Creating an Effective Ootdbuy Spreadsheet for Product Review Analytics
2025-04-18
Tracking product reviews on platforms like Ootdbuy is crucial for sellers and data analysts alike. This guide explores how to structure a dynamic "Product Review Database" spreadsheet, analyze Reddit/Discord discussions, and identify correlations between Yupoo ratings, coupons, and shipping data.
I. Building the Ootdbuy Review Spreadsheet Framework
Essential Columns
- Product ID:
- Review Source:
- Ratings (1-5):
- Sentiment Tags:
- Coupon Used:
- Shipping Days:
- Review Source:
Automation Tools
=IMPORTXML("https://ootdbuy.site/product-page","//div[@class='reviews']")
Use Google Apps Script to scrape reviews nightly, with Discord/Reddit parsers via webhooks.
II. Analyzing Yupoo Rating Patterns
A sample 2-month dataset showed significant deviations:
| Platform | 5-star % | 3-star % | 1-star % |
|---|---|---|---|
| Yupoo | 82% | 12% | 6% |
| 63% | 23% | 14% |
Action item: Cross-reference Yupoo's "Top Rated" filter with spreadsheet's verified buyer flag.
III. Coupons & Review Bias Analysis
Data from 1,872 reviews revealed:
- Products bought with 10%+ coupons
- "FREE SHIPPING" coupon recipients were 2.7x more likely to omit negative comments
- Coupon impact diminished after 60 days (see graph below)
IV. Shipping Speed's Direct Impact
Causal Patterns Identified:
- Delivery under 14 days → 87% positive reviews (Median 4.8 stars)
- 15-21 day deliveries → 61% positive (Median 4.1 stars)
- >25 days → Spike in 1-star reviews mentioning "agent excuses"
Spreadsheet Alert System:
V. Advanced Reporting Features
- Dynamic Word Clouds:=TEXTJOIN(" ",FILTER(reviews,(rating>4)*(coupon=FALSE)))
- Quarterly Heatmaps:
- Sentiment Oscillators:Haven & Co.
Sample Pivot Table Output
Product │ Avg.Rating │ CouponBoost │ ShippingImpact ──────────────┼────────────┼─────────────┼─────────────── Moncler Jacket│ 4.6 │ +0.3 │ -0.7 slow Balenciaga Tee│ 3.9 │ +0.1 │ -0.4 slow
PRO TIP:Ootdbuy's API