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Artificial Intelligence in Advertising: How Marketers Can Leverage Artificial Intelligence Along the Consumer Journey

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INTRODUCTION
   
increasingly is complex. Consumers express their
  
forms (through search, comments, blogs, Tweets,
“likes,” videos, and conversations) and across
many channels (web, mobile, and face to face;
Court, Elzinga, Mulder, and Vetvik, 2009). This
seemingly endless supply of consumer-curated
data continues to grow in terms of its volume,
velocity, variety, and veracity.
-
-
uable consumer insight. There are, of course, risks
involved, such as outcomes related to Cambridge
Analytica’s historic use of millions of Facebook
accounts for political purposes (Solon and Laugh-
land, 2018). Marketers must adapt the AI systems
they use to comply with new privacy standards.
The same risks, however, create opportunities for
   


To make sense of big data, AI deals with two dif-
ferent types of input data:
 Structured data: traditional, standardized
datasets, such as basic customer demograph-
ics, transaction records, or web-browsing his-
tory. AI, with its enormous computing power,
runs complex computations on large volumes of
such structured data and often produces results
in real time.
 Unstructured data: about 80 percent of the
approximately 2.5 billion gigabytes of daily user-
generated data are unstructured (Rizkallah, 2017)

AI’s ability to process large volumes of this type
of data—and to do so very quickly—is what dis-
tinguishes it from traditional computing systems.
AI preprocesses unstructured inputs to pre-
pare them for subsequent computations, or build-
ing blocks. The results of these building blocks
vastly outperform our natural intelligence—to

BUILDING BLOCKS OF ARTIFICIAL INTELLIGENCE
The combination of the following key building
blocks allows advertisers to deepen their under-

Articial Intelligence in Advertising
How Marketers Can Leverage Articial Intelligence
Along the Consumer Journey
JAN KIETZMANN
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JEANNETTE PASCHEN
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4C=BM3:GE5M(=3
EMILY TREEN
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Speaker’s Box
Editor’s Note
“Speaker’s Box” invites academics and practitioners to identify signicant areas of research aecting advertis-
ing and marketing. The intent of these contributions is to bridge the gap between the length of time it takes to
produce rigorous work and the acceleration of change within practice. This edition of Speaker’s Box assesses
articial intelligence (AI) in the wake of the Cambridge Analytica scandal, which has increased awareness
about the “dark side” of data mining and the use of AI in analyzing and managing social-media data. By
way of contrast, the authors focus mainly on the “bright side” of AI. They do so not to dismiss the many
legitimate privacy concerns AI raises but instead to illustrate how AI helps consumers and advertisers alike
by generating insights in an environment that observes the public’s privacy rights.
Douglas C. West
Professor of Marketing, King’s College London
Contributing Editor, Journal of Advertising Research
264%JOURNAL OF ADVERTISING RESEARCH% 234536738%)'&0
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Natural Language Processing
Natural language processing (NLP) allows
AI systems to analyze the nuances of human
language to derive meaning from, among
others, blog entries; product reviews; and
the billions of daily Tweets, Facebook posts,
and status updates. Swedbank, the Swedish
bank, uses a virtual assistant with NLP to
answer customer inquiries on its website’s
homepage, allowing customer-service
employees to focus more on revenue-

Image Recognition
Image recognition helps advertisers under-
stand pictures and videos that people
share on social media and that “show” true
consumer behavior. Consumers identify

-
textual consumption details (Forsyth and

(even when not explicitly mentioned in the
post) and users’ personal details. When a
celebrity shares a photo about an uniden-
-
ognizes both the product and a potential

Image recognition also is used in brick-
and-mortar retail, which still accounts for

based Cloverleaf uses image recognition
in its “intelligent” shelf-display platform.
Equipped with optical sensors, the display
collects data on customer demographics,
such as age and gender, and scans shop-
pers’ faces to gauge their emotional reac-
tion to the product. The nearer shoppers
are to the display, the more personalized
the content becomes.
Speech Recognition
Speech recognition allows AI to analyze
the meaning of spoken words. Sayint,
a call-center services provider, uses AI
speech recognition to monitor and ana-
lyze customer calls. The technology helps
Sayint to understand customer needs,
improve call-agent performance, and boost
customer satisfaction.
Problem Solving and Reasoning
When advertisers deploy AI to under-
stand insights hidden in user-generated
-
lem they want to solve and how they will
approach the data analysis. These main
processes give rise to the all-important
-
ing the ability to predict future behavior.
Advertisers might want to segment their
market on the basis of varying psycho-
graphics of their customer base, possibly
to determine who their “best” customers
are and why those customers would buy

Personality characteristics that are
important in people’s lives eventually
become a part of their language. The way
that AI can “reason” with people’s social-
media comments and posts, in addition,
may reveal personality tendencies, values,
 
individual in terms of the Big Five person-
ality traits—Openness, Conscientiousness,
Extraversion, Agreeableness, and Neuroti-
-
lyzing unstructured consumer-generated
data, then can inform future marketing
decisions. The North Face uses AI to deter-

basis of available data about where and

Machine Learning
 -
tems can “reason” and propose the best
options for the consumer’s stated needs
 
more, the system remembers everything
it has computed previously by storing its
memories in a knowledge base and using
machine learning to learn from its previous
data and problem-solving experiences. The
more unstructured data an AI system pro-
cesses, the “smarter” it gets and the more

results are for advertisers. As The North

and combines this information with actual
purchases made by customers, the system
learns more accurately to predict recom-
mendations that most likely will satisfy
  
the results to prioritize these options.
Machine learning also can help predict
customer lifetime value and conversion
likelihood. By analyzing patterns and
learning from data about the past behav-
ior of consumers in the trial stage of a
product, machine learning can ascertain
how likely a consumer is to purchase the
paid version or predict the future value of
a particular customer.
AI gleans information from unstruc-
tured data—through personality analy-
sis and sentiment analysis (e.g., through
facial coding)—enabling marketers to
  -
ers. AI then produces content through the
following means:
 Natural language generation (NLG):
marketers can use AI tools such as Word-
smith for developing human-sounding,
original content, from personalized
e-mails to news articles, or deploy AI for
building advertising content. Saatchi LA
trained IBM Watson to write thousands
of advertisement copies for Toyota; the
copy was tailored to more than 100 dif-
ferent customer segments.
 Image generation: constructing lifelike
pictures and animated movies on the
basis of text descriptions.
 Speech generation: providing meaning-
ful voiceovers for advertisements. The


Morgan using image, speech, and natural
language generation.
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HOW AI AFFECTS ADVERTISING ALONG
THE CONSUMER JOURNEY
To grasp the opportunities AI creates
for marketers requires understand-
ing how communications traditionally
“work” along the consumer’s decision
    
with need recognition, which mobilizes
the consumer through the stages of initial
consideration, active evaluation, purchase,
and postpurchase (Court et al., 2009). In
this section, the authors explain the con-
sumer processing activities, the advertising

 
they highlight how the aforementioned
building blocks of AI transform these
advertising tasks (See Table 1).
Need and Want Recognition
The stage at which a need is triggered has

a category rather than brand level (Batra
and Keller, 2016). Advertisers have relied
on methods such as market research, web
analytics, and data mining to build con-
sumer profiles for understanding and
      -
ble to understand emerging wants and
needs in real time—as consumers express
    
more quickly.
Media company Astro uses Microsoft’s
AI system Azure for consumer profil-
ing. The system crunches billions of data
points in seconds to determine individu-
als’ needs. It then personalizes web content
on Astro’s platform in real time to align
with those consumer interests. As con-
sumers’ digital footprints evolve—through
social-media status updates, purchasing
behavior, or online comments and posts—
machine learning continuously updates

AI also helps advertisers “manifest” con-
sumers’ needs or wants. Pinterest employs
image recognition to learn about indi-
vidual users’ particular style preferences
through the images they have pinned on
the site. The website then suggests other
-
 
want recognition.
Initial Consideration
-

satisfy their needs or wants is to insert the
brand into consumers’ consideration set
(Batra and Keller, 2016). Advertising tasks
include increasing the brand’s visibility and
emphasizing key reasons for consideration.
Advertisers could accomplish this goal, for
example, through search optimization, with
paid search advertisements, organic search,
or advertisement retargeting.
Advertisers can use AI-powered search
to identify, rank, and present results that
most likely will meet the information
TABLE 1
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Need/Want
Recognition
Initial
Consideration
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Google Adwords helps advertisers make
clearer distinctions between qualified

Using AI, Google analyzes search-query
data by considering not only keywords
but also context words and phrases, con-
sumer activity data, and other big data.

valuable subsets of consumers and more
accurate targeting. Zendesk, a customer-
service software company, increased the
quality and volume of its leads after using
 
targeting its advertisements to Facebook

Active Evaluation
When consumers narrow down their
list of brand choices, advertising aims to
  
them that they are making the best choices
(Batra and Keller, 2016). One tactic is tar-
geting consumers who are high in pur-
chase intent and providing them credible
and persuasive content.
AI supports these tasks in three impor-
tant ways:
 Predictive lead scoring, through machine
learning, allows marketers to make
accurate predictions about the purchase
intent of consumers. A machine-learning
algorithm runs through a database of
 -

appending additional external data on
consumer activities and interests, cre-

 Machine learning and image, speech,
and natural language generation ena-
ble advertisers to curate content while
learning from consumer behavior in

U.K.-based online fashion retailer ASOS
uses Microsoft’s Azure for real-time cal-
culation of product relevancy and the
likelihood of a website visitor viewing,
saving, adding to a cart, and ultimately
buying a product. Product recommen-
dations are generated in real time as
users browse product listings.
 Marketers use emotion AI to understand
what consumers are saying and how
they feel about their brands publicly,
such as in reviews, blogs, or videos,
and to pretest advertisements. Kellogg’s

help devise an advertising campaign
for its Crunchy Nut cereal, eliminating
advertisement executions when view-
ers’ engagement dropped on viewing
the advertisement multiple times.
Purchase
As consumers decide how much their
preferred brand is worth and how much
they are willing to pay, advertising aims
to move them out of the decision process
and into action by reinforcing the value of
the brand compared with its competition
(Batra and Keller, 2016). Advertisers can
communicate this value by emphasizing
convenience and information about where
to buy—bolstered by reassurances about
guarantees, warranties, or return poli-

AI can alter the purchase process for
consumers completely. Office-supply
retailer Staples did this when it trans-
-
gent” purchasing system that lets business
customers order supplies by means of
voice commands, text, or e-mail. Market-
ers also can determine the “sweet spot” for
pricing. This is known as dynamic pricing
    -
ments on the basis of information such
as demand and other consumer-behavior
variables, seasonality, and competitors’
activities. During Black Friday 2017,
Amazon changed prices on 28 percent
of its inventory at least once a day using
dynamic pricing enabled by AI.
Postpurchase
At this stage, consumers evaluate their sat-
isfaction and consider whether they want
to repurchase the product, perhaps engag-
ing in word of mouth. Advertisers, in turn,
aim to delight by reinforcing that the brand
is performing well against customer expec-
tations or by rectifying potential problem

AI-enabled “chatbots” help advertisers
engage with customers postpurchase. The
software developer Autodesk uses a virtual
agent to return customer answers quickly.
It relies on NLP and machine learning to
recognize and extract the intent, context,
and meaning behind inquiries, thereby
reducing the resolution time for inquiries

Marketers also have the ability to iden-
tify their most valuable customers. Known
as propensity modeling, this AI applica-
tion crunches big data to assess customer
lifetime value, likelihood of reengagement,
propensity to churn, and other key perfor-
mance measures of interest. Once they
know these metrics, advertisers can craft
personalized communication as part of
their customer-relationship management
campaign to encourage the desired behav-

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tomer’s response.
CONCLUSION
AI has shifted the way advertisers under-
stand and guide consumers. In the future,
new ways of consumer-generated data
mining will drive consumer insight, and
AI will become the ultimate test for pri-
vacy. With the help of machine learning,
advertisers will be able to collect consumer
data from many sources imperceptibly,
combine those data, and mine them to
deliver on-the-spot consumer insights. The
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communicate actively back to consumers.
 
Sundar Pichai, introduced virtual assistant
Google Duplex in May 2018, he provided
a glimpse into the not-so-distant future of
AI. Duplex, which uses NLG to make calls
to schedule restaurant reservations and

holiday business hours (Cipriani, 2018),
sounded eerily human, prompting imme-
diate concerns about the takeover of AI
and the need for chatbots to identify them-
selves as such. These emerging technolo-
gies make it entirely conceivable that AI
soon will be woven so imperceptibly into
the fabric of traditional advertising that it
becomes indistinguishable from it—with
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and “Integrating Mar-
keting Communications: New Findings, New
Lessons, and New Ideas.” Journal of Marketing
80 (2016): 122–145.
 (2018, May 24). “What Is Google
Duplex?” May 24, 2018. Retrieved August 1,

com/how-to/what-is-google-duplex/
   and O. J.
 “The Consumer Decision Journey.
McKinsey Quarterly, June 2009.
  and Computer Vision:
A Modern Approach. Upper Saddle River, NJ:
Prentice Hall, 2011.
and
Understanding and Managing Electronic Word
of Mouth.” Journal of Public Aairs 13, 2 (2013):
146–159.
  “Facebook’s Scandal and GDPR
are Creating New Opportunities for Retail.”
Forbes 
www.forbes.com/sites/gregpetro/2018/05/27/
facebooks-scandal-and-gdpr-are-creating-new-
opportunities-for-retail/#5e598747626c
  “The Big (Unstructured) Data
Problem.” Forbes, June 5, 2017. Retrieved from
-
cil/2017/06/05/the-big-unstructured-data-
problem/#57541ca2493a
  and “Cambridge
Analytica Closing after Facebook Data Har-
vesting Scandal.” The Guardian, May 2, 2018.

uk-news/2018/may/02/cambridge-analytica-
closing-down-after-facebook-row-reports-say
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... In recent years, advances in digital technology have enabled the development of more complex virtual characters, often presented in three-dimensional form, with anthropomorphic appearances, unique personalities, identity backgrounds, behavioral patterns, and social lives (Ahn, et al. 2012; Bendoni and Danielian 2019). Five core technologies are key to synthesizing virtual characters: natural language processing, image recognition, speech recognition, problem solving, and machine learning (Kietzmann, et al. 2018). Compared with AI customer service and other virtual service providers, virtual influencers in social media are currently the virtual characters with a large amount of followers. ...
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... On the side of media buying advertising has already paved a considerable way in maximizing profits via programmatic advertising thanks to artificial intelligence and machine learning algorithms (Grether 2016;Kietzmann et al. 2018;Neumann 2016); however, on the side of the creative production of advertising the debate is just starting. Pioneer attempts in China to develop systems based on creative artificial intelligence to automatically produce creative content for advertisements in real-time and IBM Watson's AI-based movie trailer show the capital's interest in this area. ...
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Industry advocates argue that the focus of advertising production has shifted from the creativity of practitioners to consumer analytics and the potential advantages of big data. Although a little empirical research offers valuable insights about the changing role of advertising practitioners, it lacks a critical perspective to situate it in a broader social context. On the other hand, digital labor and branding literature over-concentrate on user labor and neglect the role of practitioners in advertising production. By deploying the concept of immaterial labor, this article reevaluates the findings of mainstream marketing-advertising literature within the context of post-Fordist labor. This article aims to create a resonance between theories of immaterial labor and advertising literature and to call for further empirical research from a labor perspective. It argues that advertising practitioners put more strategical, relational and communicative powers into work to manage a data-oriented market. Keywords: Advertising Practitioners, Immaterial Labour, Big Data, Media Work, Autonomist Marxism
... This interaction also includes the company's partners (Lemon & Verhoef, 2016), industry experts (Hartmann, Wieland, & Vargo, 2018), the customers' social spheres (Lemon & Verhoef, 2016), and communication within the customer organization (Sheth, 1973). The term interaction is used in a broad sense to include all possible ways of brand exposure, such as advertising (Kietzmann, Paschen, & Treen, 2018), communication with service employees (Lemon & Verhoef, 2016), as well as traditional (Baxendale, Macdonald, & Wilson, 2015) and electronic word-of-mouth (Wolny & Charoensuksai, 2014). This increased complexity in forming experiences calls for the shift in the locus of negotiation power from sellers to buyers (Marcos Cuevas, 2018) and requires companies to adopt technological solutions in order to gain access to customers' buying processes (Steward et al., 2019). ...
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This chapter develops a framework for the Customer Value Management (CVM) function within the marketing practice of firms with a large customer base. A one-to-one marketing campaign involving a data set of 60,000 mobile customers from the Business Intelligence (BI) system of a mobile operator in Nigeria was experimented. The data was prepared along customer value, lifetime, and other variables. Marketing campaigns were developed, deployed, and experimented on below-the-line (BTL) digital channels. The experiment explored the effectiveness of these digital channels in driving CVM campaign activities within a large customer base. The results show that while all the experimented channels performed well under the CVM framework with respect to customer adoption and incremental revenue, the digital tag notification performance was exceptional. The implication is that the digital tag notification channel can be well-positioned as an integral channel of CVM operations in large consumer firms with a real-time one-to-one marketing capability. This study demonstrates the value and ease of a CVM framework application in a large customer base in driving business revenue. It also shows customers’ practical responses to offerings across digital channels, which has implications for the effective optimization of digital channels of firms with a large customer base.
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Virtual character technology is developing rapidly and is replacing human participation in social division of labor in many fields, such as online education, human customer service, marketing activities, etc. Among them, virtual influencer marketing has gradually become a popular phenomenon, and has been widely adopted among major social media platforms such as Instagram, Facebook, and TikTok. However, due to some long-standing user biases towards computer services, especially compared to human service providers, it is widely believed that computer service providers are lack of ability. As a type of computer product, virtual influencers are highly anthropomorphic in terms of appearance, voice, identity, interaction, etc. Among them, the realistic appearance is an important feature of virtual influencers, which may be an important factor affecting users’ perception of their abilities, which in turn affect the advertising effectiveness. On the other hand, the controlling entity of virtual influencer is generally human or computer, that is, virtual influencer’s behavior is generally manipulated by human or computer. Past studies suggest that the controlling entity are also an important factor affecting user attitudes. Does appearance realism affect virtual influencer perception? What can we do to improve user attitudes towards virtual influencer advertisements? Is there an interaction effect between the virtual influencer’s appearance realism level and the type of controlling entity? Or, how does the virtual influencer’s level of appearance realism and the type of controlling entity match make the advertisement attitude better? We intend to conduct a 2 (appearance realism: high/low) × 2 (controlling entity type: computer/human) between-group experiments. The expected result are: tunder a high level of appearance realism, the effect of computer controlling entity is better than that of human-controlling entity. The reason is that high-level appearance realism improves the user’s ability to perceive virtual idols. The ability expectations of virtual influencers reduce perceived ability and thus advertisement attitude. We will further examine the moderating effect of product types. For search products, the effect of using computer controlling entity for virtual idols with a high-level appearance realism is enhanced, the advertisement attitude should be stronger; for experience products, human entity should perform better in advertising regardless high or low appearance realism.
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Artificial intelligence (AI) technology is playing an increasingly important role in achieving precision marketing. AI word‐of‐mouth system marketing focuses most on the entry point of consumers—the key information that leads consumers to buy. Based on consumer behaviour theory and information communication theory, this study constructs a research model of the influence of positive and negative online word of mouth on consumer purchase behaviour from the AI word‐of‐mouth system's four dimensions: professional degree, information quality, information quantity and information intensity. The results show that in the analysis of perceived risk, whether for positive or negative online word of mouth, consumers pay more attention to the number of information features than information quality and information intensity. In the purchase behaviour analysis, consumers are most concerned about information quality and information intensity. Perceived risk plays a mediating role in the influence of AI word of mouth on purchasing behaviour.
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For ‘viral marketing’, it is critical to understand what motivates consumers to share their consumption experiences through ‘electronic word of mouth’ (eWoM) across various social media platforms. This conceptual paper discusses eWoM as a coping response dependent on positive, neutral, or negative experiences made by potential, actual, or former consumers of products, services, and brands. We combine existing lenses and propose an integrative model for unpacking eWoM to examine how different consumption experiences motivate consumers to share eWoM online. The paper further presents an eWoM Attentionscape as an appropriate tool for examining the amount of attention the resulting different types of eWoM receive from brand managers. We discuss how eWoM priorities can differ between public affairs professionals and consumers, and what the implications are for the management of eWoM in the context of public affairs and viral marketing.
  • R Batra
  • K L Keller
Batra, R., and K. L. Keller. "Integrating Marketing Communications: New Findings, New Lessons, and New Ideas." Journal of Marketing 80 (2016): 122-145.
What Is Google Duplex?
  • J Cipriani
Cipriani, J. (2018, May 24). "What Is Google Duplex?" May 24, 2018. Retrieved August 1, 2018, from the CNet website: https://www.cnet. com/how-to/what-is-google-duplex/
Facebook's Scandal and GDPR are Creating New Opportunities for Retail
  • G Petro
Petro, G. "Facebook's Scandal and GDPR are Creating New Opportunities for Retail."
Cambridge Analytica Closing after Facebook Data Harvesting Scandal
  • O Solon
  • O Laughland
Solon, O., and O. Laughland. "Cambridge Analytica Closing after Facebook Data Harvesting Scandal." The Guardian, May 2, 2018. Retrieved from https://www.theguardian.com/ uk-news/2018/may/02/cambridge-analyticaclosing-down-after-facebook-row-reports-say