Churning customers is a normal thing I mean even for RapidMiner you I know it's
hard to believe but it does happen we lose a customer or two like maybe from
time to time very seldomly of of course but it happens so that is not
the problem but you should be aware of when this is going to happen with whom
and then of course you you want to know what what you need to do about this so
let me help you on this let's set up another call and we'll figure it out
okay buddy all right okay talk to you soon bye hey Ingo hey you know what time
it is of course it's time for five minutes with Ingo yes so let's talk
about churn prediction for a second I mean you just heard it it is a common
problem really um in fact I would say customer analytics in general is per the
most frequently solved type of data and problems people are using
machine learning for and yeah sure there's IOT and like sensor data and
everything else and that's all very important but customer analytics is probably
still like 60 or 70 percent of the overall data science work most people are doing
and within customer analytics there's a couple of different problems one of them
is predicting who are customers you are going to lose and that is exactly
this kind of churn prediction problem and that is important why because getting a
customer or replacing a customer back by a new one is actually a very
expensive thing versus keeping a customer you already have so it's so
much better to keep actually your existing customers versus having the
need for replacing them over and over again and that is why you should be
aware who are your customers who are most likely to leave who will have the
highest likelihood of churn point one and then of course also you should
figure out what is the best course of action so you can actually well do the
right things so first of all this actually sounds not immediately like a
machine learning program so how do you transform this business use case here
into a machine learning problem or problem with the machine learning algorithm
really and you might remember in the first season we talked about the four
main categories of machine learning one is for classification one is regression
classification is basically putting things into different groups regression
is answering the question how many things will happen one is around
clustering or segmentation so what are the natural groups in your data and one
is about recommendations or figuring out what are the things which go together so
those are the four main categories and churn actually falls into the category
of classification that means that you have two groups of customers and we know
those two groups the loyal customers who stay with us and the customers who are
looking into the offers of competitors or might want to think about like
getting rid of this offer altogether so those are the churn customers so we
have two groups loyal versus churn customers and we want to separate those
two groups and that is the typical application area of a classification
algorithm so in a sense this is very very similar to the pieces here the
pawns on this chess board we have the white pawns here and let's say those
are the customers who are not loyal they're more likely to churn so
we want to identify those and then we have our loyal customers on this side of
the chessboard and we really would like to identify differentiators between
those two groups so let's actually use some machine
learning here thank you so for example in a linear regression scheme using this
little stick here you could for example find models separating those two groups
then finding out who are the customers most far away based on this
side of the linear regression model basically saying like oh look those are
people here they are more likely to churn so you should try and do something
about them so that's how you transform churn into a classification problem and
then you can solve the classification problem basically with any machine
learning methods you could think about but Ingo
are there any machine learning algorithms that are better for churn
prediction well yes and no so in general you can use any machine learning model as
I just said you can use for classification but there is one thing which is
important you need to figure out what you want to do afterwards and this is
the thing if you have a linear regression line like this this might be
something you might be still able to understand you see what are the
important factors but those important factors influence factors of your data
set they are true for all the data points but what about this dude here I
mean he is about to churn and the model doesn't really tell you right away what
is the reason for this particular individual so and there are other
machine learning methods which may be little better for this so for example
decision trees they are easy to understand and you can follow the path
of the decision trees to see well why exactly is this particular person going
to churn and if you know the reason for this particular person you can do
something about this you can give them a discount so they stay or you can improve
the service for this client so they actually like your products and your
company even more so and that depends on what the problems with this client are
and that is really what other models are better for but even for let's say the
more fancy models like gradient boost the trees or others you can even use
some techniques for example lime is a technique which is also available on RapidMiner
but you can actually figure out well even for a model that you don't
understand it what are the main reasons for a particular individual so even for
the more complex models which are maybe less they don't allow you to do all the
action there is still a way to figure out well what's the best course of
action by combining them with other techniques as well and I guess that's it
for five minutes with Ingo today