Operations Management For Online Retailers With Data-Driven And Data-Disclosure Policies

By 2018,the number of online shopping consumers in China reached 610 million,accounting for 76.06% of the total number of internet users,and the online shopping transaction volume reached 6.6 trillion,accounting for 22.7% of the total retail sales of consumer goods.In order to capture the online retail dividend,many online retailers have intensified competition in the industry,forcing online retailers to seek more effective market and operational strategies.Moreover,with the development of data mining and analysis technology,the data accumulated by online retailers has received more and more attention as a basis for understanding consumers and optimizing operating models.Therefore,it is of great practical significance to carry out data-based research to solve the operational problems faced by online retailers,and it has become a hot topic in the field.Traditional research on online retailers mostly uses model optimization methods,ignoring the role of data.However,in practice,the data records the relevant information of the consumer in the process of purchasing the product,and it is important for the online retailer to understand the consumer shopping rules,analyze the product sales characteristics,and optimize the operation mode.Furthermore,the use and disclosure of data has been widely adopted in the online retail industry.However,different online retailers face different problems,and there is currently no targeted,well-established theoretical system.Therefore,based on the practice of domestic online retailers,this paper focuses on the use and disclosure of data to solve the problems faced by online retailers.The specific research contents are as follows:(1)We discusse an allocation issue of promotion budget and traffic volume,faced by Vipshop.Due to different categories of products and different brands of products having different responses to the same promotion budget and different profit margins,it is very necessary for Vipshop to allocate promotion budget and traffic volume among all brands of products.Based on the real historical data of VIP.com,we first find main elements that influence the sales revenues of different brands of products through machine learning.We then predict the sales of all brands of products by multiplicative regression model.Finally,we develop allocation optimization models with objectives of maximizing Vipshop's total sales and total sales profit,respectively.The results from VIP.com's real data tests show that under the same resource investment the presented allocation optimization approach can yield a significant increase in Vipshop's total sales and sales profit.(2)We present our study with Vipshop,as an example of how an online retailer can use data on consumers' purchase history to develop a data-driven size prediction and recommendation system for apparel products and reduce customers' ill-fit returns.We first propose a new method for extracting consumers' statistical size characteristics,i.e.,combining consumers' statistical size characteristics with individual size features,to improve the accuracy of size prediction.Then,grounded in reinforcement learning theory,we propose a two-step nested Thompson sampling algorithm to solve the size recommendation problem for consumers with no parameter input and examine its efficacy.We demonstrate through simulation based on Vipshop's data that the proposed algorithm performs at 95.86% accuracy,reducing consumers' ill-fit return rate by 62% and increasing profit by 8.6%.We also consider two size recommendation modes,namely full-size recommendation(FSR)and limited-size recommendation(LSR)and compare the performance of the TSN Thompson sampling algorithm under the two modes.We find that,under both the FSR and LSR modes,the ill-fit return rate is significantly reduced and profit increases,compared with the case with no size recommendation.In addition,we find that if the inventory/demand level is low,LSR can yield a higher profit;otherwise,FSR can generate more profit.Furthermore,our simulation analysis indicates that the modified method of extracting consumers' size characteristics enables the size-recommendation algorithm to operate more accurately than the traditional method.(3)We aim at the selection issues of product pricing mode and whether to disclose historical sales data for an online retailer selling monopoly products considering information cost.We employ the theory of consumer utility theory and depict the heterogeneity of consumers from two dimensions: the retention value of the product and the expected retention value of the historical sales of the product.The research results show that:(1)Whether to disclose the historical sales data of products to consumers depends on the cost of collecting information about products and the sensitivity of the historical sales data of the products,only when the information collecting cost of consumers is low,and when the sensitivity of historical sales is high,the historical sales information disclosure strategy will be selected;(2)When the retailer discloses the historical sales information of the product,in the fixed pricing mode,the low-price sales strategy should be adopted,and in the dynamic pricing mode,they should first adopt a low price and then adopt a high-priced sales strategy.(4)Based on research question(3),for online retailers considering disclosing product history sales data to consumers,in the scenario of selling products through third-party shopping sites,we consider the reference price effect to study the optimal strategy that whether the online retailers should adjust the price or whether it should disclose historical sales data.The study points out that,first of all,it is always better to disclose historical sales data of products or padjust prices than to conceal relevant information;second,if online retailer only adjusts the price of products,they should adopt a high-low price strategy,and if the online retailer only discloses the historical sales data of products,low price strategy is better;third,When an online retailer chooses to disclose historical sales data,the price of the product should be adjusted at the same time to obtain better sales performance;IV,the reference price effect conflicts with the impact of the disclosure of historical sales data,so the hybrid strategy will not always be superior to the single strategy,but depends on the relevant characteristics of the consumer.