Analytics in promotional pricing and advertising

Big data and the internet are shifting the paradigm in promotional pricing and advertising. The amount of readily available data from both point-of-sale systems and web cookies has grown, enabling a shift from qualitative design to quantitative tools. In this work, we address how firms can utilize the power of analytics to maximize profits in both their offline and online channels. First, we consider an online setting, in which an advertiser can target ads to the customer in question. The goal of the advertiser is to determine how to target the right audience with their ads. We study this problem as a Multi-Armed Bandit problem with periodic budgets, and develop an Optimistic-Robust Learning algorithm with bounded expected regret. Practically, simulations on synthetic and real-world ad data show that the algorithm reduces regret by at least 10-20% compared to benchmarks.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Operations Research Center

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Baardman, Lennart.

Advisor dc:contributor.advisor

• Georgia Perakis.

• Operations Research Center.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/122389

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122389