{"id":"https://openalex.org/W4404962639","doi":"https://doi.org/10.3390/fi16120453","title":"Customer Churn Prediction Approach Based on LLM Embeddings and Logistic Regression","display_name":"Customer Churn Prediction Approach Based on LLM Embeddings and Logistic Regression","publication_year":2024,"publication_date":"2024-12-03","ids":{"openalex":"https://openalex.org/W4404962639","doi":"https://doi.org/10.3390/fi16120453"},"language":"en","primary_location":{"id":"doi:10.3390/fi16120453","is_oa":true,"landing_page_url":"https://doi.org/10.3390/fi16120453","pdf_url":null,"source":{"id":"https://openalex.org/S34838331","display_name":"Future Internet","issn_l":"1999-5903","issn":["1999-5903"],"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":"Future Internet","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/fi16120453","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114976636","display_name":"Meryem Chajia","orcid":null},"institutions":[{"id":"https://openalex.org/I81605866","display_name":"Sidi Mohamed Ben Abdellah University","ror":"https://ror.org/04efg9a07","country_code":"MA","type":"education","lineage":["https://openalex.org/I81605866"]}],"countries":["MA"],"is_corresponding":true,"raw_author_name":"Meryem Chajia","raw_affiliation_strings":["LISAC Laboratory, Department of Computer Science, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco"],"raw_orcid":"https://orcid.org/0009-0000-5728-5850","affiliations":[{"raw_affiliation_string":"LISAC Laboratory, Department of Computer Science, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco","institution_ids":["https://openalex.org/I81605866"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085575952","display_name":"El Habib Nfaoui","orcid":"https://orcid.org/0000-0002-5816-0897"},"institutions":[{"id":"https://openalex.org/I81605866","display_name":"Sidi Mohamed Ben Abdellah University","ror":"https://ror.org/04efg9a07","country_code":"MA","type":"education","lineage":["https://openalex.org/I81605866"]}],"countries":["MA"],"is_corresponding":false,"raw_author_name":"El Habib Nfaoui","raw_affiliation_strings":["LISAC Laboratory, Department of Computer Science, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco"],"raw_orcid":"https://orcid.org/0000-0002-5816-0897","affiliations":[{"raw_affiliation_string":"LISAC Laboratory, Department of Computer Science, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco","institution_ids":["https://openalex.org/I81605866"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5114976636"],"corresponding_institution_ids":["https://openalex.org/I81605866"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":2.781,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.91074897,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"16","issue":"12","first_page":"453","last_page":"453"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9983999729156494,"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/T10609","display_name":"Digital Marketing and Social Media","score":0.9872000217437744,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.82343989610672},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.6648385524749756},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.656981348991394},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5941628813743591},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.5803171992301941},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5709850788116455},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5358383059501648},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5046693086624146},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.48411357402801514},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4765109121799469},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4615630507469177},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.4359477162361145},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.4111970067024231},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3939819037914276},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.22708192467689514}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.82343989610672},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.6648385524749756},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.656981348991394},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5941628813743591},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.5803171992301941},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5709850788116455},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5358383059501648},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5046693086624146},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.48411357402801514},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4765109121799469},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4615630507469177},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.4359477162361145},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.4111970067024231},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3939819037914276},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.22708192467689514},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/fi16120453","is_oa":true,"landing_page_url":"https://doi.org/10.3390/fi16120453","pdf_url":null,"source":{"id":"https://openalex.org/S34838331","display_name":"Future Internet","issn_l":"1999-5903","issn":["1999-5903"],"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":"Future Internet","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:gam:jftint:v:16:y:2024:i:12:p:453-:d:1535879","is_oa":false,"landing_page_url":"https://www.mdpi.com/1999-5903/16/12/453/","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},{"id":"pmh:oai:doaj.org/article:bb4be8eb98d94042aea17b5a17374f06","is_oa":false,"landing_page_url":"https://doaj.org/article/bb4be8eb98d94042aea17b5a17374f06","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Future Internet, Vol 16, Iss 12, p 453 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/fi16120453","is_oa":true,"landing_page_url":"https://doi.org/10.3390/fi16120453","pdf_url":null,"source":{"id":"https://openalex.org/S34838331","display_name":"Future Internet","issn_l":"1999-5903","issn":["1999-5903"],"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":"Future Internet","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.4399999976158142}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1494264608","https://openalex.org/W1978501154","https://openalex.org/W2128532956","https://openalex.org/W2789071465","https://openalex.org/W2922127534","https://openalex.org/W2963912736","https://openalex.org/W2979826702","https://openalex.org/W2993325324","https://openalex.org/W3016662809","https://openalex.org/W3094636950","https://openalex.org/W3121579735","https://openalex.org/W3132726656","https://openalex.org/W3133224387","https://openalex.org/W3135085797","https://openalex.org/W3138417868","https://openalex.org/W3183576350","https://openalex.org/W4200316600","https://openalex.org/W4205936864","https://openalex.org/W4210273262","https://openalex.org/W4211010593","https://openalex.org/W4282934475","https://openalex.org/W4293528099","https://openalex.org/W4312750750","https://openalex.org/W4313398738","https://openalex.org/W4323543254","https://openalex.org/W4372324497","https://openalex.org/W4380521660","https://openalex.org/W4381094450","https://openalex.org/W4387079051","https://openalex.org/W4387107857","https://openalex.org/W4391504026","https://openalex.org/W4391819204","https://openalex.org/W4399249625","https://openalex.org/W6635943578","https://openalex.org/W6771152903","https://openalex.org/W6785246689","https://openalex.org/W6791471509","https://openalex.org/W6798770808","https://openalex.org/W6853771583","https://openalex.org/W6853813770","https://openalex.org/W6870233455"],"related_works":["https://openalex.org/W4389954502","https://openalex.org/W2771255398","https://openalex.org/W2930428186","https://openalex.org/W3200027047","https://openalex.org/W4385770464","https://openalex.org/W4224262160","https://openalex.org/W3120363735","https://openalex.org/W2394323384","https://openalex.org/W2994772185","https://openalex.org/W1528227026"],"abstract_inverted_index":{"Nowadays,":[0],"predicting":[1,64],"customer":[2,57],"churn":[3,75],"is":[4,168],"essential":[5],"for":[6,51,78],"the":[7,29,36,79,169,177,181],"success":[8,21],"of":[9,31,104,158,188],"any":[10],"company.":[11],"Loyal":[12],"customers":[13,33],"generate":[14],"continuous":[15],"revenue":[16],"streams,":[17],"resulting":[18],"in":[19,165],"long-term":[20],"and":[22,59,90,122,149,200],"growth.":[23],"Moreover,":[24],"companies":[25],"are":[26],"increasingly":[27],"prioritizing":[28],"retention":[30],"existing":[32],"due":[34],"to":[35,93,124,152],"higher":[37],"costs":[38],"associated":[39],"with":[40],"attracting":[41],"new":[42],"ones.":[43],"Consequently,":[44],"there":[45],"has":[46],"been":[47],"a":[48,73,102,154,194],"growing":[49],"demand":[50],"advanced":[52],"methods":[53],"aimed":[54],"at":[55],"enhancing":[56],"loyalty":[58],"satisfaction,":[60],"as":[61,63],"well":[62],"churners.":[65,96],"In":[66],"our":[67,166],"work,":[68],"we":[69,129,174],"focused":[70],"on":[71,83],"building":[72],"robust":[74,155],"prediction":[76],"model":[77,156,164,192],"telecommunications":[80],"industry":[81],"based":[82],"large":[84,87],"embeddings":[85],"from":[86,117,180],"language":[88],"models":[89],"logistic":[91,134,170],"regression":[92,171],"accurately":[94],"identify":[95],"We":[97],"conducted":[98],"extensive":[99],"experiments":[100,167],"using":[101,176],"range":[103],"embedding":[105],"techniques,":[106],"including":[107,133],"OpenAI":[108,182],"Text-embedding,":[109],"Google":[110],"Gemini":[111],"Text":[112],"Embedding,":[113],"bidirectional":[114],"encoder":[115],"representations":[116],"transformers":[118],"(BERT),":[119],"Sentence-Transformers,":[120],"Sent2vec,":[121],"Doc2vec,":[123],"extract":[125],"meaningful":[126],"features.":[127],"Additionally,":[128],"tested":[130],"various":[131],"classifiers,":[132],"regression,":[135],"support":[136],"vector":[137],"machine,":[138],"random":[139],"forest,":[140],"K-nearest":[141],"neighbors,":[142],"multilayer":[143],"perceptron,":[144],"naive":[145],"Bayes,":[146],"decision":[147],"tree,":[148],"zero-shot":[150],"classification,":[151],"build":[153],"capable":[157],"making":[159],"accurate":[160],"predictions.":[161],"The":[162,190],"best-performing":[163],"classifier,":[172],"which":[173],"trained":[175],"extracted":[178],"feature":[179],"Text-embedding-ada-002":[183],"model,":[184],"achieving":[185],"an":[186],"accuracy":[187],"89%.":[189],"proposed":[191],"demonstrates":[193],"high":[195],"discriminative":[196],"ability":[197],"between":[198],"churning":[199],"loyal":[201],"customers.":[202]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}