1
Department of Computer and Applied Sciences, Barleti University,Tirana, Albania
(e.kazia@umb.edu.al)
2
Software Engineering Department, Canadian Institute of Technology,Tirana, Albania
(bledi.kazia@cit.edu.al)
Abstract :
Customer Relationship Management (CRM) is a crucial aspect of modern business that enables companies to maintain healthy relationships with their customers. In today's digital age, customers interact with companies through multiple channels, including social media, email, and phone. Therefore, analyzing customer feedback and sentiment has become increasingly important in understanding their needs and improving the overall customer experience. To this end, this work proposes a new system that applies deep learning for sentiment analysis in a way that improves the performance of CRM by analyzing customer feedback from various sources, companies can gain valuable insights into customer needs and preferences and identify areas for improvement in their products and services. Then, we present a case study of a company that implemented the proposed system in its CRM strategy. The results showed that our system could improve customer satisfaction and retention rates and enable the company to identify and address customer concerns more efficiently.Our approach can be applied as a powerful tool to enable companies to gain valuable insights into customer needs and preferences, identify areas for improvement in their products and services, and develop targeted marketing campaigns and personalized communication strategies.
Keywords :
Sentiment Analysis; Customer Relationship; Management; Social Media Analysis
References :
[1] Nasr, M. M., Shaaban, E. M., & Hafez, A. M. (2017). Enhancing Social Customer Relationship Management by Using Sentiment Analysis. International Journal of Science and Research, 6, 803-807.
[2] Deb, S. K., Jain, R., & Deb, V. (2018, January). Artificial intelligence―creating automated insights for customer relationship management. In 2018 8th international conference on cloud computing, data science & engineering (Confluence) (pp. 758-764). IEEE.
[3] Ahmed, A. A. A., Agarwal, S., Kurniawan, I. G. A., Anantadjaya, S. P., & Krishnan, C. (2022). Business boosting through sentiment analysis using Artificial Intelligence approach. International Journal of System Assurance Engineering and Management, 13(Suppl 1), 699-709.
[4] Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New avenues in opinion mining and sentiment analysis. IEEE Intelligent systems, 28(2), 15-21.
[5] Berry, M. J., & Linoff, G. S. (2004). Data mining techniques: for marketing, sales, and customer relationship management. John Wiley & Sons.
[6] Soltani, Z., & Navimipour, N. J. (2016). Customer relationship management mechanisms: A systematic review of the state of the art literature and recommendations for future research. Computers in Human Behavior, 61, 667-688.
[7] Fan, Z. P., Che, Y. J., & Chen, Z. Y. (2017). Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis. Journal of business research, 74, 90-100.
[8] Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowledge-based systems, 89, 14-46.
[9] Capuano, N., Greco, L., Ritrovato, P., & Vento, M. (2021). Sentiment analysis for customer relationship management: an incremental learning approach. Applied Intelligence, 51, 3339-3352.
[10] Seyfioğlu, M. S., & Demirezen, M. U. (2017, September). A hierarchical approach for sentiment analysis and categorization of Turkish written customer relationship management data. In 2017 Federated Conference on Computer Science and Information Systems (FedCSIS) (pp. 361-365). IEEE.
[11] Schmunk, S., Höpken, W., Fuchs, M., & Lexhagen, M. (2013). Sentiment analysis: Extracting decision-relevant knowledge from UGC. In Information and Communication Technologies in Tourism 2014: Proceedings of the International Conference in Dublin, Ireland, January 21-24, 2014 (pp. 253-265). Springer International Publishing.
[12] AL-Rubaiee, H. S., Alomar, K., Qiu, R., & Li, D. (2018). Tuning of Customer Relationship Management (CRM) via Customer Experience Management (CEM) using sentiment analysis on aspects level.
[13] Ledro, C., Nosella, A., & Vinelli, A. (2022). Artificial intelligence in customer relationship management: literature review and future research directions. Journal of Business & Industrial Marketing.
[14] Plaza, L., & de Albornoz, J. C. (2012). Sentiment analysis in business intelligence: a survey. In Customer Relationship Management and the Social and Semantic Web: Enabling Cliens Conexus (pp. 231-252). IGI Global.
[15] Păvăloaia, V. D., Teodor, E. M., Fotache, D., & Danileţ, M. (2019). Opinion mining on social media data: sentiment analysis of user preferences. Sustainability, 11(16), 4459.
[16] Hernes, M. (2015). Performance evaluation of the customer relationship management agent’s in a cognitive integrated management support system. In Transactions on Computational Collective Intelligence XVIII (pp. 86-104). Springer Berlin Heidelberg.
[17] Younis, E. M. (2015). Sentiment analysis and text mining for social media microblogs using open source tools: an empirical study. International Journal of Computer Applications, 112(5).
[18] Nadeem, M. (2012). Social customer relationship management (SCRM): How connecting social analytics to business analytics enhances customer care and loyalty?. International journal of business and social science, 3(21).
[19] Umamaheshwari, S., Harikumar, K., & Allinjoe, D. (2021, October). Customer Relationship Management using Sentimental Analysis. In 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA) (pp. 1-6). IEEE.
| Style | # |
|---|---|
| MLA | Esmeralda Kazia, Bledar Kazia. "Enhancing Customer Relationship Management through Sentiment Analysis and Social Media Data Mining." American Journal of Business and Operations Research, Vol. 10, No. 1, 2023 ,PP. 16-24 (Doi : https://doi.org/10.54216/AJBOR.100102) |
| APA | Esmeralda Kazia, Bledar Kazia. (2023). Enhancing Customer Relationship Management through Sentiment Analysis and Social Media Data Mining. Journal of American Journal of Business and Operations Research, 10 ( 1 ), 16-24 (Doi : https://doi.org/10.54216/AJBOR.100102) |
| Chicago | Esmeralda Kazia, Bledar Kazia. "Enhancing Customer Relationship Management through Sentiment Analysis and Social Media Data Mining." Journal of American Journal of Business and Operations Research, 10 no. 1 (2023): 16-24 (Doi : https://doi.org/10.54216/AJBOR.100102) |
| Harvard | Esmeralda Kazia, Bledar Kazia. (2023). Enhancing Customer Relationship Management through Sentiment Analysis and Social Media Data Mining. Journal of American Journal of Business and Operations Research, 10 ( 1 ), 16-24 (Doi : https://doi.org/10.54216/AJBOR.100102) |
| Vancouver | Esmeralda Kazia, Bledar Kazia. Enhancing Customer Relationship Management through Sentiment Analysis and Social Media Data Mining. Journal of American Journal of Business and Operations Research, (2023); 10 ( 1 ): 16-24 (Doi : https://doi.org/10.54216/AJBOR.100102) |
| IEEE | Esmeralda Kazia, Bledar Kazia, Enhancing Customer Relationship Management through Sentiment Analysis and Social Media Data Mining, Journal of American Journal of Business and Operations Research, Vol. 10 , No. 1 , (2023) : 16-24 (Doi : https://doi.org/10.54216/AJBOR.100102) |