{"id":"https://openalex.org/W2763992219","doi":"https://doi.org/10.1109/cig.2017.8080412","title":"Games and big data: A scalable multi-dimensional churn prediction model","display_name":"Games and big data: A scalable multi-dimensional churn prediction model","publication_year":2017,"publication_date":"2017-08-01","ids":{"openalex":"https://openalex.org/W2763992219","doi":"https://doi.org/10.1109/cig.2017.8080412","mag":"2763992219"},"language":"en","primary_location":{"id":"doi:10.1109/cig.2017.8080412","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cig.2017.8080412","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Conference on Computational Intelligence and Games (CIG)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1710.02262","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Paul Bertens","orcid":null},"institutions":[{"id":"https://openalex.org/I2800271857","display_name":"Sumco Corporation (Japan)","ror":"https://ror.org/01yp72g57","country_code":"JP","type":"company","lineage":["https://openalex.org/I2800271857"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Paul Bertens","raw_affiliation_strings":["Game Data Science Department Silicon Studio, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Game Data Science Department Silicon Studio, Tokyo, Japan","institution_ids":["https://openalex.org/I2800271857"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Anna Guitart","orcid":null},"institutions":[{"id":"https://openalex.org/I2800271857","display_name":"Sumco Corporation (Japan)","ror":"https://ror.org/01yp72g57","country_code":"JP","type":"company","lineage":["https://openalex.org/I2800271857"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Anna Guitart","raw_affiliation_strings":["Game Data Science Department Silicon Studio, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Game Data Science Department Silicon Studio, Tokyo, Japan","institution_ids":["https://openalex.org/I2800271857"]}]},{"author_position":"last","author":{"id":null,"display_name":"Africa Perianez","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Africa Perianez","raw_affiliation_strings":["Game Data Science Department, Silicon Studio, 1-21-3 Ebisu Shibuya-ku, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Game Data Science Department, Silicon Studio, 1-21-3 Ebisu Shibuya-ku, Tokyo, Japan","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2800271857"],"apc_list":null,"apc_paid":null,"fwci":7.7009,"has_fulltext":false,"cited_by_count":44,"citation_normalized_percentile":{"value":0.96878209,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"33","last_page":"36"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.996399998664856,"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":0.996399998664856,"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/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.9900000095367432,"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/T11674","display_name":"Sports Analytics and Performance","score":0.9854000210762024,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/monetization","display_name":"Monetization","score":0.7975000143051147},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6800000071525574},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.6611999869346619},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5309000015258789},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5167999863624573},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.3898000121116638},{"id":"https://openalex.org/keywords/mobile-device","display_name":"Mobile device","score":0.3352999985218048},{"id":"https://openalex.org/keywords/mobile-computing","display_name":"Mobile computing","score":0.3319999873638153}],"concepts":[{"id":"https://openalex.org/C2780602052","wikidata":"https://www.wikidata.org/wiki/Q289845","display_name":"Monetization","level":2,"score":0.7975000143051147},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.77920001745224},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6800000071525574},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.6611999869346619},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5309000015258789},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5167999863624573},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3898000121116638},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.34850001335144043},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.3352999985218048},{"id":"https://openalex.org/C144543869","wikidata":"https://www.wikidata.org/wiki/Q2738570","display_name":"Mobile computing","level":2,"score":0.3319999873638153},{"id":"https://openalex.org/C95491727","wikidata":"https://www.wikidata.org/wiki/Q992968","display_name":"Mobile telephony","level":3,"score":0.32710000872612},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31700000166893005},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3116999864578247},{"id":"https://openalex.org/C73795354","wikidata":"https://www.wikidata.org/wiki/Q287618","display_name":"Sequential game","level":3,"score":0.3068000078201294},{"id":"https://openalex.org/C200594392","wikidata":"https://www.wikidata.org/wiki/Q812529","display_name":"Bayesian game","level":4,"score":0.303600013256073},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30329999327659607},{"id":"https://openalex.org/C113336015","wikidata":"https://www.wikidata.org/wiki/Q574010","display_name":"Complete information","level":2,"score":0.28929999470710754},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2874999940395355},{"id":"https://openalex.org/C177142836","wikidata":"https://www.wikidata.org/wiki/Q44455","display_name":"Game theory","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C54276265","wikidata":"https://www.wikidata.org/wiki/Q1061635","display_name":"Video game development","level":3,"score":0.26589998602867126},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C41826821","wikidata":"https://www.wikidata.org/wiki/Q844005","display_name":"Game Developer","level":3,"score":0.25839999318122864},{"id":"https://openalex.org/C2778079155","wikidata":"https://www.wikidata.org/wiki/Q288500","display_name":"Potential game","level":3,"score":0.25209999084472656}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cig.2017.8080412","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cig.2017.8080412","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Conference on Computational Intelligence and Games (CIG)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1710.02262","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1710.02262","pdf_url":"https://arxiv.org/pdf/1710.02262","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1710.02262","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1710.02262","pdf_url":"https://arxiv.org/pdf/1710.02262","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2003528492","https://openalex.org/W2087097402","https://openalex.org/W2115108310","https://openalex.org/W2123998733","https://openalex.org/W2144922697","https://openalex.org/W2204198178","https://openalex.org/W2564904255","https://openalex.org/W3147894994"],"related_works":[],"abstract_inverted_index":{"The":[0],"emergence":[1],"of":[2,23,33,55,74,130,139,152,159],"mobile":[3,62],"games":[4,156],"has":[5],"caused":[6],"a":[7,21,49,53,75,127],"paradigm":[8],"shift":[9],"in":[10],"the":[11,61,71,99,107,140],"video-game":[12],"industry.":[13],"Game":[14],"developers":[15],"now":[16],"have":[17],"at":[18,101],"their":[19,26,110],"disposal":[20],"plethora":[22],"information":[24],"on":[25,89,97],"players,":[27],"and":[28,42,109,124],"thus":[29],"can":[30,37],"take":[31],"advantage":[32],"reliable":[34],"models":[35],"that":[36,114],"accurately":[38],"predict":[39],"player":[40,66,104],"behavior":[41],"scale":[43],"to":[44,52,84,120,126,148],"huge":[45],"datasets.":[46],"Churn":[47],"prediction,":[48],"challenge":[50],"common":[51],"variety":[54],"sectors,":[56],"is":[57,68,118],"particularly":[58],"relevant":[59],"for":[60,70,136,155],"game":[63,86,108],"industry,":[64],"as":[65],"retention":[67],"crucial":[69],"successful":[72],"monetization":[73],"game.":[76],"In":[77],"this":[78],"article,":[79],"we":[80],"present":[81],"an":[82],"approach":[83],"predicting":[85],"churn":[87],"based":[88],"survival":[90],"ensembles.":[91],"Our":[92],"method":[93],"provides":[94],"accurate":[95],"predictions":[96],"both":[98],"level":[100],"which":[102],"each":[103],"will":[105],"leave":[106],"accumulated":[111],"playtime":[112],"until":[113],"moment.":[115],"Further,":[116],"it":[117],"robust":[119],"different":[121],"data":[122],"distributions":[123],"applicable":[125],"wide":[128],"range":[129],"response":[131],"variables,":[132],"while":[133],"also":[134],"allowing":[135],"efficient":[137],"parallelization":[138],"algorithm.":[141],"This":[142],"makes":[143],"our":[144],"model":[145],"well":[146],"suited":[147],"perform":[149],"real-time":[150],"analyses":[151],"churners,":[153],"even":[154],"with":[157],"millions":[158],"daily":[160],"active":[161],"users.":[162]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":8}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2017-10-20T00:00:00"}