RESEARCH ON METHODS OF DETERMINING CUSTOMER LOYALTY AND ASSESSING THEIR LEVEL OF SATISFACTION

Authors

DOI:

https://doi.org/10.30837/ITSSI.2023.24.104

Keywords:

unified assessment; customer satisfaction level; customer loyalty; reviews; questionnaires

Abstract

The subject of research in the article is methods of data collection and processing to assess the level of customer satisfaction and loyalty to the company, as well as the possibility of evaluating the collected data. The purpose of the work is the analysis of methods for determining customer loyalty and assessing their level of satisfaction, the development of a unified assessment algorithm based on various types of data. The article deals with the following tasks: analysis of information acquisition methods – questionnaires and reviews, evaluation methods definition and comparison questionnaires with closed answers, reviews and open answers’ text tonality evaluation methods analysis using an artificial intelligence, development of an algorithm for determining a unified evaluation and conducting an experiment. The following methods are used: theoretical research methods for determining existing data collection methods, as well as methods for assessing the level of customer loyalty and satisfaction using CSI, CSAT indexes, NLP methods for determining the text tonality, bringing the calculated values to one scale, determining the method of a unified assessment; empirical research methods for conducting an experiment, determining and proving the feasibility of applying the method. The following results were obtained: a method of assessing customer loyalty and their level of satisfaction based on the analysis of various types of information with further results unification was proposed. Various types of date are responses to questionnaires and user reviews. The questionnaires are analyzed using KPI, and reviews – using artificial intelligence methods. After normalizing the results (bringing them to one scale), the additive convolution method is used to unify the overall result. A prototype of the software system has been developed, which allows you to carry out a full cycle from collecting information to calculating both KPI-metrics and a unified assessment. Conclusions: Experimentally, it was determined that the method of assessing customer loyalty and their level of satisfaction based on the unification of a comprehensive assessment of various types of data is efficient and can be used to optimize business processes by reducing time and efforts spent on analyzing the gathered data. The use of this method is fully justified, since the measurement error is low, and the margin of error is acceptable.

Author Biographies

Yana Leiba, Kharkіv National University of Radio Electronics

Master’s degree at the Department of Software Engineering

Mariya Shirokopetleva, Kharkіv National University of Radio Electronics

Senior Lecturer at the Department of Software Engineering, Deputy Director of the Center for Postgraduate Education

Iryna Gruzdo, Kharkіv National University of Radio Electronics

PhD (Engineering Sciences), Associate Professor at the Department of Software Engineering

References

References

Hogreve, J., Iseke, A., Derfuss, K., (2021), "The service-profit chain: reflections, revisions, and reimaginations", Journal of service research, Vol. 25(3), Р. 460–477. DOI: 10.1177/10946705211052410

Narayan, R., Gehlot, A., Singh, R., Akram, S. V., Priyadarshi, N., Twala, B., (2022), "Hospitality feedback system 4.0: digitalization of feedback system with integration of industry 4.0 enabling technologies", Sustainabilit, Vol. 14(19), 12158 р. DOI: 10.3390/su141912158

Capuano, N., Greco, L., Ritrovato, P., Vento, M., (2020), "Sentiment analysis for customer relationship management: an incremental learning approach", Applied intelligence, Vol. 51, Р. 3339–3352. DOI: 10.1007/s10489-020-01984-x

Cherednichenko, O., Yanholenko, O., Vovk, M., Sharonova, N., (2020), "Towards structuring of electronic marketplaces contents: items normalization technology", Proceedings of the 4th International Conference on Computational Linguistics and Intelligent Systems (COLINS 2020), P. 44–55, available at: https://ceur-ws.org/Vol-2604/paper4.pdf

Nichols, Р., (2000), Social Survey Methods: A field guide for development workers, Oxfam GB. Practical Action Publishing, 132 р.

McDougall G. H. G., Levesque, T., (2000), "Customer Satisfaction with Services: Putting Perceived Value into the Equation", Journal of Services Marketing, Vol. 14(5), Р. 392–410. DOI:10.1108/08876040010340937

Gonchar, O., Polishchuk, I., (2019), "Integration factors of today as a prerequisite for forming a mechanism for managing the marketing potential of the enterprise", Journal of European Economy, Vol. 18(2), Р. 213–225. DOI: https://doi.org/10.35774/jee2019.02.213

Saher, L., Kolesnyk, A. (2018), "Customer Loyalty: the Essence and Program Types", Market Infrastructure, Vol. 20, Р. 176–186, аvailable at: http://www.market-infr.od.ua/journals/2018/20_2018_ukr/32.pdf

Sinkovska, V., (2019), "Measuring consumer loyalty in competition", Marketing and digital technologies, Vol. 4, No. 3, Р. 40–53. DOI: 10.15276/mdt.3.4.2019.4

Holovachov, I., (2023), "Digital marketing strategies of the enterprise", International Scientific Journal "Internauka". Series: "Economic Sciences", No. 2(70), Р.95-100. DOI: https://doi.org/10.25313/2520-2294-2023-2-8658

Fedorchenko A., Ponomarenko I., (2019), "A/B-testing as an efficient tools for digital marketing", The problems of innovation and investment-driven development, No. 19, Р. 36–42, аvailable at: https://doi.org/10.33813/2224-1213.19.2019.4

Pavlyk, S., Chunikhina, T., (2023), "Marketing in electronnic kommerce", Brand Management: Marketing Technologies, Р. 389-392, аvailable at: https://knute.edu.ua/file/MzEyMQ==/dfa2684085a58809d90b630a0fe26059.pdf

Balan, V., (2023), "Fuzzy hybrid model for forming a system of indicators for assessing the efficiency of an enterprise", Ekonomy and society, No. 48. DOI: https://doi.org/10.32782/2524-0072/2023-48-70

Parmenter, D. (2020), Key Performance Indicators: developing, implementing, and using winning KPIs, Hoboken, New Jersey: John Wiley & Sons. 384 р., аvailable at: https://digilibdprdsumselprov.id/index.php?p=fstream-pdf&fid=53&bid=44

Fritze, A., Schnup, C., Moller, K., (2017), "Strategy-based prioritisation of KPIs using the fuzzy analytic network process–An application in the context of shared services", Magazine for success-oriented corporate management, No. 29(2), Р. 58–68. аvailable at: https://www.alexandria.unisg.ch/entities/publication/d0787a67-8732-4e0d-ba51-08f6027eac9f/details

Hordiienko, I., (2010), "Modeling of the system of key performance indicators of the organization", Collection of Scientific Papers "ECONOMY AND ENTREPRENEURSHIP", No. 25. Р. 205–215, аvailable at: https://ir.kneu.edu.ua/handle/2010/2044?locale-attribute=en

Martynova, O., (2018), "Basic principles and provisions of the company's evaluation modeling using a balanced scorecard", Young Scientist, No. 11(63). Р. 1158–1165, аvailable at: http://repository.hneu.edu.ua/bitstream/123456789/21361/1/4%20%d0%9c%d0%b0%d1%80%d1%82%d0%b8%d0%bd%d0%be%d0%b2%d0%b0_%d0%b6%d1%83%d1%80%d0%bd%d0%b0%d0%bb%20%d0%9c%d0%be%d0%bb%d0%be%d0%b4%d0%b8%d0%b9%20%d0%b2%d1%87%d0%b5%d0%bd%d0%b8%d0%b9.pdf

Feshchur, R., Samuliak, V., (2010), "The groups of indexes (indicators) of evaluation of level of enterprises development", Bulletin of the National University "Lviv Polytechnic", No. 691. Р. 231–239, available at: https://ena.lpnu.ua/bitstreams/970e4a3b-b052-494d-9be9-25c9d3c5ed4a/download

"Quality management systems–Requirements", (2015), ISO 9001:2015(en), available at: https://www.iso.org/obp/ui/#iso:std:iso:9001 (last accessed 11.04.2023)

Picha, V. (2018), Sociology: basic terms and concepts encyclopedic dictionary-reference, New World-2000. 658 р.

Panchenko, D., Maksymenko, D., Turuta, O., Yerokhin, A., Daniiel, Y., Turuta, O. (2022), "Evaluation and Analysis of the NLP Model Zoo for Ukrainian Text Classification", Communications in Computer and Information Science, available at: https://link.springer.com/chapter/10.1007/978-3-031-20834-8_6

Batiuk, T., Dosyn, D. (2023), "Implementation of the intellectual system of sentiment analysis and clusterization of publications in the Twitter social network", Innovative Technologies and Scientific Solutions for Industries, No. 1 (23), P. 25–44. DOI: https://doi.org/10.30837/ITSSI.2023.23.025

Yuvchenko, K., Yesilevskyi, V., Sereda, O. (2022), "Human emotion recognition system using deep learning algorithms", Innovative Technologies and Scientific Solutions for Industries, No. 3(21). Р. 60–69. DOI: https://doi.org/10.30837/ITSSI.2022.21.060

Farris, Р., Bendle, N., Pfeifer, P., Reibstein, D. (2010), Marketing metrics: the definitive guide to measuring marketing performance, Upper Saddle River, N.J: Wharton School Pub. 53 р., available at: https://ptgmedia.pearsoncmg.com/images/9780137058297/samplepages/9780137058297.pdf

Rachitsky, L. (2021), "Choosing your north star metric", Future, available at: https://future.com/north-star-metrics/ (last accessed 12.04.2023).

Erdem, E., Kuyu, M., Yagcioglu, S., Frank, A., Parcalabescu, L., Plank, B., Babii, A., Turuta, O., Erdem, A., Calixto, I., Lloret, E., Apostol, E.-S., Truică, C.-O., Šandrih, B., Martinčić-Ipšić, S., Berend, G., Gatt, A., Korvel, G. (2022), "Neural Natural Language Generation: A Survey on Multilinguality, Multimodality, Controllability and Learning", Journal of Artificial Intelligence Research, No. 73, Р.1131–1207. DOI: https://doi.org/10.1613/jair.1.12918

Sharonova, N., Kyrychenko, N., Gruzdo, I., Tereshchenko, G. (2022), "Generalized Semantic Analysis Algorithm of Natural Language Texts for Various Functional Style Types", Proceedings of the 6th International Conference on Computational Linguistics and Intelligent Systems (COLINS 2022), Gliwice, Poland, May 12-13, Р. 16–26, available at: https://ceur-ws.org/Vol-3171/paper4.pdf

Kaur, G., Sharma, A. (2023), "A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis", Journal of big data. No. 10(5), 4227 р. DOI: https://doi.org/10.1186/s40537-022-00680-6

Haque, R., Islam, N., Tasneem, M., Das, A. K. (2023), "Multi-class sentiment classification on bengali social media comments using machine learning", International journal of cognitive computing in engineering, Vol. 4, Р. 21–35. DOI: https://doi.org/10.1016/j.ijcce.2023.01.001

Smelyakov, K., Karachevtsev, D., Kulemza, D., Samoilenko, Y., Patlan, O., Chupryna, A. (2020), "Effectiveness of Preprocessing Algorithms for Natural Language Processing Applications", 2020 IEEE International Conference on Problems of Infocommunications. Science and Technology (PIC S&T), Kharkiv, Ukraine, Р. 1–5. DOI: https://doi.org/10.1109/picst51311.2020.9467919

Xu, Y., Wu, G., Chen, Y. (2022), "Predicting patients’ satisfaction with doctors in online medical communities", Of organizational and end user computing, Vol. 34(6), Р.1–17. DOI: https://doi.org/10.4018/joeuc.287571

Avrunin, O., Vlasov, O., Filatov, V. (2020), "Model of semantic integration of information systems properties in relay database reengineering problems", Innovative Technologies and Scientific Solutions for Industries, No. 4(14), Р. 5–12. DOI: https://doi.org/10.30837/ITSSI.2020.14.005

Kosenko, V. (2017), "Principles and structure of the methodology of risk-adaptive management of parameters of information and telecommunication networks of critical application systems", Innovative Technologies and Scientific Solutions for Industries, No. 1(1), P. 46–52. DOI: https://doi.org/10.30837/2522-9818.2017.1.046

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Published

2023-11-13

How to Cite

Leiba, Y., Shirokopetleva, M., & Gruzdo, I. (2023). RESEARCH ON METHODS OF DETERMINING CUSTOMER LOYALTY AND ASSESSING THEIR LEVEL OF SATISFACTION. INNOVATIVE TECHNOLOGIES AND SCIENTIFIC SOLUTIONS FOR INDUSTRIES, (2 (24), 104–117. https://doi.org/10.30837/ITSSI.2023.24.104