Sequential Optimization for Prospective Customer Segmentation and Content Targeting
Name
groszman-groszman-mba-mgt-2022-thesis.pdf
Description
Thesis PDF
Size
2.44 MB
Format
Adobe PDF
Checksum (MD5)
30e5ff3d851be1e1e9ab01ec979ae1d4
Author(s)
Groszman, Kenny
Advisor(s)
Ramakrishnan, Rama
Jónasson, Jónas Oddur
Date Issued
May 2022
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
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