{"title":"查询管理:联系人决策规则","authors":"Behram J. Hansotia","doi":"10.1002/dir.4000090104","DOIUrl":null,"url":null,"abstract":"<div><p>This article develops the technology for evaluating contact decisions over time. It is assumed that a company has a database of inquirers and wishes to determine who should be selected to receive a potential stream of contacts. Individuals are first evaluated for receiving the initial contact with a break-even rule based on customers’ lifetime values and the potential of receiving up to four contacts. Those contracted who fail to respond are reevaluated for the second contact, and so on. The latter half of the article discusses the longitudinal response model that is needed to predict conditional response probabilities for evaluating the decision rules. This model is based on recent work on discrete survival models using logistic regression.</p></div>","PeriodicalId":100774,"journal":{"name":"Journal of Direct Marketing","volume":"9 1","pages":"Pages 17-28"},"PeriodicalIF":0.0000,"publicationDate":"1995-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/dir.4000090104","citationCount":"2","resultStr":"{\"title\":\"Inquiry management: contact decision rules\",\"authors\":\"Behram J. Hansotia\",\"doi\":\"10.1002/dir.4000090104\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This article develops the technology for evaluating contact decisions over time. It is assumed that a company has a database of inquirers and wishes to determine who should be selected to receive a potential stream of contacts. Individuals are first evaluated for receiving the initial contact with a break-even rule based on customers’ lifetime values and the potential of receiving up to four contacts. Those contracted who fail to respond are reevaluated for the second contact, and so on. The latter half of the article discusses the longitudinal response model that is needed to predict conditional response probabilities for evaluating the decision rules. This model is based on recent work on discrete survival models using logistic regression.</p></div>\",\"PeriodicalId\":100774,\"journal\":{\"name\":\"Journal of Direct Marketing\",\"volume\":\"9 1\",\"pages\":\"Pages 17-28\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1002/dir.4000090104\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Direct Marketing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0892059195703159\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Direct Marketing","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0892059195703159","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This article develops the technology for evaluating contact decisions over time. It is assumed that a company has a database of inquirers and wishes to determine who should be selected to receive a potential stream of contacts. Individuals are first evaluated for receiving the initial contact with a break-even rule based on customers’ lifetime values and the potential of receiving up to four contacts. Those contracted who fail to respond are reevaluated for the second contact, and so on. The latter half of the article discusses the longitudinal response model that is needed to predict conditional response probabilities for evaluating the decision rules. This model is based on recent work on discrete survival models using logistic regression.