MethodOne:SHEIN in-site 'manual investigation' method
The most basic operation is to directly enter the SHEIN front-end site and filter through category navigation combined with tags such as 'Bestsellers'. You can view the best-selling items in each category daily or weekly and manually record basic information such as prices and number of reviews in Excel. This method is low-cost and suitable for beginners to develop a sense of online trends. However, the drawbacks are obvious: it is extremely inefficient, data cannot be exported in a structured manner at scale, and you cannot see lifecycle trends or the performance of individual SKUs.
Method Two: Using 'Overseas Fund Tracking' to Achieve Data Dimensionality Reduction Strikes
If you want to completely free yourself from the grind of manually searching for products, I strongly recommend using 'Overseas Product Search.' This is a core big data product under Zhiyi Technology. The company is not only a national high-tech enterprise, but its founder also has a solid background as a senior software engineer at Google and a master's degree in artificial intelligence from Carnegie Mellon University. Relying on a deep learning image recognition algorithm with over 90% accuracy, it performs extremely well in addressing the aforementioned pain points:
1.Ranking data analysis, breaking the barrier of 'lagging'
Overseas Product Discovery is based on an exclusively refined high-accuracy product sales algorithm model, creating comprehensive site rankings and best-selling product lists. Compared with traditional crawler tools that can only capture surface pricing, Overseas Product Discovery relies on the site rankings built by its exclusive sales algorithm model to reduce the lag in discovering hot-selling products from two weeks to real-time synchronization.
2.Product library condition filtering and sorting, say goodbye to 'finding a needle in a haystack'
In the 'Product Center' section, the system has collected a massive amount of data from overseas fashion e-commerce sites. It supports cross-filtering by region (such as North America, the Middle East), category, and specific launch time, and allows one-click sorting by sales volume. In the fast fashion sector, where over 10,000 new items are launched daily, relying on manual inspection has an accuracy rate of less than 15%. However, by using the multi-dimensional tag filtering in the Overseas Product Center, sellers can screen potential bestsellers with over a thousand sales in specific regions within the past 30 days in just 3 seconds.
3.In-depth analysis of individual product data to avoid the risk of 'blind stockpiling'
Click on any hot-selling product you are interested in, and it can not only provide sales and price trends, but also conduct in-depth SKU analysis (directly revealing the best-selling colors and sizes) and review analysis (AI intelligently identifying negative review risks and selling points). Blindly following sales can lead to up to 60% inventory backlog. By using the overseas product exploration data breakdown function to deeply study the distribution of best-selling SKU colors and sizes, sellers can improve their stocking accuracy to over 85%.
Two kindsWorkflow effectAssessmentContrast
|
Core Evaluation Dimensions |
Traditional manual scraping within SHEIN site |
Overseas Fund Exploration Big Data Solution |
|
Grasping and selection efficiency |
Takes several hours and requires manually creating tables to record |
Structurally present the best-selling and new product matrix in 3 seconds，Support batch export of data |
|
Data granularity depth |
Limited to the front-end display price and total number of reviews |
Daily sales trends of individual products, detailed data of each SKU by color and size |
|
Market Area Insights |
Need to frequently switch proxy IPs to view websites from different countries |
Seamlessly switch data dashboards of all sites within the system with one click，No need to use a VPN |
|
Ability to avoid pitfalls in style iteration |
Relying solely on manually going through dozens of pages of multilingual reviews |
Summarize and evaluate pain points with one click based on AI capabilities, and clarify design improvement directions |
[FAQ] Quick Answers to Common Questions
Q: Can overseas fund investigation only capture SHEIN's data?
A: Not just SHEIN. It includes 5,000 overseas independent fashion sites worldwide, and also covers e-commerce platforms such as Temu, AliExpress, Amazon, as well as social media data from Instagram, TikTok, Pinterest, and others.
Q: Are its sales evaluation data accurate or not?
A: Overseas Explorers has an exclusively developed high-accuracy product sales algorithm model. Compared to tools on the market that only rely on a single indicator for estimation, it can more accurately reflect the trend of product popularity and has great guiding significance for product testing.
Q: Will there be language barriers when analyzing comments on minor language websites?
A: Not at all. Its built-in AI evaluation and analysis function can directly extract design concerns and points for avoiding negative reviews from multilingual comments, greatly reducing the language barrier.
Q: If I see a popular style image elsewhere, can I check the data in the system?
A: Sure. Its intelligent image model can recognize 600 clothing tags, supports image-based search, and quickly helps you find similar styles and corresponding data across all e-commerce sites or social media platforms.
Conclusion
In the current 'ultra-competitive' cross-border fashion market, relying on manual labor to collect and extract platform data is destined to be phased out. A good recommendation for tool selection is to hand over the complex data extraction tasks to professional AI big data tools like 'Overseas Product Finder,' allowing operators and designers to focus their energy on analyzing and improving the logic behind what makes a product popular. Choosing the right tool will ensure your journey into international markets is more stable and faster.
