Was it the lowest price yesterday, and will it be today as well?
We check every morning whether our product is currently at the lowest price by opening one shopping mall at a time.
It is a common scene in the fiercely competitive e-commerce industry. Managers go through Coupang and Naver Shopping every day, transferring their own product and competitors' prices to Excel. The problem is that the moment this task is completed, the data becomes outdated.
- Competitors lowered prices last night, but we found out half a day later.
- We discovered that prices vary by channel for the same product, but it was too late.
- With hundreds of items to manage, there aren't enough people to look through them all.
Manual comparisons become increasingly difficult as the number of channels and products grows. Missing out on a day can directly lead to a loss in sales.
Open market prices move in real-time
E-commerce platforms like Coupang, Gmarket, and 11th Street operate dynamic pricing. Depending on demand, competition, and seller settings, prices for the same product can be automatically adjusted multiple times a day. In a structure where exposure is concentrated on sellers offering the lowest prices like Coupang's Item Winner, the speed of price changes accelerates.
This fluctuation is particularly sensitive for manufacturers and suppliers. Even if they do not sell directly, knowing where their products are exposed and at what price on each channel directly impacts brand value and trading relationships.
- If the lowest price on open markets collapses, conflicts over prices arise with offline distributors and trading partners.
- Discovering a specific seller dumping below the set price long after the fact.
- Manufacturers themselves are unaware of the online selling prices of their products in real-time.
In an environment where prices move on their own, a system to monitor these changes without missing out is necessary. Tracking market prices with data for decision-making is called price intelligence.
Automatically collect product and price data daily
By utilizing Hashscraper's Coupang and Naver Shopping crawlers, you can automatically collect price information for specified products and keywords daily. From search rankings to selling prices, discount rates, and shipping conditions, you can gather all this information at once for direct comparison.
Actual Crawling Data Example - Naver Shopping Product
{
"Channel": "네이버 쇼핑",
"Keyword": "무선 청소기",
"Rank": 3,
"Product Name": "○○ 무선 청소기 Z7",
"Store": "○○공식스토어",
"List Price": 399000,
"Sale Price": 259000,
"Discount Rate": "35%",
"Delivery Fee": 0,
"Review Count": 1842,
"Rating": 4.7,
"Collected At": "2026-07-06 09:00"
}
Actual Crawling Data Example - Coupang Product
{
"Channel": "쿠팡",
"Keyword": "무선 청소기",
"Rank": 1,
"Product Name": "○○ 무선 청소기 Z7",
"Seller": "○○공식판매처",
"Delivery": "로켓배송",
"Original Price": 399000,
"Sale Price": 249000,
"Coupon Price": 229000,
"Discount Rate": "42%",
"Review Count": 5210,
"Rating": 4.8,
"Collected At": "2026-07-06 09:00"
}
By collecting list prices, selling prices, and coupon applied prices together, you can compare channel prices based on actual payments. Including search rankings and seller information allows for understanding not only prices but also exposure competition and seller composition.
Use Case: Multichannel Lowest Price Management for Consumer Brands
A domestic consumer brand was selling its products in over 20 shopping malls. Despite having the same item number, prices were scattered across channels, requiring daily tracking of the lowest and second-lowest prices. This used to be a manual task of filling out Excel sheets.
Crawling Settings
- Targets: Over 20 shopping malls, based on the company's item numbers
- Collected Items: Seller, selling price, lowest and second-lowest prices, shipping conditions, coupon applied prices
- Crawling Frequency: Once daily (regular morning collection)
Analyzable Items with Crawling Data
| Analysis Item | Utilization Method |
|---|---|
| Tracking lowest prices by item number | Daily verification of the market's lowest prices and sellers for the company's products |
| Detecting price changes | Automatically identifying products whose prices have risen or fallen compared to the previous day |
| Price discrepancy between channels | Checking the consistency of selling prices between the company's store and open markets |
| Competitor positioning | Comparing price ranges of competing products in the same category |
| Changes in seller composition | Identifying new seller entries or exits, emergence of unofficial sellers, etc. |
| Promotion timing | Confirming competitors' discount timing and extent through data |
Quantitative Performance
| Item | Figures |
|---|---|
| Monthly collection volume | Approximately 240,000 to 300,000 items |
| Collection frequency | Once daily |
| Target channels | Over 20 shopping malls |
Receiving organized data every morning allowed the team to focus on price response decisions instead of spending time on manual comparisons. By promptly confirming channels where the lowest prices had fallen, the response speed itself improved.
Questions answered by price data
Even with the same data, if you accumulate it, it's just numbers. By comparing and aggregating price data collected daily, you can immediately answer the following questions.
- What is the channel-specific lowest price of our product now, and where is it being sold?
- How many times and by how much did competitors adjust prices in the past month?
- At what point was the company store price higher than the open market's lowest price?
- Are there any sellers selling below the set minimum advertised price (MAP)?
When you start answering with data instead of intuition, the basis for price policies and promotion decisions becomes clear.
It is important to detect changes as they happen
Knowing "when it changed" is more critical than just accumulating price data. By comparing data collected daily with the previous day, you can immediately identify products with price reductions, sold-out items, or new sellers. By setting criteria such as lowest price departure or MAP violation, you can prioritize responding to those specific products.
The collected data is provided in raw data format. You can either download it directly as an Excel file or receive it via email automatically at a set time every day. If you have your own dashboard or analysis system, you can also directly connect through API integration or DB integration.
It can also be used in these cases
Minimum Advertised Price (MAP) Management - Monitor whether distributors or sellers are selling below the set price to detect price policy violations early.
New Product Price Tracking - Understand how competitors are responding with prices right after a new product launch.
Promotion Effect Confirmation - During discount periods, observe how competitors' prices and exposure rankings change.
Consistency between Company Store and Open Markets - If you have entered multiple channels simultaneously, check daily to ensure that selling prices do not diverge between channels.
Summary
The success of price competition lies in "who knows first." There is no guarantee that yesterday's lowest price will remain so today, especially since open market prices move on their own. Whether a manufacturer or seller, those who monitor how their products are sold online daily have the advantage.
Hashscraper handles crawler operation, maintenance, and monitoring. Even if there are changes in shopping mall policies or data collection errors, there is no need for customers to respond directly. Once set up, price data is collected daily, allowing you to maintain a monitoring system without additional manpower.
Start Now
With Hashscraper, you can automatically collect product and price data from Coupang and Naver Shopping daily.
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