A machine learning approach to consumer behavior in supermarket analytics

Tasos Stylianou , Aikaterina Pantelidou
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Abstract

The rapid advancement of Big Data technologies has significantly influenced multiple sectors, with the retail industry being a key impact area. This study explores the relationship between Big Data analytics and consumer behavior, focusing on supermarket transaction data to extract macroeconomic insights. Using a dataset of over two million records from a multinational supermarket chain, the research employs a suite of advanced analytical techniques, including the Apriori algorithm for association rule mining, K-Means clustering for customer segmentation, collaborative filtering for recommendation systems, and AutoRegressive Integrated Moving Average (ARIMA) for time series forecasting. The study identifies purchasing patterns, segment-specific preferences, and temporal shopping behaviors. The results reveal distinct associations among frequently co-purchased products, clear segmentations based on shopping habits, and predictive trends in consumer demand, offering valuable input for marketing, inventory management, and policy-making. Beyond the operational insights, this study highlights the potential of transactional data to reflect broader economic shifts, such as changes in consumption patterns during periods of economic uncertainty, thus linking consumer micro-behaviors to macroeconomic indicators. The novelty of this work lies in its integration of multiple machine learning techniques within a unified framework that connects retail analytics to economic policymaking, thereby extending the application of Big Data from commercial strategy to public economics.
超市分析中消费者行为的机器学习方法
大数据技术的快速发展对多个行业产生了重大影响,零售业是一个关键的影响领域。本研究探讨了大数据分析与消费者行为之间的关系,重点关注超市交易数据以提取宏观经济见解。该研究使用了来自跨国连锁超市的超过200万条记录的数据集,采用了一套先进的分析技术,包括用于关联规则挖掘的Apriori算法,用于客户细分的K-Means聚类,用于推荐系统的协同过滤,以及用于时间序列预测的自回归综合移动平均(ARIMA)。该研究确定了购买模式、特定细分市场的偏好和时间购物行为。结果揭示了经常共同购买的产品之间的明显关联,基于购物习惯的明确细分,以及消费者需求的预测趋势,为营销,库存管理和决策提供了有价值的输入。除了运营方面的见解,本研究还强调了交易数据反映更广泛经济变化的潜力,例如经济不确定时期消费模式的变化,从而将消费者的微观行为与宏观经济指标联系起来。这项工作的新颖之处在于它将多种机器学习技术集成在一个统一的框架内,将零售分析与经济政策制定联系起来,从而将大数据的应用从商业战略扩展到公共经济学。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
3.90
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