Research on AIGC Empowering E-commerce Product Innovation and Private Domain Traffic Operation Mechanism

Abstract

With the in-depth evolution of the digital economy, China’s e-commerce sector has stepped out of the crude growthstagereliantontrafficdividends.Escalatingcustomeracquisitioncostsinthepublicdomain,widespreadproduct homogenization issues, and the absence of refined private-domain operational systems have become key bottlenecks restricting the sustainable growth of e-commerce enterprises. Featuring multimodal content generation, intelligent user datamining,andfull-linkautomateddecision-makingcapabilities,ArtificialIntelligenceGeneratedContent(AIGC)has thoroughly reshaped the operational paradigms of e-commerce product development and private domain traffic management. Adopting literature review, comparative analysis, and case study approaches, this paper systematically summarizes domestic and international research outcomes concerningAIGC adoption in e-commerce and establishes a closed-loop collaborative framework covering technological empowerment, product upgrading, and private domain efficiency optimization. This study elaborates on six core dimensions, including theoretical foundations, empowering mechanisms, operational restructuring, model comparison, practical obstacles, and targeted optimization strategies. Supported by quantitative comparison data, industrial statistical indicators, and typical enterprise cases, this paper clarifies the practical value and internal operational logic of AIGC deployment in e-commerce scenarios. The research findings indicate that AIGC tools can effectively shorten the research and development cycle of e-commerce products, lower the trial-and-error expenses of innovative practices, and substantially enhance the refined management level as well as conversion and repurchase performance of private domain user groups. Nevertheless, industrial-scale AIGC application still confronts prominent challenges, including homogeneous content output, latent copyright disputes, insufficient dataprivacy supervision, andunreasonable human-machinecollaborationstructures. Basedonthe research results, this study proposes multi-dimensional optimization strategies covering technological iteration, procedural standardization, compliance governance, and talent development, and forecasts future industrial development trends. This research aims to provide credible theoretical