Modeling relationships between retail prices and consumer reviews: A machine discovery approach and comprehensive evaluations

价(化学) 计算机科学 情感(语言学) 计量经济学 认知 数据科学 营销 机器学习 业务 经济 心理学 物理 沟通 量子力学 神经科学
作者
Xian Yang,Yang Gao,Jiangning Wu,Yanzhong Dang,Weiguo Fan
出处
期刊:Decision Support Systems [Elsevier BV]
卷期号:145: 113536-113536 被引量:10
标识
DOI:10.1016/j.dss.2021.113536
摘要

Setting the retail price as a part of marketing would affect customers' cognition regarding products and affect their post-purchase behavior of review writing. To deeply understand the relationships between retail prices and reviews, this paper designs an intelligent data-driven Generate/Test Cycle using a machine learning technique to automatically discover the relationship model from a huge amount of data without a prior hypothesis. From a unique dataset, various free-form relationship models with their own structures and parameters have been discovered. By the comprehensive evaluations of candidate models, a guided map was offered to understand the relationship between dynamic retail prices and the volume/valence of reviews for different types of products. Experimental results show that 37.69% of products in our sample exhibit the following trend: When the price is increased to a certain level, the volume of reviews shifts from a decreasing trend to an increasing trend. Results also demonstrate that a linearly increasing relationship model between prices and the valence of reviews is more suitable for the low-involvement products than for the high-involvement products. In addition to the new findings, this research provides a powerful tool to assist domain experts in building relationship models for decision making in a highly efficient manner.
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