- [Instructor] You want to be able to understand
how to best manage your customer churn.
If you know what signals a paying customer to leave,
then you can take steps to prevent that
from happening in the first place,
and this is a great way to drive up revenue.
Keep the customers that you have.
So what you can do is
you can create a machine learning model
to make this prediction.
So, let's set one up.
The first thing that we want to do is
we want to bring in some data,
so what you can do is click on the left side,
Datasets, in the Datasets menu,
and then click on in the bottom left, Add New.
From this window, you can click on from local file,
and now you can click on choose file,
and in your exercise files, select 02-04-Churn.csv
and go ahead and click on Open.
And now, go ahead and click on the checkbox
at the bottom right of that screen.
And now that we have the message
that our upload has completed,
let's click on OK in that bottom right ribbon
that popped up, just to tuck that out of the way.
Now, let's click on Experiments in the left side menu.
On the bottom left, click on New,
and click on Blank Experiment.
This is going to set up a new canvas for us.
Let's go ahead and bring that data set in
that we just loaded,
so in the left side menu, click on the right arrow
for Save Datasets,
and you'll see a subcategory called My Datasets
and there's our file that we just loaded in,
02-04-Churn.csv so once you locate that,
you want to click and drag that over to the canvas
and drop it right in that topmost outline.
Next thing we want to do is bring in our split data module
so in the search bar, at the top left,
we can just quickly search for that,
and so let's search for split data
and then once you find that module that's called spit data,
click and drag that to the canvas,
and then back over to the search window, top left.
We're going to run an anomaly detection on this,
so I'm going to search for PCA-Based Anomaly,
and once you find that particular module,
click on that and bring that over to the canvas as well.
And then we want to search for Train Model
and click and drag that to the canvas
and what could be really useful
to determine the quality of our model
is to score and to evaluate the output
from the model itself, so, in the text search window,
let's once again type Score Model this time,
and once you find Score Model,
let's drag that to the bottom of your canvas,
and so you can just zoom out
just to give yourself a little bit more room.
And then lastly, we want to bring in our evaluation module.
And so let's search for evaluate
and once you find the module called Evaluate Model,
you want to bring that into the canvas at the very bottom.
And now we just want to wire all of this together,
so we're going to feed our data into our split data module.
We're going to feed our split data now into our train model.
We're also going to wire up our algorithm
into the train model as well.
We want to click on Train Model
and that brings up our properties in this right side pane.
And let's click on launch column selector
and if you put any text in this field here,
it will bring up the options that we have available to us,
and so, what we're looking to predict
is this for all duration
and we'll click on the check mark.
So, to finish up wiring up our experiment,
we can connect Train Model to Score Model.
We can then connect our Score Model to Evaluate Model
and then the last thing we can do is,
we can connect our right side of our split data
to our score model.
Now that we're set up, let's go ahead and run this.
And while that's processing,
let's talk about how to expose your machine learning model
in a way that you can put it to work
for your marketing programs.
What we're going to do once our model finishes running
is we're going to spin up a web service
that any application can integrate with.
So we're going to click on Deploy Web Service
and I'm going to select Predictive Web Service
and let that spin up, and you can see
it's going to make some changes to the canvas here.
Now, this model that we just created
is available now in the cloud for us to integrate with.
So in this video, we looked at the process
you would go through to establish a machine learning model
to predict churn.
We ran the model and evaluated its performance
and, we spun up a web service
that we can ingest into our marketing applications.
Taking this approach can provide a clear signal
for when a customer may go elsewhere,
so you can point marketing programs to customers
in that state, to prevent them from leaving.