Studying customer behavior and anticipating future trends is a challenging task, as customer behavior is complex and constantly evolving. To effectively anticipate future trends, businesses need to analyze large amounts of data, use sophisticated analytical techniques, and stay up-to-date with the latest research and industry trends. In this paper, we propose a comprehensive framework to identify trends in consumer behavior using multiple layers of processing, including clustering, classification, and association rule learning. The aim is to help a major retailer in Saudi Arabia better understand customer behavior by utilizing the power of big data analysis. The proposed framework is presented as being generalized to gain insight into the generated big data and enable data-driven decision-making in other relevant domains. We developed this framework in collaboration with a large supermarket chain in Saudi Arabia, which provided us with over 1,000,000 sales transaction records belonging to around 30,000 of their loyal customers. In this study, we apply our proposed framework to those data as a case study and present our initial results of consumer clustering and association rules for each cluster. Moreover, we analyze our findings to figure out how we can further utilize intelligence to predict customer behavior in clustered groups.

中文翻译：

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提高市场绩效指标的消费者行为分析框架：以沙特零售业为例

研究客户行为并预测未来趋势是一项具有挑战性的任务，因为客户行为非常复杂且不断变化。为了有效预测未来趋势，企业需要分析大量数据，使用复杂的分析技术，并及时了解最新的研究和行业趋势。在本文中，我们提出了一个综合框架，使用多层处理（包括聚类、分类和关联规则学习）来识别消费者行为趋势。目的是利用大数据分析的力量帮助沙特阿拉伯的一家大型零售商更好地了解客户行为。所提出的框架被提出为通用的，以深入了解生成的大数据并在其他相关领域实现数据驱动的决策。我们与沙特阿拉伯的一家大型连锁超市合作开发了这个框架，该超市为我们提供了属于其大约 30,000 名忠实客户的超过 1,000,000 条销售交易记录。在本研究中，我们将我们提出的框架应用于这些数据作为案例研究，并展示我们的消费者聚类和每个聚类的关联规则的初步结果。此外，我们分析我们的发现，以找出如何进一步利用情报来预测集群中的客户行为。