{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/126956"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/126956","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Learning who to target with what via adaptive experimentation to optimize long-term outcomes","abstract":"This paper develops a framework for learning and implementing optimal targeting policies via a sequence of adaptive experiments to maximize long-term customer outcomes. Our framework builds on literature on doubly robust off-policy evaluation and optimization from computer science, statistics, and economics, and can also adapt to potential changes in the environment. We apply our framework to learn optimal discount targeting policies to the current subscribers at Boston Globe to maximize long-term revenue. Since the long-term revenue is not observable, we use intermediate outcomes such as subscribers' short-term revenue and their content consumption to construct a surrogate index and use it to impute the missing long-term revenues. Our method improves the average 1.5-year revenue by $15 and projected 3-year revenue by $40 per subscriber compared to several competitive targeting policies such as a policy that targets no one, a random policy, and a policy that targets subscribers with the highest churn risk. Over a three year period, our approach has a net-positive revenue impact in the range $1.7-$2.8 million compared to the status quo.","abstract_html":"This paper develops a framework for learning and implementing optimal targeting policies via a sequence of adaptive experiments to maximize long-term customer outcomes. Our framework builds on literature on doubly robust off-policy evaluation and optimization from computer science, statistics, and economics, and can also adapt to potential changes in the environment. We apply our framework to learn optimal discount targeting policies to the current subscribers at Boston Globe to maximize long-term revenue. Since the long-term revenue is not observable, we use intermediate outcomes such as subscribers' short-term revenue and their content consumption to construct a surrogate index and use it to impute the missing long-term revenues. Our method improves the average 1.5-year revenue by $15 and projected 3-year revenue by $40 per subscriber compared to several competitive targeting policies such as a policy that targets no one, a random policy, and a policy that targets subscribers with the highest churn risk. Over a three year period, our approach has a net-positive revenue impact in the range $1.7-$2.8 million compared to the status quo.","abstract_has_math":true,"creators":["Yang, Jeremy(Jeremy Zhen)"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Sloan School of Management","school":null,"contributors":[],"advisors":["Sinan Aral."],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020","date_published":"2020","updated_at":"2026-07-22T22:21:29Z","subjects":["Sloan School of Management."],"languages":["eng"],"rights":["MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/126956","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Sinan Aral."]},{"key":"dc:contributor.department","label":"Department","values":["Sloan School of Management","Sloan"]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Sloan School of Management."]},{"key":"dc:creator","label":"Author","values":["Yang, Jeremy(Jeremy Zhen)"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2020-09-03T16:44:58Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2020-09-03T16:44:58Z"]},{"key":"dc:date.issued","label":"Date","values":["2020"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Sloan School of Management."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/126956"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis: S.M. in Management Research, Massachusetts Institute of Technology, Sloan School of Management, May, 2020","Cataloged from the official PDF of thesis.","Includes bibliographical references (pages 29-33)."]},{"key":"dc:description.abstract","label":"Abstract","values":["This paper develops a framework for learning and implementing optimal targeting policies via a sequence of adaptive experiments to maximize long-term customer outcomes. Our framework builds on literature on doubly robust off-policy evaluation and optimization from computer science, statistics, and economics, and can also adapt to potential changes in the environment. We apply our framework to learn optimal discount targeting policies to the current subscribers at Boston Globe to maximize long-term revenue. Since the long-term revenue is not observable, we use intermediate outcomes such as subscribers' short-term revenue and their content consumption to construct a surrogate index and use it to impute the missing long-term revenues. Our method improves the average 1.5-year revenue by $15 and projected 3-year revenue by $40 per subscriber compared to several competitive targeting policies such as a policy that targets no one, a random policy, and a policy that targets subscribers with the highest churn risk. Over a three year period, our approach has a net-positive revenue impact in the range $1.7-$2.8 million compared to the status quo."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M. in Management Research"]},{"key":"dc:title","label":"Title","values":["Learning who to target with what via adaptive experimentation to optimize long-term outcomes"]}]}],"canonical_facts":{"dc:contributor.advisor":["Sinan Aral."],"dc:contributor.department":["Sloan School of Management","Sloan"],"dc:contributor.other":["Sloan School of Management."],"dc:creator":["Yang, Jeremy(Jeremy Zhen)"],"dc:date.accessioned":["2020-09-03T16:44:58Z"],"dc:date.available":["2020-09-03T16:44:58Z"],"dc:date.issued":["2020"],"dc:description":["Thesis: S.M. in Management Research, Massachusetts Institute of Technology, Sloan School of Management, May, 2020","Cataloged from the official PDF of thesis.","Includes bibliographical references (pages 29-33)."],"dc:description.abstract":["This paper develops a framework for learning and implementing optimal targeting policies via a sequence of adaptive experiments to maximize long-term customer outcomes. Our framework builds on literature on doubly robust off-policy evaluation and optimization from computer science, statistics, and economics, and can also adapt to potential changes in the environment. We apply our framework to learn optimal discount targeting policies to the current subscribers at Boston Globe to maximize long-term revenue. Since the long-term revenue is not observable, we use intermediate outcomes such as subscribers' short-term revenue and their content consumption to construct a surrogate index and use it to impute the missing long-term revenues. Our method improves the average 1.5-year revenue by $15 and projected 3-year revenue by $40 per subscriber compared to several competitive targeting policies such as a policy that targets no one, a random policy, and a policy that targets subscribers with the highest churn risk. Over a three year period, our approach has a net-positive revenue impact in the range $1.7-$2.8 million compared to the status quo."],"dc:description.degree":["S.M. in Management Research"],"dc:identifier.uri":["https://hdl.handle.net/1721.1/126956"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Sloan School of Management."],"dc:title":["Learning who to target with what via adaptive experimentation to optimize long-term outcomes"],"dc:type":["Thesis"],"thesis:degree_name":["Master"]},"updated_at":"2026-07-22T22:21:29Z"}