Studi Bibliometrik Implementasi Teknik Machine learning dalam Bidang Customer Relationship Management
Keywords:
Customer Relationship Management, machine learning, bibliometric analysis, customer behavior predictionAbstract
Application of machine learning techniques in Customer Relationship Management has proven to have significant potential in enhancing the efficiency and effectiveness of managing customer interactions. This study aims to conduct a comprehensive bibliometric analysis regarding the application of machine learning techniques in CRM. Through this analysis, we seek to identify recent research trends, gaps, and future research opportunities. This study utilizes data from various prestigious international scientific journals indexed by Scopus to explore the application of machine learning techniques for churn prediction models. The method employed in this research is bibliometric analysis on machine learning techniques in the field of CRM. This study reveals a significant trend in the application of machine learning techniques in Customer Relationship Management. The results indicate that the use of machine learning in CRM has increased, particularly since 2021, reflecting a high interest and the relevance of this technology in enhancing the efficiency and effectiveness of CRM. The analysis also shows that the discipline of computer science dominates this research, followed by engineering, business and management, and mathematics. The contribution of this research to existing knowledge is providing a deeper understanding of current research trends and identifying gaps and future research opportunities.
References
L. Schoonbee, W. R. Moore, and J. H. van Vuuren, “a Machine-Learning Approach Towards Solving the Invoice Payment Prediction Problem,” South African J. Ind. Eng., vol. 33, no. 4, pp. 126–146, 2022, doi: 10.7166/33-4-2726.
S. Sharma and A. A. Waoo, “An efficient machine learning technique for prediction of consumer behaviour with high accuracy,” Int. J. Comput. Artif. Intell., vol. 4, no. 1, pp. 12–15, 2023, doi: 10.33545/27076571.2023.v4.i1a.59.
W. Du, J. Ge, X. Liu, and J. Ai, “Convolutional neural network model based on text similarity for customer service,” J. Phys. Conf. Ser., vol. 1550, no. 3, 2020, doi: 10.1088/1742-6596/1550/3/032045.
G. Mena, K. Coussement, K. W. De Bock, A. De Caigny, and S. Lessmann, “Exploiting time-varying RFM measures for customer churn prediction with deep neural networks,” Ann. Oper. Res., 2023, doi: 10.1007/s10479-023-05259-9.
H. H. Triyanto, A. U. P., A. Djadjadi, and H. Santoso, “Smart Relationship Development in Community Based Marketing using Machine learning,” CSRID (Computer Sci. Res. Its Dev. Journal), vol. 14, no. 1, p. 55, 2022, doi: 10.22303/csrid.14.1.2021.66-78.
A. A. Jamjoom, “The use of knowledge extraction in predicting customer churn in B2B,” J. Big Data, vol. 8, no. 1, 2021, doi: 10.1186/s40537-021-00500-3.
A. Chouiekh and E. H. I. El Haj, “Deep Convolutional Neural Networks for customer churn prediction analysis,” Int. J. Cogn. Informatics Nat. Intell., vol. 14, no. 1, pp. 1–16, 2020, doi: 10.4018/IJCINI.2020010101.
J. Faritha Banu, S. Neelakandan, B. T. Geetha, V. Selvalakshmi, A. Umadevi, and E. O. Martinson, “Artificial Intelligence Based Customer Churn Prediction Model for Business Markets,” Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/1703696.
S. Chen Du, T. Chen, K. Zhu, and J, “Research hotspots and trends of exercise on parkinson’s disease: a global bibliometric analysis from 2012 to 2021,” Front. Hum. Neurosci., vol. 16, 2022, doi: 10.3389/fnhum.2022.908049.
w Lim and S. Kumar, “Guidelines for interpreting the results of bibliometric analysis: a sensemaking approach,” Glob. Bus. Organ. Excell., vol. 43, no. 2, pp. 17–26, 2023, doi: 10.1002/joe.22229.
R. Ananda and A. Nandiyanto, “Bibliometric analysis of publication on protein nanoparticle using vosviewer,” J. Kedokt. Diponegoro (Diponegoro Med. Journal), vol. 11, no. 6, 2022, doi: 10.14710/dmj.v11i6.35942.
I. Passas, “Bibliometric analysis: the main steps,” Encyclopedia, vol. 4, no. 2, pp. 1014–1025, 2024, doi: 10.3390/encyclopedia4020065.
C. Erdo?mu? and Ö. Korkmaz, “Trends in educational technologies according to articles published in the last 20 years in international literature,” Int. Online J. Prim. Educ., vol. 11, no. 1, pp. 232–259, 2022, doi: 10.55020/iojpe.1083925.
N. Ellili, “Bibliometric analysis and systematic review of environmental, social, and governance disclosure papers: current topics and recommendations for future research,” Environ. Res. Commun., vol. 4, no. 9, p. 92001, 2022, doi: 10.1088/2515-7620/ac8b67.
N. Liu, “The state of astragaloside iv research: a bibliometric and visualized analysis,” Fundam. Clin. Pharmacol., vol. 38, no. 2, pp. 208–224, 2023, doi: 10.1111/fcp.12956.
E. Demiray and S. Alkan, “Bibliometric analysis of amebiasis research,” J. Clin. Med. Kazakhstan, vol. 19, no. 6, pp. 38–42, 2022, doi: 10.23950/jcmk/12677.
A. Saltali and E. Aslanjar, “Bibliometric analysis on pediatric caudal anesthesia,” Pediatr. Pract. Res., vol. 11, no. 1, pp. 7–12, 2023, doi: 10.21765/pprjournal.1228593.
N. Gzahli, H. Mutalib, and A. Noor, “Bibliometric analysis of cash waqf,” J. Intelek, vol. 17, no. 2, pp. 63–73, 2022, doi: 10.24191/ji.v17i2.18032.
M. Alhajj et al., “Bibliometric analysis and evaluation of the journal of prosthodontic research from 2009 to 2021,” J. Prosthodont. Res., vol. 66, no. 4, pp. 525–529, 2022, doi: 10.2186/jpr.jpr_d_21_00311.
M. Hossain, “Current status of global research on novel coronavirus disease (covid-19): a bibliometric analysis and knowledge mapping ,” F1000research , vol. 9. p. 374, 2020. doi: 10.12688/f1000research.23690.1.
K. Kammerer, M. Göster, M. Reichert, and R. Pryss, “Ambalytics: a scalable and distributed system architecture concept for bibliometric network analyses ,” Future Internet , vol. 13, no. 8. p. 203, 2021. doi: 10.3390/fi13080203.
J. Gläser, W. Glänzel, and A. Scharnhorst, “Same data—different results? towards a comparative approach to the identification of thematic structures in science ,” Scientometrics , vol. 111, no. 2. pp. 981–998, 2017. doi: 10.1007/s11192-017-2296-z.
R. Ragadhita and A. Nandiyanto, “Computational bibliometric analysis on publication of techno-economic education ,” Indonesian Journal of Multidiciplinary Research , vol. 2, no. 1. pp. 213–222, 2021. doi: 10.17509/ijomr.v2i1.43180.
K. Kamaludin, “Two decades of bibliometric research in indonesia ,” The Light Journal of Librarianship and Information Science , vol. 3, no. 1. pp. 32–43, 2023. doi: 10.20414/light.v3i1.7034.
P. Kokol, H. Vošner, and J. Završnik, “Application of bibliometrics in medicine: a historical bibliometrics analysis ,” Health Information & Libraries Journal , vol. 38, no. 2. pp. 125–138, 2020. doi: 10.1111/hir.12295.
R. Sureka, S. Kumar, S. Mangla, and F. Hourneaux, “Fifteen years of international journal of productivity and performance management (2004–2018) ,” International Journal of Productivity and Performance Management , vol. 70, no. 5. pp. 1092–1117, 2020. doi: 10.1108/ijppm-11-2019-0530.
A. Deshmukh, “A machine learning approach for cleaning crm data ,” International Journal of Advanced Trends in Computer Science and Engineering , vol. 9, no. 2. pp. 2253–2258, 2020. doi: 10.30534/ijatcse/2020/204922020.
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