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MOBILE MARKETING 2.0: STATE OF
THE ART AND RESEARCH AGENDA
Unnati Narang and Venkatesh Shankar
ABSTRACT
Mobile marketing, the two- or multi-way communication and promotion of
an offer between a firm and its customers using a mobile medium, device,
platform, or technology, has made rapid strides in the past several years.
Mobile marketing has entered its second phase or Mobile Marketing 2.0.
The surpassing of desktop by mobile devices in digital media consumption,
diffusion of wearable devices among customers, and an overall integration
and interconnectedness of devices characterize this phase. Against this back-
drop, we present a synthesis of the most recent literature in mobile marketing.
We discuss three key advances in mobile marketing research relating to
mobile targeting, personalization, and mobile-led cross-channel effects. We
outline emerging industry trends in mobile marketing, including mobile app
monetization, augmented reality, data and privacy, wearable devices, driver-
less vehicles, the Internet of Things, and artificial intelligence. Within each
extant and emerging area, we delineate the future research opportunities in
mobile marketing. Finally, we discuss the impact of mobile marketing on cus-
tomer, firm, and societal outcomes.
Keywords: Mobile; technology; platform; app; shopping; retailing;
advertising; promotions; personalization
INTRODUCTION
The penetration of mobile devices has reached unprecedented levels. In 2017,
68.9% of the United States (US) population or 224.3 million people owned a
smartphone (Statista, 2018). An average US adult spends about five hours every
Marketing in a Digital World
Review of Marketing Research, Volume 16, 97119
Copyright r2019 by Emerald Publishing Limited
All rights of reproduction in any form reserved
ISSN: 1548-6435/doi:10.1108/S1548-643520190000016008
97
day on his/her smartphone (TechCrunch, 2017). Mobile apps are increasingly
dominating mobile device use. Mobile apps account for 87% of mobile usage for
an average adult in the United States, which constitutes the bulk of digital media
time (eMarketer, 2017).
Given the rising popularity of mobile devices and apps, it is unsurprising
that mobile marketing has become a strategic priority for firms (Shankar,
2012; Shankar & Balasubramanian, 2009). Mobile marketing refers to the
two- or multi-way communication and promotion of an offer between a firm
and its customers using a mobile medium, device, platform, or technology
(Shankar & Balasubramanian, 2009). For example, 51% of all digital ad bud-
gets in 2016 were spent on mobile alone (IAB, 2016). By 2018, mobile adver-
tising spending in the United States will surpass TV advertising spending and
account for 69.9% of all digital advertising spending, close to US$40 billion
(eMarketer, 2018). Thus, mobile marketing is a topic of key interest for both
academics and practitioners.
Research on mobile marketing has exploded in the last decade but has largely
remained scattered. Prior reviews of mobile marketing research (Shankar, 2012;
Shankar & Balasubramanian, 2009) have provided a solid foundation to under-
stand mobile marketing. With advances in mobile apps, augmented reality
(AR), and wearables, mobile marketing has entered its second phase or Mobile
Marketing 2.0. This phase exhibits three unique aspects. First, mobile device
usage for digital media consumption has surpassed desktop usage in this new
era. Second, the scope of mobile devices has expanded beyond smartphones and
tablets to wearables and other smart devices such as smart speakers. Third, the
integration and interconnectedness of devices are more ubiquitous with the
spread of Internet of Things (IoT).
Recent mobile marketing research has primarily addressed questions relating
to the adoption of smartphones, tablets, and apps, the effectiveness of targeting
in mobile promotions, the influence of mobile display and in-app advertising,
and the cross-channel impact of mobile devices and apps on offline behaviors in
contexts such as retailing, healthcare, and gaming. The methods and data used
to address relevant mobile marketing questions have become increasingly
sophisticated and have leveraged large-scale field experiments, natural experi-
ments, and structural models. Because mobile marketing is still nascent and
evolving, and huge sums of money are being invested in mobile, there is poten-
tial to continue to gather new and unique data to better understand and predict
the future of mobile-led marketing.
The Mobile Marketing 2.0 era also exhibits characteristics that are either new
or not previously apparent. Emerging trends in app monetization, mobile AR,
data privacy and user experience, wearables, IoT, and self-driving cars pose new
questions for both practitioners and researchers.
In this chapter, we provide an overview of mobile marketing research, in par-
ticular, in the post-mobile app phase. Based on the above discussion, we first
outline insights from extant research along the three themes of targeted mobile
promotions, personalized mobile advertising, and mobile-led cross-channel
effects. These are areas where mobile marketing research has made significant
98 UNNATI NARANG AND VENKATESH SHANKAR
advances in recent years. Next, we focus on underexplored emerging mobile
marketing trends. We first identify and describe these new phenomena.
Subsequently, for each area of extant and emerging mobile marketing theme, we
outline a future research agenda for mobile marketing researchers for creating
further knowledge in the Mobile Marketing 2.0 era.
REVIEW OF MOBILE MARKETING RESEARCH
What makes mobile devices unique for marketers? It is useful to briefly review
the unique characteristics and capabilities of mobile devices that lend themselves
to marketing activities. Mobile devices are uniquely characterized by personabil-
ity, portability, location specificity, interactivity via touch interfaces, and com-
patibility with customer’s lifestyles and their use of other devices (Shankar &
Balasubramanian, 2009). Mobile devices have expanded to wearable devices,
such as fitness trackers and virtual reality (VR) headsets. Mobile devices enable
marketers to target customers based on their temporal, geographical, behavioral,
or contextual factors, personalize the message delivered to them, and engage in
two-way interactions and touchpoints across different channels. These unique
capabilities can be leveraged by marketers for improved mobile advertising, pro-
motions, search, and shopping.
Initial research in mobile marketing focused on topics such as wireless
e-commerce (Shankar, O’Driscoll, & Reibstein, 2003), mobile advertising
(Shankar & Hollinger, 2007), mobile couponing (Dickinger & Kleijnen, 2008),
and mobile retailing (Shankar, Venkatesh, Hofacker, & Naik, 2010). The first
large scale set of commercial apps came to market in 2008 when iPhone created
the app store. However, research on mobile marketing through mobile apps was
slow to take off. As shopper marketing quickly became a central practice for
marketers, in particular, consumer packaged goods companies, mobile apps
started to play an important role in shopper marketing, leading the emergence
of mobile shopper marketing (for a review of mobile shopper marketing, see
Shankar et al., 2016).
In the rest of this section, we provide an overview of marketing research in its
second phase or mobile marketing 2.0. We focus on research that leverages the
emerging capabilities of mobile marketing and focuses on three central themes:
targeted mobile promotions, personalized mobile advertising, and mobile-led
cross-channel effects. Targeted mobile promotions related to the coupons and
offers sent to the customers on their devices based on different types of target-
ing. Personalized mobile advertising refers to the use of mobile banner and dis-
play ads, and the personalization of ads displayed to each individual. Finally,
mobile-led cross-channel effects relate to the impact of mobile app and device
usage on shopping in online and offline channels.
Fig. 1 shows the broad dimensions of these three developments in mobile
marketing research, while Table 1 reports how these areas emerge from the
unique capabilities and benefits offered by mobile devices and apps.
99State of the Art and Research Agenda
Targeted Mobile Promotions
Mobile devices are portable and personable, making them suited for targeted
promotional messages (Danaher, Smith, Ranasinghe, & Danaher, 2015; Hui,
Inman, Huang, & Suher, 2013). Mobile technologies allow marketers to target
customers with promotions based on their specific location, time, and context.
Fig. 1. Recent Advances in Mobile Marketing.
Table 1. Mobile Attributes and Related Marketing Advantages.
Mobile Device Attributes Related Mobile
Marketing Advantages
Description
Portability, location
specificity, personability
Targeted mobile
promotions
Because each individual typically carries a
personal mobile device(s) all the time, mobile
offers a unique opportunity to marketers to
target at different times and locations
Personability,
interactivity
Personalized mobile
advertising
Because each individual typically owns
personal mobile device(s) that allow two-way
interactions, mobile marketers can personalize
the mobile experience for each user based on
their specific preferences, past click-thru
behavior, etc.
Anytime-anywhere
access, interactivity
Mobile-led cross-
channel effects
Because mobile devices can provide anytime-
anywhere information benefits to shoppers
about products and nearby stores as well as
immediate access to shopping and exposure to
deals and offers on-the-go, they can influence
shopping in other channels
100 UNNATI NARANG AND VENKATESH SHANKAR
Mobile marketers can leverage several factors that influence consumers at the
right moment with the right message through context (e.g., crowdedness), location,
time, saliency, and historical shopping patterns or trajectory (Ghose, 2017).
Contextual targeting is about reaching the customer at the appropriate
“micro-moment.”It is the sum of all factors, circumstances, and associated
behaviors that guide decision-making (Ghose, 2017). Customers are more likely
to be influenced by contextual mobile marketing efforts. Customers respond
more positively to in-app restaurant recommendations that are designed to be
“in-the-moment”rather than expert-driven (Adamopoulos & Tuzhilin, 2015).
Commuters in crowded subway trains are twice as likely to respond to a mobile
offer by making a purchase as those in non-crowded trains mainly because
crowdedness makes people turn inward and immerse more in their handheld
devices (Andrews, Luo, Fang, & Ghose, 2015). Even different types of commut-
ing contexts can influence consumers differently. Commuters who travel to or
from work are about three times as likely to redeem their first mobile coupon
compared to non-commuters (Ghose, Kwon, Lee, & Oh, 2018). Commuters also
differ from non-commuters in responsiveness to coupons, expiration periods,
and time taken for coupon redemptions. Weather also influences customers in
their purchases and coupon redemption decisions. Purchase responses to promo-
tions tend to be greater (smaller) and faster (slower) in sunny (rainy) weather
(Li, Luo, Zhang, & Wang, 2017).
Locational targeting leverages the customer’s location when a promotion is
delivered through mobile devices. Customers are more likely to make a purchase
in response to a promotional offer received when they are closer to the store
than when they are farther from it (Luo, Andrews, Fang, & Phang, 2013).
Furthermore, location-based targeting is used as a tool to gain competitive
advantage and/or to drive shoppers away from the competition to the focal firm.
Competitive locational targeting is the practice of sending promotions to custo-
mers when they are near a competitor’s location. It results in greater returns to
promotional discount depth than targeting customers when they are close to the
focal firm’s location (Fong, Fang, & Luo, 2015). Thus, mobile promotions have
a competitive influence, in particular, when firms target based on historic, real-
time location, and peak vs off-peak time. Furthermore, the impact of mobile
promotions based on such “geoconquesting”is low when the competitor also
engages in similar targeting (Dubé, Fang, Fong, & Luo, 2017). Co-locational
targeting further incorporates customer’s network structure based on the idea
that an individual’s location reveals the underlying preference for a type of place
and event and that co-located individuals who are at the same place at approxi-
mately the same time reflect similar preferences. Indeed, there is a significant
relationship between co-location and response to mobile coupons in the same
product category (Zubcsek, Katona, & Sarvary, 2017).
Temporal targeting involves sending timely messages to customers based on
real-time targeting. Promotions that temporally target customers increase their
unplanned spending (Heilman, Nakamoto, & Rao, 2002). The effects of tempo-
ral targeting are less straightforward to analyze when it is combined with loca-
tional targeting. For mobile users close to the focal firm’s location, there is a
101State of the Art and Research Agenda
negative sales-lead time relationship. Thus, sending them promotions on the
same day is more effective than sending them promotions two days prior to the
promoted event. For non-proximal mobile users, there is an interesting inverted-
U relationship between response and temporal targeting. For example, large-
scale field experiments show that targeting non-proximal users with a one-day
prior promotion is more effective than both same-day and two-day prior target-
ing (Luo et al., 2013). Thus, the interactions between the different types of tar-
geting efforts result in different implications for customers and marketers.
Salience or position effects is an important consideration for mobile marketers
in targeting and search. Mobile devices have a smaller screen size than desktops
and laptops. As a result, ranking effects are higher on mobile phones suggesting
higher search costs. Because of these search frictions, links that appear at the top
of the screen are more likely to be clicked on mobile phones than on desktops
and laptops (Ghose, Goldfarb, & Han, 2012). In many settings, such as Yelp
and other recommendation-based apps, user-generated content influences the
formation of consideration sets through search. In such a setting, the sizes of
mobile shoppers’consideration sets may be small due to search frictions. If a
lower-ranked item in a search is not a part of the shopper’s consideration set,
it can result in suboptional choices and decision-making (Muir & Tsai, 2016).
Overall, when people search on mobile, it tends to lead to action: 92% of those
who searched on their phone made a related purchase, and 68% of people use
search to enable decisions at some point in the future (Google, 2018).
Finally, shoppers’historical movement patterns and offline trajectories pro-
vide rich insights into how they reach a specific location and what experiences
might shape their subsequent shopping choices and coupon usage. A large-scale
field experiment in one of Asia’s largest malls leverages this idea of offline walk-
ing patterns or trajectories’impact on the redemption rates of mobile coupons
(Ghose, Li, & Liu, 2016). Their results show that such targeting leads to
increased redemption probability, faster redemption behavior, and higher trans-
action amount. This effect is weaker on weekends when shoppers are more likely
to be in exploratory shopping mode and less impulsive than during weekdays
(Ghose, Guo, & Li, 2017).
Mobile influences shopping within the store. Grewal, Ahlbom, Beitelspacher,
Noble, & Nordfält (2018) show that mobile phone use can increase point-of-
purchase sales through eye-tracking technology used in a field experiment.
Mobile messages divert consumers from their conventional shopping loop and
induce them to spend extra time in the store examining products and shelf
prices.
Personalized Mobile Advertising
Mobile advertising includes banner/display advertising, search (paid and unpaid)
advertising, and personalized advertising. Research on mobile advertising (e.g.,
Grewal, Bart, Spann, & Zubcsek, 2016) offers a framework in which contextual
and consumer factors determine advertising goals, leading to the choice of
advertising elements and advertising outcomes, such as clicks and purchases.
102 UNNATI NARANG AND VENKATESH SHANKAR
Market and firm factors moderate the relationship between advertising goals
and advertising elements.
Mobile devices are uniquely tied to an individual and allow marketers to
leverage data on individual preferences, movement patterns, co-located social
connections, and other individual-specific variables to personalize advertising
and marketing communication messages. Thus, mobile devices offer a unique
marketing opportunity for personalization. In the mobile advertising context,
mobile display advertisements result in favorable attitudes toward products and
purchase intentions, in particular, for high-involvement and utilitarian products
(Bart, Stephen, & Sarvary, 2014). The use of mobile ads with other communica-
tion channels (e.g., television) can result in a “tech mix”that facilitates informa-
tion retrieval. In general, such ads are more effective when viewers are close to
purchasing and are highly involved and invested in the decision-making process.
Furthermore, with personalization, advertisements can be shown uniquely
and/or in unique sequences to each user. There has been at least one recent effort
in marketing research to develop a personalized dynamic framework for optimal
ad allocation and sequence in which ads are shown to a mobile user (Rafieian &
Yoganarasimhan, 2018). The rationale for incorporating past ad exposures is to
better understand user response to a current ad and user app usage. This is par-
ticularly true because seeing a sequence of ads within a short duration can influ-
ence users differently relative to seeing a sequence of ads over a long duration.
Therefore, understanding the impact of previous sequence of ad exposures and
ways of optimizing ad placement strategy within a session is critical in the
mobile contexts. Furthermore, it requires personalization at the individual user
level, since users are heterogeneous in exposures and their responses.
Personalization also captures customer heterogeneity, an increasingly impor-
tant theme for enriching the understanding of different user experiences in
mobile interfaces. Different users interact with and are influenced by mobile
devices differently. A recent working paper shows that retail app users who are
more loyal, have greater prior digital channel use, and who experienced failures
less attributable to the retailer, are less sensitive to failures and crashes in the
app than other retail app users (Narang, Shankar, & Narayanan, 2018).
Furthermore, the interface and combination of interfaces on which the ads
are displayed also affects shopper responses. Combining both web and mobile
display advertising triggers more clicks and purchases than using only web or
only mobile for display advertising (Ghose, Han, & Park, 2013). Within mobile
devices such as smartphones and tablets, ads can be delivered through apps
using in-app advertisements (IAA), text messages, or mobile browser webpages.
The effects of these different interfaces might differ. The use of SMS (short mes-
saging service) for advertising leads to higher awareness generation, brand atti-
tudes, and direct behavioral responses (Barwise & Strong 2002).
Finally, mobile advertising strategies may operate differently for different
cultures and countries. In emerging economies, a popular mobile marketing
strategy is Missed Call Marketing (MCM), where the receiver can indicate a yes
or no response with a free missed call (Knowledge @ Wharton, 2016). It is also
known as miskol in the Philippines, beep in Africa, memancing in Indonesia,
103State of the Art and Research Agenda
and flashcall in Pakistan. A missed-call-based campaign typically includes a call-
to-action that requests a missed call back if the recipient wishes to get more
information. The low cost and fast speed of missed-call campaigns are central to
their appeal, in particular, in emerging and low-income economies. However,
the impact of MCM is a relatively less researched area.
Mobile-led Cross-channel Effects
Mobile devices not only impact shopper behavior within the channel but also
have wider cross-channel implications (Shankar et al., 2016). In omnichannel
retailing, mobile devices can facilitate purchases in stores and online. The unique
characteristics of mobile devices that make this possible are anytime-anywhere
accessibility, location specificity, and interactivity. Shoppers can glean product-,
offers-, and store- related information from apps (e.g., nearest store locator,
stores’open hour, latest products on offer). These capabilities enhance conve-
nience for shoppers, the ease of finding relevant information (Dubé et al., 2017;
Fong et al., 2015), and the ability to quickly shop and checkout (Shankar, 2012;
Shankar & Balasubramanian, 2009). Many apps are linked with the retailer’s
loyalty programs and can provide shoppers easy access to their loyalty benefits
across channels (e.g., Macy’s and Kohls apps) as well as allow searches for
rewards redeemable across channels.
Both mobile devices and apps have unique cross-channel effects relating to
shopper purchases. Cross-device effects of mobile devices for m-commerce
include effects on frequency, quantity, and monetary value of purchases in other
devices. In a study of the introduction of Taobao’s tablet app, Xu, Chan,
Ghose, and Han (2016) find that overall e-commerce revenues increase after the
tablet app launch. They also show that tablet commerce substitutes desktop
commerce but complements smartphone commerce. Thus, the effects of mobile
device addition to the shopper’s purchase devices are not straightforward as
they can lead to both synergies and cannibalization. Shoppers who transact
more using a mobile device than a desktop computer purchase more frequently
but spend less in monetary value (Lee, Zhuang, Kozlenkova, & Fang, 2016).
Within mobile devices, apps offer a unique opportunity to create and enhance
shopper loyalty. In the e-commerce context, eBay’s app is associated with higher
aggregate revenues from the platform (Einav, Levin, Popov, & Sundaresan,
2014). This is true even for apps that provide access to multiple retailers, such as
air miles loyalty rewards apps, that let people redeem their points through vari-
ous retailers. In such apps, shoppers’point accruals increase once they start
using the app and two app features, information lookup, and check-ins contrib-
ute most in increased shopper activity (Kim, Wang, & Malthouse, 2015). Even
promotional campaigns to encourage shoppers to use mobile apps result in
higher spending once shoppers adopt e-commerce (Wang, Malthouse, &
Krishnamurthi, 2015). For non-retail settings, such as news apps, shoppers’web-
site visits increase after the mobile channel’s introduction (Xu, Forman, Kim, &
Van Ittersum, 2014).
104 UNNATI NARANG AND VENKATESH SHANKAR
While most studies examine the impact of mobile channel addition or adop-
tion on online or e-commerce revenues, the impact on offline or in-store
outcomes is less well-known. Mobile apps drive purchases in both online and in-
store channels (Narang & Shankar, 2019). In-store effects are largely explained
by the use of the app in close geographical-temporal proximity, in particular, for
features, such as reward coupons discoverable through the app that can be
redeemed in store and product discovery through the app. The app drives pur-
chases of a larger diversity of products, in particular, less popular products.
Despite higher purchases, app adopters are also likely to return more products
than non-adopters after the app is introduced.
Marketers can utilize mobile devices in conjunction with retail kiosks to influ-
ence shopping. Such combination can be implemented through retail mobile
integrated kiosks to allow marketer’s information to flow seamlessly across the
kiosk, the retailer’s website, and mobile apps. Using both field experiments and
lab studies, Grewal, Ahlbom, Noble et al. (2018) show that inspirational com-
munication content (e.g., food recipe) increases unplanned spending and sales
more than promotional communication content (e.g., coupons). They further
reveal that these effects are mediated by the activation of category-related
thoughts and purchases of substitute products related to inspirational communi-
cation content and that the effects are greater for shoppers with low budget and
for those who process information concretely. Their findings suggest that retai-
lers should enhance sales by offering inspirational ideas to shoppers through
mobile integrated kiosks mainly for low-budget, frequent, and concrete proces-
sing shoppers.
A summary of the three emerging mobile marketing themes, their representa-
tive studies, and key findings appears in Table 2. These research studies also
point to the emergence of new trends in mobile marketing.
EMERGING TRENDS
In what new ways does mobile marketing influence customer preference and
decision-making? There is growing academic research on this issue, but it is still
nascent to fully capture all the emerging trends and issues. In this section, we
identify and describe seven emerging trends, consistent with our characterization
of the Mobile Marketing 2.0 era. These trends relate to developments in existing
mobile technologies (e.g., app monetization, AR in apps), introduction of new
smarter mobile technologies (e.g., self-driving vehicles, wearables, IoT), and the
engines powering these developments (e.g., big data, artificial intelligence).
These trends are consistent with industry reports that offer an early glimpse into
customer perceptions and behavior in an increasingly mobile-first world. We
next delve into each of these emerging trends.
Mobile App Monetization
Mobile apps offer engagement, convenience, easy access, and shopping all
through a single tap. In the past two years, mobile app downloads have surged
105State of the Art and Research Agenda
by 60% globally (App Annie, 2018). Mobile marketing is the “primary tool for
the digital omnivore”and we are now in the “app age”with mobile apps becom-
ing the hottest emerging trend for engaging users beyond one-off promotional
redemption (comScore, 2017).
An important emerging trend in mobile apps is app monetization. Global
consumer spending for apps has now reached US$86 billion. Several app catego-
ries are increasingly popular sources of revenue for app providers. These catego-
ries include ride sharing, bike sharing, home rentals, health tracking, pro-social
Table 2. Summary of Key Issues in Mobile Marketing.
Key Issue Representative Studies What We Know Current Evidence
Targeted mobile
promotions
Andrews et al. (2016)
Danaher et al. (2015)
Dubé et al. (2017)
Fong et al. (2015)
Ghose et al. (2018)
Hui et al. (2013)
Luo et al. (2013)
Li et al. (2017)
Zubcsek et al. (2017)
Mobile promotions targeted based on user’s context
(e.g., crowdedness, weather), location, time, saliency,
and historical shopping patterns or trajectory impact
coupon redemption rates, time, etc. depending on the
type of targeting and their different combinations.
Mobile promotions have a competitive influence, in
particular, when firms target based on historic, real-
time location, and peak vs off-peak time
Personalized
mobile
advertising
Bart et al. (2014)
Grewal et al. (2016)
Rafieian and
Yoganarasimhan (2018)
Mobile display advertisements result in favorable
attitudes towards products and purchase intentions, in
particular, for high-involvement and utilitarian
products. Personalization and optimal mobile ad
sequencing can further improve user response to ads.
Customer heterogeneity is relevant for mobile
marketing. Retail app users who are more loyal, have
greater prior digital channel use, and who experienced
failures less attributable to the retailer are less sensitive
to negative in-app experiences, such as app crashes and
failures
Mobile-led cross-
channel effects
Einav et al. (2014)
Grewal, Ahlbom,
Beitelspacher et al.
(2018)
Grewal, Ahlbom,
Noble et al. (2018)
Kim et al. (2015)
Lee et al. (2016)
Narang and Shankar
(2019)
Narang et al. (2018)
Wang et al. (2015)
Xu et al. (2016)
Mobile apps and devices impact outcomes in online and
offline channels. Specifically, mobile app adoption
results in higher frequency, quantity and monetary
value of purchases in online and offline channels, as
well as higher product returns. Tablet commerce
substitutes desktop commerce but complements
smartphone commerce
106 UNNATI NARANG AND VENKATESH SHANKAR
activity, and creative apps. In-app purchases (IAP), IAA, and paid apps are
three formats for monetizing apps (Kumar, Dogan, & Lahiri, 2016). App retai-
lers such as Apple and Google incentivize apps that monetize content through
subscription sign-ups and renewals. Finally, apps also provide benefits that influ-
ence spending and expenses outside of the app. For example, the adoption of an
mHealth platform significantly reduces blood glucose and glycated hemoglobin
levels, hospital visits, and medical expenses (Ghose et al., 2017). Similarly, push
notifications in apps can influence donation decisions and donation amounts
(Lee, Gopal, & Lee, 2017).
Mobile Augmented Reality (AR)
AR is the confluence of VR with reality. Mobile technologies, such as built-in
camera, sensors, and computational resources, have made AR apps possible on
mobile devices. Apps that leverage these technologies can generate new reven-
ues. Estimates suggest that AR apps could boost iPhone and App Store sales by
as much as US$8 billion (CNBC, 2018). A popular AR app is Nintendo’s
Pokemon Go game that lets users chase game characters in public areas based
on projections from their phone screens. While mobile AR apps engage users
and integrate the online and offline environments, they can also have adverse
consequences, including road accidents (Faccio & McConnell, 2018).
In addition to gaming, mobile AR is quickly catching on in retail. For example,
Amazon introduced AR view, an AR shopping tool, in 2017. AR View allows
shoppers to see before buying how items will look in their homes (Engadget,
2018). Apart from improving shopping experience and satisfaction, AR tools can
also potentially improve brand engagement and loyalty. For example, the wine
brand 19 Crimes offers an AR app for free. Once users download the free app
(iOS and Android), they can hold it up to the label on the wine, which features an
image of a former convict. The convicts come alive with animation and narrate
their story (Forbes, 2017). IKEA offers a mobile app that lets users overlay the
furniture in the store against their home picture to visualize how the furniture will
appear in their own homes.
Data Privacy and Mobile User Experience
While mobile devices and apps improve user experience by making it more rel-
evant and engaging, the algorithms that make this possible rely on huge
amounts of user data. Apps elicit different types of data from their users
(Kesler, Kummer, & Schulte, 2018). Data breaches and mishandling of cus-
tomer data have led to more stringent regulatory oversight. European Union’s
introduction of general data protection regulations (GDPR) in May 2018 puts
ownership of customer data in the hands of users and requires firms to seek
permission from users to use their data and be responsible custodians of cus-
tomer data. These regulatory changes impact the way companies collect, store
and use data, as well as how transparent they are in their communications
with the users about these data-handling processes. The California consumer
107State of the Art and Research Agenda
privacy act of 2018 is likely to reshape how companies handle data in the most
populous state of the US (Wakabayashi, 2018). Under this law, firms will be
required to disclose the data they collect from users and to let users decide
whether their data can be sold.
These regulations raise a number of interesting research questions. What do
these and similar regulations imply for mobile marketers? First, marketers need
to invest in strengthening opt-in messaging, for example, highlighting a benefit
of the app before asking users to allow push notifications. The New York-based
health app, ZocDoc, does this by letting patients know that they can be updated
of their appointments with doctors if they sign up for notifications before the
users are shown the push notification prompt from the App Store/Google Play.
Personalized content in notifications can deliver a four-time lift in open rates vs
generic content and leveraging behavioral-based user actions can boost open
rates by nine times (Leanplum, 2017). However, opt-in can mitigate the privacy
concerns surrounding notifications.
Wearables and Other Smart Devices
Another emerging trend and area of future investigation for mobile marketers is
the ability to leverage the connections between mobile and other smart devices
(Shankar et al., 2016). For example, using the Amazon Alexa app, consumers
can utilize voice-based devices, such as Amazon echo, or echo dot to ask Alexa
to perform different tasks, including shopping. Amazon has also tied up with
hotels, such as Marriott by placing Amazon Echo speakers in hotel rooms so
that guests can use voice for different services (Forbes, 2018). Amazon creates
engagement with Alexa by emailing users specific commands to use to get their
voice assistant to perform certain tasks (e.g., “Alexa, reorder deodorant”or
“Alexa, track my order”). Other ways apps link with wearables for improved
user experience and convenience include recording and presenting archived data
from tracking devices. For example, smart watches like Fitbit that track health
data link back to the app to allow users to check their statistics at any time and
enjoy additional features, such as connecting with social networks and friends
who use the same device. In this way, these devices leverage both direct and
indirect network effects.
Self-driving Vehicles
An important development in recent years is the emergence of self-driving vehi-
cles, with several companies testing these vehicles on the roads. Self-driving cars
are expected to hit the roads in the next five years. Estimates suggest that by
2035, 12 million fully autonomous cars would be sold (BCG, 2017). This pros-
pect would open up huge opportunities and challenges for marketers. Because
marketers would be able to communicate with individual cars, the cars would
serve as personalized advertising platforms, vehicles for delivery, and as a mar-
keting channel (Gelb, 2017).
108 UNNATI NARANG AND VENKATESH SHANKAR
Self-driving cars would provide marketers rich customer data and the ability
to reach them directly and personally. Marketers can map and leverage custo-
mers’interactions with cars at a granular level. Furthermore, self-driving cars
will offer customers greater convenience. Already, large-scale retailers, such as
Kroger, are teaming with self-driving car manufacturers and robotics firms to
test these technologies for supplying groceries directly to their customers’homes.
In addition, self-driving vehicles would have spillover effects on other industries,
including ride-hailing and car-sharing services (Belk, 2014). In this way, autono-
mous or self-driving cars will likely not only present new challenges for existing
auto-related services but also create new opportunities for industries, such as
retailing.
Internet of Things (IoT)
The IoT, a network of gadgets, devices, appliances, vehicles, and sensors embed-
ded in objects, all connected to the Internet, is shaking up marketing. The net-
work collects and exchanges data from and with all these items. As such, these
items are mobile and serve to further expand the potential of mobile marketing.
Marketers can use the network to better engage the customers in their journey.
They can also harness customer interactions with all the items in the network in
real time and make more effective decisions on the fly. The IoT can be viewed
as an endless highway of touchpoints that can feed data on customers’digital
footprints everywhere. Naturally, IoT provides a treasure trove of data on a
myriad of customer activities.
The IoT can redefine marketing in different areas such as repeat purchases
and customer services. For example, a washing machine connected to the IoT
ecosystem can automatically reorder laundry detergent, and an inkjet printer on
the IoT can replenish refills in advance, skipping customers’need recognition
stage in their shopping journey. In the customer service realm, KONE, an eleva-
tor and escalator company, analyzes the data collected from the sensors in its
elevators to anticipate symptoms and addresses them through technicians before
they become problems (Kone, 2018). The connectivity of multiple devices to the
Internet through a giant network gives rise to the development of innovative ser-
vices that could be aptly termed Internet of Services (IoS).
Artificial Intelligence (AI)
AI, “programs, algorithms, systems or machines that demonstrate intelligence,
or more generally, a set of tools that can enhance the intelligence of a product,
service, or solution,”is rapidly growing in importance and is reshaping retailing
(Shankar, 2018) and marketing, in general. AI is helping marketers leverage
data to better understand and anticipate customer needs and make optimal deci-
sions to maximize customer lifetime value. AI involves the use of machine learn-
ing models developed on Big Data (voluminous data from a variety of sources
collected at high velocity) to predict customer behavior. Much of the data comes
from mobile interactions of customers. In its most ubiquitous form, AI is
109State of the Art and Research Agenda
embedded in the virtual assistants of smartphones (e.g., Siri, Cortana, Alexa)
and smart speakers (e.g., Echo, Google Home, Apple Homepod), all mobile
devices. AI is also assisting in specialized marketing decisions such as salesforce
planning (e.g., Einstein) and retailing (e.g., IBM Watson).
Many firms are already using AI to improve and automate some of their
marketing decisions. For example, French retailer, L’Occitane,usesAItoana-
lyze customer data on its desktop and mobile websites and to personalize site
layout, having experienced a 159% jumpinmobileconversionsonitsUKsite
(Sandler, 2018). Thank God It’sFriday(TGIF),aUSrestaurantchain,uses
AI to integrate data from mobile app, email, loyalty program, and in-store
purchases and to make personalized offers to its customers through text mes-
sages (Marshall, 2018). Thanks to their efforts over a year, TGIF’scustomer
engagement multiplied by a factor of five and sales grew by US$150 million
(Marshall, 2018).
A RESEARCH AGENDA
With a better understanding of the ways in which academics have explored the
evolving landscape of mobile marketing interventions, including those related to
targeting, personalization, and cross-channel spillovers, we next develop an
agenda for future research in mobile marketing. We incorporate central ideas
from mobile marketing trends to recommend future research questions along the
themes of current and emerging interest.
Research Agenda Based on Gaps in Extant Literature
While mobile marketing research has advanced in the three areas of mobile tar-
geting, personalization, and mobile-led cross-channel effects, there are several
unexplored questions worthy of future investigation.
Research on targeted mobile promotions has examined the effects of loca-
tional, temporal, and contextual targeting on the immediate response to one-
time promotions (Andrews, Goehring, Hui, Pancras, & Thornswood, 2016;
Fong et al., 2015). However, only a few studies have considered the impact of
combining two or more different targeting strategies (Luo et al., 2013).
Therefore, there is a need for further research on the effectiveness of different
targeting approaches when used together. Opportunities also exist for under-
standing newer ways of targeting not explored before. Thus, two potential future
research questions are as follows: (1) What are other innovative and unexplored
ways of targeting mobile promotions (e.g., based on dissimilarity between consu-
mers, reinforcement learning based on past redemption behaviors)? and (2) How
do various types of targeting work when implemented together?
Research on personalized mobile advertising is limited to one or two applica-
tions on developing personalized recommendation systems or personalizing the
sequence of ads shown to users (Rafieian & Yoganarasimhan, 2018). However,
much work remains to be done in this area. Three potential ideas for future
research are as follows: (1) What are some effective ways of optimizing both ad
110 UNNATI NARANG AND VENKATESH SHANKAR
sequence and ad content in mobile web and apps for maximizing user exposure,
engagement, and actions? (2) What is the impact of personalization in different
formats, such as in-app personalization, recommendations, and push notifica-
tions on purchases? and (3) How should marketers allocate mobile ad spending
across different personalization strategies?
Research on mobile-led cross-channel effects has assessed the impact of
mobile devices and apps on primarily online outcomes (Wang et al., 2015; Xu
et al., 2016). Not much is known about how mobile augments exposure to shop-
ping in other channels or messaging from other channels. Furthermore, previous
research has examined data from a single retailer or app provider. Thus, three
potential areas of future research are as follows: (1) How do different apps and/
or portfolio of apps drive customers’offline choices? (2) What is the impact of
mobile web vs app on shopper preferences and behaviors? and (3) What are
effective ways to optimize mobile shopping experiences and create synergies
with other channels? Some illustrative potential research questions based on
gaps in extant literature appear in Table 3.
Research Agenda Based on Emerging Trends
While the industry is fast embracing the emerging trends in mobile marketing,
particularly led by behemoths like Amazon, there is a huge untapped opportu-
nity for academics to understand newer forms of interaction and engagement
that augment mobile marketing strategies.
Table 3. Research Agenda based on Analysis of Gaps in Extant Literature.
Key Issue Future Research Agenda
Targeted mobile
promotions
1. What are other innovative and unexplored ways of targeting
mobile promotions (e.g., based on dissimilarity between
consumers, reinforcement learning based on past redemption
behaviors)?
2. How do various types of targeting work when implemented
together?
Personalized mobile
advertising
1. What are some effective ways of optimizing both ad sequence and
ad content in mobile web and apps for creating maximum user
exposure, engagement, and actions?
2. What is the impact of personalization in different formats, such as
in-app personalization, recommendations, and push notifications
on purchases?
3. How should marketers allocate mobile ad spending across
different personalization strategies?
Mobile-led cross-channel
effects
1. How do different apps and/or portfolio of apps drive customers’
offline choices?
2. What is the impact of mobile web vs app on shopper preferences
and behaviors?
3. What are effective ways to optimize mobile shopping experiences
and create synergies with other channels?
111State of the Art and Research Agenda
Research on mobile app monetization can focus on several unanswered ques-
tions. Firms follow different app monetization approaches, ranging from free
apps with IAP to freemium apps with paid subscription for upgrades (Kumar
et al., 2016). Which revenue models work best under what scenarios and at what
prices? Some pending questions in the app monetization domain include the fol-
lowing: (1) What are some ways of effectively monetizing mobile apps? (2) How
can providers create mobile app stickiness and engagement in the long term?
and (3) What in-app features and functionalities do app users value based on
heterogeneity in user and app characteristics?
Mobile AR has allowed another new breakthrough in engagement by blur-
ring the lines between on-screen and off-screen experiences (Shankar et al.,
2016). Because AR requires significant time and effort, in this respect, firms and
researchers alike would benefit from understanding: (1) What is the impact of
AR apps on user engagement and actions in and outside of the app? (2) How do
AR apps impact firm’s brand image? What are some ways in which firms can
leverage such apps in different settings and industries? and (3) What are the
implications of combining AR with other non-mobile devices and settings (e.g.,
wearable in stores)?
In the mobile privacy and data sensitivity context, it is unclear how the new
capabilities of mobile devices that leverage rich user-level data will attract and
invite regulatory purview and concerns for providers. It is also unclear how firms
should communicate the data-handling processes in a simple but transparent
manner with end users when eliciting information (Kesler et al., 2018). Several
unanswered but pressing concerns can be delineated in this area: (1) What is the
impact of stricter privacy regulations on mobile marketing? (2) Are there trade-
offs associated with handling data privacy concerns and optimizing user experi-
ences to be more relevant? What are some optimal approaches? (3) How do
customers vary in their privacy preferences and how can marketers glean and
leverage this information for creating unique mobile experiences for them?
and (4) What is the broad spectrum of privacy for mobile users (e.g., different
levels of data sharing)? Does the effect of different levels of privacy on user
experience and sensitivity to promotions and advertising vary? How?
Another emerging trend in mobile marketing is cross-device integration with
wearables and smart devices, such as smart speakers and watches (Shankar
et al., 2016). In this domain, the key questions of interest are as follows:
(1) What are the future avenues for the growth of voice-enabled assistants?
(2) How are voice-enabled assistants likely to impact different industries, for
example, retail and hospitality? (3) What are the ways in which mobile devices
and apps can create and sustain synergies with other devices, including
wearables? (4) Are there any potential cannibalization effects? and (5) What do
we know about the usage patterns across devices?
From both marketing and policy perspectives, research on self-driving cars
can address some unanswered questions (Waldrop, 2015). Early advances in
research relating to self-driving cars suggest that their broader adoption will
depend on their price, ownership (individual or service companies), and liability
for accidental damage. Potential research questions for marketing researchers
112 UNNATI NARANG AND VENKATESH SHANKAR
are as follows: (1) What are the effective ways of marketing self-driving cars?
(2) What will be the impact of self-driving cars on directly and indirectly related
markets, such as ride sharing and leasing? and (3) In what ways can retailers
leverage self-driving car technologies for improved delivery?
Research on IoT is still in its infancy but can rapidly evolve with the avail-
ability of increased data from the ever-growing number of mobile devices con-
nected to the IoT ecosystem. Analysis of such data can lead to more accurate
predictions of customer behavior. In this regard, some important research ques-
tions are as follows: (1) What models can help marketers deliver timely, person-
alized messages and offerings to customers based on customers’stages in their
shopping journeys for different products? (2) How can marketers overcome pri-
vacy issues involved in accessing multiple devices to collect relevant data and
provide useful services to customers? and (3) In what ways can marketers seam-
lessly develop IoS by leveraging the data from IoT?
Research on AI can have a fundamental impact on marketing. The prolifera-
tion of AI in consumers’daily lives and marketing decision makers’arsenal
opens up an exciting new research frontier in marketing. Researchers are devel-
oping newer models for personalization that can form the engine behind AI rec-
ommendation systems (e.g., Darani & Shankar, 2018; Jacobs, Donkers, & Fok,
2016). Consistent with Shankar (2018), we identify three broad research ques-
tions of importance. (1) How do we develop AI-driven, mobile-enabled market-
ing systems for an array of marketing decisions ranging from product portfolio
to pricing to promotion to personalization? (2) How do we develop new models
of shopping behavior of AI- and mobile-assisted shoppers and consumers? and
(3) How can we apply neuroscience-based AI models that leverage mobile data
to develop a deep understanding of consumer psychology? The search for
answers to these broad questions will spur new research on narrower and
focused questions that can be addressed using the latest advances in data,
machine learning, causal modeling, and experimental research.
Illustrative potential research questions based on emerging trends in mobile
marketing appear in Table 4. These questions are grouped by the emerging trends.
MOBILE MARKETING PRACTICE
The cumulative research insights and the emerging trends in mobile usage and
shopping offer important implications for mobile marketing practice. Mobile
marketers should consider adopting the following broad recommendations.
View mobile as an integral part of the overall marketing strategy, not just a
component of the media/communication mix. Mobile is fast becoming the pri-
mary digital tool for marketers, occupying the center stage in the customer’s
lives and lifestyles. Given the wide range of spillover effects of any mobile inter-
vention on customer’soffline decisions, as well as the impact of offline trajecto-
ries on their mobile usage, marketers should view mobile more holistically and
plan for spillovers across channels. For example, many brands, including
Gillette, have adopted the direct-to-customer model to compete directly with
online competition (Forbes, 2016). In doing so, they have embraced mobile
113State of the Art and Research Agenda
Table 4. Research Agenda based on Emerging Trends.
Key Issue Future Research Agenda
Mobile app monetization 1. What are some ways of effectively monetizing mobile apps?
2. How can providers create mobile app stickiness and
engagement in the long term?
3. What in-app features and functionalities do app users value
based on heterogeneity in user and app characteristics?
Mobile Augmented Reality
(AR)
1. What is the impact of AR apps on user engagement and
actions in and outside of the app?
2. How do AR apps impact firm’s brand image? What are some
ways in which firms can leverage such apps in different
settings and industries?
3. What are the implications of combining AR with other non-
mobile devices and settings (e.g., wearable in stores)?
Data privacy and mobile
user experience
1. What is the impact of stricter privacy regulations on mobile
marketing?
2. Are there trade-offs associated with handling data privacy
concerns and optimizing user experiences to be more
relevant? What are some optimal approaches?
3. How do customers vary in their privacy preferences and how
can marketers glean and leverage this information for
creating unique mobile experiences for them?
4. What is the broad spectrum of privacy for mobile users (e.g.,
different levels of data sharing)? Does the effect of different
levels of privacy on user experience and sensitivity to
promotions and advertising vary? How?
Wearables and other smart
devices
1. What are the future avenues for the growth of voice-enabled
assistants?
2. How are voice-enabled assistants likely to impact different
industries, for example, retail and hospitality?
3. What are the ways in which mobile devices and apps can
create and sustain synergies with other devices, including
wearables?
4. Are there any potential cannibalization effects?
5. What do we know about the usage patterns across devices?
Self-driving vehicles 1. What are the effective ways of marketing self-driving cars?
2. What will be the impact of self-driving cars on directly- and
indirectly- related markets, such as ride sharing and leasing?
3. In what ways can retailers leverage self-driving car
technologies for improved delivery?
Internet of Things (IoT) 1. What models can help marketers deliver timely, personalized
messages and offerings to customers based on customers’
stages in their shopping journeys for different products?
2. How can marketers overcome privacy issues involved in
accessing multiple devices to collect relevant data and
provide useful services to customers?
3. In what ways can marketers seamlessly develop Internet of
Services (IoS) by leveraging the data from IoT?
Artificial intelligence (AI) 1. How do we develop AI-driven, mobile-enabled marketing
systems for an array of marketing decisions ranging from
product portfolio to pricing to promotion to personalization?
114 UNNATI NARANG AND VENKATESH SHANKAR
marketing. For example, the Gillette razor on-demand program allows custo-
mers to reorder blades simply through a text message.
Re-evaluate targeting strategy. A mobile device allows marketers to target in
several ways based on locational, temporal, and contextual factors. Marketing
managers should carefully evaluate the different approaches. Rather than view-
ing the targeting decision as a one-time, piecemeal decision, managers should
also consider the long-term effects of targeting and incorporate shopper learning
and heterogeneity in their decisions. The combined returns from the two differ-
ent types of targeting on shoppers as well as those from retargeting can be very
different from a one-time, one-way targeting approach. Managers must develop
creative ways of leveraging the new tools at their disposal based on data and AI
to learn from shoppers’repeated responses to different combinations of target-
ing approaches.
Personalize, personalize, personalize. Mobile offers unique opportunities to
directly target and reach shopper on their devices. Marketers should leverage this
personalization potential by designing mobile strategy that understands customer
needs, perspectives, and contexts by leveraging holistic data rather than narrow
data. For example, Hilton hotels allows their guests to personalize their stay and
offers them digital key and check-in room selection (Forbes, 2016). If personaliza-
tion at the individual level is not possible in certain channels, managers should still
leverage differences among subgroups of shoppers and identify shopper segments
based on granular purchase and mobile device usage behaviors.
Get customers to opt-in and be transparent with customer data usage.Ina
data-sensitive age, the majority of customers do not appreciate unsolicited mes-
sages on their mobile devices, even if these messages are highly personalized and
relevant. Respect for customer data and privacy preferences requires that firms
get users to opt-in to receive mobile marketing communications. Highlighting
the benefits of opting-in as well as personalizing the communications if users are
already using the provider’s app can be effective strategies. Firms can adopt
effective strategies to get users to opt into their specific permissions by highlight-
ing the value proposition to them before the default prompt to enable permis-
sions in apps. For example, NHL has a full-screen “soft-prompt”for its app
users describing the benefits of notification and location enablement such as
access to real-time news and game scores.
Experiment wisely. It is easy to get carried away by the hype of a new wave
of innovations in wearables, AR, and smart devices. Marketers need to create a
unique mix and meet their customers wherever they are. Before making huge
Table 4. (Continued )
Key Issue Future Research Agenda
2. How do we develop new models of shopping behavior of AI-
and mobile-assisted shoppers and consumers?
3. How can we apply neuroscience-based AI models that
leverage mobile data to develop a deep understanding of
consumer psychology?
115State of the Art and Research Agenda
investments, learning about what customers value (e.g., do they value the conve-
nience of voice assistants, or the control over their privacy if there are trade-
offs?) can result in mutual win-wins. Again, personalization based on what kind
of trade-offs each customer may be making, as learned through their revealed
preferences can offer marketers a unique competitive edge.
CONCLUSION
Mobile marketing has gained prime importance for consumers and managers in
recent years. In the omnichannel environment, managers are constantly seeking
to integrate customers’experience seamlessly across devices and channels.
Mobile devices, due to their personability and location specificity, offer tremen-
dous opportunity for engaging customers at the right time and right place.
However, the full potential of mobile marketing in the Mobile 2.0 era has not
been realized in research and in practice. Several trends have surfaced in areas,
such as mobile app monetization, AR, data and privacy, wearable devices, self-
driving vehicles, the IoT, and AI. These trends present both challenges and
opportunities for marketers, consumers, and society, warranting deeper investi-
gation by marketing researchers.
In this chapter, we presented the conceptual underpinnings of mobile market-
ing and built on previous reviews of mobile marketing research. We offered an
overview of recent advances in the mobile marketing literature by discussing
three broad themes, namely targeted mobile promotions, personalized mobile
advertising, and mobile-led cross-channel effects. Within each research stream,
we discussed the impact of mobile marketing on relevant customer, firm, and
societal outcomes. Finally, we outlined emerging trends in practice for each of
these themes and delineated the future research opportunities in mobile market-
ing. Our synthesis offers some useful insights, several directions for future
research, and actionable implications for mobile marketing executives.
ACKNOWLEDGMENT
We thank the editor and an anonymous reviewer for their useful comments.
REFERENCES
Adamopoulos, P., & Tuzhilin, A. (2015). The business value of recommendations: A privacy-
preserving econometric analysis. Working Paper.
Andrews, M., Goehring, J., Hui, S., Pancras, J., & Thornswood, L. (2016). Mobile promotions:
A framework and research priorities. Journal of Interactive Marketing,34,1524.
Andrews, M., Luo, X., Fang, Z., & Ghose, A. (2015). Mobile ad effectiveness: Hyper-contextual tar-
geting with crowdedness. Marketing Science,35(2), 218233.
App Annie. (2018). 2017 retrospective: A monumental year for the app economy. Retrieved from
https://tinyurl.com/ycrlmq56
Bart, Y., Stephen, A. T., & Sarvary, M. (2014). Which products are best suited to mobile advertising?
Afield study of mobile display advertising effects on consumer attitudes and intentions.
Journal of Marketing Research,51(3), 270285.
Barwise, P., & Strong, C. (2002). Permission-based mobile advertising. Journal of Interactive
Marketing,16(1), 1424.
116 UNNATI NARANG AND VENKATESH SHANKAR
BCG. (2017). Revolution in the driver’s seat: The road to autonomous vehicles. Retrieved from https://
tinyurl.com/ycx69d7c
Belk, R. (2014). You are what you can access: Sharing and collaborative consumption online. Journal
of Business Research,67(8), 15951600.
CNBC. (2018). Augmented reality could be an $8 billion ‘revenue opportunity’for Apple, analyst says.
Retrieved from https://tinyurl.com/y89wl59v
comScore. (2017). Mobile hierarchy of needs. Retrieved from www.comscore.com/mobile-hierarchy
Danaher, P. J., Smith, M. S., Ranasinghe, K., & Danaher, T. S. (2015). Where, when, and how long:
Factors that influence the redemption of mobile phone coupons. Journal of Marketing
Research,52(5), 710725.
Darani, M., & Shankar, V. (2018). Topic hidden Markov modeling: A new machine learning approach
to make dynamic purchase predictions. Working Paper, Texas A&M University.
Dickinger, A., & Kleijnen, M. (2008). Coupons going wireless: Determinants of consumer intentions
to redeem mobile coupons. Journal of Interactive Marketing,22(3), 2339.
Dubé, J. P., Fang, Z., Fong, N., & Luo, X. (2017). Competitive price targeting with smartphone cou-
pons. Marketing Science,36(6), 944975.
Einav, L., Levin, J., Popov, I., & Sundaresan, N. (2014). Growth, adoption, and use of mobile
e-commerce. American Economic Review,104(5), 489494.
eMarketer. (2017). eMarketer unveils new estimates for mobile app usage. Retrieved from https://
tinyurl.com/ydyz4hsm
eMarketer. (2018). eMarketer: Mobile ad spending to surpass TV in 2018. Retrieved from https://
tinyurl.com/yckmugq5
Engadget. (2018). Amazon’s AR shopping tool is now available on Android. Retrieved from https://
tinyurl.com/y7v7b27m
Faccio, M., & McConnell, J. J. (2018). Death by Pokémon GO: The economic and human cost of
using apps while driving. National Bureau of Economic Research (No. w24308).
Fong, N. M., Fang, Z., & Luo, X. (2015). Geo-conquesting: Competitive locational targeting of
mobile promotions. Journal of Marketing Research,52(5), 726735.
Forbes. (2016). How leading brands are winning the ‘direct-to-customer’conversation. Retrieved from
https://tinyurl.com/yd242dk5
Forbes. (2017). 19 Crimes wine is an amazing example of adult targeted augmented reality. Retrieved
from https://tinyurl.com/yah8e23t
Forbes. (2018). Amazon and Marriott want to modernize hotel experiences. Retrieved from https://
tinyurl.com/y8k5lhok/
Gelb. (2017). Branding, marketing, and the impact of self-driving cars. Retrieved from https://tinyurl.
com/ydehc3p7
Ghose, A. (2017). TAP: Unlocking the mobile economy. Massachusetts: MIT Press.
Ghose, A., Goldfarb, A., & Han, S. P. (2012). How is the mobile internet different? Search costs and
local activities. Information Systems Research,24(3), 613631.
Ghose, A., Guo, X., & Li, B. (2017). Empowering patients using smart mobile health
platforms: Evidence from a randomized field experiment. Working Paper, New York
University, NY.
Ghose, A., Han, S., & Park, S. (2013). Analyzing the interdependence between Web and mobile
advertising: A randomized field experiment. Working Paper, Leonard N. Stern School of
Business New York University, NY.
Ghose, A., Kwon, H. E., Lee, D., & Oh, W. (2018). Seizing the commuting moment: Contextual tar-
geting based on mobile transportation apps. Information Systems Research. Forthcoming.
Ghose, A., Li, B., & Liu, S. (2016). Mobile targeting using customer trajectory patterns. Working
Paper, New York University, NY.
Google. (2018). Mobile has changed search intent and how people get things done: New consumer
behavior data. Retrieved from https://www.thinkwithgoogle.com/consumer-insights/mobile-
search-consumer-behavior-data
Grewal, D., Ahlbom, C. P., Beitelspacher, L., Noble, S. M., & Nordfält, J. (2018). In-store mobile
phone use and customer shopping behavior: Evidence from the field. Journal of Marketing,
82(4), 102126.
117State of the Art and Research Agenda
Grewal, D., Ahlbom, C. P., Noble, S. M., Shankar, V., Narang, U., & Nordfält, J. (2018). Mobile
integrated kiosks: How communication content increases spending. Working Paper, Babson
College, MA.
Grewal, D., Bart, Y., Spann, M., & Zubcsek, P. P. (2016). Mobile advertising: A framework and
research agenda. Journal of Interactive Marketing,34,314.
Heilman, C. M., Nakamoto, K., & Rao, A. G. (2002). Pleasant surprises: Consumer response to
unexpected in-store coupons. Journal of Marketing Research,39(2), 242252.
Hui, S. K., Inman, J. J., Huang, Y., & Suher, J. (2013). The effect of in-store travel distance on
unplanned spending: Applications to mobile promotion strategies. Journal of Marketing,
77(2), 116.
IAB. (2016). Digital ad spend reaches an all-time high of $88 billion in 2017, with mobile upswing
unabated, accounting for 57% of revenue. Retrieved from https://tinyurl.com/y9frnohx
Jacobs, B. J. D., Donkers, B., & Fok, D. (2016). Model-based purchase predictions for large assort-
ments. Marketing Science,35(3), 389404.
Kesler, R., Kummer, M., & Schulte, P. (2018). Mobile applications and access to private data: The
supply side of the Android ecosystem. Working Paper.
Kim, S. J., Wang, R. J. H., & Malthouse, E. C. (2015). The effects of adopting and using a brand’s
mobile application on customers’subsequent purchase behavior. Journal of Interactive
Marketing,31,2841.
Knowledge @ Wharton. (2016). Why ‘missed call’marketing has taken hold in India. Retrieved from
http://knowledge.wharton.upenn.edu/article/missed-call-marketing-taken-hold-india/
Kone. (2018). AI, more of a reality than you think. Retrieved from https://tinyurl.com/y2qoa6ar
Kumar, V., Dogan, O. B., & Lahiri, A. (2016). Engaging customers in the app world through smart
analytics. The Journal of World Marketing Summit,2(1).
Leanplum. (2017). Personalize or bust? The impact on app engagement. Retrieved from https://tinyurl.
com/y8vcj625
Lee, D., Gopal, A., & Lee, D. (2017). Micro-giving: On the use of mobile devices and monetary sub-
sidies in charitable giving. Working Paper. Retrieved from https://ssrn.com/abstract=3280553
Lee, J., Zhuang, M., Kozlenkova, I., & Fang, E. (2016). The dark side of mobile channel expansion
strategies. MSI Report.
Li, C., Luo, X., Zhang, C., & Wang, X. (2017). Sunny, rainy, and cloudy with a chance of mobile
promotion effectiveness. Marketing Science,36(5), 762779.
Luo, X., Andrews, M., Fang, Z., & Phang, C. W. (2013). Mobile targeting. Management Science,
60(7), 17381756.
Marshall, M. (2018). TGI Friday’s AI-powered marketing drives $150 million (with virtual bartenders
and more), Venturebeat. Retrieved from https://tinyurl.com/yd5mzjbr
Muir, D., & Tsai, Y. (2016). Search costs and consideration set formation with UGC: Fixed v. mobile
devices. Working Paper.
Narang, U., & Shankar, V. (2019). The effects of mobile apps on shopper purchases and product
returns. Marketing Science. Forthcoming.
Narang, U., Shankar, V., & Narayanan, S. (2018). The impact of mobile app failures on online and
offline purchases. Working Paper.
Rafieian, O., & Yoganarasimhan, H. (2018). Optimal sequencing of mobile advertisements. Working
Paper, University of Washington, Seattle, WA.
Sandler, E. (2018). How L’Occitane is using AI to improve customer experiences online. Glossy.
Retrieved from https://tinyurl.com/yazasd48
Shankar, V. (2012). Mobile marketing strategy. In V. Shankar & G. S. Carpenter (Eds.), Handbook
of marketing strategy (pp. 217230). Massachusetts: Edward Elgar Publishing.
Shankar, V. (2018). How artificial intelligence (AI) is reshaping retailing. Journal of Retailing,94(4),
611.
Shankar, V., & Balasubramanian, S. (2009). Mobile marketing: A synthesis and prognosis. Journal of
Interactive Marketing,23(2), 118129.
Shankar, V., & Hollinger, M. (2007). Online and mobile advertising: Current scenario, emerging
trends, and future directions. Marketing Science Institute,31(3), 206207.
118 UNNATI NARANG AND VENKATESH SHANKAR
Shankar, V., Kleijnen, M., Ramanathan, S., Rizley, R., Holland, S., & Morrissey, S. (2016). Mobile
shopper marketing: Key issues, current insights, and future research avenues. Journal of
Interactive Marketing,34,3748.
Shankar, V., O’Driscoll, T., & Reibstein, D. (2003). Rational exuberance: The wireless industry’s
Killer ‘B’.Strategy þBusiness,31(Summer), 6877.
Shankar, V., Venkatesh, A., Hofacker, C., & Naik, P. (2010). Mobile marketing in the retailing envi-
ronment: Current insights and future research avenues. Journal of Interactive Marketing,
24(2), 111120.
Statista. (2018). Number of smartphone users in the United States from 2010 to 2022 (in millions).
Retrieved from https://tinyurl.com/j2mzjqh
TechCrunch. (2017). U.S. consumers now spend 5 hours per day on mobile devices. Retrieved from
https://tinyurl.com/y7dvmx7b
Wakabayashi, D. (2018). California passes sweeping law to protect online privacy. The New York
Times. Retrieved from https://tinyurl.com/y7ojx39l
Waldrop, M. M. (2015). No drivers required. Nature,518(7537), 20.
Wang, R. J. H., Malthouse, E. C., & Krishnamurthi, L. (2015). On the go: How mobile shopping
affects customer purchase behavior. Journal of Retailing,91(2), 217234.
Xu, J., Forman, C., Kim, J. B., & Van Ittersum, K. (2014). News media channels: Complements or
substitutes? Evidence from mobile phone usage. Journal of Marketing,78(4), 97112.
Xu, K., Chan, J., Ghose, A., & Han, S. P. (2016). Battle of the channels: The impact of tablets on
digital commerce. Management Science,63(5), 14691492.
Zubcsek, P. P., Katona, Z., & Sarvary, M. (2017). Predicting mobile advertising response using con-
sumer colocation networks. Journal of Marketing,81(4), 109126.
119State of the Art and Research Agenda
