00:00:00
all right so next let's think about how
00:00:02
to conceptualize different data clean
00:00:04
room use cases so what's on the screen
00:00:07
right now what's on the slide this might
00:00:09
be the most important concept from this
00:00:13
entire presentation so if you end up
00:00:15
forgetting everything that I've said
00:00:17
everything that I'm going to cover later
00:00:18
please remember this part okay so data
00:00:22
clean rooms allow one party to use
00:00:25
another party's data to reveal new data
00:00:28
points about their own users
00:00:30
all use cases are ultimately unlocked by
00:00:33
this concept so if you're using a data
00:00:35
cleaning room you're not actually adding
00:00:39
or activating the other party's data
00:00:41
what you're doing is you're using the
00:00:44
other party data to reveal insights
00:00:46
about your own data and with those
00:00:48
insights you're then able to unlock new
00:00:51
use cases so it's like one of those
00:00:53
stone tablets from like an Indiana Jones
00:00:55
movie that reveal the truth when the Sun
00:00:58
Shines on it at the right angle at the
00:01:00
right time of day the tablet is your
00:01:02
data and the Sun is the other party's
00:01:05
data um probably not the best example
00:01:08
but I think I think you understand what
00:01:10
I'm getting at uh so let's go through
00:01:12
some use case categories to illustrate
00:01:14
and for our purposes we'll assume the
00:01:16
perspective of a brand being the party
00:01:18
that's setting up the DCR that said most
00:01:21
of the these use cases can apply to
00:01:24
Media owners and Publishers as well okay
00:01:27
addressable identity and audience
00:01:29
Activation so so here the brand wants to
00:01:32
discover existing or new audiences for
00:01:34
media targeting and or measurement at
00:01:36
scale party a here would be the brand
00:01:38
and party B could be a media owner or it
00:01:41
could be a data provider and in these
00:01:43
cases brands use the data provided by
00:01:46
the media owner or data provider to
00:01:47
reveal new attributes about their own
00:01:49
users or customers which can then inform
00:01:52
Downstream auding Spas use cases like
00:01:54
Discovery targeting expansion look like
00:01:58
modeling or segmentation
00:02:00
and it's worth noting that the benefits
00:02:02
here can work both ways and that a media
00:02:04
owner can also take these insights to
00:02:06
better understand what audiences on
00:02:08
their properties can drive more impact
00:02:10
for the brand
00:02:12
partner next we'll look at data
00:02:14
enrichment and inside Generation Um so
00:02:17
here brands are looking to enrich their
00:02:20
existing first-party data to learn more
00:02:22
about their customers to improve
00:02:24
everything from product development
00:02:26
creative messaging uh and media planning
00:02:29
and here party a would be again be the
00:02:31
brand party B could be anyone with value
00:02:34
adding first-party data and in this
00:02:36
category brands use the collaborating
00:02:39
partners data to enrich their existing
00:02:41
customer data to reveal new data points
00:02:44
whether that's demographic psychographic
00:02:46
behavioral uh Behavioral or any other uh
00:02:49
consumer attribute and that uh this
00:02:51
allows them to better understand their
00:02:54
customers and optimize uh things like
00:02:56
identity resolution so moving more
00:02:59
toward it's a holistic single customer
00:03:01
view uh and worth noting that identity
00:03:05
resolution requires the use of one or
00:03:07
more ID graphs which we'll cover more
00:03:09
later in this section and this can help
00:03:12
with inside generation enrichment and
00:03:14
modeling as well and then attribution
00:03:17
measurement and optimization so here
00:03:19
Brands want to better understand the
00:03:21
impact and returns generated by their ad
00:03:23
spend so party a again would be brand
00:03:25
and party B uh would be the media owner
00:03:28
where the brand is running their media
00:03:29
media activity so here Brands would
00:03:32
leverage the media owner data to reveal
00:03:34
the data points that are required to
00:03:36
better quantify the effectiveness of
00:03:38
their marketing advertising campaigns so
00:03:40
things like ad exposure engagement
00:03:42
conversion sales all of these um could
00:03:45
be the data points that are required to
00:03:47
carry out these use cases and then uh
00:03:49
the outcomes would inform Downstream use
00:03:52
cases like campaign reach and frequency
00:03:54
audience validation so the brand making
00:03:57
sure that they're actually reaching the
00:03:58
audience's the they want and
00:04:00
incrementality which is a um a technique
00:04:04
to measure how much of the campaign
00:04:06
outcomes are actually being driven by
00:04:08
advertising versus the advertising
00:04:10
simply taking credit for outcomes that
00:04:12
would have happened with or without the
00:04:14
ads being placed then lastly uh
00:04:16
predictive modeling of different kinds
00:04:18
and different
00:04:20
types so I touched on identity graphs
00:04:23
earlier and the main purpose of identity
00:04:26
graphs or ID graphs in clean rooms um um
00:04:30
are to improve match rates between data
00:04:33
sets from different parties um why is
00:04:35
this important well cleaning rooms work
00:04:37
by joining different data sets right and
00:04:39
we've established that um earlier on
00:04:40
this presentation and this is done
00:04:43
typically using some sort of user ID as
00:04:46
the common field and Beyond scenarios
00:04:49
where data sets um already share the
00:04:52
same identifier the ability to join data
00:04:55
will depend on an ID graph or multiple
00:04:58
ID graphs working together and this is
00:05:00
crucial for data collaborations as match
00:05:03
rates really or they often dictate the
00:05:06
scale and effectiveness of use cases so
00:05:09
as an example if you look on a slide
00:05:11
starting from uh starting from the left
00:05:13
side and moving moving right if data set
00:05:16
a contains a user record with user ID
00:05:20
equals 1 2 3 4 5 um so that's the the
00:05:22
purple circle on the left and data set B
00:05:26
contains a user record with person ID
00:05:29
equals 3 4 5 6 7 that's the um you know
00:05:32
the light orange circle on the left um
00:05:35
these these are two different ID forms
00:05:37
right user ID and person uh person ID so
00:05:40
they have to be linked somehow and this
00:05:42
is done through what's called an ID
00:05:44
crosswalk um or walking from or crossing
00:05:48
over from one ID set to another and then
00:05:51
it's determined through this crosswalk
00:05:53
that user ID 1 2 3 45 is the same as
00:05:56
person ID
00:05:58
34567 this indicates that the identity
00:06:01
um between those records have been
00:06:03
resolved or matched and that process um
00:06:07
leads to a higher match rate which leads
00:06:09
to better results and better utility
00:06:11
from the clean
00:06:14
room so we we lightly touched on this
00:06:17
when going through the use case
00:06:19
categories but for more advanced use
00:06:21
cases or at least more analytical use
00:06:23
cases data scientists can deploy data
00:06:26
models within clean rooms in uh a couple
00:06:29
different ways uh the first is um and
00:06:32
this is the the like the Box on the left
00:06:35
side the first is essentially building a
00:06:37
new model using the match data set as
00:06:40
training data and it's important to
00:06:42
remember that the match data set that
00:06:44
comes from a collaboration use case um
00:06:46
it can be used like any other data set
00:06:49
as long as the data doesn't leave the
00:06:51
DCR environment and the model can then
00:06:53
be deployed on existing customer data
00:06:56
again within the DCR where analyst
00:06:58
analyst can direct D insights and
00:07:00
because these insights come from
00:07:03
aggregated data this Ure ensures that no
00:07:06
uh Pi is exposed and then the second way
00:07:09
which is the the right side box um the
00:07:11
second way is import meaning bringing an
00:07:14
existing pre-trained data model into the
00:07:17
DCR for deployment against the data
00:07:19
that's already inside for similar use
00:07:22
cases and in terms of the types of
00:07:24
models that can either um be built or
00:07:28
imported into to a uh DCR there's really
00:07:32
no limitation right so any kind of data
00:07:34
model that's used within advertising and
00:07:36
marketing things like audience
00:07:37
segmentation models attribution LTV
00:07:40
propensity CH prediction and so on and
00:07:42
so forth everything's fair
00:07:44
game so at this point you might be
00:07:47
wondering whether um DCR simply exist or
00:07:51
S simply allows for existing data use
00:07:53
cases to be carry out albe it in a
00:07:55
secure privacy Safe Way or whether they
00:07:58
actually facilitate say net new use
00:08:00
cases the answer as best as I can tell
00:08:03
is that most of these use cases most of
00:08:06
the um the use cases we outline they
00:08:08
aren't um they aren't new per se but
00:08:11
dcrs provide users with access to
00:08:14
first-party data sources with whom to
00:08:16
collaborate that were previously
00:08:18
practically impossible so to give you a
00:08:21
personal anecdote uh along time ago back
00:08:24
in the early days of my career um in New
00:08:26
York City I was more of a standard
00:08:29
digital planner buyer and at that time a
00:08:32
lot of the job um was basically working
00:08:35
directly with Publishers so tons and
00:08:37
tons of Publishers um negotiating rates
00:08:40
asking about inventory and then data as
00:08:42
programmatics started taking off and
00:08:44
there was a time when we were looking to
00:08:47
and in hindsight hindsight this is crazy
00:08:50
but we were we were trying to get a
00:08:51
publisher or several Publishers to allow
00:08:54
us to either pixel their site or do a
00:08:58
DMP sync to basically gain access to the
00:09:01
first- party data um and every time I
00:09:03
had that conversation they look at me
00:09:06
like at three heads right uh or they'd
00:09:08
ask for what would be a practically
00:09:10
impossible um amount of investment or in
00:09:13
order to unlock it so the point is that
00:09:16
kind of conversation was theoretically
00:09:19
possible back then but practically off
00:09:21
limits um and these days with data clean
00:09:24
rooms that kind of thing is unlocked so
00:09:27
it's a big deal in terms of unlocking a
00:09:30
whole new way of working um at a time
00:09:33
when we really need it when it comes to
00:09:35
first party data
![The Power Of NOT Reacting (How To Regulate Emotions) [Japanese Dub Available]](https://ruhnrawpgxrzdrgaohnf.supabase.co/storage/v1/object/public/images/video/laQIz0hLeII/thumbnail.jpg)
