July 22, 2024 • 46 mins
What if your daily drive could be the key to safer roads and smarter cities? Join us as we sit down with Jeff Schlitt from Arity, a subsidiary of Allstate Corporation, to uncover the power of quantified driving data. Discover how Arity collects and analyzes data from connected cars and mobile phones, all while prioritizing consumer consent and transparency. Jeff shares eye-opening insights on how this data enhances safety features like crash detection, boosts fuel efficiency, and even sends you timely oil change notifications—truly revolutionizing the driving experience.
We also dive deep into the ethical use of driving data for equitable insurance pricing. Jeff explains how artificial intelligence and machine learning refine actuarial tables, leading to fairer pricing frameworks. We tackle the evolving landscape of driving behaviors post-COVID and the role of advanced automotive technologies in aiming for net zero accidents and deaths. If you’re curious about the future of the automotive sector and how data is shaping safer, smarter roads, this episode is a must-listen.
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
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (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
(00:33):
new data sources.
All right, Thanks everybody forjoining.
My name is Mike Chung, I'm thehost of this session and I'm
pleased to have Jeff Schlitt ofArity join us, and the reason
why I asked Jeff to join us isbecause he and his team have a
really great view on quantifieddriving data and behavior.
So, Jeff, maybe you take a fewmoments to introduce yourself.
(00:56):
Tell us about your role andwhat your company does.
Speaker 2 (00:58):
Great, mike.
Thanks, appreciate it and veryhappy to be here within the
group here talking aboutmobility and telematics.
As Michael said, jeff Schlittfrom Arity For those that don't
know, arity is a wholly ownedsubsidiary of the Allstate
Corporation.
We connect with about 40million consumers today,
collecting information ondriving data, where they drive,
(01:19):
how they drive, what risks theyaccumulate.
And what we do at Arity is weanalyze that.
We are, by trade, a data andanalytics company and our goal
is to make transportation safer,smarter and more useful for
consumers.
We take that information.
We really understand thingslike the routes and routines
that people do.
We understand their behaviorslike how hard do they break, how
fast do they drive.
(01:40):
We then work directly with theconsumer, use that information
to really make that consumer'slife easier, whether it's
providing safety.
Really quite an amazing companywith the amount of data that we
process, the insights andanalytics we can use that data
(02:08):
for and really impact society.
So that's kind of the excitingpart.
My role as a solution engineeris I spend a lot of time with
our customers really anythingfrom insurance companies to
mobile publishers, to the OEMs,to individual retailers and
repair facilities that anythingthat has to do with mobility we
work with and we try to findsolutions that create tighter
customer relationships, create asafer environment towards net
(02:31):
zero power insurance to a moreequitable price.
So I work with all of thosefolks.
My background is a data andanalytics person, so I really
live and thrive in this dataworld.
Speaker 1 (02:39):
Fantastic, really appreciate that introduction and
I think you'll be right at homein this new podcast.
So kudos to our team forsetting this up so we can dive a
little bit more into data whatit means, but also how it
affects our industry.
So one of the things youmentioned was collecting data,
and I'm a little bit familiarwith Arity, being a data partner
(03:00):
of Auto Care Association.
We get the VMT data from yourgroup.
Maybe, just for the benefit oflisteners who may not be as
familiar with your data, tell usa little bit about how that
driving data is recorded.
Speaker 2 (03:13):
It's a great question and today it's really important
to recognize that the consumerowns this data right.
It's coming from their vehicles, their interactions with
mobility.
So it's really important tostart out with a very
transparent way of working withthe consumers, the way that we
do that at Arity.
We really get data from one oftwo sources.
We get it directly fromconnected cars through
relationships with OEMs, and wealso get it from mobile phones,
(03:35):
right, and those mobile phonesare apps like GasBuddy, myradar,
life360.
These are apps that have amobility background.
One of the ways we do that isthrough things like family
safety.
Arity has analytics like crashdetection from a mobile device.
We can use the sensors on thephone to really understand
whether or not there haspotentially been an accident.
We use machine learning,artificial intelligence, to
(03:55):
really infer that those sensorsand those readings may signify
there's been an accident.
And then what we do is wesurface that data to the mobile
publisher like like a Life360,and they can take action with it
.
They can get emergency responsethere.
So, primary with us, when wework with a consumer, we're
asking for consent to use thedata from their phone both the
sensors and location data to tryto create an experience that
(04:18):
helps them right.
In the case of Life360, it'shelping them stay safe and
helping them understand wheretheir family is.
I think there's a lot ofdiscussion in the industry about
transparency and how do you getthat consent, and I think at
Arity, we work very hard withthat.
Obviously, being a wholly ownedsubsidiary of Allstate, brand
recognition is super critical,so our focus is to do it with
(04:39):
the consumer first, rightMeaning, if you were to log into
any of the mobile apps that ourtechnology is running in, you
will be presented with consentforms that are basically clearly
explaining why we're collectingthe data, what we do with it.
At any point, you can choose toopt in, opt out.
Of course, some of that datacan be used for advertising, and
we try to do that, and again,you'll have the option to opt
(05:01):
out of that.
But when used well, there's alot of value for the consumers.
We do a lot with things likefuel efficiency.
We do a lot with just overallhelping things become efficient.
When you're driving a vehicleand maybe you're on your way
home and a quick servicerestaurant wants to reach out,
or a service repair facilityknows that maybe it's time for
(05:22):
an oil change and they offer youa discount because it's
convenient for you.
You'll be driving by theirroute.
These are things that you canpower with that data.
But again, what's first andforemost is the consumer has to
be comfortable to share thatinformation and you've got to be
very transparent when sharingit.
Speaker 1 (05:37):
Oh, that's really helpful, and one of the things
that I'm thinking about is as aconsumer of your data and
proponent of your data.
I'm able to see it in aggregatetotal vehicle miles traveled
for the United States and I'vebeen sharing in presentations
here at Connect that in 2023, wehad maybe 3.15 trillion miles
driven nationally, about 0.65%higher than 2022.
(06:03):
And it gets interesting whenyou peel the onion layers back
at a regional level, at a metrolevel time of day, and what I'm
hearing is in terms of that databeing aggregated.
I'm not necessarily going tosee Jeff Schlitt's mileage and
so tell me a little bit abouthow sliceable that data is from
(06:27):
an aggregated standpoint.
Could you highlight that for us?
Speaker 2 (06:31):
Absolutely, and I think you raise a really
important point, which is wecollect data about individuals,
but a lot of that data, whenit's used out in the industry,
it's used in aggregated formats,like you alluded to.
So one of the first things thatwe start to do and I think
there's some really excitingexamples, you spoke about it
earlier Pre-COVID, I thinkeverybody kind of knew how
people drove, right, you droveinto work, you drove home.
(06:52):
There was a very definedmorning rush hour, a very
defined afternoon rush hour.
Covid hit and what we noticedwere patterns changed and what's
unique?
We're past COVID now, but thosepatterns are still different.
You know, as an example, youknow in the morning, the morning
rush hour still runs aboutthree to 4% less than it did
pre-COVID, right, which meansobviously a lot of people work
(07:14):
from home.
You start to see a new rushhour around afternoon hours,
right, you know people at lunchbreak, since they're working
from home, start to go out andmove around.
So in aggregate, we can startto understand that behavior
change, right, and that means alot.
You know, really, if you're inthe quick service restaurant
space or you're in an autoservice area, right, where you
choose real estate, where youput those brick and mortar
(07:35):
footprints.
That matters.
Now people are out working indifferent ways, they're
commuting in different ways andso in aggregate we can start to
understand where is that shiftin mileage right, and we've seen
that shift go from heavilyurban, concentrated, you know,
during the day, to now you'restarting to see that spread out
and there's benefits to thatright.
Density's down in most of theurban areas.
(07:57):
But there's also drawbacks fromthat.
Speeds are way up right,because there's less density on
the roads.
Overall speeds are up and I'msure everybody who's out there
driving knows their insurancerates are up and that's somewhat
of a problem because if speedsare up, the severity of crashes
are up.
When the severity of crashesare up, the cost to repair those
crashes go up, and thatobviously has to work in
(08:19):
conjunction with how theinsurance carriers work.
So all of these things aregrounded in data and I think
what's really exciting intoday's society and some of the
work that Arity does, we're ableto use that data to create
insights that can power the nextgeneration of change, help
departments of transportationput in the appropriate
infrastructure, help thoseretailers in the automobile
(08:41):
industry really start to learnabout the new behaviors, the new
driving styles and then tailorproducts for those consumers.
Speaker 1 (08:49):
Super helpful.
So let's kind of double click,as we like to say in some of
those areas.
So, if I'm hearing youcorrectly, with the additional
driving data you're able toaggregate that and tell a retail
location at this time of a day,traffic volume is up X percent
and that could help them perhapswith advertising.
(09:12):
It could be a digital billboard, for instance, it could be
mailings.
So tell me a little bit aboutsome of the use cases of that,
as well as kind of getting tothe how sliceable the data is.
Is it simply this many vehiclesare going by?
How much more color are youable to give that with regard to
(09:34):
it?
Is this type of consumer, it isthis type of vehicle, Because I
know it's anonymous, but atwhat level can it be kind of
stratified for those types ofuse cases?
Speaker 2 (09:46):
It's a great question , michael, and I throw out a
couple of things here and great,I love the use cases, so we'll
give you two.
One thing that we work on is aproduct called Retail Traffic
Analytics, and what's reallyinterested about Retail Traffic
Analytics?
All of this comes together withthe ability to use cloud
compute and have unlimitedcompute.
But the way that product worksis you know we work with certain
(10:08):
retail locations, right?
So take any brick and mortarfootprint again could be you
know an auto repair facility,you know that has a chain
background, and so they havehundreds of stores in the United
States.
What we will do is we willunderstand the footprint of
those stores.
You know geographically we callthem shape files, right and
really what that is.
If you go to any map and you'regoing to see an outline of the
(10:29):
property, including parking lots, it's like drawing a polygon.
It's like drawing a polygonaround the location, right.
So if you've got a couplehundred of these you know arity
with our 40 million consumersthat have entrusted us with
their data we can basicallystart to understand
statistically.
That's extremely relevant.
There's about 270 milliondrivers in the US today, so
we're sitting on a large,statistically relevant portion
(10:52):
of that.
So if you are that retailer, wecan start to understand traffic
trends.
We can start to understand thaton the streets adjacent to your
location, you're getting 10% ofthat traffic will stop into the
store over this duration.
But you know, maybe just downthe street, you know your
competitive competitors, maybethey're getting 12% right, and
(11:16):
so we can start to understandthat.
Now, what's maybe somethingthat we can segment into as well
?
Is it a time of day difference,right?
Are these people getting themin the afternoon been into as
well?
Is it a time of day difference,right?
Are these people getting themin the afternoon?
Are they getting a more traffic?
Because it's a right turn intothe location versus a left turn?
You know, those are some of thethings you can start to slice
and dice to really understand.
You know, how is that trafficinteracting with that brick and
(11:38):
mortar location?
What's even better is.
Then you can start to enactsome changes.
Right, you know, maybe you wantto do some additional signage
and hold that thought We'll getthere with your digital out of
home.
You know, maybe you want to dosome marketing campaigns to
attract people.
This is where our predictivemobility and our predictive
commuting comes in.
What that's all about is ifit's convenient for a consumer,
(11:59):
right, we know we basicallylearn what we call our routes
and routines, and a routine isbasically an origin and a
destination.
The origin may be your worklocation and the destination may
be where you work out and weknow that you do that three to
four days a week and then, aftera period of time, we learn the
routes you take.
Now one of those auto servicerepair facilities may want to
(12:20):
market to you proactively andsay you know you may be driving
right by our store.
Stop in $3 off on our oilchange, you know, stop in for a
$5 coupon on retail parts.
That's the type of conveniencethat consumers really value.
We don't actually know who thatconsumer is.
We just know that there is amobile device or a car that
(12:40):
tends to go this pattern right,and we cut off things like
private locations, like home andanything of certain areas of
interest that are consideredvery personal.
We don't track or work withanything there, but what we'll
end up doing is we'll enable youto engage that consumer.
We'll learn those patterns.
We can help you understand thatduring rush hour you don't see
(13:02):
as much, so maybe you want torun specials in rush hour, maybe
you want to put some additionalsignage out and then you can
measure that.
Going to the signage, it'sreally interesting.
We partner with some digitalout-of-home sign companies and
what's really interesting withdigital signs is they can work
with multiple advertisers todisplay a digital ad and usually
those spots run from anywherefrom 10 to 15 seconds.
(13:23):
And what's critical is theywant to know who was exposed and
how many people were exposed.
Not because we're targeting,like you said, we're not
targeting that individual, butwhat we can start to understand
is the profile of drivingbehavior that passes by a
digital sign.
Let me give you a few examplesof that.
So you know, let's say that wehave people that tend to drive
(13:46):
high mileage, right, and we knowthat this particular sign in
Northern Illinois right thatruns digital ads for, you know,
an auto care repair facility isseeing an inordinate amount of
high mileage drivers.
We can actually index each ofthose signs based on those
behaviors, what that does for anadvertiser.
If you are an auto repair andyou're interested in servicing
(14:09):
high mileage vehicles, you mayrun a digital spot and pick the
signs that see an inordinateamount of high mileage drivers.
And that's some of the signalthat we can do in aggregate.
We're not targeting anindividual, we are basically
just grouping and understandingof the behavior that drives by
the signs and letting theadvertisers really tailor that
message to try to engage thatconsumer approach to how your
(14:46):
group is approaching it.
Speaker 1 (14:47):
So I'm relieved because I think there's always
going to be that, or notnecessarily always, but I can
see where people might think BigBrother is watching right and,
if I'm hearing you correctly,that data is being collected and
aggregated so that that data isreported to organizations that
can use it.
But it's really at the overalllevel and you highlight and I'm
(15:10):
hearing that correct yeah, thatis correct.
So one thing you mentioned washigh mileage drivers, and I
remember in previous discussionsresident mileage, where the
trips are originating from.
So is that part of the calculushere where you're able to say,
oh, we know where the trips areoriginating from because the
signal is being captured throughthat app and we know that that
(15:33):
driver is driving from, let'ssay, 40 miles away, 10 miles
away.
So tell us a little bit aboutthat aspect of things, how
you're able to separateresidents, non-residents, high
mileage, if you could.
Speaker 2 (15:45):
Yeah, absolutely.
And so when you think aboutthese again, the way Airdy works
is when our technology goesinside of a mobile app.
It is all anonymous, right?
You know, we know that there isa certain vehicle that is
driving.
You know, we know the patterns.
So, for instance, to your pointof the origin and the
destination, we only wake up.
The technology wakes up on thephone when it senses that
(16:08):
somebody is driving a vehicle.
Right and again, like everythingin today's world, this is
artificial intelligence, machinelearning, algorithms that take
signals such as speed and takesignals such as location Are you
on a road?
Because if you're going 20miles an hour, you could be on a
bike.
So we have to understand you'reon a physical road, somewhere
that traffic may be.
(16:28):
All of those signals get putinto a model that basically make
an inference that, yep, I thinkand feel pretty good that this
is somebody in a vehicle driving.
That's when our technologywakes up, starts recording
information about thosebehaviors, and those are
anything from you know how fastare you driving right, what
routes are you taking and whatroads are you on.
(16:48):
And when you think about thatagain, it's just tracking a user
ID.
It's not an actual individual.
I have zero personalinformation on that individual
right, but what I do have is Ihave kind of the trails they
take and how they move, and thenwhat we're able to do is we can
(17:09):
start to associate that youknow these trips keep starting
in this geographic area and sowe can infer that that must be
an important point of interestfor you, such as an area where
you live or an area where youwork, and so we actually look at
that about monthly.
Every month we'll updatebecause consider the you know
individual individual that maybevacations up in Northern
Michigan in the summer.
We may not see you if they'rein Illinois, we may not see them
in Illinois, but then we seethem over and over again
(17:29):
originating trips up in Michigansomewhere.
So for that month they may betagged as a resident of Michigan
and what that allows us to do.
Then, when you aggregate allthat data up, we can see those
shifting patterns, right, and wecan even mark it.
As you know these may bevacationers, right, you know
summer vacationers at a cottage,and so we can start to
(17:49):
understand you know how manymileage in Northern Michigan is
due to people summering thereversus people that live there
year round.
And that's important, becauseif you're a business and you're
looking at inventory or out ofstock or forecasting sales,
those are all signals you coulduse.
Again, very privacy safe.
It's not about an individual.
It's about individual behaviorrolling up into geographic and
temporal patterns that you cananalyze.
(18:11):
You would ask the questionaround, like where they come
from.
And this is another veryimportant thing for brick and
mortar retailers isunderstanding the reach.
If you want to put a newfootprint of a brick and mortar
store, what you want tounderstand is, let's say you
have 100, 150 stores already,you'll know that the average
distance that you get people tocome to your store is maybe 10,
(18:31):
12 miles right.
But when you see people gettingfurther out, right, and you're
seeing enough that you knowmaybe your competition is
starting to pull that businessaway.
Those are some of the signalsthat may say there is enough
population to drive the salesyou need and there's a
convenience in that you canshorten that window, which in a
lot of cases leads to higherconversion rates in those retail
(18:52):
footprints.
So all of that data, all ofthat information is driven by
the data that we collect, right,and it really gives you that
opportunity.
There are opportunities to useour data individually.
And again, that usually getsinto things around insurance
pricing.
But again, it's extremelyimportant for any consumer to
realize that none of thathappens without their consent.
Right, and even to the pointwhere, if you're, let's say,
(19:13):
getting an insurance quote, youwould be asked exactly.
We are what you call a creditreporting agency, just like
TransUnion and Equifax for yourcredit scores.
So, being a credit reportingagency, you know we are what you
call a credit reporting agency,just like TransUnion and
Equifax for your credit scores.
So, being a credit reportingagency, we are subject to all of
the fair credit requirements.
So if you go and you are askeddo you consent to use the data
to price and insurance, you cansay no with no impact.
(19:36):
You can say yes, you consent tothat, and we are required to
tell you why we got these scores, what we understood.
So really, to me, that is theright way to do this.
Right it is if you don't engagethe consumer, not only will
they be suspect of you, but theyalso want to understand the
value in it.
Right?
So we take a lot of pride inthat engagement with the
consumers.
Speaker 1 (19:57):
I think about.
When I signed up for a safedriving app to track my hard
braking, my sudden accelerationsand late night driving, I opted
in.
The default was not that youhave to opt out.
So it sounds like if anindividual wants to have a
direct impact on theirindividual bill, they physically
(20:18):
have to opt in.
Correct, but if I may ask thisas an insurance organization, I
would imagine that's just a.
Really the data that you'regetting is very helpful to
understand how the population isdriving, to assess risk and
perhaps how that can kind ofsharpen the actuarial tables for
(20:39):
writing policies.
Is that fair to say?
Speaker 2 (20:41):
It's absolutely fair to say.
And, again, I think one of themost important things is equity
and pricing.
Insurance is a reallyinteresting industry.
I've worked in and around itfor many years and insurance is
one of those.
It's a highly regulatedindustry, right, and the general
sense of insurance is to createan equitable pricing framework.
Right Now, everybody who paysan insurance bill probably has a
(21:03):
hard time believing that, butit truly is that way.
Every state has a department ofinsurance.
Every department of insuranceis trying to make sure that if
you're going to raise your ratesright, there's justification
for doing so.
If you're going to price oneperson differently than another
person, it has to be done in abalanced, equitable manner.
So things like credit scoreshave been used in insurance for
(21:23):
years and it is very predictiveof loss.
Now, why is it predictive ofloss?
That's really a hypothesis thatyou've got to kind of unpack to
understand.
It could be predictive of lossbecause of the fact that people
with higher credit scores maylive in the suburbs, right, and
suburbs tend to have lessdensity of traffic, and if you
have less density of traffic,therefore, you may have less
(21:43):
losses Still equitable.
But if you're an individual wholives in a city, maybe in a
very urban area, and you're avery safe driver, you're
actually, to a certain extent,being penalized for where you
live there.
Where driving data becomes superimportant is it's rating you as
an individual based on yourdriving behavior.
You're in control of that rightand that's why we became a
(22:04):
credit reporting agency becauseyou get to dispute, you get to
understand your credit.
That is your right.
But it is a fair way.
If you choose to drive in riskymanners, you should pay
equitably more for that right.
If you want to drive 100 milesan hour the expressway right
there, is a higher chance thatyou will get in an accident, and
when you do, those costs haveto be passed on to policyholders
(22:26):
.
So it's much better to havethat cost passed on based on
risk than it is spreading thatcost across everybody.
So, truly, telematics is a veryequitable way of doing things.
But you're right, it does meanwe have to understand how you're
driving, and that is a scaryproposition.
Speaker 3 (22:43):
This is DTP IT Director and Sustainability
Committee Staff Liaison at AutoCare Association.
Are you passionate aboutshaping our industry's future?
Join an Auto Care AssociationAdvisory Committee and make a
real impact as a volunteer.
You will drive innovation,tackle key challenges and
collaborate on cutting-edgesolutions for the entire supply
(23:04):
chain.
Don't miss out.
Join us at our upcomingLeadership Days event to start
making a difference.
Learn more at autocareorg slashleadership days and find
information on currentcommittees at autocareorg slash
committees.
Speaker 1 (23:30):
And I think from if I go back to Economics 101,
because we were talking aboutsons in college taking core
classes it's the informationasymmetry problem.
Right, and with this technologyavailable to fill in those data
gaps for lack of a better wordit allows a company to price its
product better.
It allows a company to priceits product better and in a
field that has many competitors,it's kind of just upping the
state of technology and thestate of the art so as to have a
(23:50):
stronger foundation for makingthose pricing decisions.
Speaker 2 (23:53):
That's 100% accurate and I think what the consumer
has to realize in this equationand regardless if it's insurance
or if it's how you engage withyour repair facility, the more
information that you can providesomebody, the better you know.
Imagine if you go to yourdoctor and you're not feeling
well.
The first thing they ask you iswhat are your symptoms?
How do you feel?
(24:13):
They want you to share datawith them and insight so they
can give you a diagnosis.
So what we have to really focuson is how to build trust with
the consumer such that they'rewilling to share that
information.
And that's why, when we workwith these mobile apps, when we
work with the OEMs, we want tomake sure the experiences that
we put in front of the consumerright our experiences they value
, they are in control not us onhow that data is used right and,
(24:36):
quite honestly, we givefeedback right.
If you are driving using riskybehaviors, you know we want to
give that feedback back to you.
We want to give you theopportunity to improve those
behaviors, to lower the risk.
So a big part of what we do,you know, whether that's through
the departments oftransportation and in our public
sector areas, whether it's thecoaching experiences that we put
(24:56):
inside of these apps.
It is really to educate theconsumer that they are in
control.
Right, it's not big brotherwatching.
It is your data that you cancontrol how it's used, and you
get to change your behavior toimpact what you pay for
insurance Makes a lot of sense.
Speaker 1 (25:11):
One thing that you alluded to were things like
high-speed driving is up.
The use cases of peoplevacationing in northern Michigan
Broad open-ended question.
But what are some of thechanges that you're seeing?
Speaker 2 (25:26):
Yeah, you know the speeding and again, a lot of
these are hypotheses to beproven out over time with the
data but the speeding, I dobelieve and this is Jeff's
opinion is that because drivingspread out more and people are
working from home, you're onroads that are less populated,
driving spread out more andpeople are working from home
you're on roads that are lesspopulated, you know, and if
you're on roads that are lesspopulated, you probably feel
(25:48):
like you know you can push thataccelerator a little bit harder,
right, nobody's around you.
The risk with that is we'realso seeing continually higher
distracted driving rates.
Distracted driving is up over32% from pre-COVID, right, which
again I think emboldens thesebehaviors that nobody else is
maybe immediately right in frontof you, so you're just going to
(26:09):
peek right at that phone,you're going to look, take one
more look, and so we take thatvery seriously because, again,
that is one of the largest riskfactors.
And so, instead of just callingit out, really what we want to
do is start to change thatbehavior.
Right, and you know whetherit's the way.
Predictive commuting is a greatopportunity, right, you know we
don't want to advertise to youwhile you're driving.
Right, we don't want to makenotice of that.
(26:30):
That's why we built predictivecommuting, because what we want
to do is say I'm pretty sureyou're going to be on this route
, so why don't I go ahead andnotify you before you get in the
car?
I'm going to make it moreconvenient and also safer to
have that conversation withpeople.
You know, as people change, Ithink one of the other behaviors
that we're seeing is you knowthe length of trips and where
(26:51):
people are going and how they'redoing it and when human
behavior changes.
Right, you get to adapt as abusiness to.
You know, meet the consumerwhere they are Cliche but it
actually is true.
Right, you know when peopledecide hey, you know, I'm bored
of working out of the house, I'mgoing to go to the Starbucks.
Right, you know, maybe you caninfluence that behavior.
Right, people do like tosocialize, they like to get out,
(27:12):
and I think that's where thisdata, you know, with some of the
changes post-COVID, is valuable.
Right, we can actually engagecustomers in different areas, we
can create new situations, andthat mobility data really helps
you understand what thatconsumer's preference is.
Right, and it's not all oneright, different consumers value
different things, but I thinkthat's where this driving data
can really help you understandsome of those changes, and then
(27:35):
again you can meet the consumerwhere they are, with goods and
services that they value.
Speaker 1 (27:39):
Thanks for that, jeff .
Two things came up as you weretalking.
The first one was thedistracted driving going up 32%.
If I can just go down a littlebit of a rabbit hole here, can
you describe, define distracteddriving?
And part of the reason I ask isthis If I'm driving my 2012
Nissan, which has Bluetooth, butthat's my wife's car, I don't
(28:00):
connect my phone to it.
If I pick up my phone and lookat it, maybe it unlocks.
To me that's an obviousdistracted driving.
If I had a rental car that wasmaybe a 2021 and I paired my
phone with it and I could seemessage coming in from so-and-so
(28:21):
, you could hit a button toignore it.
Or I'm driving or not engage itotherwise.
Or if the phone rings and I hitthe button on the steering
wheel to answer it, which ofthose might count as distracted
driving, and can you just tellus a little bit more about what
constitutes it please?
Speaker 2 (28:38):
Yeah, it's a great question.
And again, you can take dataand display it anywhere you want
.
So, at Arity, the way wedescribe distracted driving when
initiated from a mobile deviceis kind of what you alluded to.
It's an unlock of the phone,okay, it's followed by movement.
So phones have accelerometersinside of them, so we sense that
change in acceleration of thephone as it moves through the
(28:59):
air.
So the unlock event, followedby the accelerometer right, is
what we typically will calldistraction, right, and we have
to sense it for a short periodof time, right, you know, we
kind of give everybody theopportunity.
We also look at the speed of thevehicle moving, right, if
you're stopped at a light, right, we won't necessarily call that
distracted because the vehicleisn't moving okay.
(29:21):
So you know, all of thosesignals are processed and again,
we also look at them in thecontext of society, right, in
the context of insurance loss,and when we tie that data
together we can start tounderstand that this type of
movement on the phone is reallywhat we consider distracting,
because it's highly predictiveof loss severity, loss frequency
, you know, accidents happening,and so it's never just one loss
severity, loss frequency,accidents happening, and so it's
(29:43):
never just one set of rulesthat stay forever right.
The really important thing withanalytics and statistics and
machine learning and artificialintelligence is it's constantly
learning and it's constantlygetting smarter right at to what
the distraction means, and so Ithink today that's how it's
defined.
Tomorrow it may be differentand again, as society changes,
(30:04):
as technology changes, we'lladapt those models.
But again, really what we'relooking at is that signal of
risk and in our world that is aninsurance claim or a loss on a
vehicle.
We look at that and we say whatbehaviors led up to that, so we
can help coach those behaviorsand become safer.
Speaker 1 (30:20):
Is there an industry standard definition?
Because if I'm National HighwayTransportation Safety
Administration, I think that'swhat NHTSA stands for I'm
thinking about tell me thedifference between somebody
physically scrolling versustexting, versus initiating a
call, versus X, y, z.
So is there a standard in theindustry for the different types
(30:41):
of distracted driving and areyou able to detect that?
Speaker 2 (30:44):
Right?
Yes, as far as I know, there isno standard yet and I think
it's incumbent on companies likeArity and maybe the insurance
sectors.
There are conversationsstarting, right, if you've heard
of the net zero initiatives andTell us a little more about net
zero.
Yeah, net zero is one of thosethings in the Department of
Transportation and withinmunicipalities and the public
(31:06):
sector that are trying to sayhow do we get to the point where
there's no accidents, no deaths, right?
You know, we have reallyintelligent technology with
these cars, whether it's theadvanced driver assist systems
they call that ADAS right.
Whether it is, you know, theself-driving vehicles, whether
it's the material we're using inthe car, all of these things
(31:27):
have the potential to lead to,you know, zero accidents, zero
deaths right.
But again, the consumer has tobalance that with the desire and
the fun of driving a vehicle,right, like everybody loves to
drive a car.
You know you turn 16, you can'twait to get your license.
But I think you can have both,right.
You can have a safe environment, and that's the promise of
technology, right, and?
(31:48):
But I think you can have both,right.
You can have a safe environment, and that's the promise of
technology, right.
And so I think what you'regoing to see is you will
probably see the industrystandardized on this.
The government has to saylocation data in general is a
very hot topic right now.
It's a hot topic in Washington,it's a hot topic in insurance,
a hot topic in advertising.
I don't think it goes awaybecause it's extremely valuable.
(32:10):
I think we need to make acontract with consumers that
they value right.
So and again, you needcompanies like an Allstate
Corporation, and that's what Ilove about working for Arity.
Being owned by Allstate givesus the investment and the
opportunity to do it the rightway, right.
And again, I think, if you lookat the leadership at Allstate,
right, they're heavily vested insafety.
(32:31):
Right, you're in good hands.
That's the nature of theirmotto and we live into that, and
I'd like to believe that that'swhere we need to take society
length of trips and earlier youtalked about.
Speaker 1 (32:51):
We can detect because I'm connected to this phone and
you can see for this device, atleast if I'm hearing it
correctly, I have, my patternshave changed and now I'm driving
in Florida.
Maybe he's not resident, right,so you can tie it to an
individual, but you don't knowthat it's Mike Chung, age X,
gender, et cetera, et cetera.
So I'm seeing you nodding yourhead.
(33:11):
It sounds like I'm on the rightpath and tell me a little bit
more about the length of trips.
You mentioned earlier thatthere could be combined purposes
.
Could you just tell me a littlebit about what you've seen
broadly over the last few years?
Speaker 2 (33:29):
Yeah, I think what you see with the length of trips
is they tend to become longer,right, more mileage is being
accumulated, and I think thathas to do with, again and this
is really important in theretail space, right, because
we're starting to understandchanges Everybody has to go get
groceries.
You've got to go charge yourcar or fill up your car, um,
you've got to go get, you know,material and clothes and go to
home depot and do the thingsthat you do on a daily basis.
(33:51):
But when you're working fromhome, right, people tend to try
to go to a convenient method ofof purchase.
Anyways, right, if I'm on theway home, I'm going to hit the
fuel station, the grocery storein, you know, my neighborhood uh
drugstore to pick up aprescription.
So, now, what we're getting to,though, is when people venture
out, they're learning newpatterns themselves, right, so
(34:13):
we're seeing longer trips.
We are seeing people ventureout at lunch hours and run
errands that they typicallywould do on the way home, right,
and so, you know, the length oftrips are increasing, right,
and, again, I think that has afunction to do with, just, if
you live in the suburbs, right,houses aren't usually right next
to you know retail locations,but when you're at work, there's
retail locations all aroundthat, because you drove to work,
(34:35):
put your car away, then youwalk to lunch.
You walked over to get ahaircut.
So we're seeing that change inbehavior right Anywhere from.
If you start to look at youknow parking lots.
When's the last time you'vegone to a parking lot and it's
full right.
Those are things you'restarting to see.
We even and I'll give a greatexample when the Key Bridge
incident happened in Baltimore.
(34:56):
What's fun is almostimmediately we can start to
analyze what happens in thatscenario.
And I think earlier, about ayear earlier, on I-95, they had
to shut down I-95 for four orfive days and I-95, major
thoroughfare and we were able tolook that was just outside of
Philadelphia, correct, outsideof Philadelphia.
We were able to look at theimpact to the infrastructure.
(35:19):
Where did that traffic go?
You can't go on I-95.
Where did you go?
And what we saw were timesballooned by 20, 30% when I-95
happened.
What typically would be maybe a10-minute ride is now 15, 18
minutes, right, just ballooningtimes.
And you can see the density onthe side routes that weren't
(35:41):
designed to handle that.
So that was a real difficultpoint for consumers.
With the Key Bridge, theinfrastructure there really kind
of absorbed that change quite abit.
Now that could be the type oftraffic that went there across
the Key Bridge, but what we didend up seeing is that the
infrastructure in that areaabsorbed that change pretty
(36:03):
easily and again that's asuccess story for the
Departments of Transportationand that's kind of the promise
of what this data can do.
I mean, consumers have a veryshort fuse right.
If you're sitting in your carand you're stuck in a red light,
your anxiety goes up, you'refrustrated and if we can plan
(36:24):
around that and we can plan witha little bit of leeway in some
of this infrastructure, thatreally goes a long way in
society.
So this is some of the realpositive benefits of having
access to this kind of data and,again, making that contract
with the consumer that you willbenefit from the sharing
Terrific.
Speaker 1 (36:37):
One thing I thought of as we were talking was
possible noise you talked aboutyou might be going 20 miles an
hour.
It might be a bicycle.
I remember signing up for thatsafe driving app and it would
say it would thought I wasdriving but I was taking the
subway and I had an opportunityto say I was not driving.
How about if you're taking acar service, you get picked up
(36:58):
in a Lyft or Uber.
I remember I think you hadshared the acceleration is
different in the front seatversus the back seat.
Can you tell us a little bitabout that noise reduction
process?
Speaker 2 (37:09):
Absolutely.
So you'll start to sense reallyquick how mathematic and how
hard it is to actually gleansome of the signal.
But with the advancement of AIand everything else, these are
positive opportunities for it.
So we have something calledvehicle mode, right, and vehicle
mode is a methodology where wetry to look at and determine,
you know, are you on a bus, areyou on a bike, are you on an
(37:31):
airplane?
Some of these things are easy,right?
You know, if you're at 10,000feet, going 300 miles an hour,
I'm pretty sure you're not in acar anymore.
Okay, so those are pretty easyinferences to make.
But take rideshare like yousaid, that's actually pretty
difficult, except if you look at.
And what's really interesting ifyou look at, and what's really
interesting if you visualize it,if you look at that vehicle,
that rideshare vehicle, and youlook at what it does, the
(37:53):
pattern on a map is really,really gives it away.
I mean, it goes somewhere, itstops, it goes back somewhere,
it stops.
It just looks like it's drivingin circles, right, so that
pattern can be recognized bymachine learning.
And when you recognize that asa consumer, if you get inside of
a vehicle, you know, and thatvehicle is zigzagging in a
pattern that looks like a rideshare.
(38:14):
We can market as that.
Now we always give you theopportunity to override it right
, meaning that a lot of theexperiences you know we can.
We call it tagging the trip.
You know the consumer can go inand say no, no, no, I was on a
bus.
You, no, no, no, I was on a bus, you know they can click that.
That is great too, because itactually helps us get better at
these analytics, right.
That's that continual trainingand learning we do, but we're
pretty good at it right.
Our accuracy at figuring it outpretty good, and it's again,
(38:36):
it's the positive aspect of datais extremely important in
understanding behavior, right,and when you understand behavior
, you know when used for theappropriate manners, it really
does make people's lives better,and that's what we do.
Speaker 1 (38:50):
Terrific.
One thing that I'm thinkingabout here is earlier you talked
about advertising.
One way, two way, so tell mewhat that means.
Does that mean my data cannotbe used for advertising purposes
?
Does it mean advertisementsthat are shown to me?
Can you just distinguishbetween the two for us?
Speaker 2 (39:10):
And the advertising industry is highly regulated.
So I'll start with that Meaningthat, again, as a consumer, you
have a ton of choices, right.
It's also a extremely complextechnical environment, but it's
super interesting.
So we'll go back to thatpredictive commutes and I'll
even give you some statisticsagain.
We do a lot of what we callin-app advertising, right, and
in-app advertising is when yousign up with an app, you know,
(39:36):
pick any of the apps that ourtechnology is running in.
You'll be asked do you consentto sharing your data for uses in
advertising?
If you say yes, right, it meansthat we will display ads in
that app.
You know, that's one way mostmobile publishers make their
revenue.
Right, in order to use that app, whether it's a weather app,
whether it is a family safetyapp, a lot of those free
versions are powered byadvertising, right, so there's a
benefit to the consumer rightthere, which is you're getting
free software and in return,you've got to look at the
(39:57):
advertisement.
I mean, the whole purpose ofmass media and television is
built on that model.
Now, what's really unique,though, is it's not just about,
you know, what we call pray andspray, right.
It's not just put the ad up andhope that the people looking at
it are interested in theproduct.
Now, when we start tounderstand those behaviors, we
can actually tailor theseproducts and again, the consumer
(40:18):
will value this.
I'll give you an example.
We worked with a fuel retailerwhere we were doing our
predictive commutes, which againis us making a prediction that
you may be driving by one ofthose locations in the next hour
, two hours, three hours, andagain the purpose of doing that
is we know what routes thatphone or that device is taking
and so, within the app thatyou've entrusted to advertise to
(40:39):
, that advertiser can basicallysay I want to contact people who
are going to be driving by oneof my locations this afternoon.
So you may be just be using aweather app and you may get a
coupon for food or auto repairor whatever it may be.
You may look at that and go.
You know what.
I'm going to drive right bythat location and I'm going to
(40:59):
get $5 off.
This is a good deal.
I'm happy with that.
That's what our advertising canpower and, amazingly enough,
when we did that with this fuelretailer, we ran this for 60
days, I think, on about 100locations and over that 60 days.
When you compare it to the 60days when you weren't running
that predictive ad you were justdoing the pray and spray we got
(41:22):
a 39% higher conversion.
What does that mean?
It means that the people thatsaw the ad that was convenient
converted bought a product 39%more times than they did when it
was just putting a generic adup.
Right, and again, what thattells me is the consumer valued
that experience and thatadvertisement.
(41:43):
Right.
Again, you get to choosewhether or not you want to see
those ads.
Speaker 1 (41:47):
Right.
And then that's on the mobilephone and, similarly, on an
outdoor billboard you have statson how many people are driving
by it, Correct?
And that can just fuel thetargeting as well.
That's right, Yep, Fascinating,so I think.
One last question I have for youas we close up, and I'll say
(42:10):
this it's been fascinatingtalking to you.
I feel like we could talk forhours.
We should probably do thisagain.
So much thanks for joining ushere.
What do you see in three tofive years with regard to data
that's going to become available?
And I'll jam in one morequestion here, because when I
gave a presentation here atConnect, I showed the inferred
(42:35):
high impact collisions month bymonth, and it was a slightly
positively sloping curve right.
And the question came up doesthat account for ADOS?
Because this man conjectured ifthere is ADOS, I would expect
(42:55):
that to go down.
So I was thinking about that.
Is there a way to mash thosedata sets together?
So kind of a two-part questionIs ADOS helping?
Is there a way to prove that?
And then what's the future?
Speaker 2 (43:11):
Yeah.
So it's a really good question,by the way, and I think time
will tell.
What I will tell you is, again,the average age of vehicles on
the road, I think today isaround 12, 13 years, and so when
you look at that, there's a lotof vehicles on the road that do
not have 8S, right?
The second thing is a lot ofpeople turn 8S off, right?
I have a Honda 2019 that has 8S.
I love the system, but you know, it was also kind of like Gen 1
(43:33):
.
And you know, at times it'slike, okay, I'm just going to
turn it off right.
So you've got those two thingsat play.
So what is the real number ofcars doing that?
It's actually, I'd say, a scaryproposition, right, because if
things are still rising and wehave safety features preventing
it, it's probably rising at ahigher slope or even more
exponential than you think,because ADAS, I guarantee you,
(43:54):
is working, but we're stillseeing them rising in high
severity collisions, right?
So parlaying that into thethree to five-year plan net zero
you just can't complain aboutthat, right?
There's nothing negative abouttrying to save lives, right?
You know, I have four children.
Two of them are driving, twoare going to get their permits
this year.
Yeah, I want them to be safe,right.
(44:16):
So anything around familysafety to me is a net positive.
So, using data, building thatcontract with the consumer to be
able to build a safer world tolive in, you know?
Yes, three to five years, I dobelieve.
And there's a lot ofconversation around the use of
that data for insurance andpricing.
I do believe, from an equitableposition, it's a more equitable
(44:39):
way to price.
It's way more equitable thancredit, than your gender, your
age, those things today arebeing used.
I think the way you drive youcontrol it.
If you want to drive riskier,you should pay more.
Used.
I think the way you drive, youcontrol it right.
If you want to drive riskier,you should pay more.
So I think in three to fiveyears, the insurance industry
believes in this.
The numbers don't lie.
I think what we've got to do isfigure out how to get the
consumer confident with it.
(44:59):
So I think over the next threeto five years you're going to
see a continued effort to bringthat consumer along.
Whether that's the way that weset up the businesses, whether
that's the contract we make withthem, whether that's the
education, I would say that'simportant.
And then I think outside of that.
Quite honestly, I think adsthat are tailored towards
(45:19):
convenience, that are meaningfulto a consumer, are way more
valuable than the pray and spray.
So what you may.
Actually, in a perfect world,maybe you're going to see less
ads, but the ads you see aregoing to be more worthwhile.
Right now the advertiser isgoing to pay more for that, but
in general, their spending willstay flat because they're not
going to spend as much on thepray and spray and they're going
(45:40):
to use data like this to reallytarget the consumer with what
they want.
So those are maybe utopianviews of the world, but I do
think that's the promise of thisdata and analytics.
And again, it's just importantthat we do that collaboratively
with the consumer so they'recoming along with the journey.
They're not being forced upon.
Speaker 1 (45:56):
That makes a lot of sense from an efficiency
standpoint, as well as acustomized content and higher
return on investment, becauseyou have to do the cost benefit
analysis as you're alluding to.
That's right.
Well, jeff, so great to haveyou.
Always a pleasure to talk.
I hope we can.
Thanks for tuning in to anotherepisode of Auto Care On Air.
(46:20):
Make sure to subscribe to ourpodcast so that you never miss
an episode.
Don't forget to leave us arating and review.
It helps others discover ourshow.
Auto Care On Air is proud to bea production of the AutoCare
Association, dedicated toadvancing the auto care industry
and supporting professionalslike you.
To learn more about theassociation and its initiatives,
visit autocareorg.
Thank you.
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
(00:33):
new data sources.
All right, Thanks everybody forjoining.
My name is Mike Chung, I'm thehost of this session and I'm
pleased to have Jeff Schlitt ofArity join us, and the reason
why I asked Jeff to join us isbecause he and his team have a
really great view on quantifieddriving data and behavior.
So, Jeff, maybe you take a fewmoments to introduce yourself.
(00:56):
Tell us about your role andwhat your company does.
Speaker 2 (00:58):
Great, mike.
Thanks, appreciate it and veryhappy to be here within the
group here talking aboutmobility and telematics.
As Michael said, jeff Schlittfrom Arity For those that don't
know, arity is a wholly ownedsubsidiary of the Allstate
Corporation.
We connect with about 40million consumers today,
collecting information ondriving data, where they drive,
(01:19):
how they drive, what risks theyaccumulate.
And what we do at Arity is weanalyze that.
We are, by trade, a data andanalytics company and our goal
is to make transportation safer,smarter and more useful for
consumers.
We take that information.
We really understand thingslike the routes and routines
that people do.
We understand their behaviorslike how hard do they break, how
fast do they drive.
(01:40):
We then work directly with theconsumer, use that information
to really make that consumer'slife easier, whether it's
providing safety.
Really quite an amazing companywith the amount of data that we
process, the insights andanalytics we can use that data
(02:08):
for and really impact society.
So that's kind of the excitingpart.
My role as a solution engineeris I spend a lot of time with
our customers really anythingfrom insurance companies to
mobile publishers, to the OEMs,to individual retailers and
repair facilities that anythingthat has to do with mobility we
work with and we try to findsolutions that create tighter
customer relationships, create asafer environment towards net
(02:31):
zero power insurance to a moreequitable price.
So I work with all of thosefolks.
My background is a data andanalytics person, so I really
live and thrive in this dataworld.
Speaker 1 (02:39):
Fantastic, really appreciate that introduction and
I think you'll be right at homein this new podcast.
So kudos to our team forsetting this up so we can dive a
little bit more into data whatit means, but also how it
affects our industry.
So one of the things youmentioned was collecting data,
and I'm a little bit familiarwith Arity, being a data partner
(03:00):
of Auto Care Association.
We get the VMT data from yourgroup.
Maybe, just for the benefit oflisteners who may not be as
familiar with your data, tell usa little bit about how that
driving data is recorded.
Speaker 2 (03:13):
It's a great question and today it's really important
to recognize that the consumerowns this data right.
It's coming from their vehicles, their interactions with
mobility.
So it's really important tostart out with a very
transparent way of working withthe consumers, the way that we
do that at Arity.
We really get data from one oftwo sources.
We get it directly fromconnected cars through
relationships with OEMs, and wealso get it from mobile phones,
(03:35):
right, and those mobile phonesare apps like GasBuddy, myradar,
life360.
These are apps that have amobility background.
One of the ways we do that isthrough things like family
safety.
Arity has analytics like crashdetection from a mobile device.
We can use the sensors on thephone to really understand
whether or not there haspotentially been an accident.
We use machine learning,artificial intelligence, to
(03:55):
really infer that those sensorsand those readings may signify
there's been an accident.
And then what we do is wesurface that data to the mobile
publisher like like a Life360,and they can take action with it
.
They can get emergency responsethere.
So, primary with us, when wework with a consumer, we're
asking for consent to use thedata from their phone both the
sensors and location data to tryto create an experience that
(04:18):
helps them right.
In the case of Life360, it'shelping them stay safe and
helping them understand wheretheir family is.
I think there's a lot ofdiscussion in the industry about
transparency and how do you getthat consent, and I think at
Arity, we work very hard withthat.
Obviously, being a wholly ownedsubsidiary of Allstate, brand
recognition is super critical,so our focus is to do it with
(04:39):
the consumer first, rightMeaning, if you were to log into
any of the mobile apps that ourtechnology is running in, you
will be presented with consentforms that are basically clearly
explaining why we're collectingthe data, what we do with it.
At any point, you can choose toopt in, opt out.
Of course, some of that datacan be used for advertising, and
we try to do that, and again,you'll have the option to opt
(05:01):
out of that.
But when used well, there's alot of value for the consumers.
We do a lot with things likefuel efficiency.
We do a lot with just overallhelping things become efficient.
When you're driving a vehicleand maybe you're on your way
home and a quick servicerestaurant wants to reach out,
or a service repair facilityknows that maybe it's time for
(05:22):
an oil change and they offer youa discount because it's
convenient for you.
You'll be driving by theirroute.
These are things that you canpower with that data.
But again, what's first andforemost is the consumer has to
be comfortable to share thatinformation and you've got to be
very transparent when sharingit.
Speaker 1 (05:37):
Oh, that's really helpful, and one of the things
that I'm thinking about is as aconsumer of your data and
proponent of your data.
I'm able to see it in aggregatetotal vehicle miles traveled
for the United States and I'vebeen sharing in presentations
here at Connect that in 2023, wehad maybe 3.15 trillion miles
driven nationally, about 0.65%higher than 2022.
(06:03):
And it gets interesting whenyou peel the onion layers back
at a regional level, at a metrolevel time of day, and what I'm
hearing is in terms of that databeing aggregated.
I'm not necessarily going tosee Jeff Schlitt's mileage and
so tell me a little bit abouthow sliceable that data is from
(06:27):
an aggregated standpoint.
Could you highlight that for us?
Speaker 2 (06:31):
Absolutely, and I think you raise a really
important point, which is wecollect data about individuals,
but a lot of that data, whenit's used out in the industry,
it's used in aggregated formats,like you alluded to.
So one of the first things thatwe start to do and I think
there's some really excitingexamples, you spoke about it
earlier Pre-COVID, I thinkeverybody kind of knew how
people drove, right, you droveinto work, you drove home.
(06:52):
There was a very definedmorning rush hour, a very
defined afternoon rush hour.
Covid hit and what we noticedwere patterns changed and what's
unique?
We're past COVID now, but thosepatterns are still different.
You know, as an example, youknow in the morning, the morning
rush hour still runs aboutthree to 4% less than it did
pre-COVID, right, which meansobviously a lot of people work
(07:14):
from home.
You start to see a new rushhour around afternoon hours,
right, you know people at lunchbreak, since they're working
from home, start to go out andmove around.
So in aggregate, we can startto understand that behavior
change, right, and that means alot.
You know, really, if you're inthe quick service restaurant
space or you're in an autoservice area, right, where you
choose real estate, where youput those brick and mortar
(07:35):
footprints.
That matters.
Now people are out working indifferent ways, they're
commuting in different ways andso in aggregate we can start to
understand where is that shiftin mileage right, and we've seen
that shift go from heavilyurban, concentrated, you know,
during the day, to now you'restarting to see that spread out
and there's benefits to thatright.
Density's down in most of theurban areas.
(07:57):
But there's also drawbacks fromthat.
Speeds are way up right,because there's less density on
the roads.
Overall speeds are up and I'msure everybody who's out there
driving knows their insurancerates are up and that's somewhat
of a problem because if speedsare up, the severity of crashes
are up.
When the severity of crashesare up, the cost to repair those
crashes go up, and thatobviously has to work in
(08:19):
conjunction with how theinsurance carriers work.
So all of these things aregrounded in data and I think
what's really exciting intoday's society and some of the
work that Arity does, we're ableto use that data to create
insights that can power the nextgeneration of change, help
departments of transportationput in the appropriate
infrastructure, help thoseretailers in the automobile
(08:41):
industry really start to learnabout the new behaviors, the new
driving styles and then tailorproducts for those consumers.
Speaker 1 (08:49):
Super helpful.
So let's kind of double click,as we like to say in some of
those areas.
So, if I'm hearing youcorrectly, with the additional
driving data you're able toaggregate that and tell a retail
location at this time of a day,traffic volume is up X percent
and that could help them perhapswith advertising.
(09:12):
It could be a digital billboard, for instance, it could be
mailings.
So tell me a little bit aboutsome of the use cases of that,
as well as kind of getting tothe how sliceable the data is.
Is it simply this many vehiclesare going by?
How much more color are youable to give that with regard to
(09:34):
it?
Is this type of consumer, it isthis type of vehicle, Because I
know it's anonymous, but atwhat level can it be kind of
stratified for those types ofuse cases?
Speaker 2 (09:46):
It's a great question , michael, and I throw out a
couple of things here and great,I love the use cases, so we'll
give you two.
One thing that we work on is aproduct called Retail Traffic
Analytics, and what's reallyinterested about Retail Traffic
Analytics?
All of this comes together withthe ability to use cloud
compute and have unlimitedcompute.
But the way that product worksis you know we work with certain
(10:08):
retail locations, right?
So take any brick and mortarfootprint again could be you
know an auto repair facility,you know that has a chain
background, and so they havehundreds of stores in the United
States.
What we will do is we willunderstand the footprint of
those stores.
You know geographically we callthem shape files, right and
really what that is.
If you go to any map and you'regoing to see an outline of the
(10:29):
property, including parking lots, it's like drawing a polygon.
It's like drawing a polygonaround the location, right.
So if you've got a couplehundred of these you know arity
with our 40 million consumersthat have entrusted us with
their data we can basicallystart to understand
statistically.
That's extremely relevant.
There's about 270 milliondrivers in the US today, so
we're sitting on a large,statistically relevant portion
(10:52):
of that.
So if you are that retailer, wecan start to understand traffic
trends.
We can start to understand thaton the streets adjacent to your
location, you're getting 10% ofthat traffic will stop into the
store over this duration.
But you know, maybe just downthe street, you know your
competitive competitors, maybethey're getting 12% right, and
(11:16):
so we can start to understandthat.
Now, what's maybe somethingthat we can segment into as well
?
Is it a time of day difference,right?
Are these people getting themin the afternoon been into as
well?
Is it a time of day difference,right?
Are these people getting themin the afternoon?
Are they getting a more traffic?
Because it's a right turn intothe location versus a left turn?
You know, those are some of thethings you can start to slice
and dice to really understand.
You know, how is that trafficinteracting with that brick and
(11:38):
mortar location?
What's even better is.
Then you can start to enactsome changes.
Right, you know, maybe you wantto do some additional signage
and hold that thought We'll getthere with your digital out of
home.
You know, maybe you want to dosome marketing campaigns to
attract people.
This is where our predictivemobility and our predictive
commuting comes in.
What that's all about is ifit's convenient for a consumer,
(11:59):
right, we know we basicallylearn what we call our routes
and routines, and a routine isbasically an origin and a
destination.
The origin may be your worklocation and the destination may
be where you work out and weknow that you do that three to
four days a week and then, aftera period of time, we learn the
routes you take.
Now one of those auto servicerepair facilities may want to
(12:20):
market to you proactively andsay you know you may be driving
right by our store.
Stop in $3 off on our oilchange, you know, stop in for a
$5 coupon on retail parts.
That's the type of conveniencethat consumers really value.
We don't actually know who thatconsumer is.
We just know that there is amobile device or a car that
(12:40):
tends to go this pattern right,and we cut off things like
private locations, like home andanything of certain areas of
interest that are consideredvery personal.
We don't track or work withanything there, but what we'll
end up doing is we'll enable youto engage that consumer.
We'll learn those patterns.
We can help you understand thatduring rush hour you don't see
(13:02):
as much, so maybe you want torun specials in rush hour, maybe
you want to put some additionalsignage out and then you can
measure that.
Going to the signage, it'sreally interesting.
We partner with some digitalout-of-home sign companies and
what's really interesting withdigital signs is they can work
with multiple advertisers todisplay a digital ad and usually
those spots run from anywherefrom 10 to 15 seconds.
(13:23):
And what's critical is theywant to know who was exposed and
how many people were exposed.
Not because we're targeting,like you said, we're not
targeting that individual, butwhat we can start to understand
is the profile of drivingbehavior that passes by a
digital sign.
Let me give you a few examplesof that.
So you know, let's say that wehave people that tend to drive
(13:46):
high mileage, right, and we knowthat this particular sign in
Northern Illinois right thatruns digital ads for, you know,
an auto care repair facility isseeing an inordinate amount of
high mileage drivers.
We can actually index each ofthose signs based on those
behaviors, what that does for anadvertiser.
If you are an auto repair andyou're interested in servicing
(14:09):
high mileage vehicles, you mayrun a digital spot and pick the
signs that see an inordinateamount of high mileage drivers.
And that's some of the signalthat we can do in aggregate.
We're not targeting anindividual, we are basically
just grouping and understandingof the behavior that drives by
the signs and letting theadvertisers really tailor that
message to try to engage thatconsumer approach to how your
(14:46):
group is approaching it.
Speaker 1 (14:47):
So I'm relieved because I think there's always
going to be that, or notnecessarily always, but I can
see where people might think BigBrother is watching right and,
if I'm hearing you correctly,that data is being collected and
aggregated so that that data isreported to organizations that
can use it.
But it's really at the overalllevel and you highlight and I'm
(15:10):
hearing that correct yeah, thatis correct.
So one thing you mentioned washigh mileage drivers, and I
remember in previous discussionsresident mileage, where the
trips are originating from.
So is that part of the calculushere where you're able to say,
oh, we know where the trips areoriginating from because the
signal is being captured throughthat app and we know that that
(15:33):
driver is driving from, let'ssay, 40 miles away, 10 miles
away.
So tell us a little bit aboutthat aspect of things, how
you're able to separateresidents, non-residents, high
mileage, if you could.
Speaker 2 (15:45):
Yeah, absolutely.
And so when you think aboutthese again, the way Airdy works
is when our technology goesinside of a mobile app.
It is all anonymous, right?
You know, we know that there isa certain vehicle that is
driving.
You know, we know the patterns.
So, for instance, to your pointof the origin and the
destination, we only wake up.
The technology wakes up on thephone when it senses that
(16:08):
somebody is driving a vehicle.
Right and again, like everythingin today's world, this is
artificial intelligence, machinelearning, algorithms that take
signals such as speed and takesignals such as location Are you
on a road?
Because if you're going 20miles an hour, you could be on a
bike.
So we have to understand you'reon a physical road, somewhere
that traffic may be.
(16:28):
All of those signals get putinto a model that basically make
an inference that, yep, I thinkand feel pretty good that this
is somebody in a vehicle driving.
That's when our technologywakes up, starts recording
information about thosebehaviors, and those are
anything from you know how fastare you driving right, what
routes are you taking and whatroads are you on.
(16:48):
And when you think about thatagain, it's just tracking a user
ID.
It's not an actual individual.
I have zero personalinformation on that individual
right, but what I do have is Ihave kind of the trails they
take and how they move, and thenwhat we're able to do is we can
(17:09):
start to associate that youknow these trips keep starting
in this geographic area and sowe can infer that that must be
an important point of interestfor you, such as an area where
you live or an area where youwork, and so we actually look at
that about monthly.
Every month we'll updatebecause consider the you know
individual individual that maybevacations up in Northern
Michigan in the summer.
We may not see you if they'rein Illinois, we may not see them
in Illinois, but then we seethem over and over again
(17:29):
originating trips up in Michigansomewhere.
So for that month they may betagged as a resident of Michigan
and what that allows us to do.
Then, when you aggregate allthat data up, we can see those
shifting patterns, right, and wecan even mark it.
As you know these may bevacationers, right, you know
summer vacationers at a cottage,and so we can start to
(17:49):
understand you know how manymileage in Northern Michigan is
due to people summering thereversus people that live there
year round.
And that's important, becauseif you're a business and you're
looking at inventory or out ofstock or forecasting sales,
those are all signals you coulduse.
Again, very privacy safe.
It's not about an individual.
It's about individual behaviorrolling up into geographic and
temporal patterns that you cananalyze.
(18:11):
You would ask the questionaround, like where they come
from.
And this is another veryimportant thing for brick and
mortar retailers isunderstanding the reach.
If you want to put a newfootprint of a brick and mortar
store, what you want tounderstand is, let's say you
have 100, 150 stores already,you'll know that the average
distance that you get people tocome to your store is maybe 10,
(18:31):
12 miles right.
But when you see people gettingfurther out, right, and you're
seeing enough that you knowmaybe your competition is
starting to pull that businessaway.
Those are some of the signalsthat may say there is enough
population to drive the salesyou need and there's a
convenience in that you canshorten that window, which in a
lot of cases leads to higherconversion rates in those retail
(18:52):
footprints.
So all of that data, all ofthat information is driven by
the data that we collect, right,and it really gives you that
opportunity.
There are opportunities to useour data individually.
And again, that usually getsinto things around insurance
pricing.
But again, it's extremelyimportant for any consumer to
realize that none of thathappens without their consent.
Right, and even to the pointwhere, if you're, let's say,
(19:13):
getting an insurance quote, youwould be asked exactly.
We are what you call a creditreporting agency, just like
TransUnion and Equifax for yourcredit scores.
So, being a credit reportingagency, you know we are what you
call a credit reporting agency,just like TransUnion and
Equifax for your credit scores.
So, being a credit reportingagency, we are subject to all of
the fair credit requirements.
So if you go and you are askeddo you consent to use the data
to price and insurance, you cansay no with no impact.
(19:36):
You can say yes, you consent tothat, and we are required to
tell you why we got these scores, what we understood.
So really, to me, that is theright way to do this.
Right it is if you don't engagethe consumer, not only will
they be suspect of you, but theyalso want to understand the
value in it.
Right?
So we take a lot of pride inthat engagement with the
consumers.
Speaker 1 (19:57):
I think about.
When I signed up for a safedriving app to track my hard
braking, my sudden accelerationsand late night driving, I opted
in.
The default was not that youhave to opt out.
So it sounds like if anindividual wants to have a
direct impact on theirindividual bill, they physically
(20:18):
have to opt in.
Correct, but if I may ask thisas an insurance organization, I
would imagine that's just a.
Really the data that you'regetting is very helpful to
understand how the population isdriving, to assess risk and
perhaps how that can kind ofsharpen the actuarial tables for
(20:39):
writing policies.
Is that fair to say?
Speaker 2 (20:41):
It's absolutely fair to say.
And, again, I think one of themost important things is equity
and pricing.
Insurance is a reallyinteresting industry.
I've worked in and around itfor many years and insurance is
one of those.
It's a highly regulatedindustry, right, and the general
sense of insurance is to createan equitable pricing framework.
Right Now, everybody who paysan insurance bill probably has a
(21:03):
hard time believing that, butit truly is that way.
Every state has a department ofinsurance.
Every department of insuranceis trying to make sure that if
you're going to raise your ratesright, there's justification
for doing so.
If you're going to price oneperson differently than another
person, it has to be done in abalanced, equitable manner.
So things like credit scoreshave been used in insurance for
(21:23):
years and it is very predictiveof loss.
Now, why is it predictive ofloss?
That's really a hypothesis thatyou've got to kind of unpack to
understand.
It could be predictive of lossbecause of the fact that people
with higher credit scores maylive in the suburbs, right, and
suburbs tend to have lessdensity of traffic, and if you
have less density of traffic,therefore, you may have less
(21:43):
losses Still equitable.
But if you're an individual wholives in a city, maybe in a
very urban area, and you're avery safe driver, you're
actually, to a certain extent,being penalized for where you
live there.
Where driving data becomes superimportant is it's rating you as
an individual based on yourdriving behavior.
You're in control of that rightand that's why we became a
(22:04):
credit reporting agency becauseyou get to dispute, you get to
understand your credit.
That is your right.
But it is a fair way.
If you choose to drive in riskymanners, you should pay
equitably more for that right.
If you want to drive 100 milesan hour the expressway right
there, is a higher chance thatyou will get in an accident, and
when you do, those costs haveto be passed on to policyholders
(22:26):
.
So it's much better to havethat cost passed on based on
risk than it is spreading thatcost across everybody.
So, truly, telematics is a veryequitable way of doing things.
But you're right, it does meanwe have to understand how you're
driving, and that is a scaryproposition.
Speaker 3 (22:43):
This is DTP IT Director and Sustainability
Committee Staff Liaison at AutoCare Association.
Are you passionate aboutshaping our industry's future?
Join an Auto Care AssociationAdvisory Committee and make a
real impact as a volunteer.
You will drive innovation,tackle key challenges and
collaborate on cutting-edgesolutions for the entire supply
(23:04):
chain.
Don't miss out.
Join us at our upcomingLeadership Days event to start
making a difference.
Learn more at autocareorg slashleadership days and find
information on currentcommittees at autocareorg slash
committees.
Speaker 1 (23:30):
And I think from if I go back to Economics 101,
because we were talking aboutsons in college taking core
classes it's the informationasymmetry problem.
Right, and with this technologyavailable to fill in those data
gaps for lack of a better wordit allows a company to price its
product better.
It allows a company to priceits product better and in a
field that has many competitors,it's kind of just upping the
state of technology and thestate of the art so as to have a
(23:50):
stronger foundation for makingthose pricing decisions.
Speaker 2 (23:53):
That's 100% accurate and I think what the consumer
has to realize in this equationand regardless if it's insurance
or if it's how you engage withyour repair facility, the more
information that you can providesomebody, the better you know.
Imagine if you go to yourdoctor and you're not feeling
well.
The first thing they ask you iswhat are your symptoms?
How do you feel?
(24:13):
They want you to share datawith them and insight so they
can give you a diagnosis.
So what we have to really focuson is how to build trust with
the consumer such that they'rewilling to share that
information.
And that's why, when we workwith these mobile apps, when we
work with the OEMs, we want tomake sure the experiences that
we put in front of the consumerright our experiences they value
, they are in control not us onhow that data is used right and,
(24:36):
quite honestly, we givefeedback right.
If you are driving using riskybehaviors, you know we want to
give that feedback back to you.
We want to give you theopportunity to improve those
behaviors, to lower the risk.
So a big part of what we do,you know, whether that's through
the departments oftransportation and in our public
sector areas, whether it's thecoaching experiences that we put
(24:56):
inside of these apps.
It is really to educate theconsumer that they are in
control.
Right, it's not big brotherwatching.
It is your data that you cancontrol how it's used, and you
get to change your behavior toimpact what you pay for
insurance Makes a lot of sense.
Speaker 1 (25:11):
One thing that you alluded to were things like
high-speed driving is up.
The use cases of peoplevacationing in northern Michigan
Broad open-ended question.
But what are some of thechanges that you're seeing?
Speaker 2 (25:26):
Yeah, you know the speeding and again, a lot of
these are hypotheses to beproven out over time with the
data but the speeding, I dobelieve and this is Jeff's
opinion is that because drivingspread out more and people are
working from home, you're onroads that are less populated,
driving spread out more andpeople are working from home
you're on roads that are lesspopulated, you know, and if
you're on roads that are lesspopulated, you probably feel
(25:48):
like you know you can push thataccelerator a little bit harder,
right, nobody's around you.
The risk with that is we'realso seeing continually higher
distracted driving rates.
Distracted driving is up over32% from pre-COVID, right, which
again I think emboldens thesebehaviors that nobody else is
maybe immediately right in frontof you, so you're just going to
(26:09):
peek right at that phone,you're going to look, take one
more look, and so we take thatvery seriously because, again,
that is one of the largest riskfactors.
And so, instead of just callingit out, really what we want to
do is start to change thatbehavior.
Right, and you know whetherit's the way.
Predictive commuting is a greatopportunity, right, you know we
don't want to advertise to youwhile you're driving.
Right, we don't want to makenotice of that.
(26:30):
That's why we built predictivecommuting, because what we want
to do is say I'm pretty sureyou're going to be on this route
, so why don't I go ahead andnotify you before you get in the
car?
I'm going to make it moreconvenient and also safer to
have that conversation withpeople.
You know, as people change, Ithink one of the other behaviors
that we're seeing is you knowthe length of trips and where
(26:51):
people are going and how they'redoing it and when human
behavior changes.
Right, you get to adapt as abusiness to.
You know, meet the consumerwhere they are Cliche but it
actually is true.
Right, you know when peopledecide hey, you know, I'm bored
of working out of the house, I'mgoing to go to the Starbucks.
Right, you know, maybe you caninfluence that behavior.
Right, people do like tosocialize, they like to get out,
(27:12):
and I think that's where thisdata, you know, with some of the
changes post-COVID, is valuable.
Right, we can actually engagecustomers in different areas, we
can create new situations, andthat mobility data really helps
you understand what thatconsumer's preference is.
Right, and it's not all oneright, different consumers value
different things, but I thinkthat's where this driving data
can really help you understandsome of those changes, and then
(27:35):
again you can meet the consumerwhere they are, with goods and
services that they value.
Speaker 1 (27:39):
Thanks for that, jeff .
Two things came up as you weretalking.
The first one was thedistracted driving going up 32%.
If I can just go down a littlebit of a rabbit hole here, can
you describe, define distracteddriving?
And part of the reason I ask isthis If I'm driving my 2012
Nissan, which has Bluetooth, butthat's my wife's car, I don't
(28:00):
connect my phone to it.
If I pick up my phone and lookat it, maybe it unlocks.
To me that's an obviousdistracted driving.
If I had a rental car that wasmaybe a 2021 and I paired my
phone with it and I could seemessage coming in from so-and-so
(28:21):
, you could hit a button toignore it.
Or I'm driving or not engage itotherwise.
Or if the phone rings and I hitthe button on the steering
wheel to answer it, which ofthose might count as distracted
driving, and can you just tellus a little bit more about what
constitutes it please?
Speaker 2 (28:38):
Yeah, it's a great question.
And again, you can take dataand display it anywhere you want
.
So, at Arity, the way wedescribe distracted driving when
initiated from a mobile deviceis kind of what you alluded to.
It's an unlock of the phone,okay, it's followed by movement.
So phones have accelerometersinside of them, so we sense that
change in acceleration of thephone as it moves through the
(28:59):
air.
So the unlock event, followedby the accelerometer right, is
what we typically will calldistraction, right, and we have
to sense it for a short periodof time, right, you know, we
kind of give everybody theopportunity.
We also look at the speed of thevehicle moving, right, if
you're stopped at a light, right, we won't necessarily call that
distracted because the vehicleisn't moving okay.
(29:21):
So you know, all of thosesignals are processed and again,
we also look at them in thecontext of society, right, in
the context of insurance loss,and when we tie that data
together we can start tounderstand that this type of
movement on the phone is reallywhat we consider distracting,
because it's highly predictiveof loss severity, loss frequency
, you know, accidents happening,and so it's never just one loss
severity, loss frequency,accidents happening, and so it's
(29:43):
never just one set of rulesthat stay forever right.
The really important thing withanalytics and statistics and
machine learning and artificialintelligence is it's constantly
learning and it's constantlygetting smarter right at to what
the distraction means, and so Ithink today that's how it's
defined.
Tomorrow it may be differentand again, as society changes,
(30:04):
as technology changes, we'lladapt those models.
But again, really what we'relooking at is that signal of
risk and in our world that is aninsurance claim or a loss on a
vehicle.
We look at that and we say whatbehaviors led up to that, so we
can help coach those behaviorsand become safer.
Speaker 1 (30:20):
Is there an industry standard definition?
Because if I'm National HighwayTransportation Safety
Administration, I think that'swhat NHTSA stands for I'm
thinking about tell me thedifference between somebody
physically scrolling versustexting, versus initiating a
call, versus X, y, z.
So is there a standard in theindustry for the different types
(30:41):
of distracted driving and areyou able to detect that?
Speaker 2 (30:44):
Right?
Yes, as far as I know, there isno standard yet and I think
it's incumbent on companies likeArity and maybe the insurance
sectors.
There are conversationsstarting, right, if you've heard
of the net zero initiatives andTell us a little more about net
zero.
Yeah, net zero is one of thosethings in the Department of
Transportation and withinmunicipalities and the public
(31:06):
sector that are trying to sayhow do we get to the point where
there's no accidents, no deaths, right?
You know, we have reallyintelligent technology with
these cars, whether it's theadvanced driver assist systems
they call that ADAS right.
Whether it is, you know, theself-driving vehicles, whether
it's the material we're using inthe car, all of these things
(31:27):
have the potential to lead to,you know, zero accidents, zero
deaths right.
But again, the consumer has tobalance that with the desire and
the fun of driving a vehicle,right, like everybody loves to
drive a car.
You know you turn 16, you can'twait to get your license.
But I think you can have both,right.
You can have a safe environment, and that's the promise of
technology, right, and?
(31:48):
But I think you can have both,right.
You can have a safe environment, and that's the promise of
technology, right.
And so I think what you'regoing to see is you will
probably see the industrystandardized on this.
The government has to saylocation data in general is a
very hot topic right now.
It's a hot topic in Washington,it's a hot topic in insurance,
a hot topic in advertising.
I don't think it goes awaybecause it's extremely valuable.
(32:10):
I think we need to make acontract with consumers that
they value right.
So and again, you needcompanies like an Allstate
Corporation, and that's what Ilove about working for Arity.
Being owned by Allstate givesus the investment and the
opportunity to do it the rightway, right.
And again, I think, if you lookat the leadership at Allstate,
right, they're heavily vested insafety.
(32:31):
Right, you're in good hands.
That's the nature of theirmotto and we live into that, and
I'd like to believe that that'swhere we need to take society
length of trips and earlier youtalked about.
Speaker 1 (32:51):
We can detect because I'm connected to this phone and
you can see for this device, atleast if I'm hearing it
correctly, I have, my patternshave changed and now I'm driving
in Florida.
Maybe he's not resident, right,so you can tie it to an
individual, but you don't knowthat it's Mike Chung, age X,
gender, et cetera, et cetera.
So I'm seeing you nodding yourhead.
(33:11):
It sounds like I'm on the rightpath and tell me a little bit
more about the length of trips.
You mentioned earlier thatthere could be combined purposes
.
Could you just tell me a littlebit about what you've seen
broadly over the last few years?
Speaker 2 (33:29):
Yeah, I think what you see with the length of trips
is they tend to become longer,right, more mileage is being
accumulated, and I think thathas to do with, again and this
is really important in theretail space, right, because
we're starting to understandchanges Everybody has to go get
groceries.
You've got to go charge yourcar or fill up your car, um,
you've got to go get, you know,material and clothes and go to
home depot and do the thingsthat you do on a daily basis.
(33:51):
But when you're working fromhome, right, people tend to try
to go to a convenient method ofof purchase.
Anyways, right, if I'm on theway home, I'm going to hit the
fuel station, the grocery storein, you know, my neighborhood uh
drugstore to pick up aprescription.
So, now, what we're getting to,though, is when people venture
out, they're learning newpatterns themselves, right, so
(34:13):
we're seeing longer trips.
We are seeing people ventureout at lunch hours and run
errands that they typicallywould do on the way home, right,
and so, you know, the length oftrips are increasing, right,
and, again, I think that has afunction to do with, just, if
you live in the suburbs, right,houses aren't usually right next
to you know retail locations,but when you're at work, there's
retail locations all aroundthat, because you drove to work,
(34:35):
put your car away, then youwalk to lunch.
You walked over to get ahaircut.
So we're seeing that change inbehavior right Anywhere from.
If you start to look at youknow parking lots.
When's the last time you'vegone to a parking lot and it's
full right.
Those are things you'restarting to see.
We even and I'll give a greatexample when the Key Bridge
incident happened in Baltimore.
(34:56):
What's fun is almostimmediately we can start to
analyze what happens in thatscenario.
And I think earlier, about ayear earlier, on I-95, they had
to shut down I-95 for four orfive days and I-95, major
thoroughfare and we were able tolook that was just outside of
Philadelphia, correct, outsideof Philadelphia.
We were able to look at theimpact to the infrastructure.
(35:19):
Where did that traffic go?
You can't go on I-95.
Where did you go?
And what we saw were timesballooned by 20, 30% when I-95
happened.
What typically would be maybe a10-minute ride is now 15, 18
minutes, right, just ballooningtimes.
And you can see the density onthe side routes that weren't
(35:41):
designed to handle that.
So that was a real difficultpoint for consumers.
With the Key Bridge, theinfrastructure there really kind
of absorbed that change quite abit.
Now that could be the type oftraffic that went there across
the Key Bridge, but what we didend up seeing is that the
infrastructure in that areaabsorbed that change pretty
(36:03):
easily and again that's asuccess story for the
Departments of Transportationand that's kind of the promise
of what this data can do.
I mean, consumers have a veryshort fuse right.
If you're sitting in your carand you're stuck in a red light,
your anxiety goes up, you'refrustrated and if we can plan
(36:24):
around that and we can plan witha little bit of leeway in some
of this infrastructure, thatreally goes a long way in
society.
So this is some of the realpositive benefits of having
access to this kind of data and,again, making that contract
with the consumer that you willbenefit from the sharing
Terrific.
Speaker 1 (36:37):
One thing I thought of as we were talking was
possible noise you talked aboutyou might be going 20 miles an
hour.
It might be a bicycle.
I remember signing up for thatsafe driving app and it would
say it would thought I wasdriving but I was taking the
subway and I had an opportunityto say I was not driving.
How about if you're taking acar service, you get picked up
(36:58):
in a Lyft or Uber.
I remember I think you hadshared the acceleration is
different in the front seatversus the back seat.
Can you tell us a little bitabout that noise reduction
process?
Speaker 2 (37:09):
Absolutely.
So you'll start to sense reallyquick how mathematic and how
hard it is to actually gleansome of the signal.
But with the advancement of AIand everything else, these are
positive opportunities for it.
So we have something calledvehicle mode, right, and vehicle
mode is a methodology where wetry to look at and determine,
you know, are you on a bus, areyou on a bike, are you on an
(37:31):
airplane?
Some of these things are easy,right?
You know, if you're at 10,000feet, going 300 miles an hour,
I'm pretty sure you're not in acar anymore.
Okay, so those are pretty easyinferences to make.
But take rideshare like yousaid, that's actually pretty
difficult, except if you look at.
And what's really interesting ifyou look at, and what's really
interesting if you visualize it,if you look at that vehicle,
that rideshare vehicle, and youlook at what it does, the
(37:53):
pattern on a map is really,really gives it away.
I mean, it goes somewhere, itstops, it goes back somewhere,
it stops.
It just looks like it's drivingin circles, right, so that
pattern can be recognized bymachine learning.
And when you recognize that asa consumer, if you get inside of
a vehicle, you know, and thatvehicle is zigzagging in a
pattern that looks like a rideshare.
(38:14):
We can market as that.
Now we always give you theopportunity to override it right
, meaning that a lot of theexperiences you know we can.
We call it tagging the trip.
You know the consumer can go inand say no, no, no, I was on a
bus.
You, no, no, no, I was on a bus, you know they can click that.
That is great too, because itactually helps us get better at
these analytics, right.
That's that continual trainingand learning we do, but we're
pretty good at it right.
Our accuracy at figuring it outpretty good, and it's again,
(38:36):
it's the positive aspect of datais extremely important in
understanding behavior, right,and when you understand behavior
, you know when used for theappropriate manners, it really
does make people's lives better,and that's what we do.
Speaker 1 (38:50):
Terrific.
One thing that I'm thinkingabout here is earlier you talked
about advertising.
One way, two way, so tell mewhat that means.
Does that mean my data cannotbe used for advertising purposes
?
Does it mean advertisementsthat are shown to me?
Can you just distinguishbetween the two for us?
Speaker 2 (39:10):
And the advertising industry is highly regulated.
So I'll start with that Meaningthat, again, as a consumer, you
have a ton of choices, right.
It's also a extremely complextechnical environment, but it's
super interesting.
So we'll go back to thatpredictive commutes and I'll
even give you some statisticsagain.
We do a lot of what we callin-app advertising, right, and
in-app advertising is when yousign up with an app, you know,
(39:36):
pick any of the apps that ourtechnology is running in.
You'll be asked do you consentto sharing your data for uses in
advertising?
If you say yes, right, it meansthat we will display ads in
that app.
You know, that's one way mostmobile publishers make their
revenue.
Right, in order to use that app, whether it's a weather app,
whether it is a family safetyapp, a lot of those free
versions are powered byadvertising, right, so there's a
benefit to the consumer rightthere, which is you're getting
free software and in return,you've got to look at the
(39:57):
advertisement.
I mean, the whole purpose ofmass media and television is
built on that model.
Now, what's really unique,though, is it's not just about,
you know, what we call pray andspray, right.
It's not just put the ad up andhope that the people looking at
it are interested in theproduct.
Now, when we start tounderstand those behaviors, we
can actually tailor theseproducts and again, the consumer
(40:18):
will value this.
I'll give you an example.
We worked with a fuel retailerwhere we were doing our
predictive commutes, which againis us making a prediction that
you may be driving by one ofthose locations in the next hour
, two hours, three hours, andagain the purpose of doing that
is we know what routes thatphone or that device is taking
and so, within the app thatyou've entrusted to advertise to
(40:39):
, that advertiser can basicallysay I want to contact people who
are going to be driving by oneof my locations this afternoon.
So you may be just be using aweather app and you may get a
coupon for food or auto repairor whatever it may be.
You may look at that and go.
You know what.
I'm going to drive right bythat location and I'm going to
(40:59):
get $5 off.
This is a good deal.
I'm happy with that.
That's what our advertising canpower and, amazingly enough,
when we did that with this fuelretailer, we ran this for 60
days, I think, on about 100locations and over that 60 days.
When you compare it to the 60days when you weren't running
that predictive ad you were justdoing the pray and spray we got
(41:22):
a 39% higher conversion.
What does that mean?
It means that the people thatsaw the ad that was convenient
converted bought a product 39%more times than they did when it
was just putting a generic adup.
Right, and again, what thattells me is the consumer valued
that experience and thatadvertisement.
(41:43):
Right.
Again, you get to choosewhether or not you want to see
those ads.
Speaker 1 (41:47):
Right.
And then that's on the mobilephone and, similarly, on an
outdoor billboard you have statson how many people are driving
by it, Correct?
And that can just fuel thetargeting as well.
That's right, Yep, Fascinating,so I think.
One last question I have for youas we close up, and I'll say
(42:10):
this it's been fascinatingtalking to you.
I feel like we could talk forhours.
We should probably do thisagain.
So much thanks for joining ushere.
What do you see in three tofive years with regard to data
that's going to become available?
And I'll jam in one morequestion here, because when I
gave a presentation here atConnect, I showed the inferred
(42:35):
high impact collisions month bymonth, and it was a slightly
positively sloping curve right.
And the question came up doesthat account for ADOS?
Because this man conjectured ifthere is ADOS, I would expect
(42:55):
that to go down.
So I was thinking about that.
Is there a way to mash thosedata sets together?
So kind of a two-part questionIs ADOS helping?
Is there a way to prove that?
And then what's the future?
Speaker 2 (43:11):
Yeah.
So it's a really good question,by the way, and I think time
will tell.
What I will tell you is, again,the average age of vehicles on
the road, I think today isaround 12, 13 years, and so when
you look at that, there's a lotof vehicles on the road that do
not have 8S, right?
The second thing is a lot ofpeople turn 8S off, right?
I have a Honda 2019 that has 8S.
I love the system, but you know, it was also kind of like Gen 1
(43:33):
.
And you know, at times it'slike, okay, I'm just going to
turn it off right.
So you've got those two thingsat play.
So what is the real number ofcars doing that?
It's actually, I'd say, a scaryproposition, right, because if
things are still rising and wehave safety features preventing
it, it's probably rising at ahigher slope or even more
exponential than you think,because ADAS, I guarantee you,
(43:54):
is working, but we're stillseeing them rising in high
severity collisions, right?
So parlaying that into thethree to five-year plan net zero
you just can't complain aboutthat, right?
There's nothing negative abouttrying to save lives, right?
You know, I have four children.
Two of them are driving, twoare going to get their permits
this year.
Yeah, I want them to be safe,right.
(44:16):
So anything around familysafety to me is a net positive.
So, using data, building thatcontract with the consumer to be
able to build a safer world tolive in, you know?
Yes, three to five years, I dobelieve.
And there's a lot ofconversation around the use of
that data for insurance andpricing.
I do believe, from an equitableposition, it's a more equitable
(44:39):
way to price.
It's way more equitable thancredit, than your gender, your
age, those things today arebeing used.
I think the way you drive youcontrol it.
If you want to drive riskier,you should pay more.
Used.
I think the way you drive, youcontrol it right.
If you want to drive riskier,you should pay more.
So I think in three to fiveyears, the insurance industry
believes in this.
The numbers don't lie.
I think what we've got to do isfigure out how to get the
consumer confident with it.
(44:59):
So I think over the next threeto five years you're going to
see a continued effort to bringthat consumer along.
Whether that's the way that weset up the businesses, whether
that's the contract we make withthem, whether that's the
education, I would say that'simportant.
And then I think outside of that.
Quite honestly, I think adsthat are tailored towards
(45:19):
convenience, that are meaningfulto a consumer, are way more
valuable than the pray and spray.
So what you may.
Actually, in a perfect world,maybe you're going to see less
ads, but the ads you see aregoing to be more worthwhile.
Right now the advertiser isgoing to pay more for that, but
in general, their spending willstay flat because they're not
going to spend as much on thepray and spray and they're going
(45:40):
to use data like this to reallytarget the consumer with what
they want.
So those are maybe utopianviews of the world, but I do
think that's the promise of thisdata and analytics.
And again, it's just importantthat we do that collaboratively
with the consumer so they'recoming along with the journey.
They're not being forced upon.
Speaker 1 (45:56):
That makes a lot of sense from an efficiency
standpoint, as well as acustomized content and higher
return on investment, becauseyou have to do the cost benefit
analysis as you're alluding to.
That's right.
Well, jeff, so great to haveyou.
Always a pleasure to talk.
I hope we can.
Thanks for tuning in to anotherepisode of Auto Care On Air.
(46:20):
Make sure to subscribe to ourpodcast so that you never miss
an episode.
Don't forget to leave us arating and review.
It helps others discover ourshow.
Auto Care On Air is proud to bea production of the AutoCare
Association, dedicated toadvancing the auto care industry
and supporting professionalslike you.
To learn more about theassociation and its initiatives,
visit autocareorg.
Thank you.
Popular Podcasts
On Purpose with Jay Shetty
I’m Jay Shetty host of On Purpose the worlds #1 Mental Health podcast and I’m so grateful you found us. I started this podcast 5 years ago to invite you into conversations and workshops that are designed to help make you happier, healthier and more healed. I believe that when you (yes you) feel seen, heard and understood you’re able to deal with relationship struggles, work challenges and life’s ups and downs with more ease and grace. I interview experts, celebrities, thought leaders and athletes so that we can grow our mindset, build better habits and uncover a side of them we’ve never seen before. New episodes every Monday and Friday. Your support means the world to me and I don’t take it for granted — click the follow button and leave a review to help us spread the love with On Purpose. I can’t wait for you to listen to your first or 500th episode!
The Breakfast Club
The World's Most Dangerous Morning Show, The Breakfast Club, With DJ Envy And Charlamagne Tha God!
The Joe Rogan Experience
The official podcast of comedian Joe Rogan.