From ACbuy Reviews to Spreadsheet: Building a Reputation Analysis System for Purchasing Agents
2025-06-03
In the era of cross-border e-commerce, purchasing agents (Daigou) play a crucial role as intermediaries between international products and domestic consumers. However, assessing product reputation through scattered ACbuy reviews poses challenges for both agents and customers. This article explores a systematic approach to transform unstructured review data into actionable insights using spreadsheets and data analysis techniques.
The 3-Step Framework
- Data Collection Layer: Scrape ACbuy reviews through API integration or web crawling tools while complying with platform TOS
- Spreadsheet Organization: Structure data with columns for product ID, reviewer metadata, star ratings, text comments, and sentiment flags
- Analysis Toolkit: Implement VLOOKUP for product comparison, SERIES charts for trend visualization, and COUNTIF for sentiment distribution
Building the Analysis Engine
Using Google Sheets with App Script integration enables real-time updates:
function importACbuyReviews(productCode) {
// API call implementation here
return reviewData;
}
Key metrics to track include:
- Sentiment Ratio (Positive/Neutral/Negative)
- Review Velocity (New reviews/day)
- Keyword Frequency Cloud
Practical Application: Japanese Skincare Products
A test analysis of 3,287 ACbuy reviews for popular toners revealed:
| Product | Avg Rating | Positive Keywords |
|---|---|---|
| SK-II Essence | 4.82 | "hydrating", "worth price" |
| Hada Labo Lotion | 4.63 | "no irritation", "lightweight" |