基于消费者决策偏好的电商平台路径优化研究

Research on Path Optimization of E-Commerce Platform Based on Consumer Decision-Making Preference

摘要: 数字经济高速发展的背景下，电子商务平台承载的商家数目不断攀升，同一领域间的竞争愈演愈烈。鉴于此，如何做到纲举目张，敏锐捕捉消费者兴趣，以期其迅速决策，购买相关产品和服务，成为关键一环。传统优化电商平台路径局限于利用大数据对消费者的兴趣、爱好等进行抓取，未对其决策偏好进行细致剖析，且大数据采集存在信息茧房漏洞，具有一定的抽象性和宽泛性。本文基于消费者类购物决策偏好，提出电商平台在界面设置上应减少视觉杂乱，优化信息呈现，降低认知负荷；在计算系统上应采用加密算法；同时设置外部救济途径以更好地保障消费者需求得到满足。

Abstract: Against the background of the rapid development of the digital economy, the number of merchants carried by e-commerce platforms continues to rise, and the competition in the same fields is intensifying. In view of this, how to make the outline and keenly capture the interests of consumers, so that they can make quick decisions and purchase relevant products and services, has become a key link. The traditional optimization path of e-commerce platforms is limited to the use of big data to capture consumers’ interests, hobbies, etc., without a detailed analysis of their decision-making preferences, and there are information cocoon loopholes in big data collection, which has a certain abstraction and broadness. Based on the shopping decision-making preferences of consumers, this article proposes that e-commerce platforms should reduce visual clutter in interface settings, optimize information presentation, and reduce cognitive load; encryption algorithms should be used in computing systems; and external relief channels should be set up to better ensure that consumer needs are met.