{"id":"https://openalex.org/W4388937323","doi":"https://doi.org/10.1109/icccnt56998.2023.10306384","title":"Data Transformation, Modelling and Prediction of Customer Churn using Deep Learning","display_name":"Data Transformation, Modelling and Prediction of Customer Churn using Deep Learning","publication_year":2023,"publication_date":"2023-07-06","ids":{"openalex":"https://openalex.org/W4388937323","doi":"https://doi.org/10.1109/icccnt56998.2023.10306384"},"language":"en","primary_location":{"id":"doi:10.1109/icccnt56998.2023.10306384","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icccnt56998.2023.10306384","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)","raw_type":"proceedings-article"},"type":"article","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/A5069695739","display_name":"S. Menaka","orcid":null},"institutions":[{"id":"https://openalex.org/I145286018","display_name":"SRM Institute of Science and Technology","ror":"https://ror.org/050113w36","country_code":"IN","type":"education","lineage":["https://openalex.org/I145286018"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"S. Menaka","raw_affiliation_strings":["SRM Institute of Science and Technology,Department of Computer Science and Engineering,Chennai,India"],"affiliations":[{"raw_affiliation_string":"SRM Institute of Science and Technology,Department of Computer Science and Engineering,Chennai,India","institution_ids":["https://openalex.org/I145286018"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111488982","display_name":"B. Raghu Ram","orcid":null},"institutions":[{"id":"https://openalex.org/I145286018","display_name":"SRM Institute of Science and Technology","ror":"https://ror.org/050113w36","country_code":"IN","type":"education","lineage":["https://openalex.org/I145286018"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"B. Raghu Ram","raw_affiliation_strings":["SRM Institute of Science and Technology,Department of Computer Science and Engineering,Chennai,India"],"affiliations":[{"raw_affiliation_string":"SRM Institute of Science and Technology,Department of Computer Science and Engineering,Chennai,India","institution_ids":["https://openalex.org/I145286018"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029465789","display_name":"N Gowtham","orcid":"https://orcid.org/0000-0002-5070-8424"},"institutions":[{"id":"https://openalex.org/I145286018","display_name":"SRM Institute of Science and Technology","ror":"https://ror.org/050113w36","country_code":"IN","type":"education","lineage":["https://openalex.org/I145286018"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"N.V.S. Gowtham","raw_affiliation_strings":["SRM Institute of Science and Technology,Department of Computer Science and Engineering,Chennai,India"],"affiliations":[{"raw_affiliation_string":"SRM Institute of Science and Technology,Department of Computer Science and Engineering,Chennai,India","institution_ids":["https://openalex.org/I145286018"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112408918","display_name":"G. Likhith Reddy","orcid":null},"institutions":[{"id":"https://openalex.org/I145286018","display_name":"SRM Institute of Science and Technology","ror":"https://ror.org/050113w36","country_code":"IN","type":"education","lineage":["https://openalex.org/I145286018"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"G.V. Madhu Reddy","raw_affiliation_strings":["SRM Institute of Science and Technology,Department of Computer Science and Engineering,Chennai,India"],"affiliations":[{"raw_affiliation_string":"SRM Institute of Science and Technology,Department of Computer Science and Engineering,Chennai,India","institution_ids":["https://openalex.org/I145286018"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5069695739"],"corresponding_institution_ids":["https://openalex.org/I145286018"],"apc_list":null,"apc_paid":null,"fwci":0.6832,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.77708786,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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/T11536","display_name":"Consumer Retail Behavior Studies","score":0.9818999767303467,"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":0.9778000116348267,"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/computer-science","display_name":"Computer science","score":0.7442227005958557},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6225845813751221},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5851837992668152},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5360550880432129},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.47274836897850037},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.44234147667884827},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.44210177659988403},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.43161740899086},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.41798311471939087},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.40333548188209534},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40279167890548706}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7442227005958557},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6225845813751221},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5851837992668152},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5360550880432129},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.47274836897850037},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.44234147667884827},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.44210177659988403},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.43161740899086},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.41798311471939087},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.40333548188209534},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40279167890548706},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccnt56998.2023.10306384","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icccnt56998.2023.10306384","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.4399999976158142,"id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1541696305","https://openalex.org/W2003083941","https://openalex.org/W2084341220","https://openalex.org/W2084571098","https://openalex.org/W2561686941","https://openalex.org/W2601171548","https://openalex.org/W2779766484"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4230611425","https://openalex.org/W2731899572","https://openalex.org/W4304166257","https://openalex.org/W4294635752","https://openalex.org/W4387478977","https://openalex.org/W3034267371","https://openalex.org/W1792679987","https://openalex.org/W2923727989"],"abstract_inverted_index":{"Client":[0],"prediction":[1,31,128],"of":[2,8,69,108,119,138,207],"churn":[3,29,63,79,141],"is":[4,132,151],"a":[5,17,27,85,98,103,106,152,161],"difficult":[6],"field":[7],"study":[9,131],"that":[10,164],"adds":[11],"to":[12,33,59,82,90,115,203],"client":[13],"retention":[14],"strategies.":[15,209],"In":[16,196],"saturated":[18],"and":[19,61,122,182,194],"competitive":[20],"market,":[21],"telecommunications":[22,145],"companies":[23],"must":[24],"rely":[25],"on":[26,40,177],"viable":[28],"customers":[30],"model":[32,127],"retain":[34],"consumers.":[35],"Previous":[36],"research":[37,101],"has":[38],"focused":[39],"predicting":[41],"customer":[42,140,149],"attrition":[43,150],"in":[44,88,135,143],"the":[45,91,117,136,144,156,205],"present":[46,70],"moment":[47],"or":[48],"next":[49],"month,":[50],"when":[51],"telecom":[52],"businesses":[53],"have":[54],"not":[55],"got":[56],"enough":[57],"time":[58],"create":[60],"implement":[62],"control":[64],"solutions.":[65],"The":[66],"predictive":[67],"performance":[68,184],"machine":[71,170],"learning":[72,171],"algorithms,":[73],"which":[74],"are":[75],"frequently":[76],"used":[77,187,200],"by":[78],"communities,":[80],"appears":[81],"be":[83],"at":[84],"stalemate,":[86],"owing":[87],"part":[89],"algorithms'":[92],"weak":[93],"feature":[94,123],"extraction":[95],"capacity.":[96],"As":[97],"result,":[99],"this":[100,197],"proposes":[102],"unique":[104,162],"technique,":[105],"mix":[107],"neural":[109],"networks":[110],"with":[111,169],"self-":[112],"attention":[113],"augmentation,":[114],"increase":[116],"efficiency":[118],"features":[120],"filtering":[121],"extraction,":[124],"hence":[125],"enhancing":[126],"performance.":[129],"This":[130],"being":[133],"undertaken":[134],"context":[137],"forecasting":[139],"(CCP)":[142],"industry":[146],"(TCI),":[147],"wherein":[148],"typical":[153],"occurrence.":[154],"For":[155],"CCP":[157],"problem,":[158],"we":[159,199],"developed":[160],"solution":[163],"combines":[165],"data":[166],"transformation":[167],"approaches":[168],"models.":[172],"We":[173],"ran":[174],"our":[175],"tests":[176],"publicly":[178],"accessible":[179],"TCI":[180],"datasets":[181],"evaluated":[183],"using":[185],"widely":[186],"assessment":[188],"metrics":[189],"(e.g.,":[190],"AUC,":[191],"accuracy,":[192],"recall,":[193],"F-measure).":[195],"work,":[198],"extensive":[201],"comparisons":[202],"verify":[204],"impact":[206],"modification":[208]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}
