{"id":"https://openalex.org/W2072837080","doi":"https://doi.org/10.1145/1014052.1016912","title":"Cross channel optimized marketing by reinforcement learning","display_name":"Cross channel optimized marketing by reinforcement learning","publication_year":2004,"publication_date":"2004-08-22","ids":{"openalex":"https://openalex.org/W2072837080","doi":"https://doi.org/10.1145/1014052.1016912","mag":"2072837080"},"language":"en","primary_location":{"id":"doi:10.1145/1014052.1016912","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1014052.1016912","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining","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/A5060856151","display_name":"Naoki Abe","orcid":"https://orcid.org/0000-0002-4048-3989"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Naoki Abe","raw_affiliation_strings":["IBM T. J. Watson Res. Ctr., Yorktown Heights, NY","IBM T. J. Watson Research Ctr., Yorktown Heights, NY"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Res. Ctr., Yorktown Heights, NY","institution_ids":["https://openalex.org/I1341412227"]},{"raw_affiliation_string":"IBM T. J. Watson Research Ctr., Yorktown Heights, NY","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000803049","display_name":"Naval K. Verma","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Naval Verma","raw_affiliation_strings":["IBM T. J. Watson Res. Ctr., Yorktown Heights, NY","IBM T. J. Watson Research Ctr., Yorktown Heights, NY"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Res. Ctr., Yorktown Heights, NY","institution_ids":["https://openalex.org/I1341412227"]},{"raw_affiliation_string":"IBM T. J. Watson Research Ctr., Yorktown Heights, NY","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111994276","display_name":"Chid Apte","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chid Apte","raw_affiliation_strings":["IBM T. J. Watson Res. Ctr., Yorktown Heights, NY","IBM T. J. Watson Research Ctr., Yorktown Heights, NY"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Res. Ctr., Yorktown Heights, NY","institution_ids":["https://openalex.org/I1341412227"]},{"raw_affiliation_string":"IBM T. J. Watson Research Ctr., Yorktown Heights, NY","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075299895","display_name":"Robert Schroko","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Robert Schroko","raw_affiliation_strings":["Saks Fifth Avenue, New York, NY"],"affiliations":[{"raw_affiliation_string":"Saks Fifth Avenue, New York, NY","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5060856151"],"corresponding_institution_ids":["https://openalex.org/I1341412227"],"apc_list":null,"apc_paid":null,"fwci":2.5171,"has_fulltext":false,"cited_by_count":54,"citation_normalized_percentile":{"value":0.90963105,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"767","last_page":"772"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.9991999864578247,"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/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.9991999864578247,"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/T12384","display_name":"Customer churn and segmentation","score":0.996999979019165,"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":0.9952999949455261,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.7858201265335083},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7344836592674255},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.7291368246078491},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.6709431409835815},{"id":"https://openalex.org/keywords/testbed","display_name":"Testbed","score":0.6390671730041504},{"id":"https://openalex.org/keywords/ibm","display_name":"IBM","score":0.5784174799919128},{"id":"https://openalex.org/keywords/customer-relationship-management","display_name":"Customer relationship management","score":0.5030011534690857},{"id":"https://openalex.org/keywords/maximization","display_name":"Maximization","score":0.4526505172252655},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.44927653670310974},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.34212470054626465},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.2881450355052948},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2752191424369812},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.12339502573013306},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.11282956600189209},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.10197228193283081},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09288686513900757},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09095656871795654}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7858201265335083},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7344836592674255},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.7291368246078491},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.6709431409835815},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.6390671730041504},{"id":"https://openalex.org/C70388272","wikidata":"https://www.wikidata.org/wiki/Q5968558","display_name":"IBM","level":2,"score":0.5784174799919128},{"id":"https://openalex.org/C98825075","wikidata":"https://www.wikidata.org/wiki/Q485643","display_name":"Customer relationship management","level":2,"score":0.5030011534690857},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.4526505172252655},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.44927653670310974},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.34212470054626465},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2881450355052948},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2752191424369812},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.12339502573013306},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.11282956600189209},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.10197228193283081},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09288686513900757},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09095656871795654},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C171250308","wikidata":"https://www.wikidata.org/wiki/Q11468","display_name":"Nanotechnology","level":1,"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":1,"locations":[{"id":"doi:10.1145/1014052.1016912","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1014052.1016912","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W16521680","https://openalex.org/W100053436","https://openalex.org/W1515851193","https://openalex.org/W1520048352","https://openalex.org/W1575592356","https://openalex.org/W1583837637","https://openalex.org/W1983236732","https://openalex.org/W2072758956","https://openalex.org/W2107726111","https://openalex.org/W2121863487","https://openalex.org/W2153947321","https://openalex.org/W4210868164","https://openalex.org/W4212774754","https://openalex.org/W6676007687"],"related_works":["https://openalex.org/W2883256816","https://openalex.org/W2171408034","https://openalex.org/W3003320923","https://openalex.org/W2106140982","https://openalex.org/W2152313554","https://openalex.org/W2064303750","https://openalex.org/W3048672182","https://openalex.org/W1509300825","https://openalex.org/W3092582874","https://openalex.org/W2065450024"],"abstract_inverted_index":{"The":[0,80],"issues":[1,41],"of":[2,14,46,58,73,93,101,125],"cross":[3,208],"channel":[4,135,209],"integration":[5],"and":[6,31,65,136,153],"customer":[7,20,138,165],"life":[8],"time":[9],"value":[10],"modeling":[11],"are":[12],"two":[13,39],"the":[15,26,56,71,104,118,123,129,137,180,207,213],"most":[16],"important":[17,40],"topics":[18],"surrounding":[19],"relationship":[21],"management":[22],"(CRM)":[23],"today.":[24],"In":[25,51],"present":[27],"paper,":[28],"we":[29,53,83,116],"describe":[30],"evaluate":[32],"a":[33,43,59,86,109,145,188],"novel":[34],"solution":[35,146],"that":[36,92,169,199],"treats":[37],"these":[38],"in":[42,103,133,141,156,179,204,212],"unified":[44],"framework":[45],"Markov":[47],"Decision":[48],"Processes":[49],"(MDP).":[50],"particular,":[52],"report":[54],"on":[55,151],"results":[57,197],"joint":[60],"project":[61],"between":[62,128],"IBM":[63],"Research":[64],"Saks":[66],"Fifth":[67],"Avenue":[68],"to":[69,76,112,122,174],"investigate":[70],"applicability":[72],"this":[74,148],"technology":[75],"real":[77,214],"world":[78],"problems.":[79],"business":[81],"problem":[82,110,149],"use":[84],"as":[85,170,172],"testbed":[87],"for":[88,99,147],"our":[89,194,200],"evaluation":[90,162],"is":[91,202],"optimizing":[94],"direct":[95],"mail":[96],"campaign":[97],"mailings":[98],"maximization":[100],"profits":[102,182],"store":[105,181],"channel.":[106],"We":[107,143],"identify":[108],"common":[111],"cross-channel":[113],"CRM,":[114],"which":[115],"call":[117],"Cross-Channel":[119],"Challenge,":[120],"due":[121],"lack":[124],"explicit":[126],"linking":[127],"marketing":[130],"actions":[131],"taken":[132],"one":[134],"responses":[139],"obtained":[140],"another.":[142],"provide":[144],"based":[150],"old":[152],"new":[154],"techniques":[155],"reinforcement":[157],"learning.":[158],"Our":[159],"in-laboratory":[160],"experimental":[161],"using":[163],"actual":[164],"interaction":[166],"data":[167],"show":[168],"much":[171],"7":[173],"8":[175],"per":[176],"cent":[177],"increase":[178],"can":[183],"be":[184],"expected,":[185],"by":[186,193],"employing":[187],"mailing":[189],"policy":[190],"automatically":[191],"generated":[192],"methodology.":[195],"These":[196],"confirm":[198],"approach":[201],"valid":[203],"dealing":[205],"with":[206],"CRM":[210],"scenarios":[211],"world.":[215]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":6},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}