Tracking Mobile: The Rise of Smartphones in Marketing

Flagship Research Quarterly of the Indian School of Business
Capturing Value
in the Digital Era
Indian School of Business
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COVER STORY 22 ISB INSIGHT April-June 2014
One of the most exciting developments in the digital
media arena is the explosion of mobile phone data and
the possibilities for analysing it. Brands, marketers and
strategists have invested considerable time and money
in trying to decipher such data and develop insights
from it. Mobile data provides us the opportunity
and the ability to ask a plethora of interesting and
sometimes highly intuitive questions. This article
presents a series of studies that were undertaken in
four different countries − the US, Germany, China
and South Korea. These studies helped us access
detailed granular atomic datasets that enabled us to
comprehend and identify actionable insights about
what brands and marketers can do with mobile data.
The Mobile Phenomenon
Increasingly, all evidence points to the fact that we are
more and more engaged in one way or the other with
our mobile phones. Whether it is a smartphone or
a feature phone, the mobile device is very personal,
with almost constant accessibility, and it is a veritable
storehouse of data. As a result, the device is constantly
storing data on what we might be doing at a given
point in time, for instance, walking into a restaurant
and checking in, using a mobile application (“app”) or
writing a review of the restaurant and sharing updates
with friends. The smartphone may be a device for
customer engagement, but it has had a visible impact
on various other parts of the digital media industry
and on a fragmented mobile ecosystem made up of
several different institutions.
Mobile traffic accounts for more than 50% of all
Web traffic in a number of countries. India, the
world’s second most-populous country, sees 48.24%
of its mobile traffic coming from mobile phones
(Information Week 2012). With the rapid growth
in mobile content generation, the chances of our
lives being affected by mobile apps in one way or
another will increase. Furthermore, the practice
of “showrooming” − where consumers examine
and compare merchandise in a traditional brick and
mortar retail store and then shop for the product
online at a lower price − is gaining prominence in
mobile commerce as well.
Mobile usage as a proportion of Internet traffic continues to skyrocket, but mobile advertising often gets
the rap for being ineffective. In this article, Professor Anindya Ghose unravels the myths and paradoxes of
mobile marketing through findings from a series of studies conducted in four different countries − the United
States (US), Germany, China and South Korea. By conducting various randomised experiments and deploying
sophisticated econometric models on historical data, brands can determine the right time, right place and
right device to reach the right customer.
Tracking Mobile:
The Rise of Smartphones
in MarketingBY ANINDYA GHOSE

Cover Story
COVER STORYApril-June 2014 ISB INSIGHT 23
Consumer Travel Patterns and Mobile
Purchases
In an effort to gauge consumer behaviour across
devices and platforms, our projects looked at data
collected from different countries. Although there are
differences in the manner in which data is generated
and stored across these countries, at a fundamental
level, the underlying factors affecting consumer
behaviour remain the same.
A fascinating aspect about the data we gathered
is that it records interactions on a second-to-second
basis. In other words, for the sample of people under
observation, we can capture every single interaction
through the smartphone, including locational
movements and interactions with other people.
My co-author, Sangpil Han, and I were interested
in examining what insights on users’ content
consumption and creation behaviour could be derived
from consumer data on the mobile Internet (that is
on mobile optimised sites of brands). For instance,
in Seoul, South Korea, consumer data from two
of the leading telecommunications operators, KT
Corporation and SK Telecom, yielded a great deal of
information about consumption and mobile content
creation. By collating information on mobile content
generation on Youtube, Facebook or Twitter, content
consumed via the Maeil Business Newspaper mobile
app and user location data, we can gain a tremendous
amount of insight into a consumer’s behaviour.
To better comprehend the implications of the
data observed, imagine a hypothetical consumer X in
Hyderabad, who on a regular day commutes between
her residence in Begumpet and her ofﬁce in Jubilee
Hills. Her routine includes a client visit to Banjara
Hills. On certain days, her routine includes additional
client visits to Gachibowli, followed by Madhapur, and
additional visits unrelated to her work. By studying the
mobile usage and daily travelling habits of a large panel
of consumers like X above, we examined how brands
could reach a potential customer at the right time, at
the right place and on the right device. Using this data,
we tested our hypothesis that mobile advertising was
more effective when a consumer deviated from the
standard path of travel than when she adhered to it. In
other words, the consumer is more likely to respond
to mobile advertising and make a purchase when she
has to make additional visits resulting in deviations in
standard travelling patterns than on a regular day when
she sticks to her usual travel patterns. And consistent
with this hypothesis, we found that variance in the
user’s travel patterns is a much stronger predictor
of mobile Internet usage than the mean (Ghose and
Han 2011).
Impact of Geographic Distance
We also studied the impact of the distance between
the consumer and a store on the consumer’s response
to mobile coupons. While redemption rates increased
as the customer’s proximity to a store increased, this
occurred in a non-linear manner. It is not always the
case that the closer a customer is to a store, the more
likely she is to respond to a mobile coupon, all else
being equal.
In the case of the mobile, its limited screen size
is an additional factor. An iPhone is about two inches
smaller than an Android device and both are more
than a few inches smaller than a personal computer
(PC). This signiﬁcantly reduces the number of offers
one can view on the screen. Thus, a customised or
targeted advertisement becomes more critical on a
By studying the mobile usage and daily travelling habits
of a large panel of consumers, we examined how brands
could reach a potential customer at the right time, at
the right place and on the right device. Using this data,
we tested our hypothesis that mobile advertising was
more effective when a consumer deviated from the
standard path of travel than when she adhered to it. And
consistent with this hypothesis, we found that variance in
the user’s travel patterns is a much stronger predictor
of mobile Internet usage than the mean

Cover Story
mobile phone. Its position on the mobile screen and
the distance between a customer and the nearest store
advertised produces an interesting set of three-way
interactions for marketers to exploit. For example, if
the Barista coffee shop outlet you regularly pass by is
able to figure out when you are at an optimal distance
from it, it can send you a targeted coupon of the right
value on your phone. Many retailers will thus jostle for
your attention via their mobile coupons but there is
only limited space on the mobile screen. The topmost
slots on the mobile screen will therefore have greater
value for retailers and be priced at a premium.
To further examine these interactions, my co-
authors (Dominik Molitor, Martin Spann and Philipp
Reichhart) and I worked closely with a set of German
firms and consumers spread across multiple cities. As
part of the experiment, the retailers had to advertise
through a particular mobile app, which would also
be made available to all the consumers on their
smartphones. The app would provide details on the
most recent discounts from the retail stores and the
consumers’ distance from the various stores based on
their exact position in real time.
The consumers were divided into multiple
groups, where one group was able to access
information related only to distance, another only
to price and a third to both distance and price. The
order of the sale was randomised to see how the rank
of the offer on the screen incentivised consumers.
The products on sale were all high frequency retail
products such as coffee, sandwiches, food, books and
movie tickets. The data revealed that, on average, if
the discount was increased by 10%, it was the same
as reducing the distance between the consumer
and the store by 131 metres. In other words, if the
consumer receives a coupon when she is 131 metres
away from the nearest Starbucks outlet, then she is
as likely to redeem that coupon as she is to avail of a
10% discount on her coffee. Similarly, every one unit
additional improvement in rank of appearance on
screen produces the same effect as a price reduction
of about 12%. Our study (Molitor et al., 2014) clearly
indicates the remarkable possibilities of reaching out
to the mobile shopping segment while experimenting
with distance, face value of the coupon and the rank
of the mobile offer on the screen.
Impact of Geo-fencing
The above research targets advertisements or coupons
based on distance but not on time. Say, for instance,
that a person is walking past a Starbucks outlet at 2
P.M. His friend is walking past a pub at the same time.
Both retailers send discount offers: Buy one coffee
and get one free at 2 P.M., or buy one beer and get
one beer free at 2 P.M.
Which offer is more likely to be redeemed? In
all probability, it will be the Starbucks coffee offer.
However, if the time of the offers is switched from 2
P.M. to 8 P.M., the consumer is probably more likely
to go to the pub and redeem the beer offer. Mobile
targeting should take context into account to increase
its effectiveness. This means that combining location
with time is a much more powerful strategy than
using either one of them individually.
Narrowing down or defining the periphery within
which the consumer is most likely to be at a particular
time is known as geo-fencing. The idea behind geo-
fencing is to combine location-based and time-based
Consumers were divided into multiple groups, where
one group was able to access information related
only to distance, another only to price and a third to
both distance and price. The order of the sale was
randomised to see how the rank of the offer on the
screen incentivised consumers. The products on sale
were all high frequency retail products such as coffee,
sandwiches, food, books and movie tickets. The data
revealed that, on average, if the discount was increased
by 10%, it was the same as reducing the distance
between the consumer and the store by 131 metres.

Cover Story
COVER STORYApril-June 2014 ISB INSIGHT 25
targeting. Another recent academic study on this topic
was conducted by Luo et al. (2014) on tickets to a
movie theatre advertised via a mobile app in Shanghai,
China. In Shanghai, people often buy movie tickets
through a movie app on their smartphones. The
experiment involved targeting people with discount
offers using the two dimensions of time and location.
The researchers would send targeted offers to
consumers on three occasions: the day the movie was
going to be screened, one day prior and two days prior.
The other targeting dimension was the consumer’s
distance from the movie theatre: 200 feet or so
(“near”), 200 feet to 500 feet (“medium”) and up to
a kilometre (“far”). One would expect that a discount
offered on the day of the movie screening when the
consumer was within 200 feet of the theatre would
be more likely to be redeemed. However, they found,
interestingly, that one-day prior offers actually had the
highest rate of redemption. Interestingly, for those
who get these discount coupons up to a kilometre
away from where they are in real time, instead of the
same day offers, the one-day offer yield the highest
redemptions. Why exactly is this happening? Does it
have something to do with how people plan?
Offersarecostly,sobusinessesneedtomakethem
as targeted as possible. The idea is to narrow down the
scope and the geo-fencing perimeter beyond which
they are unlikely to reach a consumer. I am working
with several different companies to execute additional
experiments that would help us answer questions like
the ones above.
Mobile Apps
Mobile apps may appear to be a trivial part of the
digital media ecosystem, but they force us to think
about deep and strategic questions. For example,
how would you price an app? Two-thirds of the apps
on iTunes are free and roughly three-fourths of the
apps on Android are free. If you are an app developer,
one way to go is to charge for the app and make it
advertisement free since a lot of people are annoyed
by advertisements. The other option is to make the
app free, but monetise it by showing advertisements.
In 2013, the global mobile app market was estimated
at over US$50 billion and is expected to grow to
US$150 billion in the next two years (Ghose and Han
2014).
In a recently published research project (Ghose
and Han 2014), we used data from both Apple and
Android platforms and estimated the optimal price of
an app that would maximise the developer’s revenues.
How should you price an app so that you make the
same revenues as a developer whether you are giving
it for free and then monetising it through
advertisements or charging a price for
the app and promising not to show any
advertisements? We built a structural
econometric model to quantify the vibrant
platform competition between mobile
(smartphoneandtablet)appsontheApple
iOS and Google Android platforms. We
found that app demand increases with the
in-app purchase option wherein a user can
complete transactions within the app. On
the contrary, app demand decreases with
the in-app advertisement option where
consumers are shown advertisements
while they are engaging with the app.
The direct effect on app revenue from
the inclusion of an in-app purchase and
in-app advertisement option is equivalent
to offering a 28% price discount and
App demand increases with the in-app purchase option
wherein a user can complete transactions within the
app. On the contrary, app demand decreases with the in-
app advertisement option where consumers are shown
advertisements while they are engaging with the app.
The direct effect on app revenue from the inclusion of
an in-app purchase and in-app advertisement option
is equivalent to offering a 28% price discount and
increasing price by 8%, respectively.
Anindya Ghose is a
Professor of Information,
Operations and
Management Sciences
and a Professor of
Marketing at New York
University’s Stern School
of Business. He is also
co-Director of the Center
for Business Analytics at
NYU Stern and co-Chair of
the New York University-
American International
Group (NYU-AIG)
partnership on Innovation
for Global Resilience.

Cover Story
increasing price by 8%, respectively. We also find that
a price discount strategy results in a greater increase in
app demand in Google Play compared to Apple App
Store. Using the estimated demand function, we find
that mobile apps have enhanced consumer surplus by
approximately US$33.6 billion annually in the US,
and discuss various implications for mobile marketing
analytics, app pricing and app design strategies.
Interestingly, app usage is challenging television
usage: average app usage across the world is about 128
minutes and average television consumption is about
164 minutes. For advertisers interested in allocating
their budgets across traditional and digital media, this
is a useful statistic to keep in mind as they devise their
future digital marketing strategies.
Future Mobile Marketing Experiments
There are new opportunities globally for exploring
the tremendous impact of mobile technology. The
Shanghai government has decided to start introducing
Wi-Fi on buses, both public and private. This is a
marketer’s dream: once we know the bus route and
where potential customers get on and off, we can
incentivise them with the right kind of advertisements
based on that knowledge. We can customise the
advertisements based on the route of the bus. For
example, if there is a particular shopping mall on
the bus route, we can send targeted advertisements
based on which stores are in that shopping mall. This
large scale availability of internet access on public
transportation is going to be a key feature of China’s
“smart cities.” The technology infrastructure required
for this is not especially complex and we have seen
early implementation of the core idea in Canada. My
prediction is that we will soon see it in countries like
India and the US as well.
Ghose, A, and S Han (2014). “Estimating Demand for Mobile Apps
in the New Economy”, Management Science (forthcoming).
Ghose, A, and S Han (2011). “An Empirical Analysis of User
Content Generation and Usage Behavior on the Mobile Internet”,
Management Science, 57(9): 1671-1691.
Information Week. 2012. 10% of Web Traffic Now From Mobile
Devices http://www.informationweek.com/mobile/mobile-
devices/10--of-web-traffic-now-from-mobile-devices/d/d-
id/1104298?
Molitor, D, P Reichhart, M Spann, and A Ghose (2014). “Measuring
the Effectiveness of Location-based Advertising: A Randomized
Field Experiment”, Working Paper.
Luo X, A Michelle, F Zheng, P Chee Wee (2014). “Mobile Targeting”,
Management Science (forthcoming).
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Tracking Mobile: The Rise of Smartphones in Marketing

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