Okay, off we go.
So this data set, it's got a few columns.
It's got row number customer ID surname.
So what we're looking at is a data set of a bank.
of course it's all fictional.
It's not a real bank, but it's very realistic.
And here we've got a snapshot of
if you scroll down to the bottom is going to be 10,000 customers.
So 10,000 rows in this data set.
And what the bank
did is they measured some things about these customers.
Right.
Why do why did they undergo this whole thing.
And what's the challenge here.
Well the bank has been seeing unusual churn rates.
So churn is when people leave the company
and they've seen customers leaving, at unusually high rates.
And they want to understand what the problem is,
and they want to assess and address that problem.
And that's why they've hired you to look into this data set for them.
And give them some insights.
And how did this dataset come to be?
Well, six months ago, the bank said, all right, there's a big problem.
We got to take a sample of our customers.
By the way, this is a sample.
That 10,000 is a tiny number for this bank.
This bank has millions of customers.
It operates this this fictional bank operates in Europe
in three countries France, Spain and Germany.
And they have lots and lots of customers.
So what they did is they took this sample of 10,000 customers
and they measured six months ago everything they knew
about them, their customer ID, their surname, credit score,
their geography, their gender, their age, their tenure.
So how long they've been with the bank, the balance of the customers at that point
in time, the number of products they had at that point in time.
So a number of products says
things like how many products do they have to have a savings account?
They have a credit card.
do they have a loan?
did the customer have a credit card or not?
So there's a yes no flag.
Is the customer an active member? Another yes.
No flag?
Active member can be measured differently by different organizations.
It could be whether or not
the customer logged into their online banking in the past month,
whether they did a transaction in the past two months or some other.
A measure like that, and estimated salary.
So the bank doesn't know the salary of the customers,
but based on the other things they know, they could estimate a salary
for that customer. And they also gave you this information.
So six months ago they measured all of these things
and said, all right, so for these 10,000 randomly selected
customers, we're going to just, watch them.
So we're just going to wait six months and six months down the track.
We're going to check
who all of those customers left and who those customers stayed.
And that's what this column exited represents here.
It tells you whether or not the person left the bank within those six months.
So this person over here, sometime within the six months he left the bank.
And as of a couple of days ago, he's no longer with the bank.
This person over here, on the other hand, is still off the bank.
So there's a zero here.
This person left the bank, this person stayed and so on.
So if you see a one, that means the person is no longer for the bank.
Is there a person still for the bank?
And your goal is to create a geographic segmentation model
to tell the bank which of their customers are at highest risk of leaving.
What I wanted to mention today is that for a lot of customer
centric organization, this is going to be valuable.
So I've done this personally.
I've done this so many times.
It is such an a value add to any customer centric organization.
So when whenever an organization deals with customers this is a lot of value add.
And then the other thing is that this skill that you're going to
learn is very transferable.
It doesn't have to be for a bank.
It doesn't have to be for churn rates.
Geo demographic segmentation models can be applied
to millions of scenarios.
So here, for instance, even in a bank, the same scenario could work.
should the person get a loan or not?
Should the person be approved for credit or not?
And once again, you'd have a binary outcome.
So based on prior experience you would know, whether or not
a person is reliable and you would build a model and say
which people are
more likely to be reliable and which people are more likely to default.
And that could govern the bank's decision on whether or not to give loans.
This could be in terms of fraudulent transactions,
not even in a bank, in a different financial institutions.
You could, find out which transactions
are more likely to be fraudulent, which are less likely.
there's lots of, scenarios where you could apply
a geo demographic segmentation model,
and it doesn't even have to be geo demographic.
So whenever you have a scenario, when you have a binary outcome
and you have lots of, independent variables, you can build a
proper robust model and that will tell you which factors influence the outcome.
So you can apply this knowledge.
You're going to learn in any kind of scenario
where you have a binary outcome and lots of independent variable.