Xueying Liu

Problem: Digital commerce trends were scattered across multiple sources, making it difficult for teams to efficiently monitor industry changes.

Role: Designed the intelligence framework, prompt structure, information filtering criteria, dashboard structure, and automated delivery workflow.

Approach: Combined automated data collection, AI summarization, trend signal extraction, GitHub-based dashboard visualization, and scheduled email reporting.

Output: Created a reusable Digital Commerce Intelligence Hub that supports continuous retail trend monitoring and business discussions.

Problem: Raw user comments were noisy and difficult to interpret at scale.

Role: Designed the analysis logic and insight extraction process.

Approach: Combined LLM API, NLP preprocessing, sentiment classification, and topic clustering.

Output: Transformed user comments into structured signals for content and product understanding.

Problem: Customer behaviors were not clearly prioritized for retention action.

Role: Conducted segmentation and customer value modeling.

Approach: Used RFM analysis, BG/NBD, Gamma-Gamma, and CLV estimation.

Output: Identified priority customer segments and supported differentiated retention strategy.