Analyzing SuperBuy Review Data: How Cross-border E-commerce Agents Can Extract Improvement Insights
In the competitive world of cross-border e-commerce, user feedback is a goldmine for improvement. Platforms like SuperBuy
Step 1: Organizing Feedback in Spreadsheets
Consolidate user reviews into a structured spreadsheet (e.g., Excel or Google Sheets) with columns for:
- Product ID/NameReview Text
- Rating (1-5 stars)
- Timestamp
- User Geography
Step 2: Tagging & Sentiment Analysis
Utilize two powerful techniques to categorize feedback:
A. Keyword Tagging
Create tags for common themes: #ShippingDelay, #ProductQuality, #CustomerService etc. Use spreadsheet filters to quantify issue frequency.
B. Sentiment Analysis
Categorize reviews as Positive/Neutral/Negative using formulas that detect emotional words (e.g., "great" ➔ Positive; "broken" ➔ Negative)
Step 3: Pivot Tables & Visualization
Transform raw data into insights:
- Create pivot tables showing negative review % by product category
- Generate charts illustrating sentiment trends over time
- Map geographical clusters of specific complaints
Turning Insights into Action
Example findings from a SuperBuy case study:
Problem:
Solution:
Source: SuperBuy's internal case study
Continuous Feedback Optimization
By implementing this spreadsheet-based analysis framework monthly, e-commerce agents can systematically:
- Quantify satisfaction across product categories
- Detect emerging issues before they escalate
- Measure the impact of service improvements
Platforms like SuperBuy