{"title":"CRISP:基于客户反应的迭代分割程序,用于直接营销中的反应建模","authors":"Wayne S. DeSarbo , Venkatram Ramaswamy","doi":"10.1002/dir.4000080304","DOIUrl":null,"url":null,"abstract":"<div><p>We present a system of <em>empirical</em> segmentation procedures called CRISP (<strong>C</strong>ustomer <strong>R</strong>esponse-based <strong>I</strong>terative <strong>S</strong>egrnentation <strong>P</strong>rocedures) for <em>simultaneously</em> deriving market segments and estimating models of customer response in each of these segments. While the common practice in response modeling is to estimate a single response model for all customers in the database, we allow for <em>customer heterogeneity</em> by calibrating response models for different (unknown) customer segments. We describe a system of iterative segmentation procedures that simultaneously estimate the number of customer segments, the sizes of each derived segment, the values of segment-level response parameters, and their statistical significance, all in a maximum likelihood framework that can accommodate various types of commonly collected response data. To illustrate the CRISP system, we discuss an empirical application entailing typical binary response data for a large number of households for a mail subscription offer from a major magazine publisher. We describe the specific implementation of CRISP to this particular problem of list segmentation, and discuss its Potential usefulness to direct mail marketers. We conclude by discussing the general uses of the CRISP system for response modeling in other direct marketing contexts besides list segmentation.</p></div>","PeriodicalId":100774,"journal":{"name":"Journal of Direct Marketing","volume":"8 3","pages":"Pages 7-20"},"PeriodicalIF":0.0000,"publicationDate":"1994-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/dir.4000080304","citationCount":"64","resultStr":"{\"title\":\"CRISP: Customer response based iterative segmentation procedures for response modeling in direct marketing\",\"authors\":\"Wayne S. DeSarbo , Venkatram Ramaswamy\",\"doi\":\"10.1002/dir.4000080304\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>We present a system of <em>empirical</em> segmentation procedures called CRISP (<strong>C</strong>ustomer <strong>R</strong>esponse-based <strong>I</strong>terative <strong>S</strong>egrnentation <strong>P</strong>rocedures) for <em>simultaneously</em> deriving market segments and estimating models of customer response in each of these segments. While the common practice in response modeling is to estimate a single response model for all customers in the database, we allow for <em>customer heterogeneity</em> by calibrating response models for different (unknown) customer segments. We describe a system of iterative segmentation procedures that simultaneously estimate the number of customer segments, the sizes of each derived segment, the values of segment-level response parameters, and their statistical significance, all in a maximum likelihood framework that can accommodate various types of commonly collected response data. To illustrate the CRISP system, we discuss an empirical application entailing typical binary response data for a large number of households for a mail subscription offer from a major magazine publisher. We describe the specific implementation of CRISP to this particular problem of list segmentation, and discuss its Potential usefulness to direct mail marketers. We conclude by discussing the general uses of the CRISP system for response modeling in other direct marketing contexts besides list segmentation.</p></div>\",\"PeriodicalId\":100774,\"journal\":{\"name\":\"Journal of Direct Marketing\",\"volume\":\"8 3\",\"pages\":\"Pages 7-20\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1002/dir.4000080304\",\"citationCount\":\"64\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Direct Marketing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0892059194703815\",\"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/S0892059194703815","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
CRISP: Customer response based iterative segmentation procedures for response modeling in direct marketing
We present a system of empirical segmentation procedures called CRISP (Customer Response-based Iterative Segrnentation Procedures) for simultaneously deriving market segments and estimating models of customer response in each of these segments. While the common practice in response modeling is to estimate a single response model for all customers in the database, we allow for customer heterogeneity by calibrating response models for different (unknown) customer segments. We describe a system of iterative segmentation procedures that simultaneously estimate the number of customer segments, the sizes of each derived segment, the values of segment-level response parameters, and their statistical significance, all in a maximum likelihood framework that can accommodate various types of commonly collected response data. To illustrate the CRISP system, we discuss an empirical application entailing typical binary response data for a large number of households for a mail subscription offer from a major magazine publisher. We describe the specific implementation of CRISP to this particular problem of list segmentation, and discuss its Potential usefulness to direct mail marketers. We conclude by discussing the general uses of the CRISP system for response modeling in other direct marketing contexts besides list segmentation.