Hi, I'm Craig Warman,
Field CTO Data Platform Architect,
and I'm excited to be sharing this presentation today
on the Snowflake Media Data Cloud.
Data has become a core element
in the media, advertising, and entertainment industry. How businesses leverage and collaborate
with data, while maintaining strict consumer privacy, is essential for success.
In this video, I'm going to show you how the Snowflake Media Data Cloud
enables businesses to collaborate
across media and advertising ecosystems, while applying strict data security and governance.
The first challenge for publishers is creating a single view of their customer with their
consented first-party data, and then joining that with advertisers.
We call that an Identity Graph.
Now, matching data sets together between two sources works great when you have a common key
or data that is basically identical, but in a media and advertising context, it can be
much more difficult. For example, what if you want to match up a data set that has offline names and
addresses with one that just has email addresses? And what if the people in those data sources use
different email addresses, even though they're the same person? How about if one of the data sets is
much older than the other? These are just a couple of the challenges that brands and publishers face
because of things they don't know or can't know using their data alone. Until recently, one solution
was to just send data to an external identity resolution provider, but that can be a high
friction process because it requires coordination across multiple parties and introduces PII privacy
concerns because you're moving data outside of your own environment. Instead, we now have several
identity resolution providers that run natively on Snowflakes Media Data Cloud, including Experian,
Neustar, Liveramp, and Acxiom. When you leverage one of these identity resolution services, the
resolution process happens in your own environment. You don't have to move your data anywhere else.
In other words, the service applies keys and IDs to the data right there
in your secure Snowflake environment.
Here's an example use case: Suppose a media platform wants to measure performance of an
advertising campaign. Let's look at the data that's likely to be present in that sort of scenario.
On the left side, you've got subscriber data, which might be names and addresses of people who use
that particular platform. On the right side, you've got impression or action data that resulted from
the campaign. These data sets are quite different, and confidently matching them together isn't easy.
That's where an identity resolution service can help. I'll use Experian for this example.
What they do is match those disparate records against their consumer reference data.
The IDs their service returns are unique to each party, however. This allows the parties to match
data in a Snowflake clean room environment and talk to each other as though they had
identical data sets without exposing PII, thus keeping the separation and privacy intact.
What's important to note here is that the Identity Graph is brought to the data.
Each party's data stays right where it's at, in their own secure Snowflake environments.
Once the Identity Graph is in place, publishers and advertisers can create an overlap analysis
that enables them to build a targeted audience for a marketing campaign. Suppose my sports brand wants
to better predict return on advertising spend by understanding the overlap of their customers by
segment with a media publisher subscriber viewing patterns. Let's say we want to run an ad campaign
for sports equipment using that media publisher's weekly sports show, but before we do that, we need
to understand the overlap between our customer data and the media publisher's subscriber base.
In this demonstration, my sports brand has 4.7 million customers that we want to match against
5.7 million publisher subscribers. At this point , both my brand and the media publisher will have
leveraged an identity service provider for that identity resolution, so that's what gives us the
overlap. I'll now use a Snowflake data clean room to analyze the overlapping customer population,
without revealing our sports brand customer PII to the media publisher, and likewise, they won't reveal
their customer PII to us. I'll start my analysis by choosing a query template for audience overlap
and segment creation, which allows me to group overlapping customers and build segments for
audience planning. Next, I'll configure my query to group or filter by the segments I want to analyze.
The query this generates will return my sports brand customer data joined to the
publisher subscriber data, giving me the overlap count grouped by the attributes I selected.
Now I can run this analysis and view the status. We can make decisions, like running a campaign on
football cleats in a specific age range, or running shoes across all age ranges, and see what type of
viewing habits they display.This lets us decide when to run our ads to achieve a minimum exposure
threshold. In this case, I want to target customers that have purchased shoes and also watched the
publisher's weekly sports program. With Snowflake's data clean room capabilities, I was able to get to
a customer-overlap analysis aggregated on values across both population sets, without moving data
or sharing PII. Now, we know how important it is to measure results of your campaigns.
Let's look at how campaign-exposure analysis is executed on the Snowflake Media Data Cloud.
We can use the same Snowflake data cleanroom technology to link our customers buying patterns
with their viewing habits on the publisher side. For example, after running the campaign for a
couple of weeks, we can track campaign exposure by linking customer data coming from our CRM
or POS systems with clickstream data coming from the media publisher. This lets us answer
questions like, how many customers were exposed to the ad? What products were purchased and where?
What demographic did they belong to? This information allows us to iterate on our strategy or inform
future campaigns, so that we drive new sales by maximizing ad exposure within the media
publisher's audience base. This is just one example of the many sophisticated analytics
that our customers can now do using Snowflake's data cleanroom capabilities.
So there you have it.
I've just shown you how Snowflake enables publishers to connect with their audiences
and advertisers, while maintaining data privacy and data governance.
Thanks for watching.
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