{"id":"https://openalex.org/W3176004440","doi":null,"title":"AI Based Nose for Trace of Churn in Assessment of Captive Customers","display_name":"AI Based Nose for Trace of Churn in Assessment of Captive Customers","publication_year":2021,"publication_date":"2021-06-28","ids":{"openalex":"https://openalex.org/W3176004440","mag":"3176004440"},"language":"en","primary_location":{"id":"mag:3176004440","doi":null,"is_oa":true,"landing_page_url":"https://www.tojqi.net/index.php/journal/article/view/1193","pdf_url":null,"source":{"id":"https://openalex.org/S2736657409","display_name":"Turkish Online Journal of Qualitative Inquiry","issn_l":"1309-6591","issn":["1309-6591"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["poland-20"],"host_organization":"https://openalex.org/P4310318383","host_organization_name":"Turkish Online Journal of Qualitative Inquiry (TOJQI)","host_organization_lineage":["https://openalex.org/P4310318383"],"host_organization_lineage_names":["Turkish Online Journal of Qualitative Inquiry (TOJQI)"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Turkish Online Journal of Qualitative Inquiry","raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.tojqi.net/index.php/journal/article/view/1193","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076543846","display_name":"Drumil Joshi","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Drumil Joshi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038677809","display_name":"Aayush Parikh","orcid":"https://orcid.org/0009-0006-5713-1572","observed_orcids":["https://orcid.org/0009-0006-5713-1572"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aayush Parikh","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0009-0006-5713-1572","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069760346","display_name":"Ritika Mangla","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ritika Mangla","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030255252","display_name":"Fawzan Sayed","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fawzan Sayed","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5025891306","display_name":"Sunil Karamchandani","orcid":"https://orcid.org/0000-0001-6607-1440","observed_orcids":["https://orcid.org/0000-0001-6607-1440"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sunil Karamchandani","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-6607-1440","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6478,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.75026042,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":"12","issue":"6","first_page":"1102","last_page":"1108"},"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/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/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/customer-churn-prediction","display_name":"customer churn prediction","score":0.9810000061988831},{"id":"https://openalex.org/keywords/telecommunications-customers","display_name":"telecommunications customers","score":0.6890000104904175},{"id":"https://openalex.org/keywords/customer-retention","display_name":"customer retention","score":0.6909999847412109},{"id":"https://openalex.org/keywords/xgboost","display_name":"XGBoost","score":0.925000011920929},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"hyperparameter optimization","score":0.9120000004768372},{"id":"https://openalex.org/keywords/classifiers","display_name":"machine learning classifiers","score":0.6169999837875366}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.6562472581863403},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6509779095649719},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6422032713890076},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6170392632484436},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5593549013137817},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5567928552627563},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.42602914571762085}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3176004440","doi":null,"is_oa":true,"landing_page_url":"https://www.tojqi.net/index.php/journal/article/view/1193","pdf_url":null,"source":{"id":"https://openalex.org/S2736657409","display_name":"Turkish Online Journal of Qualitative Inquiry","issn_l":"1309-6591","issn":["1309-6591"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["poland-20"],"host_organization":"https://openalex.org/P4310318383","host_organization_name":"Turkish Online Journal of Qualitative Inquiry (TOJQI)","host_organization_lineage":["https://openalex.org/P4310318383"],"host_organization_lineage_names":["Turkish Online Journal of Qualitative Inquiry (TOJQI)"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Turkish Online Journal of Qualitative Inquiry","raw_type":null}],"best_oa_location":{"id":"mag:3176004440","doi":null,"is_oa":true,"landing_page_url":"https://www.tojqi.net/index.php/journal/article/view/1193","pdf_url":null,"source":{"id":"https://openalex.org/S2736657409","display_name":"Turkish Online Journal of Qualitative Inquiry","issn_l":"1309-6591","issn":["1309-6591"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["poland-20"],"host_organization":"https://openalex.org/P4310318383","host_organization_name":"Turkish Online Journal of Qualitative Inquiry (TOJQI)","host_organization_lineage":["https://openalex.org/P4310318383"],"host_organization_lineage_names":["Turkish Online Journal of Qualitative Inquiry (TOJQI)"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Turkish Online Journal of Qualitative Inquiry","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1496317909","https://openalex.org/W1506285740","https://openalex.org/W1966021193","https://openalex.org/W2006153161","https://openalex.org/W2067594023","https://openalex.org/W2123504579","https://openalex.org/W2131064753","https://openalex.org/W2540642777"],"related_works":["https://openalex.org/W4296949392","https://openalex.org/W3130989307","https://openalex.org/W4308750674","https://openalex.org/W4408400663","https://openalex.org/W3021028588","https://openalex.org/W2074780813","https://openalex.org/W4391576473","https://openalex.org/W4405635511","https://openalex.org/W4385701313","https://openalex.org/W4385258152"],"abstract_inverted_index":{"Customer":[0],"churn":[1,26,49],"speculation":[2],"is":[3],"aimed":[4],"at":[5],"finding":[6],"customers":[7,47,79],"with":[8,151],"a":[9,25,62,135],"high":[10],"potential":[11],"for":[12,46,130],"attraction.":[13],"Predictive":[14],"accuracy,":[15],"Precision,":[16],"and":[17,50,95,134,157],"justification":[18],"are":[19],"the":[20,33,37,43,66,75,81,85,102,107,110,121,131],"three":[21],"critical":[22],"elements":[23],"of":[24,36,65,77,88,138,154,159],"predictive":[27],"model.":[28],"According":[29],"to":[30,41,48,73,119],"domain":[31],"knowledge,":[32],"Accurate":[34],"standards":[35],"model":[38],"allow":[39],"us":[40],"identify":[42],"main":[44],"drivers":[45],"develop":[51],"an":[52,152],"effective":[53],"retention":[54],"strategy.":[55],"In":[56,84,106,117],"this":[57],"research":[58],"paper,":[59],"we":[60,126],"present":[61],"comparative":[63],"study":[64],"most":[67,122],"popular":[68,103],"machine":[69],"learning":[70],"classifiers":[71],"used":[72],"solve":[74],"problem":[76],"churning":[78],"in":[80],"telecommunications":[82],"sector.":[83],"first":[86],"phase":[87],"our":[89],"test,":[90],"all":[91],"models":[92],"were":[93],"implemented":[94],"tested":[96],"using":[97],"statistical":[98],"evaluative":[99],"measures":[100],"on":[101],"telecom":[104],"database.":[105],"second":[108],"phase,":[109],"performance":[111],"improved":[112],"by":[113],"boosting":[114],"was":[115,144],"studied.":[116],"order":[118],"determine":[120],"efficient":[123],"parameter":[124],"combinations,":[125],"performed":[127],"hyperparameter":[128],"tuning":[129,150],"best":[132,141],"classifier":[133,143,147],"wide":[136],"range":[137],"parameters.":[139],"The":[140],"overall":[142],"XG":[145],"Boost":[146],"after":[148],"Hyperparameter":[149],"accuracy":[153],"almost":[155],"82%":[156],"Precision":[158],"0.8..":[160]},"counts_by_year":[{"year":2024,"cited_by_count":4}],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2021-07-05T00:00:00"}