Home > From LoveBuy Reviews to Spreadsheets: Building a Reputation Analysis System for Purchasing Agents

From LoveBuy Reviews to Spreadsheets: Building a Reputation Analysis System for Purchasing Agents

2025-07-29
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The Journey Begins: Collecting LoveBuy Reviews

In today's global e-commerce landscape, purchasing agents (daigou) play a crucial role in bridging international markets. Our project began with collecting reviews from platforms like LoveBuy—a popular website for Asian cosmetics and skincare products sold overseas.

We utilized web scraping tools to extract:

  • Product ratings (1-5 stars)
  • Verbatim customer comments
  • Purchase dates and locations
  • Verified purchase markers

Tip: Always comply with review platforms' Terms of Service when scraping data. We used LoveBuy's API where available and respected robots.txt restrictions.

Building the Spreadsheet Infrastructure

The core of our analysis system lives in a structured Google Sheet with five key components:

Tab Name Purpose
Raw Data Unprocessed review imports with metadata
Sentiment Analysis Automated scoring of review tone
Product Summary Aggregated scores by SKU
Brand Dashboard Comparison metrics across product lines
Trend Tracker Temporal analysis of review patterns

We connected this to Google Apps Script for automatic data refreshing from our LoveBuy API endpoints.

From Raw Data to Product Insights

The magic happens when translating spreadsheet data into actionable intelligence:

Example: Sheet Masks Market Trend Analysis

By tracking "alcohol-free" mentions in reviews over time, coupled with rating data, we identified:

Growing Preference for Alcohol-Free Formulas

Practical Implementation Suggestions

  1. Review Metadata Matters
  2. Normalize Ratings
  3. Leverage AI Categorization
  4. Visualization is Key

This integrated system from LoveBuy data collection through spreadsheet analysis helps purchasing agents make data-driven sourcing decisions and identify quality issues before they affect reputation.

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