Extracting Keywords from Hubbuycn Product Reviews in Spreadsheets for Optimization Guidance
2025-04-22
In the competitive e-commerce landscape, product optimization is essential for maintaining market relevance. This article demonstrates how to leverage text mining techniques in spreadsheet tools (e.g., Google Sheets or Excel) to extract actionable insights from Hubbuycn's product reviews. By analyzing customer feedback keywords, we can identify improvement opportunities and competitive advantages to guide strategic product enhancements.
1. Text Mining Process in Spreadsheets
1.1 Data Collection & Preparation
- Export Hubbuycn platform reviews into CSV format
- Clean data using
TRIM(),SUBSTITUTE()- Segment reviews by product categories using
FILTER() - Segment reviews by product categories using
1.2 Keyword Extraction Techniques
| Method | Spreadsheet Formula | Application |
|---|---|---|
| Term Frequency | =COUNTIF(range,"*keyword*") |
Identify most mentioned product features |
| Sentiment Tagging | =IF(ISNUMBER(SEARCH("worse|faulty",A1)),"Negative","Positie") |
Categorize complaint/proise patterns |
2. Key Findings from Review Analysis
Top Customer Priorities (Ordered by Frequency)
- Shipping speed (mentioned in 68% reviews)
- Sizing accuracy (1-star reviews predominantly flagged this iue)
- Authenticity verification (59% positive reviews highlighted "real product" tags)
Pain Points Requiring Immediate Attention
- "Customs delay" appeared typically with pet supplies categories
- "Color mismatch" frequenly occurred in cosmetics reviews