{"id":"https://openalex.org/W4387914505","doi":"https://doi.org/10.1109/codit58514.2023.10284430","title":"A Review on Machine Learning-Based Customer Churn Prediction in the Telecom Industry","display_name":"A Review on Machine Learning-Based Customer Churn Prediction in the Telecom Industry","publication_year":2023,"publication_date":"2023-07-03","ids":{"openalex":"https://openalex.org/W4387914505","doi":"https://doi.org/10.1109/codit58514.2023.10284430"},"language":"en","primary_location":{"id":"doi:10.1109/codit58514.2023.10284430","doi":"https://doi.org/10.1109/codit58514.2023.10284430","is_oa":false,"landing_page_url":"https://doi.org/10.1109/codit58514.2023.10284430","pdf_url":null,"source":{"id":"https://openalex.org/S4363607900","display_name":"International Conference on Control, Decision and Information Technologies (CoDIT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 9th International Conference on Control, Decision and Information Technologies (CoDIT)","raw_type":"proceedings-article"},"volume":null,"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/A5018506034","display_name":"Sawsan Barham","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I158749337","display_name":"Princess Sumaya University for Technology","ror":"https://ror.org/01jy46q10","country_code":"JO","type":"education","lineage":["https://openalex.org/I158749337"]}],"countries":["JO"],"is_corresponding":false,"raw_author_name":"Sawsan Barham","raw_affiliation_strings":["Princess Sumaya University for Technology, Amman, Jordan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Princess Sumaya University for Technology, Amman, Jordan","institution_ids":["https://openalex.org/I158749337"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093120768","display_name":"Nowfal Aweisi","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I158749337","display_name":"Princess Sumaya University for Technology","ror":"https://ror.org/01jy46q10","country_code":"JO","type":"education","lineage":["https://openalex.org/I158749337"]}],"countries":["JO"],"is_corresponding":false,"raw_author_name":"Nowfal Aweisi","raw_affiliation_strings":["Princess Sumaya University for Technology, Amman, Jordan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Princess Sumaya University for Technology, Amman, Jordan","institution_ids":["https://openalex.org/I158749337"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023950656","display_name":"Ala' Fathi Khalifeh","orcid":"https://orcid.org/0000-0003-3600-8090","observed_orcids":["https://orcid.org/0000-0003-3600-8090"]},"institutions":[{"id":"https://openalex.org/I230091363","display_name":"German Jordanian University","ror":"https://ror.org/02jgpyd84","country_code":"JO","type":"education","lineage":["https://openalex.org/I230091363"]}],"countries":["JO"],"is_corresponding":false,"raw_author_name":"Ala' Khalifeh","raw_affiliation_strings":["German Jordanian University, Amman, Jordan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"German Jordanian University, Amman, Jordan","institution_ids":["https://openalex.org/I230091363"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.2516,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.96469143,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2659","last_page":"2664"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":1.0,"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":1.0,"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/T10154","display_name":"Customer Service Quality and Loyalty","score":0.0,"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"}},{"id":"https://openalex.org/T11122","display_name":"Online Learning and Analytics","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/customer-churn-prediction","display_name":"customer churn prediction","score":0.9990000128746033},{"id":"https://openalex.org/keywords/telecommunications-industry","display_name":"telecommunications industry","score":0.824999988079071},{"id":"https://openalex.org/keywords/machine-learning-algorithms","display_name":"machine learning algorithms","score":0.843999981880188},{"id":"https://openalex.org/keywords/customer-retention","display_name":"customer retention","score":0.8569999933242798},{"id":"https://openalex.org/keywords/customer-loyalty","display_name":"customer loyalty","score":0.8870000243186951},{"id":"https://openalex.org/keywords/customer-behavior-prediction","display_name":"customer behavior prediction","score":0.6729999780654907},{"id":"https://openalex.org/keywords/forecasting-accuracy-comparison","display_name":"forecast accuracy comparison","score":0.5820000171661377}],"concepts":[{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5846207141876221},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5530593395233154},{"id":"https://openalex.org/C191511416","wikidata":"https://www.wikidata.org/wiki/Q999278","display_name":"Customer satisfaction","level":2,"score":0.5478763580322266},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.507190465927124},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.37561118602752686},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.35792580246925354},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.332854688167572},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.32133811712265015},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/codit58514.2023.10284430","doi":"https://doi.org/10.1109/codit58514.2023.10284430","is_oa":false,"landing_page_url":"https://doi.org/10.1109/codit58514.2023.10284430","pdf_url":null,"source":{"id":"https://openalex.org/S4363607900","display_name":"International Conference on Control, Decision and Information Technologies (CoDIT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 9th International Conference on Control, Decision and Information Technologies (CoDIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2792328488","https://openalex.org/W2806801170","https://openalex.org/W2900706564","https://openalex.org/W2939289155","https://openalex.org/W2973549182","https://openalex.org/W2981033936","https://openalex.org/W3007755116","https://openalex.org/W3047167994","https://openalex.org/W3130989307","https://openalex.org/W3132353323","https://openalex.org/W3133219337","https://openalex.org/W3139139994","https://openalex.org/W3153480621","https://openalex.org/W3200156107","https://openalex.org/W3204207740","https://openalex.org/W4200409524","https://openalex.org/W4200444575","https://openalex.org/W4205936864","https://openalex.org/W4210345259","https://openalex.org/W4210609593","https://openalex.org/W4210643267","https://openalex.org/W4211189082","https://openalex.org/W4220775133","https://openalex.org/W4221065845","https://openalex.org/W4226237685","https://openalex.org/W4226293701","https://openalex.org/W4312337747","https://openalex.org/W4313462038"],"related_works":["https://openalex.org/W4407869002","https://openalex.org/W4401222579","https://openalex.org/W7104565474","https://openalex.org/W3158965464","https://openalex.org/W4407320280","https://openalex.org/W7140310154","https://openalex.org/W4312337747","https://openalex.org/W4402096244","https://openalex.org/W4396230774","https://openalex.org/W7220617790"],"abstract_inverted_index":{"In":[0],"today's":[1],"business":[2],"landscape,":[3],"companies":[4],"are":[5,22,71,114,161],"facing":[6],"significant":[7],"difficulties":[8],"in":[9,37,122,148,189,193],"achieving":[10],"positive":[11],"client":[12],"interactions":[13],"and":[14,32,68,80],"maintaining":[15],"customers'":[16,47,78,109,146,164,191],"satisfaction.":[17],"As":[18],"a":[19,130,168],"result,":[20],"businesses":[21,70],"striving":[23],"to":[24,30,35,54,75,103,128,138,144,180],"focus":[25,170],"on":[26,171],"every":[27],"aspect":[28],"related":[29],"customers":[31,64],"their":[33,178],"behaviors":[34,79],"compete":[36],"the":[38,50,89,105,149,155,172,184,194],"industry.":[39,151,196],"This":[40,125],"has":[41,100],"become":[42],"increasingly":[43],"important":[44],"for":[45,59,163],"building":[46],"loyalties,":[48],"given":[49],"many":[51],"opportunities":[52],"available":[53],"customize":[55],"services":[56,91],"or":[57,86,92],"products":[58],"each":[60],"customer.":[61],"To":[62],"prevent":[63],"from":[65,136],"becoming":[66],"dissatisfied":[67],"leaving,":[69],"deploying":[72],"various":[73,133],"techniques":[74,121,143,188],"efficiently":[76],"predict":[77,145],"identify":[81],"those":[82],"who":[83],"may":[84],"churn":[85,110,147,165,192],"stop":[87],"using":[88],"company's":[90],"products.":[93],"The":[94,152],"rise":[95],"of":[96,107,132,186],"machine":[97,119,157],"learning":[98,120,158],"applications":[99],"significantly":[101],"contributed":[102],"addressing":[104,190],"challenge":[106],"predicting":[108],"rates.":[111],"Researchers":[112],"worldwide":[113],"now":[115],"moving":[116],"towards":[117],"applying":[118],"this":[123],"area.":[124],"paper":[126,153],"aims":[127],"present":[129],"review":[131],"studies":[134],"conducted":[135],"2019":[137],"2022":[139],"that":[140,160],"utilized":[141],"machine-learning":[142],"telecom":[150,173,195],"summarizes":[154],"different":[156],"algorithms":[159],"used":[162],"prediction,":[166],"with":[167],"particular":[169],"industry,":[174],"as":[175,177],"well":[176],"accuracy,":[179],"provide":[181],"insights":[182],"into":[183],"effectiveness":[185],"these":[187]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":1}],"updated_date":"2026-10-30T12:39:25.321706","created_date":"2023-10-25T00:00:00"}