Anyway, that's the last time I buy raw oysters from a gas station.
Whoa!
(Speaking in German) Ingo Ingo!
English, English!
It's time for Five Minutes with Ingo!
Five Minutes with Ingo!
Alright let's go.
Ok, cool.
Last time we really talked about a lot of problems around data scientists and they are
unicorns and difficult to find, but really the interesting thing is that there is a really,
really difficult question right in the beginning, before we even start figuring out really how
to create all of these great analytical work flows and solve your problems, you need to
really make a mapping between your business problem, and an analytical technique.
And that turns out to be really really difficult.
So many people out there don't get it right.
But it's actually super simple, and I'm going to explain to you how.
It boils down really to two questions only.
So the first question in about the time.
So Q1, not the quarter, the question.
So question number one is: what are we actually talking about.
So this is now, and this is the past, and this here is the future.
If I have questions about the past, like, 'ok, what did happen, how many times did we
sell something, how many customers did we lose in the past?'
This is really the area of BI; reporting, and all those good stuff.
And that's great, and that's very important, but seriously, that's about the past I know,
I do not really care.
So the second aspect here is what is the realm of predictive analytics.
So those are questions about the future.
And you might think like 'yeah, sure that is easy and that is simple' but you would
be surprised how many people are not getting that right.
They think like 'ok yeah data mining and predictive analytics kind of help us hear as well,' but
that's completely different techniques, different methods.
They're not getting the difference between the two.
Exactly.
So that's part of the problem.
We can talk about this another time, but that's not the topic for today.
You need to get it right.
You need to get it right and you need to focus on 'ok, where am I?'
If I'm here, ok, so use all those BI tools, but I'm talking about predictive analytics,
I use a completely different approach.
And that's now the second question; this is actually leading to this portion, this second
question is about the mapping from your business problem, to the analytical basic methods you're
using.
Ok, so, question number two.
So this here is about time, and this here is about mapping the business problem and
an analytical solution.
Ok?
So that's how this really works.
And there are only four, so that's the good news.
It's not, not too many.
The first thing is really about putting things into buckets, A and B. Every single time you
ask yourself ok, that will sort something.
So you see a new situation and you ask yourself 'OK, is this an A situation, is this a B situation?'
You see a new customer and you would like to understand is this a B customer, an A customer,
so this is really one question.
The next one is: there's a development in the past, and you would like to know, how
is this really developing in the future.
So you take the forecasts, the regressions, so you can come to that.
The third category: you have a description of data, of objects, of people, and you would
like to know what are the natural groups those people, those options actually form to, so
we're cementing things.
And the last one is you would like to understand what's happening very often together.
Those are the four basic categories.
So they have names, I won't get these perfectly.
This is called classification, regression, this is called clustering or segmentation,
and those are called association rules.
OK, let's do an exercise here.
Tell me just one use case.
Give me one.
Uuuuuuuuuuuh.
Churn prediction?
Well that is an excellent idea, really.
I love this one.
OK, well let's first think about churn prediction.
We are predicting something that is about the future, so it falls into this bucket,
good.
Next step is now: what is it really?
Well, we only have two cases: a customer can leave you, class number one, or A, or the
customer can stay a customer class number B, class B. So this is clearly a classification
task.
That is actually easy.
So there are like, lots of methods you can use for that like decision trees, neural networks,
and that's, like, a place where tools like RapidMiner can help you.
But the important question is to figure out this actual classification.
Can you give me a second one?
Campaign selection?
I couldn't make a better selection.
Campaign selection though is interesting because if you think about your customer base you
don't really know who are the people who are interested in you new offer, and you would
like to make a targeted campaign, so it's important to understand who are your customers,
and what are the common properties to make a targeted campaign for those people and select
the right people for this campaign.
But often, it can be a classification itself, but often it's really in this clustering bucket
that you really let people fall into the natural buckets and then focus on one bucket and do
a targeted campaign.
OK, do we have a last one?
Cross selling?
Well that's coming as a surprise now.
Cross selling is a typical case for associations.
Associations really means like, whenever A happens, B is also likely to happen, or the
other way around.
And here in this case, like whenever C happens D happens.
A typical example is Amazon, we all know this.
People who are interested in this book, they will also buy another book.So that's a typical
example for an association rule.
What are the things which are associated with each other.
You can use this for cross selling.
So whenever you know somebody bought a product, you can make a recommendation for buying their
second product as well.
So you see, its super simple, its super easy.
You start by identifying 'ok, this is actually really predictive stuff,' and then you really
bring the things into those four buckets, and that's about it.
And this has been your Five (plus one) Minutes with Ingo.