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

Abstract

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.