{"id":"https://openalex.org/W3038146351","doi":"https://doi.org/10.1080/08839514.2020.1790248","title":"Bank CRM Optimization Using Predictive Classification Based on the Support Vector Machine Method","display_name":"Bank CRM Optimization Using Predictive Classification Based on the Support Vector Machine Method","publication_year":2020,"publication_date":"2020-07-04","ids":{"openalex":"https://openalex.org/W3038146351","doi":"https://doi.org/10.1080/08839514.2020.1790248","mag":"3038146351"},"language":"en","primary_location":{"id":"doi:10.1080/08839514.2020.1790248","is_oa":false,"landing_page_url":"https://doi.org/10.1080/08839514.2020.1790248","pdf_url":null,"source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"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/A5006866789","display_name":"Vladimir Djurisic","orcid":"https://orcid.org/0000-0002-1679-2327"},"institutions":[{"id":"https://openalex.org/I5936373","display_name":"University of Montenegro","ror":"https://ror.org/02drrjp49","country_code":"ME","type":"education","lineage":["https://openalex.org/I5936373"]}],"countries":["ME"],"is_corresponding":true,"raw_author_name":"Vladimir Djurisic","raw_affiliation_strings":["Faculty of Economics, University of Montenegro, Podgorica, Montenegro"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Economics, University of Montenegro, Podgorica, Montenegro","institution_ids":["https://openalex.org/I5936373"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035980595","display_name":"Ljiljana Ka\u0161\u0107elan","orcid":"https://orcid.org/0000-0001-9831-7599"},"institutions":[{"id":"https://openalex.org/I5936373","display_name":"University of Montenegro","ror":"https://ror.org/02drrjp49","country_code":"ME","type":"education","lineage":["https://openalex.org/I5936373"]}],"countries":["ME"],"is_corresponding":false,"raw_author_name":"Ljiljana Kascelan","raw_affiliation_strings":["Faculty of Economics, University of Montenegro, Podgorica, Montenegro"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Economics, University of Montenegro, Podgorica, Montenegro","institution_ids":["https://openalex.org/I5936373"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000466231","display_name":"Sun\u010dica Rogi\u0107","orcid":"https://orcid.org/0000-0003-3591-1245"},"institutions":[{"id":"https://openalex.org/I5936373","display_name":"University of Montenegro","ror":"https://ror.org/02drrjp49","country_code":"ME","type":"education","lineage":["https://openalex.org/I5936373"]}],"countries":["ME"],"is_corresponding":false,"raw_author_name":"Suncica Rogic","raw_affiliation_strings":["Faculty of Economics, University of Montenegro, Podgorica, Montenegro"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Economics, University of Montenegro, Podgorica, Montenegro","institution_ids":["https://openalex.org/I5936373"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088399439","display_name":"Boban Melovi\u0107","orcid":"https://orcid.org/0000-0001-6330-9425"},"institutions":[{"id":"https://openalex.org/I5936373","display_name":"University of Montenegro","ror":"https://ror.org/02drrjp49","country_code":"ME","type":"education","lineage":["https://openalex.org/I5936373"]}],"countries":["ME"],"is_corresponding":false,"raw_author_name":"Boban Melovic","raw_affiliation_strings":["Faculty of Economics, University of Montenegro, Podgorica, Montenegro"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Economics, University of Montenegro, Podgorica, Montenegro","institution_ids":["https://openalex.org/I5936373"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5006866789"],"corresponding_institution_ids":["https://openalex.org/I5936373"],"apc_list":{"value":2195,"currency":"USD","value_usd":2195},"apc_paid":null,"fwci":3.0088,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.91404645,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"34","issue":"12","first_page":"941","last_page":"955"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9993000030517578,"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":0.9993000030517578,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9905999898910522,"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/computer-science","display_name":"Computer science","score":0.8351399302482605},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7885082960128784},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5923170447349548},{"id":"https://openalex.org/keywords/market-segmentation","display_name":"Market segmentation","score":0.5854476690292358},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5574632287025452},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5452568531036377},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5380818843841553},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5024387836456299},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.423453152179718},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4173160195350647},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.41170182824134827},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33220335841178894}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8351399302482605},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7885082960128784},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5923170447349548},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.5854476690292358},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5574632287025452},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5452568531036377},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5380818843841553},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5024387836456299},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.423453152179718},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4173160195350647},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.41170182824134827},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33220335841178894},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/08839514.2020.1790248","is_oa":false,"landing_page_url":"https://doi.org/10.1080/08839514.2020.1790248","pdf_url":null,"source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:bfdc9b38c66a475ba376fa9f62a87c0c","is_oa":false,"landing_page_url":"https://doaj.org/article/bfdc9b38c66a475ba376fa9f62a87c0c","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":"Applied Artificial Intelligence, Vol 34, Iss 12, Pp 941-955 (2020)","raw_type":"article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W7175315","https://openalex.org/W639324889","https://openalex.org/W1532325895","https://openalex.org/W1541815793","https://openalex.org/W1588039922","https://openalex.org/W1594031697","https://openalex.org/W1966039095","https://openalex.org/W1966517947","https://openalex.org/W1970365789","https://openalex.org/W1982165141","https://openalex.org/W1998612608","https://openalex.org/W2008299439","https://openalex.org/W2015557855","https://openalex.org/W2016821125","https://openalex.org/W2018363110","https://openalex.org/W2051224630","https://openalex.org/W2064873396","https://openalex.org/W2065080511","https://openalex.org/W2101857386","https://openalex.org/W2115280476","https://openalex.org/W2125055259","https://openalex.org/W2127218421","https://openalex.org/W2146057527","https://openalex.org/W2149070311","https://openalex.org/W2149706766","https://openalex.org/W2156909104","https://openalex.org/W2216604007","https://openalex.org/W2330820318","https://openalex.org/W2341234495","https://openalex.org/W2474310541","https://openalex.org/W2503127566","https://openalex.org/W2563011108","https://openalex.org/W2585105539","https://openalex.org/W2783497756","https://openalex.org/W2893616889","https://openalex.org/W2907006747","https://openalex.org/W2938453849","https://openalex.org/W2971607317","https://openalex.org/W3117254546","https://openalex.org/W3124119219","https://openalex.org/W3209367410","https://openalex.org/W4200425518","https://openalex.org/W4234670385","https://openalex.org/W4236137412","https://openalex.org/W4237922488","https://openalex.org/W4239944110","https://openalex.org/W4241611050"],"related_works":["https://openalex.org/W2090763504","https://openalex.org/W1630076647","https://openalex.org/W2371138613","https://openalex.org/W2048963458","https://openalex.org/W43109613","https://openalex.org/W2359952343","https://openalex.org/W148178222","https://openalex.org/W2080152487","https://openalex.org/W1948992892","https://openalex.org/W4224922629"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,106,124,162],"predictive":[4,34,85,120],"approach":[5,19,164],"to":[6,15,77,90,111,136,140,192],"segmenting":[7],"credit":[8],"card":[9],"users,":[10],"based":[11],"on":[12],"their":[13],"value":[14],"the":[16,21,30,37,49,53,63,79,91,97,130,138,149,155,167,170,179],"bank.":[17],"The":[18,116,144],"combines":[20],"Recency,":[22],"Frequency":[23],"and":[24,33,57],"Monetary":[25],"(RFM)":[26],"method,":[27,32],"clustering":[28],"using":[29,154],"k-means":[31],"classification":[35,86,121,159],"by":[36,44],"Support":[38,98],"Vector":[39,99],"Machine":[40,100],"(SVM)":[41,101],"method.":[42,160],"Clustering":[43],"non-encoded":[45],"RFM":[46,73],"attributes":[47],"overcomes":[48],"subjectivity":[50],"in":[51,62,84,134],"selecting":[52],"number":[54,93],"of":[55,65,71,81,94,108,119,126,146,169],"segments":[56,133],"losing":[58],"information":[59],"(small":[60],"differences":[61],"values":[64],"these":[66],"attributes)":[67],"which":[68],"are":[69],"problems":[70],"classic":[72],"segmentation.":[74],"In":[75],"order":[76,135],"overcome":[78],"problem":[80],"class":[82],"imbalance":[83],"(which":[87],"occurs":[88],"due":[89,110,191],"small":[92,171],"valuable":[95,181,195],"customers),":[96],"method":[102],"was":[103,152],"applied":[104],"as":[105],"pre-processor":[107],"data":[109],"its":[112],"extraordinary":[113],"generalization":[114],"capabilities.":[115],"end":[117],"result":[118],"should":[122],"be":[123],"set":[125],"rules":[127,147],"that":[128,165],"describes":[129],"identified":[131],"customer":[132],"tailor":[137],"offer":[139],"each":[141],"segment":[142],"individually.":[143],"extraction":[145],"from":[148],"SVM":[150],"output":[151],"achieved":[153],"Decision":[156],"Tree":[157],"(DT)":[158],"Using":[161],"proposed":[163],"addresses":[166],"issue":[168],"class,":[172],"marketing":[173],"managers":[174],"can":[175],"more":[176],"effectively":[177],"target":[178],"most":[180],"customers,":[182],"thereby":[183],"increasing":[184],"revenue,":[185],"but":[186],"also":[187],"reducing":[188],"unnecessary":[189],"costs":[190],"wrongly":[193],"targeted":[194],"clients.":[196]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}