In the digital era, Big Data Analytics has emerged as a crucial tool for optimizing marketing strategies. This research explores the integration of Big Data into marketing, aiming to identify effective analytical techniques and their impact on marketing outcomes. The study utilized secondary data from various sources, including sales transactions, social media interactions, customer demographics, and web analytics. The analysis process involved data cleaning, integration, predictive modeling, clustering, sentiment analysis, and data visualization. The findings reveal that promotional campaigns and seasonal discounts significantly boost sales, with customer segmentation identifying three key groups: discount hunters, loyal customers, and occasional shoppers. Sentiment analysis shows positive customer feedback, though logistics-related issues warrant improvement. These results underscore the importance of targeted and personalized marketing strategies driven by data insights. The research contributes to marketing theories by providing empirical evidence on the effectiveness of Big Data Analytics in enhancing marketing strategies. Further research is recommended to explore its applicability across different industries, incorporate more diverse data sources, and utilize advanced analytical techniques to refine marketing strategies.
                        
                        
                        
                        
                            
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