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It's actually the next wave is 
actually going back to how you 

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can make marketing decisions or 
targeting decisions with data. 

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One of the very strong ones in 
it. 

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And you will see this not just 
name be students but actually 

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entrepreneurs. 
It's better when they say we are

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spending on marketing. 
What they actually mean is, we 

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are spending on Advertising. 
This is called segmentation in 

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marketing. 
It's also about cluster analysis

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in statistics and machine 
learning. 

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They are one the state. 
Hello, welcome to data, shatter 

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the podcast on all things data. 
This podcast is a series of 

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conversations with experts and 
Industry leaders in data. 

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And each week. 
We aim to unpack a different 

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compartment of the data 
suitcase. 

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I am your host Catholic charity.
That I'm a blogger newspaper. 

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Columnist book, author, and a 
former data Etc. 

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Consultant at currently head, 
analytics, and business 

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intelligence for delivery. 
One of India's largest. 

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Just logistics companies. 
You can follow me on Twitter at 

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Karthik s that is Kar thi KS and
read my blog at. 

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No Intruder.com. 
That is n 0e. 

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N th you be a.com all opinions 
expressed in his podcast belong 

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to me and my podcast guests. 
And it do not reflect the views 

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of any organizations. 
We might be associated with 

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nothing. 
Disgusting, this podcast should 

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be taken as Financial or legal 
advice. 

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If you go to business school in 
India, you would end up with the

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impression that marketing is not
a quantitative subject. 

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Instead of weird process happens
in the first deal. 

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When you see a more 
mathematically inclined students

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gravitate towards finance and 
marketing gets labeled as for 

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creatives donut. 
However, marketing has for a 

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very long time, in an extremely 
quantitative discipline as the 

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amount of data explodes. 
It only seems to be getting more

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quantitative so that it's only 
right to get an assistant 

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professor of marketing from I am
angle to talk about marketing 

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being a quantitative subject. 
My guest today is between us 

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because G, he teaches courses on
marketing management, and 

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marketing research for MBA, 
students and doctoral students 

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apart from this. 
He has a book title 

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quantitative, marketing 
research. 

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Search available on edx and 
swipe his research interests 

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include behavior and 
decision-making. 

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Very models biases and digital 
marketing. 

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When he investigates influence 
of Fraud and click. 

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Delete. 
So you teach marketing at IIM 

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Bangalore, you have done a mooc 
in, you teach a mooc and 

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quantitative marketing. 
So, now based on 15 years back, 

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back when I was a student at I 
am banged up. 

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There was a strong correlation 
between sort of people who are 

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not so good at maths by MB 
standards and people who wanted 

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to pursue marketing. 
It is almost like it is like if 

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you don't do well at maths in in
your first year in the NBA, then

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you are better off. 
Specializing in marketing and 

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back then marketing is all about
feeling and personality and 

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things like that. 
But I understand that like 

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there's a fairly long history of
numbers and quantitative methods

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in marketing. 
And since you also sort of work 

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in that and teach a course on 
that, can you give us a sort of 

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a brief introduction on numbers 
in marketing and how it came 

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about in for? 
Sure. 

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Thanks, cryptic. 
So, let's start with what you 

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said about feeling. 
And what is the other words? 

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Personality, I do a lot of 
engineering elective in 

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marketing. 
So yeah, so a lot of this 

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actually, what when you talk 
about feelings and personality 

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characteristics Etc, by the I 
just correct you. 

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These are also these also have 
some quantitative basis. 

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So let's start off with that. 
But I will go a little back. 

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Let's do a little bit of history
as to how marketing is a field 

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evolved. 
So it's all businesses are more 

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or less derivatives of the 
so-called classical feelings. 

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So most of marketing comes from 
econ with I think, you know, is 

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a Quantrill. 
Most people will identify it 

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with it as a cornfield 
psychology or consumer Behavior.

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Now, if your audience is 
primarily engineering, the 

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probably don't know, but 
psychology is a intensively 

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cornfield, and that's where the 
feelings and that's it. 

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So the problem of measurement is
a quantitative problem. 

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Sociology against astrology, has
a lot of quantitative 

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underpinnings, the entire ideas 
of network science. 

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Etc. 
Lot of it comes from sociology. 

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Yep. 
So the social networks that we 

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see etcetera, which also has 
quantitative underpinnings and 

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it's of course, statistics and 
operations research. 

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This tends to be most of how 
marketing is the field evolved. 

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So people from this field. 
So you may have heard of Philip.

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Outlet is an economist, 
actually, another famous name 

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Galilee. 
He comes from an operations 

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research background. 
And even today, if you see 

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marketing departments, at least 
academic marketing, departments 

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hire psychologists, they hire 
sociologists. 

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They hired a and of course, 
today, computer scientist, but 

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historically, statisticians and 
operations research people. 

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Okay, so you may want to ask 
where numbers? 

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Come in actually numbers. 
Come in almost everywhere. 

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It is a small aspect of 
marketing called qualitative 

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inference, but it's not just 
about your intuition. 

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It is a rigorous field in 
itself. 

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It's something that I do not 
specialize in so I will not go 

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into. 
So let's go into the 

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quantitative. 
Underpin. 

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At the hut. 
A very reductionist approach to 

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marketing is what that you need 
to make something and sell it 

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for money. 
Yeah, that's the most 

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reductionist idea of marketing. 
Of course, that is not entirely 

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true, there's more to marketing.
You can be a non-profit. 

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You may want to have more 
nuanced idea of marketing is 

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that you have something that you
want to exchange with somebody 

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else for something else. 
Yeah, so you may want to develop

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an impression of yourself and 
then go certain actions Etc. 

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That would also come under the 
Ambit of marketing. 

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Let's go to the most reduction 
is but the one that most of us 

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can understand that you have to 
iron Sell you to buy and sell 

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right now. 
So, here, I'll use some 

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technical terms. 
So the idea is that you have a 

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product and you want somebody to
buy it. 

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Yeah. 
Well, you know, that not 

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everybody is going to buy what 
you sell. 

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This is simply because different
people have different needs. 

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Yep. 
All of these products that are 

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selling our services. 
They address some certain need, 

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and why is the other person 
going to pay for it? 

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Because the amount of money here
she is going to part with Is. 

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Worth less than the benefits 
that he or she receives from 

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your product product. 
Yeah. 

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So add the heart of it is an 
exchange is a profit loss 

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calculation. 
That itself is quantitative. 

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Yes, right. 
So initially, let us say the 

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1950 which are considered some 
of the earlier days post wash. 

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There's a boom that but you 
don't have enough data to know 

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who wants one because it's you 
don't have databases difficult 

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to track a person. 
What do you do with at the store

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level? 
You know, that today? 

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I sold certain things at certain
prices and I observed profit 

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plus Etc. 
Now that storekeeper may 

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increase prices decrease prices 
Etc. 

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And that is the rough proxy 
fuzzy price elasticity. 

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Now that is not completely 
accurate because he does know 

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who. 
But what me as a person is very 

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different from you as a person 
or a third party the person 

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Later what Market has devised is
something called the coupon 

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direct mail. 
This at least when I was growing

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up. 
This was not very popular in 

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India, but apparently in the 
western economies u.s. 

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Especially the coupon was a 
very, you are. 

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Do you say way of tracking, who 
was buying, what you find 

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coupons in your mail? 
And you can go get discounts or 

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whatever else. 
But based on that, what ever you

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have redeemed? 
They're actually keeping a 

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database. 
And then the next coupon you get

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is going to be very different 
from the next group on somebody 

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else gets. 
So essentially they are using 

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some sort of statistical 
modeling. 

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Track you in a way and go deeper
and deeper into understanding. 

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What your preferences are. 
Now, of course, coupons are 

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expensive. 
It you have to pay postage 

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having a thinking that there be 
in India have sort of Leap Frog 

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coupons. 
We leap from Cooper really did 

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get coupons, but I do not 
remember getting that they take 

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referrals started Cooper. 
The first time I saw targeted 

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coupons was actually in France 
in a supermarket. 

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So I have a loyalty card. 
This is this is last decade the 

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right thing, but I'm afraid. 
It still goes ahead. 

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I have a loyalty card. 
After I make my purchase, I get 

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a coupon which is very different
from the coupon that the next 

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person. 
In my, in the line, gets my 

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coupons are somehow eliminated 
based on my purchase Behavior. 

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So, coupons exists to this day. 
Of course, there is a for the 

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inertia or language or you call 
it. 

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The original idea of coupons was
that you sent posted? 

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Yes. 
This is still inefficient 

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expensive and all of that. 
So the what happened? 

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The next wave? 
And this game in the 1980s. 

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And again, we have kind of leaps
of this. 

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We are in this scenario to some 
extent, the idea of Supermarket 

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scanner panel data. 
Okay? 

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Yeah. 
Now, scanners panel data will 

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tell you who bought what they 
can track you based on your 

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library card. 
They can track you based on your

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credit card Etc. 
You are not going to get 

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everybody because I can still 
walk into a store. 

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I can be a walk-in. 
Yep, take cash and get out. 

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So they don't know where. 
But a large number of people in 

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the western economies were using
credit cards, of course, right? 

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So they can track you with the 
credit card. 

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They can track you. 
If you have a loyalty card with,

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where they give you a discount, 
Etc, today. 

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However is very interesting. 
Amazon doesn't need to give you 

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a ticket because they have all 
your details in. 

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Yeah, you have your login ID, so
it's slick. 

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00:11:33,800 --> 00:11:36,500
Yeah, so literally, you can 
track every customer and their 

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purchase history. 
Yep, essentially. 

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Of inference is how you can use 
data of the past. 

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To make decisions. 
And when I say decisions, I 

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00:11:51,600 --> 00:11:56,100
mean, can I tell a product? 
So you can you tell her offers 

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for you? 
Can I tailor ads with yeah, 

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right. 
The same thing. 

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If you take a look at 
advertising, you have TV ads and

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00:12:04,600 --> 00:12:07,300
you have been assets, you have 
newspaper ads which are not 

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00:12:07,300 --> 00:12:10,100
really track you. 
Of course you have cookie ads 

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00:12:10,600 --> 00:12:15,400
online ads, or you have mobile 
ads which can track you. 

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00:12:15,900 --> 00:12:21,100
Yes, so individual. 
Double date actually increases 

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00:12:21,700 --> 00:12:24,500
and this is why actually Google 
became the multibillion-dollar 

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00:12:24,500 --> 00:12:26,700
company, it is right? 
And Facebook. 

206
00:12:26,800 --> 00:12:30,500
Yep, because if you remember 
pre-google, there was no way to 

207
00:12:30,500 --> 00:12:33,900
find out who you are. 
What your preferences are and 

208
00:12:33,900 --> 00:12:35,400
Target you. 
Yeah. 

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00:12:36,800 --> 00:12:40,400
But to some extent you can so I 
can say that. 

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00:12:40,400 --> 00:12:45,700
Yes, if you are in a posh area, 
Supermarket. 

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00:12:45,900 --> 00:12:48,800
Yeah, then maybe your 
preferences are going to be a 

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00:12:48,800 --> 00:12:51,600
little different from if you're 
not in a posh area, tailbone 

213
00:12:51,600 --> 00:12:56,300
down to your foot down, Etc. 
But it still approximate on 

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Google. 
However, you are searching for 

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exactly. 
Something. 

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00:13:00,800 --> 00:13:03,600
Yep. 
They have a much better idea of 

217
00:13:03,600 --> 00:13:06,400
what your preferences are and 
they can use that information. 

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00:13:07,500 --> 00:13:10,300
So now that has been the 
evolution of how quantitative 

219
00:13:10,300 --> 00:13:13,700
data has been used. 
So previously people, you should

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do a lot of surveys, Etc. 
They still do today. 

221
00:13:17,100 --> 00:13:21,100
However, you can actually find 
out what people want. 

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00:13:21,400 --> 00:13:23,700
So, surveys are essentially what
I tell you what. 

223
00:13:23,700 --> 00:13:26,400
I what I tell you may or may not
be true all the time. 

224
00:13:28,700 --> 00:13:31,900
You asked me? 
Do you like this? 

225
00:13:33,700 --> 00:13:45,600
Yeah. 
I miss, you know, let's say it 

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00:13:46,400 --> 00:13:50,400
was exactly that Google knows 
XYZ about me. 

227
00:13:50,700 --> 00:13:54,500
But doesn't like resting this 
evil is Yes, are accusing 

228
00:13:54,500 --> 00:13:56,300
another classic coming in the 
literature. 

229
00:13:56,300 --> 00:13:58,500
They call it as like stated and 
revealed preference. 

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00:13:58,500 --> 00:14:00,100
Right? 
Like we used to trade another 

231
00:14:00,100 --> 00:14:03,000
based on stated preferences 
until now, but now we have 

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00:14:03,000 --> 00:14:05,600
enough data to kind of like, we 
don't need to know your status 

233
00:14:05,600 --> 00:14:09,700
Bristol. 
Yes, for a lot of things, you 

234
00:14:09,700 --> 00:14:11,900
know, you're you know, revealed 
preference is not for 

235
00:14:11,900 --> 00:14:14,200
everything. 
Yeah, but for a lot of things 

236
00:14:14,800 --> 00:14:19,100
now, however interestingly this 
is GDP, our rule that's come up 

237
00:14:19,100 --> 00:14:24,800
in Europe where you are not 
allowed to store certain kinds 

238
00:14:24,800 --> 00:14:25,900
of data. 
Yep. 

239
00:14:26,000 --> 00:14:30,400
So, if I were to take the times 
of India, homepage in India, 

240
00:14:30,500 --> 00:14:32,900
versus the same time, the 
finding home page in Europe. 

241
00:14:33,200 --> 00:14:39,100
Let's say in Paris. 
I would have to authorize a lot 

242
00:14:39,100 --> 00:14:41,800
more gas. 
I have to obtain and most people

243
00:14:41,800 --> 00:14:49,000
don't while here automatically a
lot of stuff is being recorded. 

244
00:14:49,100 --> 00:14:51,000
Yeah, it is true for any online 
player. 

245
00:14:51,200 --> 00:14:54,100
According to European Lungi 
repair less this happens. 

246
00:14:54,600 --> 00:14:57,800
So actually the next wave is 
actually going back to how you 

247
00:14:57,800 --> 00:15:04,200
can make marketing decisions or 
targeting decisions with data. 

248
00:15:04,200 --> 00:15:09,400
Yeah, we were going up. 
And again, there is a wave of at

249
00:15:09,400 --> 00:15:13,600
least in certain economies where
limited data is Rule. 

250
00:15:13,900 --> 00:15:15,900
Yeah. 
Okay. 

251
00:15:16,200 --> 00:15:18,100
Thanks for the demolition. 
I mean, like this is used. 

252
00:15:18,100 --> 00:15:20,500
So what I understand from you is
that like marketing is always 

253
00:15:20,500 --> 00:15:24,000
been a highly quantitative 
subject and like you have people

254
00:15:24,000 --> 00:15:28,800
from like sociology economics, 
psychology statistics and so on 

255
00:15:28,800 --> 00:15:30,500
which are all highly 
quantitative discipline. 

256
00:15:31,000 --> 00:15:35,900
So why is it that we in India or
at least in is we think? 

257
00:15:36,100 --> 00:15:41,600
Think that we think that 
marketing is a soft subject 

258
00:15:41,600 --> 00:15:43,700
without much Quant. 
Why is it that we have this 

259
00:15:44,000 --> 00:15:47,300
weenie? 
Okay, that's an interesting 

260
00:15:47,300 --> 00:15:50,500
question. 
There are parts of it, which I 

261
00:15:50,500 --> 00:15:53,500
will not answer why you in 
irons. 

262
00:15:53,500 --> 00:15:56,000
Think something is something. 
I'm not qualified to answer. 

263
00:15:56,000 --> 00:15:58,400
Okay, that's Michael. 
And I have never been an iron 

264
00:15:58,400 --> 00:16:00,300
student. 
I've only seen it from one side 

265
00:16:01,600 --> 00:16:04,500
by exposure, to marketing has 
been from a European schoolnet 

266
00:16:04,500 --> 00:16:08,400
was pretty cool and heavy so I 
actually did not have the same 

267
00:16:08,500 --> 00:16:13,800
Notions that you did. 
That said there are certain 

268
00:16:13,800 --> 00:16:15,500
marketing role. 
This. 

269
00:16:16,600 --> 00:16:20,600
That add the face of it. 
Do not seem very pointed and 

270
00:16:20,600 --> 00:16:24,200
this is absolutely true if 
you're working for Unilever and 

271
00:16:24,200 --> 00:16:28,000
they send you, this is rap to 
rural beer. 

272
00:16:28,400 --> 00:16:32,600
Yeah. 
That, that will be an initial 

273
00:16:32,600 --> 00:16:36,800
training placed right now. 
Of course, I think everybody has

274
00:16:36,800 --> 00:16:39,000
to do it, especially in the top 
of Energy company. 

275
00:16:39,300 --> 00:16:42,400
What if you're doing B2B sales, 
where you have to negotiated 

276
00:16:42,400 --> 00:16:51,500
deal with a buyer Prime, see a 
lot of these marketing change. 

277
00:16:55,800 --> 00:17:00,100
Now, the pills does not see 
until you can only guess that 

278
00:17:00,100 --> 00:17:02,600
this is probably one. 
Is driving the perception. 

279
00:17:03,000 --> 00:17:08,599
However, at the back end you 
have to maintain says, Diaries 

280
00:17:08,900 --> 00:17:11,200
salesforce.com is a huge 
business. 

281
00:17:11,200 --> 00:17:16,099
So is as it is associate, switch
does things like calibrating 

282
00:17:16,099 --> 00:17:21,900
Salesforce incentives, and 
training Salesforce people Etc. 

283
00:17:22,300 --> 00:17:25,500
And the back end. 
There is the lot of machine 

284
00:17:25,500 --> 00:17:27,300
learning. 
There's a lot of statistical 

285
00:17:27,300 --> 00:17:30,800
analysis, economic analysis, 
even psychologically. 

286
00:17:32,900 --> 00:17:35,600
So at the end of the day 
negotiation is this type is a 

287
00:17:35,600 --> 00:17:40,600
psychological process. 
At the back end, I did big 

288
00:17:40,600 --> 00:17:44,100
companies at the back. 
End will be keeping tabs of a 

289
00:17:44,108 --> 00:17:47,400
lot of data. 
Yeah, in B2B settings. 

290
00:17:47,400 --> 00:17:49,600
They do. 
Keep that record, sales persons 

291
00:17:49,600 --> 00:17:52,200
died. 
Is it is to be die, least lead 

292
00:17:52,200 --> 00:17:54,600
generation in cetera. 
Nowadays. 

293
00:17:54,600 --> 00:18:01,600
There are software systems that 
track at what level the customer

294
00:18:01,600 --> 00:18:02,900
is. 
If you are talking to the 

295
00:18:02,900 --> 00:18:08,600
customer, what exactly happened 
is the customer a On to the next

296
00:18:08,600 --> 00:18:10,700
level. 
Then they try to do some 

297
00:18:10,700 --> 00:18:14,900
analysis, regression analysis or
something higher trying to 

298
00:18:14,900 --> 00:18:18,200
either segment customers into 
different groups or trying to 

299
00:18:18,200 --> 00:18:22,700
understand why, or why you did 
not get a deal, etc. 

300
00:18:23,200 --> 00:18:26,600
So a primer facie right in the 
front end. 

301
00:18:26,600 --> 00:18:29,000
It may not seem to be very 
quantitative. 

302
00:18:30,400 --> 00:18:34,000
But at the back end, there is a 
lot of quantitative analysis 

303
00:18:34,000 --> 00:18:37,900
going on. 
Similarly, with banks at the 

304
00:18:37,900 --> 00:18:40,500
front end. 
You have some guy calling you up

305
00:18:41,100 --> 00:18:45,700
and pitching an investment 
product or whatever or credit 

306
00:18:45,700 --> 00:18:47,900
card or whatever. 
But at the backing they're 

307
00:18:47,900 --> 00:18:51,300
tracking your transactions. 
They're tracking how much money 

308
00:18:51,300 --> 00:18:54,100
you have balance, Etc, and 
trying to make some sort of 

309
00:18:54,100 --> 00:18:56,200
influences in particular. 
So, okay. 

310
00:18:56,200 --> 00:18:59,900
So you're saying that. 
Like, while we might think that 

311
00:18:59,900 --> 00:19:02,500
it's Bit of a sort of 
non-quantitative sort of 

312
00:19:03,000 --> 00:19:04,400
discipline. 
There is a lot of quantitative 

313
00:19:04,400 --> 00:19:06,300
stuff. 
Just that people are not exposed

314
00:19:06,300 --> 00:19:09,200
to it in the earlier, not 
necessarily exposed to it in the

315
00:19:09,200 --> 00:19:11,500
earlier years of the jobs 
include cigarette. 

316
00:19:12,600 --> 00:19:16,100
Earlier years of the jobs in, 
the second Factor will be 

317
00:19:16,100 --> 00:19:20,000
probably the core course in 
marketing that started in. 

318
00:19:20,000 --> 00:19:24,500
I am at least I am Bangalore, is
not very quantity. 

319
00:19:24,500 --> 00:19:25,500
Me. 
Now. 

320
00:19:25,500 --> 00:19:28,500
Remember you are bringing in 
people actually from an 

321
00:19:28,500 --> 00:19:30,900
engineering background. 
Very few people have actually 

322
00:19:30,900 --> 00:19:34,400
exposure to marketing. 
Yes, and you have to teach them 

323
00:19:34,400 --> 00:19:37,300
a lot of stuff to. 
One of the things that we find 

324
00:19:37,300 --> 00:19:40,600
is that there's a preconceived 
notion and my marketing is equal

325
00:19:40,600 --> 00:19:44,800
to advertising. 
And a lot of people have this 

326
00:19:44,800 --> 00:19:47,700
notion even experienced. 
People have this notion that 

327
00:19:48,300 --> 00:19:50,500
marketing means, you're going to
be the next person. 

328
00:19:50,500 --> 00:19:56,700
Joshi ZJ you spawned a huge 
point is no, the old gray. 

329
00:19:56,700 --> 00:20:00,500
It is. 
Alec presidency. 

330
00:20:00,700 --> 00:20:07,500
Yeah, so not a lot, but quite a 
few people tend to have certain 

331
00:20:07,500 --> 00:20:10,800
preconceived notions, right? 
One of the very strong ones. 

332
00:20:10,800 --> 00:20:11,900
In the end. 
You will see this. 

333
00:20:12,200 --> 00:20:14,800
Not just name be students, but 
actually entrepreneurs. 

334
00:20:14,800 --> 00:20:17,800
It's better when they say we are
spending on marketing. 

335
00:20:17,900 --> 00:20:20,100
What they actually mean is, we 
are spending on Advertising. 

336
00:20:20,400 --> 00:20:21,200
Yep. 
Yeah. 

337
00:20:21,200 --> 00:20:24,600
Product designed. 
So you need to convince somebody

338
00:20:24,600 --> 00:20:29,200
that this product design, which 
was also part of marketing that 

339
00:20:29,200 --> 00:20:35,900
is Retail format retail store 
designed choosing the retail 

340
00:20:35,900 --> 00:20:37,900
format and things about the 
place. 

341
00:20:37,900 --> 00:20:42,700
The 40's essentially, which is 
also marketing pricing is also 

342
00:20:42,700 --> 00:20:47,700
marketing, Etc. 
So I think there's a lot to 

343
00:20:47,700 --> 00:20:53,000
unpack in the first marketing 
course that people take the S 

344
00:20:53,000 --> 00:20:56,300
marketing quiz that people take.
If anybody is interested is 

345
00:20:56,300 --> 00:21:01,800
usually during the search. 
We This, you do end up doing a 

346
00:21:01,808 --> 00:21:05,600
lot of construction. 
Like this is, you probably did 

347
00:21:05,600 --> 00:21:08,500
not do that in your vad problem.
It's an elective at. 

348
00:21:08,500 --> 00:21:11,900
I am back and I did it and ended
up with a backdrop. 

349
00:21:12,300 --> 00:21:15,800
Let's not take names here. 
But like I did and they ended up

350
00:21:15,800 --> 00:21:18,300
with a profit made me lose all 
of my critics. 

351
00:21:18,300 --> 00:21:21,500
Oh, yeah. 
Well, okay. 

352
00:21:21,500 --> 00:21:23,600
That's that's beyond the scope 
of this discussion. 

353
00:21:23,600 --> 00:21:25,000
Yeah, that's beyond this little 
marketing. 

354
00:21:25,700 --> 00:21:28,600
Yeah, so marketing these 
hatches. 

355
00:21:29,400 --> 00:21:35,300
You start doing very shallow 
dive into quantitative methods, 

356
00:21:35,300 --> 00:21:39,100
but there's a lot more actually 
than what is typically taught in

357
00:21:39,100 --> 00:21:42,800
a course. 
Okay, and it took us to sort of 

358
00:21:42,800 --> 00:21:45,200
before we sort of dive into the 
actual contents. 

359
00:21:45,200 --> 00:21:47,600
Only one other question. 
Is that like so everything. 

360
00:21:47,600 --> 00:21:50,200
I mean you are an academic. 
So everything that you have told

361
00:21:50,200 --> 00:21:52,600
me probably comes from an 
academic perspective in the in 

362
00:21:52,600 --> 00:21:56,400
terms of like how Quant is being
used in marketing, how it's how 

363
00:21:56,400 --> 00:22:00,200
it can be used how it has been 
used over the years but What's 

364
00:22:00,200 --> 00:22:03,300
the typical translation like of 
what happens in Academia to 

365
00:22:03,500 --> 00:22:06,500
Industry? 
Like, sort of like, is the from 

366
00:22:06,500 --> 00:22:08,800
the industry standpoint also 
apart from your digital 

367
00:22:08,800 --> 00:22:11,200
marketing and stuff, which is 
still very quantitative, or is 

368
00:22:11,200 --> 00:22:16,000
it sort of like, very different 
from what it is, in Academia? 

369
00:22:17,300 --> 00:22:20,900
Okay, so whatever I told you it.
So I have a purpose, my 

370
00:22:20,900 --> 00:22:27,700
experiences are colored by the 
By the academic good point. 

371
00:22:27,800 --> 00:22:29,400
But whatever. 
I told you that actually use a 

372
00:22:29,400 --> 00:22:35,600
lot in Industry. 
So for example, when you want to

373
00:22:35,600 --> 00:22:37,700
design a product, let's say you 
want to design a hotel room. 

374
00:22:38,500 --> 00:22:41,300
Okay? 
Now ideally you would like the 

375
00:22:41,600 --> 00:22:45,900
best most luxurious hotel room 
at the lowest price. 

376
00:22:45,900 --> 00:22:52,000
Giving free Wi-Fi, giving like 
screen TV, whatever. 

377
00:22:52,000 --> 00:22:54,800
It's I access whatever. 
That's not feasible. 

378
00:22:55,500 --> 00:22:59,500
It. 
What you want to do is kind of 

379
00:22:59,500 --> 00:23:06,300
try to measure for example. 
For a given price how prices it 

380
00:23:06,300 --> 00:23:08,000
is? 
A customer is of a singer. 

381
00:23:08,800 --> 00:23:12,600
But along with that. 
Where do you get your most bang 

382
00:23:12,600 --> 00:23:14,200
for buck? 
Okay. 

383
00:23:14,700 --> 00:23:18,000
Should I give free spy Axis, or 
is it better to be free Wi-Fi? 

384
00:23:18,300 --> 00:23:21,500
Which one do I think will lead 
to more customer satisfaction or

385
00:23:21,900 --> 00:23:24,700
bring in more customers? 
Right? 

386
00:23:24,800 --> 00:23:26,700
Ideally, I would like to give 
birth but I don't want to 

387
00:23:26,700 --> 00:23:30,700
because the cost money. 
So there is a method called 

388
00:23:30,700 --> 00:23:34,200
stick conjoint analysis. 
For example, where They say that

389
00:23:34,200 --> 00:23:38,100
this hypothetical. 
Sorry, let's dive into conjoint 

390
00:23:38,100 --> 00:23:39,800
analysis. 
Now that you have have let you 

391
00:23:39,800 --> 00:23:41,900
do it alone. 
Yeah, okay. 

392
00:23:42,300 --> 00:23:46,700
So this is just one example of 
where industry actually uses 

393
00:23:46,800 --> 00:23:49,900
quantitative methods. 
So what? 

394
00:23:51,600 --> 00:23:57,300
So what happens then is that? 
I will give a survey. 

395
00:23:58,300 --> 00:24:01,600
To a customer or potential 
customer. 

396
00:24:03,100 --> 00:24:11,900
That gives him a set of him or 
her a set of hypothetical hotel 

397
00:24:11,900 --> 00:24:15,600
rooms and I'm purposely using 
hotel rooms here because the 

398
00:24:15,600 --> 00:24:19,400
Hilton group has very same. 
Mostly used conjoint analysis. 

399
00:24:19,400 --> 00:24:24,400
I think 30, 40 years ago to 
design its various offerings, 30

400
00:24:24,400 --> 00:24:28,300
40 years ago, ha ha It's an old 
method. 

401
00:24:28,600 --> 00:24:31,600
Okay, well worth it. 
So I give you. 

402
00:24:31,600 --> 00:24:36,600
So, for example, I say 10,000 
rupees the night, free spy axis.

403
00:24:37,900 --> 00:24:43,600
No internet, okay, option one, 
another hypotheticals. 

404
00:24:43,600 --> 00:24:46,600
And now you can see the more 
so-called attributes. 

405
00:24:46,600 --> 00:24:52,800
I give I can actually have 
hundreds and thousands and even 

406
00:24:52,800 --> 00:24:55,100
millions of potential 
combinations of permutation 

407
00:24:55,100 --> 00:24:57,800
combination problem, right? 
So I can change the price for 

408
00:24:57,808 --> 00:25:00,500
anything. 
I want Wi-Fi. 

409
00:25:01,100 --> 00:25:04,100
I can say free Wi-Fi or paid 
Wi-Fi, but I can also increase 

410
00:25:04,100 --> 00:25:07,500
that to 1GB a day. 25gb a death 
certificate. 

411
00:25:07,600 --> 00:25:11,700
Yes, all of them cost money Etc.
I can have spy axis. 

412
00:25:12,200 --> 00:25:15,700
I can have free breakfast on. 
Not wreckage. 

413
00:25:15,700 --> 00:25:18,300
Very the price of breakfast, 
etc. 

414
00:25:18,300 --> 00:25:21,200
Etc. 
So I can give you, hypothetical 

415
00:25:21,200 --> 00:25:26,200
scenarios like this. 
This room has a king-size bed 

416
00:25:26,700 --> 00:25:30,100
free Wi-Fi. 
No Spa access option. 

417
00:25:30,100 --> 00:25:37,900
One option, two. 
It is 8000 rupees. 35 axis, free

418
00:25:37,900 --> 00:25:40,700
Wi-Fi, Etc. 
In the process. 

419
00:25:40,700 --> 00:25:46,000
What I am trying to do is I am 
trying to find out the consumer 

420
00:25:46,100 --> 00:25:50,200
or the customers value system. 
What you value this, what if it 

421
00:25:50,200 --> 00:25:53,000
doesn't. 
So some then maybe a group of 

422
00:25:53,000 --> 00:25:58,200
customers that just wants a sea 
view, you know, time in Bombay. 

423
00:25:59,300 --> 00:26:01,900
While another guy doesn't care 
about this review, but once free

424
00:26:01,900 --> 00:26:05,000
Wi-Fi, yep, maybe maybe. 
He's on a business trip. 

425
00:26:05,000 --> 00:26:07,400
You want sleeve is like the 
third guy. 

426
00:26:07,400 --> 00:26:09,900
Maybe he doesn't like either of 
these two, but would prefer free

427
00:26:09,900 --> 00:26:11,400
breakfast. 
Yep. 

428
00:26:11,500 --> 00:26:14,800
Stuff like this. 
So what I will try to do is 

429
00:26:14,800 --> 00:26:19,600
figure out what customers want 
or what groups of customers are 

430
00:26:20,100 --> 00:26:22,900
yet accordingly. 
I will Design my rooms. 

431
00:26:23,500 --> 00:26:26,500
Yeah. 
Now for the same price, I might 

432
00:26:26,500 --> 00:26:29,500
give customer x a City View. 
Rules. 

433
00:26:30,800 --> 00:26:34,300
Because he doesn't care about 
the Sea View, but free breakfast

434
00:26:34,600 --> 00:26:37,500
yet because we might prefers. 
The breakfast were at the same 

435
00:26:37,500 --> 00:26:39,000
price. 
Well, another, I will say, I 

436
00:26:39,000 --> 00:26:43,200
don't care about free breakfast.
Happy to pay for basic first, 

437
00:26:43,500 --> 00:26:46,500
but at the same price, I want a 
TV room. 

438
00:26:49,500 --> 00:26:52,200
So this is what conjoint 
analysis does. 

439
00:26:52,400 --> 00:26:55,600
Conjoint actually means 
considered jointly multiple 

440
00:26:55,600 --> 00:26:58,400
attributes considered jointly. 
Take a look at cell phone 

441
00:26:58,400 --> 00:26:59,600
design. 
Yeah. 

442
00:26:59,900 --> 00:27:01,500
It's a classic. 
Listen, conjoint. 

443
00:27:01,500 --> 00:27:04,700
Analysis comes in. 
Do you want more battery life, 

444
00:27:04,700 --> 00:27:06,300
or do you want more RAM in your 
phone? 

445
00:27:08,800 --> 00:27:13,500
They are at the same price and 
you are price sensitive or not 

446
00:27:14,000 --> 00:27:16,600
yet. 
But that price goes with Allure.

447
00:27:16,600 --> 00:27:20,300
Am sensitive or not. 
Are you screen size sensitive or

448
00:27:20,300 --> 00:27:21,600
not? 
Etc. 

449
00:27:21,800 --> 00:27:23,600
So I will give you hypothetical 
Community. 

450
00:27:23,600 --> 00:27:25,100
I cannot be all the 
combinations. 

451
00:27:25,100 --> 00:27:26,200
There are statistical 
techniques. 

452
00:27:26,200 --> 00:27:31,100
That let me reduce the number of
hypothetical scenarios. 

453
00:27:31,100 --> 00:27:37,200
I give you and in the process, I
try to figure out what you want 

454
00:27:37,200 --> 00:27:39,200
the most. 
Megan's take my customers, 

455
00:27:39,200 --> 00:27:42,000
bitch. 
Yeah, okay, that I was coming to

456
00:27:42,000 --> 00:27:43,500
that. 
Like if you can also cluster and

457
00:27:43,500 --> 00:27:46,000
sort of segments to people 
because like they will do a 

458
00:27:46,900 --> 00:27:49,900
bunch of people who may be 
willing willing to pay 2,000 

459
00:27:49,900 --> 00:27:53,300
Rupees per night, except for the
Sea View, while another Bunch 

460
00:27:53,300 --> 00:27:55,400
who might there, not value it at
all, in things like that. 

461
00:27:55,400 --> 00:28:00,800
So because exactly, Exactly. 
So, ideally what I would like is

462
00:28:01,200 --> 00:28:03,700
I would like to give you a room 
that exactly fits your 

463
00:28:03,700 --> 00:28:05,600
specification and maximizes my 
product. 

464
00:28:05,900 --> 00:28:07,500
That's not going to be possible.
Yes. 

465
00:28:07,500 --> 00:28:10,300
I have a finite set of rooms. 
Of course. 

466
00:28:10,400 --> 00:28:13,900
I have a finite set of rooms. 
So what I want to see is, can I 

467
00:28:14,000 --> 00:28:19,500
find clusters of people? 
Yeah, who value C View and 

468
00:28:19,500 --> 00:28:24,000
breakfast and this. 
Similarly, so can I have a, this

469
00:28:24,000 --> 00:28:25,800
is called segmentation in 
marketing. 

470
00:28:25,800 --> 00:28:29,100
It's also about cluster analysis
in Statistics and machine 

471
00:28:29,100 --> 00:28:30,300
learning. 
They are one in the same. 

472
00:28:30,500 --> 00:28:33,600
Actually. 
Can I find a group of people who

473
00:28:33,600 --> 00:28:37,800
have similar needs or 
preferences? 

474
00:28:38,600 --> 00:28:40,800
And then I will give them 
something where they are. 

475
00:28:43,000 --> 00:28:46,700
Where they're more likely to 
like my product. 

476
00:28:46,900 --> 00:28:49,400
Yeah. 
And healthy make more money at 

477
00:28:49,400 --> 00:28:52,700
the end of the day. 
It's today made for money. 

478
00:28:54,200 --> 00:28:56,900
Yeah, so I guess identity for 
phones. 

479
00:28:57,300 --> 00:29:01,800
It can be for false. 
It can be for hair saloons. 

480
00:29:01,800 --> 00:29:05,200
It can be for IT consulting. 
Contracts. 

481
00:29:05,200 --> 00:29:08,500
It can be for hospitals, can be 
any of this. 

482
00:29:09,600 --> 00:29:11,300
But I'm sober. 
So tell me anything like that. 

483
00:29:11,300 --> 00:29:13,800
So I think conjoint analysis 
since its 30. 40 years old. 

484
00:29:13,800 --> 00:29:17,100
I'm assume assuming it was 
initially designed for the 

485
00:29:17,400 --> 00:29:19,900
sampling world where you do a 
survey and then do this 

486
00:29:19,900 --> 00:29:22,400
analysis. 
Now, I guess, like, if you are, 

487
00:29:23,000 --> 00:29:25,300
if you are, let's say I make my 
trip you. 

488
00:29:25,500 --> 00:29:28,200
Possibly have the data that 
taking your hotel room example, 

489
00:29:28,200 --> 00:29:31,200
you probably have the data to 
actually do this with what I 

490
00:29:31,200 --> 00:29:35,100
would call as population data, 
rather than sample data as that 

491
00:29:35,100 --> 00:29:40,800
actually happened as in like as 
and are has marketing evolved to

492
00:29:40,800 --> 00:29:44,800
sort of, take into account, all 
this the amount of data that we 

493
00:29:44,800 --> 00:29:47,800
sort of collect and use 
nowadays. 

494
00:29:49,700 --> 00:29:54,000
That's an interesting question. 
The answer can best be given by 

495
00:29:54,000 --> 00:29:57,800
companies who have this data. 
It's very unlikely that they're 

496
00:29:57,800 --> 00:30:00,800
going to tell you what exactly 
they're doing with it. 

497
00:30:01,200 --> 00:30:06,700
Of course, on my guess is yes. 
My guess is that there must be 

498
00:30:07,200 --> 00:30:09,800
some sort of background 
analysis. 

499
00:30:09,800 --> 00:30:13,800
Done travel booking websites, 
are a classic case of where you 

500
00:30:13,800 --> 00:30:17,700
can take. 
So you cannot exactly. 

501
00:30:17,700 --> 00:30:20,800
Do conjoint analysis, which 
travel booking data, by the way,

502
00:30:21,300 --> 00:30:23,300
organically because I have given
you only one choice. 

503
00:30:23,300 --> 00:30:26,200
You have given me certain 
options which actually exists 

504
00:30:26,200 --> 00:30:29,000
and I've given you one choice 
and your clothes. 

505
00:30:29,500 --> 00:30:34,100
You don't know my relative 
references for XYZ, over a large

506
00:30:34,100 --> 00:30:37,100
number of people, you can do 
some sort of statistical 

507
00:30:37,100 --> 00:30:41,500
inference, but at the individual
level, I have given you very few

508
00:30:41,700 --> 00:30:50,300
data points, so But yes, what 
you can do is you can a find out

509
00:30:50,300 --> 00:30:54,300
across a large area, almost 
population level. 

510
00:30:55,100 --> 00:31:03,000
What people's preferences are, 
you can also track me and my 

511
00:31:03,000 --> 00:31:07,100
past purchases assuming that 
I've been loyal to make my trip.

512
00:31:07,100 --> 00:31:10,700
Now, that if you were taking the
example of make my trip, now, 

513
00:31:10,700 --> 00:31:15,400
that is not necessarily true, of
course, along with make my trip.

514
00:31:15,600 --> 00:31:18,400
We'll be able to clear trip. 
I may be going to booking.com. 

515
00:31:18,800 --> 00:31:21,900
Yeah, and a lot of other 
websites that actually takes me 

516
00:31:21,900 --> 00:31:24,800
to another very interesting 
problem in marketing. 

517
00:31:25,000 --> 00:31:31,200
Okay, which is, which is 
modeling vertical, steamer 

518
00:31:31,500 --> 00:31:34,000
attrition and Customer Loyalty. 
Okay. 

519
00:31:34,000 --> 00:31:35,800
It's a very, very big problem in
bucket. 

520
00:31:36,100 --> 00:31:37,600
Okay. 
Let me give an example. 

521
00:31:37,700 --> 00:31:39,900
Let's give you the example of 
make my trip itself. 

522
00:31:40,200 --> 00:31:44,300
Yeah, so, okay. 
We'll go back to for Simplicity 

523
00:31:44,300 --> 00:31:45,400
sake. 
Let's go back up. 

524
00:31:45,500 --> 00:31:46,800
Recovery data, okay. 
Okay. 

525
00:31:46,900 --> 00:31:49,000
Yep. 
It complicates a lot of things. 

526
00:31:49,000 --> 00:31:54,500
Yeah, we don't yet know how the 
models are to think, that's it. 

527
00:31:54,700 --> 00:31:56,200
Let's go back to the 
precordillera. 

528
00:31:57,200 --> 00:31:59,800
So, make my trip has my phone 
number. 

529
00:32:00,400 --> 00:32:03,300
Yeah, so they know. 
It's me when I'm booking also. 

530
00:32:03,300 --> 00:32:06,200
Actually, when I booked a flight
ticket, they have all my 

531
00:32:06,200 --> 00:32:08,500
details, so, they know exactly. 
That it's me terribly. 

532
00:32:08,900 --> 00:32:12,000
Yes. 
Let's say I travel on 1st 

533
00:32:12,000 --> 00:32:17,100
January and make my first ever 
transaction one for Jen. 2010. 

534
00:32:17,800 --> 00:32:22,100
Okay, one month later. 
I make one more trouble for 

535
00:32:22,100 --> 00:32:23,800
months later. 
They make my next travel. 

536
00:32:25,200 --> 00:32:27,700
One month later. 
I made it my next travel, Etc. 

537
00:32:27,700 --> 00:32:29,100
So they're seeing a pattern, 
right? 

538
00:32:31,300 --> 00:32:35,400
Now after that, sadly they say, 
see a break or six months. 

539
00:32:37,100 --> 00:32:41,300
Yes, okay, and they don't know 
the future at that point. 

540
00:32:42,100 --> 00:32:44,200
It's been six months since I 
last traveled. 

541
00:32:45,400 --> 00:32:49,000
So the question I have not 
signed any contract with me by 

542
00:32:49,000 --> 00:32:51,600
trip. 
It's not like my mobile. 

543
00:32:51,800 --> 00:32:53,900
It's not like a postpaid mobile 
phone, operator. 

544
00:32:53,900 --> 00:32:58,500
Yes, I signed a contract and 
they know when I have pulled out

545
00:32:59,200 --> 00:33:03,700
the question is, am I still 
active on make my trip or not? 

546
00:33:03,900 --> 00:33:07,100
Yeah. 
Now you can think of it's a make

547
00:33:07,100 --> 00:33:09,900
my trip against Amazon, then 
snip it buzzer. 

548
00:33:10,200 --> 00:33:14,700
Yes, is it this is called, I am 
a non-contractual customer. 

549
00:33:15,200 --> 00:33:19,900
Has been frequently the store 
and at each time, they know how 

550
00:33:19,900 --> 00:33:22,800
much I have but how much is 
spent it? 

551
00:33:25,600 --> 00:33:29,500
So, now the question is, how do 
they know when I will be back 

552
00:33:29,500 --> 00:33:31,500
next? 
If at all, I will be back. 

553
00:33:31,900 --> 00:33:38,200
Yep, very tough problem to say. 
Yep, a first-rate here. 

554
00:33:38,200 --> 00:33:40,700
You can do things. 
Like, you can assume that if I'm

555
00:33:40,700 --> 00:33:44,300
in active for six months M1. 
Yep, but is it really true? 

556
00:33:45,200 --> 00:33:48,800
Is there a better method? 
One of the things, one of the, 

557
00:33:50,800 --> 00:33:54,300
this kind of modeling was 
started in the 1980s early 80s, 

558
00:33:54,400 --> 00:33:58,300
okay. 
Buy some days called David 

559
00:33:58,300 --> 00:34:01,400
Schmidt line. 
MIT guys, anything they wish 

560
00:34:01,400 --> 00:34:05,400
with line Colombo is the third 
day Morris and Donald Morrison. 

561
00:34:05,400 --> 00:34:06,800
How can I forget Donald 
Morrison? 

562
00:34:07,800 --> 00:34:11,400
So what then suggested is that 
these patterns that you see are 

563
00:34:11,400 --> 00:34:15,600
actually a person process. 
So I am like a, yeah, I enter 

564
00:34:15,600 --> 00:34:20,300
purchase times. 
Our poisson process are a 

565
00:34:20,300 --> 00:34:23,800
person. 
I'm assuming you're actually in 

566
00:34:23,800 --> 00:34:25,199
the processing will be a 
connection. 

567
00:34:26,100 --> 00:34:30,400
Interposition stammer, his 
exponential, took its time would

568
00:34:30,400 --> 00:34:33,100
be exponential. 
Okay, exactly. 

569
00:34:33,199 --> 00:34:38,900
Yeah, except at some point. 
There isn't stopped. 

570
00:34:39,100 --> 00:34:41,199
Maybe I got the stuff. 
Yes. 

571
00:34:41,199 --> 00:34:43,800
Be a located out yet. 
Maybe I just don't want to 

572
00:34:43,800 --> 00:34:45,500
travel anymore. 
Or maybe I actually died. 

573
00:34:45,900 --> 00:34:48,000
Yes, follow the first possible. 
Yes. 

574
00:34:48,400 --> 00:34:51,199
And in this literature is the 
estimate. 

575
00:34:51,199 --> 00:34:54,800
This is actually called Death. 
It's quarter after my death. 

576
00:34:54,800 --> 00:34:59,400
So Ellie What they came up with 
something called which is one of

577
00:34:59,400 --> 00:35:01,800
the bike till you die murder. 
Okay. 

578
00:35:02,600 --> 00:35:07,800
I am a customer with a certain 
exponential distribution. 

579
00:35:09,900 --> 00:35:13,600
That models my enterprise 
system. 

580
00:35:13,800 --> 00:35:16,400
Yes, but there's also an 
exponential distribution and 

581
00:35:16,400 --> 00:35:20,000
it's like a time to failure in a
way, which bottles my lifetime. 

582
00:35:20,500 --> 00:35:21,700
Exactly. 
Yes. 

583
00:35:22,600 --> 00:35:25,400
There are two things that are 
simultaneously in done. 

584
00:35:25,700 --> 00:35:29,200
So based on their past 
purchases, you will actually 

585
00:35:29,200 --> 00:35:33,500
have a likelihood function. 
Oh, there is a second step to 

586
00:35:33,500 --> 00:35:34,100
this. 
Now. 

587
00:35:34,100 --> 00:35:37,100
There are thousands of other 
customers, right? 

588
00:35:37,300 --> 00:35:39,400
Yes. 
Who may not have the same. 

589
00:35:40,000 --> 00:35:42,300
Exponential distribution 
parameters that I do. 

590
00:35:42,800 --> 00:35:47,500
Yes, on top of this you need to 
have a distribution that models 

591
00:35:47,500 --> 00:35:50,500
the heterogeneity of this, 
right? 

592
00:35:50,500 --> 00:35:55,500
If you have to have fun is my 
Lambda 2 Lambda mu 1 is from a 

593
00:35:55,508 --> 00:35:57,300
lifetime and one is from in to 
possess. 

594
00:35:57,400 --> 00:36:03,100
Yeah. 
But you and me and thousand 

595
00:36:03,100 --> 00:36:07,900
other people, our same 
parameters, then come from. 

596
00:36:09,800 --> 00:36:13,600
Let's say beta distribution. 
All are my distribution, 

597
00:36:13,600 --> 00:36:17,900
depending on Etc, depending on 
certain assumptions. 

598
00:36:17,900 --> 00:36:20,600
You can actually change that. 
So there's a lot of research 

599
00:36:20,600 --> 00:36:23,400
going on here to use a 
distribution instead of an 

600
00:36:23,400 --> 00:36:26,800
exponential distribution Etc, to
model in to purchase type. 

601
00:36:27,100 --> 00:36:30,400
The simplest is it approaches 
the times you see, exponential 

602
00:36:30,400 --> 00:36:32,900
distributions? 
And so they came up with a very 

603
00:36:33,000 --> 00:36:36,900
important result which says that
I don't need to store every 

604
00:36:37,300 --> 00:36:41,800
single But just timing know it. 
Remember databases are expensive

605
00:36:41,800 --> 00:36:43,800
in the 80s in the 80s. 
It was exist. 

606
00:36:43,800 --> 00:36:47,800
I thought I did if I use even 
now even now by the way, are 

607
00:36:47,800 --> 00:36:50,500
even now even if you were to 
store everything and not use an 

608
00:36:50,500 --> 00:36:52,900
exponential distribution and try
to calculate a likelihood 

609
00:36:52,900 --> 00:36:56,300
function, your computer is where
even the very most powerful 

610
00:36:56,300 --> 00:36:58,500
computers. 
They run for weeks possibly have

611
00:36:58,500 --> 00:37:03,700
to what their true. 
So again, if you are you will 

612
00:37:03,700 --> 00:37:07,400
probably know you don't know if 
your leadership sorry. 

613
00:37:07,400 --> 00:37:12,200
Isness. 
Your listeners, the exponential 

614
00:37:12,200 --> 00:37:14,200
distribution has a memory less 
property. 

615
00:37:14,700 --> 00:37:17,700
Of course. 
Yes, which means there are three

616
00:37:17,700 --> 00:37:20,000
parameters that you need to 
stir. 

617
00:37:20,500 --> 00:37:23,400
Yes, the timing of the last 
purchase, which one is the 

618
00:37:23,400 --> 00:37:26,300
recency. 
Yeah, how many times you have 

619
00:37:26,300 --> 00:37:29,300
purchased in the past, which is 
known as frequency and how much 

620
00:37:29,300 --> 00:37:32,400
money you have spent? 
Yes. 

621
00:37:32,800 --> 00:37:35,400
It is. 
The last key, I think in the 

622
00:37:35,400 --> 00:37:38,700
last or maybe the monetary 
transaction, you may need. 

623
00:37:39,800 --> 00:37:43,000
The money part, I remember what 
the recency and frequency. 

624
00:37:43,200 --> 00:37:46,400
Yet are sufficient to start 
modeling your future medium. 

625
00:37:47,400 --> 00:37:49,500
Yeah. 
I got an aggregate level. 

626
00:37:49,500 --> 00:37:55,500
I can totally believe that at at
an aggregate limit and So 

627
00:37:55,500 --> 00:38:00,800
further from people, simplified 
this Etc, these at an aggregate 

628
00:38:00,800 --> 00:38:06,600
level are excellent in 
predicting future volumes of 

629
00:38:06,600 --> 00:38:10,000
purchases, from a knot at an 
individual level. 

630
00:38:10,300 --> 00:38:16,300
Yes, but If you have a cohort of
customers, it is very good at 

631
00:38:16,300 --> 00:38:19,300
predicting how much they're 
going to buy in the future. 

632
00:38:20,000 --> 00:38:21,700
Yep. 
Now, the other advantage of this

633
00:38:21,700 --> 00:38:26,500
person processes You Can Count. 
Yes, there is actually a method.

634
00:38:26,800 --> 00:38:30,100
There is actually a closed form 
solution where you can count the

635
00:38:30,100 --> 00:38:33,200
number of transactions in any 
given time period, what they 

636
00:38:33,200 --> 00:38:37,300
show is that out-of-sample 
predictions are amazingly good 

637
00:38:37,800 --> 00:38:42,500
for this. 
What is not good? 

638
00:38:42,500 --> 00:38:44,900
However, is how do you tracks an
individual? 

639
00:38:45,500 --> 00:38:51,300
So the next level is can you 
make individual estimates of 

640
00:38:51,900 --> 00:38:56,100
Lambda, get a little 
distribution parameters, 

641
00:38:56,100 --> 00:39:01,000
etcetera at the individual level
that there has been mixed 

642
00:39:01,000 --> 00:39:04,300
success, but people do that a 
lot using using hierarchical 

643
00:39:04,300 --> 00:39:09,400
Bayes kind of analysis. 
Very computationally expensive 

644
00:39:09,400 --> 00:39:10,800
though. 
If you try to go at the 

645
00:39:10,800 --> 00:39:14,000
individual, Level things get a 
lot more complicated. 

646
00:39:14,500 --> 00:39:18,300
And of course the next step is 
then at what point do I send you

647
00:39:18,300 --> 00:39:20,300
a coupon? 
What kind of poop and you send 

648
00:39:20,300 --> 00:39:22,900
you? 
How do you react to my marketing

649
00:39:22,900 --> 00:39:24,400
messages at the individual 
level? 

650
00:39:24,900 --> 00:39:30,200
So this is a lot of where it's 
called customer lifetime value 

651
00:39:30,200 --> 00:39:32,600
modeling sometimes or customer 
churn modeling. 

652
00:39:32,800 --> 00:39:37,700
Yep, another very important. 
I would say quantitative area of

653
00:39:37,700 --> 00:39:39,600
marketing analytics. 
Yeah. 

654
00:39:39,600 --> 00:39:41,200
Okay. 
I think I think by this point, 

655
00:39:41,300 --> 00:39:42,600
Point. 
I think if anybody has risen 

656
00:39:42,600 --> 00:39:46,300
done till now, any doubts, they 
have about marketing being a 

657
00:39:46,300 --> 00:39:48,600
quantitative description would 
have been dispelled. 

658
00:39:48,600 --> 00:39:51,900
I mean, just this one thing of 
having to talk about customer 

659
00:39:51,900 --> 00:39:55,400
lifetime value, which seems like
a sort of, I mean, sort of a 

660
00:39:55,408 --> 00:39:57,900
global concept. 
We've kind of broken with down 

661
00:39:57,900 --> 00:40:01,100
to a exponential distribution 
and parameters such as like 

662
00:40:01,100 --> 00:40:04,700
frequency and decency and so on.
And so it's very clear about how

663
00:40:05,200 --> 00:40:07,500
how quantitative marketing is. 
I don't think anybody will have 

664
00:40:07,500 --> 00:40:10,000
a doubt about it. 
So why do I say so slightly 

665
00:40:10,000 --> 00:40:13,100
switching tracks? 
So in the last 10 years, I think

666
00:40:13,100 --> 00:40:15,500
one new career that has come up,
which wasn't there before? 

667
00:40:15,500 --> 00:40:18,000
Was this whole thing is a thing 
called digital marketing. 

668
00:40:18,600 --> 00:40:21,200
And what do you find? 
Especially when I go around on 

669
00:40:21,200 --> 00:40:24,700
LinkedIn is that like sort of 
the people who call the age of 

670
00:40:24,700 --> 00:40:28,300
digital marketers? 
Have they don't seem like. 

671
00:40:28,500 --> 00:40:32,000
So they sort of seem disjoint 
from traditional Market is, why 

672
00:40:32,000 --> 00:40:36,700
is it that digital marketing? 
In your opinion has sort of 

673
00:40:36,800 --> 00:40:40,400
grown in a completely different 
way and like, from from the 

674
00:40:40,500 --> 00:40:43,300
traditional Market, Because the 
way I see it, it's all 

675
00:40:43,300 --> 00:40:44,800
marketing. 
It's just a different medium. 

676
00:40:46,500 --> 00:40:49,900
Okay. 
It depends on how you define 

677
00:40:49,900 --> 00:40:52,900
digital marketing. 
I would say the pioneer of 

678
00:40:52,900 --> 00:40:59,200
digital marketing comes from 
mobile essentially, because they

679
00:41:00,200 --> 00:41:02,500
pioneered. 
And these are not mbas or even 

680
00:41:02,500 --> 00:41:04,300
business activities in any 
sense. 

681
00:41:04,600 --> 00:41:09,300
These are computer scientists 
who, who realize that I can find

682
00:41:09,500 --> 00:41:13,000
data about you. 
I can get data about you and I 

683
00:41:13,000 --> 00:41:18,800
will then pass on that data. 
Our because to advertisers, you 

684
00:41:18,800 --> 00:41:21,300
can Target you with with 
something very specific. 

685
00:41:21,600 --> 00:41:26,700
So, for example, if I look for, 
let's say, but our shoes I want 

686
00:41:26,700 --> 00:41:30,300
to put on Google. 
Then Google will pass on the 

687
00:41:30,300 --> 00:41:33,500
message to not just butter, but 
pursue retailer, or maybe Nike 

688
00:41:33,500 --> 00:41:36,600
or maybe Puma. 
Yeah, when we're competitors to 

689
00:41:36,600 --> 00:41:42,300
butter. 
And so It is better than sending

690
00:41:42,300 --> 00:41:46,700
me a leaflet on in my mailbox, 
where they don't know if I'm 

691
00:41:46,700 --> 00:41:48,100
actually looking for shoes or 
not. 

692
00:41:48,500 --> 00:41:53,000
You have the fit between the 
person and the advertisers is 

693
00:41:53,100 --> 00:42:01,900
established. 
The next level is probably at 

694
00:42:01,900 --> 00:42:04,200
this point. 
The typical digital marketer 

695
00:42:04,200 --> 00:42:06,800
that you may be talking about is
the so-called social media 

696
00:42:06,800 --> 00:42:13,500
marketer, you know, yeah, which 
is doing social media campaigns.

697
00:42:16,300 --> 00:42:19,300
And the so-called influences, 
where it is, somebody with a 

698
00:42:19,308 --> 00:42:25,200
large social media following. 
Who is then missing whose tent 

699
00:42:25,200 --> 00:42:27,200
going and telling the brand. 
Then? 

700
00:42:27,200 --> 00:42:29,900
Look, I have so many followers. 
So, I am a very popular food 

701
00:42:29,900 --> 00:42:35,400
blogger. 
Yeah, if you are selling The 

702
00:42:35,400 --> 00:42:38,600
nonstick cookware. 
What I, I will. 

703
00:42:39,900 --> 00:42:42,600
Endorse it, and there's a 
benefit to you. 

704
00:42:42,600 --> 00:42:44,600
So you pay me for it. 
Yep. 

705
00:42:46,800 --> 00:42:50,700
Social media marketing is an 
extension of what Google 

706
00:42:50,700 --> 00:42:53,400
Pioneer. 
Google tried a lot to capture 

707
00:42:53,400 --> 00:42:56,900
social media, but for some 
reason they would not all of 

708
00:42:56,900 --> 00:42:59,200
these are now. 
Yep. 

709
00:42:59,700 --> 00:43:03,900
What bviously future? 
Nobody can say right now. 

710
00:43:03,900 --> 00:43:09,500
Social media is a strange Beast 
because Social media lets, you 

711
00:43:09,500 --> 00:43:11,900
know, not just what your 
preferences are, what you share.

712
00:43:11,900 --> 00:43:16,200
Our reference is economics, if I
move on to the XYZ restaurant 

713
00:43:16,200 --> 00:43:20,500
and I put a picture then. 
They have some idea. 

714
00:43:20,500 --> 00:43:22,200
What kind of Cuisine zelig 
etcetera. 

715
00:43:22,700 --> 00:43:27,000
The fun part about social media 
is that it can map your social 

716
00:43:27,000 --> 00:43:30,400
network for certain degree? 
Yes. 

717
00:43:30,400 --> 00:43:33,100
It knows who your friends are. 
We'll see your posts. 

718
00:43:33,600 --> 00:43:36,600
Yes, what there. 
And this is where actually it. 

719
00:43:36,600 --> 00:43:38,700
So I told you about sociology 
yet. 

720
00:43:38,700 --> 00:43:41,600
This is where social logical 
Concepts become, very important 

721
00:43:41,600 --> 00:43:49,900
Network science, of course not. 
So, now Facebook and do 

722
00:43:49,900 --> 00:43:53,900
something that Google cannot. 
It knows not just about you but 

723
00:43:53,900 --> 00:43:55,300
a body of Rex. 
Yes. 

724
00:43:56,400 --> 00:44:00,400
Yes, and essentially, that is 
the reason why if Facebook is so

725
00:44:00,400 --> 00:44:03,200
valuable, this guide, the fact 
that you're not searching for 

726
00:44:03,207 --> 00:44:10,800
information on Facebook also. 
Also, of course, Now, you have 

727
00:44:10,900 --> 00:44:13,400
killing a lot of time on 
Facebook, Twitter, Instagram, 

728
00:44:13,400 --> 00:44:16,500
Etc, which means that you have 
in this sticky on this platform,

729
00:44:16,500 --> 00:44:19,900
which means by definition, there
are 24 hours a day. 

730
00:44:20,800 --> 00:44:23,900
So your DV time has now become 
okay? 

731
00:44:24,300 --> 00:44:27,200
Yes. 
Okay, for example, a there are 

732
00:44:27,207 --> 00:44:29,400
two aspects to it one is they 
have granular data. 

733
00:44:29,400 --> 00:44:32,500
The second is it is in their 
interest to people who come to 

734
00:44:32,500 --> 00:44:39,200
the platform because Only if a 
brand gets eyeballs, will 

735
00:44:39,200 --> 00:44:42,400
advertisement this network, 
right? 

736
00:44:45,700 --> 00:44:52,500
So now, Digital campaigns. 
In that sense, a little 

737
00:44:52,500 --> 00:44:55,900
different from a flight Camp. 
It's little mention Machinery. 

738
00:44:55,900 --> 00:44:59,400
Any to get your view on this. 
How has AI? 

739
00:44:59,900 --> 00:45:04,000
Ml Big Data Cloud. 
How has that changed marketing? 

740
00:45:06,400 --> 00:45:08,000
Well, not right? 
Okay. 

741
00:45:08,000 --> 00:45:09,900
Look at how Google is in 
marketing. 

742
00:45:09,900 --> 00:45:17,700
That's your answer a lot. 
So if you use your data in a 

743
00:45:18,700 --> 00:45:22,100
rigorous fashion economically 
residence, okay, let's be clear 

744
00:45:22,100 --> 00:45:24,000
is it is scientifically 
rigorous. 

745
00:45:24,000 --> 00:45:27,400
Let's say fashion. 
There's a lot that can be done 

746
00:45:27,700 --> 00:45:31,500
with big data. 
So this for example, I started 

747
00:45:31,500 --> 00:45:34,500
off by telling you that store 
inventory right in the face, 

748
00:45:35,500 --> 00:45:38,800
too. 
Is Amazon knows what you 

749
00:45:38,800 --> 00:45:41,500
clicked? 
When you abandoned your cut 

750
00:45:41,800 --> 00:45:45,500
shopping cart? 
Yes, they can do pricing 

751
00:45:45,500 --> 00:45:49,100
experiments into price 
discrimination experiments where

752
00:45:49,500 --> 00:45:51,500
they keep one person. 
One tries on the person on the 

753
00:45:51,500 --> 00:45:53,400
pricing, Etc. 
Yep. 

754
00:45:53,400 --> 00:45:56,200
You can do large A/B tests. 
They can do a meters from their 

755
00:45:56,200 --> 00:45:58,400
format Etc. 
And by the way, not just online 

756
00:45:58,400 --> 00:46:00,900
even offline, big store can do 
it. 

757
00:46:00,900 --> 00:46:04,200
And record the results are in a 
database. 

758
00:46:05,600 --> 00:46:08,700
So it has revolutionized 
marketing, a lot, the more data 

759
00:46:08,700 --> 00:46:13,800
you have. 
Oh, the more relevant data that 

760
00:46:13,800 --> 00:46:17,600
you have to keep the board. 
Informed decisions. 

761
00:46:17,800 --> 00:46:21,400
You can make the trick is in how
you use that data. 

762
00:46:22,100 --> 00:46:23,600
So, I'll give you a word of 
caution. 

763
00:46:23,600 --> 00:46:26,700
Same way that it has changed 
Marketing in a lot of ways. 

764
00:46:27,200 --> 00:46:30,700
There is a lot of improper usage
as well. 

765
00:46:31,000 --> 00:46:35,300
Okay, so a classic example is, 
let's say you are modeling my 

766
00:46:35,300 --> 00:46:40,500
behavior. 
And you have a 1 million meter 

767
00:46:40,700 --> 00:46:43,000
model, OKAY? 
In your opinion. 

768
00:46:43,000 --> 00:46:45,200
Is that a good model that a bad 
Body by Body? 

769
00:46:45,200 --> 00:46:47,500
Is that a good point? 
A little bit further Back Body. 

770
00:46:47,500 --> 00:46:50,300
Why to my parameters, too many 
parameters. 

771
00:46:50,300 --> 00:46:52,200
I mean like, it's just lucky to 
have a lot of 

772
00:47:02,900 --> 00:47:06,100
articles in the Press actually 
said that all. 

773
00:47:06,100 --> 00:47:09,500
Look there. millions of data 
points and they're using 

774
00:47:09,700 --> 00:47:15,100
millions of parameters to A 
Target, you to predict your 

775
00:47:15,100 --> 00:47:18,900
behaviors and you'll find a lot 
of people actually say that. 

776
00:47:18,900 --> 00:47:21,800
Look, I can predict your 
behavior etcetera with this. 

777
00:47:22,500 --> 00:47:25,400
The problem is a serious. 
Statistician will tell you, 

778
00:47:25,400 --> 00:47:26,700
then. 
This is a very permanent. 

779
00:47:27,700 --> 00:47:31,400
So at the heart of the matter, 
it's still the same. 

780
00:47:32,800 --> 00:47:35,600
The principles of Statistics are
still the same way, you need 

781
00:47:35,600 --> 00:47:38,500
that. 
You need pasta money and you 

782
00:47:38,500 --> 00:47:42,800
need some sort of. 
Critical justification for the 

783
00:47:42,808 --> 00:47:46,400
model pure data. 
If you were just to fix 

784
00:47:46,400 --> 00:47:51,400
ourselves into a data, it's very
unlikely to have out-of-sample 

785
00:47:51,500 --> 00:47:55,100
predictive validity. 
The principles are the same, the

786
00:47:55,100 --> 00:47:57,400
number of data points increase, 
do you? 

787
00:47:58,000 --> 00:48:00,400
So the statistical power of what
you do? 

788
00:48:01,500 --> 00:48:05,100
Goes up a great deal as you mean
that you're using the data 

789
00:48:05,100 --> 00:48:12,000
appropriately. 
So, yes, in short the data, the 

790
00:48:12,200 --> 00:48:17,600
greater availability of data. 
If used by the right person, in 

791
00:48:17,600 --> 00:48:23,800
the right way, can help a lot. 
It can also be misused. 

792
00:48:26,200 --> 00:48:28,600
And mislead you into thinking 
that you are. 

793
00:48:30,000 --> 00:48:33,400
You have very sophisticated 
analytics which may not always 

794
00:48:33,400 --> 00:48:36,300
be the case. 
Now, by the way, I don't 

795
00:48:36,300 --> 00:48:42,400
normally plugs and be but I am 
be actually has started a new 

796
00:48:42,400 --> 00:48:48,700
MBA called Indian analytics. 
It's it is an analytics focused 

797
00:48:48,700 --> 00:48:55,700
MBA. 
Where's a lot of your coursework

798
00:48:55,700 --> 00:48:58,900
is different from the standard. 
India does, if you can intake of

799
00:48:58,900 --> 00:49:01,700
only 40 people in the first 
batch, they will be graduating 

800
00:49:01,700 --> 00:49:07,000
next year with the intention. 
Of course, when I say MBA in 

801
00:49:07,000 --> 00:49:09,500
analytics, it doesn't mean MBA 
in marketing analytics. 

802
00:49:09,700 --> 00:49:13,200
Yeah, let's go operations 
analytics HR analytics and all 

803
00:49:13,200 --> 00:49:18,300
of that Financial analytics and 
marketing analytics actually 

804
00:49:18,300 --> 00:49:26,100
placed a large. 
Parole today in the so-called 

805
00:49:26,100 --> 00:49:27,100
analytics. 
Boom. 

806
00:49:27,100 --> 00:49:32,500
That fits in yep, whether it's 
music miles of the world or 

807
00:49:32,700 --> 00:49:35,300
earlier, the Googles and absence
Facebook's Etc. 

808
00:49:36,100 --> 00:49:39,200
Other special genetics company, 
Sigma a lot of others. 

809
00:49:39,600 --> 00:49:41,600
A lot of them actually look 
marketing analytics. 

810
00:49:42,000 --> 00:49:46,700
So MBA is across the world are 
kind of you to live in this way.

811
00:49:47,300 --> 00:49:50,600
I am be, has an analytics and 
behold a few American and 

812
00:49:50,600 --> 00:49:53,400
European. 
Schools have specialized Masters

813
00:49:53,400 --> 00:49:59,000
or MBA programs that tend to 
focus more on analytics than a 

814
00:49:59,000 --> 00:50:23,800
traditional lb Etc. 
Thank you for listening to data 

815
00:50:23,800 --> 00:50:26,700
shatter. 
If you like this show, please 

816
00:50:26,700 --> 00:50:29,700
leave a comment, share and 
subscribe to the podcast. 

817
00:50:30,000 --> 00:50:33,300
You can find this podcast on 
Apple podcasts Spotify. 

818
00:50:33,400 --> 00:50:36,200
By or wherever else, you go to 
get your podcasts. 

819
00:50:37,000 --> 00:50:39,200
Once again, this is Karthik 
signing off. 

820
00:50:39,500 --> 00:50:40,000
Thank you.