Learning from Click Transition Data: The Effectiveness of Greedy Pricing Policy under Dynamic Product Availability

55 Pages Posted: 22 Jul 2022 Last revised: 10 Jan 2024

See all articles by Mo Liu

Mo Liu

University of California, Berkeley - Department of Industrial Engineering and Operations Research

Junyu Cao

University of Texas at Austin - Red McCombs School of Business

Zuo-Jun Max Shen

University of California, Berkeley - Department of Industrial Engineering & Operations Research (IEOR)

Date Written: July 8, 2022

Abstract

We study how to utilize random clicking behaviors of customers to benefit online retailers' pricing strategies. We introduce a new dynamic attraction click model based on a Markov chain, which describes both purchase and click behaviors under product availability. Based on our click model, we propose an efficient data-driven framework to determine product prices that maximize expected revenue. To learn customers' preference efficiently under the high-dimensional click transition data, we explore the similarities in click transition patterns across products, which are captured by the low-rank structure of the attraction matrix in our click model. Driven by the dynamic availability of products in practice, we also provide an algorithm to estimate the attraction matrix under dynamic product availability. This approach yields a small estimation error bound by leveraging the low-rank structure. When considering estimation and pricing decisions simultaneously, we demonstrate the effectiveness of a greedy online algorithm and derive a sublinear regret bound under dynamic product availability. Empirical investigations conducted on real-world data show that using click data along with purchase data can significantly reduce the prediction error associated with purchase behaviors, leading to a substantial increase in the anticipated revenue from pricing decisions.

Keywords: dynamic attraction model, optimal pricing policy, click model, product availability

Suggested Citation

Liu, Mo and Cao, Junyu and Shen, Zuo-Jun Max, Learning from Click Transition Data: The Effectiveness of Greedy Pricing Policy under Dynamic Product Availability (July 8, 2022). Available at SSRN: https://ssrn.com/abstract=4158054 or http://dx.doi.org/10.2139/ssrn.4158054

Mo Liu (Contact Author)

University of California, Berkeley - Department of Industrial Engineering and Operations Research ( email )

4141 Etcheverry Hall
Berkeley, CA 94720-1777
United States

Junyu Cao

University of Texas at Austin - Red McCombs School of Business ( email )

Austin, TX
United States

Zuo-Jun Max Shen

University of California, Berkeley - Department of Industrial Engineering & Operations Research (IEOR) ( email )

IEOR Department
4135 Etcheverry Hall
Berkeley, CA 94720
United States

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