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The Use of Data-Driven Technologies for Customer-Centric Marketing
By Mark Anthony Camilleri1 2 (Ph.D. Edinburgh)
University of Malta, Malta.
How to Cite: Camilleri, M.A. (2019). The Use of Data-Driven Technologies
for Customer-Centric Marketing, International Journal of Big Data
Management. Forthcoming.
Abstract
The latest technologies are shifting how businesses capture, analyse and distribute data
from the individual users’ online activity. Therefore, this contribution critically
reviews the latest developments on big data analytics and programmatic advertising.
Moreover, it sheds light on the use of blockchain; as this distributed ledger technology
provides secure, verified transactions among marketplace stakeholders. The findings
suggest that the service providers are increasingly utilising data-driven technologies
including programmatic advertising tools to target and re-target individuals online or
on their mobile. However, individuals and organisations are becoming increasingly
aware on data protection issues, as they often block marketers from tracking them and
serving them ads. In conclusion, this contribution puts forward a theoretical
framework that explains how, why, where and when practitioners are capturing,
analysing and distributing data. In sum, it implies that the data-driven technologies are
facilitating the businesses’ customer-centric marketing.
Keywords: Big Data, Analytics; Programmatic Advertising; Blockchain; Customer-
Centric Marketing; Data-Driven Marketing; Digital Media.
1 Department of Corporate Communication, Faculty of Media and Knowledge
Sciences, University of Malta, Msida, MSD2080, MALTA. Tel: +356 2340 3742 Email:
mark.a.camilleri@um.edu.mt
2 The Business School, University of Edinburgh, Bucchleuch Place, Edinburgh, EH8
9JS, Scotland.
1
Introduction
The ongoing advances in technology have brought significant improvements in the
processing speed and storage of large volumes of data. Tech-savvy organisations have
already started using big data with a goal to improve their decision making, agility,
and customer-centric approaches (Erevelles, Fukawa and Swayne, 2016; George, Haas
and Pentland, 2014). Today, many marketers are hyper-targeting consumers through
real-time mobile ad campaigns to drive conversions. They use analytics to identify
how exogenous variables, including; the broader economy, competitive offerings, and
even the weather can affect their organisational performance (Akter, Wamba,
Gunasekaran, Dubey and Childe, 2016). Similarly, the smaller enterprises are
economically gathering and storing data from each and every customer transaction.
They use analytics to customise their offerings and improve their customer
engagement (Ransbotham and Kiron, 2018).
Therefore, this paper builds on the previous theoretical underpinnings on smart
technologies, including big data and analytics (Li, Hu, Huang and Duan, 2017;
Baesens, Bapna, Marsden, Vanthienen and Zhao, 2016; Gretzel, Sigala, Xiang and
Koo, 2015; Buhalis and Amaranggana, 2013; Wang, Li and Li, 2013). It clarifies how
disruptive technologies have led to endless opportunities for networked businesses to
gain a competitive advantage. It explains how they are leveraging themselves by
utilising contemporary marketing strategies and tactics that are customer-focused.
Specifically, this contribution examines the use of big data, analytics, programmatic
advertising and blockchain technologies. It adds value to academic knowledge as it
presents a theoretical framework that clarifies the data processing cycle. It shows how
practitioners capture, analyse and distribute data. In conclusion, this paper outlines the
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implications for practitioners and identifies future research avenues in the realms of
data-driven marketing.
The Uses of Big Data and Analytics
For decades, organisations have been gathering and analysing digital data in some way
or another, to improve their performance (Ji-fan Ren, Fosso Wamba, Akter, Dubey and
Childe, 2017; Akter et al., 2016; George et al., 2014). The new advantages of
crunching big data analytics are based on the discovery of how to improve
productivity levels and agility to enhance the businesses’ financial performance
(Wang, Kung and Byrd, 2018; Chen and Zhang, 2014). The latest technological
advances have enabled many businesses, including airlines and hotels to manage their
operations in a more efficient and economical way (Pan and Yang, 2017; Liu, Teichert,
Rossi, Li and Hu, 2017). For instance, several airlines and hotels are increasingly
using revenue management systems that are quickly adjusting offers (Pan and Yang,
2017). The pricing of their products is usually based on a variety of situations and
circumstances, as they optimise communications and transactions. By using data and
analytics on the customers’ behaviours, many travel businesses are taking advantage of
channel-based distribution. Hence, the distribution networks have come a long way
from the ticket counter. Evidently, travel and tourism businesses are leveraging
themselves with data-driven marketing, as they seek new customers and prospects
(Camilleri, 2018a, b). They may decide to target high-yield customers through
elaborated pricing and revenue management systems. Hence, the travel distribution is
evolving from its current passive, rigid, and technology-centric state to a more
flexible, dynamic, and passenger-centric environment as airlines and hotels monitor
and detect any changes in their consumers’ behaviours.
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The terms of “big data” and “analytics” are increasingly being used to describe data
sets and analytical techniques in applications ranging from sensors to social media that
require advanced and unique storage, management, analysis, and visualisation
technologies (Boyd and Crawford, 2012; Chen, Chiang and Storey, 2012). There are a
number of concepts, including; volume, variety, velocity, veracity and value that are
associated with big data (Grover, Chiang, Liang and Zhang, 2018; Côrte-Real,
Oliveira, and Ruivo, (2017). Usually, big data analytics are dependent on an extensive
storage capacity and processing power, requiring a flexible grid that can be
reconfigured for different needs (Stergiou, Psannis, Kim and Gupta, 2018; Erevelles et
al., 2016). Information technology (IT) systems may reveal valuable insights on the
businesses’ customers. They can indicate which products or services are sought by
customers. For instance, they may reveal where and what customers eat, where and
when they go on vacation, how much products they buy, et cetera.
Big data environments ought to make sense of the content they gather. This means that
IT applications need to scrutinise and report on a wide variety of dimensions;
including customer interactions, product usage, service actions and other dynamic
measures. Such content may include; text messages, document images, pictures, video
clips, web logs, et cetera). The explosion of online and mobile activity ranging from
ecommerce and social media has led to the dissemination of meaningful data that is
being created online (Li et al., 2017).
Capturing Data
The business environment is currently witnessing a sea of change in IT activity
(Baesens et al., 2016). The expansion of big data use has been generated by the web
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and its online communities. Ecommerce vendors including Amazon and eBay have
surely transformed the market through their innovative and highly scalable e-
commerce platforms and product recommender systems. Moreover, internet giants like
Google and Facebook are leading the development of web analytics, cloud computing
and social media platforms. The emergence of user-generated content on fora,
newsgroups, social media networks and crowd-sourcing platforms have offered
endless opportunities for researchers and practitioners to “listen” to marketplace
stakeholders; including customers, employees, investors and the media (Abbasi,
Sarker and Chiang, 2016). As a result, businesses are increasingly collecting and
analysing data from various sources to improve their customer-centric marketing
(Côrte-Real et al., 2017).
The online review sites and personal blogs provide opinion-rich information that may
be explored through textual and sentiment analysis (Gao, Tang, Wang and Yin, 2018;
Xiang, Du, Ma, and Fan, 2017; Cambria, 2016). Social media analytics are
increasingly capturing fast-breaking trends on customer sentiments about products,
brands and companies. Businesses may take advantage of these insights as it is in their
interest to get to know whether there are changes in consumer behaviour and how it
may correlate with changes in sales over time. In big data environments it’s important
to analyse, decide and act expeditiously (Baesens et al., 2016). Businesses should be
quick in their decision making and have to take remedial action, if necessary. They
may have to establish specific processes which determine alternative courses of action.
Successful businesses regularly analyse their customer service records. They gather
data on the consumer sentiment toward products and brands as they continuously
monitor the marketing environment (Cambria, 2016). They may collect data in real-
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time and use it well to personalise every aspect of their users’ experience. Data-driven
organisations strive to understand their customers’ personas so that they target them
the right content with the relevant tone, imagery and value propositions (Grover et al.,
2018). Usually they take advantage of the consumers’ cognitive behaviour as they try
to uncover consumer frailty at their individual level (Boucher Ferguson, 2013). It may
appear that many companies are increasingly gathering data on their customers as they
try to understand their needs, wants and desires. They may use big data to delve into
the enormous volumes of information that they collect during sales transactions in
their day to day operations. The companies may use what they know about human
psychology and consumer behaviour to set prices, draft contracts, minimise
perceptions of danger or risk, or extract as much information as possible from their
consumers (Boucher Ferguson, 2013).
In this day and age, marketing automation is helping businesses to engage with
individuals whether they are customers or not. Behavioural targeting is nothing new in
digital marketing. When firms hold detailed information about their consumers, they
may customise every aspect of their interaction with them. Direct marketing tactics
could prove as the most effective way how to reach consumers, when it is used wisely
and diligently. There is scope for business to consider using data-driven marketing.
Many firms have learned how to use databases as they gather valuable information in
order to communicate with customers and prospects (Côrte-Real et al., 2017).
Businesses could use databases to deliver promotional content to remind customers on
their offerings. Eventually, the gathered data may translate to new revenue streams and
can possibly build long-term loyalty. The consumer lists whether they are automated
or in the cloud should always be used to deliver enhanced customer experiences.
Technology is instrumental for today’s businesses in their ongoing interactions with
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people. Nevertheless, such marketing practices could possibly lead to unnecessary
nuisances to the customers. Online users are frequently bombarded with marketing
endeavours including email promotions that are often picked up as spam. Therefore,
one-size-fits-all messages could have negative implications on prospective customers.
Large technology conglomerates are increasingly using anonymous, cookieless data
capture methods to gather the consumers’ data. Very often, they track the individual
users’ movements as they measure traffic and other real-time phenomena (Fong, Fang
and Luo, 2015). They may be using browsing session data combined with the
consumers’ purchase history to deliver “suitable” items that consumers like. As more
consumers carry smartphones with them, they are increasingly receiving compelling
offers that instantaneously pop up on their mobile devices. Recent advances in mobile
communication and geo-positioning technologies have also presented marketers with a
new way how to target prospects, based on their location. Location-targeted mobile
advertising involves the provision of ad messages to cellular subscribers. This digital
technology allows marketers to deliver ads and coupons that are customised to
individual consumers’ likings, geographic location and the time of day. Given the
ubiquity of mobile devices, location-targeted mobile advertising seems to offer
tremendous marketing benefits (Frizzo-Barker, Chow-White, Mozafari and Ha, 2016).
The consumers who have social media apps on their smartphone are usually indicating
their geo location as they move out and about. This same data can be used to identify
where people gather. Such information may be valuable to brands as they seek to
improve their consumer engagement and marketing efforts. Yet, to date there has been
little empirical evidence about the immediate and cumulative effectiveness of such
mobile advertising (Malthouse and Li, 2017).
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EMarketer (2018) forecasted that mobile spending would account for about 82.5 of all
US digital display ads. Notwithstanding, mobile marketers like Google or Facebook
are benefiting of the smart phones and tablets’ geo-location capabilities. Large
technology giants use geolocation capabilities to capitalise on these mobile
technologies as they leverage location and context to obtain better information on
shopping habits, lifestyle preferences and the like (Aksu, Babun, Conti, Tolomei, and
Uluagac, 2018). At the same time, the consumers are becoming increasingly
acquainted with these data-driven marketing technologies. Therefore, they may decide
to block advertisers and publishers from serving them unwanted ads. There may be
customers who may be wary of giving their personal data due to privacy concerns.
Recently, The New York Times and The Observer reported that a British consulting
firm, namely; Cambridge Analytica had acquired and used the personal data of
thousands of Facebook users who explicitly shared their data via a third-party app. As
a result, Cambridge Analytica accessed these individuals’ information and obtained
other data on their friends via Facebook. These Facebook users should have known
very well what they were getting into. Out of their own volition, they consented a
third-party app to use their personal data.
Very often, the advances in technology are faster than legislation and its deployment,
as the use of digital data pushes the limits of consumer protection law. For the time
being, there are different regulatory guidelines, and they are still geographically-
fragmented. As a result, there are different level playing fields across various
jurisdictions. For instance, the European Union (EU) Parliament has put forward its
general data protection regulation (GDPR) that became effective as of the 25th May
2018. In sum, the GDPR has replaced the Data Protection Directive 95/46/EC. The
GDPR was designed to harmonise the data privacy laws across EU countries; to
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protect their citizens from privacy and data breaches, in an increasingly data-driven
world (EU, 2018; Goddard, 2017). The GDPR applies to all companies processing the
personal data of online users (that are referred to as the subjects, residing in the EU). It
transpires that the organisations that will breach the EU’s GDPR can be fined up to 4%
of annual global turnover or €20 Million (whichever is greater). This is the maximum
fine that can be imposed for the most serious infringements, like not having the
customers’ consent to process their data, or for violating the core of “Privacy by
Design” concepts (EU, 2018). This EU regulation has strengthened the conditions for
consent as companies will no longer be allowed to use long illegible terms and
conditions; as the request for consent must be presented in an accessible form.
Therefore, the online users’ consent or withdrawal of consent, must be presented in
clear and plain language.
GDPR stipulates that the subjects have a right to obtain confirmation from the data
holder as to whether or not their personal data is being processed, as organisations
should explain where and for what purpose they are collecting the data. The data
holders are expected to provide a copy of the personal data, free of charge, in an
electronic format; if this data is requested by the subjects. Notwithstanding, the
subjects can ask the data holders to erase their personal data, cease further
dissemination of their data, and potentially third parties should not be allowed to
process this data. Therefore, the gathered data will no longer remain relevant for its
original purposes without the subjects’ consent.
Analysing Data
Organisations can always make use of the consented data that was voluntarily
provided to them by the online users. However, they may avail themselves of other
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data sources from the macro environment, including; competitive activities, marketing
actions, customer service records, et cetera. They may use analytics to create a good
picture of their marketing performance, as it can provide them with important insights
for their customer-centric strategies (Akter et al., 2016). Companies can use the
gathered data for many purposes. They could quantify the contribution of each element
of advertising; run scenarios for business planning; and allocate resources across
marketing activities. Organisations need to adopt a more continuous approach to
analysis and decision-making, that are based on a series of hunches and hypotheses in
real-time monitoring contexts, (Davenport, 2014; Davenport et al., 2012).
The analytics software could provide detailed information on sales data and
advertising metrics, by medium and location. A data analysis of one campaign may
reveal that the marketing communications may work independently of one another
(Camilleri, 2015; Nichols, 2013). The latest analytics packages can even reveal the
impact of the companies’ marketing activities in different media. This happens because
the consumer behaviours might change in response to the advertising stimuli. For
instance, an analysis could pick up a spike in consumers’ clicking through online
banner ads, after they have watched a TV ad, came across a social media placement or
experienced an in-store promotion (Kumar, Choi and Greene, 2017). Therefore,
analytics can reveal the “assist effects” of the traditional marketing communications
on digital media (Cheng and Edwards, 2015 ).
Once the marketers have quantified the relative contribution of each marketing
communications channel, they may use predictive-analytical tools to run scenarios for
business planning (Wang, Gunasekaran, Ngai and Papadopoulos, 2016; Siegel, 2016).
These analytics reveal the implications of increasing or decreasing marketing
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expenditures to optimise the marketing mix, and to guide the spending allocation on
particular media. The statistical models could shed light on the effect of advertising on
the consumers’ behaviours as they may reveal the relationships (if any) with the
market conditions, other marketing communications, and competitive activities. Data
on the consumers’ response to the marketing activities must be fed into the analytics’
systems to fine-tune the corporate spending through different media. Such
optimisation software can generate realistic contexts along with relevant marketing
recommendations to achieve them. For example, the analytics software can test
specific what-if scenarios, measure outcomes, validate models, and make corrections
before making corporate decisions (Siegel, 2016). Today’s marketers can readily adjust
or re-allocate online advertising budgets in different markets in a fraction of a second
(Camilleri, 2018c). Analytics will help them understand which marketing activities are
driving leads to websites, and intermediaries.
Today’s companies are in a position to quantify the precise combination of ads that
will be the most effective (Kumar et al., 2017). They can identify how advertising of
one product category influences the purchasing of others. Therefore, firms are already
benefiting from such information, as they make fact-based decision making when they
allocate resources for their advertising and promotions (Malthouse and Li, 2017). They
can also monetise data by increasing revenue whilst reducing expenses. In addition,
the gathered data may be used to improve the speed to market and to enhance
customer service levels (Woerner and Wixom, 2015; Kwon, Lee and Shin, 2014).
Many businesses are increasingly recognising the value of building data-driven
organisational cultures as they are increasingly dealing with massive volumes of data,
analytics, algorithms and user interfaces (Grover et al., 2018; Hartmann, Zaki,
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Feldmann and Neely, 2016). Some firms have experienced optimal results after they
have integrated analytics within their core businesses’ operational and production tasks
(Abbasi et al., 2016; Loebbecke and Picot, 2015).
However, the greatest barrier to achieving this is the management and employee’
attitudes toward technology and change (Russell Neuman, Guggenheim, Mo Jang, and
Bae, 2014; Andrejevic, 2014). Therefore, organisations should train and recruit
competent individuals with new digital literacies. Recently, there has been an ongoing
requirement for skilled professionals in data-driven marketing. Tomorrow’s marketing
employees should be knowledgeable in computer science, management information
systems or network-oriented social sciences (Horlacher and Hess, 2016; Davenport et
al., 2012). Highly qualified individuals are expected to support the businesses’ data
and information management. In some service sectors, data scientists have to become
an integral part of the research and development team (Horlacher and Hess, 2016).
In the past, the marketing information systems function involved monitoring,
controlling processes and notifying management about anomalies. The most vaunted
business and IT capabilities used to be stability and scale (Davenport et al., 2012).
Today, the analysis of data can be categorised as descriptive, predictive analytics, and
prescriptive analytics (Wang et al., 2016; Deka, 2016; Halavais, 2015; Ransbotham,
Kiron and Kirk Prentice, 2015; Camilleri, 2015). Descriptive analytics focus on what
happened in the past. Very often, it canexplain why it happened. Predictive analytics
uses models to forecast the future (Fu, Hao, Li and Hsu, 2018), as data models could
quantify the likelihood that a particular person will do something in the foreseeable
future — whether it is defaulting on a loan, upgrading to a higher level of cable
service or seeking another job (Camilleri, 2015; Siegel and Davenport, 2013). It may
12
appear that predictive analytics anticipates human behaviours that have not happened
as yet (Fu et al., 2018). For instance, predictive tools and smart cards have enabled
Singapore Land Transit Authority to provide a more convenient transportation system
to commuters and leisure passengers. Siegel and Davenport (2013) explained how
quantitative techniques can be deployed to find valuable patterns in data that can
enable companies to predict the likely behaviour of customers, employees and others.
They distinguished between forecasting and predictive analytics. They maintained that
forecasting could estimate future sales, whereas predictive data will provide additional
details on customer personas, segments and prospects. Siegel and Davenport (2013)
referred to the “Prediction Effect” as they suggested that minor increases in the data
accuracy of predictions can often lead to substantial savings in the long term.
Prescriptive analytics anticipates what will happen, when it will happen, and explains
why it will happen (Deka, 2016). Therefore, it prescribes better decision options.
Although, individuals tend to regularly repeat their habitual behaviours, (for the time
being) predictive analytics cannot determine when and why they may decide to change
their future preferences. Yet, the possibility of “one off” events must never be
discounted (Gandomi and Haider, 2015).
Distributing Data
The use of advanced analytics has led to the development of programmatic advertising
(or real-time advertising); where buyers and sellers of online advertising connect to
exchange available inventory (Busch, 2016; Rayport, 2015). The programmatic
advertising environment offers remarkable speeds and sophistication levels, as online
users click on URLs. In a short period (less than a second), an algorithm evaluates an
optimal bid for an advertiser. As a result, a real-time auction determines the winning
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bid (Jerath and Savary, 2017). Consequentially, the winning ad is displayed, and the
advertiser is notified that the ad had been viewed by online prospects. The analytics
technology has enabled the technology giants to deliver targeting and re-targeting
solutions with instantaneous pricing and access, across digital channels (including;
mobile, video, social, etc.) (Rayport, 2015). Therefore, programmatic advertising is set
to continue captivating the cross-media creative and media industry as it forms the
basis of distributive advertising and marketing on every level (Busch, 2016). Another
disruptive innovation, namely, blockchain’s distributed ledger technology involves a
decentralised environment where online transactions are recorded in a public ledger
that are visible to everyone (Wang, Chen, Yao, Liu, Xu and Zhu, 2018; Yli-Huumo,
Ko, Choi, Park and Smolander, 2016). Blockchain is a new platform technology
enabling an improved ability to verify and record the exchange of value among an
interconnected set of users. It is a secure and transparent way to track the ownership of
assets before, during, and after any transaction (Lakhani, 2017; Yli-Huumo et al.,
2016). Hence, it may appear that this technology meets the user’s confidentiality and
consistency requirements.
In the past, companies could have struggled to determine the value of their intellectual
property; including patents and trade secrets. However, emerging blockchain
technologies create marketplaces for such intangible data, whilst improving data
privacy. Blockchain has stringent requirements for privacy and confidentiality, but
also for auditability (FEDS, 2016). Hence, organisations are increasingly sharing their
data in a safe environment, because there's more protection in terms of cryptography
and protocols. Blockchain users track the ownership of assets before, during, and after
any online transaction. Therefore, its distributed ledger technology can be used by
different businesses hailing from various industry sectors, to facilitate their
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transactions with marketplace stakeholders, including suppliers, intermediaries, and
consumers across borders. Blockchain records a history of all transactions within it,
and all users in the network have an identical copy of the record/ledger. The network
using the blockchain agrees on and governs the rules for its use. It protects all data and
information, and the use of digital keys and digital signatures is required to access data
in the ledger (Lakhani, 2017). The Blockchain’s data is permanent and the details of
all recorded transactions cannot be altered retroactively without the full agreement of
the network.
Moreover, the Blockchain’s digital ledger reflects any changes in real time, and the
records of all transactions are securely maintained and updated by the users
themselves. These benefits enable the development of many potential products and
applications for different industry practitioners, including government entities and
business organisations. Current blockchain products include: (i) the clearing of
payments and settlement functions in financial services companies, (ii) the creation
and use of a digital identity within an enterprise, and (iii) smart contracts (Iansiti and
Lakhani, 2017; Yli-Huumo et al., 2016; Kosba, Miller, Shi, Wen and Papamanthou,
2016), among other applications. Blockchain can facilitate cross-borders payments
among marketplace stakeholders, including; suppliers, intermediaries, and consumers.
Very often, the businesses’ payment options are highly intermediated, costly, and time
consuming. Additionally, the blockchain’s online transactions are secure, private, and
verifiable (Iansiti and Lakhani, 2017). This technology could potentially lead to
operational efficiencies and cost savings that may be passed on to the consumers
themselves. On the other hand, many commentators maintain that Blockchain has
potential challenges and limitations in terms of its scalability issues. The distributed
ledger technology’s performance is limited by the network’s latency (Wang et al.,
15
2018). Currently, Blockchain is restricted by its transaction speeds. For instance, the
maximal transaction throughput of the bitcoin is 27 transactions per second
(Georgiadis, 2019).
Discussion and Conclusion
Overall, it may appear that the data-driven marketing technologies are supporting the
businesses’ marketing mix elements as they improve their interactive engagement with
individual prospects. This contribution posited that they can even enhance customer
centric marketing, in terms of the increased personalisation of services, whilst
providing secure pricing options. Evidently, many firms are evolving from their
passive, rigid, and product-centric state to a more flexible, dynamic, and customer-
focused environment, as they monitor and detect any changes in consumer sentiment.
Data-driven companies are increasingly capturing and analysing the online and mobile
activity of prospective customers, as they delve into ecommerce and review sites,
personal blogs and social media (Kumar et al., 2017). Their analytics captures the
consumers’ interactions with brands and companies through digital media. Therefore,
big data is enabling them to target and re-target individu als and online communities
with instantaneous pricing and access options, across multiple channels (via web-site
activity, mobile, video, social media, ecommerce, among others). Large technology
giants use mobile tracking technologies, to gather information on the consumer
behaviours, including their shopping habits, lifestyle preferences, et cetera (Aksu et
al., 2018).
Tech-savvy firms have learnt how to take advantage of on-demand, real-time
information from sensors, radio frequency identification and other geo-location
tracking devices to better understand their marketing environments at a more granular
16
level (Storey and Song, 2017; Kwon et al., 2014). This way business could come up
with personalised products and services, that are demanded by individual customers
themselves (Li et al., 2017). The challenge for many business practitioners is to
recognise the value of big data analytics as they can truly provide insightful
information onthe marketing environment, including customers and the competitors
(Grover et al., 2018). The predictive-analytical tools can examine different scenarios;
as the prescriptive analytics anticipate what shall happen in the future. Such digital
technologies monetise data by identifying revenue-generating opportunities and / or
through the identification of cost savings. The use of the big data analytics’ automated
inferences and data modelling can provide quick and effective reaction to new
opportunities, threats or regulations, thereby increasing the business’s agility
(Erevelles et al., 2016). From a business perspective, it may prove difficult to manage
data in an efficient manner, as internal as well as external data may often be
unstructured or loosely structured.
However, recently there have been significant advances in technological innovations
in mobile, social media, video streaming, wearable devices, virtual and augmented
reality, 5G networks and the Internet-of-Things (IoT), among others. These disruptive
technologies have inevitably provided new opportunities to acquire more information
from the marketing environment. For instance, many businesses are already benefiting
of the programmatic advertising environment; whereby buyers and sellers of digital
advertising are increasingly connecting online to exchange available inventory
(Malthouse and Li, 2017; Busch, 2016). The perennial issue is whether businesses will
(or will not) continue to rely on the insights from big data analytics to improve their
organisational performance. Nevertheless, this contribution posited that the data-
17
driven technologies are helping businesses to achieve a competitive advantage. For
instance, a relevant literature review suggests that the blockchain applications can be
used by businesses to facilitate their transactions with marketplace stakeholders,
including suppliers, intermediaries, and consumers across borders (Wang et al., 2018;
Lakhani, 2017). Despite its inherent limitations, the blockchain’s distributed ledger
technology will probably be more convenient than other payment options, in terms of
time and money. Therefore, blockchain can possibly offer better customer service
levels and operational efficiencies for the many businesses. At the same time, this
paper reported that it has improved the data privacy among an interconnected set of
online users, as it tracks the ownership of assets before, during, and after any online
transaction.
This paper reported that smart, data-driven technologies are shifting how organisations
collect, analyse and utilise and distribute data (Hashem, Chang, Anuar, Adewole,
Yaqoob, Gani, Ahmed and Chiroma, 2016; George et al., 2014). Table 1 illustrates
how insightful data is increasingly being captured through online and mobile activity.
Hence, this data is analysed and / or distributed for monetisation or strategic purposes.
Table 1. The Data Processing Cycle
Capturing Data
Online and mobile users’ behaviour in
real-time
Customer service records
Referral sources and product
recommender systems
Consumers’ personal preferences
Website activity
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Social media networks
Ecommerce platforms
Comprehensive information in a
database
Analysing Data Descriptive analytics
Predictive analytics
Prescriptive analytics
Distributing Data Programmatic advertising
Blockchain distributed ledger
technology
Emerging Trends and Future Research
Disruptive technologies can be used to anticipate the discerned consumers’
requirements. For example, the use of programmatic advertising will probably increase
the individuals’ intuitive shopping experiences and can tap into the travellers’
discretionary purchases.
In the foreseeable future, it is very likely that there will be more sales via mobile
commerce, with increased consumer interactions through speech and voice recognition
software. The service providers may possibly rely on artificial intelligence (AI) and
other forms of cognitive learning capabilities, like machine learning and deep learning.
Many businesses’ distributive systems could interface with virtual reality software to
help online intermediaries to merchandise their products in captivating customer
19
experiences. Notwithstanding, online consumers may possibly use blockchain’s secure
technology to purchase products through their mobile devices.
In conclusion, this contribution calls for further empirical research that could explore
the use of data-driven marketing innovations to better serve individuals and
organisations. Future research avenues may include; the personalisation and
behavioural modelling for mobile apps, programmatic advertising, blockchain, AI, and
IoT, among other areas.
Acknowledgements
The author thanks the editor as well as the reviewers for their constructive remarks and
suggestions. Their feedback has improved the quality of this contribution.
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