{"id":"https://openalex.org/W2557639246","doi":"https://doi.org/10.1145/3006299.3006324","title":"Not too late to identify potential churners","display_name":"Not too late to identify potential churners","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2557639246","doi":"https://doi.org/10.1145/3006299.3006324","mag":"2557639246"},"language":"en","primary_location":{"id":"doi:10.1145/3006299.3006324","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3006299.3006324","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies","raw_type":"proceedings-article"},"type":"conference-paper","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/A5056363072","display_name":"Jingjiao Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingjiao Zhang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050099843","display_name":"Jiaqing Fu","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaqing Fu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100633263","display_name":"Chunhong Zhang","orcid":"https://orcid.org/0000-0003-3008-1887"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunhong Zhang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102979295","display_name":"Xin Ke","orcid":"https://orcid.org/0000-0002-5728-1689"},"institutions":[{"id":"https://openalex.org/I4210136246","display_name":"China Telecom (China)","ror":"https://ror.org/03jgnzt20","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210136246"]},{"id":"https://openalex.org/I4387153335","display_name":"China Telecom","ror":"https://ror.org/05p67dv18","country_code":null,"type":"company","lineage":["https://openalex.org/I4387153335"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Ke","raw_affiliation_strings":["China Telecom Corporation Limited Beijing Research Institute, Beijing China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Telecom Corporation Limited Beijing Research Institute, Beijing China","institution_ids":["https://openalex.org/I4210136246","https://openalex.org/I4387153335"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075198788","display_name":"Zheng Hu","orcid":"https://orcid.org/0000-0002-8874-5466"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Hu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"194","last_page":"199"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9872999787330627,"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/T10154","display_name":"Customer Service Quality and Loyalty","score":0.9829000234603882,"subfield":{"id":"https://openalex.org/subfields/1407","display_name":"Organizational Behavior and Human Resource Management"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/churning","display_name":"Churning","score":0.9583078622817993},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7071282863616943},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.5845486521720886},{"id":"https://openalex.org/keywords/interval","display_name":"Interval (graph theory)","score":0.5745826959609985},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.5047763586044312},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48023444414138794},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4539364278316498},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45279937982559204},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42631715536117554},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10695889592170715},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09838664531707764}],"concepts":[{"id":"https://openalex.org/C161664118","wikidata":"https://www.wikidata.org/wiki/Q1089933","display_name":"Churning","level":2,"score":0.9583078622817993},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7071282863616943},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.5845486521720886},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.5745826959609985},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.5047763586044312},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48023444414138794},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4539364278316498},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45279937982559204},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42631715536117554},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10695889592170715},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09838664531707764},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C145236788","wikidata":"https://www.wikidata.org/wiki/Q28161","display_name":"Labour economics","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3006299.3006324","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3006299.3006324","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies","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":0.6200000047683716}],"awards":[{"id":"https://openalex.org/G1867295167","display_name":null,"funder_award_id":"61302077,61421061","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2028342077","display_name":null,"funder_award_id":"2014AA01A706","funder_id":"https://openalex.org/F4320321540","funder_display_name":"Ministry of Science and Technology of the People's Republic of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321540","display_name":"Ministry of Science and Technology of the People's Republic of China","ror":"https://ror.org/027s68j25"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1546088349","https://openalex.org/W1732973385","https://openalex.org/W1986969158","https://openalex.org/W2005755239","https://openalex.org/W2010345901","https://openalex.org/W2033670306","https://openalex.org/W2039655141","https://openalex.org/W2053193751","https://openalex.org/W2069300565","https://openalex.org/W2071128523","https://openalex.org/W2115383008","https://openalex.org/W2121946324","https://openalex.org/W2122838852","https://openalex.org/W2127199396","https://openalex.org/W2138906630","https://openalex.org/W2160505632","https://openalex.org/W2161634631","https://openalex.org/W2163922914","https://openalex.org/W2241784456","https://openalex.org/W2259915738","https://openalex.org/W2291250397","https://openalex.org/W4285719527","https://openalex.org/W4394224089","https://openalex.org/W6760352437"],"related_works":["https://openalex.org/W2586853404","https://openalex.org/W3125064327","https://openalex.org/W2370947527","https://openalex.org/W1595793304","https://openalex.org/W2366222894","https://openalex.org/W2377544927","https://openalex.org/W2376686040","https://openalex.org/W4318612353","https://openalex.org/W4389829534","https://openalex.org/W4390939596"],"abstract_inverted_index":{"Churn":[0,114],"prediction,":[1],"which":[2,109,123],"is":[3,13,82,138,173],"to":[4,9,20,46,95],"identify":[5],"who":[6],"are":[7,36],"prone":[8],"abandon":[10],"the":[11,18,22,32,34,38,42,50,65,74,93,97,104,120,142,152,165,170],"subscription,":[12],"of":[14,53,68,119,130,151],"high":[15,107],"significance":[16],"for":[17],"operators":[19,43,154],"retain":[21,47],"potential":[23],"churners.":[24],"It":[25],"should":[26],"be":[27],"noted":[28],"that,":[29],"in":[30,56,133,155],"practice,":[31],"earlier":[33,51],"churners":[35,98],"identified,":[37],"more":[39],"effective":[40],"strategies":[41],"can":[44],"develop":[45],"them.":[48],"While":[49],"prediction":[52,171],"customer":[54],"churn":[55],"telecommunication":[57],"industry":[58],"has":[59],"not":[60],"been":[61],"well":[62],"investigated":[63],"and":[64,79,128],"predicting":[66],"accuracy":[67,105],"previous":[69,166],"work":[70,167],"degrades":[71],"unacceptably":[72],"when":[73,169],"interval":[75,172],"between":[76],"observed":[77],"attributes":[78,127],"predicted":[80],"labels":[81],"prolonged.":[83],"In":[84],"this":[85],"paper,":[86],"from":[87],"a":[88,145],"different":[89],"perspective,":[90],"we":[91,110],"study":[92],"effectiveness":[94],"find":[96],"as":[99,101,112],"early":[100],"possible":[102],"with":[103],"being":[106],"enough,":[108],"define":[111],"Early":[113],"Prediction.":[115],"The":[116,157],"predictive":[117],"performance":[118],"proposed":[121],"model,":[122],"takes":[124],"time":[125],"series":[126],"influence":[129],"churning":[131],"contacts":[132],"social":[134],"network":[135],"into":[136],"consideration,":[137],"investigated.":[139],"We":[140],"evaluate":[141],"method":[143],"using":[144],"12-month-long":[146],"dataset":[147],"collected":[148],"by":[149],"one":[150],"largest":[153],"China.":[156],"results":[158],"show":[159],"that":[160],"our":[161],"model":[162],"significantly":[163],"outperforms":[164],"especially":[168],"larger":[174],"than":[175],"3":[176],"months.":[177]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}