January 14, 2020 • 12 mins
James Kotecki of C Space Studio speaks with Gregg Lindner, President of Americas, GfK & Chairman of MRI-Simmons
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Episode Transcript
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Speaker 1 (00:01):
Hi, I'm James Kotechi. You're listening to the C Space
Studio podcast interviews with technology, media and marketing leaders from
c E Hi, you're in the C Space Studio with me,
James Kotechi, your host here at c E S twenty
twenty and here with us today, Greg Linder, President of
(00:23):
GfK America's and chairman m R. Simmons, Welcome to pleasure
James the studio. Thank you so much for being here.
So first, kind of defined for us what those two
brands are, just kind of catch everybody up on what
you're doing. Sure, So m R m R. I and
Simmons UH came together as a joint venture in February
of so we're coming close to our one year anniversary
(00:43):
as a joint venture. And it really was a way
to bring together two pre eminent brands that collect media data,
consumer insights, information, psychographics data and for them they're very
complementary services. And so it was a way to start
a joint venture with those two very well known brands. Um,
go sorry, go ahead, And I said, in recent years,
our data has become more relevant to the digital ecosystem
(01:06):
through segmentations and through DSPs in order to help people
through digital activation. So it's obviously it's all about data
and who are the customers that are getting this data?
So the customers are a media uh clients that we have, print, broadcast, audio,
cable clients as well as advertisers different brands who buy
the service and agency clients throughout it's important that the
(01:29):
agencies are users of the data. It helps with the
work that gets done by the other media clients that
we have. And just to contextualize for the audience, GfK Americas,
how does that fit into this too? So GfK America's
is uh. The way that it works is that the
GfK has different regions throughout the world. It's a global business.
The Americas are one region within that with a different
(01:49):
set of products that are part of it. And the
joint venture is a large part of the GfK North
America got it. So what kind of insights do you
get from this data? I think that's the most interesting
thing that a lot of people like se space would
be interested to me. Yeah. So one of the things
that I think you hear a lot around here, certainly
on the media side, is that content is king and
(02:10):
UH sometimes to me. It's what's old is also new.
And so, just as a little anecdote, UM, I remember
being a child, uh sitting with a five dollar Westinghouse
radio with an earpiece that I would have under the
pillow so that way I could listen to a New
York Met game at night and my parents wouldn't hear
me up until eleven o'clock listening to the Met game.
So fast forward thirty forty years and I have children
(02:33):
that are sitting there listening to a Met game only
using a device that's maybe a hundred times more costly
than my five dot or Westinghouse radio with earbuds that
probably cost more than the rent that my parents paid
for the apartment. They're listening to the Met game on
the radio. The unfortunate thing is that the result that
they had when I was a kid, and the result
of the games as they were children were the same results.
(02:56):
But I guess you can't really blame them for for
using technology in that way. And so is that what
your fire? Do? You do? You do you split the
data up by kind of demographics and find that I mean,
one thing that I've always kind of felt about a
lot of the success of YouTubers is that they're targeting
like teenagers and tweens who can't watch that stuff in
the main room, so they kind of watch it in
their bedroom and their parents are watching. And so we
(03:16):
do collect information from from an adult in the household,
and that adult does tell us what it is that
they're doing from a media perspective. We also have kids
in teen studies for which we get from the same
same households information about the kings and the and the
kids and what it is that they're doing on different
media and their consumer behavior as well. So, yes, the
(03:37):
data is demographically driven. The data psychographically driven, and that's
what helps creates different segments with psychographics a little bit
more so, it's just to be able, let's say, lifestyle information.
What is it that makes you different or a group
of people to be different than another group of people.
What are their feelings on different types of batteries of questions,
(03:58):
whether it's environmental questions, whether it's media related questions that
they have, and from that you're able to draw segments.
Those segments then can be looked at with all your
media and your consumer behavior insights as well. And how
are you collecting all this data? So that data gets
collected in a multitude of ways. Uh. In for the
m r I study that we have, it's one where
(04:18):
you actually still go door to door to collect information
with an area probability sample very high representation of what
the what the country is. So if you're able to
look at that data, you would see something that would
mirror the country. So it's someone with an iPad, for example,
walking up to a door, ringing the doorbell and and
hoping the answer and then asking them some questions about
(04:39):
the media. That's correct. So a randomly selected uh, household
and then a random respondent within that household huh and
and why do it that way? That sounds like an
old school way to do it, but maybe that's the
best way to get certain kind of as well. One
one of the things that should remembered is that in
an age of large data or big databases, it's important
to have in from a should be that can be
(05:01):
considered truth sets. And when you have information which you're
collecting with a lot of rigor around that information and
it's something that can be trendable over time and the
demographics match the population, it gives people a comfort that
there is a truth set that they can work with.
And as you're working with digitally activated information, to have
seed data that has that type of value to it
(05:22):
is really incredibly important. And do you adjust for the
fact that people might not be completely truthful with some
stranger coming to the door and asking them about all
their media consumption? How there maybe some guilty pleasures? I'm
not willing to Yeah, and and I'm sure that there
are some things that people are less prone to to say.
But what you find out is when you go door
to door and you're able to sit down and look
(05:43):
eye to eye to the responding through an interviewer that's
gone through a whole lot of training, you're able to
break through that and there's a bond that forms between
the interviewer and the respondent. I'm talking to Greg Linda
right now is the chairman of the m R. Simmons
joint venture. UM, let's talk about privacy concerned. UM, so
you've collected all this data, what do you what do
you tell the people when you're at the front door,
(06:04):
and then what do you actually do with the Data's
first privacy? Well, I should say that privacy is an
essential cornerstone to what it is that we do as
a business. So for m RI I Simmons um, if
we did not take care of the privacy concerns, we
would not be able to mail questionnaires two respondents and
have them return it. We wouldn't be able to go
door to door to households and be able to have
(06:25):
them feel comfortable about giving us the information about UH,
the economics and the education and all the other detailed
information ends up being collected. So it's essential from that
end if we ever broke that bond, it would end
up impacting what it is that we could deliver to
our clients. So we take great pride in what we do.
We have both internal and external audits that are done
(06:45):
on the systems, and we have investments that are made
throughout the business to make sure we take care of that.
We also integrate our data with other data sets, and
there's a lot of care that's taken to ensure that
the information that would be PI data ends up being
left out of any of those transactions. And obviously, with
what's going on with California and the c c p A,
it's important that we're stay on top of that because
(07:07):
we know the concerns that both respondents and businesses have
around privacy and do you have an example of how
one of your clients and media, for example, might use
this data to make a decision or do something that
they wouldn't otherwise do if they didn't have so the
information could be used. If you have an audience estimate
the number of people that read an average issue of
a magazine, you're able to use that information in order
(07:30):
to determine how you might charge for the advertising that
ends up being within that So it's a way to
use that information and a quantitative fashion. And then there's
also a qualitative aspect that radio networks or cable networks
the broadcast would be able to use the information. And
then it's for decision making on the brand side of
the business. When they take a look at the data,
they're able to look at people that might buy their
(07:51):
own brand as well as people that are you know,
looking at other brands, and it gives them what's hot,
what's not. You know. Marketers have so much data and
obviously it's to their benefit largely, but it also makes
their job very difficult and very complicated. Is it is it?
Is there a sense that it's just harder to be
a marketer today, because if you could go back and
promise marketers all this data in an era before they
had it, they would salivate, right, But then once they
(08:12):
get it, how do they know how to apply it?
How do they know what to do with it? It
seems like it's just getting more challenging, not less. Now,
that's that's a great question, and certainly one of the
reasons for the joint venture that we had that was
m R. Simmons was to bring together the rigor of
the data and also to have new platforms that we're
able to roll out to our users and it makes
it easier for them to work with that information. And
(08:34):
you know, the intent is to be able to use
machine learning and AI in order to help clients make
a decision about how to work with the data and
not just look at things in a descriptive fashion, but
to be able to look at it in a predictive
analytic way in the future. So it enables them to
get different uses out of the data. But you're right,
it's a very complicated in a world as far as
(08:55):
all the different data, and we try to differentiate what
we do with the business again by having that truth
set of data that people can feel very confident with. So,
speaking of AI, look in the crystal ball for me
and tell me what's going to be happening with AI
in five years that I'm gonna be able to apply
a I d R data and get what kind of
insight that I can't get today. If I can do that,
I could probably be out at the casino. But uh,
(09:18):
it's certainly UM. We have to take cues from our
users as they're working with the data sets that we
have and with the machine learning that we're working with
them on. We have to take cues from them as
to how to make it more actionable for them, how
to make the data sing more for the users that
the uses that they have for the information. So I
think we're going to use to our technologists or data
(09:40):
scientists UM work with our clients. So we have a
lot of clients, UM UM groups that we UH that
we work with in order to make sure that we
get the input from them to provide the uses of
the data and to expand on our consumer platforms that
we have. And by focusing on the data, you may
be focusing on exactly the right challenge for the moment,
right Because I think a lot of people I to
(10:00):
do AI m L initiatives and then find out that
the data wasn't as good as they thought. But if
you're able to kind of control the quality of the
data and then apply AI to it, you may be
actually better off having that I don't know, closed ecosystem
or whatever you way. And and that's how we talked
to our clients about the data. It's really is the
truth set. It's used as the foundation for other things
that may they may want to do, because as people
(10:21):
work with data more, they're looking to integrate more data sets,
and as you're integrating that information, you have to make
sure that you have truth sets as part of that
as part of the foundation of that data. Otherwise the
decisions you make may not be the right decisions. Um
GfK as A as a company has been around for
eighty five years. What stays the same as technology continues
(10:41):
to change. We talked about equality of information certainly needs
to to be their consultancy UH work and in order
to make sure that when clients have questions about the information,
we have the people who have the expertise or having
a human on the other end of the phone and
it's honest it's important to do that, and it's important
to go visit our clients and to look at them
face to face and to make sure that we have
(11:03):
you know, the right the right type of analytics that
we can that we can use for them. Okay, one
more crystal ball question, a little more nearer term to
close us out. Twenty will be the year of blank.
Is unlikely to be different than other years relative to
making sure that you're able to innovate and to constantly
(11:23):
look at ways that you're working with with with data
sets that you're able to innovate. And because what we
think is the most important thing in January may not
be the most important thing in July of so you
have to make sure that you're that you're going through
the innovation within your business and to stay up to
speed on that innovation. Is the same as because things
(11:45):
are continuing to change, a change all the time. Well,
Greg Lender, President GfK America's Chairman of m R Simmons
Jode Venture, thanks so much for joining us here in
the pleasure. Thank you very much for having me here.
Thank you. This podcast is in partnership with the iHeart
Podcast Networks. M
Hi, I'm James Kotechi. You're listening to the C Space
Studio podcast interviews with technology, media and marketing leaders from
c E Hi, you're in the C Space Studio with me,
James Kotechi, your host here at c E S twenty
twenty and here with us today, Greg Linder, President of
(00:23):
GfK America's and chairman m R. Simmons, Welcome to pleasure
James the studio. Thank you so much for being here.
So first, kind of defined for us what those two
brands are, just kind of catch everybody up on what
you're doing. Sure, So m R m R. I and
Simmons UH came together as a joint venture in February
of so we're coming close to our one year anniversary
(00:43):
as a joint venture. And it really was a way
to bring together two pre eminent brands that collect media data,
consumer insights, information, psychographics data and for them they're very
complementary services. And so it was a way to start
a joint venture with those two very well known brands. Um,
go sorry, go ahead, And I said, in recent years,
our data has become more relevant to the digital ecosystem
(01:06):
through segmentations and through DSPs in order to help people
through digital activation. So it's obviously it's all about data
and who are the customers that are getting this data?
So the customers are a media uh clients that we have, print, broadcast, audio,
cable clients as well as advertisers different brands who buy
the service and agency clients throughout it's important that the
(01:29):
agencies are users of the data. It helps with the
work that gets done by the other media clients that
we have. And just to contextualize for the audience, GfK Americas,
how does that fit into this too? So GfK America's
is uh. The way that it works is that the
GfK has different regions throughout the world. It's a global business.
The Americas are one region within that with a different
(01:49):
set of products that are part of it. And the
joint venture is a large part of the GfK North
America got it. So what kind of insights do you
get from this data? I think that's the most interesting
thing that a lot of people like se space would
be interested to me. Yeah. So one of the things
that I think you hear a lot around here, certainly
on the media side, is that content is king and
(02:10):
UH sometimes to me. It's what's old is also new.
And so, just as a little anecdote, UM, I remember
being a child, uh sitting with a five dollar Westinghouse
radio with an earpiece that I would have under the
pillow so that way I could listen to a New
York Met game at night and my parents wouldn't hear
me up until eleven o'clock listening to the Met game.
So fast forward thirty forty years and I have children
(02:33):
that are sitting there listening to a Met game only
using a device that's maybe a hundred times more costly
than my five dot or Westinghouse radio with earbuds that
probably cost more than the rent that my parents paid
for the apartment. They're listening to the Met game on
the radio. The unfortunate thing is that the result that
they had when I was a kid, and the result
of the games as they were children were the same results.
(02:56):
But I guess you can't really blame them for for
using technology in that way. And so is that what
your fire? Do? You do? You do you split the
data up by kind of demographics and find that I mean,
one thing that I've always kind of felt about a
lot of the success of YouTubers is that they're targeting
like teenagers and tweens who can't watch that stuff in
the main room, so they kind of watch it in
their bedroom and their parents are watching. And so we
(03:16):
do collect information from from an adult in the household,
and that adult does tell us what it is that
they're doing from a media perspective. We also have kids
in teen studies for which we get from the same
same households information about the kings and the and the
kids and what it is that they're doing on different
media and their consumer behavior as well. So, yes, the
(03:37):
data is demographically driven. The data psychographically driven, and that's
what helps creates different segments with psychographics a little bit
more so, it's just to be able, let's say, lifestyle information.
What is it that makes you different or a group
of people to be different than another group of people.
What are their feelings on different types of batteries of questions,
(03:58):
whether it's environmental questions, whether it's media related questions that
they have, and from that you're able to draw segments.
Those segments then can be looked at with all your
media and your consumer behavior insights as well. And how
are you collecting all this data? So that data gets
collected in a multitude of ways. Uh. In for the
m r I study that we have, it's one where
(04:18):
you actually still go door to door to collect information
with an area probability sample very high representation of what
the what the country is. So if you're able to
look at that data, you would see something that would
mirror the country. So it's someone with an iPad, for example,
walking up to a door, ringing the doorbell and and
hoping the answer and then asking them some questions about
(04:39):
the media. That's correct. So a randomly selected uh, household
and then a random respondent within that household huh and
and why do it that way? That sounds like an
old school way to do it, but maybe that's the
best way to get certain kind of as well. One
one of the things that should remembered is that in
an age of large data or big databases, it's important
to have in from a should be that can be
(05:01):
considered truth sets. And when you have information which you're
collecting with a lot of rigor around that information and
it's something that can be trendable over time and the
demographics match the population, it gives people a comfort that
there is a truth set that they can work with.
And as you're working with digitally activated information, to have
seed data that has that type of value to it
(05:22):
is really incredibly important. And do you adjust for the
fact that people might not be completely truthful with some
stranger coming to the door and asking them about all
their media consumption? How there maybe some guilty pleasures? I'm
not willing to Yeah, and and I'm sure that there
are some things that people are less prone to to say.
But what you find out is when you go door
to door and you're able to sit down and look
(05:43):
eye to eye to the responding through an interviewer that's
gone through a whole lot of training, you're able to
break through that and there's a bond that forms between
the interviewer and the respondent. I'm talking to Greg Linda
right now is the chairman of the m R. Simmons
joint venture. UM, let's talk about privacy concerned. UM, so
you've collected all this data, what do you what do
you tell the people when you're at the front door,
(06:04):
and then what do you actually do with the Data's
first privacy? Well, I should say that privacy is an
essential cornerstone to what it is that we do as
a business. So for m RI I Simmons um, if
we did not take care of the privacy concerns, we
would not be able to mail questionnaires two respondents and
have them return it. We wouldn't be able to go
door to door to households and be able to have
(06:25):
them feel comfortable about giving us the information about UH,
the economics and the education and all the other detailed
information ends up being collected. So it's essential from that
end if we ever broke that bond, it would end
up impacting what it is that we could deliver to
our clients. So we take great pride in what we do.
We have both internal and external audits that are done
(06:45):
on the systems, and we have investments that are made
throughout the business to make sure we take care of that.
We also integrate our data with other data sets, and
there's a lot of care that's taken to ensure that
the information that would be PI data ends up being
left out of any of those transactions. And obviously, with
what's going on with California and the c c p A,
it's important that we're stay on top of that because
(07:07):
we know the concerns that both respondents and businesses have
around privacy and do you have an example of how
one of your clients and media, for example, might use
this data to make a decision or do something that
they wouldn't otherwise do if they didn't have so the
information could be used. If you have an audience estimate
the number of people that read an average issue of
a magazine, you're able to use that information in order
(07:30):
to determine how you might charge for the advertising that
ends up being within that So it's a way to
use that information and a quantitative fashion. And then there's
also a qualitative aspect that radio networks or cable networks
the broadcast would be able to use the information. And
then it's for decision making on the brand side of
the business. When they take a look at the data,
they're able to look at people that might buy their
(07:51):
own brand as well as people that are you know,
looking at other brands, and it gives them what's hot,
what's not. You know. Marketers have so much data and
obviously it's to their benefit largely, but it also makes
their job very difficult and very complicated. Is it is it?
Is there a sense that it's just harder to be
a marketer today, because if you could go back and
promise marketers all this data in an era before they
had it, they would salivate, right, But then once they
(08:12):
get it, how do they know how to apply it?
How do they know what to do with it? It
seems like it's just getting more challenging, not less. Now,
that's that's a great question, and certainly one of the
reasons for the joint venture that we had that was
m R. Simmons was to bring together the rigor of
the data and also to have new platforms that we're
able to roll out to our users and it makes
it easier for them to work with that information. And
(08:34):
you know, the intent is to be able to use
machine learning and AI in order to help clients make
a decision about how to work with the data and
not just look at things in a descriptive fashion, but
to be able to look at it in a predictive
analytic way in the future. So it enables them to
get different uses out of the data. But you're right,
it's a very complicated in a world as far as
(08:55):
all the different data, and we try to differentiate what
we do with the business again by having that truth
set of data that people can feel very confident with. So,
speaking of AI, look in the crystal ball for me
and tell me what's going to be happening with AI
in five years that I'm gonna be able to apply
a I d R data and get what kind of
insight that I can't get today. If I can do that,
I could probably be out at the casino. But uh,
(09:18):
it's certainly UM. We have to take cues from our
users as they're working with the data sets that we
have and with the machine learning that we're working with
them on. We have to take cues from them as
to how to make it more actionable for them, how
to make the data sing more for the users that
the uses that they have for the information. So I
think we're going to use to our technologists or data
(09:40):
scientists UM work with our clients. So we have a
lot of clients, UM UM groups that we UH that
we work with in order to make sure that we
get the input from them to provide the uses of
the data and to expand on our consumer platforms that
we have. And by focusing on the data, you may
be focusing on exactly the right challenge for the moment,
right Because I think a lot of people I to
(10:00):
do AI m L initiatives and then find out that
the data wasn't as good as they thought. But if
you're able to kind of control the quality of the
data and then apply AI to it, you may be
actually better off having that I don't know, closed ecosystem
or whatever you way. And and that's how we talked
to our clients about the data. It's really is the
truth set. It's used as the foundation for other things
that may they may want to do, because as people
(10:21):
work with data more, they're looking to integrate more data sets,
and as you're integrating that information, you have to make
sure that you have truth sets as part of that
as part of the foundation of that data. Otherwise the
decisions you make may not be the right decisions. Um
GfK as A as a company has been around for
eighty five years. What stays the same as technology continues
(10:41):
to change. We talked about equality of information certainly needs
to to be their consultancy UH work and in order
to make sure that when clients have questions about the information,
we have the people who have the expertise or having
a human on the other end of the phone and
it's honest it's important to do that, and it's important
to go visit our clients and to look at them
face to face and to make sure that we have
(11:03):
you know, the right the right type of analytics that
we can that we can use for them. Okay, one
more crystal ball question, a little more nearer term to
close us out. Twenty will be the year of blank.
Is unlikely to be different than other years relative to
making sure that you're able to innovate and to constantly
(11:23):
look at ways that you're working with with with data
sets that you're able to innovate. And because what we
think is the most important thing in January may not
be the most important thing in July of so you
have to make sure that you're that you're going through
the innovation within your business and to stay up to
speed on that innovation. Is the same as because things
(11:45):
are continuing to change, a change all the time. Well,
Greg Lender, President GfK America's Chairman of m R Simmons
Jode Venture, thanks so much for joining us here in
the pleasure. Thank you very much for having me here.
Thank you. This podcast is in partnership with the iHeart
Podcast Networks. M
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