I am certain
that he will be grillingscallops for us, which I love,
so that is on the menu for thisweekend and I am pretty excited
about it.
(49:58):
Fresh out of the ocean, thatsounds like a treat.
Mike Chung (50:01):
Thanks for tuning in to another episode of Auto Care
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(50:23):
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Welcome to Auto Care ON AIR, a candid podcast for a
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I'm Mike Chung, Senior Directorof Market Intelligence at the
Auto Care Association, and thisis Indicators, where we identify
and explore data that will helpyou monitor and forecast
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This includes global economicdata, industry indicators and
new data that will help youmonitor and forecast industry
performance.
(00:34):
Hello everybody, I'm very happyto introduce my good friend and
colleague, Nate Chenenko ofDucker Carlisle.
So, Nate, welcome to the show.
Nate Chenenko (00:44):
Thanks, Mike, happy to be here.
Thanks for inviting me.
Absolutely, so maybe tell us alittle bit about your company
and their involvement in theaftermarket.
Sure, I work for a strategyconsulting company called Ducker
Carlisle.
We're US based, with offices inbasically every continent that
you could think of.
(01:05):
We do work primarily in theautomotive aftermarket at least
my group within the DuckerCarlisle Umbrella Organization.
We do a lot of work with OEMs,plenty of work with tier one
suppliers, service organizations, parts retailers, et cetera.
I personally have been here for12 years, spent the entirety of
(01:27):
that 12 years in the automotiveaftermarket, again with a
primary focus on OEMs and theirrelationship to the aftermarket,
and have not even come close tomastering the aftermarket.
And it continues to engage meregularly.
So I'm still here.
Mike Chung (01:45):
Oh, that's great and it is a complex market and you
mentioned your company beinginvolved with OEMs.
Do you also have clients in theI guess in the parts
manufacturing distributor space,if I may ask?
Nate Chenenko (01:58):
Yep, we do.
We work with pretty much theentire value chain, you might
call it, ranging all the wayfrom mostly tier one suppliers
but occasionally tier two, tierthree suppliers.
We have a research arm as wellthat does research into some of
the raw materials.
Particularly like aluminum is aspecialty of ours and that
(02:21):
research gets consumed by a lotof tier suppliers.
We do work with lightweighting.
So that's all on the partssupply side, even like more of
the raw materials side, and alot of that research is consumed
directly by, like I said,primarily tier one suppliers.
And then our work with OEMs.
(02:42):
They're buying from tier onesuppliers, pricing those things,
distributing those things.
We do a lot of work with OEMs.
They're buying from tier onesuppliers, pricing those things,
distributing those things.
We do a lot of work with partssupply chain, parts pricing and
then some growth strategy workfor aftermarket companies as
well.
Mike Chung (02:57):
Oh, that's exciting and that gives a little bit more
color into the strategyconsulting.
So some of the things I heardthere are pricing studies,
research from materials and howdoes that affect the cost of a
good that an OEM might sell?
And you mentioned research.
So it could be research ofcustomers, it could be research
with surveys with suppliers,things like that.
Nate Chenenko (03:20):
Yeah.
And then because we do quite abit of work with OEMs and they
yeah.
And then because we do quite abit of work with OEMs and they
in many cases, especially in theAmerican market, can't sell or
don't sell directly to consumers.
We also have to be very attunedto how all of that information
plays with dealers.
So the OEM sets a price, thenthe dealer can change that price
(03:41):
.
The OEM sets a strategy andthat strategy is deeply reliant
on the dealers buying in andhelping execute that strategy.
So that adds an additionallayer of complexity.
I think the complexity you saidearlier that it's a very
complex market.
I think that complexity is whatkeeps me interested here.
Mike Chung (03:58):
Yeah, and you said you've been there for 12 years.
So tell me a little bit aboutthe journey to Ducker Carlisle,
what brought you there, and tellme a little bit about your
background.
Nate Chenenko (04:08):
I had the misfortune of graduating from
university during the globalfinancial crisis, so I ended up
finding my way into strategywork in sort of a roundabout
fashion, by starting with somegovernment contract consulting
work, which was the only thingthat had any money back then.
Luckily, it didn't take me toolong to get into the private
(04:29):
sector and ended up at Carlyleand Company, which later merged
with Ducker to become DuckerCarlyle and hit the automotive
aftermarket, like I said, in2012.
Then worked on quite a bit ofsupply chain work related to
aftermarket and after salessupply chains for OEMs and other
(04:51):
companies, but also was exposedto a lot of our work in pricing
.
And then, over the years, asI've become more and more
interested and started to readmore and engage with content
from a variety of sources likethe Auto Care Association, among
others, ended up leading a lotof our thought leadership
activities over that period oftime, and so now I spend about
(05:16):
half of my time doing some morestrategic research rather than
trying to answer one specificquestion, more so trying to
figure out hey, where is thisindustry going to be in 5, 10,
15 years?
What are the things we need todo now in order to prepare for
that.
How are the differentstakeholders going to react to
(05:37):
those things that we have to do?
How can we challenge somepre-existing maybe assumptions
that exist?
And then also, one of myfavorite topics is what is
everybody paying attention toright now?
That is important.
And what is everybody payingattention to right now?
That may not be as important aswe think it is, and I'm often
(05:57):
wrong on those things, butluckily I'm able to be right a
little bit more than I'm wrong,so people are still interested
in taking a look at what wecreate.
Mike Chung (06:09):
Oh, that's exciting and I think I appreciate that
description and the overview,because when we think about
trend spotting, trend tracking,we have our finger on a number
of different things right, andwhat I'm hearing is you're
looking at the market, you'relooking at qualitative
indicators, you're probablylooking at some quantitative
(06:30):
metrics and indices as well asyour kind of knowledge of being
in the space.
So I guess, with that in mind,what are some of the
quantitative metrics that youand your team might look at to
think about informing that fiveto 10 year horizon.
Nate Chenenko (06:45):
Yeah, you really so accurately characterized that
.
It is a mix of qualitative andquantitative, and oftentimes
we'll start digging into aquantitative metric after
hearing somebody tell us thatthey have a qualitative problem.
So I over the years, boththrough my personal life and
professionally, have developedsome friendly relationships with
(07:08):
people that work at autodealerships, for example, and
I'll just give you a specificexample Earlier this year, like
in February or March, a localfriend I'm from Rochester, new
York, and a local friend of minewho works at a dealership said
hey, we've been working like 60%time since the beginning of
January.
(07:28):
Things are super slow here andI'm not sure what's going on now
.
He works for one dealership outof the like 15,000 plus
dealerships in the country.
So his experience may not berepresentative at all, is not
representative at all.
But that little seed we plantthat and then we can collect
(07:48):
information from, especiallyfrom OEMs which, as I've
mentioned, is the bulk of ourclientele to understand whether
that's a real thing or not.
And so we use that to set up a.
We do some monthly metricsgathering from OEMs.
It's a product that we havecalled Metric Watch.
(08:10):
It's available just to OEMs inthe after sales space and we
collect basic information fromthem, like sales information,
and we communicate thisinformation in generally an
anonymized fashion so that we'reabove board on all regulations
and other regulatory issues.
But sales information lines,shipped fill rates from a supply
(08:36):
chain perspective, whatpercentage of the parts are
actually in stock at the timethat the person orders it and
then some warehouse metrics aswell.
And we can often validate thequalitative input that we're
getting with the quantitativedata and that, in this
particular example, did not holdtrue.
So if I had just listened to myfriend who works at this local
(08:59):
dealership, we would have hadbig, big concerns about the
health of the service industry.
Now it just so happened thathis brand and two or three
others were down on volume, butindustry-wide there was not
enough of a degradation inservice volume for us to be
worried about it.
It was a bit slow, but it musthave been slower locally than we
(09:22):
realized, but it must have beenslower locally than we realized
.
So having the data, or beingable to get the data, is a great
way to know whether whatsomebody is telling you is
really representative of theentire market or there may be a
local issue going on.
So that's a fairly specificexample, but there's a lot of
different ways that we try toaccomplish that across the
(09:43):
various pieces of the industrywe touch parts and service both
ways that we try to accomplishthat across the various pieces
of the industry.
Mike Chung (09:48):
We touch parts and service both.
Yeah, let me dial into that alittle bit more, because in this
case you were able to conductprimary research to get data
firsthand from the audience andthe market of interest, and when
you have a big pool of 15,000plus dealerships, that's a
pretty sizable data source right.
And you mentioned somethinginteresting in there and it's
(10:08):
kind of came from that antitrustperspective of aggregation and
anonymization.
So just to expand on that alittle bit, the first things
that came to mind for me are onebrand's data, like Toyota, for
example.
They may not be able to seeHonda's, but you can kind of
look at it in the aggregate andsay, well, maybe there are cuts
(10:31):
that are available to everybody,and it's foreign makes versus
domestic makes or body styles,let's just say light trucks
versus passenger cars.
So are those some of thecontours that go along with the
legalities that you mentioned?
Nate Chenenko (10:51):
Yeah, we have a really good antitrust attorney
that we work with and antitrustdrives pretty much everything
that we do.
To be candid, we're workingwith, especially in the
automotive industry, 18different companies who are
retailing a substantial volumeof parts in the market and the
(11:12):
ability to enter the market isincredibly small.
The OEM market specifically,like the manufacturer market
Tier ones I'll hold for aseparate conversation.
So antitrust is deeplyimportant to us.
We also work with heavyequipment companies and on that
(11:33):
side of the business antitrustis even more of an issue because
there are a lot of very, verysmall players but a few very
large players who do control aneven bigger portion of the
market than the biggest of theauto manufacturers.
So everything that we releaseto our clients that has data
from more than one company wereview from an antitrust
(11:57):
perspective.
A lot of information we areable to share attributed to the
company that submits it to us,so something like warehouse
quality, for example.
I'm sure you've had an incidentwhere you've ordered product A
from an e-commerce vendor, let'ssay, and they ship you product
B, or you order two of product Aand they only ship you one, and
(12:18):
that's frustrating as acustomer and that happens in
supply chains all the timehappens in supply chains all the
time.
It's not an antitrust risk foranybody to know how everybody
else's warehouse qualitycompares to one another.
So we know and we can tell ourclients you know company A, you
(12:38):
did 500 errors per million linesshipped.
That's good industryperformance.
Per million lines shipped,that's good industry performance
, not best in class, but goodCompany B 3,000.
You should do these things thatcompany A does to improve their
performance.
(12:59):
So we use that benchmark dataand then we develop case studies
around it and that's whatallows us to that sort of feeds
a lot of our strategy consultingwork from especially the supply
chain and service perspective,because it's all quantifiable
and most of the information thatwe collect can be shared.
That way, where we can't shareany information, we don't do any
price benchmarking and if wedid we wouldn't be able to
(13:21):
attribute that information toone another.
But we don't collect anyinformation on prices from our
clients and anything related tosales has to be or is generally
anonymized.
But we're really careful aboutthat because if we cause a
problem with that, then not onlydoes our entire business
evaporate and we cease to exist,we've really hurt a lot of our
(13:43):
clients.
So in many cases we'reovercautious and we probably
could push the limits a littlebit more.
It's just not a smart decisionfor the long run for us.
Mike Chung (13:51):
Oh, that's, that's really helpful to understand.
Thanks, and just kind of onelast question about the, the,
the study with the, theindividual you know in the
Rochester area, was that like aone-time study or was that did
that get established into like arecurring study?
Nate Chenenko (14:07):
So the after effect of that our monthly
metric watch product has beenrecurring.
So every month we collect basicsupply chain metrics from a
variety of OEMs and couldcertainly do it for more
companies if there was broaderinterest and we collect all of
that information and it's onlythe core, most important pieces
(14:28):
that really fluctuate quite abit month over month.
So something like inventorylevels, which again are not at
dollar cost level, they're atlike a months of supplier and
inventory turns level, some ofthe common inventory management
metrics that the supply chainprofessionals use, and so we
compare those every month andthen once a month there's a 45
(14:50):
minute webinar that I lead.
Hey, you're up, can you explainwhy?
This has been reallyinteresting also because, like,
seeing the data is one thing,but of course you see somebody,
you see an OEMs number changemonth over month and you want to
know why, and so we always makesure to combine our industry
(15:11):
benchmark work and our datacollection work with a at least
digital meeting to discuss theresults and understand why
things are happening the waythat they were happening.
So, mike, you probably recalllast summer there were quite a
few, or late last summer I guessAugust, september there were
multiple OEMs had strikes acrossthe production environment and
(15:36):
some of those companies sawthose strikes echo into the
after-sales environment as welland they affected their
aftermarket warehouses and theiraftermarket distribution.
So of course that has a hugeimpact on the metrics that we
see your lines shipped and thedollars that you ship drop
precipitously.
And so we saw that in the data,which was not a surprise.
(16:05):
Everybody knew why, but it wasreally interesting to see the
recovery.
What recovered first?
Which warehouses?
How did it come back?
How did inventory follow on?
How was warehouse performanceaffected when there were some
temp staff, some headquartersstaff, some full-time staff
doing the work that typicallyunion staff would have done
during that time?
(16:25):
So it's an excellent way for usto understand what's going on.
And then we've developed goodrelationships around the
industry, so the companies thatparticipate in this research are
quite willing to talk to oneanother about what's going on.
And then again back to yourantitrust point.
We facilitate all of thosediscussions and we've got really
strict rules about what can andcan't be discussed, and people
(16:48):
understand the rules.
We rarely have an issue, but ifwe do ever stray close to the
boundaries one of my teammatesor myself I facilitate a lot of
them, but not all of them.
We just make sure to put up anappropriate boundary and push
things back into the safeterritory so we don't have any
issues there.
So it's a great learningopportunity and it allows us to
(17:09):
understand why the numbers aremoving the way that they're
moving, because, like, just anumber often just spawns a lot
of very good questions and thatallows us to get the answers
often just spawns a lot of verygood questions and that allows
us to get the answers Right.
Mike Chung (17:27):
And what I'm hearing is just that having the
discussion allows for thatadditional color and kind of
validation, as well as to tellme more from people in the
industry, and I don't know ifthis will come off funny, but I
thought about the example of thefill rates when you had the
managerial staff going in andtrying to load things and I can
see that perhaps being some funbetween the regular labor staff
(17:48):
and the managerial staff sayinghey yeah fill rates, leave it to
us.
Nate Chenenko (17:54):
Exactly yeah.
The numbers are a lot betterwhen the union staff come to do
it.
Mike Chung (18:02):
They're there for a reason, yeah.
Nate Chenenko (18:04):
Partly because they do it all day, every day,
and partly because there aremore of them, and but yes, I
that that definitely is one ofthe things that we take a look
at, and also the whole thing.
It's trust building.
If somebody just gives you anumber and you don't know them,
you're inclined to disbelievethe number.
(18:27):
And when you know the personand they've explained to you why
the number looks the way thatit looks, and you believe them.
And you've seen many, manyother numbers that they've
submitted, all of which areright and you believe.
The whole thing builds trustfor the entire endeavor, which
is helpful.
And then we also get veryvaluable data to use to work
(18:51):
with everybody to improve theirbusinesses as well.
So it's foundational for ourapproach.
Mike Chung (18:56):
Yeah, thanks for sharing that.
And one last question before Ipivot over to the dealer
retention rate.
And I feel like this could be anice segue, because when I
think about the type of studywhere it's a monthly metric, I
think about the data you'relooking for, but also the ease
of people on the receiving endto provide that data.
Say for that monthly metricthat you're that data.
(19:22):
Say for that monthly metricthat you're referring to, how is
it for the people taking thesurvey I would imagine they're
kind of industry standards.
There's some data points thatthey should be able to access
fairly easily and I guess asubset of that question could be
.
If it turns out it's adifficult question to answer.
How does that affect you from adata collector standpoint?
Do you alter the question?
Do you go to something else?
Nate Chenenko (19:43):
Yeah, I'm going to try not to give you too long
of an answer because I couldtalk about this all day and I've
been doing this for a while.
But benchmarking is.
The foundation of benchmarkingare good data definitions.
So if I ask you somethingsimple like how tall are you
definitions?
So if I ask you somethingsimple like how tall are you,
you could answer that questionbecause society has defined what
is a foot, what is an inch,what is a meter, all of the
(20:06):
basic building blocks formeasuring things.
But the supply chain is verycomplicated and the service
business, which we'll get to ina moment when we start talking
about retention, as youmentioned, is even more
complicated.
So trying to create a metricwithout a standard definition is
almost worse than useless.
Like if I simply ask somebodywhat's your fill rate, that is
(20:31):
worse than not knowing, in myopinion, because one company may
say 88 and another company maysay 96, and they could both be
right.
But they're talking about twodifferent things.
The metaphor that everybodyuses, or the idiom, I suppose,
because it's so popular, isapples to oranges comparisons,
and it's really our job to makesure that that doesn't happen.
(20:52):
So long before I joined thecompany or got into this
industry.
Other smarter people created aset of industry standard
definitions especially for thesecore metrics that are, like the
most important ones to measurethe supply chain and measure the
service business which we'llget to, and those definitions,
(21:16):
because we work with so manycompanies around the industry,
have become the industrystandard.
So we still, of course, have adetailed, like.
When I say facing fill, the 10people on the phone know what
that means.
Case of FacingFill like 33years ago somebody wrote a 90
(21:42):
word definition that everybodyhas just intuited and put into
their systems and that's the waythat the systems generate this
information.
So in many cases gathering thedata now is easy because they
push a button and then it spitsout these 10 numbers and then
they just put them into oursystem.
So it takes a few minutes whenwe try to create a new
definition.
Like, for example, we'velaunched an electric vehicle
(22:03):
after sales benchmark.
Because electric vehicles are.
They consume parts differently,they consume service
differently, they require adifferent set of capabilities.
It's hard to warehouse thebatteries, it's hard to train
dealers on how to service thosevehicles.
So we've got a new benchmarkwhich requires an adapted set of
definitions and the definitionof even what makes up an
(22:25):
electric vehicle is like 80words, because some people call
an electric vehicle like aplug-in hybrid.
They may consider an electricvehicle or even a regular hybrid
like a not plug-in hybrid, forexample a traditional Prius.
So setting those definitions istricky and the earlier we are
in the process, the harder it isto standardize on those
(22:45):
definitions.
But as time goes on, thedefinition generally expands to
clarify some inconsistenciesthat may have happened in year
one or year two and, with thebenefit of having access to
historical data, we can betterunderstand whether companies are
accurately interpreting thedefinition.
(23:07):
So we do a big validationprocess whenever we collect this
information to make sure it'saccurate, because if we're
communicating benchmarkinformation that's not truly
comparable, again I considerthat worse than not having any
information.
Mike Chung (23:22):
Right, it can create confusion because the 88 to 96,
.
It could be something like isthat units, is that percent?
Is that overall for aparticular category?
What's the time span we'relooking at?
So what I'm hearing isdefinitions are critical, and I
would imagine just going to thesame person or making sure that
he or she can loop in theappropriate person.
(23:43):
Those are some of thefoundations for having
consistent, reliable data.
Nate Chenenko (23:47):
Yes, yeah, and we also keep.
We've had relationships for along time as well.
But we take notes too.
So when a company switches over, you know people rotate around
companies all the time.
Either they have some positiveattrition and they get promoted,
or they go to a different jobwithin the company, or maybe
they leave and go to a differentcompany, and so we take notes
(24:09):
as well for each of theparticipants.
In many cases we'll know hey,you get this information from
this system and I can't tell youexactly what buttons to push,
but I can tell you what systemit comes from.
We've got the historicalinformation, and if it's
somebody new to the role, weoften help them out early on in
the process, which helps ouraccuracy, and then it's helpful.
(24:31):
It saves them a lot of time andstress as well.
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(25:16):
com and I hope to see you there.
Mike Chung (25:21):
For the benefit of our listeners, can you give us
just a little thumbnail on whatthe dealer retention rate is, a
little bit about its history andwhat it means for the industry?
Nate.
Nate Chenenko (25:31):
Yeah.
So when assessing a market andin this case we're talking about
the service market I've talkedquite a lot about supply chain
metrics, but we're going topivot over to talk about service
and, again, for purposes ofthis discussion, we'll focus
specifically on service that'sperformed in the dealer service
(25:51):
lane, or in the dealer channel,as I will refer to it.
So this is taking your car to aFord dealer or Toyota dealer, as
many of your listeners probablydo, and whenever we assess a
market like the dealer servicemarket, I really care about two
things.
Primarily, I want to know howbig is the market, which the
(26:13):
Auto Care Factbook does a verygood job of communicating, and
then I also want to know what ismy share of the market, what is
the company's share of themarket and the Auto Care
Factbook does a good job ofcommunicating.
And then I also want to knowwhat is my share of the market,
what is the company's share ofthe market and the Auto Care
Factbook does a good job ofcommunicating that as well, at a
very, very high level.
But each individual companythat we work with also wants to
know what their share of themarket is, and it is
(26:35):
surprisingly hard to calculate,and it is surprisingly hard to
calculate share of a servicemarket because it is hard to get
all of the data, particularlybecause a lot of this data
historically lived with dealersand was not easily accessible to
the OEM up until like the mid2000s so again prior to my
(27:01):
joining the company, we createda metric called dealer service
retention and the definitionwhich we talked about is deeply
important is the percentage ofcustomers who have come to the
dealer for customer pay serviceat least once in a calendar year
.
So not warranty service notwarranty service, and the
(27:22):
definition is much longer thanthat In practice.
It excludes no cost repairorders.
It excludes internal repairorders, like a dealer working on
VIN 1, 2, 3, 4, 5, 6 to refurbit for the used car sales.
Those are excluded.
It has to be like a quote,unquote real customer who owns a
(27:42):
real vehicle and goes to thedealer for service.
We also exclude fleet vehiclesand fleet service from that
metric.
So sounds easy, right, just goask all the dealers how many
customers came back for customerpay service.
But you have to define it soprecisely.
So I talked about what weexclude, but we will include
(28:04):
somebody who goes once.
That counts.
We'll also include somebody whogoes five times.
That counts too.
And so that's nice because thatgives you the numerator, like I
might know.
Okay, I had exactly 1 million,just to make the numbers easy.
I had exactly 1 million, just tomake the numbers easy.
I had exactly 1 millioncustomers come back to me in the
(28:25):
calendar year for service.
Well, that's nice to know andthat's great if you have a
million customers on the road,but it's a lot less good if
you're like General Motors andyou've got tens of millions of
customers on the road, and so weneed to divide that number by
something and we divide it bythe units in operation which we
(28:46):
buy from the vehicleregistration data that I suspect
a excuse me who've returned toa dealer for customer pay
service at least once in thecalendar year, and divide that
(29:09):
by the total number of customersor the total number of vehicles
on the road for that brand, andthat gives us the dealer
service retention metric.
And there's a lot of reasonsthat metric is not great, which
I suspect we will get to.
There's a lot of reasons thatmetric is not great, which I
(29:36):
suspect we will get to, but thatis better than what we, the
industry, knew prior to 2008,when we created the metric,
which was just a top line numberof customers who had returned,
and a lot of companies werecalculating their own version of
service retention but wereunable to compare to one another
accurately.
Mike Chung (29:45):
Apples and oranges phenomenon.
Nate Chenenko (29:47):
Exactly.
Mike Chung (29:47):
Referring to earlier .
So if I think about it from adealer perspective, an OEM might
say nationwide the numerator,the denominator you described as
a, perhaps a benchmark, andthen applying that across the
country to say, okay, dealer X,dealer Y, dealer 243 in
California, dealer 596 in DesMoines, iowa.
(30:10):
This is sort of the metric andwe're going to set the bar here
and I would imagine, with thedata sources you're referring to
, you're able to delineate anddrill down to the state, the
local level.
I would think.
Nate Chenenko (30:27):
Yeah, the calculation methodology is used
by most of the OEMs as well.
So when they calculate theirinternal like for all of their
dealerships, they do exactlywhat you were just mentioning
and I just had a conversationwith an OEM about this on Friday
where they calculate thatnationwide number as you were
(30:48):
mentioning.
Then they calculate a statewidenumber, but when you get down
to the metro area, it can betricky because there could be
one dealer in the metro area orthere could be four dealers in
the metro area, depending on thesize.
There could be 10 in really bigcities or more.
And so there's, even when youget down to that, like the more
granularity you want to go into,the more complexity it adds.
(31:10):
And that's again, if the themeof the conversation is
complexity, like that's whatkeeps us coming back and trying
to build more detail in withoutcreating too much complexity so
that you're giving somebody 200numbers and they look at none of
them.
One number is often not enough,but 200 numbers is too many and
(31:30):
we have to find the rightbalance of how many metrics can
people actually keep in theirheads and strive for versus,
which may not be perfectlycomprehensive.
They are accurate, but they maynot be comprehensive, and so
(31:52):
that drives a lot of what we do,as well as trying to say, okay,
here's the best way we can dothis to balance the amount of
different data points andmetrics that we have to
communicate with our ability todrive action, because ultimately
, the point here is to enablepeople to change their behavior,
to sell more, do more, providebetter customer service, provide
a better value and thenultimately increase their
(32:13):
revenues and profits.
It's not just to give people agiant list of numbers.
Sure.
Mike Chung (32:18):
And so, like the complexities that you mentioned
in terms of whether it's a datagap or availability of
information going to that dealerretention rate, what are some
of the things that you and yourteam have sort of climbed up the
mountain towards?
Nate Chenenko (32:38):
So there's a problem with dealer service
retention, which is probablyfairly obvious.
When I talked about when Iintroduced the concept, I talked
about market size, which isalways in dollars, and then I
talked about market share, whichis a percentage.
But typically when people thinkabout market share, they think
about market share as apercentage of the dollars, not
(33:02):
as a percentage of the peoplethat go somewhere.
And because of the complexityof the industry, the best the
industry has been able to dountil fairly recently has been
the service retention metricthat I've described, the
percentage of people that go toa dealer.
The problem with that metric isthat I maybe I'm a personally
(33:27):
good example of this.
I went to a dealer for customerpay service last year and I
also went to an aftermarket likea chain shop by my house
because I needed a stateinspection, which counts as
customer pay work, and I alsowent to the advanced auto parts
down the street from where Ilive and I bought wiper blades
and I bought washer fluid and Ibought a cabin air filter for my
(33:50):
other car.
But according to our and theindustry's service retention
metric, I still count asretained, but I would argue I'm
not fully retained because Iwent elsewhere.
This is sort of like McDonald'ssaying Mike bought a
cheeseburger from us on January1st, therefore he's our customer
(34:13):
.
Well, you might've bought one,but perhaps you go to Wendy's
like every Thursday, and soyou're retained by McDonald's,
but McDonald's might have 1% ofthe amount of money that you
spend.
Sure, that's the biggestproblem with the service
retention metric.
There are some secondary issues.
Service retention is still veryinformative.
(34:35):
It's relatively easy tocalculate and it is certainly
better than what the industryhad before.
Some of the other problems withservice retention are and these
are solvable, and I'll explainhow we've solved them.
Service retention differs agreat deal based on the age of
the vehicle.
So a customer of a brand newlet's say, one-year-old,
(34:58):
two-year-old premium vehicle BMW, lexus, mercedes, et cetera
about 80% of them, 85% of themreturn to the dealer for service
at least once a year.
By the time you're looking at a10-year-old non-premium vehicle
Chevy, hyundai, toyota, etcetera it's more like 20%.
(35:20):
So this is where we can go allthe way back to what you asked
earlier, where we segment theindustry.
So we'll take a look at premiumbrands.
There's zero to three yearretention, or zero to four, zero
to seven year retention, sevento 10.
We break those age categoriesdown pretty narrowly and we can
compare across those agecategories and then also we look
(35:45):
at the slope of that line.
So you can envision that linegoes all the way from starting
at 85% all the way down to maybe30% for a premium brand, 20%
for a non-premium brand.
The slope of that line reallymatters because that shows how
good an OEM and its dealers areat retaining customers as the
vehicle ages.
And as the vehicle ages theownership transitions.
(36:06):
The car leaves lease after 36months.
Generally that may be 50, 55,60% of the vehicles on the road.
For a premium brand it maychange hands again year seven or
year eight.
Now you're into a third owner.
You've got less connection tothem.
It's probably not going back tothe original dealer who
retailed the vehicle when it wasnew.
So it gets incrediblychallenging for dealers to
(36:29):
retain those customers as timegoes on.
And so we can address that.
We can solve that problem bylooking at specific narrow age
segments.
So that's a solvable problem.
But that does not address theissue of you buying one
cheeseburger at McDonald's andthen 99 of them at Wendy's and
(36:50):
then McDonald's being happy thatyou're a customer.
So the industry needs to gainsome sophistication in that area
.
Mike Chung (36:57):
Right, and I think from a quote unquote share of
wallet perspective too right.
Let's say I have aseven-year-old vehicle.
What is my total spend on thatvehicle?
You, have some great examples ofthe local parts shop, the
inspection station, thedealership, as well as maybe you
(37:18):
purchase something on ane-commerce outlet and you do it
yourself.
So I think, looking out interms of all the data that's
available, what's in the realmof reasonable and how much can
we get for a particular vehicleand, kind of like you said, what
data makes sense for yourpurchasing audience, if you will
(37:42):
right, because I think aboutthe number of people, the number
of vehicles, what percentage ofthose vehicles are going back
and, to the extent that you'reable to segment it, like you
mentioned, by age of vehicle,type of vehicle, whether premium
or non-premium, and it could beinteresting too Are you able to
do any sort of?
Here is an approximation of thedollars share that the dealer
(38:08):
is getting for spend on that car.
Nate Chenenko (38:11):
Yeah, we finally started doing that this year at
an industry aggregated basis, sowe're not yet able to do it in
as much accuracy as I'mcomfortable with on the brand
level for individual OEMs, butat the industry level we were
able to pull it together thisyear.
So we took the serviceretention data we've been
(38:33):
talking about and just as apoint of reference.
If you exclude all of thevehicle age segments that we're
looking at here, dealer serviceretention is about 50%, so about
half of vehicles go to a dealerfor customer pay work at least
once a year.
That seems really good, butthat doesn't again communicate
(38:54):
any of the dollars and we can'tuse that information to get a
share of wallet.
So I finally got a little bitfed up with being the data
arbiter for the industry and notactually having a good answer
to this question, and we boughta data set that allowed us to
(39:14):
calculate with a reasonabledegree of accuracy it is
definitely not perfect andprobably will never be perfect
the share of wallet for dealers.
So their service retention is50%.
That sounds quite good.
Their share of wallet is only43%, which is still good, but
it's not 50%.
(39:35):
So customers that dealersperceive as loyal must be going
somewhere else for service.
And that's really where you see, in particular, the independent
aftermarket.
We call them independent repairfacilities or IRFs, and they
have also 43% share of wallet.
(39:56):
So dealers with all of theinfrastructure, all of the
branding, all of the built-incustomer base.
I bought a car from a dealer.
The easiest thing for me to do,as the customer, should be to
go back to that dealer.
I know they exist, I know wherethey are, I'm in the CRM system
.
They have all of my information.
(40:18):
I have some degree of trustwith them.
Presumably In many cases thatshould be good.
Some cases, of course, it's not.
That should be the default andit is the default.
But what we find is that theindependent aftermarket with low
to no marketing budget, minimalonline booking technology, no
(40:39):
business development center toanswer the phone when people
call, unlikely to have Saturdayhours, don't sell genuine parts,
don't have loaner vehicles,don't offer Uber or Lyft as a
standard part of the serviceexperience to their customers,
don't serve coffee in thewaiting room, may not even have
a waiting room.
They're getting 43% of thedollars in the market and that's
(41:04):
a pretty striking differencewhen you look at the costs that
dealers put into their servicebusiness versus the costs that
the independent aftermarket putsinto their service business.
Mike Chung (41:18):
Right, yeah, really fascinating and just a testament
to the strength of theindependent repair facilities in
terms of the ability to retaincustomers as well as presumably
offer a compelling valueproposition.
And I think we've got aboutfive minutes left here, and one
of the things that I'm kind ofthinking about here, too, is you
(41:42):
mentioned purchasing a new dataset.
Tell us a little bit about whatthat looks like from a process.
What types of things are youlooking at from a data provider
when you make that type ofdecision?
Nate Chenenko (41:54):
Yeah, I'll give my personal take here and then
I'll layer on some other.
Of course we have plenty ofcompany policies as well that
I'll try to skip past.
That's the more boring piece.
When we're buying data, I wantthe most granular information
that I can possibly get my handson.
The more aggregation that'sperformed in whatever I'm buying
(42:16):
, the less useful it is for me.
So what we bought in this casewas a giant batch of credit card
data with 28.7 billiontransaction records in it.
From that was over the courseof three years, so it's not
quite that many on an annualbasis.
And there are companies whowould have sold us the answer to
(42:40):
the question, which is what isthe share of wallet for dealers
for?
For the independent aftermarket, for chains in the aftermarket,
for parts retailers in theaftermarket.
But I want the individualpieces because I want to know
and have 100% trust in what I'vegot and I want to be able to
understand the limitations ofwhat we have as well, because in
(43:03):
order to provide a service thatI feel good about that I think
our clients can really trust, weneed to lean into the problems
with the methodology that wehave, and if I don't have all
the granularity, then I can'tunderstand the problems and then
I can't communicate theproblems and that puts me in a
(43:23):
very awkward could put me, if welet this happen, in a very
awkward position where somebodyasks a question and I don't have
the answer.
That's like the worst casescenario for me.
I'm fine to say I don't know,let me find out and get back to
you.
But if my answer is, if I'msaying something and somebody
can give me a compellingargument why I'm wrong and I
(43:44):
can't at least address thatargument, that's a huge failure
for our business.
It's a trust killer.
I can't imagine trying to run abusiness that way, especially a
consulting business that way.
(44:06):
I also want to buy that datafrom somebody who has a similar
attitude to my attitude toconsuming the data.
I want somebody who's going totell me what I can do and also
what I can't do.
It's sort of like a goodattorney.
A bad attorney will tell youwhat you can't do.
A good attorney will tell youwhat you can't do, but then also
what you can do, and I thinkthe opposite may be true for
data that we buy.
I want to know both of thosethings.
What are the limitations?
(44:28):
And for the credit card dataspecifically, the big limitation
is that I don't get to see anyPII, any of the names of the
people whose credit cards it is,and I also don't get to see
what type of vehicle they own.
And I want to buy from somebodywho's going to own that and
make that very clear to me earlyon in the process.
Mike Chung (44:49):
That's really helpful and, I think, going
along with some of the otherthings we talked about with
regard to what are thelimitations of my data, what
questions can I answer and can'tI answer.
Because if I buy something fromxyzcom and install it myself,
you don't necessarily see inthat credit card transaction my
VIN number, and there's only somuch you can conclude or infer
(45:14):
from my transaction and how itmight apply to a particular
brand, make, year, etc.
Cetera.
And perhaps similarly, if Itake my vehicle to an
independent repair facility,that VIN number may or may not
be included in the credit carddata.
(45:35):
So being able for you as acollector and analyzer of data,
analyst of data, to be able tosay what can and can't we answer
, so really appreciate thoseperspectives.
Nate Chenenko (45:43):
Yeah, that's critical for us just knowing
what and also being able to saythis is what we can't do,
because otherwise people havedoubts and then they are not
interested in working with usand I can't argue with them.
I wouldn't want to work with useither if we weren't upfront
about it.
Sure, sure.
Mike Chung (46:00):
Yeah, so I guess one last topical question here is,
in the next, say, five, tenyears, the dealer service
retention.
Do you see any significantchanges in terms of being able
to be able to say now we can addthis type of analysis because
XYZ data is available, or we'reable to incorporate a new
(46:22):
analytical methodology to tellmore?
Nate Chenenko (46:27):
I'll answer this question.
I'll try to touch on the dataaspect and then the non-data
aspect.
Dealer service retention hasbeen slipping very slowly for
like a decade plus, and it movesslowly because the units in
operation, like the body of carson the road, changes very
slowly, especially as peoplekeep their cars for longer.
(46:47):
So I'm 40% concerned withgetting better data and
understanding that more.
I'm 60% concerned withunderstanding exactly why that's
happening and trying to helpall of our clients improve their
share of wallet.
So from a data standpoint, Ithink we will get better at
(47:10):
calculating share of wallet.
The more data sources we get,the better we can triangulate on
this information.
At the brand level, we can geta lot of data on specific
service points or serviceproviders and see what they're
doing well and what they may notbe doing well.
We can see in that data whethera tire chain, for example, is
(47:34):
growing more rapidly than adealer group.
We can see whether a partsretailer is growing faster than
another parts retailer, and wecan get that information before
they release their quarterlyresults.
Now we're not investors, wedon't invest on that information
, but it's nice to have it earlybecause it gives us an
indication of where things aregoing At an aggregate level,
(47:55):
like beyond the data.
Dealers are increasing theirprices at a higher rate than the
aftermarket.
Either chains or independentshops are increasing their
prices and the dealers start outat a higher rate than the
aftermarket.
Either chains or independentshops are increasing their
prices and the dealers start outat a higher point.
So increasing a $500 price tagby 10% is a lot more than
increasing a $300 price tag by5%, and the wider that price gap
(48:20):
gets, the harder it's going tobe for dealers to keep their
retention high and keep theirshare of wallet high.
They are trending towardsgetting an ever-increasing
amount of their revenue from anever-shrinking population of
people, and that is for anon-luxury business.
We're not talking about LouisVuitton.
(48:41):
We're not talking about Hermeshere, where that's a viable
strategy.
We're not talking about LouisVuitton.
We're not talking about Hermeshere, where that's a viable
strategy.
We're talking about a massmarket business.
That's a risky strategy that Ithink has to reverse course at
some point.
Otherwise service retention andshare of wallet for dealers
will continue to fall.
Mike Chung (48:57):
I'd love to keep the conversation going, perhaps in
a future episode more on creditcard data and considerations
there, as well as new trends incustomer behavior and provider
offerings, but I'll just endwith this last non-topical
related question.
(49:17):
You mentioned having familyget-togethers recently.
Let's say you have anotherget-together this weekend.
It's at your house.
You and your wife are servingfood.
What kind of foods might youprepare for the gathering?
Nate Chenenko (49:38):
Mike, I am a very simple person and I would
happily eat the same food everyday for the rest of my life, if
I'm one of those people.
But we are going to mybrother-in-law's house this
weekend and I am certain thatthey live in Beverly,
massachusetts, like up on theNorth shore, and I am certain
that he will be grillingscallops for us, which I love,
so that is on the menu for thisweekend and I am pretty excited
about it.
(49:58):
Fresh out of the ocean, thatsounds like a treat.
Mike Chung (50:01):
Thanks for tuning in to another episode 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
Association, dedicated toadvancing the auto care industry
and supporting professionalslike you.
(50:23):
To learn more about theassociation and its initiatives,
visit autocareorg.
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