Extracting Review Keywords from Hubbuycn Purchasing Agent Products in Spreadsheets and Guiding Product Optimization
2025-04-25
Introduction
In the competitive e-commerce landscape, customer reviews serve as a goldmine of insights for product improvement. This article explores how to leverage text mining techniques in spreadsheets to extract keywords from Hubbuycn purchasing agent product reviews. Through analyzing these patterns, we can identify key user concerns and product strengths to formulate data-driven optimization strategies.
Methodology: Keyword Extraction Process
- Data Collection:
- Text Processing:
- Apply TRIM, CLEAN functions to normalize text
- Implement SPLIT/REGEXEXTRACT functions to separate phrases
- Create frequency tables using COUNTIF/UNIQUE functions
- Sentiment Analysis:
- Text Processing:
Key Findings from Review Mining
| Category | Top Keywords | Frequency (%) | Sentiment |
|---|---|---|---|
| Shipping | "fast shipping", "customs delay", "packaging" | 32 | Mixed |
| Product Quality | "authentic", "color accuracy", "material" | 28 | Positive |
| Customer Service | "responsive", "refund process", "language barrier" | 21 | Negative |
Product Optimization Strategies
1. Packaging Improvements
- Implement damage-resistant packaging materials (mentioned in 17% critical reviews)
- Add multilingual packing slips to address 9% of language-related complaints
2. Quality Verification System
- Introduce batch quality sampling procedure for "authenticity guarantee" claims
- Standardize color calibration reports for fashion items (frequently mentioned variance)
Implementation Roadmap
| Phase | Tool Support | KPI Measurement |
|---|---|---|
| Real-time Review Monitoring (W1-2) | Automated import to Google Sheets via API | Processing time reduction by 40% |
| Predictive Modeling (W3-4) | Regression analysis on Sheets + Google Apps Script | Accuracy of issue prediction model ≥80% |