Embracing AI and Big Data in customer journey mapping: from literature review to a theoretical framework

Abstract

Nowadays, Big Data and Artificial Intelligence (AI) play an important role in different functional areas of marketing. Starting from this assumption, the main objective of this theoretical paper is to better understand the relationship between Big Data, AI, and customer journey mapping. For this purpose, the authors revised the extant literature on the impact of Big Data and AI on marketing practices to illustrate how such data analytics tools can increase the marketing performance and reduce the complexity of the pattern of consumer activity. The results of this research offer some interesting ideas for marketing managers. The proposed Big Data and AI framework to explore and manage the customer journey illustrates how the combined use of Big Data and AI analytics tools can offer effective support to decision-making systems and reduce the risk of bad marketing decision. Specifically, the authors suggest ten main areas of application of Big Data and AI technologies concerning the customer journey mapping. Each one supports a specific task, such as (1) customer profiling; (2) promotion strategy; (3) client acquisition; (4) ad targeting; (5) demand forecasting; (6) pricing strategy; (7) purchase history; (8) predictive analytics; (9) monitor consumer sentiments; and (10) customer relationship management (CRM) activities.

Full text

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Innovative Marketing, Volume 15, Issue 4, 2019
http://dx.doi.org/10.21511/im.15(4).2019.09
Abstract
Nowadays, Big Data and Articial Intelligence (AI) play an important role in dierent
functional areas of marketing. Starting from this assumption, the main objective of this
theoretical paper is to better understand the relationship between Big Data, AI, and
customer journey mapping. For this purpose, the authors revised the extant literature
on the impact of Big Data and AI on marketing practices to illustrate how such data
analytics tools can increase the marketing performance and reduce the complexity of
the pattern of consumer activity. e results of this research oer some interesting
ideas for marketing managers. e proposed Big Data and AI framework to explore
and manage the customer journey illustrates how the combined use of Big Data and
AI analytics tools can oer eective support to decision-making systems and reduce
the risk of bad marketing decision. Specically, the authors suggest ten main areas of
application of Big Data and AI technologies concerning the customer journey map-
ping. Each one supports a specic task, such as (1) customer proling; (2) promotion
strategy; (3) client acquisition; (4) ad targeting; (5) demand forecasting; (6) pricing
strategy; (7) purchase history; (8) predictive analytics; (9) monitor consumer senti-
ments; and (10) customer relationship management (CRM) activities.
Mario D’Arco (Italy), Letizia Lo Presti (Italy), Vittoria Marino (Italy),
Riccardo Resciniti (Italy)
Embracing AI and Big Data
in customer journey
mapping: from literature
review to a theoretical
framework
Received on: 4 of December, 2019
Accepted on: 18 of December, 2019
INTRODUCTION
In recent years, the word “Big Data” has become increasingly pop-
ular. Both academics and non-academics use this term to designate
large volumes of extensively varied data that are generated, captured,
and processed at high velocity (Laney, 2001). In concomitance with
the rise of Big Data technologies, Articial Intelligence (AI) is be-
ing revitalized and has again become an appealing topic for research.
According to Duan, Edwards, and Dwivedi (2019), the term AI is used
to designate “the ability of a machine to learn from experience, adjust
to new inputs and perform human-like tasks” (p. 63). AI tools can
support the processing of large amounts of data and turn them into
useful information.
As highlighted by Huang (2019), Big Data and AI are widely used in
many dierent elds, “such as robotics, speech recognition, image rec-
ognition, machine translation, automatic response, natural language
processing and automatic driving” (p. 165). Furthermore, Big Data
and AI are transforming the business environment and many areas
of marketing. e correlation between these methods and marketing
© Mario D’Arco,
Letizia Lo Presti, Vittoria Marino,
Riccardo Resciniti, 2019
Mario D’Arco, Ph.D. in Management
and Information Technology,
Department of Business Science
- Management and Innovation
Systems/DISA-MIS, University of
Salerno, Italy.
Letizia Lo Presti, Research Associate
in Management, Department of Law
and Economics, University of Rome
Unitelma Sapienza, Italy.
Vittoria Marino, Associate Professor
in Marketing, Department of
Business Science - Management
and Innovation Systems/DISA-MIS,
University of Salerno, Italy.
Riccardo Resciniti, Full Professor
in Marketing, Department of Law,
Economics, Management and
Quantitative Methods, University of
Sannio, Italy.
consumer analytics, data-driven, decision support
systems, marketing analytics
Keywords
JEL Classification M15, M30, M31
is is an Open Access article,
distributed under the terms of the
Creative Commons Attribution 4.0
International license, which permits
unrestricted re-use, distribution,
and reproduction in any medium,
provided the original work is properly
cited.
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BUSINESS PERSPECTIVES

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discipline is related to the technological progress, which enables broad implementation of Big Data
and AI applications in practice, such as marketing analytics toolbox based on machine learning (Sun,
Huang, Wu, Song, & Wunsch, 2017; Choi, Wallace, & Wang, 2018).
Since consumer behavior is being inuenced more and more by digital applications, the generation
and availability of data is growing at a faster rate than ever before. e convergence of Big Data, AI,
and marketing can create greater customer value and several advantages for companies. For example,
marketers and organizations can use Big Data-related analytics techniques to gain important informa-
tion about transactions, purchase quantities, and customer credentials (ackeray, Neiger, Hanson, &
McKenzie, 2008). Additionally, data are useful to better understand the consumer behavior, purchase
preferences, and marketing trends (Yin & Kaynak, 2015). Furthermore, company management, sup-
ported by Big Data analytics tools, can make better decision about production quantity, stock control
and inventory, sales forecasting, logistics optimization, supplier coordination, and purchase channels
selection (Schneider & Gupta, 2016; Bradlow, Gangwar, Kopalle, & Voleti, 2017).
Based on these premises, it is important to investigate how Big Data and AI should be leveraged strate-
gically to plan the customer journey. Customer journey is a metaphor to conceptualize the customer ex-
perience during the purchase cycle. Specically, both researchers and practitioners with this metaphor
designate the sequence of customer’s direct and indirect encounters with a specic product, service,
or brand (Meyer & Schwager, 2007). Such encounters are mediated by dierent types of touchpoints,
namely, online and oine channels that aect the customer’s experiences and purchase intentions.
As highlighted by Lemon and Verhoef (2016), customer journey consists of three phases: the prepur-
chase phase, the purchase phase, and the postpurchase phase. e rst phase encompasses the behaviors
such as need recognition, search, and the formation of consideration set assembled from exposure to
information found on the web, ads, user-generated contents, words of mouth, or other stimuli. In the
second phase, consumers, based on the information provided, select what they want and proceed with
the payment. e third phase is characterized by such behaviors as usage and consumption, and posi-
tive or negative postpurchase engagement phenomena.
e main objective of this theoretical paper is to systematize the relationship between Big Data, AI, and
customer journey map. Starting from an exploration of the extant literature concerning the impact of
Big Data and AI on marketing practices, the authors aim at developing a theoretical framework focused
on strategic use of Big Data and AI across the customer journey mapping. Specically, the ndings re-
veal how such data analytics tools can increase the marketing performance (i.e., media spend and touch
point selection (see Edelman, 2010), and reduce the complexity of the purchase patterns and consumer
activities.
1. THEORETICAL BASIS
As highlighted by Fink (2005), “A literature re-
view is a systematic, explicit, and reproducible
design for identifying, evaluating, and interpret-
ing the existing body of recorded documents” (p.
3). Literature reviews are conducted for a variety
of purposes. First, they present in a rigorous way
the knowledge already available on a specic topic.
Second, the collection of previous works helps the
researchers to identify new patterns and themes
that can contribute to theory development.
In this research, the purpose of the literature re-
view is (1) to dene those existing research con-
cerning the usefulness of Big Data and AI adop-
tion in the marketing, (2) to provide a framework
for implementing and managing these technolo-
gies to understand the customer journey.
Due to the existence in the academic literature of
a large number of articles about Big Data and AI,
we followed a specic inclusion/exclusion proto-
col. Firstly, publications were selected from 2014
onwards, since as highlighted by Grover and Kar

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(2017) before this date, the number of publications
focusing on the benets of Big Data for marketers
and organizations is not so prominent. Secondly,
we focused on the articles that appeared in peer-re-
viewed academic journals, excluding other types
of publications, such as books or conference pro-
ceedings. Studies that were not written in English
were also excluded, as well as those ones that were
not published in business and management area.
Searches were carried out on Scopus and Web of
Science databases during the period from July 1,
2019 to July 8, 2019. We selected the two afore-
mentioned databases because they are considered
the most exhaustive sources of scholarly articles
and academic productions in the social sciences
(Vieira & Gomes, 2009). At this stage of the review
process, we selected the articles by reading the title,
abstract and, in some cases, the entire document.
Table 1 illustrates the combinations of keywords
and the limitations performed in the Scopus da-
tabase. The first combination was determined
to capture the articles about the use of Big Data
in the marketing area. Using this combination,
181 articles were identified, but only 26 docu-
ments satisfied the purpose of this research. The
second combination was applied to capture the
articles about the use of AI in marketing prac-
tices. The number of articles identified was 50,
but only 15 were selected. The third combina-
tion was introduced to extract those documents
concerning the relationship between Big Data
and customer journey. The total number of arti-
cles identified was two, but only one article was
selected for further analysis. Finally, the fourth
combination was applied to capture the articles
about the relationship between AI and custom-
er journey. Two documents emerged from the
search. Both documents were considered rele-
vant for this study.
e same combinations of keywords and limita-
tions were used to perform the searches in the Web
of Science database (see Table 2). With regard to the
rst combination, the number of documents identi-
ed was 128, but only 21 articles were selected. e
second combination, introduced to identify those
articles concerning the role of the AI in the market-
ing functions, revealed 11 documents, but only three
were selected for further analysis. e total number
of articles identied with the third combination was
three, but only two were selected. Finally, with re-
gard to the fourth combination concerning the rela-
tionship between articial intelligence and customer
journey, the total number of articles identied was
one, but this document was discarded because it did
not t with the topic of our research.
Aer this searching process, 78 articles in the lit-
erature were identied. e next step consisted of
manually cleaning the dataset from duplications.
e nal dataset included 43 articles considered
as capable of helping the researchers to better un-
derstand the role of Big Data and AI in specic
marketing practices such as customer journey
mapping. Specically, 26 articles deal with topics
related to the domain of Big Data, other 16 articles
explore the relationship between AI and market-
ing, and one article focuses strictly on the relation-
ship between Big Data and customer journey.
e 43 studies selected for the literature review
were subjected to a descriptive analysis in order to
collect the information about the distribution of
publication over time, the distribution of publica-
tions by journals, and the recurring terms in the
titles and abstracts.
e year wise publication of the documents se-
lected for this literature review is given in Figure
1. Only eight papers were published in the period
2014–2015. e majority of publications, in fact,
Table 1. Combinaon of keywords and limitaons in the Scopus database
Searches Combinaon of keywords and limitaons
First combinaon TITLE-ABS-KEY (“Big Data” AND “markeng”) AND DOCT YPE (ar) AND PUBYEAR > 2013 AND (LIMIT-TO
(DOCT YPE, “ar”)) AND (LIMIT-TO (SUBJAREA, “BUSI”)) AND (LIMIT-TO (LANGUAGE, “English”))
Second combinaon TITLE-ABS-KEY (“Arcial Intelligence” AND “markeng”) AND DOCT YPE (ar) AND PUBYEAR > 2013 AND
(LIMIT-TO (DOCTYPE, “ar”)) AND (LIMIT-TO (SUBJAREA , “BUSI”)) AND (LIMIT-TO (LANGUAGE, “English”))
Third combinaon TITLE-ABS-KEY (“Big Data” AND “customer journey”) AND DOCTY PE (ar) AND PUBYEAR > 2013 AND (LIMIT-TO
(DOCT YPE, “ar”)) AND (LIMIT-TO (SUBJAREA, “BUSI”)) AND (LIMIT-TO (LANGUAGE, “English”))
Fourth combinaon TITLE-ABS-KEY (“Arcial Intelligence” AND “customer journey”) AND DOC TYPE (ar) AND PUBYEAR > 2013
AND (LIMIT-TO (DOCT YPE, “ar”)) AND (LIMIT-TO (SUBJAREA, “BUSI”)) AND (LIMIT-TO (LANGUAGE, “English”))

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appear aer 2015, this means that in recent years,
there has been growing interest in topics such as
the adoption of Big Data and AI tools in the mar-
keting area. e major number of articles about
the relationship between Big Data and marketing
were published in 2016. Conversely, in the period
from January 1, 2019 to July 8, 2019, researches
paid signicant attention to AI adoption in the
marketing. Specically, in this time-frame, seven
articles were published.
With regard to the journals that contributed to the
development of such issue, it is possible to notice
that 43 collected articles appear in 36 dierent aca-
demic journals (see Figure 2). e jou rnals with the
major number of articles are Applied Marketing
Analytics (3), Journal of Destination Marketing
and Management (3), Decision Support Systems
(2), Journal of Travel and Tourism Marketing (2),
and Journal of Travel Research (2).
The publications collected for this review have
appeared principally in the journals focusing on
substantive issues in the domain of marketing
and management, such as theories in the sup-
port of enhanced decision-making, aspects con-
cerning the measurement and analysis of mar-
keting performance to improve its effectiveness,
and researches in the field of manufacturing
operations management. It is interesting to note
that six journals deal with tourism and hospital-
ity topics. The importance given to the applica-
tion of Big Data analytics in the tourism indus-
try is also confirmed by the text analysis of the
titles and abstracts of the collected documents
for the literature review. The word cloud used to
illustrate those terms with the higher frequency
reveals the presence of words such as “tourism,”
“travels,” and “tourists” (see Figure 3). Other
prominent words that appear in the text corpus
are “analysis,” “analytics,” “customer,” “knowl-
edge,” and “management.” This means that the
main researches about how Big Data and AI can
help marketing are focusing on themes like cus-
tomer analytics, and the importance of knowl-
edge in decision-making.
Table 2. Combinaon of keywords and limitaons in the Web of Science database
Searches Combinaon of keywords and limitaons
First combinaon
TOPIC: (“Big Data” AND “markeng”) Rened by: LANGUAGES: (ENGLISH) AND RESEARCH ARE AS: (BUSINESS
ECONOMICS) AND DOCUMENT T YPES: (ARTICLE) AND WEB OF SCIENCE CATEGORIES: (BUSINESS OR
MANAGEMENT) Timespan: 2014–2019. Indexes: SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-
SSH, ESCI.
Second combinaon
TOPIC: (“Arcial Intelligence” AND “markeng”) Rened by: LANGUAGES: (ENGLISH) AND RESEARCH AREAS:
(BUSINESS ECONOMICS) AND DOCUMENT TYPES: (ARTICLE) AND WEB OF SCIENCE C ATEGORIES: (BUSINESS)
Timespan: 2014–2019. Inde xes: SCI-E XPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI -S, BKCI-SSH, E SCI.
Third combinaon
TOPIC: (“Big Data” AND “customer journey”) Rened by: LANGUAGES: (ENGLISH) AND RESE ARCH AREA S:
(BUSINESS ECONOMICS) AND DOCUMENT TYPES: (ARTICLE) AND WEB OF SCIENCE C ATEGORIES: (BUSINESS
OR MANAGEMENT ) Timespan: 2014–2019. Indexes: SCI-E XPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI -S,
BKCI-SSH, ESCI.
Fourth combinaon
TOPIC: (“Arcial Intelligence” AND “customer journey”) Rened by: RESEARCH AREAS: (BUSINESS
ECONOMICS) AND DOCUMENT T YPES: (ARTICLE) AND WEB OF SCIENCE CATEGORIES: (MANAGEMENT)
Timespan: 2014–2019. Inde xes: SCI-E XPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI -S, BKCI-SSH, E SCI.
Figure 1. Distribuon of publicaons by year
0
1
2
3
4
5
6
7
8
2014 2015 2016 2017 2018 2019
Artificial intelligence Big data Big data and customer journey

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2. RESULTS
Compared to the previous literature review, this
paper makes something totally new. Firstly, its in-
tent is not to carry out a historical reconstruction
of the use of Big Data and AI in the eld of market-
ing and management (i.e., Soon, Lee, & Boursier,
2016). Secondly, this research is not oriented to-
wards the analysis of a specic sector such as hos-
pitality and tourism (i.e., Mariani, Baggio, Fuchs,
& Höepken, 2018). irdly, this research does not
limit the eld of investigation to a single market-
Figure 2. Distribuon of publicaons by journal
0
1
2
3
4
Applied Marketing Analytics
Business Process Management Journal
Decision Support Systems
Digital Policy, Regulation and Governance
Electronic Commerce Research and Applications
Electronic Markets
European Journal of Marketing
Industrial Management and Data Systems
Industrial Marketing Management
International Journal of Computer Information Systems and…
International Journal of Electronic Commerce
International Journal of Hospitality Management
International Journal of Market Research
International Journal of Operations and Production…
International Journal of Production Research
Journal of Business & Industrial Marketing
Journal of Business Research
Journal of China Tourism Research
Journal of Cleaner Production
Journal of Consumer Marketing
Journal of Destination Marketing and Management
Journal of Interactive Marketing
Journal of Personal Selling and Sales Management
Journal of Service Research
Journal of Services Marketing
Journal of the Academy of Marketing Science
Journal of the Operational Research Society
Journal of Travel and Tourism Marketing
Journal of Travel Research
Management Science
Marketing Science
Production and Operations Management
Research Technology Management
Technological Forecasting and Social Change
Tourism Management
Figure 3. Word cloud of the tles and abstracts of the selected arcles for this research

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ing level (i.e., Wiencierz & Röttger, 2016). Fourthly,
this literature review does not investigate sepa-
rately Big Data and AI (i.e., Fosso Wamba, Akter,
Edwards, Chopin, & Gnanzou, 2015; Nassirtoussi,
Aghabozorgi, Wah, & Ngo, 2014), but its main ob-
jective is to combine these two technologies in or-
der to understand how they can be used by mar-
keters to analyze and model the customer jour-
ney. Reviewing the existing literature, the authors
found out that marketers and organizations can
use Big Data analytics and AI systems to collect
the consumer data from many sources and deliver
the important insights to support marketing deci-
sion-making. Specically, several areas of applica-
tion were identied and further described.
2.1. Customer profiling
Marketers can use Big Data and AI to support cus-
tomer proling. e customer proling is possible
thanks to the enormous availability of data that
individuals voluntarily and involuntarily leave in
almost all their online actions. Verhoef et al. (2017)
highlight that thanks to the advances in informa-
tion and communication technology, consumers
are capable of connecting with three components:
(1) People (other consumers and rms’ representa-
tives), (2) Objects, and (3) their Physical environ-
ments. is framework called POP allows both re-
searchers and practitioners to create the customer
proles by aggregating and synthesizing very het-
erogeneous data coming from dierent sources,
such as environmental sensors, smartphones, and
wearables. According to Trusov, Ma, and Jamal
(2016), processing the massive records of user on-
line activity coming from search engines, web vis-
its, and display advertising enables to identify the
consumer behavioral proles. Unlike traditional
retailer, pure-click companies such as Google,
Facebook, eBay/PayPal, and Amazon have at their
disposal specic apps that help them to prole
their customers and use data-driven marketing
to make specic decision (Pousttchi & Hufenbach,
2014). Liu, Huang, Bao, and Chen (2019) highlight
that user-generated content (UGC), that is, “me-
dia content created by users to share information
and/or opinions with other users” (Tang, Fang, &
Feng, 2014, p. 41), such as online reviews in the
tourism sector, represents another source of in-
formation about consumers. Utilizing lexicon l-
tering and machine learning enables to conduct a
sentiment analysis of Big Data in the form of UGC
and collect the information on a specic theme.
Finally, a decision support system (DSS) that uses
of AI techniques can help managers to improve
the client acquisition and development. For ex-
ample, the main purpose of the BIG CHASE, a
DSS tool experimented in the nancial context by
Banco Santander S.A., as highlighted by Quijano-
Sanchez and Liberatore (2017), using the social
structure obtained from client relations and op-
erations, is to identify “the most reliable sequence
of clients that a manager should contact to reach
a dened target (a client or a non-client)” (p. 51).
2.2. Promotion strategies
e information obtained from customer proling
can be used to support sales promotion and oth-
er promotion strategies (Buhalis & Foerste, 2015).
In order to reach potential customers, Miralles-
Pechuán, Ponce, and Martínez-Villaseñor (2018)
suggest the adoption of micro-targeting tech-
niques based on a machine-learning based click-
through rate model to program the display ad-
vertising campaigns. e information retrieved
from the web and processed through the specic
algorithm proposed by Miralles-Pechuan et al.
(2018) is useful to congure the parameters of a
campaign, such as age, time, browser, operating
system, and device type. In this way, advertisers
can increase the performance of online publicity
(i.e., more conversions) by selecting a very specif-
ic public.
2.3. Demand forecasting
Companies can utilize Big Data and AI to forecast
the sales of products. Some researches (Chong, Li,
Ngai, Ch’ng, & Lee, 2016; Chong, Ch’ng, Liu, & Li,
2017; Park, Yang, & Wang, 2019) highlight the im-
portance to investigate online reviews, sentiments,
customer questions and answers, and online pro-
motional variables, such as free delivery and price
discount oerings. e information obtained
from the analysis of this type of data can be used
for developing the models capable of predicting
the sales products online. Other information con-
cerning the demand forecasting can be obtained
from the analysis of the web trac volume data.
Specically, Yang et al. (2014) found out that in the
tourism sector, it is possible to predict the demand

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for hotel rooms at a destination, and potentially
even local businesses’ future revenue and perfor-
mance, analyzing web trac data. Huge amount
of data from consumers can be also collected
through mobile apps. According to Trabucchi,
Buganza, and Pellizzoni (2017), “this data rep-
resents a powerful new source of value” (p. 43),
because companies can use them to predict con-
sumer product demands and “to build deep un-
derstanding of customers’ needs and wants, and of
how products are used” (p. 50).
2.4. New product/service
development
Cao, Duan, and El Banna (2019) show that Big
Data can support the product development pro-
cess. Generally, product development is a very long
and delicate process that requires as much infor-
mation as possible on the target markets’ shopping
preferences. One of the main risks for companies
belonging to sectors with a high rate of innovation
is the long time it takes to develop a new prod-
uct. e use of Big Data analytics tools oers the
opportunity to reduce the product development
time by 20 to 50% because it allows the marketing
managers to collect the insights about the custom-
ers’ needs and expectations, as well as competi-
tors’ new designs, and key product features. Xu,
Frankwick, and Ramirez (2016) highlight that by
adopting Big Data analytics tools, rms can de-
termine if a new product will become successful.
For example, Netix examines vast quantities of
real-time data produced by its users to predict
if a pilot will become a successful new show. To
understand the consumer preferences regard-
ing the dierent features and dierent congura-
tions of a new product, the most commonly used
method is conjoint analysis. e results obtained
from this method sometimes do not provide the
clear indications. at is why López, Maldonado,
and Montoya (2017) introduced a novel choice-
based conjoint approach based on Support Vector
Machines, a branch of AI. is novel approach
has “superior predictive performance and compu-
tational eciency […] Additionally, the method
can be further extended to deal with clusters of
consumers instead of an unimodal representation
of preference heterogeneity” (Lopez et al., 2017, p.
15). Kühl et al. (2019) underline the importance to
monitor on social media the consumer needs and
wants in order to design customer-centric prod-
ucts and services and control marketing activities.
Specically, the authors stated that it is possible to
utilize machine learning algorithms “to detect m
characteristics (“needs”) in n instances (“tweets”)”
(p. 14). Big Data analytics, as highlighted by some
researchers (Marine-Roig & Clavé, 2105; Önder,
2017), can be adopted in the tourism sector to
predict the travels destination too. Tourism and
hospitality industry can use this information to
understand the tourist trend, create the specic
travel product, and provide the sucient local
transportation options.
2.5. Pricing strategy
According to Danaher, Huang, Smith, and Telang
(2014) Big Data analytics techniques can be adopt-
ed to optimize the pricing strategies of specic
products that are inuenced by periods, trends,
and fashions, for example, in digital music market,
it is possible to determine own- and cross-price
elasticities for songs and albums. Both Weber and
Schütt (2019) and Wirth (2018) are interested in
the potential of AI to inform the marketing deci-
sions with regard to four areas of the marketing
mix, that is, “product,” “price,” “place”, and “pro-
motion.” Specically, according to Wirth (2018)
“AI is powerful enough to inform decisions such
as will person A like product B and will consumer
X purchase the car Y at price Z” (p. 436). anks
to AI, retail is changing. For example, using the
automatic algorithms is possible to perform such
an operation as dynamic pricing, namely, a pric-
ing strategy in which companies adjust the prices
for products or services in real-time based on the
current market demand. is model of calculating
the price would be dicult for a human being, but
with the help of AI this task can be automated and
completed very quickly.
2.6. Distribution choices
As highlighted by Wu, Ho, Lam, and Ip (2015)
AI approach can be adopted to analyze the com-
petitiveness and protability of specic distribu-
tion channels. For example, the authors propose
a franchising decision support system capable of
collecting the data from the external environment
and help the franchisor in formulating the mar-
keting strategies.

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2.7. Customer service
Big Data analytics can help the r ms to understand
how to serve customers better. Specically, from a
service delivery point of view, Big Data analytics
can help the frontline employees to interact with
customers (Motamarri, Akter, & Yanamandram,
2017). Services are complex to dene because
they have many dimensions, and dier from each
other. For example, some services require low in-
teractions and low customization; others, instead,
are based on the customer’s active involvement.
Companies, to better understand the consumers’
preferences and develop the schemes of service
contexts that facilitate the frontline employees in
service delivery, can use the information collected
from the web (i.e., internet searches, click stream,
Facebook chats, Twitter exchanges, peer network-
ing sites, etc.). Nevertheless, as highlighted by
Montamarri et al. (2017), one of the risks is to vio-
late the consumers’ privacy.
Pradana, Sing, and Kumar (2017) suggest that
companies, to facilitate the interaction with the
consumers on the corporate or e-commerce
websites, can utilize Intelligent Conversational
Bot, that is, “an implementation of Articial
Intelligence (AI) in a form of soware or applica-
tion which users can interact by having conversa-
tions” (Pradana et al., 2017, p. 265). Such a tool can
act as a salesperson to help the companies adver-
tise their products. Furthermore, consumers can
pose simple questions to the bot in order to gain
specic information.
2.8. Analysis of consumer behavior
e emergence of user-generated content on the
internet has provided a new source of data con-
cerning the human behavior. As highlighted by
Hofacker, Malthouse, and Sultan (2016), consum-
ers provide on social media information about
their relationship with a brand or a company. An
examination of what is being said online can help
the marketers to identify the warning signs of
consumer dissatisfaction, such as negative word
of mouth, or complaints about the product, the
service or the brand in general. Furthermore, text
mining and other emerging technologies, such
as Automated Sentiment Analysis, oer the pos-
sibility to measure the customer satisfaction (i.e.,
Kiri lenko, Stepchenkova, Kim, & Li, 2018; Park, Ok,
& Chae, 2016; Park et al., 2019), loyalty and com-
mitment (Hofacker et al., 2016). McColl-Kennedy
et al. (2019) introduce a conceptual framework for
measuring and understanding the customer expe-
rience that takes into consideration the customer
perspective, such as emotions (i.e., joy, love, sur-
prise, anger, sadness, and fear), and cognitive re-
sponse to the dierent touchpoint that occur dur-
ing the purchase decision journey (i.e., complaints,
compliments, and suggestions). Specically, they
recommend to utilize linguistics-based text min-
ing model to capture the details about consumers
that matter for making the marketing decisions.
2.9. Customer relationship
management
e online contexts create the challenges and op-
portunities for customer relationship management
(CRM). For example, e-commerce, AI technolo-
gies (i.e., chatbots, avatars, and virtual assistants),
and Big Data analytics used for generating the en-
hanced customer insights that can be used to per-
sonalize the products and services constitute an
important building block for online relationships
(Steinho, Arli, Weaven, & Kozlenkova, 2019).
George and Wakeeld (2018) highlight that both
marketers and researchers should use Big Data “to
better understand how consumers respond to con-
tact strategies over time” (p. 113), such as direct
mail, email, telephone and salesperson contacts.
Specically, service rms can adopt Big Data ana-
lytics to leverage useful information to more eec-
tively attract, serve, and retain the customers.
2.10. Brand analysis
Brand managers and marketers can adopt the
intelligent systems based on fuzzy logic, an area
of AI, for modelling and evaluating the brand-
ing strategies. For example, Identimod is a deci-
sion support system proposed by Chica, Cordón,
Damas, Iglesias, and Mingot (2016) appropriate
to analyze the intangible variables related to the
brands (i.e., brand loyalty, brand awareness, per-
ceived quality, brand associations, and other pro-
prietary assets). Putting all the available linguis-
tic or numerical data in the system, Identimod
can simulate dierent scenarios and support the
marketing decision-making. For example, this

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technology can help brand managers to take im-
portant decision such as keep the current brand
image, rebrand the company, restyle the current
brand, reduce the size of a brand portfolio, etc.
3. DISCUSSION
e task of understanding the customer jour-
ney is complex because there are many factors
and variables that need to be considered (i.e., so-
ciodemographic and psychographic characteris-
tics, consumer buying motives, duration of the
journey, buying frequency, number of the dif-
ferent touchpoints used during the search, etc.).
Fortunately, according to Erevelles, Fukawa, and
Swayne (2015), consumers have become an “inces-
sant generator of both structured, transactional
data as well as contemporary unstructured be-
havioral data” (p. 898). erefore, with the help of
Big Data and AI, marketers and organizations can
collect the consumer data from many sources and
deliver the important insights about his/her path
to purchase.
Assuming that the customer journey consists of
three dierent phases, namely prepurchase, pur-
chase, and postpurchase (Lemon & Verhoef, 2016),
the proposed framework illustrates the type of
data that researchers or practitioners can col-
lect from dierent sources to better understand
and manage the customer journey at any stage.
Furthermore, the framework shows dierent spe-
cic tasks concerning the customer journey mod-
elling that can be improved by Big Data and AI
utilization (see Figure 4).
The prepurchase phase includes “customer’s
entire experience before purchase” (Lemon &
Verhoef, 2016, p. 76). Generally, the “journey”
starts with the consumer intention to purchase
something he/she needs or desires. If in the tra-
ditional offline world, the consumer had trou-
ble finding the alternatives, nowadays, with the
explosion of the digital markets, the problem
has too many alternatives (Hofacker et al., 2016).
Therefore, the customer decision process is not
linear anymore, but it is iterative and some-
times very long. Thanks to Big Data analytics
consumer search activities on website, e-com-
merce, and shopping app are recorded and ana-
lyzed (Trusov et al., 2016). Marketers can easily
retrieve the information regarding which items
have been searched, clicked on, added to a shop-
ping cart or wish list, abandoned, or purchased.
Furthermore, as highlighted by Hofacker et
al. (2016), it is possible to know “which search
terms attracted prospective customers from
search engines, and whether it was a paid search
term or an organic one” (p. 91). All the infor-
mation collected at this stage can be used to
create the customer profiles. Customer profil-
ing will help the marketers to understand their
customers, highlighting who they are, what
their interests are, and what they want. This in-
sight will help the companies to recognize their
customer’s characteristics (demographics), and
behavior (psychographics). In addition, col-
lecting the customer data gives the research-
ers and practitioners the possibility to map the
touchpoints that occur throughout the journey
from the customers’ perspective. Having a bet-
ter understanding of the customers helps the
marketers to allocate their resources efficient-
ly, such as advertising spend (Edelman, 2010)
and ad targeting (Miralles-Pechuan et al., 2018).
Furthermore, leveraging customer data organ-
ization is useful to know each customer more
individually and deliver better value to the cus-
tomers because they get things they want. This
means happier customers, reduced client churn,
and bigger profits. Finally, accessing to a vast
and growing ocean of data retailers have the
ability to quickly gain the information that can
be utilized for pricing strategies. For example,
the automatic tracking of metrics such as page
views, cart abandonment, and conversion rates
can signal to retailers if their pricing strategy is
wrong.
Purchase is the second phase of the customer jour-
ney. According to Lemon and Verhoef (2016), this
phase “covers all customer interactions with the
brand and its environment during the purchase
event itself. It is characterized by behaviors such as
choice, ordering, and payment” (p. 76). Generally,
this phase of the journey is “the most temporally
compressed” (Lemon & Verhoef, 2016, p. 76), but
it is reach of detailed data about transactions, geo-
graphic location of the client, inuence of price on
customer’s purchase decision, and bestseller prod-
ucts. Choosing the right technology, data collected

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during this phase of the journey can be analyzed
“in terms of purchase history, credit and return
history available” (Chauhan, Mahajan, & Lohare,
2017, p. 486). erefore, all the information col-
lected in this phase is useful for customer prol-
ing, demand forecasting and prot optimization.
Such data, in fact, could be the source of a decision
support system and help marketers to make better
decision.
The postpurchase phase, as highlighted by
Lemon and Verhoef (2016), “encompasses cus-
tomer interactions with the brand and its en-
vironment following the actual purchase. This
stage includes behaviors such as usage and con-
sumption, post-purchase engagement, and ser-
vice requests” (p. 76). Theoretically, this phase
of the journey can last from the purchase to the
end of the customer’s life. During this phase,
customers “evaluate the gap between their ex-
pectations and their consumption experience
during and after consumption” (Hofacker et
al., 2016, p. 92). Therefore, e-word of mouth, re-
views, tweets, shared pictures or videos about a
product represent a large amount of data capa-
ble of producing the knowledge about custom-
er satisfaction, commitment, and attitudinal
loyalty. For example, if people complain about
a product/service on social media or on a re-
view site, marketers should treat this data as
the material to investigate. Understanding how
consumer feel about the product features or the
service experience, if they are satisfied or not,
is fundamental in the development of sustain-
able competitive advantage of brands and com-
panies. During this phase of the customer jour-
ney, Big Data analytics and AI can be used to
monitor the consumer sentiments (Marine-Roig
& Clavé, 2015; Culotta & Cutler, 2016; Kirilenko
et al., 2018; Buhalis & Sinarta, 2019), or to auto-
matically quantify the customer needs from so-
cial media (Kühl, Mühlthaler, & Goutier, 2019).
In addition, during this phase, marketers can
utilize Intelligent Conversational Bot (Pradana
Source: Built by the authors.
Figure 4. Big Data and AI framework for the customer journey mapping

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et al., 2017) to enhance the customer service and
simultaneously collect the useful data. Finally,
as highlighted by George and Wakefield (2017),
Big Data analytics can be used to plan the CRM
strategy. For example, implementing the data
into a predictive model could help the market-
ers readily identify the at-risk customers and
adopt the specific strategies in reactions.
CONCLUSION
As we could conclude from the literature review carried out in this article, Big Data analytics tools and
AI nd large application in the marketing eld, especially in the domain of customer analytics and
decision support systems. An interesting observation was noted that only two studies (i.e., George &
Wakeeld, 2017; McColl-Kennedy, 2019) deal about how Big Data analytics and AI can be applied to ex-
plore and manage the customer journey. erefore, the theoretical contribution of this paper consists of
proposing a Big Data and AI framework (Figure 4) capable of illustrating how such technologies can be
used for customer journey modelling. Specically, we suggest ten main areas of application of Big Data
and AI technologies in the customer journey modelling: (1) customer proling; (2) promotion strategy;
(3) client acquisition; (4) ad targeting; (5) demand forecasting; (6) pricing strategy; (7) purchase history;
(8) predictive analytics; (9) monitor consumer sentiments; and (10) customer relationship management
(CRM) activities. Each one supports a specic task, such as understanding the customer needs and
wants at each stage of the “journey” or at each touchpoint; identifying the dierent buyer personas in
order to provide the specic pricing strategy or customer engagement solutions; collecting information
to create better products or service, to improve the customer experience, and to make advertising more
relevant.
e proposed framework poses a new perspective on Big Data an AI literature since it essentially focuses
on the customer journey mapping. Furthermore, it can be very useful for managers and decision-mak-
ers. Creating a good customer journey represents one of the main sources of competitive advantage.
erefore, it is important for practitioners to learn how to turn the data into insights that can be used
to solve the problems and improve their capabilities of increasing the customer value and competitive
performance.
Future studies should explore more deeply the relationship between Big Data, AI and customer journey
map. is is, in fact, a relatively unexplored area. Specically, researchers should highlight the main
technologies behind Big Data and AI, that is, the platforms or soware useful to collect the data, analyze
the data, and produce the knowledge to solve the complex problems.
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