BRUNO AZIZA: Hi, everyone.
This is Bruno Aziza.
And today I'm excited to introduce you
to a catalog of reference patterns.
These are code guidance that are going
to help you take advantage of multiple products.
So BigQuery, data flow, Pub/Sub, Looker,
our AI platform in the context of your business needs.
So what do I mean by that?
Well, let's say you're trying to build a product recommendation
system, or you're trying to predict your prospects,
propensity to buy.
Well, you have the opportunity to use
these solutions, if you will, to accelerate your deployment.
We've learned this from some of the best
customers we worked with.
And today we're making those references available to you.
Now, to tell you how they work and how
you can take advantage of this, I've
invited Robert Saxby on my team, who
is joining us from Amsterdam to talk to you about them.
So first, Robert, thank you for taking the time so late
in your day.
Let's start at the top.
What are these patterns?
ROBERT SAXBY: These reference patterns, they
cover the kind of spectrum of use cases
that we've seen with different customers.
So let's give some examples.
Anomaly detection.
Let's say that you have--
you're a telco and you're monitoring the system logs
coming from your calls.
And you want to make sure that all the traffic on your network
is doing proper things.
You might want to spot a bad actor, someone
that's going to DDoS one of your services, for example.
So seeing those anomalies has real value.
And with the technologies that we make available in the Cloud
today, we've seen our customers put these things together.
And then rather than have every customer go out there and start
from the beginning, we want to give them a kind of leap start,
let's say, and get them 70% of the way there.
So as you mentioned, we bring the code, sample data,
instructions on both why the pattern is useful
and how to use the pattern, how to run the sample,
and then how to customize it, and really importantly,
how to activate it.
One of the things I've seen that I think
is really important when you're in the Analytics space
is you're getting all these good insights.
Once you've got those insights, the next part
is how to operationalize it, how to take
the results of that work and actually activate it and make
it work for you and then put the automation in there
so you can start using the other folks in your company
to work on the next thing.
BRUNO AZIZA: That is great.
So in fact, today we have a video
from [INAUDIBLE] that's going to talk
about product recommendation.
If I'm a customer out there-- actually it's
a Halloween video, so it's also fun to watch.
If I'm a customer out there, what
can I expect from that resource in particular?
ROBERT SAXBY: Yeah, so let's take a step back and think
about what a recommendation is.
So during this prolonged period of partial lockdowns
and things, I found myself doing more DIY.
And then I go online, and I buy the next tool
or the thing I need.
And often when you buy a product or service,
there are some recommendations there.
For example, if you buy a drill, then they
might suggest you some drill bits that
will help you drill through wood or metal or something
like that.
But there's a whole bunch of stuff they could suggest you.
They could suggest an extra battery or something.
Now, obviously, some folks compile these lists
manually using their specialist knowledge
that they have about the area.
But now imagine that you don't have a catalog of tens
of products or hundreds.
You have thousands or tens of thousands.
And rather than trying to compile a recommendation
list for all these products, all these tens of thousands
of products, why not let your customer website behavior,
for example, actually drive that?
So we have a recommendation system.
We're looking at the feedback that we
get from customers, whether it's either explicit or implicit.
Implicit is the one we're going after here because what we're
trying to do is infer what customers like
by how often they go back to a page
or how long they look at an article, these kinds of things.
And then when we take that, we're
able to correlate what the item that someone
was looking at with other items that they look at next.
And this we put into a system.
And basically what we draw out of that
is a list of products or services
that our customers are actively, based
on their current behavior, interested in buying
when they look at this product or service.
And then when we get a customer and they look at a product,
we can then recommend them one of these alternative
or complementary products.
So it's real value there.
It helps you to upsell, cross-sell.
It helps you to build a better customer experience.
And there are many ways of actually
going about building this.
The way that we've got here is really
taking a canonical approach based on the systems
that we make available in Google Cloud.
So BigQuery, if you're a data warehouse.
We're using Google Analytics data,
which is a way to instrument your website in order
to see what folks are doing, how long they spend on page, which
page they go to next, all that kind of stuff,
like the click-through rates, convergence,
the things that are important to analyze from your website.
We have a service that brings that data into BigQuery.
And then in BigQuery, as well, we actually
have an ML capability.
So we're able to generate, based on the Analytics data,
recommendations using a model--
matrix factorization, in this case--
and generate that list of recommendations per product.
And now, here's the great thing.
It's not just-- as I said, it's not just
about creating that list or getting those insights.
The next step is how to activate that.
So when you look at [INAUDIBLE] video as well,
you'll see the whole journey here-- how to get the data,
how to do the preparation of the data, how to train the model,
and then how to do the predictions
and then how to activate that.
So you might be asking yourself like, OK,
that sounds like a lot of work.
How long is it going to take?
Well, as I said, the sample data of the code is there.
I would set someone a challenge and say,
spend a couple of hours running through the sample.
And then based on our sample data on this,
you'll be generating those recommendations.
And then follow the steps on how to customize and add
in your own data.
And then before you know it, you'll
be looking at your own data, your own Analytic data,
and start generating recommendations
for your own company.
BRUNO AZIZA: That was great.
So all these come with a simplified two-pager
that is going to allow you to really structure
your deployment, simplify your approach,
and then scale this inside your organization.
We're putting the link down here of all the other ones
that are available to you.
We hope that you're going to enjoy them.
We hope that they're going to help you accelerate
value inside your organization.
And of course, if there's anything
that you would like us to build for us,
or you'd like to find out more about how other customers are
building, let us know simply by putting a comment down here.
Robert, thank you so much for staying up late to talk to us
about this.
I'm sure lots of people are going to reach out to you.
We'll talk to you soon.
ROBERT SAXBY: You're welcome.
Thanks a lot, Bruno.
Bye-bye.