{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/146666"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/146666","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Sequential Optimization for Prospective Customer Segmentation and Content Targeting","abstract":"ResMed is a global leader in medical devices for the treatment of obstructive sleep apnea (OSA). Due to the high prevalence and underdiagnosis of OSA, a key pillar of ResMed's business strategy is to increase awareness of the disease and encourage treatment. This work seeks to optimize an emerging OSA awareness channel for ResMed: online paid advertising. Specifically, a sequential optimization approach (batched sequential model-based algorithm configuration, or B-SMAC) is developed to automatically and intelligently target online advertisements through iterative batch experimentation. The result, verified through simulation and field experiment, is the maximization and characterization of ad performance over a search space of 960 mutually exclusive customer segments. Further, re-aggregation methods are developed and tested in order to transform the outputs of B-SMAC into an economically viable targeting strategy for an online ad platform, leading to improved ad effectiveness when compared to baseline strategies. These results are a proof-of-concept for sequential optimization-based ad targeting and represent a promising future direction for increasing the number of patients entering ResMed's diagnostic funnel and receiving life-altering OSA treatment.","abstract_html":"ResMed is a global leader in medical devices for the treatment of obstructive sleep apnea (OSA). Due to the high prevalence and underdiagnosis of OSA, a key pillar of ResMed's business strategy is to increase awareness of the disease and encourage treatment. This work seeks to optimize an emerging OSA awareness channel for ResMed: online paid advertising. Specifically, a sequential optimization approach (batched sequential model-based algorithm configuration, or B-SMAC) is developed to automatically and intelligently target online advertisements through iterative batch experimentation. The result, verified through simulation and field experiment, is the maximization and characterization of ad performance over a search space of 960 mutually exclusive customer segments. Further, re-aggregation methods are developed and tested in order to transform the outputs of B-SMAC into an economically viable targeting strategy for an online ad platform, leading to improved ad effectiveness when compared to baseline strategies. These results are a proof-of-concept for sequential optimization-based ad targeting and represent a promising future direction for increasing the number of patients entering ResMed's diagnostic funnel and receiving life-altering OSA treatment.","abstract_has_math":false,"creators":["Groszman, Kenny"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Operations Research Center","school":null,"contributors":[],"advisors":["Ramakrishnan, Rama","Jónasson, Jónas Oddur"],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05","date_published":"2022-05","updated_at":"2026-07-22T22:21:34Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"rights_urls":["https://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/146666","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Ramakrishnan, Rama","Jónasson, Jónas Oddur"]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. 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Due to the high prevalence and underdiagnosis of OSA, a key pillar of ResMed's business strategy is to increase awareness of the disease and encourage treatment. This work seeks to optimize an emerging OSA awareness channel for ResMed: online paid advertising. Specifically, a sequential optimization approach (batched sequential model-based algorithm configuration, or B-SMAC) is developed to automatically and intelligently target online advertisements through iterative batch experimentation. The result, verified through simulation and field experiment, is the maximization and characterization of ad performance over a search space of 960 mutually exclusive customer segments. Further, re-aggregation methods are developed and tested in order to transform the outputs of B-SMAC into an economically viable targeting strategy for an online ad platform, leading to improved ad effectiveness when compared to baseline strategies. These results are a proof-of-concept for sequential optimization-based ad targeting and represent a promising future direction for increasing the number of patients entering ResMed's diagnostic funnel and receiving life-altering OSA treatment."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M.","M.B.A."]},{"key":"dc:title","label":"Title","values":["Sequential Optimization for Prospective Customer Segmentation and Content Targeting"]}]}],"canonical_facts":{"dc:contributor.advisor":["Ramakrishnan, Rama","Jónasson, Jónas Oddur"],"dc:contributor.department":["Massachusetts Institute of Technology. Operations Research Center","Sloan School of Management"],"dc:creator":["Groszman, Kenny"],"dc:date.accessioned":["2022-11-30T19:39:59Z"],"dc:date.available":["2022-11-30T19:39:59Z"],"dc:date.issued":["2022-05"],"dc:description.abstract":["ResMed is a global leader in medical devices for the treatment of obstructive sleep apnea (OSA). Due to the high prevalence and underdiagnosis of OSA, a key pillar of ResMed's business strategy is to increase awareness of the disease and encourage treatment. This work seeks to optimize an emerging OSA awareness channel for ResMed: online paid advertising. Specifically, a sequential optimization approach (batched sequential model-based algorithm configuration, or B-SMAC) is developed to automatically and intelligently target online advertisements through iterative batch experimentation. The result, verified through simulation and field experiment, is the maximization and characterization of ad performance over a search space of 960 mutually exclusive customer segments. Further, re-aggregation methods are developed and tested in order to transform the outputs of B-SMAC into an economically viable targeting strategy for an online ad platform, leading to improved ad effectiveness when compared to baseline strategies. 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