An Integrated Randomized Pricing Strategy for Omni-Channel Retailing

ABSTRACT

Omni-channel supply chains provide customers with increased convenience to experience products and gather price information. Having more information under omni-channel retailing means customers can strategically choose where to purchase (which retail channel) and when to purchase (purchase now or later), which makes channel integration a challenge for omni-channel retailers. This paper focuses on the joint pricing optimization for omni-channel retailers. Specifically, we propose a randomized pricing strategy for omni-channel retailers by considering manufacturer’s wholesale price, customers’ discrepant perception between two channels, and customers’ strategic-waiting behavior. Under this strategy, while the retailer offers a fixed price in the offline channel, it may randomly provide a discount according to a preset probability in the online channel to discriminate between customers and gain more profit. This study explores how the manufacturer’s wholesale price and strategic customer behavior affect the randomized pricing strategy and profits for both omni-channel retailer and manufacturer. We show that if a randomized pricing strategy exists, it can benefit both the retailer and manufacturer. Finally, hiding the promotion probability is not always beneficial to the retailer. However, the retailer can benefit from price differentiation. Therefore, this study also provides guidelines to develop effective pricing strategies for omni-channel retailing.

KEY WORDS AND PHRASES:

• Retail supply chain
• omni-channel retailing
• randomized pricing
• price discrimination
• online retail
• e-tail
• online pricing

Acknowledgments

The authors thank the Editor-in-Chief, Professor Vladimir Zwass, and the anonymous referees for their constructive suggestions and comments on the early version of the paper.  This research is supported by fund for building world-class universities (disciplines) of Renmin University of China Project No. KYGJC2020002.

Notes

1. The data source in Figure 1 (https://product.suning.com/0000000000/10620937351.html).

Figure 2. Net utility under the high(regular) and low prices

Figure 3. Relationship between customer type and behavior when the online price is high

Figure 4. Effect of $\delta$ on the optimal retailer’s and manufacturer’s profits ($t=0.9,a=0.07,n=2,w=0.3$)

Figure 5. Effect of $\delta$ on optimal $\alpha$ and prices ($t=0.9,a=0.07,n=2,w=0.3$)

Figure 6. Effects of $\delta$ and$a$ on optimal$\alpha$ and retailer’s profits ($t=0.9,n=2,w=0.3$)

Figure 7. Effects of $\delta$ and$n$ on optimal$\alpha$ and prices ($t=0.9,a=0.07,w=0.3$)

Figure 8. Effects of $\delta$ and$n$ on retailer’s and manufacturer’s profits ($t=0.9,a=0.07,w=0.3$)

Figure 9. Effects of $t$ on optimal $\alpha$ and prices ($a=0.07,n=2,\delta =0.9,w=0.3$)

Figure 10. Effects of $t$ on retailer’s and manufacturer’s profits ($a=0.07,n=2,\delta =0.9,w=0.3$)

Figure 11. Effects of $w$ on optimal profits ($a=0.07,n=2,\delta =0.9,t=0.9$)

Figure 12. Effects of $w$ on optimal $\alpha$ and prices ($a=0.07,n=2,\delta =0.9,t=0.9$)

2. Customer loyalty is often considered one of the most important conditions for the success of e-commerce [14]. We focus on the purchasing behavior of the loyal customers of a retailer (e.g., customers with memberships of Costco, JD). Therefore, we do not explicitly consider competition across retailers. However, because the retailer may lose part of the customers when the price is high, this model implies competition.

3. Our research has some distinct features compared with Wu et al. [54]. In terms of the research problem, Wu et al. [54] study the randomized pricing strategy on the single online channel, whereas we study this pricing strategy in a supply chain with the omni-channel retailer. From the perspective of model setup, we consider customers’ select-and-wait behaviors regarding the channel and the impact of the manufacturer’s wholesale price on the randomized pricing strategy. Therefore, this research generates several novel conclusions. First, we show that the randomized pricing strategy comparatively reduces wholesale price $w$, thus alleviating the double marginalization and achieving Pareto improvement. Second, hiding the promotion probability is not always beneficial to the supply chain, which is influenced by consumer psychology and the discount factor. Third, price differentiation leads to an increase in retailer’s and manufacturer’s profits.

4. Because we should at least ensure that the utility of offline purchase of customers with a valuation of 1 is greater than 0, then $U_0=v-p_h-a\le 1-p_h-a\le 1-w-a$. Thus, we assume $1-w-a>0$.

5. See Appendix B for the details of the optimal price in cases $R1-R4$ and $R6-R7$.

Additional information

Notes on contributors

Jianghua Wu

Jianghua Wu ([email protected]) is a professor of operations management at the School of Business, Renmin University, China. He earned his Ph.D. in Operations Management from Purdue University. His principal research interests include supply chain management, inventory control, revenue management, and Marketing Operations Management interfaces. His work has been published in Computers & Operations Research, Decision Support Systems, OMEGA, Operations Research Letters, and other journals. Dr. Wu’s research has been supported by National Natural Science Foundation of China and research grants from Ministry of Education.

Chenchen Zhao

Chenchen Zhao ([email protected]) is a doctoral student of Management Science at School of Business, Renmin University of China.

Xinghao Yan

Xinghao Yan ([email protected]; corresponding author) is an associate professor of supply chain management at the College of Business and Innovation, University of Toledo. Dr. Yan’s research addresses various aspects of supply chain management and contracting. He has published in many journals, including Operations Research, Production and Operations Management, Decision Sciences, IIE Transactions on Healthcare System Engineering, and International Journal of Production Economics.

Lifei Wang

Lifei Wang ([email protected]) was a graduate student at School of Business, Renmin University, China.