{"id":"https://openalex.org/W4387846339","doi":"https://doi.org/10.1145/3583780.3615021","title":"Quantifying the Effectiveness of Advertising: A Bootstrap Proportion Test for Brand Lift Testing","display_name":"Quantifying the Effectiveness of Advertising: A Bootstrap Proportion Test for Brand Lift Testing","publication_year":2023,"publication_date":"2023-10-21","ids":{"openalex":"https://openalex.org/W4387846339","doi":"https://doi.org/10.1145/3583780.3615021"},"language":"en","primary_location":{"id":"doi:10.1145/3583780.3615021","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583780.3615021","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","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/A5046476046","display_name":"W. Liu","orcid":"https://orcid.org/0009-0007-0936-4424"},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wanjun Liu","raw_affiliation_strings":["LinkedIn Corporation, Sunnyvale, CA, USA"],"raw_orcid":"https://orcid.org/0009-0007-0936-4424","affiliations":[{"raw_affiliation_string":"LinkedIn Corporation, Sunnyvale, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023532963","display_name":"Xiufan Yu","orcid":"https://orcid.org/0000-0003-2027-6402"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiufan Yu","raw_affiliation_strings":["University of Notre Dame, Notre Dame, IN, USA"],"raw_orcid":"https://orcid.org/0000-0003-2027-6402","affiliations":[{"raw_affiliation_string":"University of Notre Dame, Notre Dame, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064390627","display_name":"Jialiang Mao","orcid":"https://orcid.org/0009-0007-1234-1004"},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jialiang Mao","raw_affiliation_strings":["LinkedIn Corporation, Sunnyvale, CA, USA"],"raw_orcid":"https://orcid.org/0009-0007-1234-1004","affiliations":[{"raw_affiliation_string":"LinkedIn Corporation, Sunnyvale, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022838176","display_name":"Xiaoxu Wu","orcid":"https://orcid.org/0009-0002-5739-7769"},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaoxu Wu","raw_affiliation_strings":["LinkedIn Corporation, Sunnyvale, CA, USA"],"raw_orcid":"https://orcid.org/0009-0002-5739-7769","affiliations":[{"raw_affiliation_string":"LinkedIn Corporation, Sunnyvale, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018396797","display_name":"Justin S. Dyer","orcid":null},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Justin Dyer","raw_affiliation_strings":["LinkedIn Corporation, Sunnyvale, CA, USA"],"raw_orcid":"https://orcid.org/0009-0004-4259-1571","affiliations":[{"raw_affiliation_string":"LinkedIn Corporation, Sunnyvale, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2093,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.62111726,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1627","last_page":"1636"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.9977999925613403,"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":0.9977999925613403,"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/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.9944000244140625,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/lift","display_name":"Lift (data mining)","score":0.7457947134971619},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5413988828659058},{"id":"https://openalex.org/keywords/statistical-hypothesis-testing","display_name":"Statistical hypothesis testing","score":0.5318037867546082},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5169715285301208},{"id":"https://openalex.org/keywords/test","display_name":"Test (biology)","score":0.48851144313812256},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.43328750133514404},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3842553198337555},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.16524603962898254}],"concepts":[{"id":"https://openalex.org/C139002025","wikidata":"https://www.wikidata.org/wiki/Q3001212","display_name":"Lift (data mining)","level":2,"score":0.7457947134971619},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5413988828659058},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.5318037867546082},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5169715285301208},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.48851144313812256},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.43328750133514404},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3842553198337555},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.16524603962898254},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3583780.3615021","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583780.3615021","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","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":24,"referenced_works":["https://openalex.org/W1745376753","https://openalex.org/W1975566260","https://openalex.org/W2007145472","https://openalex.org/W2013528885","https://openalex.org/W2031648200","https://openalex.org/W2080770071","https://openalex.org/W2091397788","https://openalex.org/W2095056536","https://openalex.org/W2106578604","https://openalex.org/W2110093215","https://openalex.org/W2112508839","https://openalex.org/W2121878111","https://openalex.org/W2123735241","https://openalex.org/W2126002144","https://openalex.org/W2144143723","https://openalex.org/W2479725793","https://openalex.org/W2541820485","https://openalex.org/W2564917231","https://openalex.org/W2964213576","https://openalex.org/W3122414560","https://openalex.org/W3166529875","https://openalex.org/W4231114256","https://openalex.org/W4288429814","https://openalex.org/W4301914348"],"related_works":["https://openalex.org/W4389397071","https://openalex.org/W2023045191","https://openalex.org/W2952839243","https://openalex.org/W1922851888","https://openalex.org/W641782856","https://openalex.org/W3009154991","https://openalex.org/W2945555514","https://openalex.org/W1967016017","https://openalex.org/W1582343225","https://openalex.org/W2038283895"],"abstract_inverted_index":{"Brand":[0,51,212],"Lift":[1,52,213],"test":[2,36,53,63,69,92,127,135,149,156,188],"is":[3,150,160,174,189],"a":[4,34,89,198],"widely":[5],"deployed":[6],"statistical":[7,60],"tool":[8],"for":[9,136],"measuring":[10],"the":[11,29,38,42,47,59,68,73,77,95,99,112,116,125,133,138,147,154,158,172,186,211],"effectiveness":[12],"of":[13,31,62,115,185],"online":[14],"advertisements":[15],"on":[16,37,58,72,94,111],"brand":[17,23],"perception":[18],"such":[19],"as":[20],"ad":[21],"recall,":[22],"familiarity":[24],"and":[25,46,206],"favorability.":[26],"By":[27],"formulating":[28],"problem":[30],"interest":[32],"into":[33],"two-sample":[35],"binomial":[39],"proportions":[40],"from":[41,204,210],"control":[43],"group":[44,49],"(p_0)":[45],"treatment":[48],"(p_1),":[50],"evaluates":[54],"ads":[55],"impact":[56],"based":[57,71,93],"significance":[61],"results.":[64],"Traditional":[65],"approaches":[66],"construct":[67],"statistics":[70],"absolute":[74,82],"difference":[75,97],"between":[76,98],"two":[78,100],"observed":[79,101],"proportions,":[80,102],"a.k.a,":[81],"lift.":[83,105],"In":[84],"this":[85],"work,":[86],"we":[87,143],"propose":[88],"new":[90],"bootstrap":[91],"percentage":[96],"i.e.,":[103],"relative":[104],"We":[106],"provide":[107],"rigorous":[108],"theoretical":[109],"guarantees":[110],"asymptotic":[113],"validity":[114],"proposed":[117,187],"relative-lift-based":[118,126,148],"test.":[119],"Our":[120],"numerical":[121],"studies":[122],"suggest":[123],"that":[124,146],"requires":[128],"less":[129,169],"stringent":[130],"conditions":[131],"than":[132,153],"absolute-lift-based":[134,155],"controlling":[137],"type-I":[139],"error":[140],"rate.":[141],"Interestingly,":[142],"also":[144],"prove":[145],"more":[151],"powerful":[152,170],"when":[157,171],"alternative":[159,173],"positive":[161],"(i.e.,":[162,176],"p1":[163,177],"-":[164,178],"p0":[165,179],">":[166],"0),":[167],"but":[168],"negative":[175],"<":[180],"0).":[181],"The":[182],"empirical":[183],"performance":[184],"demonstrated":[190],"by":[191],"extensive":[192],"simulation":[193],"studies,":[194],"an":[195],"application":[196],"to":[197],"publicly":[199],"available":[200],"A/B":[201],"testing":[202],"dataset":[203],"advertising,":[205],"real":[207],"datasets":[208],"collected":[209],"Testing":[214],"platform":[215],"at":[216],"LinkedIn.":[217]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
