(upbeat music)
- Coming up, we'll give you a tour
of Dynamics 365 Customer Insights
that builds a 360-degree view of your customers.
Now we're going to show you what it is,
how to set it up
and how to get predictive recommended actions
with Azure Synapse.
So today I'm joined by Satish Thomas,
a leader on Customer Insights.
Welcome to Microsoft Mechanics.
- Thanks Jeremy, great to be on the show.
- So it's really a common situation
I think that people find themselves in
where it's really hard to curate customer information.
A lot of time you might be running different queries
across different data sources.
You're writing custom code sometimes to link up your data
and you might even have to customize
the data views themselves depending on
where you want to surface those insights.
So what's different in terms of the approach
that we're taking with Customer Insights?
- Yeah, so we built Customer Insights to harness
a fundamental change that's happening in the world today,
where customer data is essentially coming
from everywhere and everything.
So if you think about every customer service call,
a website visit, a purchase you make
generates data that can then be harmonized
and leveraged by organizations
to deliver personalized experiences
and over the long term,
build trust with customers,
especially very important in times like today.
And our approach actually does the hard part
of this for you,
which means being able to bring in data
from everywhere to help
build that deep understanding of the customer.
And we start by allowing
customers to bring in data
using our prebuilt connectors
and all types of data too by the way,
For example, things like transaction history
point of sale data,
behavioral data, customer preferences,
product usage, and even survey data.
And also being able to understand
how customers are interacting with you,
be it through your online website, your mobile application,
or even an in-store interaction.
And on top of all of that, of course,
you can enrich your customer information
with anonymized information
from the Microsoft Graph at an aggregate level
or third-party sources,
such as brand affinity and interest.
And then finally and probably most importantly,
all of this unified data
and deep understanding
of the customer can then be
used and be surfaced anywhere
at every touch point, wherever you engage
with your customers as an organization.
- Right so, we're providing a very sophisticated set
of capabilities really to integrate
and find insights in your data without
all the time you might've spent before
or the pain or the cost
that it would have taken in the past or even currently.
But can you show us an example of all
of this kind of working in an action?
- Sure, so let me start with the end-user view
and then later I'll show you
how we made all of this happen.
So you can see here that
for any given customer; in this case,
it's a fictitious customer Patti,
we have a rich 360 degree view of the customer
and it's not just basic information like address,
gender, phone number,
but also historical information like the previous purchases,
the events that Patti has attended
and interactions with our support team
to call out a few instances.
And of course it also shows the customer segments
that we've identified that she belongs to
and beyond the output of all of that
we have these AI models.
So all of this is possible through the
out-of-the-box models that we have available
within Customer Insights
and of course we have the ability
to build custom AI models
using Azure Machine Learning and Synapse
you mentioned earlier.
- And this is great.
So I can see then that how this can become really useful
in terms of just day-to-day use,
but aside from the Power App that you're showing now
what else, or where else can I surface that information?
- So good question, so lots of places.
So across all of your customer touch points,
be it an in-store experience when you walk in,
be it a contact center experience when you call in,
on the web or anywhere,
and this helps a turn towards making
that customer experience consistent at every touch point.
And by the way, all of this of course is opt-in information.
So for example, you can get an add in from app stores
that allows you to see this
rich 360-view of the customer within Dynamics 365.
So here you can see it contextually show up
within our Dynamics 365 for Sales application
and of course it's more than just
our first party in Dynamics 365 applications.
You can take this rich view of the customer
and act on it in third-party applications,
be it Salesforce or Adobe,
wherever your business users are.
So we have a vendor agnostic view to customer data
on the way in, in terms of being able
to bring in data from anywhere and everywhere
and also on the way out
in terms of which systems of engagement
you're able to integrate with.
- Cool, so this is Mechanics though
and I think a lot of people watching are admins,
our data engineers.
How do I actually pull all of that data in?
How do I get all this stuff working?
- The first step obviously, is to use Customer Insights
to build that unified customer profile
that I just showed a little while ago,
and being able to ingest all of that data
from those disparate sources.
Wherever it might be coming in from.
So, our challenge to customers is always
anything that can be used in the furtherance
of better understanding your customers bring it in.
So here you can see that I've already connected
Customer Insights to Dynamics 365
and that gives me information
from our sales marketing and service functions
and you can also see
some other data sources here already configured:
the StoreEvents,
the StoreCRM, Support
and our DeviceFulfillment information
which customers are waiting for what.
But there's more, in fact, I'm able to connect
to third-party systems as well
using these available connectors.
So as you can see here,
I can add a new data source,
import the data and go next here.
And this is pretty extensive.
I can essentially connect to Salesforce
a Custom SQL instance
an Oracle data source that contain any customer data,
be it survey data from customers over time,
again, super powerful to be able to bring in
all of this customer data
and be able to harmonize it here.
- Okay, so you've defined then all your data sources.
You've kind of wired everything up,
brokered the connections.
But, what are the next steps for transforming that data?
- So now this is key.
So being able to combine all of that data,
find those relationships
to create those unified profiles,
essentially creating keys where keys don't exist.
So Customer Insights makes this pretty easy
for companies to be able to do this.
So as part of the unification process
which entails map, match and merge
and we'll walk through all of that in a bit.
So in the map stage,
you'll be able to add entities.
So in my case, I've added
two for demo purposes event,
attendees and contacts
but of course I can add more.
On the right, you're able to see
to map to these entities
in our Common Data Model.
For example, if I look at the event attendees,
it's mapping attendee ID to the ID type
and discovering that this is a potential primary ID.
So you'll see that we've already mapped
all of this information,
like created on field
into the calendar date but of course
I can go in and change all of this stuff
if for some reason we got it wrong.
So again, using AI,
we're able to easily detect
and map all of this data
into the Microsoft Common Data Model
but of course you can always
go modify it if you want to.
- So if you're new to Common Data Model,
it's really a standardized common set of entities
and attributes and metadata,
which is a shared data language
that we use by business
and analytical applications
and Dynamics and the Power Platform
and beyond but so what's next here?
- Yeah, so after everything is mapped,
we can now go to the match stage.
And now I'm ready to move
on to matching entities
by going into, of course,
the match stage within unify
and set the order.
So what this means is
I can specify how I want to match
or consolidate the data that's coming
from these disparate sources
by finding matches in each cases between our contacts
and event attendees following through
from what we just mapped.
And of course, all of this data is anonymized
for demo purposes,
but you'll notice here that we have 11,000 unique customers
and we've found 4,000 matches.
So we can create rules
to define the criteria for these maps,
or I'm able to go in and tweak
all of these settings as well.
So basic matching gives me about four levels of precision,
but I can go custom and that gives me
a granularity of 100 for even
finer level of precision there.
But, I can determine that using the slider
that you see here.
So if I'm working on a finance use case, for example
I'd probably want to go pretty strict on my precision
or if I'm working on name variances
to be used in a marketing campaign for example,
Bill Gates and William Gates for example,
being the same person,
I probably will go less strict on my matching criteria.
- So now you've got all of this stuff matched
and you've kind of finished that step in the process,
how do I resolve any conflicts?
You've got two different kind of tables
that play with matching fields,
what do you do then?
- Yes, and that's a very common scenario.
And that's where the merge policies come into play.
So Customer Insights recommends attributes to merge
and how to reconcile those conflicting data.
So here I'll click on Merge
and you'll see here that we were able to determine
what the source of truth is
if you have two different sources.
And the top fields represent the fields
that are unique across both of the data sources
that we just brought in.
And if I scroll down,
you'll see that the field exists in two tables
and we're showing up
which conflict resolution rule will kick in.
And that's pretty much it at a high level.
It's a configuration-type experience
and what we just walked through is
the end-to-end unification process and what it entails
and this is the most complex part of building
that unified customer profile.
- Great so what do I do then
when most of my customer data is probably transactional?
I know that there are a lot of things
like list services, etc, to kind of enrich your data
and bring in more different insights into it.
Are there things that we can do here
to enrich our data as well?
- Yeah, so absolutely.
So this is a huge investment for us.
So in addition to all the data
that you have in your organization,
we're able to enrich it with data from the Microsoft Graph
and other third party providers.
So as you can see here from a Microsoft perspective
we have the built-in enrichment brand affinity,
interest data and we also have a very extensible approach
where third party ISVs can come and plug in
and provide their own enrichments through CI.
So the likes of lead space here
and civic science, just to call out a few examples
that you're seeing on the screen here.
- What other mechanisms do you have then
to enrich your data?
Are there other things that you can do?
- So one of the things that we're
really, really excited about is
how we're enabling folks to do AI
on top of all of this unified data.
So we enabling out-of-the-box AI models
for things like churn, next best action and lifetime value,
and these become much more powerful and effective
when you do it on top of the unified data versus silos.
So you can see here that I've added one
for subscription churn.
So when I add use this model,
I just need to enter a few basic parameters
and I'll be able to get this running
and model trained on my own data.
And of course, all of this,
you're also able to build custom AI models as well,
but think about this.
It's a powerful way for any organization to use AI
that was built by our own Microsoft data scientists
even if they don't have data scientists on staff.
So really democratizing the possibilities of AI
on top of customer data.
- Nice, so I remember though,
if we think back to our first demo,
you had some segmentation with Patti.
How we're able to build those
different segments on that app?
- So, once we have the unified view of the customer
we've enriched it now
the ability for analysts to build actionable segments
on top of it is key.
So such as what you see here:
high value customers, loyalty customers,
so these are just lists of customers
or segments and it's easy to add
or edit these segments here as well.
So for example, let me show you what the edit process
looks like for enthusiastic gamers if that is a segment.
And you can see that we're looking for purchase records
that include X-Box, that could be games
or whether they're into consoles and accessories as well.
Again, a very easy experience to build these segments
without having to write any custom code.
- Good stuff.
So now you pull all your data together
and you've merged it into a clean set
to really create that unified customer profile
and also actionable segments,
and then showed us the AI that you can run on top of it,
but how does it get surface then like
we saw in the Power App before in Dynamics
into those different apps and experiences?
- Yeah, So you can use this deep understanding
of the customer
that we've built up,
obviously in any of the applications.
So Dynamics 365 applications
or any third-party application
or build your own experiences in Power Apps,
like what I showed earlier.
So we have these inbuilt connectors
that easily allow you to export the data
from Customer Insights and use it
in the context of those applications
or perform deeper analysis on that data,
whatever your use case might be.
In fact, you'll also see,
in addition to all these third-party connectors
that we have,
we have a brand new Azure Synapse option here as well
for limitless analytics which
we're very excited about as well.
- Can you show us that Azure Synapse integration
and really what you can do there to extend it
and kind of perform deeper analytics on your data?
- Yes, sure.
So with the combined power
of Customer Insights and Azure Synapse,
you essentially can do limitless analytics
and also create your own custom machine learning models
and apply them so that you can answer the specific
and unique questions relevant for your business.
So the beauty of Customer Insights is that the work
that we did earlier all up to now
to build that deep understanding of the customer
and profiles can then be leveraged in a variety of ways.
So be it here in Synapse
and using it through a Notebook
or with our broader platform offering.
So one of the things I like to describe
all of this is by saying,
you can start with our SaaS CDP application,
which is Customer Insights
and you'll never fall off a cliff
because of the extensibility possibilities
that Azure Synapse, Azure broadly
and of course our Power Platform
beyond what we enable out-of-the-box and CI.
So it really, really enables a lot of powerful
use cases for our customers to engage with their customers
in more intelligent and personalized ways.
- But where can you go, let's say
I wanted to build one of these for myself
and build all the stuff that you showed today,
where would I go to learn more about this?
- Yeah, So you can go get started with the free trial.
So we have a five-by-five experience,
which is five seconds to start
in five minutes to get going.
And you can do that at http://www.aka.ms/TryCI
but if you want to learn more about
how to integrate and build on top of Customer Insights,
some of the scenarios we went through today,
you can look at our documentation,
http://www.aka.ms/CIdoc
and get started.
- Cool. Thanks so much for joining us today Satish
and thanks all of you for watching today
and please check back to our Microsoft Dynamics 365
Essentials for IT series to learn even more.
That's all the time we have for this show,
goodbye for now.
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