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Do you know what customers are saying about our products?
There are hundreds of reviews on Coupang and Olive Young, but I can't read them all.
When operating a beauty brand, you may have concerns like this. Reviews are piling up on each e-commerce channel, but it's difficult to monitor them systematically. It takes more time to check reviews of competitor products as well.
In the end, it becomes difficult to answer these questions:
- What are customers most satisfied with in our products?
- Are there any recurring complaints?
- What are the strengths and weaknesses of our products compared to those of competitors?
- Can we quickly assess the initial response after launching a new product?
Reading reviews one by one makes it difficult to see the big picture.
Solving with E-commerce Review Crawling + AI Analysis
By using Hashscraper, you can automatically collect reviews of your products and competitor products from e-commerce channels like Coupang and Olive Young.
Applying AI analysis allows you to quickly identify patterns from thousands of reviews.
Example of Crawling Data - Product Reviews
{
"Channel": "올리브영",
"Brand": "OO",
"Product Name": "세라마이드 모찌 토너",
"Rating": "5",
"Review Body": "수분감이 좋고 자극 없이 순해요. 환절기에 쓰기 딱 좋아요. 용량도 넉넉해서 만족합니다.",
"Review Date": "2025-01-05",
"Reviewer": "user_****",
"Helpful Count": "12"
}
Example of AI Analysis Data
{
"Channel": "올리브영",
"Product Name": "세라마이드 모찌 토너",
"Review Body": "수분감이 좋고 자극 없이 순해요. 환절기에 쓰기 딱 좋아요.",
"Sentiment": "Positive",
"Keywords": ["수분감", "저자극", "환절기"],
"Categories": [
{"category": "Texture", "subcategory": "Moisture", "type": "Positive"},
{"category": "Sensitivity", "subcategory": "Gentle", "type": "Positive"},
{"category": "Usage", "subcategory": "Seasonal", "type": "Positive"}
]
}
Along with the original reviews, sentiment analysis, keyword extraction, and category classification are automatically processed.
Use Case: Integrated Monitoring of E-commerce Reviews for a Beauty Brand
A domestic beauty brand wanted to systematically monitor customer reviews on e-commerce channels. They wanted to analyze not only their own product reviews but also those of competitors to understand their positioning in the market.
Crawling Settings
- Target Channels: Coupang, Olive Young
- Collection Targets: Own products + key competitor products
- Items Collected: Review text, ratings, posting date, helpful votes
- Crawling Frequency: Once a day
Analyzed Items from Crawling Data
- Product rating trends: Monitoring initial response after launching new products
- Positive/negative keywords: Identifying satisfaction factors and complaints
- Competitor comparison: Analyzing strengths/weaknesses compared to competing products in the same category
- Review trends: Understanding market trends through keyword changes over time
- VOC classification: Automatic classification by topic such as quality, price, packaging, and delivery
Quantitative Results
- Monitoring Channels: 4 channels
- Monthly Review Analysis Volume: 5,000 reviews
- Analysis Frequency: Daily comprehensive analysis
Previously, review monitoring was not systematically conducted, but after implementing crawling, they were able to analyze all reviews daily.
Other Use Cases
Monitoring Response to New Product Launches
By focusing on analyzing reviews in the first two weeks after launching a new product, you can quickly assess the initial response and incorporate it into marketing messages or product improvements.
Analysis of Competitors' New Products
When a competitor launches a new product, collecting and analyzing reviews of that product helps in formulating response strategies.
Seasonal Trend Analysis
Keywords mentioned by customers vary by season, such as mid-season, summer, and winter. Analyzing these patterns can be useful for seasonal marketing.
Data Integration Methods
The collected data is provided in raw data format, and you can choose the integration method according to the situation.
- Excel Download — for creating monthly review analysis reports
- Email Sending — for receiving regular weekly review data
- API Integration — for real-time integration into internal dashboards
- DB Integration — for direct loading into internal analysis systems
Summary
Customer reviews contain insights necessary for product improvement and marketing. The problem is that there are too many to analyze manually. By using crawling and AI analysis, you can quickly identify patterns from thousands of reviews.
Hashscraper handles all aspects of crawler operation, maintenance, and monitoring. Even in cases of e-commerce platform policy changes or data collection errors, customers do not need to respond directly.
By monitoring reviews of both own products and competitor products, you can objectively assess the position of your products in the market.
Get Started Now
With Hashscraper, you can automatically collect and analyze product reviews from e-commerce channels using AI.
Go to Coupang Product Review Collection Bot
If you need crawling or AI analysis for other e-commerce channels like Olive Young, feel free to inquire.
