The recent technology innovation such as big data and its applications has been adopted widely in industries in order to deal with massive datasets. Through data integration, data analysis, and data interpretation, big data technologies can assist business stakeholders in gaining the benefits in their decision-making process. In this research, we hypothesize that combining several big data analytical methods for analyzing integrated customer data can provide more effective and intelligent strategies. A hybrid model combining recency, frequency, and monetary value (RFM) model, K-means clustering, Naïve Baye's algorithm, and linked Bloom filters is proposed to target different customer segments. Our results suggest that (1) the use of big data analytics can provide marketers a direction to make marketing strategies; (2) the use of big data analytics can predict potential customer demands; and (3) the proposed linked Bloom filters can store inactive data in a more efficient way for future use.

为了处理海量数据集，诸如大数据及其应用之类的最新技术创新已在行业中广泛采用。通过数据集成，数据分析和数据解释，大数据技术可以帮助业务利益相关者在决策过程中获得收益。在这项研究中，我们假设结合几种大数据分析方法来分析集成的客户数据可以提供更有效和智能的策略。提出了一种结合了新近度，频率和货币价值（RFM）模型，K均值聚类，朴素贝叶算法和链接的Bloom过滤器的混合模型，以针对不同的客户群。我们的结果表明：（1）大数据分析的使用可以为营销人员提供制定营销策略的指导；（2）使用大数据分析可以预测潜在的客户需求；（3）建议的链接布隆过滤器可以更有效的方式存储非活动数据，以备将来使用。