Hi, I'm Yash Chechani,
a Senior Sales Engineer at Snowflake.
Today, I'm going to walk through how Snowflake and the Media Data Cloud
can help with the common use case in subscriber analytics
of improving the average revenue per customer for a streaming service.
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.
As the first step in this journey, we need to have a single view of our customers. Snowflake's Data Cloud
allows to collect and unify the customer 360 data--semi-structured or structured--
into an easy-to-query SQL data warehouse. Data sets can be continuously streamed using Snowpipe's
serverless capability, or batched into a privacy-compliant, secure raw zone hosted in Snowflake.
From here, the data is automatically and continuously validated and transformed using
features of streams and tasks to get the data into the harmonized layer. This data can then be
modeled together with additional data sets into the analytics layer. We'll talk more about this
in just a bit. This unified view can then be made available to the entire organization
to have a single version of the data.
Now, let's hop over to Snowflake's web interface to see this in action.
Here, you will see everything is represented in ANSI SQL and is relational. We can click
into our first-party database, view tables, view its structure, and even sample some of the data.
As a data engineer, I can easily create or use a compute cluster
dedicated to my work so that I don't need to contend for resources with other workloads.
I can also configure Snowpipe, a serverless, low -maintenance SQL-based feature for streaming
data in near real time. Here is an example of a pipe that loads data into the subscription billing
cycle table directly from cloud block storage. After the data has landed in the raw zone, streams
and tasks are used for automated and continuous downstream transformation, a stream like what
you see here captures changes to a base table over time. In this case, the billing cycles table.
Then a task can pick up and process the changes
from that stream as shown here in the subscriber events table.
And with that simple setup, we can start getting audience and subscriber data into Snowflake.
But in order to improve ARPU, we will need to improve marketing performance, personalization,
and customer engagement. And leveraging just first-party data is not going to be enough.
We need to integrate additional data points to do so. This is where third-party data can help.
Imagine being able to enrich your first-party data with third-party information.
The Snowflake Media Data Cloud partners publish a considerable amount of live,
ready-to-query data on Snowflakes Data Marketplace.
This data is instantly delivered to customers
through Snowflake's secure data sharing, meaning the data is always live
without the need to copy the data over or using SFTPs.
Let's go back to our Snowflake web interface into the Marketplace.
For example, let's click on the Merkle's data source offering. To gain access, you can just
click this request button and fill out the form to initiate contact with the Merkle Snowflake
administrators. Once licensed, the data is shared live directly from the Merkle Snowflake account.
In the case of this demonstration, Merkle has fabricated a dummy data set for our users that
it shares with us. The MERKLE_DATASOURCE_V is a view based on this shared data,
which is, in turn, joined with this first-party data in the user subscription history view.
Let's explore this data in Tableau, through these pre-built dashboards.
We can now begin to understand the average revenue per customer across all subscribers, and then break
it out by various segments using the demographic information we just obtained through the share.
Here we can see that the homeowners raised the ARPU, while renters lowered it.
And down here, we can see that as soon as a household eclipses four people,
there's a dramatic shift in the ARPU metric.
With these insights and comprehensive view of the customers, the marketing team can make better
decisions on how to engage with their customers and deliver a cohesive multi-channel campaign.
Next, let's see how to take action on these insights. How can the business
leverage AI and ML capabilities in Snowflake to improve engagement, thus increasing the ARPU.
This part is very exciting. Now, with Snowpark, Snowflake natively supports programming languages,
such as Java, Scala, and Python, without needing any additional non-SQL environments, while still
working on the same, single copy of your data. You can write code in your integrated development
environment of choice and execute data processing in Snowflake with push-down capabilities.
Let's hop over to a Jupyter notebook to walk through how we could develop customer segmentation
and process it in Snowflake. First, I have imported Snowpark libraries and set up a
connection to Snowflake. Then, taking advantage of the data frame abstraction, I quickly visualize
the data I want to use. For this segmentation, we choose a subset of customers and prepped the data.
Here, we have created 14 models for k-means clustering
and are using the elbow method heuristic to determine the ideal number of clusters, which is 5.
We can explore the clusters with the distribution of each feature across the cluster.
Then we define the segmentation function and register a user-defined function (UDF),
which will be deployed in Snowflake. This UDF can be called directly from SQL, as shown here ,
and gets executed within the same snowflake compute engine as the rest of SQL,
while taking advantage of Snowflake's elasticity and scalability. The customer segmentation data
can now be used for better personalization , improving engagement, and increasing the ARPU.
We just saw how Snowflake Media Data Cloud can help you centralize your audience data
seamlessly enrich external data sources, and apply models right in Snowflake,
without having to extract the data.
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