Artificial Intelligence as an Additional Tool in Customer Relationship Management and the Impact after the COVID-19 Crisis


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Table of contents

1.1 Research Background
1.2 Research Problem
1.3 Research Question
1.4 Structureofthework

2.1 Artificial Intelligence
2.2 Big Data
2.3 Machine Learning
2.4 Deep Learning
2.5 Customer-Relationship-Management (CRM)
2.5.1 Analytical CRM
2.5.2 Operational CRM
2.5.3 Collaborative CRM
2.6 COVID-19

3.1 Main Artificial Intelligence technologies used in the CRM
3.2 Explanation of AI-enabled CRM-systems in Sales
3.1.1 Example of AI-powered CRM Systems for Sales
3.1.2 Salesforce
3.2 AI-powered Marketing department
3.2.1 Chatbots in Marketing
3.2.2 Intelligent Chatbots
3.2.3 Examples for Chatbots

4.2 Threats and Challenges of Artificial Intelligence

5.1 The future of Al in the CRM sector
5.2 The situation in relation to the COVID-19 pandemic
5.3 Research interest

6.1. Methodological Approachand Research Method
6.2 Results
6.3 Answering the hypothesesandthe research questions
6.4 Summarizing the hypotheses and answering the research questions

7.1 Limitation and Future Research




The targeted use of artificial intelligence in customer relationship management to make the work of companies and their employees more efficient and of higher quality, also about the customer, is on the rise. More and more sales are generated by this technology and more and more AI-controlled tools are being developed which make customer management more successful and thus intensify the relationship between customers and companies. Thus, it is necessary to understand such technologies and how they are designed, but also what opinion the general society has on this rather revolutionary technology.

The aim of the research of this present work is to answer which criteria make a successful AI- driven CRM successful, what opportunities and challenges there are and how the general population assesses this topic. Furthermore, it is important to see that the corona pandemic is having an impact on the use of Al. For this purpose, the following two research questions were asked. How does Al strengthen the customer relationship CRM? Will the Corona pandemic accelerate the use of Al?

To answer the research questions, a quantitative study on people's current opinions and attitudes towards Al and how it will change as a part of the outbreak of the pandemic was conducted. Specifically, the study deals with artificial intelligence in customer service, the change of Al in companies due to Covid-19 and how people generally view the topic of Al. The participants surveyed were anonymous, of different age groups, occupations, and origins, as this topic affects everyone today and it is important to pay attention to the opinion of the general population.

The answers to the questions show that people are now more concerned with the topic of Al and, above all, notice the advantages and higher quality of AI-controlled CRM services. The use of Al tools has increased enormously, but the obstacles that make the use of Al difficult are still seen as problematic. It is also clear that people see the positive impact of Al in the context of employees and that Covid-19 will accelerate the use of Al in companies.

Further research in the field of AI-related CRM tools and the benefits for the general public and the economy could be developed with a qualitative research method and the survey of CXOs to what extent it has evolved after the crisis.


I would like to express my gratitude to my family, who supported me during my master’s degree and was always there for me and without the financial help I would not have been able to do it myself. I also want to thank my girlfriend Youssra Akesbi, who always motivated me to do my master's thesis and filmed my video for the defense.

My gratitude also goes to my supervisor Dr. Anis Khedhaouria, who supervised my work and has given me some good advice for the begin of the thesis.

Table of figures

Figure 1: Strong vs weak artificial intelligence (Carter,

Figure 2: CRM-Cycle

Figure 3: How CRM works

Figure 4: Alexa Skill Surpass 80k in US, Spain Adds the most Skills, New Skill Rate Falls Globally (Kinsella, 2021)

Figure 5: Gender distribution - own representation

Figure 6: Age of the participants - own representation

Figure 7: Employment status of the participants

Figure 8: Country of the particitpants (own representation)

Figure 9: Participants from the different continents

Figure 10: Interaction with the company's cutomer service - Specification in numbers

Figure 11: Abetter customer service with Al - Specification in percentages

Figure 12: Interaction with Chatbots - specification in numbers

Figure 13: Use of Alexa, Cortana or Siri

Figure 14: Use of voice-enabled Al assistants

Figure 15: More automated processes

Figure 16: Less standard work and monotonous processes are an attractive prospect (by employment status)

Figure 17: Alis more in the public conversation since the pandemic - specification in percentage

Figure 18: Advantage for companies, which one had already some integrated AI-tools

Figure 19: Those who invest in Al, will have a competitive advantage

Figure 20: Covid-19 will increase the use of Al in companies

Figure 21: The use of Al is still at the begin

Figure 22: Legal data protection slow down the use ofAI

Figure 23: The confidence ofhumans in AI-tools

Chapter 1: Introduction

1.1 Research Background

Stephen Hawking said in 2017 Success in creating Al, could be the biggest event in the history of our civilization. Or the the worst. We just don’t know.” (Kharpal, 2017) Anyone dealing with the topic of digitalisation today will inevitably come across the term artificial intelligence (Al). The progress of digitalisation can now be found in almost all areas of life, and it is hard to imagine life without it. "The term digitalisation is used to discuss topics as diverse as virtual worlds, data protection and property rights in the face of a large amount of data (big data), responsibility, artificial intelligence or the delegation of social responsibility to robots. Digitalisation and associated developments, such as artificially intelligent systems, imply a constantly expanding process that, in addition to significant potential, also harbours risks. The consideration of Al developments is not possible independently of simultaneous changes in society; they are part of society, as well as a social challenge and task.

Companies are constantly confronted with new challenges due to the rapidly advancing digitalisation. Increasing connectivity with the internet is also influencing both customers and communication between companies and customers. The result is constantly growing expectations of companies, services, and products. In addition, people should also be asked about this topic, because they too are influenced by the interaction with this technology in their lives. The masses must be considered for this new technology and people's opinions should not be ignored. Far too often it is about the advantages for the companies and what positive profit you make from it, but also the employees, self-employed and students, pupils, and everyone else should be familiarized with this topic and their opinion on quality is important, especially when it comes to the area of customer management.

The following master thesis deals with the connection between artificial intelligence and customer relationship management (CRM). The topic is something which is already since a long time an important research topic and "John McCarthy, an American computer scientist pioneer and inventor, was called the "Father of Artificial Intelligence." (Chakraborty, 2021) John McCarthy coined the term at the first artificial intelligence conference, in his proposal for the 1956 Dartmouth conference. (Chakraborty, 2021) Today, Al is an umbrella term that ranges from Robotic Process Automation (RPA) to robotics proper. Our lives and our civilization are increasingly defined by advancing artificial intelligence and ever more complex algorithms. In our everyday lives, these systems - in the form of smartphones, search engines or industrial robots, for example - have long become indispensable. (R. D. Precht, 2020)

The research field of artificial intelligence is therefore a highly topical subject. At its core is the idea that a machine learns patterns or behaviours based on a known set of data so that these patterns can then be applied to similar but new tasks. In the context of digital transformation, we keep coming across terms such as machine and deep learning as well as artificial intelligence. It would be interesting how these terms differ from each other and how they are used.

1.2 Research Problem

As the amount of customer relationship data increases and the number of transactions grows, so does the amount of unstructured data. Companies can better understand their customers, but they need to control and understand the amount of data. Using Artificial Intelligence tools, this unstructured data can be transformed into structured data. Artificial intelligence tools can improve the analysis of unstructured customer data and simplify business processes and relationships. Artificial intelligence tools can automate such processes and simplify the company's work and, above all, add value and ensure long-term success. (C. Dilmegani, 2021) Artificial intelligence is nowhere near its peak, and many companies aren't using it yet. Louise Mussat in her article "Innovation is what will get us out of the crisis" Philippe Aghion, an economist at the Paris-Jourdan Sciences Economiques (PJSE) laboratory, deals with the backlog in the use of Al and in digital transformation. But he also sees an opportunity in the outbreak ofthe Corona pandemic because many weaknesses have been exposed that can be addressed with the use of Al. Thomas Davenport is also sure that "in the future, artificial intelligence (Al) appears likely to influence marketing strategies, including business models, sales processes, and customer service options" and that digital transformation will therefore improve, as he explains in his scientific article from 2019 "How artificial intelligence will change the future of marketing". It would therefore be interesting to write a research paper on the use of artificial intelligence and see to what extent it is worthwhile for a company to use it in CRM but also how the customer itself react on this technology. Artificial intelligence is advancing and will prevail in the long run. Our lives and our civilisation are increasingly determined by advancing artificial intelligence and ever more complex algorithms. In our everyday lives, these systems - in the form of smartphones, search engines or industrial robots, for example - have long become indispensable. The research field of artificial intelligence is therefore a highly topical subject. Companies can no longer imagine life without the use of artificial intelligence. However, the use of artificial intelligence is not yet very advanced in many companies. The use of artificial intelligence in CRM has a decisive factor in the success of the company. Companies need to understand what the needs of their customers are to increase their success. To do this, it is important to complement existing expertise with the use of Al tools. In addition, it will be interesting to see what influence the corona pandemic will have on the use of Al and how the digital transformation will change as a result. The Covid-19 pandemic has been weighing on the world of work since the beginning of 2020 and is still there. Nevertheless, it will be interesting to see if this pandemic will have an impact on Al and how people react to it.

1.3 Research Question

In this paper, I want to find out what impact Artificial Intelligence has on Customer Relationship Management and which tools companies already using. The main purpose of this paper will be to find out what success Artificial Intelligence brings to companies, especially in terms of financial benefits as well as in terms of working better with the customer and if the customer itself accepts this new technology. Nevertheless, the negative aspects will also be examined and explained, because Al is not seen as positive by every person or company and that there are also dangers in it. In addition, the Corona pandemic will be examined in more detail and to what extent it has an influence on the use of Al and what opinion the population has on it.

- How does Al strengthen the customer relationship CRM?
- Will the Coronapandemic accelerate the use ofAI?

1.4 Structure ofthework

This thesis begins with the introduction, which deals with the research gap, the motivation behind this thesis, the research question, and the disposition. The following section deals with the literature review, in which important terms that are of relevance to this work are explained in more detail. The overall topic is artificial intelligence and its software and networks. Topics and terms such as machine learning, deep learning, big data, and neuronal networks are explained in more detail and used as the basis for this work. Furthermore, the second topic is customer relationship management, which is then analysed. Further topics which one get explained in this part are the covid-19 pandemic, which one is a part of this thesis, the sales and marketing department which is important for the CRM. In the following, research is already being carried out on the use of Al in CRM and, above all, the status is analyzed and examples of AI-supported tools which are already common methods in companies today.

In general, the aim is to find out to what extent this use of various methods of artificial intelligence strengthens customer satisfaction. To get a better overview of this topic, a survey will be conducted for this work, in which on the one hand it will be analysed what people think about such technologies and to what extent they deal with them. Furthermore, on the one hand, it is being researched whether people, whether employees or students, agree with the benefits for companies using Al and whether the corona pandemic will accelerate this deployment. Furthermore, it will be interesting to see whether existing obstacles have been removed by the pandemic, or whether there are still major obstacles to the use of Al. In addition, the mood of the general population is also captured, whether you feel the use of Al in CRM is positive, which tools they already know and how they assess the quality of these and whether you have often been in contact with them. This survey is realized based on four hypotheses, which should provide a rough overview, and which should offer a research approach for the future.

Finally, a conclusion is made and what the topic of Al in CRM will look like in the future or what challenges and opportunities await this technology and us humans. In addition, an outlook on further research approaches will be developed which will be based on this research and the gaps in this as a continuation.

Chapter 2: Literature Review

The literature review provides an overview of the various Al techniques and software that are currently in great demand. It is about machine learning, artificial neural networks, and deep learning. Furthermore, it is important to explain the area of the Customer relationship management (CRM) and the different types of this. In the literature review the work will give a small overview of the analytical CRM, the operative CRM, and the collaborative CRM. Afterward, a short section will show how companies are using these techniques today and what impact they have. To better understand the impact, another overview of today's work in companies without these techniques will also be presented. Furthermore, this literature review includes an explanation of the terms of customer management, especially the different forms, i.e., operational CRM, analytical CRM, communicative CRM, and collaborative CRM. It also provides a brief overview of the Covid 19 pandemic that has crippled the world for the past nearly two years and why it may have a part to play in the digital transformation of businesses. Al can help companies evolve, and this pandemic has made it clear that we have some catching up to do in almost all areas of Al in enterprises.

2.1 Artificial Intelligence

For the following work, it is important to describe the topic of artificial intelligence precisely and to highlight the various definitions and explanations. Artificial intelligence is often differentiated into two areas: strong artificial intelligence and weak artificial intelligence. For this, we can refer to the definition by Buxmann and Schmidt in their book "Mit Algorithmen zum wirtschaftlichen Erfolg" (With algorithms to economic success) from 2018 as well as the explanation and diagram by Daniel Paschek in his research paper "Automated business process management - in times of digital transformation using machine learning or artificial intelligence". A strong artificial intelligence here is that artificial intelligence has the same intelligent capabilities as humans or even exceeds them. (Buxmann and Schmidt, 2018) Karen Hao has also written some important scientific articles which may be of particular importance for this research. Karen Hao is a Senior Al editor at MIT Technology Review and in her two articles from 2018 "What is Al exactly? We drew a flowchart to work it out" and "What is machine learning?" provides interesting insights into this technology.

Artificial intelligence is a difficult concept to define, and the researchers do not come up with a suitable definition that accurately describes the subject. To explain the term a little bit more, in this thesis the topic of weak, strong, and symbolic intelligence will be presented and in addition machine learning, deep learning and big data which all belong to the phenomenon of Al. Basically, Al can be described as a prediction engine or pattern recognizer, and particularly well known today are notable examples such as self-driving cars, virtual assistants (Siri, Alexa), chatbots or CRM systems which are of relevance to this work. (Naudé, 2020)

The Distinction between strong and 'weak artificial intelligence

In science, there is a basic distinction between weak and strong artificial intelligence. The weak Al, also called Artificial Narrow Intelligence, is the current state of this technology and can solve specific tasks and problems on its own and to improve them through its learning ability. Specific problems can be excellently executed by the machine learning algorithms and can surpass human intelligence in some areas. (Paschek et al., 2017) The strong, also called Artificial General Intelligence, is mainly characterized by the fact that can solve tasks by itself, has consciousness, and while the weak Al simulates thinking, it is no longer simulated in the strong. Figure 1, which is taken from the article "Automated business process management - in times of digital transformation using machine learning or artificial intelligence" by Daniel Paschek and Caius Luminosu, is a good diagram comparing strong artificial intelligence and weak artificial intelligence.

This image was removed by the editorial team due to copyright reasons.

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Figure 1: Strong vs -weakartificial intelligence (Carter, This diagram illustrates the differences between strong Al, i.e., building systems that think exactly as we humans do, and weak Al, i.e., systems should simply work without finding out how we humans think. (Marr, 2018) Today, however, there is predominantly weak Al in use, although it is becoming stronger and stronger, but the strong Al is not yet really in use. If one should develop this strong Al however always further, then a kind of super intelligence would develop before which well-known personalities warn such as Stephan Hawking or Elon Musk. They believe that the consequences would be too large for mankind, however, calm in this regard the researchers, since one does not have today yet even the suitable means for a strong Al. However, all researchers and experts agree that one should proceed about the research and development of Al value-based and that the policy and the economy observe ethical arguments. Furthermore, a dialogue with people should be sought to avoid fears and too great a change, which encroach too much on people's privacy. (Lackemeyer, Gerhard, 2017)

2.2 Big Data

Manyika described the term Big Data back in 2011 in their research article "Big Data: The next frontier for innovation, competition, and productivity" as "data sets whose size exceeds the ability of typical database software tools to capture, store, manage, and analyse." Big data describes, on the one hand, the ever more rapidly growing amounts of data, and on the other hand, it also refers to new and explicitly powerful IT solutions and systems with which companies can process a large amount of information advantageously through new technologies such as machine learning. Unstructured data, such as from social networks like Facebook, Instagram etc. make up a not inconsiderable part of a large amount of data. Big data not only analyses large amounts of data, but also manages to process different data at high speed, which can be a great added value for companies. (F. Cena, C. Gena, G. J. Houben, and M. Strohmaier, 2017)

Big data is ultimately the analysis of large amounts of data from many sources, intending to generate economic benefits. The aim is to extract meaningful and decision-relevant insights from differently structured or unstructured information, which is already available to a previously unknown extent, or which is already available or accumulating in real time. Big data provides concepts, methods, IT architectures and tools for this purpose and is thus not only a multitude of data but also a major technological leap. (Cena et al., 2017) McKinsey describes big data as data sets that exceed the usual size from storing to collecting to managing. Big data can vary from industry to industry, but it ranges in size from many terabytes to petabytes. Volume, velocity, and variety are the keywords that best explain and understand big data. (Justhy, 2018) Gartner used this as an update to its definition of big data back in 2012: “Big Data is high-volume, high-velocity and/or high-variety information assets that demand cost­effective, innovative forms of information processing that enable enhanced insight, decision­making, and process automation.” In his scientific article "Big Data: Deep Learning for financial sentiment analysis", Sahar Sohangir has already dealt with these three concepts and explained them in a comprehensible way, which are presented below based on his literature:


The most obvious of these is the one we will start with, and that is the volume, which is what big data is all about. While a few years ago data volumes in terabytes already caused astonishment, it is not uncommon today, in petabytes, exabytes and zettabytes. This explosive development is development can be traced back to the integration of new data sources, above all the Internet. (Ilyina etal., 2021)


The growth of data and the speed in which this data comes from have changed our thinking about data. Speed means first the speed of data processing and secondly the dynamics of change. This is measured by how fast the data arrives, since all data arrives at us at different speeds. Furthermore, platforms and software cannot all process the data at the same time and at the same speed and, above all, no hasty decisions should be made. However, information can be taken faster, the faster we can work with the data. (Sonhangir, 2018)


Variety describes the variety of different data structures: structured, semi-structured and unstructured. Unstructured data is mostly impossible to analysed, but the information in the data is important for those who need it. Those data coming from a lot of different sources today, most of the “Big data coming from a variety of sources than ever before. Web sources including social media, click-streams, and logs are some examples of these resources. One of the challenges in Big Data processing is working with a variety of different data.” (Sohangir, 2018) This unstructured data is mostly in text, image and video formats and can be analysed by machine learning. This data requires a lot of work and requires high analytical skills from us. (Sohangir, 2018)

2.3 Machine Learning

Machine learning is a sub-field of artificial intelligence and is understood as a key competence of this. These machines should aim to perform tasks "intelligently", which aims to generate "knowledge" from "experience". In this case, machines should develop a complex model that can evolve through learning algorithms, i.e., through automatically acquired knowledge representation. Through the acquired knowledge representation, such machines can be applied to new, potentially unknown data. If processes become too complicated to describe, but enough data are available, machine learning can be used. (Bums 2020)

“Machine-learning algorithms use statistics tofindpatterns in massive* amounts of data. And data, here, encompasses a lot of things—numbers, words, images, clicks, what have you. If it can be digitally stored, it can befed into a machine-learning algorithm. “(Hao, 2018)

The algorithms are there to learn based on existing data and to predict the resulting outcomes. As an example, Livia Rainsberger takes bank transactions, since an algorithm can train thousands of examples if you show it the correct result beforehand, then the machine can predict the correct result of a bank transaction afterwards, i.e., whether it is fraudulent or not. (Al- Jarrah, 2015) Furthermore, unlike symbolic Al, machine learning algorithms can replicate the different types of behavior that cannot be captured by symbolic reasoning, such as face recognition or recognizing images and voices. So, these types of capabilities are all capabilities that people learn and acquire by example.

2.4 Deep Learning

A currently very successful subset of machine learning (ML) is deep learning (DL), through which Al has achieved its breakthrough. DL pushes the ML concept further and its potential lies in extracting complex relationships from large amounts of data. (Sohangir et al. 2018) Deep Learning is an important area, which was of enormous importance for the breakthrough and for the use of Artificial Intelligence. Deep Learning is based on neuronal networks in the human brain and artificially reproduces them by filtering data and long learning.

Due to the increased computing power and memory, architectures can now be applied that are designed for multiple layers of neural networks. These architectures are called deep neural networks. In addition to the traditional learning algorithms that have been around for some time, more complex tasks can now be accomplished using deep learning techniques. (Hargrave, 2020)

“Deep learning is machine learning on steroids: it uses a technique that gives machines an enhanced ability tofind—and amplify—even the smallest patterns. This technique is called a deep neural network—deep because it has many, many layers of simple computational nodes that work together to munch through data and deliver a final result in the form of the prediction.” (Hao, 2018)

These complex relationships and abstractions which DL can extract are imitated by the methods of learning from experience through a so-called layer pattern, these layer patterns resemble the functions of the human brain. To represent these processes of the brain and to process the data, the functions of the brain are tried to be simulated with the help of so-called artificial neuronal networks. The researchers use their knowledge of the natural neural networks for this and try to model and simulate these through this knowledge to imitate the functions of the brain through the artificial neural networks. The researchers' results have also been greatly improved by the knowledge that neural networks are simulated with multiple intermediate layers.

2.5 Customer-Relationship-Management (CRM)

Customer relationship management, with the growing market saturation, has become of great importance for companies and describes the relationship between customers and companies. Denise Carter already describes in her 2014 research paper "The power of 'know' and 'no' in effective customer service that “customer service is key in building a sustainable business. A business is only a business when it has a product someone wants to buy. Information teams need products that the organization wants to use. The customer is the key driver.” (Carter, 2014) Also important is the author Hajo Hippner, who deals intensively with the subject of CRM in a scientific article "Fundamentals of CRM".

As market demand has increased, it is especially important for companies to strengthen relationships with customers and improve or maintain satisfaction so as not to lose them to competitors. Thus, CRM is used to systematically manage the relationship and interaction between a company and its existing and potential customers. Through this strategy and system, a company can stay connected with customers, optimize processes, and increase profitability. Winer already stated in his 2001 article "A framework for Customer Relationship Management" that frequent interaction, between service provider and customer, is necessary as internal and external influences constantly change customer expectations. (Winer, 2001) The focus of CRM is on ensuring the continuity, stability, and intensity of an economically attractive manufacturer/retailer customer relationship, reducing the costs of acquisition and relationship maintenance, and initiating new relationships through reference effects of satisfied customers. (Rapp, 2005)

A firm relationship should grow between the customer and the company in the long term to create trust and emotionally bind the customer to the company. The satisfaction of a customer plays a major role, since satisfaction binds the customer to the company in the long term and keeps him loyal. (Winer, 2001) This also includes having had good experiences in terms of quality, service, and advice. These customers also play an important role in the acquisition of new customers, as they can recommend the company to other people based on their experience. This is one of the most important methods for companies to gain new customers, because it is a form of marketing that cannot be influenced, as people trust other people more than advertisements or marketing strategies that come from the company itself. (Hippner, 2006) The customer cycle is a closed loop as can be seen in Figure 2. In general, CRM is a scientific concept aimed at building and identifying profitable customer relationships. It aims to increase the value of the company in the long term and differs from other models of customer loyalty by its customer-oriented perspective, which aims to increase the value of a company. It is not only important to increase customer loyalty, but also to identify whether the customer is worthwhile for the company, if this is not the case, such a relationship should also be terminated. (Helmke, 2008)

This image was removed by the editorial team due to copyright reasons.

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Figure 2: CRM-Cycle

Fardoi and Monfared figured out in their research “A new design architecture for e-CRM systems that the Customer relationship management can be divided into three components. It can be divided into analytical CRM, operational CRM, and collaborative CRM, which support each other, and analytical CRM forms the basis. (Fardoi & Monfared, 2008)

2.5.1 Analytical CRM

Analytical CRM is about deeper insights and the personal needs of the customer, especially to analyse the different behaviour patterns of the customers. Through data mining, data warehouses and online analytical processing, a model can be created in which the different behaviours of the customers are stored and through which they can be analysed and thus also improved. Analytical CRM is the basis for the following components of CRM, operational CRM, and collaborative CRM. (Buttle, 2003)

The goal of analytical CRM is to gain an in-depth understanding of customer behaviour through precise evaluation of the data obtained, for example, through records of customer contacts, purchase histories, or online behaviour. The resulting findings are fed directly back into the operational business to optimally meet customer needs. This approach is often referred to as a closed-loop approach in which data is continuously improved.

2.5.2 Operational CRM

The Operational CRM forms the basis (See Figure 3) in CRM and is primarily an automated CRM that is used in marketing, service, and sales.

In the operational CRM, data is collected from customers and stored in a personalized file. However, these are not evaluated as in the analytical CRM, but only collected. Although the operational can function without the analytical, this tool does not know when the customer is available. (Cf. Hilbert 2016)

In general, the operational CRM refers to all customer-related processes and is represented by different applications in marketing, sales and service and thus support the employee in the sales service and field service in contact with customers. Through the customer data stock in the analytical CRM, the basis for the customer-oriented services along the customer life cycle is formed, to which both employees and customers can access depending on authorization, (cf. Bruck-Emden) It is true that the boundaries between the various approaches in CRM are becoming blurred time and again, and the fact that it is a holistic approach in corporate management means that operational and collaborative CRM are often regarded as a single sector. However, operational CRM, according to Hippner and Wilde, includes all processes in direct contact between customers and the company. (Cf. Hippner/Wilde, 2013) Michael Möhring divides operational CRM, in his book "CRM in the public cloud: practice-oriented fundamentals and decision support (essentials)", into marketing automation, sales automation and service automation, as can also be seen in figure three. (Cf. Möhring, 2018) In his book

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Figure 3: I low CRM -works

"Customer-Relationship-Management: Concept and Tools", Francis Buttle has divided CRM into different CRM software options. Here he deals with marketing automation, sales automation, and service automation.

Marketing Automation

Marketing in CRM is designed based on the customer life cycle and the tasks include the conception, design, and implementation of customer-oriented corporate communication. The customer should always receive the communication message at the right time with the right medium and this is done primarily through well-designed campaign management. (Cf. Neumann 2014)

To maintain this, marketing in CRM must always be developed further and kept up to date. This includes, above all, the establishment of new communication relationships, which are designed in detail and record the information and solution needs of the customers. Thus, through marketing automation, a company can use a database that contains all the data of the customers, i.e., interactions and the behavior of each person. (Cf. Süphan, 2015) Since customers usually enter a long-term relationship with a company only through the corresponding benefit, the customer relationship management must give the customer incentives that create on the acceptance of messages and the establishment of a relationship.

Because only by the appropriate consideration of goals, needs and expectations as well as experiences of the customers of a company, the better relationship and above all a long-term relationship of the customer with the enterprise makes possible. (Schnauffer and Jung, 2011)

Sales automation

The most intensive relationship with the customer is the sales department, which represents the interface between the company and the customer. Sales employees must therefore have a comprehensive knowledge of the customers as well as the products to advise the customers successfully. In sales automation, the sales department is supported in administrative tasks and the analysis of the reasons why a customer has not accepted an offer also takes place within this automation. (Cf. Schnauffer and Jung, 2011)

Findings from this analysis help to improve the strategy and give the employees a better picture of the respective customers and their concerns and wishes. As a result, sales opportunities are identified, and so-called lost order analyses become significant. These lost order analyses show the reasons that led to the failure of a sales deal and thus enable the salespeople to evaluate them and to prevent weaknesses in the future and prevent them from occurring again. (Cf. Neumann, 2014)

Service automation

The office and field service together provide the company service and customers can contact you with questions and problems and get help. Through the CRM, the possibility is created to set up complaint management, which provides customers with the necessary contact information. The administrative tasks, which are very similar to those of sales, are thus also available to the service team and are particularly important because the service team is confronted with consulting questions and complaints from customers. (Cf. Neumann, 2014)

2.5.3 Collaborative CRM

As already explained in the previous chapter, collaborative and operational CRM are often regarded as identical sectors. However, this is not 100% true, as the radius of action of collaborative CRM is much wider than that of operational CRM. The radius of action of collaborative CRM includes not only the customer-related sectors such as sales and marketing, but also extends to external sectors. These sectors also include areas outside the company itself and integrate service providers and sales channels. This collaboration serves primarily to improve the value chain. Furthermore, collaborative CRM systems manage customer interaction points and communication channels. (Hippner, 2006) Any means of contact, be it telephone, homepage, e-mail, or customer portal, are handled by the collaborative CRM and are integrated into the customer interaction center. In this type of call center, all customer requests are processed as quickly as possible, regardless of the channels through which the employees are contacted.

2.6 COVID-19

In December 2019, the People's Republic of China first officially reported to WHO a novel lung disease in the city ofWuhan, which sometime later became known as the causative agent of SARS-CoV-2. (Cf. H. Co§kun, N. Yildinm and S. Gündüz, 2021)

The CO VID-19 pandemic has spread across the planet in a very short period, causing significant strain across countries. Impacts include high infection and mortality rates, financial hardship for individuals and businesses, lockdowns, store closures, and curfews. Due to rapid infection rates and full hospitals, especially intensive care units, countries acted with varying speed to implement lockdowns to reduce the spread of the virus. Companies allowed home offices and all stores, bars and restaurants were closed, leaving only supermarkets, doctors, and pharmacies open. (Shreffler, J., Petrey, J., & Huecker, M., 2020)

Chapter 3: Artificial Intelligence in the Customer Relationship Management

In the following section, the various possibilities for the use of artificial intelligence in customer management will be presented. This is primarily about the various tools and possibilities that are possible for companies today to establish in their company and which these are at all. Furthermore, the various positive aspects of using Al in CRM will be explained and how the future of companies depends on such methods and digital transformations. In addition, it is interesting to mention which facilitation for companies, so in particular for the employees and what influence it has in terms of customer relations and satisfaction or loyalty of these effects. (Cf. Fotedar, 2020) The following chapter focuses on the extent to which companies are already dealing with artificial intelligence in CRM and why the term "big data" is so important in this area. It is about the many customer inquiries and customer data which, as already explained in the previous chapter, are processed to proactively record customer wishes and to make predictions about future customer behaviour. It will also be interesting to see whether the outbreak of the Corona pandemic has changed attitudes towards the use of Al in CRM for the better. With all the lockdowns, store closures and home offices, the use of Al would have already had a huge advantage. Now it will be interesting to see whether and to what extent attitudes towards Al have changed and whether companies and people are now in favour of its use and whether the positive conditions can be understood. In the following, I would like to take a closer look at the possibilities of artificial intelligence in customer relationship management and show for which companies this is highly relevant and what requirements companies must fulfil to make Al successful in CRM.

3.1 Main Artificial Intelligence technologies used in the CRM

The use of artificial intelligence in customer relationship management nowadays offers a wide range of possibilities to improve processes through collected customer data and thus to better understand customers. Although the use of Al in CRM is nothing new and the use of data mining and machine learning has been known for a long time, there are still enough companies for which the topic of Al in CRM is new territory. However, only very few companies have made much progress to date, which means that the topic of Al now offers the opportunity to stand out from the others.

"Infive years, the companies that were early adopters of artificial intelligence 'will be using machine learning at a level that others can't match." (Wuttke, 2021)

This quote from a study conducted by Google already shows what many companies are missing out on. This study states that the companies that are focusing strongly on the topic of Al today will no longer be able to catch up in a few years. This shows how important the topic of Al is and why many experts advocate its use in companies or describe it as a prerequisite. Seth Earley, author of “The Al-Powered Enterprise” and founder of Earley Information Science said one time “the uses of Al that are really first and foremost in organizations are customer-facing types of things.” (Pratt, 2021)

This also illustrates how important it is for companies today to properly understand their customers and thus what each need at any stage of the buying process. By using Al in CRM, the existing expertise is now supplemented by concrete predictions and thus serves as decision support. Al helps the company in CRM mainly by making better use of this data from different customers, as they often have mountains of data and do not know how to use it. Through artificial intelligence, one can understand the data correctly and thereby also use it and learn through machine learning how it will behave in the future. When companies get to that point and understand this data, they can make informed decisions and the company's goals can become more effective and efficient. Bringing this data in combination with Al will establish you ahead of the competition in the long run.

3.2 Explanation ofAI-enabled CRM-systems in Sales

Sales is used as the first example in this paper to explain the use of artificial intelligence in CRM. Sales is dealing with a large amount of data and is becoming more and more data driven. This is a huge challenge for sales, as the data is crucial for success, but mostly inaccessible. Thus, however, it can also be seen that this challenge is also the opportunity for sales to achieve higher profits and more success. However, salespeople need to b able to properly utilize this amount of data. Salespeople need to extract meaningful results from this data, but the data is usually spread across various departments in the company and is impossible for humans to analyse. That's where Al comes in, as Al can get the right data to the right employee at the right time which one follow up that “AI-driven CRM supports sales reps with findings about customers, intelligent recommendations and predictions to make proper decisions and focus on closing deals in a more efficient way.” (Acharya, 2019)

Artificial intelligence can thus add a huge part to the success of a company if used properly in sales. Al is the technology that can handle the unstructured data. These data are for example: customer data, transaction data, performance, communication, process data, news, etc. In addition, it will not only cope and analyse this data, but also qualitatively rank or increase the quality of the data as it merges the information from CRM into a high-quality source. (Cf. Rivas, 2018)

This means that a sophisticated Al tool can analyze and, above all, identify the potential new customers, moreover, the customer and prospect data can be analyzed and thus also the success rate in sales can be increased, because you can better assess the customer and cover the needs faster. Another advantage is above all that through an AI-controlled CRM system the customer interactions with the company can be observed and to what extent you interact with the competition. The probability of closing deals in sales increases on the one hand because the customer's behavior can be predicted. These are aspects of why artificial intelligence can not only relieve the sales department, but also increase the success rates of the sales department and thus improve the success of the entire company qualitatively and quantitatively.

3.1.1 Example ofAI-powered CRM Systems for Sales

The number of AI-powered CRM systems for sales is increasing today as many companies see the urgency. This is mainly since sales are becoming more and more measurable, whereas in the past the negotiation skills of the salespeople were the focus and because most meetings were face to face CRM systems were not that important. Nowadays, much more is done via e-mail, phone calls and databases, which makes CRM systems more and more important.

Sales is a big challenge for many companies and due to the huge amount of data it becomes more and more difficult without CRM systems. Gartner predicts that the expenditure for CRM software in companies will be the highest.

In the following, two CRM software will be presented, and the different tools will be considered and what functions they have. This short comparison of Oracle and Salesforce should make it clear to the reader why these tools are so important for sales and what advantages they bring for the company and the employees.


Ende der Leseprobe aus 74 Seiten


Artificial Intelligence as an Additional Tool in Customer Relationship Management and the Impact after the COVID-19 Crisis
ISBN (Buch)
artificial, intelligence, additional, tool, customer, relationship, management, impact, covid-19, crisis
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Leonard Rupperti (Autor:in), Artificial Intelligence as an Additional Tool in Customer Relationship Management and the Impact after the COVID-19 Crisis, München, GRIN Verlag,


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