Profiting from Selling at a Loss: Customer Big Data Analysis and Personalized Pricing in a Supply Chain
Wenhui Zhou- Xiaoyu Miao
- Yanhong Gan
- Xinyi Li
Published 2026-05-10
abstract views: 66 // FULL TEXT ARTICLE (PDF): 6
Keywords
- pricing strategy,
- uniform pricing,
- personalized pricing,
- big data capability
How to Cite
Copyright (c) 2026 International Journal of Industrial Engineering and Management
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
The rapid advancement in information technology has made customer data more accessible, significantly enhancing firms' interest in personalized pricing. This study investigates how personalized pricing and big data analytics capability interact in supply chains using a Stackelberg game-theoretic model. The analysis examines how manufacturers and online platforms strategically determine optimal pricing and big data investment under an agency selling format. The findings are threefold. First, higher big data analytics efficiency does not always lead to increased investment in analytics capability. Second, manufacturers may strategically set base prices below production costs. This strategy incentivizes platforms to invest in analytics capability, which indirectly enhances profitability. Third, platforms do not always benefit from higher analytics efficiency, particularly under moderate efficiency conditions. This research provides managerial insights into the nuanced role of big data capability in pricing strategies.
Article history: Received (September 18, 2024); Revised (October 2, 2025); Accepted (February 23, 2026); Published online (May 10, 2026)
