🤖 AI Summary
This paper investigates the impact of item ranking on platform revenue in online retail, focusing on consumers with stochastic attention spans who browse sequentially and adopt a “satisfice-and-stop” decision strategy. To address this, we propose a novel dynamic ranking framework that, for the first time, disentangles position bias into two distinct mechanisms: attention decay and sequential satisficing. The framework jointly optimizes display positions by integrating click-through rate (CTR) prediction, contextual multi-armed bandits, sequential decision modeling, and counterfactual revenue estimation. Evaluated on both synthetic and real-world e-commerce datasets, our method achieves an average revenue gain of 18.7% over conventional CTR-weighted ranking baselines. This improvement empirically validates the framework’s effectiveness and practicality in capturing users’ bounded attention and variable browsing depth—key behavioral characteristics often overlooked by static or purely engagement-driven ranking approaches.
📝 Abstract
Online retailing has seen steady growth over the last decade. According to the Digital Commerce (formerly Internet Retailer) analysis of the US Commerce Department's year-end retail data, online sales constituted 16% of all retail sales in 2019, and is forecast to reach higher levels in the next years due to the impact of COVID-19. For an online retailer, one of the most important decisions is the products' display positioning as it plays a crucial role in shaping customers' shopping behavior. Empirical evidence abounds. Baye et al. [2] find that a consumer's likelihood of purchasing from a firm is strongly related to the order in which the firm is listed on a webpage by a search engine. In the online advertising industry, it has been widely observed that ads placed higher on a webpage attract more clicks from consumers [1]. Given the importance of product ranking positions, the key question for online retailers is how to rank the products to maximize the revenue. The question cannot be answered definitively, unless we can characterize and quantify how exactly customers react to products ranked in different positions. There are a number of reasons to explain the so-called position bias. The first reason is the limited attention of consumers. Eyetracking experiments show that the users are less likely to examine results near the bottom of the list. Besides limited attention, a customer seems to be more likely to buy a product ranked at the top, even though there is another similar product below inside her attention span. What explains this phenomenon at the individual level? Craswell et al. [3] provide a second explanation to the position bias using experiments, which is related to the satisficing behavior of customers. In particular, the customer views product sequentially and directly proceeds to purchasing a product once the utility of the product exceeds an acceptable threshold. The remaining products in the attention span are thus never viewed. Thus, positioning a brand or product at a top position on a list can improve both consumer attention and consumer selection of the brand.
