Enhancing Customer Satisfaction
DOI:
https://doi.org/10.38152/bjtv7n4-011Abstract
Customer satisfaction is crucial for the sustained success of e-commerce platforms, necessitating effective Customer Relationship Management (CRM) strategies to bolster relationships and overall performance. While existing research provides insights into factors like payment methods, shipping times, and customer demographics that influence satisfaction, significant gaps remain in fully understanding these dynamics, particularly within specific contexts such as Olist, a prominent Brazilian online marketplace. This study aims to address these gaps by leveraging advanced machine learning techniques on a comprehensive dataset comprising 100,000 orders spanning 2016 to 2018. Through meticulous analysis, which includes rigorous feature selection and the application of predictive models like logistic regression, decision trees, and random forests, we pinpoint crucial determinants of customer satisfaction. Our findings underscore the pivotal role of operational efficiencies in shaping customer experiences and offer actionable recommendations for enhancing e-commerce performance. By bridging these research gaps, this study not only contributes to the existing body of knowledge but also informs practical strategies for optimizing customer satisfaction and guides future research endeavors in the realm of e-commerce and CRM.
References
A Al-Homery, H., Asharai, H. and Ahmad, A., (2019). The core components and types of
CRM. Pakistan Journal of Humanities and Social Sciences, 7(1), pp.121-145.
Ayyagari, M.R., (2021). A framework for analytical CRM assessments challenges and
recommendations. International Journal of Information, Business and
Management, 13(2), pp.108-121.
Chen, S., Mulgrew, B., & Grant, P. M. (1993). A Clustering Technique For Digital Communications Channel Equalization Using Radial Basis Function Networks. IEEE Transactions On Neural Networks, 4, 570–578.
Djurisic, V., Kascelan, L., & Rogic, S. (2020). Bank CRM optimization using predictive classification based on the support vector machine method. Applied Artificial Intelligence, 34(1), 1-22. https://doi.org/10.1080/08839514.2020.1712268
Doyle, W. D. (1987). Magnetization Reversal In Films With Biaxial Anisotropy. In Proceedings Of The 1987 Intermag Conference (Pp. 221–226).
Durai, S., Manoharan, G., Priya, T. S., & Jayanthi, R. (2024). Quantifying the impacts of artificial intelligence implementations in marketing. In Journal of Interactive Marketing. IGI Global.
Greenberg, P. (2010). The impact of CRM 2.0 on customer insight. Journal of Business & Industrial Marketing, 25(6), 410-419. https://doi.org/10.1108/08858621011066008
Gustafsson, A., Johnson, M. D., & Roos, I. (2005). The Effects Of Customer Satisfaction, Relationship Commitment Dimensions, And Triggers On Customer Retention. Journal Of Marketing, 69(4), 210-218. Https://Doi.Org/10.1509/Jmkg.2005.69.4.210
Iyelolu, T. V., Agu, E. E., Idemudia, C., & Ijomah, T. I. (2024). Leveraging artificial intelligence for personalized marketing campaigns to improve conversion rates. International Journal of Engineering Research and Development, 20(8), 253-270
Juette, G. W., & Zeffanella, L. E. (1990). Radio Noise Currents In Short Sections On Bundle Conductors. Presented At The Ieee Summer Power Meeting, Dallas, Tx.
Motorola Semiconductor Data Manual. (1989). Motorola Semiconductor Products Inc., Phoenix, Az.
Oliver, R. L. (2014). Satisfaction: A Behavioral Perspective On The Consumer: A Behavioral Perspective On The Consumer (2nd Ed.). Routledge. Https://Doi.Org/10.4324/9781315700892
Payne, A., & Frow, P. Strategic Customer Management: Integrating Relationship Marketing And Crm. Cambridge Core. Https://Doi.Org/10.1017/Cbo9781139057417
Poor, H. (1985). An Introduction To Signal Detection And Estimation (Ch. 4). Springer-Verlag.
Rabbi, S.(2023) Principles Of Marketing [Philip Kotler, Gary Armstrong, John Saunders].
Reinartz, W. J., & Kumar, V. (2002). The Mismanagement Of Customer Loyalty. Harvard Business Review. Accessed April 8, 2023. Https://Hbr.Org/2002/07/The-Mismanagement-Of-Customer-Loyalty
Scholtz, R. A. (1993). The Spread Spectrum Concept. In N. Abramson (Ed.), Multiple Access (Ch. 3, Pp. 121–123). IEEE Press.
Shafiq, S. (2019). 6 Factors That Influence Customer Experience. High Stuff.
Accessed 13/09/ 2024. Https://Www.Highstuff.Com/Influence-Customer-Experience/
Soler-Labajos, N., & Jiménez-Zarco, A. I. (2016). Social CRM: The role of social media in managing customer relations. In Managing Public Relations and Brand Image through Social Media (pp. 26). IGI Global. https://doi.org/10.4018/978-1-5225-0332-3.ch008
TORO Cloud. (2023). Data management for customer relationship. Lonti. Accessed 8/10/ 2024. Retrieved from https://www.lonti.com
Vidmar, R. J. (1992). On The Use Of Atmospheric Plasmas As Electromagnetic Reflectors. IEEE Transactions On Plasma Science, 21(3), 876–880. Available: Http://Www.Halcyon.Com/Pub/Journals/21ps03-Vidmar
Vishwanatham, R., Ponnavolu, S. K. R., & Others. (2024). Systems using Salesforce. In Enterprise Technology.
Young, G. O. (1964). Synthetic Structure Of Industrial Plastics. In J. Peters (Ed.), Plastics (2nd Ed., Vol. 3, Pp. 15–64). Mcgraw-Hill.
Yang, X.-S. (2019). Logistic regression, PCA, LDA, and ICA. In Introduction to Algorithms for Data Mining and Machine Learning (pp. 91–108). Elsevier. https://doi.org/10.1016/B978-0-12-817216-2.00012-0
Walker, K. W. (2021). Exploring adaptive boosting (AdaBoost) as a platform for the predictive modeling of tangible collection usage. The Journal of Academic Librarianship, 47(6), 102450. https://doi.org/10.1016/j.acalib.2021.102450
Zucco, C. (2019). Multiple learners combination: Bagging. In Encyclopedia of Bioinformatics and Computational Biology (Vol. 1, pp. 525–530). Elsevier. https://doi.org/10.1016/B978-0-12-809633-8.20345-2
