{"id":"https://openalex.org/W2913979093","doi":"https://doi.org/10.1109/bigdata.2018.8621931","title":"Predicting Individual-Level Call Arrival from Online Account Customer Activity","display_name":"Predicting Individual-Level Call Arrival from Online Account Customer Activity","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2913979093","doi":"https://doi.org/10.1109/bigdata.2018.8621931","mag":"2913979093"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2018.8621931","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8621931","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Big Data (Big Data)","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/A5048706693","display_name":"Somayeh Moazeni","orcid":"https://orcid.org/0000-0003-3631-563X"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Somayeh Moazeni","raw_affiliation_strings":["School of Business, Stevens Institute of Technology, Hoboken, NJ, USA"],"affiliations":[{"raw_affiliation_string":"School of Business, Stevens Institute of Technology, Hoboken, NJ, USA","institution_ids":["https://openalex.org/I108468826"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5048706693"],"corresponding_institution_ids":["https://openalex.org/I108468826"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.22752706,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1624","last_page":"1631"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10974","display_name":"Advanced Queuing Theory Analysis","score":0.9922000169754028,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"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/T10974","display_name":"Advanced Queuing Theory Analysis","score":0.9922000169754028,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"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/T12384","display_name":"Customer churn and segmentation","score":0.9921000003814697,"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":0.9729999899864197,"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/leverage","display_name":"Leverage (statistics)","score":0.7081798315048218},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.645889401435852},{"id":"https://openalex.org/keywords/customer-retention","display_name":"Customer retention","score":0.5647755861282349},{"id":"https://openalex.org/keywords/phone","display_name":"Phone","score":0.5311974883079529},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5116784572601318},{"id":"https://openalex.org/keywords/customer-intelligence","display_name":"Customer intelligence","score":0.5073062777519226},{"id":"https://openalex.org/keywords/customer-to-customer","display_name":"Customer to customer","score":0.465071439743042},{"id":"https://openalex.org/keywords/voice-of-the-customer","display_name":"Voice of the customer","score":0.46174222230911255},{"id":"https://openalex.org/keywords/customer-relationship-management","display_name":"Customer relationship management","score":0.4273885488510132},{"id":"https://openalex.org/keywords/customer-base","display_name":"Customer base","score":0.421736478805542},{"id":"https://openalex.org/keywords/customer-satisfaction","display_name":"Customer satisfaction","score":0.41339805722236633},{"id":"https://openalex.org/keywords/service","display_name":"Service (business)","score":0.3868647813796997},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.29310882091522217},{"id":"https://openalex.org/keywords/marketing","display_name":"Marketing","score":0.27773937582969666},{"id":"https://openalex.org/keywords/service-quality","display_name":"Service quality","score":0.20690327882766724},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.20629096031188965},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.16087698936462402},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.13939735293388367}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7081798315048218},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.645889401435852},{"id":"https://openalex.org/C101276457","wikidata":"https://www.wikidata.org/wiki/Q5196474","display_name":"Customer retention","level":4,"score":0.5647755861282349},{"id":"https://openalex.org/C2778707766","wikidata":"https://www.wikidata.org/wiki/Q202064","display_name":"Phone","level":2,"score":0.5311974883079529},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5116784572601318},{"id":"https://openalex.org/C57660159","wikidata":"https://www.wikidata.org/wiki/Q5196460","display_name":"Customer intelligence","level":5,"score":0.5073062777519226},{"id":"https://openalex.org/C63207073","wikidata":"https://www.wikidata.org/wiki/Q1076010","display_name":"Customer to customer","level":5,"score":0.465071439743042},{"id":"https://openalex.org/C43595421","wikidata":"https://www.wikidata.org/wiki/Q7939596","display_name":"Voice of the customer","level":5,"score":0.46174222230911255},{"id":"https://openalex.org/C98825075","wikidata":"https://www.wikidata.org/wiki/Q485643","display_name":"Customer relationship management","level":2,"score":0.4273885488510132},{"id":"https://openalex.org/C2777276756","wikidata":"https://www.wikidata.org/wiki/Q5196446","display_name":"Customer base","level":2,"score":0.421736478805542},{"id":"https://openalex.org/C191511416","wikidata":"https://www.wikidata.org/wiki/Q999278","display_name":"Customer satisfaction","level":2,"score":0.41339805722236633},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.3868647813796997},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.29310882091522217},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.27773937582969666},{"id":"https://openalex.org/C140781008","wikidata":"https://www.wikidata.org/wiki/Q1221081","display_name":"Service quality","level":3,"score":0.20690327882766724},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.20629096031188965},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.16087698936462402},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.13939735293388367},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2018.8621931","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8621931","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4300000071525574}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W171954822","https://openalex.org/W1480376833","https://openalex.org/W1514907448","https://openalex.org/W1517118877","https://openalex.org/W1538441360","https://openalex.org/W1569512666","https://openalex.org/W1608666872","https://openalex.org/W2039449865","https://openalex.org/W2039698234","https://openalex.org/W2067737688","https://openalex.org/W2076063813","https://openalex.org/W2098581273","https://openalex.org/W2108246181","https://openalex.org/W2119338125","https://openalex.org/W2119996119","https://openalex.org/W2121722108","https://openalex.org/W2124438244","https://openalex.org/W2147634746","https://openalex.org/W2148830179","https://openalex.org/W2153803020","https://openalex.org/W2155653793","https://openalex.org/W2165180682","https://openalex.org/W2171919935","https://openalex.org/W2378208052","https://openalex.org/W2422895071","https://openalex.org/W2578654005","https://openalex.org/W2612119519","https://openalex.org/W2889969381","https://openalex.org/W2911681169","https://openalex.org/W3099646542","https://openalex.org/W3123755823","https://openalex.org/W3124427380","https://openalex.org/W4240711941","https://openalex.org/W6607050262","https://openalex.org/W6630611256","https://openalex.org/W6758972637"],"related_works":["https://openalex.org/W3013184534","https://openalex.org/W2483231811","https://openalex.org/W2124748206","https://openalex.org/W2499260457","https://openalex.org/W4284957919","https://openalex.org/W2181766440","https://openalex.org/W2422895071","https://openalex.org/W2244494800","https://openalex.org/W2919924281","https://openalex.org/W3032838327"],"abstract_inverted_index":{"The":[0,21,117],"data":[1,55,104,119],"collected":[2,53],"from":[3,56,105,125],"a":[4,60,129],"firm's":[5,61,75],"online":[6,62,106],"account":[7,63,107],"enables":[8],"enterprises":[9],"to":[10,64,111,157,173],"understand":[11],"their":[12,18],"consumers":[13],"better":[14],"and":[15,29,99,144,183],"accordingly":[16],"adjust":[17],"business":[19],"processes.":[20],"development":[22],"of":[23,32,42,128,137,146,169],"effective":[24],"customer":[25,36,44,71,97,141,147],"relationship":[26],"management":[27],"strategies":[28],"the":[30,52,66,74,79,102,112,140,159,167,170,181],"enhancement":[31],"consumers'":[33],"experience":[34],"with":[35],"service":[37],"centers":[38,77,127],"require":[39],"accurate":[40],"prediction":[41,161],"future":[43,176],"telephone":[45,115],"queries.":[46,116],"In":[47],"this":[48,122],"paper,":[49],"we":[50],"leverage":[51],"big":[54,103],"customers'":[57],"activities":[58],"at":[59,94,179],"estimate":[65],"likelihood":[67],"that":[68],"an":[69,95],"individual":[70,96,182],"will":[72],"phone":[73],"contact":[76,126],"within":[78],"next":[80],"thirty":[81],"days.":[82],"Our":[83],"predictive":[84],"modeling":[85],"approach":[86],"has":[87],"two":[88],"distinguished":[89],"characteristics:":[90],"(i)":[91],"predicting":[92],"calls":[93,178],"level,":[98],"(ii)":[100],"incorporating":[101],"activities,":[108],"in":[109,121],"addition":[110],"customer's":[113],"past":[114],"individual-level":[118],"used":[120],"study":[123],"is":[124],"major":[130],"U.":[131],"S.":[132],"insurance":[133],"firm.":[134],"Various":[135],"classes":[136],"features":[138],"specifying":[139],"segment,":[142],"recency":[143],"frequency":[145],"interactions":[148],"are":[149,155],"considered.":[150],"Different":[151],"neural":[152],"network":[153],"architectures":[154],"investigated":[156],"achieve":[158],"best":[160],"accuracy.":[162],"Out-of-sample":[163],"performance":[164],"analyses":[165],"evince":[166],"capability":[168],"developed":[171],"model":[172],"accurately":[174],"predict":[175],"policyholders'":[177],"both":[180],"aggregate":[184],"levels.":[185]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}