As we can see, the innovation of the chat bots integration is strongly connected with the others, such as deep personalization and artificial intelligence based on machine learning.

One of the possible disputes about this innovation is an ethical problem of chat bots. Implementing them into business, not only into the customer relationship management system, can have negative effect on the employees' jobs, because if there is a software that is able to communicate with the customer and sell the product, the operators that call and communicate with the clients are not needed anymore and, therefore, they can lose their jobs.

Another possible problem is exactly the opposite for the business. As long as modern chat bots are not a perfect imitation of human interaction due to the technical limitations, they can not understand or misunderstand the requests of the customers, which means that the marketing operators are still needed, even though they have much more time for the other business tasks.

2.8 Integration with the Internet of things

The Internet of things is a concept of a computing network that combines the items of people's everyday activity into one network. This concept is aimed to make the experience of interacting with the items of everyday use easier and more pleasant and convenient for people. The innovation of the Internet of things can be used in many spheres of our lives. Among them is the concept of a smart house. It unites the items into one network, making them work together and automate some of the routine activities of people. As an example, when the sensors understand that the owner of the house has left it, they can set up a timer to a smart animal feeder to feed the cat in time. Nowadays, a lot of items of our routine everyday activity is integrated with the technology of the Internet of things (Yerpude, Singhal, 2018).

The concept of the Internet of things can also be implemented into the customer relationship management system. The aim of it is that the customer buys a product and the product begins to collect the data about the client's interaction with it and send it to the customer relationship management system. Later the algorithms analyze this data and make conclusions of the pros and cons of a current concept of the experience of interaction with the product, making it possible to improve it later. For example, a smartphone can gather and analyze the patterns of user's ways of using the smartphone, so that the company can make the interaction with the phone better in the next product of versions of the operational system.

As it can be seen, the integration with the Internet of things is also tightly connected to the innovations of deep personalization and a machine learning based artificial intelligence (Medagliani, Leguay, Duda, Dupont, 2014).

2.9 Mobile applications

A customer relationship management system can be working not only on personal computers. There are three general directions for any software program, each of which can be described by a platform that an application is located on. The first one is a desktop application. This is a program that is installed on a personal computer. The obvious advantage of this platform is the convenience of working with the customer relationship management system for an operator from an office or from home. However, this approach is limited by the fact that the desktop computers are usually not portable and it can be very inconvenient to work with the customer relationship management system from anywhere else besides the office or home, which can be very important if some urgent changes have to be done in the customer relationship management system (Liljander, Polsa, Forsberg, 2007).

This problem is covered by the last two platforms for the business software. The first one is the web platform, which is basically a web site with the interface of a customer relationship management system. The operator can work with the customer relationship management system from anywhere, especially if the web page has an adaptive design for the mobile platforms. However, working with such business software as a customer relationship management system from a browser can be not as comfortable as from a desktop application. The adaptive design for the mobile platforms is also limited with the technical issues of the browser markup nature (Verma, 2013).

The solution for this is developing a mobile application for a customer relationship management system with the set of functions of a desktop one, but with a comfortable and convenient interface adapted for the mobile platforms in order to make it easier for the operators to interact with the customer relationship management systems from their smartphones or tablets. This innovation makes it possible to use the customer relationship management system from any place, even if an employee does not have access to a personal computer (Valsecchi, Renga, Rangone, 2007).

2.10 Types of the customer relationship management systems

The customer relationship management systems can differ from each other in many ways. The businesses of different spheres and industries pursue different goals in the process of communicating with their clients and, therefore, their customer relationship management systems must be finely tuned for the specific tasks of the businesses.

The customer relationship management systems can be divided into several groups based on their purpose and the main functions that the customer relationship management systems are aimed to execute (Buttle, 2008).

Operational customer relationship management systems are designed to automate the operational processes and procedures of the marketing and analytics departments of businesses. More specifically, the goal of this type of customer relationship management systems is to automate the processes of sales, support of clients and marketing activities. The operational customer relationship management systems usually include a control panel that is designed to represent the fullest set of information about the relationship with every single client. As far as the operational customer relationship management systems are aimed to execute the function of automation of the processes of sales, customer support and marketing, the data represented for a particular client in this type of customer relationship management systems usually includes the information about previous purchases of this client, the personal specifics and preferences of a customer, the marketing solutions that have been applied to a particular customer and those of them that have been the most effective in terms of profitability of the customer for business, his or her long-term value and many other pieces of data that, when combined together in a customer relationship management system, represent the whole picture for every single one customer of a business (Buttle, 2009).

The analytical customer relationship management systems are aimed at the analysis of the gathered data about the clients in order to make sure that the management of the business understands the picture of their clients base correctly. The data is being gathered from many different sources. Such analytics is aimed to improve the businesses' understanding of their communication with their clients, figure out the possible problems of the target audience and ways to cover them with a commercial offer. The advanced analytical techniques are used in the analytical customer relationship management systems, among them the correlation analysis, patterns analysis and many others can be highlighted. The analytical customer relationship management systems are also helpful when there are some problems in communicating with the customers, for example, when the sales rates decrease. Analysis of the data with a help of analytical customer relationship management systems can solve this problem (Huang, Chai, Liu, Shen, 2019).

The main goal of the collaborative customer relationship management systems is to collect and exchange the data about the clients with the other departments of the company. The point is that the customer relationship management system is not an area of responsibility of just a marketing department of the company, but all departments have access to it and can add or change the information about the new or old customer. For example, when a client of a company calls to a technical support and it turns out that the customer's product is out of its lifetime, an operator of a technical support can add this information into the customer relationship management system for the marketing department to take care of, later the customer will receive an offer to buy a new product. Another example is the collection of feedback from the clients, which can also be added into the customer relationship management system and used as a part of deep personalization data, helping the system to discover the customers' needs and behavioral patterns (Kim, Mukhopadhyay, 2010).

It is needed to highlight that the division of the customer relationship management systems into these groups is not strict. A system can combine the elements of all groups, being a universal marketing and analytical tool for the business, executing several independent activities and goals at the same time.

2.11 The development of the customer relationship management systems

There are two general stages in the process of integrating new features into every software, and the customer relationship management systems are not an exception. The first stage includes making a decision of changing the existing customer relationship management system with some innovations, and this stage lies on the management. When the decision is made, the new function or feature has to be incepted into the existing software, or in some cases a whole new customer relationship management system must be developed. This is the second - technical - stage of the process of implementing innovations into the customer relationship management systems. This part is a sphere of responsibility of the developers and programmers (Kim, 2004).

The process of the development of a customer relationship management system includes the development of the front end part and the back end part. The front end is the part of a desktop, mobile or web application that is designed for an end user to interact with. It includes the interface of the customer relationship management system, the representation of the databases that are stored in this system, etc. The front end interface is usually designed to be user friendly in order to make it easier for users of the customer relationship management systems to interact with. This part is important for the business, because the easier the interaction with the customer relationship management system is, the less time and money it takes to educate the employees to operate it.

The technologies for developing the front end part of the customer relationship management system can be different. It mostly depends on the platform that the customer relationship management system is designed for. If it is desktop, one of the most popular solutions are C/C++ with the particular frameworks, Java, C# and others. C# is the solution that is usually used for the Microsoft Windows platform, while modern macOS interfaces are usually written in Swift. The mobile applications are also divided into two groups based on the operational systems. They are Apple's iOS and Google's Android. The technological solutions for the Android platform are Java (which is old but still the most popular programming language for this mobile platform) and Kotlin, which is much younger and easier to use than Java. On the other hand, the iOS applications situation is quite similar - there is an Objective-C language, which has been used for the mobile iOS development for a long time, and Swift, which is a young and powerful programming language on which a lot of modern iOS and macOS applications are written today. When talking about the web realization of the front end part of the customer relationship management systems, the most popular and common technologies are the web browser standard markup languages HTML and CSS and a programming language JavaScript (Chan, 2014).

If business requires a cross-platform customer relationship management system, then the front end parts have to be designed separately for every platform using different technological solutions. This usually takes more than one developer team, every single one of them specializes on one particular platform.

The back end, on the other hand, is the part of a complex computer program that includes the calculating and computing processes that are executed on the server. It executes the commands that have been given by the user of a customer relationship management system through the front end interface. It calculates and processes all the data that is located in the database of the customer relationship management systems on the servers. The back end part is the most important part of the application, because the code base of the back end is basically the program itself, while the front end part is just a shell designed to make the interaction with the program easier for the end user, however this is very important, too (Davids, 1999).

The programming technologies for the back end may vary depending on the specific needs and preferences of a particular business for its customer relationship management system. This set of technologies may include the following: Django or Flask (web framework for Python programming language), Ruby on Rails (Ruby web framework), Node.js (JavaScript open-source environment) and one of a great variety of the realizations of SQL, which is a language for creating quick and convenient databases.

2.12 Conclusion

To sum up, it can be seen that the sphere of customer relationship management is quite well covered by the existing research. There is plenty of information about many aspects of developing and using the customer relationship systems, each of them is well researched by the existing works. Many innovations in the sphere of customer relationship management systems are also described in the literature in one volume or another. However, there is still a gap in the existing research. Despite the fact that there is a secondary data about the single innovations in the customer relationship management system, none of the researches cover the issue of their effect on business processes and how some of them are better than the others in terms of business. Therefore, the current research is aimed to cover this gap and answer the four important research questions, which were stated above in the introduction paragraph and will be described more specifically in the next block of this research paper.

3. Statement of the research questions

This research is dedicated to answer several questions that have not yet been covered by existing research and that the researchers are interested in. All these questions have to correlate with the topic of the research, which is the innovations in the sphere of the customer relationship management systems and the ways in which these innovations have influence on different business processes and indexes, such as competitiveness, effectiveness of accounting clients and ways of communicating with them, costs of using the customer relationship management systems, and so on. Stating such questions correctly and answering them during the process of the analysis of the results on this research can be very useful for businesses in terms of implementing innovations into their customer relationship management systems and also this can be essential in terms of understanding the trends in the development of the customer relationship systems: what has been popular and useful before, what is now and how specifically are the customer relationship management systems going to evolve and change in the future, what new innovations will the acquire and how is all this going to be useful and helpful in the working process and the process of accounting the clients and communicating with them for the business.

First of all, the researchers are very interested in the sphere of innovations that are being implemented into the customer relationship management systems and how they influence the business processes of the businesses that use these systems. It is not wrong to suggest that different innovations in the customer relationship management systems can have very different influences on the business processes and the entire ways of accounting and communicating with the clients. Some of these innovations can bring minor improvements to the process of business, for example, they can just ease the way for the operator of the customer relationship management system to account or analyze the data that is contained in the database of the system. This can be achieved, for example, by implementing an easier and more user-friendly interface with better user experience that was before. This is an example of an innovation that has a minor influence on the business processes that has been stated above. Other new features, or innovations, on the other hand, can affect the ways of using the customer relationship management systems and, therefore, the business processes very significantly. It even can occur that such innovations do not only make a very serious influence on the business but change the entire industry of customer relationship management significantly, altering the way of accounting and communicating with the clients for the business or the marketing departments. As an example for these features that can be incepted into the customer relationship management systems and change them and the whole approach to customer relationship management, this is an artificial intelligence based on machine learning or an expert database. The feature of artificial intelligence is a very popular technology today and it is being implemented in many spheres, making it unnecessary for humans to participate. It saves lots and loads of money for the businesses, because the costs of developing such a program are significantly lower than the salary to pay to the employees that operate one or another sphere of business. The industry of customer relationship management is not an exception. An artificial intelligence based on machine learning is an innovation that can be incepted into the process of development the customer relationship management system and the processes of working and interacting with it from the side of the user, making it much easier to fill, classify, group and analyze the data that contain the information about the clients of the business. Another example of an innovation in the industry of customer relationship management that can affect the entire sphere and the methods of interacting with the customer relationship management system is an outsource services that provide a simple and easy to use online customer relationship management system or an offline program that has to be installed to the office computers or the local network of the computers that belong to the particular business. This solution can save a lot of money on developing the business's own customer relationship management system, which can cost a lot, considering the salaries of the developers. Therefore, implementing such customer relationship management systems can be very helpful to small businesses that have very limited budgets, even though these systems are not targeted at the specifics and aims of the particular business due to the fact that these customer relationship management services are supposed to satisfy the needs of a wide variety of business and fit the main needs of them all.

As it can be noticed, the innovations in the sphere of customer relationship management systems can affect differently on business, some of them can be very profitable and successful, the others may be also useful, but not that much, bringing some minor improvements into the business processes that customer relationship management is connected with, such as accounting clients, communicating with them, advertising them the best offer based on their preferences or specifics. Considering everything that has been said above, the researchers came out with the first research question of this thesis: What innovations in the CRM systems have the most positive effect on business processes?

However, considering that implementing new features into the software that the company uses can bring differently positive effects, it is correct to suggest that there is a possibility that some innovations can have negative influence on business. One possible way is that implementing an innovation brings no benefit to the usage of the customer relationship management system for the business, but costs money for the business, which can cause a minor harm if this is a large company or corporation, but it can hit the budget significantly if the innovation was implemented into the customer relationship management system of a small business that strongly depends on the budget of it and the question of money resources is essential for that business. This situation describes one possible way of how the new feature can harm business, but it is way less dangerous than another possibility of how the innovations in the customer relationship management systems can have negative influence on the business processes. It is possible that some new functions or features will mess up an entire process of using the customer relationship management system for the business, which can lead to very high costs to correct and fix. Such situations can be a result of a bug in the software that has been implemented to the business or the whole new way of communicating with it which the employees can find inconvenient, not very useful or maybe they can be not experienced enough to use it.

These statements lead to the conclusion that businesses have to approach the question of picking new features to implement into their customer relationship management system and improve the way of interacting with it very carefully in order to evade possible problems and save time and money on incepting the innovations that bring minor improvements and do not justify the costs or that can be dangerous for the business processes and cause unexpected extra costs while bring no positive result. Therefore, this research is aimed to cover this problem, which leads to the statement of the first and the main question of this research paper: What innovations can have negative effects on business processes if any?

Considering that the innovations that are implemented into the customer relationship management systems can cause different influence on the business processes, they also can be demanded differently among the businesses. Some innovations can be much more demanded by the business that the others. It has already been said that one of the possible innovations that can be incepted into the process of designing, developing and operating the customer relationship management system is an artificial intelligence. This is an example of an innovation in customer relationship management systems that is highly demanded not only in the sphere of customer relationship management systems, but in many other spheres of informational technologies industry. An artificial intelligence, or as it is sometimes called, a machine intelligence, is a wide definition that is most commonly understood as a program or a software that is capable of completing tasks that are usually claimed to be the field of activity of humans. An artificial intelligence is a very popular path of the technological progress and today it is evolving and developing very rapidly, because more and more specialists begin to consider it as one of the main directions of the future technologies and, therefore, more people begin to consider an option to connect their professional lives with the sphere of an artificial intelligence. As it has been already said before, such a sphere of computer science as an artificial intelligence includes a very wide variety of approaches and directions. As a result of such a variety of different possible activities inside the industry of an artificial intelligence, this concept is being used in a great variety of businesses, industries and spheres. Nowadays, almost every single one of the global gigantic corporations is using the concept of artificial intelligence in very different spheres of their businesses. An artificial intelligence helps businesses to collect, create, contain and analyze enormous arrays of big data which human employees or simple computer algorithms would be simply incapable of doing.

An artificial intelligence is an essential tool of such spheres of software engineering as speech recognition, which is used in the voice assistance, such as Siri from Apple, Google Assistant from Google, Alexa from Amazon, Cortana by Microsoft, Alisa by Yandex and many more. What is more, the sphere of image recognition and visual recognition are also strictly dependent on artificial intelligence, it is used in security systems of recognition of a user's face or fingerprints by many smartphone manufacturers. As a great example, Face ID and Touch ID technologies by Apple Corporation. An artificial intelligence is also used in the sphere of robotics as it helps robots to coordinate their location and movements in space. One of the wide known examples are the military cargo carrier robots by Boston Dynamics that have evolved in their ability to coordinate in space very rapidly in the last few years.

As it can be seen, in very different industries and spheres of business there is a significant demand on the concept and technologies of an artificial intelligence. However, as it has already been stated before, the definition of an artificial intelligence itself includes a wide range of specializations and technological clusters. One of the most popular and demanded in 2020 is a machine learning. Machine learning is a set of methods that is aimed to educate a computer program to solve a particular type of tasks by solving a large number of similar tasks and finding out empirical correlations. The machine learning includes inductive methods, which means the whole process of educating a program using precedents, and deductive methods, which stands for uploading a database of formalized knowledge directly to the computer program.

An artificial intelligence based on machine learning is being implemented into the customer relationship management systems for some time by now. It can be a very important, significant and effective solution. This is due to the fact that an educated program can create, sort and analyze the enormous arrays of the data about the clients without the help of humans. It can predict customers' behavior, sort clients into groups by their preferences, social statuses, levels of income. A customer relationship management system with an artificial intelligence based on machine learning can also predict the probability of purchasing a company's product for every single client, it can sort them into the groups of one-time buyers, long-term customers, etc. This opens up the opportunities that could have never been achieved before by simple customer relationship management systems. And what is much more interesting, such programs keep educating themselves in the process of analyzing the data that they consume, meaning the longer this customer relationship management system operates and the larger arrays of data it analyzes, the more accurate predictions it shares and the more effective results it brings to the business.

Considering all that has been said above, the authors of this research paper are very interested in the difference in the demand for innovations in the sphere of customer relationship management systems. It leads to the second question of this research: What innovations are the most demanded among the users of the customer relationship management systems?

The concept of the customer relationship management systems is not very new, the first prototypes of the software of the customer relationship management system appeared in the early 1970s. Since this time, the customer relationship management systems have evolved and changed very significantly, they have acquired a lot of new possibilities and functions. All this has opened a wide range of opportunities for the businesses that use the customer relationship management systems, making it possible for them to improve the business processes of accounting the information about these clients and communicating with them in order to monetize the customers in the most effective and profitable way for the business. During the whole history of the customer relationship management systems there have been a lot of innovations that were implemented into the customer relationship management systems to make them better for the business, and many of these innovations still remain a significant part of modern customer relationship management systems that many businesses use. These features that used to be innovations by the time they were invented and incepted into the customer relationship management systems cannot be called innovations today due to the fact that they exist for a relatively long time, however, knowing about them and understanding the pattern of implementation of the new features into the customer relationship management systems allows to understand the bigger picture of the entire customer relationship management sphere and possibly make a prediction of in what ways are the customer relationship management systems going to evolve and develop. Therefore, the researchers have come to the third research question of this bachelor's thesis, which is the following: Which innovations from the history of the customer relationship management are the most important part of the modern customer relationship management systems?

4. Methodology

4.1 Research Design

This research is mostly descriptive with the elements of explanatory style. As the authors are testing existing innovations in the customer relationship management systems and their impact on business processes and trying to answer a research question by using existing data and collecting experience without conducting any tests or experiments. Authors try to cover the gap in the existing literature base by collecting data from the qualitative and quantitative methods of analysis. The sources of the research include primary and secondary data. The secondary data is going to be gathered from the existing scientific literature that is related to the topic of this research paper. It is needed to understand what is innovation in the sphere of customer relationship management. Then, the researchers also need to collect their own primary data to analyze. The collection of the primary data is going to be divided into two stages. These stages include both quantitative and qualitative data.

First of all, the qualitative data will be collected. After that the qualitative part will be in a form of interviews. It needs to understand what innovations exist in the modern sphere of the CRM systems and, therefore, it is included in the survey for the following step of methodology. To do this, the researchers are going to conduct several in-depth interviews with the owners or representatives of businesses who are familiar with the customer relationship management systems and already have experience of using innovations.

The second stage of collecting the required data is going to be quantitative. To gather the quantitative data, the researchers are going to conduct a quantitative questionnaire. The content of this survey is going to be determined after the first stage of methodology is done. The reason for this fact is that during the first part of the research (in-depth interviews) the authors of the research are going to collect some information about what innovations exist in the current market of the CRM systems, and during the second step of the research this information will be included into the questions of the survey. After the collection of the data via these three stages of methodology is done, it is time for the third stage, which is analysing the data.

4.2 Data sources

The population for this stage include all available businesses that have their representations. However, the sample is limited by personal contacts of the authors of the research. The sample includes businesses that are the representatives of all available spheres. This is needed to understand if some spheres of business are interested in different innovations compared to the other spheres of business; if businesses of some sphere prefer not to use the customer relationship management systems at all, etc. Such diversity is required to identify the difference in using the CRM systems by different kinds of companies. These signs that have been gathered in the process of conducting the in-depth interviews are included into the quantitative analysis.

For the stage of the in-depth interviews, the method of snowball has been chosen. The snowball interview tactic implies the concept when a participant of the interview recruits other people who are capable of being interviewed by the researchers on a specific topic of the interview (Goodman, 1961). The fact that the researchers need this method of gathering the interviews is explained by the specifics of the sample that is required for this kind of research. The primary sample is limited by the personal contacts of the authors of this research paper. The interviewees have to be the owners or the representatives of different businesses, so it can be difficult to conduct the enough amount of the interviews with this kind of sample using only personal contacts of the authors. Therefore, the solution for this problem is using the personal contacts of the primary interviewees of the first stage of gathering the primary data for the research.

The format of the questionnaire is an evaluation of using one or the other innovation of the customer relationship management systems from 1 to 5, and also the questions with «YES» or «NO» answers. Such format is demanded due to the fact that the data gathered from the second stage of the research has to be analysed in SPSS, which requires quantitative kind of data. The sample for this stage are the participants of numerous business chats and groups that exist in social media and popular messengers, such as VK, Facebook, WhatsApp, Telegram. The researchers will also choose the sample so that many different spheres of business, their sizes, level of incomes, etc. will be represented in the sample.

4.3 Sample

In both qualitative and quantitative stages of gathering the data, the research is aimed at the owners or the representatives of Russian businesses from different spheres and industries. Out of all possible sizes and scales of the business, the researches are limited with the small and medium Russian business due to the fact that those are the ones that are available through the personal contacts of the authors and the business social media chat, which are the primary sources of gathering the data. According to the official website of the Russian Federal Taxation Service, by 2020 there are 6035035 subjects of small and medium entrepreneurship in Russia. This number is taken as the population size for the research. As a result of the process of collecting the responses for the quantitative questionnaire in the social media business chats, the authors of the research have gathered 207 responses from the business owners and representatives and entrepreneurs. This number corresponds to the size of the confidence probability of 85% and 5% of the confidence interval.

The index of the confidence probability represents the accuracy of gathered results. It shows the probability of a random answer to hit the confidence interval. The index of the confidence interval, in turn, represents the statistical error. It sets the span of a part of the distribution curve on either side of the selected point where the answers may hit.

The formula that was used for the sample size calculation is the following: , where Z is a Z-factor for the confidence probability, P is a percentage of respondents or answers of interest and C is a confidence interval.

The in-depth interviews are harder to conduct in comparison with an online questionnaire, it was conducted through the personal contacts of the authors of the research and the snowball sampling method. It is described in detail in the paragraph of the analysis results of the Description of findings block.

4.4 Analysis methods

The data gathered from the first stage is going to be qualitative, therefore, it is not possible to make mathematical conclusions based on it. The results of the in-depth interviews are not going to be analysed in the special programs such as SPSS, instead, the common theses will be looked for in the answers of the business owners or representatives and some conclusions will be made based on this qualitative analysis. On the contrary, analysis of the second part of collected data is going to be qualitative. The results are going to be formatted into an CSV file and then uploaded into SPSS. Then, a cluster analysis and linear regression for this data will be conducted. The results of this analysis are going to be visualised and interpreted by the researchers, and, therefore, the conclusions of the entire research paper will be made and the hypotheses are going to be proved or, on the contrary, disproved.

After the concoction of a set of in-depth interviews, the transcription of the interviews have been analyzed as a qualitative data.

The analysis of the results of the in-depth interviews includes the clarification of what kind of business the interviewee is related to. The questions about the segment of the business (which can include, for example, the B2B and B2C segments) and the sphere of business activity of the company are connected with the questions about the usage of a customer relationship management system. This connection helps the authors of the research to understand the correlation between the type and segment of the business and whether it uses the customer relationship management system or not.

Next, the authors analyse the type of the customer relationship management system that is used by the business if the business is using the customer relationship management system at all. This stage also has its connection with a type of the business, and also it is related to the next block of questions which includes the questions about the innovations that are used in the customer relationship management system that is integrated into the business processes of the company.

The questions about the innovations in the customer relationship management systems are directly connected to the topic of this research paper. First of all, the interviewees answer if they have any innovations in their customer relationship management systems. This part is the most important part of this stage of collecting and analyzing the qualitative primary data, because it will also be included into the quantitative questionnaire, which will be explained in details later. These questions are aimed to identify as many innovations that are used in the customer relationship management system of an interviewee as possible. The respondent explains in detail how exactly is each technique or technology used in the customer relationship management system, which helps the authors to understand whether the interviewee knows what he or she is talking about and whether he or she does not misunderstand the concept of the described innovation in the customer relationship management system. Making sure that all the innovations of the customer relationship management system that are used by the businesses of the interviewees is very important in terms of this research paper due to the fact that these points are later going to be the basis of the quantitative questionnaire.

After identifying the innovations that the respondents use in the customer relationship management systems, they are offered to state some evaluations of these innovations. These evaluations are in a fully free form, the respondent is free to express his or her opinion in every way he or she wants. The strict quantitative evaluation is included into the quantitative part of the research.

When all the interviews are done and all the primary data of this stage is collected, the authors analyse the transcriptions of the interviews and identify the most common innovations that the interviewed owner of representatives of businesses use in their customer relationship systems. Those innovations that are mentioned by the respondents most often are noted and included into the questionnaire for the next stage of the research which is aimed to collect the qualitative data. There the list of the innovations collected from the in-depth interview stage is evaluated in a strict way that can be analyzed mathematically.

Finally, the interviewees that represent different spheres of Russian entrepreneurship are offered to pick the innovations from the list of the historical innovations in the customer relationship management systems that have the most important and significant effect on the modern situation in the sphere of the customer relationship management systems. Here is the list of these innovations from the history of customer relationship management systems:

1. Cash machine

2. System for evaluating sales managers

3. Notebook with a schedule and clients list

4. Rolodex rotating file system

5. Computerization

6. Automated system to control the stages of transactions and monitor their effectiveness

7. Storage the information about the clients

8. Enterprise Resource Planning system

9. Integration with MS Office

10. Open source system

11. Social media integration

After the authors have collected data on innovations in CRM systems through in-depth interviews with representatives and business owners, they proceed to quantitative analysis. Google forms were chosen as a platform for conducting a quantitative survey. The survey can be divided into three semantic parts.

At the beginning there is a block of data about the Respondent.

1. Business type B2B, B2C or both.

2. What field does the company work in? The options were as follows:

2.1. Retailer

2.2. Wholesale

2.3. Education

2.4. Education

2.5. Taxi

2.6. Service sector

2.7. Hairdresser, beauty salon

2.8. Online store

2.9. Consulting

2.10. Logistics

2.11. Catering

The next section directly relates to the question about using the CRM system. It includes questions about whether the CRM system is used in the company and gives the following answer options: Yes, no or don't know. Similarly, if the answer is negative, the Respondent answers the question why. The answer to this question is as follows:

1. Small number of clients

2. Another way to account the clients

3. Inconvenient to use

4. Expensive to use

5. High implementation complexity

6. Low efficiency of CRM

The final section concerns the effectiveness of using innovations in CRM systems by representatives of real business in Russia. In this way, the impact of innovations on various business parameters was measured. The authors took six innovations that experts in in-depth interviews indicated as the main ones at the moment. Become them:

1. artificial intelligence

2. chat bots

3. integration with the Internet of things

4. deep personalization

5. cloud technologies

6. mobile version

The impact of each of these innovations in case of use was estimated by 5 parameters. The parameters by which the influence was measured were also obtained as a result of the interview. They were the following:

1. Ease of use of the CRM system

2. Efficiency of customer tracking

3. Effective communication with clients

4. Cost of doing business

5. Business profitability

The question of how to measure these indicators is acute for the authors of the study. Based on Finstad K., it was decided to use the 5 step Likert Scale (Finstad K., 2010). The average response in this case means that there is no wagging.

After all the respondents were interviewed, the authors began to analyze the CSV tables with responses. The analysis was performed in the SPSS program as well as using MS EXCEL. First, we worked with variables, they were recorded into numeric ones.

Then the authors calculated how many people from the respondents use the CRM system. We also received information about whether respondents use their own customer relationship management system or third-party software.

In the process of analyzing the average values, the authors created several tables. Data from tables and final results will be presented in the findings section.

4.5. Anticipated results

By the results of conducting this research, a list of innovations in the CRM systems will be created. These innovations will be classified by their types and by the effectiveness and profit that they can bring to the business. Also, the innovations that in average have a bad influence on the experience of using the CRM systems, if there are any, will also be included in this list. The final result is a visualisation that will provide an opportunity to understand what are the most efficient innovations in the modern market of the CRM spheres to be implemented into the existing CRM systems of different businesses.

The contribution of this research to the field of the customer relationship management systems is quite clear. This research paper is going to improve the understanding of the evolution of the CRM systems in the circumstances of the modern market, make the set of knowledge about the techniques and technologies that are used as the innovations in the sphere of customer relationship management more structured. The results of the research are going to create a system of evaluation of the effectiveness and profitability of different innovations in the sphere of CRM, which has not been done before by any other research.

What is more, this research paper will affect the sphere of business. The businesses will be able to indicate what innovations in the customer relationship management systems are the most suitable and effective in their current situation. This will allow companies to improve their CRM systems with only those features that are worth wasting time and money on, ameliorating the relationship between the company and the customers. On the other hand, the research will allow businesses to bypass those features that the research will determine as harmful for the business processes or get rid of them in case they are already implemented into the company's customer relationship management system.

5. Description of findings

5.1 Analysis results

During the process of the review of the existing literature, the researchers have gathered the information about the existing innovations in the sphere of customer relationship management. This review has given the authors the understanding of what innovations currently exist in this industry. The list of innovations includes artificial intelligence, machine learning, cloud technologies, Internet of things integration, chat bots integration and deep personalization technologies. The scientific articles that have been read during the literature review contain quite complete information about all these innovations. The researchers have discovered the specifics of using each particular innovation in terms of the customer relationship management system, and also that fact that some innovations depend on the others has been learned. All the customer relationship management systems innovations that have been discovered during the process of the literature review are included into the quantitative questionnaire in order to evaluate their effectiveness for the business processes and compare these innovations with each other in order to identify the most and the least effective ones.

The analysis of the history of customer relationship management development gave the authors the understanding about what innovations are already implemented and why some innovations are absolutely forgotten. Based on this, we can say that each stage of development of the CRM system was necessary in order for users to now be able to use programs that cover many different business processes.

As a result of the stage of conducting the in-depth interviews, the researchers have gathered 16 interviews with the owners or the representatives of businesses of different spheres and sectors from Russia. The respondents have been answering the questions about the sphere or the industry of their business, whether they use or do not use the customer relationship management system, what innovations are incepted in case if the customer relationship management system is used and how do they evaluate these customer relationship management systems innovations in a free form.

The authors of the research have used their personal contacts to gather the first 9 interviews. Among them are the businesses that belong to the different spheres and industries. Among the businesses that have been interviewed during the in-depth interview stage of collecting the primary data are the company that delivers and sells shrimps from Iran; a barbershop; an online store that specializes in selling the antiseptic agents and medical masks and others; a transport company that specializes on delivering bread and bakery products from the factory to different points of Saint Petersburg and Leningrad Oblast, such as shops, catering places, schools and kindergartens; a firm that provides services of forwarding for the logistics companies; an online aggregator of movies that can be seen in the cinema; an others. Some of the 9 respondents that have answered the questions of the interview about using the innovations in their customer relationship management systems have recruited the other businesses from their personal contacts by the tactics of snowball sampling, which was described in the block of methodology above. In total, the amount of the respondents that were gathered from the personal contacts of the authors of the research and those interviewees that have been recruited by the snowball sampling method is 16 conducted interviews.