Big Data with Cloud Computing Model for Customer Need Identification in E-Commerce Industry

In this study, we investigate how Big Data and Cloud computing can work together to better understand what online shoppers really want. The proliferation of e-commerce has increased the importance of listening to and meeting the requirements of customers. This research utilizes a wide variety of data sources to perform descriptive and predictive analysis, including customer transaction records, website clickstream data, customer reviews, and social media interactions. The prediction model's impressive prognostic accuracy may be attributed to its incorporation of client demographics, historical purchase history, and sentiment analysis. Text mining and natural language processing (NLP) are proven to provide insights from consumer input, while cloud computing architecture is shown to increase operational efficiency and scalability. In this paper, we explore how E-commerce enterprises may benefit from this game-changing technology by improving their customer service, cutting costs, and gaining an edge in the market. Business leaders, data scientists, cloud providers, academics, and the public all stand to gain from this study's findings. Future research directions that hold promise for keeping the E-commerce landscape at the cutting edge of innovation and customer-centric strategies include advanced deep learning techniques; real-time customer need identification; data privacy; scalability; cross-channel analysis; and industry benchmarking.