{"id":"https://openalex.org/W1533166359","doi":"https://doi.org/10.1109/icdm.2003.1250931","title":"Integrating customer value considerations into predictive modeling","display_name":"Integrating customer value considerations into predictive modeling","publication_year":2004,"publication_date":"2004-04-23","ids":{"openalex":"https://openalex.org/W1533166359","doi":"https://doi.org/10.1109/icdm.2003.1250931","mag":"1533166359"},"language":"en","primary_location":{"id":"doi:10.1109/icdm.2003.1250931","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm.2003.1250931","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Third IEEE International Conference on Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039021124","display_name":"Saharon Rosset","orcid":"https://orcid.org/0000-0002-4458-9545"},"institutions":[{"id":"https://openalex.org/I4210106905","display_name":"Amdocs (United Kingdom)","ror":"https://ror.org/01mfgkr43","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210093497","https://openalex.org/I4210106905"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"S. Rosset","raw_affiliation_strings":["Amdocs Development Centre India Private Limited","Amdocs Ltd"],"affiliations":[{"raw_affiliation_string":"Amdocs Development Centre India Private Limited","institution_ids":[]},{"raw_affiliation_string":"Amdocs Ltd","institution_ids":["https://openalex.org/I4210106905"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044020220","display_name":"Einat Neumann","orcid":null},"institutions":[{"id":"https://openalex.org/I4210106905","display_name":"Amdocs (United Kingdom)","ror":"https://ror.org/01mfgkr43","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210093497","https://openalex.org/I4210106905"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"E. Neumann","raw_affiliation_strings":["Amdocs Development Centre India Private Limited","Amdocs Ltd"],"affiliations":[{"raw_affiliation_string":"Amdocs Development Centre India Private Limited","institution_ids":[]},{"raw_affiliation_string":"Amdocs Ltd","institution_ids":["https://openalex.org/I4210106905"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5039021124"],"corresponding_institution_ids":["https://openalex.org/I4210106905"],"apc_list":null,"apc_paid":null,"fwci":4.1956,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.93119969,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"283","last_page":"290"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.988099992275238,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9664999842643738,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7333929538726807},{"id":"https://openalex.org/keywords/customer-lifetime-value","display_name":"Customer lifetime value","score":0.5924918055534363},{"id":"https://openalex.org/keywords/value","display_name":"Value (mathematics)","score":0.578643262386322},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.5696878433227539},{"id":"https://openalex.org/keywords/predictive-value","display_name":"Predictive value","score":0.5290486216545105},{"id":"https://openalex.org/keywords/customer-value","display_name":"Customer value","score":0.5070211887359619},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4378953278064728},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.404358834028244},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3910275399684906},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3524233102798462},{"id":"https://openalex.org/keywords/customer-retention","display_name":"Customer retention","score":0.24846899509429932},{"id":"https://openalex.org/keywords/marketing","display_name":"Marketing","score":0.1149565577507019},{"id":"https://openalex.org/keywords/software-engineering","display_name":"Software engineering","score":0.09454947710037231},{"id":"https://openalex.org/keywords/service","display_name":"Service (business)","score":0.07771071791648865},{"id":"https://openalex.org/keywords/service-quality","display_name":"Service quality","score":0.06438747048377991}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7333929538726807},{"id":"https://openalex.org/C130721881","wikidata":"https://www.wikidata.org/wiki/Q1146253","display_name":"Customer lifetime value","level":5,"score":0.5924918055534363},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.578643262386322},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.5696878433227539},{"id":"https://openalex.org/C3019719930","wikidata":"https://www.wikidata.org/wiki/Q3910099","display_name":"Predictive value","level":2,"score":0.5290486216545105},{"id":"https://openalex.org/C2985066332","wikidata":"https://www.wikidata.org/wiki/Q556441","display_name":"Customer value","level":3,"score":0.5070211887359619},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4378953278064728},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.404358834028244},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3910275399684906},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3524233102798462},{"id":"https://openalex.org/C101276457","wikidata":"https://www.wikidata.org/wiki/Q5196474","display_name":"Customer retention","level":4,"score":0.24846899509429932},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.1149565577507019},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.09454947710037231},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.07771071791648865},{"id":"https://openalex.org/C140781008","wikidata":"https://www.wikidata.org/wiki/Q1221081","display_name":"Service quality","level":3,"score":0.06438747048377991},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0},{"id":"https://openalex.org/C34447519","wikidata":"https://www.wikidata.org/wiki/Q179522","display_name":"Market economy","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdm.2003.1250931","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm.2003.1250931","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Third IEEE International Conference on Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4099999964237213}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W145450961","https://openalex.org/W1480376833","https://openalex.org/W1517113043","https://openalex.org/W1528905581","https://openalex.org/W1601129096","https://openalex.org/W1819386543","https://openalex.org/W2015233145","https://openalex.org/W2018928989","https://openalex.org/W2077949207","https://openalex.org/W2093802952","https://openalex.org/W2119073761","https://openalex.org/W2170913656","https://openalex.org/W2795536332","https://openalex.org/W3016133651","https://openalex.org/W4301861531","https://openalex.org/W6605907934","https://openalex.org/W6636078541","https://openalex.org/W6638261285","https://openalex.org/W6674113145","https://openalex.org/W6677870239"],"related_works":["https://openalex.org/W3124907693","https://openalex.org/W1965792358","https://openalex.org/W3151249992","https://openalex.org/W2390517436","https://openalex.org/W2026882341","https://openalex.org/W4210844823","https://openalex.org/W2368146644","https://openalex.org/W2324800818","https://openalex.org/W2322262946","https://openalex.org/W199631057"],"abstract_inverted_index":{"The":[0],"success":[1],"of":[2,26,39,67,88,102],"prediction":[3,28],"models":[4],"for":[5,29,92],"business":[6],"purposes":[7],"should":[8,18],"not":[9],"be":[10],"measured":[11],"by":[12],"their":[13],"accuracy":[14],"only.":[15],"Their":[16],"evaluation":[17,76],"also":[19],"take":[20],"into":[21],"account":[22],"the":[23,37,64,72,98],"higher":[24],"importance":[25],"precise":[27],"\"valuable\"":[30],"customers.":[31,57],"We":[32,58],"illustrate":[33],"this":[34],"idea":[35],"through":[36],"example":[38],"churn":[40,54],"modelling":[41],"in":[42,71],"telecommunications,":[43],"where":[44],"it":[45],"is":[46,83,94],"obviously":[47],"much":[48],"more":[49],"important":[50],"to":[51,97],"identify":[52],"potential":[53],"among":[55],"valuable":[56],"discuss,":[59],"both":[60],"theoretically":[61],"and":[62,77],"empirically,":[63],"optimal":[65],"use":[66],"\"customer":[68],"value\"":[69],"data":[70],"model":[73,75],"training,":[74],"scoring":[78],"stages.":[79],"Our":[80],"main":[81],"conclusion":[82],"that":[84],"a":[85],"nontrivial":[86],"approach":[87],"using":[89,104],"\"decayed\"":[90],"value-weights":[91],"training":[93],"usually":[95],"preferable":[96],"two":[99],"obvious":[100],"approaches":[101],"either":[103],"nondecayed":[105],"customer":[106],"values":[107],"as":[108],"weights":[109],"or":[110],"ignoring":[111],"them.":[112]},"counts_by_year":[{"year":2016,"cited_by_count":1}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}
