Mining competitively-priced bundle configurations

Author

Ezekiel Ong YOUNG, Singapore Management UniversityFollow
Hady W. LAUW, Singapore Management UniversityFollow

Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2022

Abstract

We examine the bundle configuration problem in the presence of competition. Given a competitor's bundle configuration and pricing, we determine what to bundle together, and at what prices, to maximize the target firm's revenue. We highlight the difficulty in pricing bundles and propose a scalable alternative and an efficient search heuristic to refine the approximate prices. Furthermore, we extend the heuristics proposed by previous work to accommodate the presence of a competitor. We analyze the effectiveness of our proposed models through experimentation on real-life ratings-based preference data.

Keywords

Economics, Heuristic algorithms, preference data

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

2022 IEEE International Conference on Big Data (Big Data): Osaka, December 17-20: Proceedings

First Page

6844

Last Page

6846

ISBN

9781665480451

Identifier

10.1109/BigData55660.2022.10020975

Publisher

IEEE

City or Country

Piscataway, NJ

Embargo Period

3-30-2023

Citation

YOUNG, Ezekiel Ong and LAUW, Hady W.. Mining competitively-priced bundle configurations. (2022). 2022 IEEE International Conference on Big Data (Big Data): Osaka, December 17-20: Proceedings. 6844-6846.
Available at: https://ink.library.smu.edu.sg/sis_research/7774

Copyright Owner and License

Authors

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Additional URL

https://doi.org/10.1109/BigData55660.2022.10020975