The explosion in digital media - web, social and now mobile - represents a departure from how things were like in the last century. This proliferation of digital media is both a threat and an opportunity for many businesses. Business Analytics can be leveraged to process data, sentiment, buzz, contacts, context and other aspects of business interest in real time, for business performance and impact. The course picks and uses use-cases from a variety of industries and geographies, to showcase the potential and impact that business analytics done properly (or not) can have on business performance.
Motivating Customer Analytics
Loading...
来自 Indian School of Business 的课程
Business Analytics and Digital Media
134 个评分
从本节课中
Customer Analytics
Introduction to and the application of some important analytical processes in Marketing Analytics
Learning outcomes by the end of week 3 should normatively be that (a) Students have an understanding of the major processes and procedures typically used in a customer analytics setting, in particular factor and cluster analyses (b) and, develop an appreciation for the possibilities that emerge from recombining procedures, data, algorithms and problem formulation perspectives in open source environments.
与讲师见面
Sudhir VoletiAssistant Professor and ISB Research Fellow
Marketing
[MUSIC]
Welcome back, folks.
The focus of today's session is going to be customer analytics.
Before I jump into customer analytics proper, let me walk you through
a motivating example for the kind of questions that would normally come up here
and some approaches later on in the session that we will use to solve them.
The 6th of July 2016 saw the launch of a mobile app game, which went on to
make mobile history and you probably know which one I'm referring to.
Pokemon Go.
Turned out to world beating not just in quantity terms,
that is in terms of numbers of users and there you can see.
Basically, it has the highest number of users beating three years lead of
Candy Crush, but also in quality terms, in terms of the time of engagement per user.
So if you see it here,
it is way above the art of the next big time consuming app which is Facebook.
For a while,
it actually briefly threatened to overtake staple apps like chat and maps and
you can see the rise that came through the first week after Pokemon Go goes launch.
And it worked wonders, of course, for parents Nintendo and Google.
They formed a company called Niantic,
which actually in some sense runs the game.
All of that is great.
Wow and all of that.
By the way, there as you can see is in some sense, the stock price of Nintendo.
Pretty much flat all the way til the 6th of July into the weekend of the 6th of
July when folks figured out something very interesting is happening.
And after that, the stock actually doubles in price within two weeks.
All of that is great.
The question is how and why did this happen?
Bunch of answers, let's explore some.
How does the game work?
Well, basically, if you don't know this already and
my guess is most of you probably do.
So, this is an Augmented Reality game and it works through your smartphone's camera.
It projects Pokemons, these little monsters into your real surroundings.
So that's what it would look like, basically.
The important thing is that actually projects does augmentations,
the same augmentations onto everyone's reality.
What that would mean, if there's a Pokemon standing right next to me and
you were to take your smartphone camera and look at it, you'd find that if you
were all to turn the camera away, you wouldn't see it anymore.
The fact that it projects the same augmentations onto everyone's reality,
essentially enables what we would call social gaming.
Now makes real the possibility that you and I could compare,
could contrast, could in some sense collaborate or compete.
Because there's something real augmented, but real in our immediate surroundings.
Interestingly, the game did not have social features built into it by design.
The game continues.
You sort of collect these Pokemons and you train them, and you form your own army,
and then you battle other teams for supremacy, and all of that.
This was the first weekend.
This is Central Park, New York and you can see all those folks playing Pokemon.
Are they all children?
I don't think so.
It's the very same scene that night.
Why are there still people there playing Pokemon?
Because some Pokemon show up only after dark.
Take a look at this one.
Don't Pokemon and drive,
which tells us that a lot of the Pokemon players actually were of driving age.
So, the question then becomes how does it make money?
Well, a few ways.
One are in app purchases, these micro-transactions.
Which in the first week, amounted to close to $170 million per day.
Then there are lures, which basically product businesses can buy from Niantic
to ensure that their business becomes, in some sense, a Pokemon hot spots.
If I'm a pizzeria, I'm an ice cream parlor, I'd rather my shop become
a Pokemon hot spot, because people hunting with Pokemons will come and sit there.
And while they are there, they might order something.
So in some sense, what we are seeing is platform functionality and
then there are PokeGyms and I'm not even going to go there.
So all of that coming through, which then brings up the big question and
that's where customer analytics in some sense is going to the picture.
If you are a Niantic, what would be the foremost question on your mind?
What would be the foremost question on your mind?
And the answer is quite simple, it's going to be the same one.
How do you make even more money and keep the tap flowing for
the existing revenue stream?
If you think about it, what are the avenues that exist for expansion?
Now you can expand your avenue stream in to ways, broadly speaking.
You can either get more from existing customer or you could find new customers.
So let me go to question 2a, how do you get more from existing customers to do
that, to raise engagement and then buy revenues?
You would have to know what it is that causes them to engage in the first place.
In other words, you would have to explore the consumer's psyche, their preferences,
their likes and dislikes, their hopes and dreams explore their world.
Question 2b for new customers, the question then would be,
how do you identify them, reach them, draw them in?
What is the picture?
What is the appeal?
What do we use?
What is it they need?
So, we'll say that we can reach out to them with.
If anybody that you make money from, remember the definition of customer.
And if anybody you make money from is a customer,
see if we cannot ignore the B2B aspect.
A B2B aspect becomes important and the questions continue.
And in this case, it's going to be a little heavier than the B2C type.
How do you get businesses, brands, enterprises to sign up?
How do you get them to pony up more money?
To think about third party developers, which is what an android would do.
Formation of alliances, investments, cross investments and so on.
Think about what new formats and innovations could help.
Think about webcasting in a PokeGym.
It would compete any SPN in a local area, why not?
Think about it.
Reality television, could some part of Pokemon Go be based on that?
Actually, it might sell and it will have an audience.
PokeClubs and PokeMerchandise, any are there already.
More questions, how do you price this in-app and off-app products?
Do you do flat pricing?
Do you do surge pricing?
Label for those, for instance?
Auctions like any of them will do?
Trading and exchange?
People who play their baseball and cricket cards for a long time.
So, how best to expand other Nintendo products?
Licensing, franchising and all of them.
And so you can see the range of questions that comes through,
very interesting, very exciting which now
in some sense brings me to the conclusion of those motivating examples.
Now we have divided questions into two classes, B2C and B2B based ones.
In Customer Analytics,
our focus is going to be primarily on the farmer type B2C.
However, B2B are also customers.
A lot of the techniques we will use can actually apply with equally facility.
You're on that kind of data with B2B type customers, as well.
The questions are going to be, who are customers?
How do we reach, acquire, retain them?
What are the needs and preferences will come to latent needs?
Why will they buy from us?
Return to us or recommend us and so on.
So, this whole thing put together in some sense becomes a domain
of customer analytics.
So we are talking in terms of factorializing the data, segmenting and
targeting, positioning and
the classic marketing treatments that would apply in a case like this.
[MUSIC]
