{"id":"https://openalex.org/W2902121735","doi":"https://doi.org/10.1109/bigdata.2018.8622151","title":"Customer Lifetime Value in Video Games Using Deep Learning and Parametric Models","display_name":"Customer Lifetime Value in Video Games Using Deep Learning and Parametric Models","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2902121735","doi":"https://doi.org/10.1109/bigdata.2018.8622151","mag":"2902121735"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2018.8622151","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8622151","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":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1811.12799","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5110353471","display_name":"Pei Pei Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Pei Pei Chen","raw_affiliation_strings":["Yokozuna Data, a Keywords Studio, Japan"],"affiliations":[{"raw_affiliation_string":"Yokozuna Data, a Keywords Studio, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068338316","display_name":"Anna Guitart","orcid":"https://orcid.org/0000-0003-0480-1131"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Anna Guitart","raw_affiliation_strings":["Yokozuna Data, a Keywords Studio, Japan"],"affiliations":[{"raw_affiliation_string":"Yokozuna Data, a Keywords Studio, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073405232","display_name":"Ana Fern\u00e1ndez del R\u00edo","orcid":"https://orcid.org/0000-0003-1465-7065"},"institutions":[{"id":"https://openalex.org/I4210095385","display_name":"Instituto de F\u00edsica Fundamental","ror":"https://ror.org/009wseg80","country_code":"ES","type":"facility","lineage":["https://openalex.org/I134820265","https://openalex.org/I4210095385"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Ana Fernandez del Rio","raw_affiliation_strings":["Dpto. F\u00edsica Fundamental, UNED, Madrid, Spain"],"affiliations":[{"raw_affiliation_string":"Dpto. F\u00edsica Fundamental, UNED, Madrid, Spain","institution_ids":["https://openalex.org/I4210095385"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065553799","display_name":"\u00c1frica Peri\u00e1\u00f1ez","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Africa Perianez","raw_affiliation_strings":["Yokozuna Data, a Keywords Studio, Japan"],"affiliations":[{"raw_affiliation_string":"Yokozuna Data, a Keywords Studio, Japan","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5110353471"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.1866,"has_fulltext":false,"cited_by_count":52,"citation_normalized_percentile":{"value":0.95763252,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2134","last_page":"2140"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9976999759674072,"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":0.9976999759674072,"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":0.9948999881744385,"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":0.9936000108718872,"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/computer-science","display_name":"Computer science","score":0.7771022319793701},{"id":"https://openalex.org/keywords/video-game","display_name":"Video game","score":0.6885541081428528},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.668263852596283},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5522435307502747},{"id":"https://openalex.org/keywords/revenue","display_name":"Revenue","score":0.5415902137756348},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5044547319412231},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5013928413391113},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4904889762401581},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.467424601316452},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46599188446998596},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.463933527469635},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4353730380535126},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4313923716545105},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.4119989275932312},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.399000883102417},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.1950775384902954},{"id":"https://openalex.org/keywords/multimedia","display_name":"Multimedia","score":0.19484657049179077}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7771022319793701},{"id":"https://openalex.org/C3018412434","wikidata":"https://www.wikidata.org/wiki/Q7889","display_name":"Video game","level":2,"score":0.6885541081428528},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.668263852596283},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5522435307502747},{"id":"https://openalex.org/C195487862","wikidata":"https://www.wikidata.org/wiki/Q850210","display_name":"Revenue","level":2,"score":0.5415902137756348},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5044547319412231},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5013928413391113},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4904889762401581},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.467424601316452},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46599188446998596},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.463933527469635},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4353730380535126},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4313923716545105},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4119989275932312},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.399000883102417},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.1950775384902954},{"id":"https://openalex.org/C49774154","wikidata":"https://www.wikidata.org/wiki/Q131765","display_name":"Multimedia","level":1,"score":0.19484657049179077},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","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/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdata.2018.8622151","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8622151","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"},{"id":"pmh:oai:arXiv.org:1811.12799","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1811.12799","pdf_url":"https://arxiv.org/pdf/1811.12799","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:1811.12799","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1811.12799","pdf_url":"https://arxiv.org/pdf/1811.12799","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":60,"referenced_works":["https://openalex.org/W303776967","https://openalex.org/W1522301498","https://openalex.org/W1523493493","https://openalex.org/W1533861849","https://openalex.org/W1538131130","https://openalex.org/W1553526580","https://openalex.org/W1568657496","https://openalex.org/W1625817316","https://openalex.org/W2016210396","https://openalex.org/W2025348367","https://openalex.org/W2026882341","https://openalex.org/W2045568966","https://openalex.org/W2059888405","https://openalex.org/W2064675550","https://openalex.org/W2072128103","https://openalex.org/W2076063813","https://openalex.org/W2081569777","https://openalex.org/W2091823618","https://openalex.org/W2097117768","https://openalex.org/W2127624016","https://openalex.org/W2133176971","https://openalex.org/W2147800946","https://openalex.org/W2156387975","https://openalex.org/W2156957852","https://openalex.org/W2167029003","https://openalex.org/W2169805405","https://openalex.org/W2189223556","https://openalex.org/W2202704248","https://openalex.org/W2288074780","https://openalex.org/W2318793669","https://openalex.org/W2327667672","https://openalex.org/W2346354117","https://openalex.org/W2415594836","https://openalex.org/W2564904255","https://openalex.org/W2575693697","https://openalex.org/W2595177306","https://openalex.org/W2727476476","https://openalex.org/W2749587125","https://openalex.org/W2763992219","https://openalex.org/W2781814582","https://openalex.org/W2794065766","https://openalex.org/W2964121744","https://openalex.org/W3098057095","https://openalex.org/W3103104966","https://openalex.org/W3103695590","https://openalex.org/W3105639507","https://openalex.org/W4231109964","https://openalex.org/W4232331595","https://openalex.org/W4237699877","https://openalex.org/W4238079052","https://openalex.org/W4254751698","https://openalex.org/W4292225646","https://openalex.org/W6610867836","https://openalex.org/W6631190155","https://openalex.org/W6631943919","https://openalex.org/W6632100814","https://openalex.org/W6682889407","https://openalex.org/W6687833805","https://openalex.org/W6696429117","https://openalex.org/W6786041110"],"related_works":["https://openalex.org/W4390608645","https://openalex.org/W4394895745","https://openalex.org/W4247566972","https://openalex.org/W2960264696","https://openalex.org/W3090563135","https://openalex.org/W2497432351","https://openalex.org/W4206777497","https://openalex.org/W2910064364","https://openalex.org/W4200136508","https://openalex.org/W2965782936"],"abstract_inverted_index":{"Nowadays,":[0],"video":[1,109,193],"game":[2,19,194],"developers":[3,222],"record":[4],"every":[5],"virtual":[6],"action":[7],"performed":[8],"by":[9,81],"their":[10,64,113,234],"players.":[11,140],"As":[12],"each":[13],"player":[14,37],"can":[15,30,100,163],"remain":[16],"in":[17,24,60,69,108,133,146],"the":[18,47,53,77,86,88,130,135,190],"for":[20,215],"years,":[21],"this":[22,41,92],"results":[23,122],"an":[25,208],"exceptionally":[26],"rich":[27],"dataset":[28],"that":[29,124],"be":[31,101],"used":[32,102],"to":[33,45,74,103,115,153,203,223],"understand":[34],"and":[35,51,111,156,170,232],"predict":[36,104],"behavior.":[38],"In":[39],"particular,":[40],"information":[42],"may":[43],"serve":[44],"identify":[46,204],"most":[48,131],"valuable":[49],"players":[50],"foresee":[52],"amount":[54],"of":[55,76,85,138,148,212],"money":[56],"they":[57,162],"will":[58],"spend":[59],"in-app":[61],"purchases":[62],"during":[63],"lifetime.":[65],"This":[66,179],"is":[67,79,188,211],"crucial":[68],"free-to-play":[70],"games,":[71,110],"where":[72],"up":[73],"50%":[75],"revenue":[78],"generated":[80],"just":[82],"around":[83],"2%":[84],"players,":[87],"so-called":[89],"whales.To":[90],"address":[91],"challenge,":[93],"we":[94],"explore":[95],"how":[96],"deep":[97],"neural":[98,126,197],"networks":[99,198],"customer":[105],"lifetime":[106],"value":[107,137],"compare":[112],"performance":[114],"parametric":[116],"models":[117],"such":[118],"as":[119,161,187,218],"Pareto/NBD.":[120],"Our":[121],"suggest":[123],"convolutional":[125,196],"network":[127],"structures":[128],"are":[129,184,199],"efficient":[132],"predicting":[134],"economic":[136],"individual":[139],"They":[141],"not":[142,173],"only":[143],"perform":[144],"better":[145],"terms":[147],"accuracy,":[149],"but":[150],"also":[151],"scale":[152],"big":[154,230],"data":[155,169],"significantly":[157],"reduce":[158],"computational":[159],"time,":[160],"work":[164],"directly":[165],"with":[166,192],"raw":[167],"sequential":[168],"thus":[171],"do":[172],"require":[174],"any":[175],"feature":[176],"engineering":[177],"process.":[178],"becomes":[180],"important":[181],"when":[182],"datasets":[183],"very":[185],"large,":[186],"often":[189],"case":[191],"logs.Moreover,":[195],"particularly":[200],"well":[201],"suited":[202],"potential":[205],"whales.":[206],"Such":[207],"early":[209],"identification":[210],"paramount":[213],"importance":[214],"business":[216],"purposes,":[217],"it":[219],"would":[220,237],"allow":[221],"implement":[224],"in-game":[225],"actions":[226],"aimed":[227],"at":[228],"retaining":[229],"spenders":[231],"maximizing":[233],"lifetime,":[235],"which":[236],"ultimately":[238],"translate":[239],"into":[240],"increased":[241],"revenue.":[242]},"counts_by_year":[{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}