Considering how many of us love to shop and spend money online, it is no surprise that global retail eCommerce sales are estimated to exceed 4.3 trillion U.S. dollars in 2025.
While many businesses are eager to jump into the eCommerce business, doing so without properly studying the market and defining effective pricing, marketing, and communication strategies can lead to failure.
To avoid a flop, making data-driven decisions is key. This is why the interest in product data has been growing steadily, with more and more solutions emerging to help collect data aimed at strategically supporting your eCommerce.
In this guide, you will learn everything you should know about collecting product data for eCommerce stores!
Main Challenges When Retrieving Product Data
Gathering product data is useful, from helping you devise effective pricing strategies to monitoring what your competitors are doing. For example, you could use that data to study when and how they present their deals.
As a rule of thumb, the more product data you have on your competitors and the market you have, the better you can strategize and organize your eCommerce business or activities.
Everyone in the industry knows that, but not all might be fully aware of the challenges in acquiring product data. So, it is important to highlight these challenges:
- Guaranteeing the collected data is accurate can be challenging, so you must rely only on trusted sources.
- eCommerce sites frequently update their content, and making decisions on outdated data is risky. This is why historical data holds less value in the fast-paced eCommerce world.
- Most eCommerce sites have advanced anti-bot systems to restrict automated data extraction.
- eStore platforms often present data in varying structures across different product pages, complicating automated data parsing and integration.
- Depending on the data source, there might be legal restrictions or licensing requirements that limit how product data can be accessed, processed, or shared.
With these issues in mind, it is clear that you must follow a consolidated approach to acquiring product data for eCommerce.
Top 3 Approaches to Getting Product Data for eCommerce
There are several methods for sourcing product data, each with its advantages and drawbacks. In this section, we will explore the most effective ways to gather product data for eCommerce.
Please look at the below summary of the best way to collect product data.
| Product Data Source | Method | Free? | Data Formats | Technical Skills Required | Data Freshness | Cost |
|---|---|---|---|---|---|---|
| Product datasets | Purchasing or downloading a dataset with the product data | Yes, typically via open data | Excel, CSV, JSON, XML, Parquet, etc. | None | Limited | Free to several thousand dollars |
| eStore scraping | Building a script to automatically retrieve product data from online stores | Yes, as open-source scraping libraries are available | Excel, CSV, JSON, XML, etc. | Advanced | Always fresh | Free to a few hundred dollars/month |
| Web scraper APIs | Getting product data from web scraping endpoints | Free trial options | Excel, CSV, JSON, XML, etc. | Basic to none | Always fresh | Typically, a fraction of a cent per request |
Time to analyze the best product data sourcing solutions!
1. Buying Ready-to-Use Product Data Datasets
Datasets provide you with access to large volumes of product data, without the need to build an in-house data retrieval process. These ready-made datasets come in easy-to-explore formats like Excel, CSV, JSON, or XML.
You can download datasets for free from open data repositories and public marketplaces, or purchase them from commercial data providers. Some providers even offer custom data generation services tailored to your specific needs.
Costs can range from zero to several thousand dollars, depending on the source, quality, freshness, and scope of the data. Licensing terms also vary, affecting how you are allowed to use the data. Additionally, the chosen source impacts whether the data is exclusive, how frequently it is updated, and whether customer support is available.
This method low-effort way to obtain valuable product data that is ideal for market research, catalog building, and trend analysis. At the same time, it may lack real-time data and flexibility.
Pros:
- Quick and easy setup, with one-time purchase or download.
- Data is already formatted for use (e.g., XLS, CSV, XML, JSON).
- No technical knowledge required.
Cons:
- Often contains only historical data, with limited access to real-time updates.
- Little to no customization, depending on the dataset provider you choose.
- Difficult to find datasets tailored exactly to your needs.
- The same dataset can be purchased by your competitors.
2. Scraping eCommerce Sites
The idea behind this approach is to collect online product data using web scraping, the process of automatically extracting data from web pages. This involves writing a custom script that:
- Connects to the target eCommerce site.
- Parses the HTML content of relevant product pages.
- Programmatically extracts the desired product data.
- Exports the scraped data into a structured format such as CSV or JSON.
Languages like Python and JavaScript offer plenty of libraries to simplify these steps, making the scraping process seem pretty straightforward. The problem is that eStore owners know how valuable their product data is, and they do not give it away easily.
Thus, most sites protect their content with anti-scraping mechanisms such as rate limiting, IP blocks, CAPTCHAs, JavaScript challenges, and browser fingerprinting. These systems are what make eCommerce scraping particularly challenging.
That being said, suppose you manage to implement a successful scraping setup by using premium solutions like rotating proxies. In that case, you would gain full control over what data to extract, how often, and in what format. That flexibility is surely powerful, but it also means you are responsible for maintaining and scaling the scraping infrastructure yourself.
This solution cost can be free if using open-source tools, but might range up to a few hundred dollars per month when factoring in hidden costs (e.g., servers, proxies, premium solutions, etc.).
Pros:
- Full control over what data to extract and how that is processed.
- Can be cost-effective, especially in simpler scenarios.
- You always get fresh data.
Cons:
- Most eCommerce sites have strong anti-scraping protections.
- Requires advanced technical skills.
- You must deal with maintenance and manage the entire scraping infrastructure.
- May need proxies and other web unlocking premium tools for consistent performance and high effectiveness.
3. Using Dedicated Web Scraper APIs
A web scraping API is a specialized endpoint that, when given the URL of a page on a specific domain, returns the content of that page in a structured format like JSON, XML, or CSV.
For example, assume you are interested in retrieving up-to-date data for a specific set of Amazon products. To do so, you can make HTTP requests to an Amazon web scraper API. In return, you will get the data in the configured format, scraped from the product pages in real-time.
Web scraper APIs are a middle-ground solution that combines most of the benefits of the other two aforementioned approaches while minimizing their drawbacks.
They give you the flexibility of custom web scraping while removing the downsides, such as the need to manage the infrastructure yourself. Also, most scraping API providers offer no-code interfaces that allow users to configure scraping processes through a visual UI.
Another key benefit of web scraper APIs is that they always provide fresh data, helping to solve the problem of data staleness. Furthermore, you can choose specific eCommerce domains and product pages to scrape, giving you greater control and customization.
It typically costs a fraction of a cent per request, making it very affordable.
Pros:
- No need to manage scraping infrastructure, with built-in integrations for proxies and other anti-scraping bypass solutions.
- Quick access to structured product data from most eCommerce domains.
- Easy integration with any language that supports HTTP clients, or through visual HTTP clients.
- Always access to fresh, up-to-date data.
Cons:
- Integration with premium providers required.
eCommerce Web Scraper APIs You Need to Know
Each of the product data sourcing methods presented earlier addresses different needs and scenarios. While there is no definitive “best” solution, eCommerce web scraper APIs stand out as one of the most flexible and powerful options.
Now, the market is filled with numerous API scraping providers, and testing all available options would take significant time and effort. Based on our research, Bright Data emerges as the top web scraper API for product data.
Currently, their Web Scraper APIs cover popular platforms such as Amazon, eBay, TikTok Shop, Google Shopping, Zara, Etsy, Walmart, Shein, Ikea, and others.
With a free trial option and pricing as low as $1.5 per 1000 requests, it makes sense to use Bright Data web scraper API to collect product data.
Conclusion
In this article, you learned about the importance of having access to product data to support your eCommerce. You also considered the challenges involved in data retrieval, digging into three effective options to bypass them:
- Downloading or purchasing a ready-to-use dataset.
- Building a web scraping script.
- Using a web scraper API.
As covered here, the web scraper API seems to be a consistent, and practical solution for collecting product data.