发现不同细分市场的特定销售模式

IF 0.5 4区 计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING International Journal of Data Warehousing and Mining Pub Date : 2020-07-01 DOI:10.4018/ijdwm.2020070103
Cheng-Hsiung Weng, Cheng-Kui Huang
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引用次数: 1

摘要

制定不同的营销策略,以适用于不同的细分市场是一个值得注意的事业,市场营销经理。因此,营销经理应该识别不同细分市场的销售模式。该研究最初应用了最近频率货币(RFM)分数的概念,将交易数据集划分为几个子数据集(细分市场),并从这些细分市场中发现RFM项目集。此外,本研究还定义了三种销售特征(独特的、常见的和特殊的销售模式)来识别各种销售模式。特别地,我们也提出了一个新的标准(对比支持)来发现不同细分市场之间显著的销售模式。本研究开发了一种称为销售模式挖掘(SPMING)的算法,用于从几个基于RFM的细分市场中发现RFM项目集,然后识别独特的、常见的和特定的销售模式。两个真实数据集的实验结果表明,SPMING算法可以在不同的细分市场中发现特定的销售模式。
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Discovering Specific Sales Patterns Among Different Market Segments
Formulating different marketing strategies to apply to various market segments is a noteworthy undertaking for marketing managers. Accordingly, marketing managers should identify sales patterns among different market segments. The study initially applies the concept of recency–frequency–monetary (RFM) scores to segment transaction datasets into several sub-datasets (market segments) and discovers RFM itemsets from these market segments. In addition, three sales features (unique, common, and particular sales patterns) are defined to identify various sales patterns in this study. In particular, a new criterion (contrast support) is also proposed to discover notable sales patterns among different market segments. This study develops an algorithm, called sales pattern mining (SPMING), for discovering RFM itemsets from several RFM-based market segments and then identifying unique, common, and particular sales patterns. The experimental results from two real datasets show that the SPMING algorithm can discover specific sales patterns in various market segments.
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来源期刊
International Journal of Data Warehousing and Mining
International Journal of Data Warehousing and Mining COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.40
自引率
0.00%
发文量
20
审稿时长
>12 weeks
期刊介绍: The International Journal of Data Warehousing and Mining (IJDWM) disseminates the latest international research findings in the areas of data management and analyzation. IJDWM provides a forum for state-of-the-art developments and research, as well as current innovative activities focusing on the integration between the fields of data warehousing and data mining. Emphasizing applicability to real world problems, this journal meets the needs of both academic researchers and practicing IT professionals.The journal is devoted to the publications of high quality papers on theoretical developments and practical applications in data warehousing and data mining. Original research papers, state-of-the-art reviews, and technical notes are invited for publications. The journal accepts paper submission of any work relevant to data warehousing and data mining. Special attention will be given to papers focusing on mining of data from data warehouses; integration of databases, data warehousing, and data mining; and holistic approaches to mining and archiving
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