{"id":"https://openalex.org/W4394595760","doi":"https://doi.org/10.1109/tce.2024.3376672","title":"Personalized and Contextualized Data Analysis for E-Commerce Customer Retention Improvement With Bi-LSTM Churn Prediction","display_name":"Personalized and Contextualized Data Analysis for E-Commerce Customer Retention Improvement With Bi-LSTM Churn Prediction","publication_year":2024,"publication_date":"2024-04-09","ids":{"openalex":"https://openalex.org/W4394595760","doi":"https://doi.org/10.1109/tce.2024.3376672"},"language":"en","primary_location":{"id":"doi:10.1109/tce.2024.3376672","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tce.2024.3376672","pdf_url":null,"source":{"id":"https://openalex.org/S126824455","display_name":"IEEE Transactions on Consumer Electronics","issn_l":"0098-3063","issn":["0098-3063","1558-4127"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Consumer Electronics","raw_type":"journal-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/A5106578837","display_name":"Lei Zhang","orcid":"https://orcid.org/0000-0002-5305-8543"},"institutions":[{"id":"https://openalex.org/I4210136682","display_name":"Heze University","ror":"https://ror.org/041zje040","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136682"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qing Wei","raw_affiliation_strings":["Computer Science Department, Heze University, Heze, China","Xiamen Innovation Research Institute, China"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, Heze University, Heze, China","institution_ids":["https://openalex.org/I4210136682"]},{"raw_affiliation_string":"Xiamen Innovation Research Institute, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5106578837"],"corresponding_institution_ids":["https://openalex.org/I4210136682"],"apc_list":null,"apc_paid":null,"fwci":4.1672,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.93491189,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"71","issue":"2","first_page":"4406","last_page":"4414"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9983999729156494,"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":0.9983999729156494,"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/computer-science","display_name":"Computer science","score":0.6108860969543457},{"id":"https://openalex.org/keywords/customer-retention","display_name":"Customer retention","score":0.4191468358039856},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.337444007396698},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.18353933095932007},{"id":"https://openalex.org/keywords/marketing","display_name":"Marketing","score":0.1600285768508911},{"id":"https://openalex.org/keywords/service","display_name":"Service (business)","score":0.11205357313156128}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6108860969543457},{"id":"https://openalex.org/C101276457","wikidata":"https://www.wikidata.org/wiki/Q5196474","display_name":"Customer retention","level":4,"score":0.4191468358039856},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.337444007396698},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.18353933095932007},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.1600285768508911},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.11205357313156128},{"id":"https://openalex.org/C140781008","wikidata":"https://www.wikidata.org/wiki/Q1221081","display_name":"Service quality","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tce.2024.3376672","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tce.2024.3376672","pdf_url":null,"source":{"id":"https://openalex.org/S126824455","display_name":"IEEE Transactions on Consumer Electronics","issn_l":"0098-3063","issn":["0098-3063","1558-4127"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Consumer Electronics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W2017027240","https://openalex.org/W2065130659","https://openalex.org/W2161634631","https://openalex.org/W2792328488","https://openalex.org/W2896750106","https://openalex.org/W2915216698","https://openalex.org/W2981772589","https://openalex.org/W2988126958","https://openalex.org/W3008569663","https://openalex.org/W3016662809","https://openalex.org/W3083748063","https://openalex.org/W3121192132","https://openalex.org/W3133744637","https://openalex.org/W3155396182","https://openalex.org/W3160811333","https://openalex.org/W3165976337","https://openalex.org/W3200742127","https://openalex.org/W3203528470","https://openalex.org/W3211688920","https://openalex.org/W3215878407","https://openalex.org/W4210642697","https://openalex.org/W4210866249","https://openalex.org/W4210929766","https://openalex.org/W4211088096","https://openalex.org/W4213303451","https://openalex.org/W4213412586","https://openalex.org/W4226382147","https://openalex.org/W4285186567","https://openalex.org/W4321209194","https://openalex.org/W4362496915","https://openalex.org/W4362499112","https://openalex.org/W4379882795","https://openalex.org/W4379929708","https://openalex.org/W4379988080","https://openalex.org/W4385210537","https://openalex.org/W4385454542","https://openalex.org/W4385565171","https://openalex.org/W4386376715","https://openalex.org/W4386933640"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W4391913857","https://openalex.org/W2350741829"],"abstract_inverted_index":{"This":[0,35,50,78,101,153,172,208],"study":[1,79,102,154,173,209],"examines":[2,174],"the":[3,9,62,81,87,104,112,124,127,139,156,175,194,197,214,225,231,238,252],"issue":[4],"of":[5,11,106,116,126,158,177,199,216,222],"customer":[6,33,42,59,167,184,205],"churn":[7,60],"in":[8,61,91,170,202,213,251],"context":[10],"e-commerce":[12,63,133],"businesses.":[13],"Customer":[14,233],"churn,":[15],"or":[16],"losing":[17],"customers,":[18],"poses":[19],"a":[20,31,54,69,117,131],"significant":[21],"challenge":[22],"for":[23,57,248],"these":[24,200],"businesses":[25],"as":[26],"they":[27],"strive":[28],"to":[29,84,110,148,165],"retain":[30],"loyal":[32],"base.":[34],"research":[36,51,195],"paper":[37,52],"delves":[38],"into":[39],"enhancing":[40],"E-commerce":[41],"retention":[43,168,185,206],"through":[44],"personalized":[45],"and":[46,96,114,180,186,235,246],"contextualized":[47],"data":[48],"analysis.":[49],"introduces":[53],"novel":[55],"method":[56],"predicting":[58],"industry.":[64],"The":[65],"proposed":[66,140],"approach":[67],"utilizes":[68],"Bidirectional":[70],"Long":[71],"Short-Term":[72],"Memory":[73],"(Bi-LSTM)":[74],"neural":[75],"network":[76],"model.":[77,118],"uses":[80],"Bi-LSTM":[82,128],"architecture":[83],"efficiently":[85],"capture":[86],"sequential":[88],"patterns":[89],"exhibited":[90],"user":[92],"interactions,":[93],"purchase":[94],"history,":[95],"other":[97],"pertinent":[98],"time-series":[99],"data.":[100],"explores":[103],"application":[105],"feature":[107],"engineering":[108],"procedures":[109],"improve":[111],"performance":[113,125],"interpretability":[115],"In":[119],"this":[120],"study,":[121],"we":[122],"assess":[123],"model":[129,141],"on":[130,183,224,230,237],"real-world":[132],"dataset.":[134],"Our":[135],"findings":[136,242],"indicate":[137,243],"that":[138],"exhibits":[142],"higher":[143,204],"predictive":[144],"accuracy":[145,220],"when":[146],"compared":[147],"conventional":[149],"machine":[150],"learning":[151,161],"methods.":[152],"emphasizes":[155],"importance":[157],"utilizing":[159],"deep":[160],"methods,":[162],"specifically":[163],"Bi-LSTM,":[164],"enhance":[166],"strategies":[169],"e-commerce.":[171],"impact":[176],"accurate":[178,249],"prediction":[179],"subsequent":[181],"actions":[182],"overall":[187],"business":[188],"performance.":[189],"Taking":[190],"an":[191,219],"alternative":[192],"perspective,":[193],"highlights":[196],"attractiveness":[198],"factors":[201],"driving":[203],"rates.":[207],"presents":[210],"promising":[211],"results":[212],"field":[215],"predictions,":[217],"achieving":[218],"rate":[221],"99.80%":[223],"IBM":[226],"Telecom":[227],"Dataset,":[228,234],"99.61%":[229],"Bank":[232],"99.22%":[236],"Orange":[239],"Dataset.":[240],"These":[241],"high":[244],"reliability":[245],"potential":[247],"predictions":[250],"given":[253],"case.":[254]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3}],"updated_date":"2026-03-06T13:50:29.536080","created_date":"2024-04-10T00:00:00"}
