{"title":"A Tool for Intelligent Customer Analytics","authors":"D. Nauck, D. Ruta, M. Spott, B. Azvine","doi":"10.1109/IS.2006.348473","DOIUrl":null,"url":null,"abstract":"Businesses collect and keep large volumes of customer data as part of their processes. Analysis of this data by business users often leads to discovery of valuable patterns and trends that otherwise would go unnoticed and that can lead to prioritization of decisions on future investments. The majority of tools currently available to business users are typically limited to computing summary statistics, simple visualization and reporting of data. More complex tools that could offer possible explanations for observations, discover knowledge, or allow making predictions are usually aimed at an academic audience or at users who are highly trained in analytics. However, it is business users with little experience in analytics who require access to tools that allow them to easily model customer behavior and build future scenarios. In this paper we present a tool we developed for business users to perform advanced analysis on customer data","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 3rd International IEEE Conference Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IS.2006.348473","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Businesses collect and keep large volumes of customer data as part of their processes. Analysis of this data by business users often leads to discovery of valuable patterns and trends that otherwise would go unnoticed and that can lead to prioritization of decisions on future investments. The majority of tools currently available to business users are typically limited to computing summary statistics, simple visualization and reporting of data. More complex tools that could offer possible explanations for observations, discover knowledge, or allow making predictions are usually aimed at an academic audience or at users who are highly trained in analytics. However, it is business users with little experience in analytics who require access to tools that allow them to easily model customer behavior and build future scenarios. In this paper we present a tool we developed for business users to perform advanced analysis on customer data