{"title":"发现不同细分市场的特定销售模式","authors":"Cheng-Hsiung Weng, Cheng-Kui Huang","doi":"10.4018/ijdwm.2020070103","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"28 1","pages":"37-59"},"PeriodicalIF":0.5000,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Discovering Specific Sales Patterns Among Different Market Segments\",\"authors\":\"Cheng-Hsiung Weng, Cheng-Kui Huang\",\"doi\":\"10.4018/ijdwm.2020070103\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":54963,\"journal\":{\"name\":\"International Journal of Data Warehousing and Mining\",\"volume\":\"28 1\",\"pages\":\"37-59\"},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2020-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Data Warehousing and Mining\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.4018/ijdwm.2020070103\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Data Warehousing and Mining","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.4018/ijdwm.2020070103","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
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.
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