Notes

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So the idea that we're going to talk
about, preference isolation, is the notion
that
you have preferences and tastes that are
different from the other people in your
neighborhood.
And as a result, local stores, local
restaurants, local
merchants aren't motivated to give you
what you'd like.
So just to motivate this a little bit
more, let's
think about an example where everyone in
your town wears ties.
most people happened to want to wear blue
ties, and you wear a red tie.
Now think about the problem faced by
manufacture and seller of ties if there's
a cost associated with producing more
variety.
And typically there, there is.
The manager of that factory and that store
might say, you know what?
most people want blue ties.
Only one fellow wants a red tie.
We're not going to make any red ties.
And as a result, the person with the taste
for the
red tie, the preference minority, is not
going to get what they want.
So I'm going to explain in the next few
slides how this problem can
be overcome through online intervention,
particularly
through sellers who operate on the
internet.
to make a more personal example here,
I often wonder myself in myself in
Philadelphia
why it is that I can't get Vegemite when I
go to the local supermarket.
Let me show you a picture of Vegemite.
I'm sure my friends in the UK Australia,
and New Zealand and other places have seen
it.
Here's a little kid with Vegemite all over
his face.
It's a delicious black paste that you have
on toast with cheese and avocado and stuff
like that.
I look for it all the time when I go
to the supermarket in Philadelphia, but I
can never find it.
Why?
Because I'm probably one of the only few
people that would actually buy it.
So the store manager who wants to stock
items that are profitable
and sell frequently, is not going to pay
attention to my preferences.
I'm a preference minority.
And therefore I'm not going to be able to
get what I want.
The internet, however, could solve this
problem.
So let's go again,
a little bit of background.
Here's a slide of our friends at Quidsi.
And, the research for this particular
article
that I wrote and published with a friend
of mine, Jeonghye Choi, at Yonsei
University, is
based on data that we got from
diapers.com.
here's the article itself, the title of
the article.
if you want to you can always go and read
it for
more details, but I'm going to give you
the flavor of the main findings.
The article is called Preference
Minorities and the Internet.
So, let's see how this works.
The notion is that if you're selling
things
online, that gives you the ability to
aggregate people.
Maybe there's only two people in
Philadelphia that like Vegemite, but in
all
the towns in America maybe there's 100,000
if we added all those people together.
And even though it wouldn't be efficient
for us to serve them in
individual shops, we could sell that
product
over the internet, that's the basic idea.
So preference isolation is going to bring
shoppers to the online marketplace instead
of the offline marketplace where
they're locked out, and it's going to do
that in a systematic way.
So now let me get into the details and
conclusions of this particular study,
again using the data from diapers.com that
we're all by now, pretty much familiar
with.
So, in order to test out our idea, that
people who were different from
their neighbors weren't getting served
adequately by
offline stores, Jonghei and I went out,
and
we did a little bit of a field study, kind
of a fun field study, and here's what we
did.
There's a chart on the slide that you can
see.
Where we went out and we visited different
supermarkets in the Philadelphia area.
Three supermarkets were from the Fresh
Grocer chain.
And two of the supermarkets or, or stores
were Walmarts.
Now, what's interesting is all of those
stores, all
the Fresh Grocers, they're all the same
size stores.
But their local markets were different
with respect
to the number of households in the local
market
that had kids.
So again, if you look at the chart, you
can see that Store 1,
about 10% of the population in the
immediate area around the store had young
children.
They were households with babies.
Store 3, on the other hand, about 16
percent of the
households in that neighborhood where that
store was located, had children.
So, what does this mean for our idea or
our theory?
Well, the people who live in the market
where there's only 10%
of households with kids are going to be
relatively more neglected by the
supermarket than in the market where there
are 16% of households with kids.
So if my friend Chris is the manager of
the store, he's going to
say, you know what, not that many people
in the 10% market have kids.
I'll just have Pampers and a few leading
brands on the
shelf, and I won't worry about having a
lot of variety.
If, on the other hand, he's managing the
store where 16% of the target market has
children, he's going to say, you know what
a lot of people in
this local neighborhood have kids I'd
better cater to their tastes and
preferences.
And so what you see in the chart is
in neighborhoods where there's a higher
fraction of households with
kids, the actual stores have more shelf
space, more linear
square feet, and more variety of product
of the shelf.
Now of course this doesn't prove our
theory, but it does indicate
that local stores pay attention to the
composition of the people who
live in the neighborhoods, and then they
stock merchandise accordingly.
As a little side note, this was kind of a
fun thing to do in Philadelphia.
Jeonghye and I had a pink measuring tape,
we were running around trying not to
get caught by the store managers measuring
how much space was allocated to these
things.
If you've been to Philadelphia, it's kind
of a tough town.
It's a place where they boo Santa Claus,
at least the football fans do, so you have
to be a little bit careful if you're
running
around with a pink tape measuring stuff in
stores.
Okay, so let me elaborate a little bit
more on this next slide
with the actual theory, that we built up
to try and explain this concept.
So imagine we have two different markets.
this is just purely a conceptual argument,
so the first market, Market A, is a
market where there are 100 households with
babies or 100 households in the target
population.
The total population of this particular
market is 200 people,
so half of the people in this local
neighborhood have
the characteristic that we want, in this
case it's whether or not
they have children, but you could think
about this idea much more generally.
It could be half the people want Vegemite,
or
any other product that you could come up
with.
And so notice in that market there is one
store, and the store is 200 square feet in
area.
And so the manager of that store says,
gee, half the people in the market have
this particular characteristic, let's say
households with kids
So I'm going to allocate half of my store
to products that cater to those people.
And again this is just a stylized example
to make the
main point but hopefully you can see where
this is going.
Now in Market B, again there are 100
people who have
the characteristic we're looking for, in
this case households with children.
But the total population of the market is
1,000 people.
So these people with kids are a little bit
more rare in this case.
They're only 10% of the population.
Now notice however, because Market B
has more people in total.
It has a 1,000 instead of 200.
there's more stores.
And we're just assuming that the number of
stores grows with the population.
So it, in a market of 200 people, if
there's one store,
in a market of a 1,000 people, there will
be five stores.
Now again my friend Chris who does a lot
of store managing I guess.
He's in market B and he says gee, I'm
running these fives
stores, and each store is two-hundred
square feet in size, and I notice
that 10% of the households in the
local market have children, therefore I'm
going to
allocate 10% or 20 feet of the space in my
store to serving that group.
So what you notice here is even though in
both Market
A and Market B, the target market is the
same size, 100
households with kids in A, 100 households
with kids in B, the
extent to which the offline market is
paying attention to them is
very, very different.
The customers who live in Market B,
everything else held constant, should be
more likely
to want to buy their products online, in
this case from our friends at diapers.com.
So let's see if that is in fact true.
So now Jeonghye and I went and we looked
at the real data.
This is just a map, it's a black and
white map, but hopefully you can get the
idea here.
This is an area of Los Angeles county in
the United States,
and what you notice is in the top map
there's an indicator
of how isolated the customers are.
We call this the Preference Minority Index
or PM Index.
And the darker the color, or the darker
the shading, that means customers are more
isolated.
That's in the top part of the map.
In the bottom part of the map, these are
the sales to diapers.com.
And again, you notice the fifth quintile,
or
the area that's having the most sales, the
darker
areas, are sort of the same dark areas in
the bottom part of the map as they
are at the top part of the map.
So this is indicating some support for our
theory that when customers
are isolated, they're more likely to
use online merchants instead of offline
merchants.
So the next thing that we did after
looking at the
raw data is do what a lot of us here at
the
Wharton School will do, whether it's Pete
or Barbara or myself, is
we ran some statistical analysis or some
econometric analysis on the data.
And what we did was we tried to see if it
was in fact the case
that sales at diapers.com were higher in
markets where
the customers that they were focused on
were more isolated.
Everything else held constant.
So we controlled for the education level.
We controlled for the number of stores in
the area.
We controlled for the population density.
We controlled for the income.
All the things that you might think would
effect online verses offline buying.
So all of those things were held constant
in our study.
And what we
found was, yes, there was a highly
statisically significant effect of
isolation on sales.
And markets where customers were more
isolated, they were more
prone to go online, and sales at
diapers.com were higher.
So now I'm going to explain the magnitude
of the effects, which I think is really
interesting.
It was also very useful for the company,
for the guys at Diapers.com.
So most of you, I think, are familiar with
the idea of a percentile.
But let me explain that,
because that's going to be important for
understanding these results.
So if you've ever taken a standardized
test like an SAT test or a GMAT test,
any test at the end of high school or to
get into college, those kinds of things.
You'll remember that when you get your
score back, in addition
to the raw score, you typically get a
measure of percentile.
So where is it that you ranked, relative
to everybody else that took the test.
So if you were in the 90th percentile,
that's pretty good.
That means only 10%
of the test takers beat you, and you beat
you know, 89 90% of everybody else.
If you're in the tenth percentile, which
I'm sure that none of you were, that means
that you only beat 10% of the people and
in fact 90% of the people beat you.
So, what Jeonghye and I does, we used this
same
concept, but we applied it to our
preference minority index.
So, we looked at all of the locations in
the United States.
All of the areas, all of the zip codes
that
were really, really isolated in terms of
people with kids being relatively rare.
And what we found was, for the zip codes
of the 90th
percentile on that index, the online sales
at diapers.com were 50% higher.
So think about that result for a moment.
If we have two zip codes that were
absolutely identical in all respects and
in particular, these two zip codes had
the same total number of households with
children,
100 here and 100 here, just like in the
example.
If this was a zip code that was more
isolated, the online demand at diapers.com
was 50% higher.
So we think that's a very interesting
finding and also one that
internet retailers can actually use when
they think about online offline
interaction.
Now if you think back to the long tail you
might
remember the different brands also have
different levels of sales so the
most popular brand has the highest level
of
sales and the baby diaper category that's
Pampers.
And the second one is the second most
popular and so on
down all the way out into the tail or into
the niche products.
So the same thing happened here.
What we found was if we looked at
particular products that were niche
products, and we compared
an isolated market versus a non-isolated
market, the
sales in the isolated market online for
niche products
were about 125% higher.
And I'm going to show you this in a
diagram to make it easier to remember.
So now here's the diagram that explains
everything that
we've discussed and brings it all together
in one place
and also, importantly, relates it back to
the other
key idea, the long tail that we've all
ready discussed.
So let me show you what's going on.
There are three pieces to this diagram
that
are important in terms of understanding
the overall concept.
So first on the left-hand side
we see Market A and Market B, those are
just those markets that
we had previously where we looked at the
fraction of households with children.
So notice that Market A is a market where
offline retailers are paying a lot of
attention to our target customers and
households with
babies, because they are 50% of the
market.
And Market B, the offline retailers,
aren't paying much attention
to our target customers who are the
households with children.
And that's reflected at the bottom of the
slide with the
little thumbs up and thumbs down in the
two markets.
So what does that mean for the way
products are sold and bought online versus
offline?
So for that, we have to go to the top
of the diagram, which is our old friend,
the long tail.
And so to just remember, by way of review,
that the long tail is an idea or concept
that
has a plot of all of the products that
are available from a particular seller or
a particular merchant.
The Y-axis
is always the sales of the product.
And the X-axis is all products lined up
from the most popular to the least
popular.
So it just so turns out in the diapers
category
the most popular brand is Pampers,
followed by Huggies, then Loves.
And then there's a whole bunch of other
products literally thousands
and thousands of different varieties of
styles and brands and so on.
And one that I've highlighted here is
called Seventh Generation.
That's a niche product
out in the tail that has lower sales than
the other tree.
Seventh Generation is not available at
every offline retailer.
So how do we relate this long-tail idea
back to
preference isolation and see how the two
things come together?
Well if we think about the online
retailer, that's our
friends at diapers.com, they carry the
entire distribution of products.
They offer everything, probably have the
largest assortment of baby-related
products and diapers.
Probably of anyone in the world actually.
Certainly bigger than any physical store.
Now if we turn to Market A, in Market A
there's also pretty good variety.
Maybe not everything is there because
physical stores have space constraints
and so forth, but Market A has a decent
amount of variety.
Meaning that the offline sellers are quite
attracted.
Market B, however, most of the sellers are
just stocking
the popular brand and not really catering
to a full range.
So in Market B, the amount of product
available is much
more limited and is just really focused on
the most popular brand.
That's why, in Market B, the customers are
more prone to shop online versus offline.
50% overall, and up to 125% more online
shopping when they're looking for those
niche brands that are really impossible to
find in those preference-isolated markets.
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