{"id":"https://openalex.org/W4414428470","doi":"https://doi.org/10.3390/make7030105","title":"Customer Churn Prediction: A Systematic Review of Recent Advances, Trends, and Challenges in Machine Learning and Deep Learning","display_name":"Customer Churn Prediction: A Systematic Review of Recent Advances, Trends, and Challenges in Machine Learning and Deep Learning","publication_year":2025,"publication_date":"2025-09-21","ids":{"openalex":"https://openalex.org/W4414428470","doi":"https://doi.org/10.3390/make7030105"},"language":"en","primary_location":{"id":"doi:10.3390/make7030105","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7030105","pdf_url":"https://www.mdpi.com/2504-4990/7/3/105/pdf?version=1758447727","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-4990/7/3/105/pdf?version=1758447727","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010769150","display_name":"Mehdi Imani","orcid":"https://orcid.org/0000-0001-9613-1125"},"institutions":[{"id":"https://openalex.org/I161593684","display_name":"Stockholm University","ror":"https://ror.org/05f0yaq80","country_code":"SE","type":"education","lineage":["https://openalex.org/I161593684"]}],"countries":["SE"],"is_corresponding":true,"raw_author_name":"Mehdi Imani","raw_affiliation_strings":["Department of Computer and System Sciences, Stockholm University, SE-16455 Stockholm, Sweden"],"affiliations":[{"raw_affiliation_string":"Department of Computer and System Sciences, Stockholm University, SE-16455 Stockholm, Sweden","institution_ids":["https://openalex.org/I161593684"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056431850","display_name":"Majid Joudaki","orcid":"https://orcid.org/0000-0003-2923-4153"},"institutions":[{"id":"https://openalex.org/I4210129530","display_name":"Ayatollah Boroujerdi University","ror":"https://ror.org/0377qcz53","country_code":"IR","type":"education","lineage":["https://openalex.org/I4210129530"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Majid Joudaki","raw_affiliation_strings":["Department of Computer Engineering, Faculty of Engineering, Ayatollah Boroujerdi University, Boroujerd 69199-69737, Iran"],"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Faculty of Engineering, Ayatollah Boroujerdi University, Boroujerd 69199-69737, Iran","institution_ids":["https://openalex.org/I4210129530"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055852290","display_name":"Ali Beikmohammadi","orcid":"https://orcid.org/0000-0003-4884-4600"},"institutions":[{"id":"https://openalex.org/I161593684","display_name":"Stockholm University","ror":"https://ror.org/05f0yaq80","country_code":"SE","type":"education","lineage":["https://openalex.org/I161593684"]}],"countries":["SE"],"is_corresponding":true,"raw_author_name":"Ali Beikmohammadi","raw_affiliation_strings":["Department of Computer and System Sciences, Stockholm University, SE-16455 Stockholm, Sweden"],"affiliations":[{"raw_affiliation_string":"Department of Computer and System Sciences, Stockholm University, SE-16455 Stockholm, Sweden","institution_ids":["https://openalex.org/I161593684"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005432053","display_name":"Hamid R. Arabnia","orcid":"https://orcid.org/0000-0003-3943-0094"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hamid Arabnia","raw_affiliation_strings":["School of Computing, University of Georgia, Athens, GA 30602, USA"],"affiliations":[{"raw_affiliation_string":"School of Computing, University of Georgia, Athens, GA 30602, USA","institution_ids":["https://openalex.org/I165733156"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5010769150","https://openalex.org/A5055852290"],"corresponding_institution_ids":["https://openalex.org/I161593684"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":33.882,"has_fulltext":true,"cited_by_count":17,"citation_normalized_percentile":{"value":0.99748385,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"7","issue":"3","first_page":"105","last_page":"105"},"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/T11536","display_name":"Consumer Retail Behavior Studies","score":0.9832000136375427,"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/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.9733999967575073,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6947000026702881},{"id":"https://openalex.org/keywords/systematic-review","display_name":"Systematic review","score":0.576200008392334},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.3366999924182892},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.33379998803138733},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.3138999938964844}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8515999913215637},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.8185999989509583},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6947000026702881},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6468999981880188},{"id":"https://openalex.org/C189708586","wikidata":"https://www.wikidata.org/wiki/Q1504425","display_name":"Systematic review","level":3,"score":0.576200008392334},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.40639999508857727},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.3366999924182892},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.33379998803138733},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.3138999938964844},{"id":"https://openalex.org/C7493553","wikidata":"https://www.wikidata.org/wiki/Q1520777","display_name":"Certainty","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.25440001487731934}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/make7030105","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7030105","pdf_url":"https://www.mdpi.com/2504-4990/7/3/105/pdf?version=1758447727","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:a7e5a57620d6470e9a2c61b545042bd8","is_oa":true,"landing_page_url":"https://doaj.org/article/a7e5a57620d6470e9a2c61b545042bd8","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning and Knowledge Extraction, Vol 7, Iss 3, p 105 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/make7030105","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7030105","pdf_url":"https://www.mdpi.com/2504-4990/7/3/105/pdf?version=1758447727","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4414428470.pdf","grobid_xml":"https://content.openalex.org/works/W4414428470.grobid-xml"},"referenced_works_count":72,"referenced_works":["https://openalex.org/W2115383008","https://openalex.org/W2115769109","https://openalex.org/W2963345615","https://openalex.org/W2992634800","https://openalex.org/W3010280706","https://openalex.org/W3016662809","https://openalex.org/W3031101592","https://openalex.org/W3045075138","https://openalex.org/W3045717404","https://openalex.org/W3081860130","https://openalex.org/W3087407824","https://openalex.org/W3098298769","https://openalex.org/W3109813342","https://openalex.org/W3112248285","https://openalex.org/W3112348427","https://openalex.org/W3130571284","https://openalex.org/W3133219337","https://openalex.org/W3137488793","https://openalex.org/W3140080668","https://openalex.org/W3148724984","https://openalex.org/W3160940115","https://openalex.org/W3164285467","https://openalex.org/W3171294772","https://openalex.org/W3195128686","https://openalex.org/W3200065545","https://openalex.org/W3202054227","https://openalex.org/W3206832504","https://openalex.org/W3215217935","https://openalex.org/W4200458019","https://openalex.org/W4205936864","https://openalex.org/W4214736071","https://openalex.org/W4220662530","https://openalex.org/W4221024633","https://openalex.org/W4221065845","https://openalex.org/W4225153176","https://openalex.org/W4293060239","https://openalex.org/W4293791119","https://openalex.org/W4296312467","https://openalex.org/W4307814854","https://openalex.org/W4309603514","https://openalex.org/W4311392818","https://openalex.org/W4313477379","https://openalex.org/W4317039498","https://openalex.org/W4319788915","https://openalex.org/W4321502812","https://openalex.org/W4366133633","https://openalex.org/W4378378090","https://openalex.org/W4382684875","https://openalex.org/W4384347655","https://openalex.org/W4385444497","https://openalex.org/W4385819751","https://openalex.org/W4386387564","https://openalex.org/W4387036236","https://openalex.org/W4387113932","https://openalex.org/W4387827723","https://openalex.org/W4388511393","https://openalex.org/W4389370792","https://openalex.org/W4390053155","https://openalex.org/W4390590974","https://openalex.org/W4391887110","https://openalex.org/W4396769276","https://openalex.org/W4396832007","https://openalex.org/W4396941396","https://openalex.org/W4398152485","https://openalex.org/W4398186150","https://openalex.org/W4399440180","https://openalex.org/W4400996623","https://openalex.org/W4403335809","https://openalex.org/W4403509218","https://openalex.org/W4403863672","https://openalex.org/W4407037069","https://openalex.org/W4407607834"],"related_works":[],"abstract_inverted_index":{"Background:":[0],"Customer":[1],"churn":[2,40,85,189],"significantly":[3],"impacts":[4],"business":[5],"revenues.":[6],"Machine":[7],"Learning":[8,12],"(ML)":[9],"and":[10,36,45,72,91,111,146,154,180,198],"Deep":[11],"(DL)":[13],"methods":[14,119,182],"are":[15,133],"increasingly":[16,134],"adopted":[17],"to":[18,50,136],"predict":[19],"churn,":[20],"yet":[21,191],"a":[22],"systematic":[23,32],"synthesis":[24],"of":[25,149,167],"recent":[26],"advancements":[27],"is":[28],"lacking.":[29],"Objectives:":[30],"This":[31],"review":[33],"evaluates":[34],"ML":[35,179],"DL":[37,128,181],"approaches":[38,129],"for":[39,66,84,108,113,188],"prediction,":[41,190],"identifying":[42],"trends,":[43],"challenges,":[44],"research":[46],"gaps":[47,192],"from":[48],"2020":[49,71,100],"2024.":[51,74],"Data":[52],"Sources:":[53],"Six":[54],"databases":[55],"(Springer,":[56],"IEEE,":[57],"Elsevier,":[58],"MDPI,":[59],"ACM,":[60],"Wiley)":[61],"were":[62,87,94,172],"searched":[63],"via":[64],"Lens.org":[65],"studies":[67,80,107],"published":[68],"between":[69],"January":[70],"December":[73],"Study":[75,174],"Eligibility":[76],"Criteria:":[77],"Peer-reviewed":[78],"original":[79],"applying":[81],"ML/DL":[82],"techniques":[83],"prediction":[86],"included.":[88],"Reviews,":[89],"preprints,":[90],"non-peer-reviewed":[92],"works":[93],"excluded.":[95],"Methods:":[96],"Screening":[97],"followed":[98],"PRISMA":[99],"guidelines.":[101],"A":[102],"two-phase":[103],"strategy":[104],"identified":[105],"240":[106],"bibliometric":[109],"analysis":[110],"61":[112],"detailed":[114],"qualitative":[115],"synthesis.":[116],"Results:":[117],"Ensemble":[118],"(e.g.,":[120,130],"XGBoost,":[121],"LightGBM)":[122],"remain":[123,193],"dominant":[124],"in":[125,194,206],"ML,":[126],"while":[127],"LSTM,":[131],"CNN)":[132],"applied":[135],"complex":[137],"data.":[138],"Challenges":[139],"include":[140],"class":[141],"imbalance,":[142],"interpretability,":[143,195],"concept":[144],"drift,":[145],"limited":[147,160],"use":[148],"profit-oriented":[150],"metrics.":[151],"Explainable":[152],"AI":[153],"adaptive":[155],"learning":[156],"show":[157],"potential":[158],"but":[159],"real-world":[161,196],"adoption.":[162],"Limitations:":[163],"No":[164],"formal":[165],"risk":[166],"bias":[168],"or":[169],"certainty":[170],"assessments":[171],"conducted.":[173],"heterogeneity":[175],"prevented":[176],"meta-analysis.":[177],"Conclusions:":[178],"have":[183],"matured":[184],"as":[185],"key":[186],"tools":[187],"deployment,":[197],"business-aligned":[199],"evaluation.":[200],"Systematic":[201],"Review":[202],"Registration:":[203],"Registered":[204],"retrospectively":[205],"OSF.":[207]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":10}],"updated_date":"2026-04-06T07:47:59.780226","created_date":"2025-10-10T00:00:00"}