The Internet of Things (IOT), Big Data and Customer Engagement

Youmna Mohamed Abdelghany Youssef

Assistant Lecturer

Faculty of Commerce

Mansoura University Egypt

Visiting PhD student

J. Mack Robinson College of Business

Georgia State University

Atlanta GA 30303 USA

yyoussef@gsu.edu

Wesley J. Johnston, Ph.D.

CBIM Roundtable Professor of Marketing

J. Mack Robinson College of Business

Georgia State University

Atlanta GA 30303 USA

Wesleyj @gsu.edu

Abstract

In the rapidly changed world due to the digital technology and other innovations, firms

had more and more ways to get engaged better with both customers and supply chain

partners than any time before.

In this study, the authors develop a framework to (a) Review the conceptualization of

Internet of things, Big data and Customer engagement in the marketing literature, (b)

Examine the relationship between Internet of Things (IOT), Big data and Customer

Engagement. (c) Investigate whether the success in using Internet of things and Big data

analytics would impact customer engagement. (d) Guide both managers and marketers to

realize how IOT and big data can enhance customer engagement through adding value to

the marketing activities that in turn leads firms to gain competitive advantage.

Keywords: Internet of Things, IOT, Big Data, Customer Engagement

Introduction

Internet of Things(IoT) is recognized as one of the most important areas of future

technology and gained enormous attention from several industrial extents, the adoption of

this technology is changing rapidly as industrial, societal, and competitive pressures drive

firms to innovate and develop themselves (Lee & Lee, 2015). In due time, Internet of

Things (IoT) used in business projects and became more concerned with the development

of data management, leverage IoT infrastructure, and change business models than using

basic sensors, devices, and smart things(Deloitte,2016).

Apparently, data provide interactive understandings about customers that marketer can

consider as a competitive advantage (Erevelles et al., 2016) Therefore, Data science

emerged to focus on, capture, create, analyze and use big data streams (Johnston &

Pattinson, 2016). Digitization allows information to be directly stored, accessed, shared and

processed through digital circuits, devices and networks (Ng and Wakenshaw, 2017).

Big data regarded as the fourth business revolution, independent computerized

manufacturing processes connected with the internet of things that will promote efficient,

flexible and customized production (Breur, 2015). As a result, there is a strong motive to

value significant changes in retailing areas due to recent innovations, so that we can

better recognize the develop the future of retailing (Grewal et al., 2017).

Therefore, to make things easier for customers is all about using smart phone apps and

websites. As soon as the Internet of Things (IoT) extends our engagement with

businesses beyond the screen into our vehicles, stores and even our houses (Saxon,

2017).For example, Machine to Machine (M2M) connections is expected to raise tightly

from 4.9 billion in 2015 up to 12.2 billion (Calif,2016). One example to show the timely

growth in using the internet and connected devices is that in 2015, Wi-Fi and mobile

phone connected devices generated about 62 % of internet traffic. By 2020, Wi-Fi and

mobile phone connected devices expected to produce 78 % of Internet traffic (Cisco,

2016).

Despite the progress in the internet of things IoT technology, several areas remain

unexplored to date including the social, behavioral, economic and managerial dimensions

of the IoT so, this represents a significant challenge for firms to adopt IoT (Lee & Lee,

2015). Likewise due to the extraordinary volume, velocity, and variety of primary data

captured from individual customers we now are in the big data revolution era that will

lead to entirely different ways of understanding consumer behavior and designing

marketing strategy (Erevelles et al., 2016). Even though, there is an important value of

big data to marketers; IoT offers enormous understanding into customer’s mind besides

changing daily life operations by helping generate more efficient towns and creativities.

As a result, both contexts of business (B2B) and customers (B2C) will gain competitive

advantage from the IoT (Newman, 2016).

Basically, innovation is an idea, practice, or object that is perceived as new by an

individual or other unit of adoption and the five characteristics that determine an

innovation’s rate of adoption are: relative advantage, compatibility, complexity, trial

ability, and observability (Rogers, 2002).

A review of the existing literature on Internet of things, big data and customer

engagement provide insights to why these concepts are essential. Traditionally, both the

need to better understand customer behavior and the interest of managers focused on

customers delivering long term profits changed how marketers viewed the world (Winer,

2001). More recently, firms face difficult challenges that make customer engagement

more important than ever due to the severe competition that required achieving high

engagement levels (Roberts & Alpert, 2010).

At this time, both marketing scholars and practitioners discuss, measure and examine

how firms can benefit from customer engagement as these engagement behaviors ‏ also

have value extraction consequences (lemon and Verhof, 2016). Specifically, Customer

engagement that was used in the last few years as an outcome of the firms' activates

(Pansari& Kumar, 2017). As a result, the firms target developed from relationship

marketing to engaging customers in every way (Pansari and Kumar, 2017).

Due to technological advances, customers today are becoming an important source of

generating data either traditional structured, transactional data or current un-structured

behavioral data (Erevelles et al., 2016 ) Besides, Supply chain inventories could generate

massive amounts of data that companies can turn into intelligence through analytics used

from POS, GPS, RFID to social media feeds, for instance, Amazon, and WalMart utilized

well from the influence of big data analytics (Sanders, 2016).

Despite the fact that numerous studies in the marketing literature discussed how firms

build customer engagement, identified its dimensions, the relationship between internet

of things, big data and customer engagement had a lot unexplored yet.

Therefore, in this study, the authors aim to examine the role of the internet of things and

big data in achieving customer engagement in the era of increasing data driven marketing

and how it will impact the marketing discipline through discussing the opportunities of

using the internet of things and big data in creating customer engagement.

In summary, the purpose of this paper is to:

Synthesis the existing literature of internet of things, big data and customer

engagement.

Identify the conceptualization of the relationships between Internet of Things

(IOT), big data and customer engagement

Highlight the research propositions and create a framework to describe the

relationships between variables.

Literature Review

1. Internet of Things (IOT)

Internet of Things (IoT) Concept

The Internet of Things (IOT) is a combined network of sensors connected to the internet,

a web platform supported devices through sensors each containing IP addresses where,

smart sensors collect and send real time information through the internet (Johnston &

Pattinson, 2016).

Lee & Lee (2015) identified five IoT technologies that needed for the successful adoption

of IoT to create products and services: radio frequency identification (RFID); wireless

sensor networks (WSN); middleware; cloud computing; and IoT application software.

wherever, mobile phone is an essential intermediary between things, people and internet

(Mattern and Floerkemeier, 2010).

Internet of things (IoT) Current state

Currently, we still at the beginning of this technology movement, Specialists estimate that in

2015, there were 25 billion connected devices, and by 2020 they will be 50 billion (FTC, 2015).

More recently, Forbes mentioned the top 10 advances for Digital Transformation in 2017

are; adaptability to achieve continuous success, growing importance of user experience,

quick innovation, hold the distant workforce, the creation of augmented reality (AR) and

virtual reality (VR),big data and analytics, Digital transformation determined by the

Internet of Things (IoT), Smart machines and artificial intelligence (AI) that are

significantly successful and finally, destroying storage tower (Newman, 2016).

Internet of things (IoT) Growth Rates

Johnston (2014) identified some future estimates for the growths of the Internet of Things

include (IoT):

Demand for advanced logistics leading to RFID tags for facilitating routing,

Inventorying and loss avoidance.

Cost saving key for the diffusion into second movement of applications with

Investigation, security, healthcare, transport, food safety, and document

Management.

Ability of devices traced at home to get geolocation signals that permit finding

people location and everyday things.

Reduction, power resourceful electronics and accessible scale bandwidth

generating teleportation and telepresence which permit monitoring and controlling

distant objects.

Software representatives and innovative sensor merging the physical world to the

Web.

In addition, the following statistics highlight the importance of Internet of Things

(IOT):

The expected total economic impact of IoT will reach a range between $3.9

trillion to $11.1 trillion a year by 2025 (Mckinsey, 2015).

According to (Cisco, 2016) the expected growth rate in IP traffic according to

different regional areas by 2020 is;

59.1 Exabyte/month for North America in 2020, 28.0 Exabyte/month for Western

Europe by 2020, 17.0 Exabyte/month for Central Europe 2020, 11.6

Exabyte/months for Latin America by 2020 and finally 10.9 Exabyte/month in the

Middle East and Africa by 2020 .

Not only IoT develops but also, the capacity of data generated grows as well. by

some estimates, connected devices will generate 507.5 zettabytes (ZB) of data per

year (42.3 ZB per month) in 2019 higher than 134.5 ZB per year (11.2 ZB per

month) in 2014. (A zettabyte is 1 trillion gigabytes) (Deloitte, 2016).

By 2020, signifying approximately half (46 percent) of total connected devices

Connected home M2M connections will increase from 2.4 billion in 2015 to 5.8

billion in 2020, representing nearly half of total M2M connections worldwide

(Cisco,2016). Similarly, by 2020, the expected number of IoT Sensors will be 50

billion and more than 200 billion Things on the Internet by 2030 (Newman,

2016).

Therefore, the previous statistics indicate that firms should focus on the benefits of using

internet of things and how to use it in engaging customers.

Internet of things (IoT) Major applications

Rogers (2002) specified four main elements in the diffusion of new ideas which are

innovation, communication channels, time, and social system.

An example of IoT technologies that used in several areas: security (access control,

security care for elderly, time reporting for home care), tracking and tracing (fleet

management, logistics for goods transportation), payment (mobile payments), health (e-

home care), remote control and maintenance (Smart houses, environmental monitoring)

and smart power grids (Andersson and Mattson, 2015).

Indeed, IoT holds remarkable opportunity to change not only industrial manufacturing

but other businesses too for instance, a recent innovative business is Uber that used

location technology and mobile phone abilities, once customer need to ride the driver gets

a notification and determine the arrival time, fare and length of the tripe (Breur, 2015).

According to (Lee & Lee, 2015), there are three IoT sets of business applications: first,

monitoring and control Second, big data and business analytics, and finally, information

sharing and collaboration. Understanding how these three IoT groups can increase the

customer value of a business which is essential to effective IoT acceptance. For example,

Siemens firm in Germany where, machines, and computers, include about 75 % of the

value chain only, with some 1,000 computerization controllers in operation all over the

production line. Every part manufacturer has a product code that allows machines to

determine its output requirements and the subsequent steps (Deloitte, 2016).

2. Big data

Big data consumer analytics "are the extraction of hidden sight about consumer behavior

from big data and the exploitation of that insight through advantageous

interpretation"(Erevelles et al., 2016, p.897).

Big data created in and across networks so, there is no strict established definition for big

data, but (Johnston & Pattinson, 2016) reviewed big data previous studies and outlined

the f ive V's of big data which are Volume, Variety, Velocity, Veracity and Value.

Whereas, Volume is the amount of data produced every second (Johnston & Pattinson,

2016). Velocity is relentless of the data generated; Variety includes different sources of

data as structured data, un structured or semi structured data (Erevelles, 2016).Veracity is

the uncertainty of data and finally, Value, the proficiency to turn data into business value

(Johnston & Pattinson, 2016).Therefore, (Sanders, 2016) stated that big data with

analytics creates the ability to have a significant vision that turns information into

intelligence and big data without analytics is relatively a huge amount of data. On the

other hand, analytics without big data are considered only as statistical and mathematical

tools that existed for years for instance correlation and regression. Obviously, Analytics

are the tools that help in finding unknown data outlines (Erevelles, 2016).

Big data and Internet of things

The Internet of Things (IOT) has the potential to increase the volume of Big data

exponentially due to, devices and machines used directly by persons as ATMs or what

individuals depend on as traffic signing systems that exchange internet messages among

each other (Breur, 2015). The issue of storing, securing and analyzing IoT data is

significant for informed marketing activities. Insight into customer behavior in real time

will be greatly enhanced through analysis of data flowing constantly from IoT sources.

The question is will the cost for all potential applications produce value in the marketing

relationship. While there are an unlimited number of applications many that are feasible

will not produce the value required. A few areas of consumer product applications (eg.,

transportation, health care, and smart homes) will be worth the effort. In business-to-

business applications customer engagement will yield significantly more worthwhile

applications in customer engagement and efficient operations (Industry 4.0

manufacturing ecosystems, JIT maintenance and service, and servitization value

propositions).

Therefore, Big data analytics is not just a new technology. It is the link to software,

computing, and technological competencies that led to an era of profoundly different

competition and is a technology interference of significant scopes (Sanders, 2016).

3. Customer Engagement

There is a growing concern that customer-centric strategies regarded as important issues

for both academic researchers and marketing practitioners due to the shift from company

centric to customer centric marketing approach (Javornik&Mandelli, 2012).

Moreover, Managers are increasingly aware that long term quality relationships between

company and customers can be a major source of profitability (Kumar et al., 2010).

Consequently, the more the value that the company provided to its customers the more

they retained (Wong, 2013).

Initially, the engagement concept has a conceptual root in fields such as psychology and

organizational behavior while emerged in the marketing literature recently (Hollebeek,

2011) Meanwhile, (Bowden, 2009& Brodie et al., 2011) defined engagement as a

sequential psychological process to become loyal to a brand. Research indicated that

engaged consumers showed greater loyalty to brands (Hollebeek, 2011). Consequently,

Customer engagement studies focused on differentiating between customer attitudes and

behaviors that go beyond purchase (lemon &Verhof, 2016).

On the one hand, (Vivek et al., 2012) stated that customer engagement is the intensity of

an individual's participation in and connection with firms' offerings or activities. On the

other hand, (Verhoef et al., 2010) considered customer engagement as a behavioral

manifestation toward the brand or company that goes beyond transactions.

These beyond purchase behavioral dimension of customer engagement includes

manifestations, such as co-creation, social influence through word of mouth and customer

referrals (lemon &Verhof, 2016).Whereas, (Van Doorn et al., 2010) stated that

engagement is a behavioral manifestation towards the company or a brand beyond

purchasing and as a result of motivation.

This perspective used mainly as the digital and social media revolution has supported the

importance of customer engagement behavior as customers become active co-produces or

put an end to value for firms (lemon &Verhof, 2016).

Thus, the first proposition, P1: There is a positive relationship between digital and social

media and better understanding of the customer journey to improve customer

engagement.

Engagement is further seen as a way to generate customer interaction and participation

(Kumar et al., 2010). Besides, Hollebeek (2013) stated that engagement would occur

between the customer and product, brand or company. More recently, (Pansari& Kumar,

2017) defined customer engagement as the mechanics of customer value added to the

firm either through both direct and indirect contributions. Therefore, more studies

conducted lately to measure engagement value dimensions particularly, customer referral

value and customer influence value ‏ (lemon &Verhof, 2016).

Nevertheless, (Hollebeek, 2011& Brodie et al., 2011) stated that antecedents of

engagement are involvement, interaction and absorption where engagement

consequences are value creation, brand experience, and perceived quality, customer 's

satisfaction, empowerment, trust, commitment, value and brand loyalty.

Additionally, (Brodie et al., 2011) stated that engagement process had an iterative nature

means that consequences of engagement may subsequently perform as antecedents.

Similarly, (Gummerus et al., 2012) confirmed that as an ongoing relationship it's difficult

to separate antecedents of customer engagement from moderators and consequences.

Analysis of CRM data can be used drive optimization of customer life cycles; improve

operational efficiency and integrate value chain collaboration creating a more customer

centric relationship of marketing activities. Furthermore, (Pansari and Kumar, 2017)

indicated that after the relationship formed based on trust and commitment, the next level

is customer engagement

Thus, we propose that P2: There is a positive relationship between customer relationship

management via analytics, operational efficiency, value chain management and customer

engagement.

Whereas (Kumar et al., 2010) proposed four core dimensions for customer engagement

value: customer lifetime value, customer referral value, customer influence value and

customer knowledge value. Researchers agreed on three dimensions for engagement

cognitive, emotional and behavioral (Brodie et al., 2011&Hollebeek, 2013). While,

(Vivek et al., 2012) added one more dimension which is the social aspect. Evidently,

several studies examined customer engagement whether focused on attitudes, behaviors

or value extraction (lemon &Verhof, 2016).

Eventually, Deloitte (2014) report presented sustainable customer engagement

framework, where sustainability regarded as a value proposition for the potential increase

in customer engagement as it increases customer loyalty, advocacy, and repeat

conversions. Similarly, (West et al., 2015) identified the reasons to gain sustainability

through customer engagement and achieved considerable growth regarding money,

profitability, revenue and relationships which are:

Transparency engagement: visible efforts companies made to inform the

customers of the sustainability performance of products.

Partnership engagement is to improve sustainability by attracting customers to

participate in collaboration with the third party business actively.

Lifecycle engagement: where the company attempts to engage customers during

the entire lifecycle is a product.

Collaborative engagement: business used modern network technology to create

typical customer value

So far, newer technologies include Internet of things or robots, business models, big data

and predictive analytics propose that the shopping process is on the edge of a significant

rise due to the fast retail setting development. However, consumers’ needs still drive their

purchase decisions (Grewal et al., 2017).

Value creation, Internet of things (IoT) and Customer Engagement

Technology provides broad vertical and horizontal information sharing in addition to

collaboration between department and network partners that sustain product and service

implementation strategy (West et al.,2015). In fact, Changes caused by the Internet of

Things are either incremental or transformative however there is a need to capture value

remains as it finely tuned (Raynor& Cotteler, 2015). Moreover, Value creation argued as

digital business services that can be recognized and datafied through Service Oriented

Architecture (SOA) or in another expression the information Services Platform (Johnston

& Pattinson, 2016).

To illustrate, (Raynor and Cotteleer, 2015) identified five stages of information value

creation showed in figure (1) that outlines information value loop: Firstly, create: using

sensors to generate information near a physical event or state. Secondly, communicate:

transmit information from one place to another. Thirdly, aggregate gather of information

created at different times or from different sources. Fourthly, analyze: make a decision of

patterns or relationships among spectacles that leads to descriptions, predictions, or

prescriptions for action. Finally, Act: Initiating, maintaining, or changing a physical event

or state.

Accordingly, revealing such information resources can not only build engagement and

transactions with customers about the product functions but also product's position,

action and image this is due to customer relationship with the product (Ng &

Wakenshaw, 2017). Equally important, Customer value is B2B marketing central concern

as marketer continuously under pressure to ensure that customers obtain value from the

suppliers' offer (Wiersema, 2013).

Therefore, the third proposition P 3: In some cases the customer will not be required to

interact directly with the manufacturer but rather customer specific automated service

functions supported by the algorithm driven processes in place there is a positive

relationship between internet of things and customer engagement.

Value Creation, Big data and Customer Engagement

Figure 1: Information value loop

Source: Raynor, M. E. &Cotteleer, M. J. (2015),“The more things change: Value creation, value capture, and the Internet of Things,” Deloitte review 17.

http://dupress.com/articles/value-creation-value-capture-internet-of-things.

According to (Mazzei&Noble, 2017), there are three levels of value creations due to big

data trend firstly, Data as a tool, secondly, Data as an industry and thirdly, data as a

strategy. For instance, Amazon.com trying to build engagement relationship with its

customer s by creating Amazon dash which is a device that can be attached with one of

the products chosen by a customer and allows this customer to reorder that product by

just pushing the button (Venkatesan, 2017).

Erevelles (2016) provided examples of how big data create value in the marketing mix

over the place. In addition, fewer studies showed that various marketing activities

influence customer engagement and that customer engagement could impact the firm's

performance (Pansari and Kumar, 2017).

Hence, the fourth proposition P4 is: Data from social media, CRM system and IoT

sensors will generate large quantities of customer behavioral data that can be used in

aggregate or with specific customers and there is a positive relationship between big

data and better informed marketing activities

For example, Amazon’s preemptive shipping and promotion; using geospatial data in

advertising messages, product; Ford motors using customer analytics for about four

million vehicles to revolutionize its innovation and design. Currently, retailers can use

big data effectively to predict consumer behavior, design more attractive offers, reach

their customers better and build up ways to make customer support their products thus

retailers can use multiple ways of technology to engage customers (Grewal et al., 2017).

Since customers continually engaged at different levels with the company and social

networks (Venkatesan, 2017).

Thus, the fifth Proposition p5: There is a positive relationship between big data better

informed marketing activities and customer engagement.

Conceptual framework

The current study proposes a robust framework for studying the relationship between

Internet of Things, Big Data and Customer Engagement as shown in Figure (2).

This framework designed to highlight the key concepts in the reviewed literature ; we

explain the concepts of Big data and identify its components as Customer Relationship

Management (CRM), Social and digital media and Internet of Things (IOT), that firms

can use one or more or even all of these data when deciding to develop marketing

practices or activities and how these marketing efforts would help customers to better

understand firms' offerings, evaluate marketing activities and in addition firms can

achieve customer engagement. This framework mainly based on the relevant literature.

Figure (2): Framework for the relationship between Internet of Things ,Big Data and

Customer Engagement

Social Media Customer relationship

Management (CRM)

Internet of Things

(IOT)

Customer Engagement

Marketing Activities

Discussion

In this study, we present a framework that allows firms to benefit from using big data and

internet of things to engage their customers. As, we provide a strong theoretical

foundation for how big data and internet of things can positively impact customer

engagement. Despite the importance of using big data and internet of things (Lee & Lee,

2015) identified five technical and managerial challenges that firms would face, data

management, data mining, privacy, security, and disorder.

Nearly, privacy risks resulted from collecting personal information, habits, locations, and

physical conditions over time, thus, IoT allow collection of sensitive behavior patterns, which

might be used in informal ways or by unauthorized persons (FTC, 2015).

Managerial implications include identifying those applications and customers that will generate

the necessary improvement in customer engagement to drive the increase in value for the required

marketing activities and the management and analysis of the data.

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