THE TRANSFORMATIVE IMPACT OF DATA MINING ON CUSTOMER TREND ANALYSIS
International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)
Download this PDF format
Abstract
Data mining has revolutionized the way businesses understand and predict customer behavior. By leveraging advanced algorithms to analyze vast amounts of data, organizations can uncover hidden patterns, trends, and insights that were previously difficult to identify. This transformative impact enables businesses to make more informed decisions, personalize customer experiences, optimize marketing strategies, and improve product offerings. The ability to track and anticipate customer preferences allows companies to stay ahead of market shifts, enhance customer satisfaction, and foster long-term loyalty. Ultimately, data mining empowers businesses to not only respond to customer needs but to proactively shape their offerings, leading to greater competitiveness and sustained growth in an increasingly data-driven marketplace.
References
1) Jusoff, Kamaruzaman, Mohd Sani, Nur Shaqifah, Mohd Taib, Shakirah, Shazi, Ainol Rahmah. "Finding knowledge in students social network". IDOSI Publications, 2011, https://core.ac.uk/download/328829224.pdf
2) Chakraborty, Partha Sarathi, Chattopadhyay, Manojit, Dan, Pranab K, Majumdar, Sitanath. "Application of artificial neural network in market segmentation: A review on recent trends". 'Growing Science', 2012, http://arxiv.org/abs/1202.2445
3) Hsu, Wenling, Jacobsen, Guy, Jin, Yu, Skudlark, Ann. "Using social media data to understand mobile customer experience and behavior". 2024, https://core.ac.uk/download/pdf/6618366.pdf
4) McGowan, Carol G., Ross, Pauline, Styger, Lee E. J.. "A comparison of theory and practice in market intelligence gathering for Australian micro-businesses and SMEs". 'Elsevier BV', 2012, https://core.ac.uk/download/11050152.pdf
5) Bloemhof, J.M., Collins, J., Ketter, W., Koppel, H., van der, Molensky, L.. "Business intelligence gap analysis: a user, supplier and academic perspective". ACM, 2024, https://core.ac.uk/download/pdf/29240104.pdf
6) Akperi, B.T., Matthews, P.C.. "Analysis of customer profiles on an electrical distribution network.". 'Institute of Electrical and Electronics Engineers (IEEE)', 2014, https://core.ac.uk/download/42127806.pdf
7) Mendes, Ema Alexandra Guilherme. "Identificação de perfis de clientes bancários : uma perspectiva de Ciência de Dados". 2023, https://core.ac.uk/download/612265748.pdf
8) Baraulya, E. V., Ivanchenko, Olyesia Valerievna, Mirgorodskaya, Olga Nikolaevna, Putilina, T. I.. "Marketing relations and communication infrastructure development in the banking sector based on big data mining". Eleftherios Thalassinos, 2019, https://core.ac.uk/download/288638643.pdf
9) Thorleuchter, Dirk, Van den Poel, Dirk. "Using webcrawling of publicly available websites to assess E-commerce relationships". 'Institute of Electrical and Electronics Engineers (IEEE)', 2012, https://core.ac.uk/download/55827049.pdf
Keywords
Data Mining, Customer Behavior, Predictive Analytics, Business Intelligence, Customer Trends, Personalized Experiences, Marketing Strategies, Product Optimization, Customer Satisfaction, Market Shifts, Customer Loyalty, Competitive Advantage, Data-Driven Decisions.
