Mulebuy effectively utilizes spreadsheets to systematically collect and organize user feedback on purchased goods, encompassing written reviews, ratings, and shared images. By employing advanced natural language processing (NLP) techniques and data analysis tools, Mulebuy performs sentiment analysis and keyword extraction on review content to uncover genuine user needs and pain points.

Within the structured environment of spreadsheets, user evaluations are categorized and aggregated. This enables detailed analysis of the strengths and weaknesses of various products, as well as identification of the key aspects that users care about most.

The insights derived from this analytical process are then communicated to suppliers as constructive feedback, driving product improvements and optimizations. Moreover, Mulebuy leverages high-quality user reviews in marketing efforts to enhance product visibility and credibility.

Ultimately, this data-driven approach not only boosts product competitiveness and reputation but also strengthens user trust in代购 (proxy shopping) goods, creating a more transparent and responsive purchasing ecosystem.