The influence of IoT on CRM. Conditions and possibilities in B2C markets

Textbook, 2020
88 Pages



List of Abbreviations

List of Figures

1 Introduction
1.1 Purpose of the Thesis
1.2 Procedure and Research

2 Selected theoretical Aspects of the Internet of Things and Customer Relationship Management
2.1 Basic Principles of the Internet of Things
2.2 Basic Principles of Customer Relationship Management

3 Study of the Influence of the Internet of Things on Customer Relationship Management
3.1 Status Quo of Technology and its Dissemination
3.2 Presentation of relevant research results on the Internet of Things in Customer Relationship Management
3.3 Presentation and Analysis of first Implementation Attempts
3.4 Effects on strategic Customer Retention through the Internet of Things

4 Conclusion


List of Abbreviations

Abbildung in dieser Leseprobe nicht enthalten

List of Figures

Figure 1 Example of a smart home platform (Marvell 2012).

Figure 2 – Illustration of a Smart Grid (Theis/Katzemich 2015 p.624)

Figure 3 – Relationship Management Modell illustrated after Leußer et. al. 2011. p.20

Figure 4 - Functional Chain of CRM, Own illustration (based on Leußer et al. p.22)

Figure 5 - AIDA Model (own illustration)

Figure 6 - Customer Decision Journey by McKinsey

Figure 7 - Customer Journey Map (based on Beisel 2018. p.212)

Figure 8 - Gartner Hype Cycle of Emerging Technologies 2018

1 Introduction

1.1 Purpose of the Thesis

“I think there is a market for maybe five computers.” (Thomas J. Watson, Chairman and CEO of IBM, 1943)

More than seven decades later, we know that Mr. Watson’s prediction did not come true. In most households of the developed countries are several computers. Moreover, this in lots of different products of our everyday lives. The digitalisation pictures the most incisively change in human lives after the industrialisation. This technological development determinates human lives in every aspect.

Started in 1941 when the first universal programmable computer had been invented, many other scientists developed the capability of automatic processes (Ramge 2019. p.48). Firstly, used for industrial purposes, to make them faster, less complicated, and more reliable. In 1983 the first personal computer, named Commodore 64, had been offered. From this day on, everyone knew that this technology is not just for big companies but for everyone. The next evolutionary step was the connected world. Tim Berners-Lee developed a system that should simplify the data exchange between two locations of the European Organisation for Nuclear Research, known as CERN, in Switzerland. After in 1990, the first website had been launched, the first browser in 1993 enabled everyone to participate (Ramge 2019. p.49). The rest is history.

After many years of improving and spreading this technology around the entire world, everyone and everything is connected. Digitalisation enables the rapid transfer of complex information content necessary for the conduct of business and private transactions. Economically, the automated collection, processing, and dissemination of data lead to significant economies of scale and cost advantages in terms of economic transactions. Among others, these advantages contain many possibilities for companies in interaction with their customers. During the last years, computers started to communicate with themselves and interact with each other. This interconnectivity between objectives is called the internet of things. Usually, in research and articles, products of this kind, which uses this connecting technology, are called smart products. According to Michael E. Porter, because of the expanded capabilities, these smart products changing the nature of “things” (Porter/ Heppelmann 2014 p.4).

For many reasons, this technology has a massive impact on how people use digital products. Furthermore, it determinates the direction of communication between companies and their (potential) customers. Especially the strategic way of building relationships to customers will change in the future because of the internet of things. The discipline of customer relationship management is going to have new possibilities and change fundamentally. However, what does that mean specifically? According to IoT Analytics, in 2019 19,4 billion connected devices will be used worldwide, which includes more than 8 billion IoT devices (that number exclude smartphones, tablets or notebooks) (IoT Analytics 2018). In 2025 this number will increase to over 70 billion IoT connected devices worldwide (IHS 2016). Furthermore, IoT was one of the essential relevant topics on the Hannover fair 2019 (IoT Analytics 2018). This indicates that the public interest in this issue is increasing. However, many managers do not entirely understand what this technological development includes for their businesses and how to implement it (IoT World Today 2019).

Moreover, the relevance of the thesis is not only justified by explaining the usage and need of this IoT technology in customer relationship management. The change in society concerning lifestyle, communication, and consumption support the legitimacy of this topic. Statistics predict that until 2021, 3,76 billion people worldwide use smartphones (The Radicati Group 2019). For instance, in Germany, 78,8% of the population is using this kind of technology (Newzoo 2019). This shows that a significant part of people is potentially connected to the internet of things. These studies underline the tremendous potential, which companies may have if they use this technology right. Therefore, it is essential to analyse the condition of technology and customer’s lifestyle in order to understand the possibilities the internet of things entails for customer relationship management.

Regarding changing consumer habits, it is to mention that the digitalisation empowers the customer to interact with businesses and brands (Pantano 2014). It created transparency by, for instance, comparing prices, quality, and ratings. Therefore, customer relations have become more complex and complicated, as well. Companies have to be more creative and closer to the customer’s lives than ever in order to keep customers loyal and increase their value. The combination of these two related topics will determine the possibilities and success of IoT.

This thesis will focus on the b-to-c market with the purpose to analyse and study the effects of the internet of things on customer relationship management. They are targeting to find ways and options for organisations to implement this technology into their CRM system.

1.2 Procedure and Research

The aim of this work is the analyses of the effects of the internet of things on customer relationship management in the b-to-c market — specifically, the possibilities for the strategic element in customer relationships.

As there are only a few studies on this subject in the literature, this paper examines the following central research questions: What is the current state of the internet of things? What are the current studies on which do scientists work on? Which relevant scientific findings give indications for the effects of IoT on customer relationship management? Moreover, what can organisations learn from first implementation attempts?

To answer these questions, a qualitative content analysis based on a detailed literature review was put into practice. The contains the analysis of three studies. All of these combine the use of the internet of things and the field of customer relationship management. These studies should gain findings from different perspectives to answer the thesis topic. Also, two practical examples will be analysed to observe what is currently possible in the use of IoT in customer relations and where its potential for the future.

To enable the reader to follow the argumentation, this work is divided into four chapters. After this short introduction in chapter 1, three more are going to follow and end up in a summarising conclusion in part 4.

The second chapter will focus on the necessary, detailed foundations. In this part, there will be illustrated general terms, definitions, and applications to give the reader a red line to follow the thesis. An elaborated literature review of prestigious authors and experts have been the basis. All necessary information about the two significant subjects internet of things and customer relationship management will be formulated. All topics are going to be explained in an extensive and in-depth manner to offer a sufficient basis for the following research part.

Chapter 3 is the research part of the thesis. This work is a qualitative study, which analyses researches and practical cases. Firstly, the latest developments and trends in the field of IoT will be presented according to a renowned research institute Gartner. Moreover, the thesis includes the presentation of research findings in the field of the internet of things and CRM. According to this, a study about a smart store concept, in which several IoT products have been tested in terms of customer acquisition and experience brought exciting findings. Three Korean scientists operated that research. In addition to that, this work contains research about the influences of feedback and ratings on customer loyalty and how IoT can support that. The third research is about two case studies, which show the effect of the utilisation of smart technology in relationship marketing. The gained information should illustrate the status quo of the technology itself and sum up the latest findings of research in this field related to CRM. Besides the purely scientific aspect of IoT, some companies have already tried to implement this technology in their businesses. In 3.3, a first practical implementation attempt will be analysed to find real problems and things, which have already been successful. The last aspect of chapter 3 will be the distillation and summary of key findings, which give answers to the thesis questions.

The fourth and last chapter will sum up all relevant findings in order to answer the mentioned research questions and to preview on further research topics.

2 Selected theoretical Aspects of the Internet of Things and Customer Relationship Management

The section will present the fundament theories about the internet of things and customer relationship management. The reader will find a detailed presentation of fundamental principles and knowledge to use this information for further studies in chapter 3.

2.1 Basic Principles of the Internet of Things

The following chapters will give in-depth information about the fundamental principles of the internet of things. Firstly, the concept of IoT, in general, will be explained and described. Moreover, the current challenges of this technology in society are going to be presented. To give a surrounded view on this subject, it is crucial to explain the technology behind the term IoT. They have completed will this chapter by describing the related names of big data, data mining, and digital footprint. This is an essential part of the upcoming connection to the CRM.

2.1.1 Definition and Concept

The internet of things is a theory that is receiving increasing attention from business and science. The idea behind this concept is the pervasive or all-encompassing presence of items or objects that can interact and cooperate to achieve common goals (Atzori et al. 2010). Such things or objects can be any technology, e.g., RFID transponders, sensors, actuators, mobile phones, or other purposes that are equipped with a microprocessor and an antenna. IoT Analytics say, there were about 17,8 billion networked objects in 2018 worldwide. According to this, the research estimates, that this number will rise to ca. 35 billion by 2025 (IoT Analystics 2018). The economic value contribution is to amount to a total of up to 11 trillion dollars by 2025, whereby cost savings through efficiency increases are also to be added (McKinsey 2015). Verizon expects the internet of things market volume to grow from $591 billion today to $1.3 trillion in 2019. This number corresponds to an annual growth rate of 17% (Verizon 2016).

The term "internet of things" goes back to the Auto-ID Center. The Auto-ID Centers (now Auto-ID Labs) are a network of academic research centers. These have done critical work in the field of RFID (Radio Frequency Identification) and sensor networks (Atzori et al. 2010). In 1999, Kevin Ashton, co-founder and former director of the Auto-ID Center at the Massachusetts Institute of Technology, described the internet of things as "uniquely identifiable interoperable connected objects with radio-frequency identification (RFID) technology" (Li et al., p. 243).

According to Friedewald et al. (2010), the term "Internet of Things" is closely related to conditions such as ubiquitous computing, pervasive computing, or ambient intelligence. Sometimes these are used synonymously. The differences between these terms, however, are more academic. Common to all is the aim of supporting people and optimising and promoting economic and social processes through a large number of microprocessors and sensors introduced into the environment (Friedewald et al., p. 45).

The European Research Cluster on the internet of things (IERC) describes the internet of things as a global infrastructure for the information culture, allowing advanced services by interconnecting physical and virtual elements based on existing and evolving interoperable information and communication technologies (Vermesan/Friess p.15). In other words, the goal of the internet of things is to enable things to connect to any other object anytime and anywhere.

Although this technology becomes more meaningful in everyday lives, many challenges have to be taken before the widespread distribution and use of it. The common subject of discussion is the aspect of safety in a legal and mental sense. Afterward, it will then be outlined where data protection problems are and how they can be addressed. A description of the technical challenges follows this.

The security aspect plays a critical role in the widespread implementation of internet of things technologies and applications (Miorandi et al. 2012). The increasing number of networked devices also increases the security risks associated with data communication. Any object that is part of a network can be misused as an entry point for malware. Thus, the number of communicating devices in a system increases the potential target for attacks. High interoperability between devices increases the potential damage caused by unauthorized access. Hacker attacks on an intelligent power grid could have severe consequences for a large number of people. Manipulation in the smart home or healthcare applications could also cause harm to people (Manyika et al. 2015).

According to Atzori et al. (2010), there are other reasons for the high vulnerability of the internet of things to attacks. On the one hand, the multitude of networked objects cannot be supervised, which enables physical access. On the other hand, wireless communication is easy to listen to. Furthermore, most objects on the internet of things often only have sufficient computing power for the transmission and storage of data. Besides, energy storage is kept as low as possible for cost reasons. These limiting factors make the development of sophisticated security algorithms complicated (Jing et al. 2014).

Only if security is guaranteed the technologies and applications of IoT can be implemented on a large scale. One challenge is the different security requirements of the respective fields of application. In the healthcare sector, for example, data protection is of great importance, while the authenticity and security of data play an important role in smart city applications (Jing et al. 2014).

Another vital factor for the spread of the internet of things is the protection of privacy. To ensure this issue, private data revealing movements, habits, or relationships with other people must be adequately protected (Sicari et al. 2015). A well-known example of public privacy concerns in 2003 was the announcement by clothing retailer Benetton that all garments would be tagged with RFID-T transponders. As a result, there was a large-scale boycott action against the company (Michael/McCathie 2005). While the collection of data on the Internet requires the active involvement of the user, applications of IoT also include persons who do not consciously use these services.

Another aspect that raises data protection concerns is the falling cost of data storage. This matter means that data that is collected once can potentially be stored for as long as desired and theoretically linked with other data that is collected much later. One solution would be to limit the use of the information to a specific period with subsequent deletion. However, this is difficult to verify in practice (Atzori et al. 2010).

Moreover, technological problems are issues as well. According to Manyika et al. (2015), the full development of the potential of the internet of things requires progress in the following three areas:

- Hardware: Lower costs and higher hardware performance,
- Software: Improving software and data analytics
- Interoperability: Development of technical standards and solutions.

To make the spread of IoT more cost-efficient, the costs for the required components such as communication modules or energy storage systems must be reduced. Notably, in environments in which many sensors have to be installed, e.g., soil monitoring in agriculture, cost reduction is of great importance. Furthermore, the networked things are often not connected to the power grid, so that the extended power supply or battery life must be guaranteed at low cost. Internet of things applications require energy-efficient and cost-saving data transmission and storage. According to IoT Analytics, the global market will increase from $151 billion in 2018 to $1,567 billion in 2025. This number would be a growth of about 37%, because of the mentioned market acceleration (IoT Analytics 2014).

In many IoT applications, value is only created when the collected data is analysed and made available for decision support. Depending on the application, different analysis software is required, which often has to be developed first. Companies need specialists to evaluate large and heterogeneous amounts of data and apply the right algorithms (Manyika et al. 2015).

Since the networked objects have different information and communication technology equipment, a high degree of interoperability must be aimed at to enable communication according to similar principles and standards (Mattern/Flörkemeier 2010). According to Friedewald et al. (2010), improving interoperability leads to higher investment security for firms and helps to achieve a critical mass of users. Another goal is to reduce complexity and avoid transaction costs (World Economic Forum, 2015).

2.1.2 Opportunities for Application

The following chapter aims to describe various relevant application areas of the internet of things in more detail. Within these fields, it will be explained what added value the use of IoT technologies could create in general. Furthermore, it will be shown which applications have already been discussed and implemented. Besides, the specific challenges that stand in the way of the full development of the potential of the internet of things in the various areas will be briefly addressed. The chapter thematises the following areas: Industrial production, smart home, smart city, smart grid, healthcare, transport and logistics, agriculture, and smart store. Industrial Production

According to a study by the McKinsey Global Institute, the most significant potential of the internet of things lies in factories (Manyika et al. 2015). The potential economic added value in this area is thus estimated at 1,2-3,7 trillion dollars per year from 2025. Factories are generally defined as standardized production environments, including hospitals, farms, or computer centers. Another frequently used term in this context is industry 4.0. This term describes the full digitalisation of production processes and is defined by the ability to monitor and control all means of production in real-time. Also, the data collected in industry 4.0 is used to increase productivity and quality (Manyika et al. 2015). Within factories, the most enormous added value potentials lie in process optimization (633-1766 billion dollars per year), maintenance (240-627 billion) and inventory management (98-342 billion) (Manyika et al. 2015). Smart Home

"Smart Home" means the integration of technology and services into the home environment to improve security, communication, comfort, and energy consumption (Friedewald et al. 2010). To this end, residential buildings are to be networked in such a way that it will be possible to control the various functions centrally via a computer (e.g., smartphone, tablet). The principle of the Smart Home is illustrated below using a smart home platform.

Abbildung in dieser Leseprobe nicht enthalten

Figure 1 Example of a smart home platform (Marvell 2012).

The networked objects in the smart home are called smart appliances in the example (figure 1). These include washing machines, dryers, air conditioners, ovens, and any other device that can be upgraded with the appropriate technologies to create added value for the user. A computer, e.g., a smartphone or tablet, is required to access the smart appliances. Communication between computers and smart devices can take place in two ways. The user can access the smart tools directly via a communication interface if he is within radio range. If the user is out of range, he can also gain access via a cloud server. The cloud server is connected to the smart appliances via a gateway (often a router) (Vermesan/Friess 2013).

In addition to manual control via a computer, there is also the option of automatic control of the smart appliances. An example illustrates this. At the beginning of 2014, Google bought Nest Labs, a manufacturer of networked smoke detectors and thermostats, for 3 billion dollars. The main product is the smart thermostat. This learns when people are in the building and when they are not and uses this information to optimize heating control automatically (Andelfinger/Hänisch 2015). According to Google, energy and cost savings of 20% are possible. Special, the aspect of energy saving in commercial real estate, e.g., office buildings, is gaining importance. Employees are often not at work but have meetings or are at customer meetings or conferences. These absences could be taken into account by the smart thermostat (Andelfinger/Hänisch 2015).

Another way to increase energy efficiency is smart metering. Smart metering involves the measurement of electrical energy, gas, water, and heat meters. The data is used to calculate optimisation options and implement them automatically. Also, the energy supplier or grid operator could access the data to relieve the load on the electricity grid and keep it stable by balancing energy demand and energy generation (Theis/Katzemich 2015).

Furthermore, to time and energy savings, there is also potential in the area of safety. Cameras and sensor networks could reduce the risk of break-ins, fire, water damage, and accidents in the home by alerting the emergency services if a predefined event occurs. It is conceivable, for example, that sensors in the pool could report when a child is in danger (Manyika et al. 2015). The risk reduction in the smart home area brought about by the internet of things could lead to lower insurance premiums (Manyika et al. 2015).

The challenges for the nationwide implementation of the smart home are manifold. On the one hand, the convenience of an intelligent home generally entails higher energy consumption, which first has to be amortised through the savings of smart appliances (Andelfinger/Hänisch 2015). There are also difficulties regarding the interoperability of the various devices. An "intelligent" vacuum cleaner that should only vacuum in the absence of its occupants requires interoperability with sensors that can detect the presence of people. Furthermore, as in figure 1, the smart appliances must be compatible with the control program on the computer (Manyika et al. 2015). Finally, data protection and data security are of great importance. Since data is stored continuously and analysed, the question arises as to who owns the data and who has access to it. If one thinks of a smart thermostat in the bedroom, it can be used to examine a part of private life that would otherwise not be possible with legal means. If this data is enriched with other sensitive data, there is an excellent potential for abuse (Andelfinger/Hänisch 2015). The data and applications must also be secure. This matter means that unauthorized persons cannot gain access to the data. Unauthorised access to a system could also result in real damage (Andelfinger/Hänisch 2015). Smart City

Even if no generally accepted definition has been established to date concerning the smart city, four main topics can still be identified in terms of content: Energy, mobility, urban planning, and governance are repeatedly cited in various scientific descriptions. The integration and networking of these areas is the elementary characteristic of a Smart City (Institute for Sustainability 2015).

In this context, three cross-cutting themes will be addressed in cooperation with the four focal points mentioned above, consisting of information and communication technologies, citizen participation, and financing. The ongoing climate change, the decreasing availability of resources, and the demographic change from the three engines are driving the smart city development. Even if the focus of the smart city concept is primarily on the active logistical networking because of information and communication technologies, this is mainly used for the collection and evaluation of big data. The smart city, for example, is now regarded as a synonym for ICT-supported urban innovations (Schieferdecker 2014).

According to the United Nations, in 2018, 55% of the world's population lived in urban areas. In Europe, the percentage is significantly higher, at 78%. By 2050 the global share is expected to rise to 68 % (United Nations 2018). The complexity of the tasks of public administration is increasing, and with it, the incentive to use information and communication technologies in the management of the public sector (Zanella et al. 2014). The common goal is to use public resources more efficiently by improving the quality of services provided to citizens while reducing the operational costs of public administration (Zanella et al. 2014). General resources include roads, public transport, airports and ports, water and energy supply and communications (Vermesan/Friess 2014).

McKinsey sees the most significant potential of the internet of things for smart cities in the areas of air and water monitoring, adaptive traffic control, and autonomous driving (McKinsey 2015. p.9).

The World Health Organisation estimates that there are about 4.2 million deaths a year due to ambient air pollution. Furthermore, 91% of the 'world's population live in places with threatening air quality (WHO 2019). The technologies of the internet of things make it possible to monitor air and water extensively and in real-time so that any problems can be identified and resolved immediately (Manyika et al. 2015). Measurability also increases awareness of pollution. Corresponding countermeasures can, therefore, be initiated promptly (Manyika et al. 2015).

Adaptive traffic control involves the use of real-time information to adapt traffic light circuits to traffic. The use of sensor networks and GPS signals would be more useful due to the higher density of information (Zanella et al. 2014).

Autonomously driving cars to reduce the likelihood of accidents, increase the average speed on roads by 5-25% and minimize fuel consumption. Furthermore, self-parking vehicles can increase the occupancy rate of parking spaces (Manyika et al. 2015). Also, as is already the case in many cities, parking guidance systems can guide cars to the appropriate parking spaces.

Besides, according to Vermesan/Friess (2014) and Zanella et al. (2014), a smart city offers further application possibilities. Monitoring of environmental factors such as vibrations, temperature, humidity, pollution, and material in public buildings ensures early detection of structural weaknesses. This avoids cost-intensive restorations as far as possible, as problems can be solved earlier and possibly not so profoundly. Moreover, sensors installed in public environments such as schools, universities, or public administration offices can measure the values of the working environment. This allows parameters to be discovered that are not optimal, such as temperature, contamination, or oxygen content of the air. Introducing countermeasures based on this knowledge increases people's comfort and productivity while at the same time-saving costs. There is also a potential for improvement in waste disposal. In this way, intelligent waste containers could communicate their filing status and use this information to optimize the routes of the refuse trucks.

Smart city projects can be found in almost all European metropolises, but also smaller cities such as Bottrop in North Rhine-Westphalia (Innovationcity Ruhr 2014). The challenges of implementation arise in political, technical, and financial terms. From a political point of view, the question arises of how decision-making powers are distributed among private and public stakeholders. There are still numerous differences in ownership, decision-making power, and responsibility (Vilajosana et al. 2013). A possible way out would be the formation of municipal departments that are only responsible for the Smart City topic. In the technological field, the main concern is to promote interoperability between different devices and systems to develop the full potential of a Smart City. From a financial point of view, the first question that arises is a suitable business model. Although there is still no clear solution to this issue, it is increasingly being discussed in the scientific community. From a financial point of view, this is compounded by the fact that the volume of investment in the public sector is declining globally (Zanella et al. 2014). Smart Grid

The term "Smart Grid" is the name for an intelligent power grid. In contrast to a conventional power grid, it is possible to adapt the power generation or the availability of electricity to the energy requirements of the grid participants. The background to this development is the expansion of renewable energy sources such as photovoltaics or wind power (Federal Office of Economics and Energy 2016). Due to its volatile nature in a generation, it requires an intelligent and flexible power grid that can react to fluctuations in a production (Vermesan/Friess 2014). In these smart grids, communication about the energy requirements of network participants is realised in real-time with the surrounding infrastructure (Theis/Katzemich 2015). According to Siebel (2015), around 2 trillion dollars will be invested in energy infrastructures worldwide this decade. All possible devices connected to the power grid will be equipped with sensors and computers to make them part of a network through which information can flow. These devices include intelligent electricity meters (smart meters), smart appliances in buildings (e.g., for heating, ventilation and air conditioning), industrial plant and machinery, transformers, transformer stations, or others (Siebel 2015).

Abbildung in dieser Leseprobe nicht enthalten

Figure 2 – Illustration of a Smart Grid (Theis/Katzemich 2015 p.624).

According to Siebel (2015), the distribution of intelligent electricity meters (smart meters) is decisive for the development of the smart grid. A smart electricity meter is a meter for electricity and gas that shows the actual current energy consumption (Theis/Katzemich 2015). This makes it possible to identify energy-intensive devices and applications and thus increase energy efficiency (Vermesan/Friess 2014). Furthermore, smart meters can automatically transmit their data electronically and offer the possibility of automatic control and switching of devices (Federal Office of Economics and Energy 2016). According to Verizon (2015), 400 million smart meters were delivered worldwide up to and including 2014, 94 million of them in 2014, and the number of installed smart meters is expected to increase to 1.1 billion by 2022 (Siebel 2015). Besides, the price of electricity could also be adjusted in the future in line with electricity consumption and availability to manage demand (Vermesan/Friess 2014) better.

The aggregation and analysis of data from the energy infrastructure provide utilities with valuable information. The information can be used to ensure the stability and reliability of the power grid and to increase the flexibility of the power supply (Song et al. 2015). Other benefits include more efficient transmission of electricity and faster responsiveness to disruptions (Verizon 2015). Utilities can go beyond the pure supply function and offer services in the form of energy management systems that help their customers increase energy efficiency and thus save costs (Verizon 2015). Healthcare

Another important application area of the internet of things is healthcare. According to Eurostat, about 11% of the German GDP is spent on medical expenses in 2015 (Eurostat 2015). Looking at the demographic development in Germany, there is a clear trend towards increasing life expectancy and lower birth rates (Federal Centre for Political Education 2014). Although age cannot be equated with the need for long-term care, the Federal Statistical Office estimates that an increase in the number of people in need of long-term care is likely (Friedewald et al. 2010). Also, the health care system is under intense financial pressure. This results in an increased willingness to use technologies to increase efficiency and thus reduce costs (Friedewald et al. 2010). On the other hand, quality improvements in medical care can also be achieved thanks to consistent information and computer-aided diagnosis and therapy decisions (Friedewald et al. 2010).

According to Friedewald et al. (2010), the use of the internet of things in the healthcare sector will primarily enable applications in two areas, namely in the domestic environment and in medical facilities.

In the area of applications for the domestic environment, the enormous potentials for increasing efficiency and quality arise in the following three design fields:

- Support in emergencies and activity detection
- health monitoring and support for chronic diseases, and
- Assistance systems and health-promoting design of the living environment.

The best-known example of support in emergencies is home emergency call systems. With conventional home emergency call systems, it is necessary to press a button, at which point the emergency call is made. IoT could make this active involvement superfluous. If the domestic environment is equipped with motion sensors, these could trigger an alarm if, for example, a person has fallen or has not moved for an unusually long time (Friedewald et al. 2010).

Health monitoring and support for chronic diseases involves automatic remote and self-monitoring to improve home care and medical care. Another goal is to promote self-sufficiency to support an independent lifestyle (Friedewald et al. 2010). For this purpose, vital functions such as blood pressure or heartbeat are frequently monitored with sensors in order to inform the wearer of the sensors and the responsible physician in the event of irregularities. The sensors can be integrated into garments and send the data to a microcomputer integrated into the belt (Friedewald et al. 2010).

Ambient Assisted Living (AAL) is a concept that has received much scientific attention in connection with assistance systems and the health-promoting design of the living environment. This means "concepts, products, and services that combine new technologies and the social environment of those concerned" (Friedewald et al. 2010. p.157). The aim is to improve the quality of life of people who need long-term support, give them the right medical treatment at the right time, and avoid unnecessary costs. The main challenges here are data protection and system interoperability (Friedewald et al. 2010. Vermesan/Friess 2014).

In the area of applications in medical facilities, the goal is "higher quality through more comprehensive information for medical and nursing staff and their relief from administrative tasks" (Friedewald et al. 2010, p. 18). Sensors monitor the condition of people in need of care and use a cloud to store and analyze the information. The analysed data will then be communicated to the responsible persons (Vermesan /Friess 2014). On the one hand, this enables a higher quality of treatment. On the other hand, nursing staff no longer have to collect and analyse data, which leads to increased efficiency and associated cost reductions (Vermesan/Friess 2014).

Although there are many different scenarios for the use of the internet of things in the healthcare sector, there are highly technical and regulatory requirements for applications in this area, especially in Germany. As already mentioned before, data protection, in particular, is a sensitive issue. Because sensitive, personal data must be communicated and stored multiple times, it needs to secure every storage location. There must also be a user-friendly way to authenticate users with different access rights. Further challenges lie in the interoperability of solutions (Vermesan/Friess 2014) and the necessary investments in technical infrastructures (Manyika et al. 2015). Transport and Logistic

The transport and logistics sector offers many possibilities for the use of IoT technologies. The use of RFID has long been widespread in logistics (Friedewald et al. 2010). There, RFID technology is used to track products and make supply chains more transparent. RFID also offers the opportunity "to equip objects in the logistics chain with the ability to independently find their way through a logistics network, whereby decentralized decision routines can be implemented" (Friedewald et al. 2010, p.127). This would allow packages to make rule-based decisions independently and take the optimal route for them.

The applications of technologies of the internet of things offer potentials to make the flow of goods and information of suppliers and companies more efficient. If containers, pallets, and products are equipped with identification and sensor technologies, the status of the goods can be queried in addition to the location (Andelfinger/Hänisch 2015). This real-time information allows demand and supply to be better coordinated. This increases delivery flexibility in terms of type, time, and quantity. On the other hand, there are advantages for customers who can check the location and status of their deliveries in real-time (Verizon 2015). Traceability also increases the transparency of the supply chain (Friedewald et al. 2010). Especially perishable products such as food are subject to strict documentation requirements (Vermesan/Friess 2014). These can be quickly followed by automation.

Another important aspect is the monitoring of company-owned vehicles, also known as fleet management (Andelfinger/Hänisch 2015). According to Verizon (2015), fleet management is the first most widespread application of the internet of things, particularly in North America. It allows the localization and monitoring of the condition of vehicles to optimise routes and increase safety (Verizon 2015). The improved route planning and the resulting increase in capacity utilisation save fuel and time. On the other hand, there are advantages for customers who can check the location and status of their deliveries in real-time (Verizon 2015).

According to Friedewald et al. (2010), these services will offer advantages to both road users and operators in the future through their integration into a traffic management system. It distinguishes between three types of services:

- Occupant related services: These include, in particular, information services such as location-based information, productivity services allowing passengers to work on the move and entertainment services for passengers.
- Vehicle-related services: These include maintenance services that would enable continuous monitoring of the vehicle condition; protection services such as a driver authorisation system and comfort services.
- Driving-related services: efficiency and mobility services that help to save fuel and time, as well as safety services (e.g., automatic braking systems).

Through networking with other road users and comprehensive information infrastructure, the car becomes a node in the network. By including environmental conditions and position data, the user-specific information needs will be met better and more flexible. This enables better planning of processes and resource use (Friedewald et al. 2010). The application of traffic management systems is not limited to motor vehicles. In Norway, shipping traffic in the Oslo fjord is accelerated considerably by a networked navigation system (Manyika et al. 2015). Agriculture

The increasing global demand for food also requires a rethink in the agricultural sector. According to the United Nations, population growth requires food production to double by 2050 to meet global demand (Accenture 2015). The use of internet of things technologies in agriculture brings valuable benefits such as higher crop yields, higher operational efficiency, and lower costs (Verizon 2016). It should be between the potential of crop cultivation and applications in livestock breeding. Smart Store

The falling number of visitors in the retail store, caused by the increasing demand for online shopping. In 2017 retail e-commerce sales were at 2,304 billion US Dollars and will increase to 4,787 billion US Dollar till 2021 (eMarketer 2018). This makes the usage of IoT products even more relevant to attract more people for offline retail. The smart store is connected through this internet of things, which implements various smart products to increase the customer experience and acquire more people. To reach this target, objects are equipped with particular features. For instance, smart CCTV is a video system technology, which recognises people coming into the store. Through facial recognition, this system knows gender and age. This information gets stored and linked to wearables from a staff member. Moreover, a smart mirror shows clothes even without trying it actually (Hwangbo et al. 2017 p.4). In this store are all implemented devices connected, which increase the customer service and experience.

According to EHI and Microsoft, 22% of dealer’s regard IoT as one of the most significant technological trends of the coming years. IoT applications are already being used by 28 percent of dealers. Another 23% plan to implement IoT applications in the coming years. Also, 23% say they are only observing the topic for the time being. A quarter of the dealers do not consider IoT applications important for their own company (EHI 2019).

2.1.3 Technological Functionality

The following chapter deals with the technologies that enable the internet of things. After a systematic literature analysis, five sources were selected which present the most analytical techniques (Fleisch/Mattern 2005) (Li et al. 2015) (Atzori et al. 2010) (Al-Fuqaha et al. 2015) (Friedewald et al. 2010). These will be used as orientation in the following.

First, RFID technology will be explained, which allows the identification of things. Subsequently, the functionality of sensor networks is described. Finally, some important communication technologies that enable wireless communication between objects are will be demonstrated. Identification

The purpose of identification is to link objects with specific information that can benefit the owner or producer. Such data can be status information, e.g., location or product identification number or also the state of the environment (Fleisch/Mattern 2005). With the help of this information, it is then possible to track and monitor things during their life cycle (Li et al. 2015). For example, a hammer could tell its owner where it is and its producer how many times it has been used (Fleisch/Mattern 2005. p.24).

Well-Known identification technology is the barcode. The barcode is a symbol that is applied to an object. Information is stored in the barcode, e.g., a product number, which can be read by an optical reader (e.g., camera or scanner). The data can only be understood if the barcode is in direct visual contact with the reader. Furthermore, the information on the barcode cannot be changed after production.

The RFID technology has established itself as the most crucial identification technology for the internet of things (Atzori et al. 2010; Fleisch/Mattern 2005; Friedewald et al. 2010). When RFID is used, data is transmitted contact-free and without visual contact by radio link. RFID is, therefore, also referred to as contactless or automatic identification (Auto-ID) (Friedewald et al. 2010).

An RFID system consists of two parts, a reader and a transponder (Kern 2006). The transponder consists of a coupling unit and a microchip. The coupling unit, an antenna or coil, is used to transmit the transponder. Also, receiving radio waves, while the chip is used to process and store information (Fleisch/Mattern 2005).

The reader is connected to a computer from which it receives commands. On the one hand, the reader can be instructed to read the information from all transponders in the vicinity. On the other hand, the reader can overwrite the data on the microchips of the transponders (Fleisch/Mattern 2005). A fundamental distinction is made between transponders according to the type of energy supply (Fleisch/Mattern 2005).

- Passive transponders only use the energy of the magnetic field generated by the reader.
- Active transponders have a battery for energy supply.
- Semi-active transponders use the energy of the reader to send the data and its battery to power the microchip.

An active transponder can send their information over a more significant distance than passive transponders. On the other hand, the production costs for passive transponders are relatively low, which is an advantage for the spread of RFID technology for the internet of things (Abdmeziem et al. 2015).

Other reasons that speak for the importance of RFID on the internet of things are the strong support from the business world and the high maturity of the technology (Atzori et al. 2010). The reduction in size, weight, energy consumption, and cost of the transponders also allows them to be attached to almost any object, regardless of the external environment. (Suresh et al. 2014. Atzori et al. 2010). The development and deployment of RFID have been decisively driven by the U.S. Department of Defense and Wal-Mart. In 2005, they required all their business partners to tag their shipments with RFID transponders to facilitate supply chain management and logistics (Sundmaeker et al. 2010). According to Kevin Ashton, co-founder and former director of Auto-ID Labs, this was the milestone in the development and deployment of RFID (Sundmaeker et al. 2010). Today, RFID technology is also used in many other industries, such as transportation and logistics, aviation, retail, and supply chain management for many large companies (Atzori et al. 2010). Sensor networks

Sensors and sensor networks represent another vital component of the internet of things. A sensor is a component that records specific physical properties of the environment and converts them into an electrical signal (Andelfinger/Hänisch 2015). Such physical properties can be light, temperature, acceleration, pressure, magnetic field, or other (Fleisch/Mattern 2005). In this way, sensors help to depict the real world of things virtually. If, for example, an RFID transponder is combined with a sensor, a reader can read not only the actual identification number but also other features of the object and its environment (Atzori et al. 2010).

Since a large number of sensors record data in a standard application, they must be collected centrally and made available for analysis. This is done via so-called sensor networks. A sensor network usually consists of a large number of sensor nodes that can communicate wirelessly with each other (Hähner et al. 2007 p.41). These are sensor nodes miniaturized computers that are equipped with sensors in addition to a microprocessor and a communication interface (Hähner et al. 2007 p.41). The initial task of a single sensor node is to observe its immediate surroundings (Fleisch/Mattern 2005). The information from the individual sensor nodes is then forwarded via neighboring sensor nodes to a gateway where all data is stored. Moreover, the two of them converge. The user can then access the aggregated information using this gateway.

Actuators are the counterpart to sensors. They convert an electrical signal into a physical quantity, e.g., a movement. An example of this is the electric motor, which turns electrical energy into mechanical energy (Holdowsky et al. 2015). Actuators are often used together with sensors to form sensor-actuator networks. A typical example of a sensor-actuator system is a fire detector that detects an increase in the carbon monoxide concentration in a room and makes a loud noise when the limit is exceeded (Whitmore et al. 2015).


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The influence of IoT on CRM. Conditions and possibilities in B2C markets
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Internet of Things, Customer Relationship Management, Customer Decision Journey, Return in Investment, Marketing, Customer Lifetime Value
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Robin Hafer (Author), 2020, The influence of IoT on CRM. Conditions and possibilities in B2C markets, Munich, GRIN Verlag,


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