Requirement analysis and service optimization of multiple category fresh products in online retailing using importance-Kano analysis

Abstract

Online fresh retailing enriches people's shopping choices and provides convenience to reduce the risk of infection during the pandemic. Online reviews contain consumers' attention, requirements and sentiments, and in-depth analysis of this information has guiding values for service optimization. To better understand this information, a requirement analysis idea based on the attention and sentiment distribution of online reviews was proposed, namely importance-Kano analysis. Seven different customer requirements were found, including express delivery, cost performance, communication, freshness, flavor, specification and packaging. Flavor and freshness are the most concerned attributes, and they and other attributes all influence satisfaction in their unique ways. Consumers care a lot about the shopping experience and product quality and they have a high degree of product involvement in fresh products. Service improvement should be considered as a systematic project, and the influence of competitive environment, category differences and technological development should not be ignored. A service optimization model was developed based on the concept of total quality management, which was constructed by three layers including supply chain, operation management and consumer experience. The systematic analysis is conducive to in-depth understanding of the complexity, systematical and timeliness nature of fresh product operation management.