ROB TAYLOR: SAS's Customer Data Platform.
CDPs are an evolving technology and a marketing buzzword
in the last couple of years, but the requirements are not.
They've always been around for many years--
the need to consolidate a customer record
and make that available to other technologies for activation.
SAS has been doing this within Customer Intelligence
for over two decades.
It's the heart of any customer-led activity.
With that being said, the industry
has generally agreed upon four key principles or capabilities
that we plan to showcase to you in this demonstration.
Firstly, the ability to ingest data.
SAS Customer Intelligence has unmatched capability
of collecting and listening to real-time data
from first-party owned websites and mobile apps.
Through the same technology, we can
collect all the MarTech data from data layers
from Adobe, Google, and utilize these in our understanding.
Secondly, identity resolution and that
ability to manage the customer across all channels
in real time.
The third capability is creating audiences for activation,
whether that is contextual data captured
in the moment or historical or third-party data
that enriches their segments.
Of course, at SAS, we differentiate
from others in the market with our analytical history
and the ability to go beyond simple segmentations.
And then finally, CDPs need to be able to provision
and activate the data asset, defining customer
journeys to guide customers through purchase cycles
whilst triggering moments that matter to customers as part
of that journey So let's take a look through a demonstration.
So let's start by navigating to our SAS retail site.
Utilizing our single codeless tag,
we start to collect events and behaviors
of the session, product categories,
as well as individual products.
All these are collected within the tool and the CDP.
This collection of granular clickstream data
relates to the listening part of the engagement process
and shows our ability to ingest real-time data into our CDP.
You will also notice at the top left-hand corner
the session ID and corresponding identity management record.
We will add to this throughout the demonstration.
If you recall, the next stage is to understand by adding context
to these events.
It also allows us to create audiences
for targeting engagement, whether that
is for third-party advertising in Google
and Facebook, on-site personalization,
or triggering a set of coordinated customer
interactions in the form of a customer journey.
SAS CDP provides all those capabilities.
In this example, I'm going to click on Weekly Specials.
And this adds the customer to a real-time segment,
as well as pushing that customer onto an individual journey,
in this case, the bargain hunter journey.
So far, our customers only interacting
with the web channel.
So let's add activity from a mobile app.
If I go to the app, which is associated
with the SDK and the CDP, straightaway you
should start to see the identity being pushed into the identity
management solution and associating it to our customer,
whether that's a simple focus and de-focus that we can see
there in the behaviors or going in and actually showing
a geo location.
Those events are all being pushed into the solution,
as well as adding individual products, so, in this case,
some slippers into the products.
All the data is consolidated into that single, unified
customer profile.
You can also utilize any engagement events
from any other previous interactions.
So, in this example, I can open up an email.
And within the Identity Management,
we will see that line to show that open, again,
another trigger or an event that we can utilize.
Furthermore, we can start to see the identity
graph and the associations between all
the sessions and identities.
We can also start to see the relationships between all
those different channels and touch
points based on the frequency of events seen between them.
SAS's real-time identity service uses a deterministic approach
to govern how to match and prioritize identifications.
This means that the service uses any imparted customer
information, including login ID, any failed interactions,
or a form submit.
There is also an ability to use any cookie, data layer, or page
element variable.
Probabilistic matching can also be
used to support the real-time identity service as shown here.
This data has been collected through the CDP
in terms of names and addresses, as well
as information such as the device and the platform.
And then we're running it through a process of data
quality transformations.
In this case, we're parsing the name
into a family name and a given name.
SAS's quality knowledgebase has the ability
to pass and standardize many different variable types,
such as address, name, as well as phone and postal code.
We can see here on the left some of those standardizations
we're doing, so in this case, a postal code.
We then have the ability to define match codes
and set a sensitivity of how we want these matches to be done.
This gives the customer full control and governance
of how they're actually going to match individuals together,
whether that be using device type
or things such as their user agent,
as well as utilizing information such as imparted information
from the address, name, and email.
And then finally, there is the ability
to use those match codes to create clusters.
So in this case, we can see the different rules, so, therefore,
that can be utilized.
Once these have been created, they
can then be uploaded via the API and then
used to match and purge identities together.
SAS's CDP allows us to go deeper and further into any process,
in this case, the purchase cycle to understand
behaviors associated with the checkout process.
If I bring back up the website, we
get to see all the interactions, such as Add to Cart,
information around View Carts, as well as any changes
are made due to the quantities in terms of updating that cart
and then going through to the checkout process.
All this captured data can be used
with advanced analytics and part of a model ops process.
You can see at the bottom right-hand corner
a decision has been made using SAS Intelligent Decisioning.
It's incorporating a real-time model to evaluate the basket.
Whether this is around stock control and management,
potentially offering a credit product as payment, or maybe
a free delivery message based on securing the purchase,
all this can be utilized within the CDP.
AI and machine learning can also be
used to define significant customer moments
that we may want to action, whether that
is as simple as using an invalid coupon code and, therefore,
from a customer perspective, a negative sentiment
or whether that is actually abandoning the cart
and, therefore, being able to actually identify that
and then utilizing it as a trigger.
SAS also has the ability to utilize embedded text analytics
to be able to identify emotion and sentiment,
whether that is from a bad product review where
the customer has an open text box
to be able to write whatever they want in
and the need, therefore, to be able to show and understand
sentiment of that comment, in this case, that you're not
happy with the color.
You can see once they have submitted,
the feedback is available through streaming into the CDP,
but, also, that moment has been identified.
This can also be for third-party chat bots
where we can scrape out any of the communications
of the customer so, in this case,
starting off with some simple messages in terms of greetings
of Carrie, the chat bot, but then this ability
to be able to bring through any kind of level of sentiment.
In this case now to actually say I'm not happy with the product.
And again, within that moment, we
should hopefully see that negative sentiment
being defined and that ability to actually reach out.
Whilst not shown here, this could also
be call center data being utilized in real-time
and actually sending an external event
into the CDP in a similar way.
Our CDP capability allows us to ingest any external data
into our CDP and utilize it, not only for triggering of moments,
but also as part of the customer profile and audience
and segment creation.
The final considerations of our CDP
is the ability to activate the CDP data
across a managed and orchestrated
omnichannel customer journey.
Here, we can see an activity map that utilizes the core CDP data
asset to engage with customers across multiple channels.
We can see and analytically-driven moment
triggering a web personalization,
potentially followed by an email and a web push.
Here, we also have the ability to integrate
into any activation layer, whether that is Google, Adobe,
Salesforce, or a partner such as Syniverse,
for Mobile Wallet, RCS messages, and WhatsApp.
Finally, each task has the ability
to utilize all data captured for targeting and offer creation,
as well as utilizing the customer's entire data
assets through on-premise segments and audiences.
Our ability to ingest any type of data
from a customer's private cloud or data center
and not requiring them to load that data into SAS's CDP
gives us a key differentiator by allowing customers full control
and governance over their data whilst
using it like any other collected data in the CDP.
This demonstration only touches on a small subset of our CDP
capabilities but hopefully goes some way
to showcasing why we go beyond a normal CDP software vendor.
Thank you for listening.