Sarcouncil Journal of Engineering and Computer Sciences

Abstract: The article provides an in-depth study of a large data-driven Rewards Analytics Platform that will be able to maximize customer relations by providing specific approaches to retailing. Its architecture uses Hadoop technologies(HDFS, HBase, MapReduce, and Pig) to process and analyze large quantities of customer transaction data found in different retail channels. The sophisticated data processing capabilities of the platform allow it to carry out an advanced customer segmentation relying on the dimensions of customer behaviour that are purchase frequency, recency of engagement, customer product affinity, campaign response rates, and seasonal shopping behaviour. Embarking on technical data pipelines incorporated with more problem-solving visualization interfaces, the system allows retailers to create customized promotion measures that can resonate with the personal preferences of various customers elaborate manner. Both the nitty-gritty of the technology implementation and the business implications, such as more meaningful promotions, less marketing waste, faster execution of a campaign, the ability to measure more, and strengthening customer loyalty, are discussed. Any forward-looking design of architecture has AI-readiness capabilities that move retailers to use higher and more advanced analytical skills as technologies advance, offering a model to follow and replicate across retail ecosystems that are data-driven and personalized as they compete.