Targeting Seasonal Marketing Campaigns: Rebalancing Exploration and Exploitation
Name
Li-keyanli-SMMR-Management-2022-thesis.pdf
Description
Thesis PDF
Size
865.58 KB
Format
Adobe PDF
Checksum (MD5)
1227f93dbd34ac7a313f91f885aa6efe
Author(s)
Li, Keyan
Advisor(s)
Zhang, Juanjuan
Date Issued
May 2022
Publisher
Massachusetts Institute of Technology
Abstract
Once a firm has a targeting policy, the firm incurs an opportunity cost when varying its action to learn how to improve that policy. This results in what is classically considered an exploration vs. exploitation tradeoff. This tradeoff is widely studied in online learning domains. However, firms are forced to learn in batches that occur infrequently in many marketing channels, such as seasonal marketing campaigns and salesperson marketing. For example, when demand is seasonal, marketing campaigns often occur annually, with retailers using data from last year to train this year’s policy. This essay identifies an information externality when assigning actions to customers in the same batch: the incremental information contributed by the focal customer depends upon the assignment decisions foe other customers. This essay investigates how to optimally rebalance exploration (more variation) and exploitation (direct implementation) in these settings leveraging this externality. The algorithm this essay proposes balances the expected value and opportunity cost of new information from each new batch. This essay validates the findings using data from a field experiment.¹
¹This essay is based on joint work with Duncan Simester.
MIT Department
Sloan School of Management
Terms of Use
In Copyright - Educational Use Permitted
Copyright MIT
Persistent DSpace Link
