{"id":"https://openalex.org/W2126002144","doi":"https://doi.org/10.1145/2433396.2433413","title":"Improving the sensitivity of online controlled experiments by utilizing pre-experiment data","display_name":"Improving the sensitivity of online controlled experiments by utilizing pre-experiment data","publication_year":2013,"publication_date":"2013-02-04","ids":{"openalex":"https://openalex.org/W2126002144","doi":"https://doi.org/10.1145/2433396.2433413","mag":"2126002144"},"language":"en","primary_location":{"id":"doi:10.1145/2433396.2433413","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2433396.2433413","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the sixth ACM international conference on Web search 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/A5075519075","display_name":"Alex Deng","orcid":"https://orcid.org/0000-0002-8116-5602"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alex Deng","raw_affiliation_strings":["Microsoft, Redmond, WA, USA","Microsoft Redmond, WA, USA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]},{"raw_affiliation_string":"Microsoft Redmond, WA, USA#TAB#","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114062370","display_name":"Ya Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ya Xu","raw_affiliation_strings":["Microsoft, Sunnyvale, CA, USA","Microsoft, Sunnyvale, CA, USA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft, Sunnyvale, CA, USA","institution_ids":["https://openalex.org/I1290206253"]},{"raw_affiliation_string":"Microsoft, Sunnyvale, CA, USA#TAB#","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037339239","display_name":"Ron Kohavi","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ron Kohavi","raw_affiliation_strings":["Microsoft, Redmond, WA, USA","Microsoft Redmond, WA, USA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]},{"raw_affiliation_string":"Microsoft Redmond, WA, USA#TAB#","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031583581","display_name":"Toby Walker","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Toby Walker","raw_affiliation_strings":["Microsoft, Redmond, WA, USA","Microsoft Redmond, WA, USA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]},{"raw_affiliation_string":"Microsoft Redmond, WA, USA#TAB#","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":18.7769,"has_fulltext":false,"cited_by_count":217,"citation_normalized_percentile":{"value":0.99255344,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"123","last_page":"132"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9740999937057495,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9740999937057495,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.9699000120162964,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9639999866485596,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7219260931015015},{"id":"https://openalex.org/keywords/revenue","display_name":"Revenue","score":0.6100966334342957},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5965511202812195},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5925003290176392},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.5570794939994812},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.5132684707641602},{"id":"https://openalex.org/keywords/randomized-experiment","display_name":"Randomized experiment","score":0.5090157985687256},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.49209755659103394},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3261999487876892},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14962515234947205},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13636592030525208},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.12057030200958252},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08670297265052795}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7219260931015015},{"id":"https://openalex.org/C195487862","wikidata":"https://www.wikidata.org/wiki/Q850210","display_name":"Revenue","level":2,"score":0.6100966334342957},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5965511202812195},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5925003290176392},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.5570794939994812},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5132684707641602},{"id":"https://openalex.org/C155108698","wikidata":"https://www.wikidata.org/wiki/Q1231081","display_name":"Randomized experiment","level":2,"score":0.5090157985687256},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.49209755659103394},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3261999487876892},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14962515234947205},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13636592030525208},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.12057030200958252},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08670297265052795},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/2433396.2433413","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2433396.2433413","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the sixth ACM international conference on Web search and data mining","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.294.8141","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.294.8141","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://robotics.stanford.edu/users/ronnyk/2013-02CUPEDImprovingSensitivityOfControlledExperiments.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.41999998688697815}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W221376126","https://openalex.org/W1486094596","https://openalex.org/W1496357020","https://openalex.org/W1506214197","https://openalex.org/W1553296127","https://openalex.org/W1975566260","https://openalex.org/W1976767421","https://openalex.org/W1981457167","https://openalex.org/W1982530130","https://openalex.org/W2015374677","https://openalex.org/W2057090997","https://openalex.org/W2061591492","https://openalex.org/W2064097590","https://openalex.org/W2074466695","https://openalex.org/W2101517004","https://openalex.org/W2108126284","https://openalex.org/W2110228583","https://openalex.org/W2136333541","https://openalex.org/W2155474210","https://openalex.org/W2169113736","https://openalex.org/W2170051345","https://openalex.org/W2324906669","https://openalex.org/W2615953416","https://openalex.org/W3215037115","https://openalex.org/W4205110562","https://openalex.org/W4212971842","https://openalex.org/W4234525067"],"related_works":["https://openalex.org/W2378757965","https://openalex.org/W4224903346","https://openalex.org/W1593262897","https://openalex.org/W2372869593","https://openalex.org/W2384194537","https://openalex.org/W4310173797","https://openalex.org/W2031011156","https://openalex.org/W1991935520","https://openalex.org/W2368745429","https://openalex.org/W2067940999"],"abstract_inverted_index":{"Online":[0],"controlled":[1],"experiments":[2,53,75,83,95],"are":[3,159],"at":[4,11],"the":[5,32,80,94,107,121,171,179,183],"heart":[6],"of":[7,15,34,36,60,64,66,74,82,89,141,178],"making":[8],"data-driven":[9],"decisions":[10],"a":[12,37,138],"diverse":[13],"set":[14],"companies,":[16],"including":[17],"Amazon,":[18],"eBay,":[19],"Facebook,":[20],"Google,":[21],"Microsoft,":[22],"Yahoo,":[23],"and":[24,110,128,145,149],"Zynga.":[25],"Small":[26],"differences":[27],"in":[28],"key":[29,142],"metrics,":[30,144],"on":[31,96,155],"order":[33],"fractions":[35],"percent,":[38],"may":[39],"have":[40],"very":[41,160],"significant":[42],"business":[43,143],"implications.":[44],"At":[45],"Bing":[46],"it":[47,146],"is":[48,135,147],"not":[49],"uncommon":[50],"to":[51,124,137,151],"see":[52],"that":[54,117],"impact":[55],"annual":[56],"revenue":[57],"by":[58,166],"millions":[59,65],"dollars,":[61,67],"even":[62],"tens":[63],"either":[68],"positively":[69],"or":[70,91,102,181],"negatively.":[71],"With":[72],"thousands":[73],"being":[76],"run":[77],"annually,":[78],"improving":[79],"sensitivity":[81],"allows":[84],"for":[85,103],"more":[86,100],"precise":[87],"assessment":[88],"value,":[90],"equivalently":[92],"running":[93],"smaller":[97],"populations":[98],"(supporting":[99],"experiments)":[101],"shorter":[104],"durations":[105],"(improving":[106],"feedback":[108],"cycle":[109],"agility).":[111],"We":[112],"propose":[113],"an":[114],"approach":[115],"(CUPED)":[116],"utilizes":[118],"data":[119],"from":[120],"pre-experiment":[122],"period":[123],"reduce":[125,164],"metric":[126],"variability":[127],"hence":[129],"achieve":[130],"better":[131],"sensitivity.":[132],"This":[133],"technique":[134],"applicable":[136],"wide":[139],"variety":[140],"practical":[148],"easy":[150],"implement.":[152],"The":[153],"results":[154],"Bing's":[156],"experimentation":[157],"system":[158],"successful:":[161],"we":[162],"can":[163],"variance":[165],"about":[167],"50%,":[168],"effectively":[169],"achieving":[170],"same":[172],"statistical":[173],"power":[174],"with":[175],"only":[176],"half":[177,182],"users,":[180],"duration.":[184]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":23},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":19},{"year":2020,"cited_by_count":47},{"year":2019,"cited_by_count":17},{"year":2018,"cited_by_count":15},{"year":2017,"cited_by_count":10},{"year":2016,"cited_by_count":16},{"year":2015,"cited_by_count":16},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
