{"id":"https://openalex.org/W2584778415","doi":"https://doi.org/10.1109/bigdata.2016.7840744","title":"Pitfalls of long-term online controlled experiments","display_name":"Pitfalls of long-term online controlled experiments","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2584778415","doi":"https://doi.org/10.1109/bigdata.2016.7840744","mag":"2584778415"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2016.7840744","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2016.7840744","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5020697231","display_name":"Pavel Dmitriev","orcid":"https://orcid.org/0000-0001-5740-5146"},"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":"Pavel Dmitriev","raw_affiliation_strings":["Analysis and Experimentation, Microsoft Corporation, Redmond, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Analysis and Experimentation, Microsoft Corporation, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089128609","display_name":"Brian Frasca","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":"Brian Frasca","raw_affiliation_strings":["Analysis and Experimentation, Microsoft Corporation, Redmond, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Analysis and Experimentation, Microsoft Corporation, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039548707","display_name":"Somit Gupta","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":"Somit Gupta","raw_affiliation_strings":["Analysis and Experimentation, Microsoft Corporation, Redmond, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Analysis and Experimentation, Microsoft Corporation, Redmond, WA, USA","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":["Analysis and Experimentation, Microsoft Corporation, Redmond, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Analysis and Experimentation, Microsoft Corporation, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003107719","display_name":"Garnet J. Vaz","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":"Garnet Vaz","raw_affiliation_strings":["Analysis and Experimentation, Microsoft Corporation, Redmond, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Analysis and Experimentation, Microsoft Corporation, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1290206253"],"apc_list":null,"apc_paid":null,"fwci":13.2521,"has_fulltext":false,"cited_by_count":78,"citation_normalized_percentile":{"value":0.98944427,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1367","last_page":"1376"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10068","display_name":"Technology Adoption and User Behaviour","score":0.9573000073432922,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10068","display_name":"Technology Adoption and User Behaviour","score":0.9573000073432922,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9449999928474426,"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/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.9375,"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/term","display_name":"Term (time)","score":0.8297457695007324},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.713689923286438},{"id":"https://openalex.org/keywords/revenue","display_name":"Revenue","score":0.6984310746192932},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.5230197310447693},{"id":"https://openalex.org/keywords/value","display_name":"Value (mathematics)","score":0.49811267852783203},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4629021883010864},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.4130955934524536},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.327637642621994},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.15875643491744995},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.157213032245636},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.12168645858764648},{"id":"https://openalex.org/keywords/software-engineering","display_name":"Software engineering","score":0.12064146995544434}],"concepts":[{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.8297457695007324},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.713689923286438},{"id":"https://openalex.org/C195487862","wikidata":"https://www.wikidata.org/wiki/Q850210","display_name":"Revenue","level":2,"score":0.6984310746192932},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.5230197310447693},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.49811267852783203},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4629021883010864},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.4130955934524536},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.327637642621994},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.15875643491744995},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.157213032245636},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.12168645858764648},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.12064146995544434},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2016.7840744","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2016.7840744","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.6200000047683716,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W57737232","https://openalex.org/W67965866","https://openalex.org/W575598456","https://openalex.org/W655637859","https://openalex.org/W1553296127","https://openalex.org/W1949486365","https://openalex.org/W1972159854","https://openalex.org/W1975566260","https://openalex.org/W2030660637","https://openalex.org/W2033741511","https://openalex.org/W2049506335","https://openalex.org/W2065378584","https://openalex.org/W2070815407","https://openalex.org/W2074380434","https://openalex.org/W2095056536","https://openalex.org/W2102546764","https://openalex.org/W2105783544","https://openalex.org/W2108126284","https://openalex.org/W2110228583","https://openalex.org/W2126002144","https://openalex.org/W2144093215","https://openalex.org/W2162538515","https://openalex.org/W2169113736","https://openalex.org/W2210543184","https://openalex.org/W2310822838","https://openalex.org/W2338665238","https://openalex.org/W2753533763","https://openalex.org/W4234525067","https://openalex.org/W6602773337","https://openalex.org/W6616411108","https://openalex.org/W6621817780","https://openalex.org/W6633031252","https://openalex.org/W6653967188","https://openalex.org/W6676237016","https://openalex.org/W7053415339"],"related_works":["https://openalex.org/W2770234245","https://openalex.org/W96612179","https://openalex.org/W4229499248","https://openalex.org/W2566006169","https://openalex.org/W1567818861","https://openalex.org/W2987774938","https://openalex.org/W4256492088","https://openalex.org/W632915154","https://openalex.org/W2055733372","https://openalex.org/W3022067003"],"abstract_inverted_index":{"Online":[0],"controlled":[1,49,161],"experiments":[2,50,162,182,227],"(e.g.,":[3],"A/B":[4],"tests)":[5],"are":[6,21,145,168],"now":[7],"regularly":[8],"used":[9,207],"to":[10,52,64,75,115,158,208,236,260],"guide":[11],"product":[12],"development":[13,266],"and":[14,26,36,126,163,183,198,201,248,255,263],"accelerate":[15],"innovation":[16],"in":[17,109,140,220],"software.":[18],"Product":[19],"ideas":[20],"evaluated":[22],"as":[23,103,231],"scientific":[24],"hypotheses,":[25],"tested":[27],"on":[28,138],"web":[29],"sites,":[30],"mobile":[31],"applications,":[32,34],"desktop":[33],"services,":[35],"operating":[37],"system":[38],"features.":[39],"One":[40],"of":[41,83,148,180,212,267],"the":[42,60,66,80,184,237,242,246,261,265],"key":[43],"challenges":[44],"for":[45,270],"organizations":[46],"that":[47,72,144,165,204],"run":[48,159],"is":[51,71,136,157,217],"select":[53],"an":[54,134],"Overall":[55],"Evaluation":[56],"Criterion":[57],"(OEC),":[58],"i.e.,":[59],"criterion":[61],"by":[62,170],"which":[63],"evaluate":[65],"different":[67],"variants.":[68],"The":[69],"difficulty":[70],"short-term":[73,92,142],"changes":[74],"metrics":[76,139],"may":[77,233],"not":[78],"predict":[79],"long-term":[81,98,149,153,166,181,222,226],"impact":[82],"a":[84,110,141],"change.":[85],"For":[86],"example,":[87],"raising":[88],"prices":[89],"likely":[90,96],"increases":[91],"revenue":[93,99],"but":[94,123],"also":[95],"reduces":[97],"(customer":[100],"lifetime":[101,131],"value)":[102],"users":[104,114],"abandon.":[105],"Degrading":[106],"search":[107,116],"results":[108,232],"Search":[111],"Engine":[112],"causes":[113],"more,":[117],"thus":[118,127],"increasing":[119,124],"query":[120],"share":[121,177,202],"short-term,":[122],"abandonment":[125],"reducing":[128],"longterm":[129,160],"customer":[130],"value.":[132,150],"Ideally,":[133],"OEC":[135],"based":[137],"experiment":[143],"good":[146],"predictors":[147],"To":[151],"assess":[152],"impact,":[154],"one":[155],"approach":[156],"assume":[164],"effects":[167],"represented":[169],"observed":[171],"metrics.":[172],"In":[173],"this":[174,271],"paper":[175],"we":[176],"several":[178],"examples":[179,254],"pitfalls":[185,239],"associated":[186],"with":[187],"running":[188,225],"them.":[189],"We":[190,250],"discuss":[191],"cookie":[192],"stability,":[193],"survivorship":[194],"bias,":[195,197],"selection":[196],"perceived":[199],"trends,":[200,223],"methodologies":[203,269],"can":[205],"be":[206,229,234],"partially":[209],"address":[210],"some":[211],"these":[213],"issues.":[214],"While":[215],"there":[216],"clearly":[218],"value":[219],"evaluating":[221],"experimenters":[224],"must":[228],"cautious,":[230],"due":[235],"above":[238],"more":[240],"than":[241],"true":[243],"delta":[244],"between":[245],"Treatment":[247],"Control.":[249],"hope":[251],"our":[252],"real":[253],"analyses":[256],"will":[257],"sensitize":[258],"readers":[259],"issues":[262],"encourage":[264],"new":[268],"important":[272],"problem.":[273]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":37},{"year":2019,"cited_by_count":12},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":4}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
