One of the biggest challenges for any ecommerce entrepreneur is figuring out what products to sell. Pick the wrong products and you could end up with a store full of items no one wants to buy. But by leveraging web scraping to extract and analyze key data points from other online stores and marketplaces, you can make much more informed, data-driven decisions about what to sell.
In this comprehensive guide, we‘ll walk through exactly how you can use web scraping to research product opportunities, source in-demand items, optimize your pricing and listings, and ultimately find the best products to sell on your own ecommerce store. Let‘s dive in!
What is Web Scraping and How Does It Work?
Web scraping is the automated process of extracting data from websites using bots or scripts. While you could manually copy and paste product information from other ecommerce sites, this is extremely time-consuming and doesn‘t scale.
Web scraping tools allow you to automate the extraction of key data points like product names, pricing, reviews, inventory levels and more. You simply need to configure the tool to target the specific data fields you want, and it will crawl through different pages and compile everything into structured formats like CSV or JSON that you can easily analyze.
Some popular web scraping tools include Octoparse, ParseHub, Scrapy, and BeautifulSoup. These make it much easier to pull data without needing to write your own code. Later on, we‘ll show you how to scrape data step-by-step using Octoparse as an example.
7 Types of Product Data to Scrape for Ecommerce Insights
So what specific data points should you focus on scraping to find profitable product ideas and optimize your ecommerce strategy? Here are 7 of the most valuable:
1. Pricing Data
Extracting pricing information across different sellers for the same or similar products allows you to gauge the overall price range in the market. This helps you determine where to price your own products to stay competitive while still maintaining healthy profit margins.
You can also analyze pricing over time to spot seasonal trends and dynamic pricing strategies used by other sellers. For example, you might notice competitors running frequent sales during certain periods that you may want to match.
2. Best-Selling Products
Examining the current best sellers on marketplaces like Amazon or in specific niche stores can clue you in to what‘s in high demand. If certain products are consistently ranking at the top, that‘s a signal there is strong market demand and an opportunity to source similar items.
In addition to just the product names and identifiers, also consider scraping the sales rank or "Best Sellers Rank" information. This gives you more concrete data on how well each product is actually selling.
3. Customer Review Data
Reviews are a gold mine of insight into what customers like and dislike about particular products. They reveal common questions, complaints, use cases, and more.
By scraping review text at scale, you can analyze it to understand the key features customers value, identify unmet needs your products could address, and surface ideas for how to make your product listings and descriptions more compelling.
Look at both the review content itself as well as structured data like average rating, % of 1-star vs 5-star reviews, most common phrases, etc. to get a comprehensive view of customer sentiment.
4. Inventory Levels
Monitoring the inventory levels of products can reveal how quickly items are selling out and spot potential gaps in the market. For example, if you notice products in a certain category frequently going out of stock across multiple sellers, there may be an opportunity to source additional inventory to meet that excess demand.
You can scrape inventory information like current stock levels, items sold in the last 30 days, restock dates and so on. Comparing this over time also provides useful insights.
5. Product Attributes
Examining the specific attributes and features of top-selling products can spark ideas for how to position and market your own. Look at what colors, sizes, materials, technical specifications, compatible devices, etc. are most common.
Also pay attention to the unique selling propositions competitors highlight in titles and bullet points. What features do they lead with? What problems do they claim to solve? Scraping this info helps generate ideas you can test.
6. Competitor Sales Ranks
For products you are already selling, benchmarking them against competitors‘ sales ranks shows how you stack up. Are your products consistently behind in the rankings? Then you likely need to reassess your pricing, descriptions, images, or other listing elements.
If certain competitors are regularly beating you for the same types of products, analyze what they‘re doing differently in their listings and consider scraping additional data points from their listings like length of description, number of images, price history, and more.
7. Search Volume Data
Knowing the relative popularity and search volume for different keywords helps gauge overall demand. If you see certain search phrases are consistently getting tens or hundreds of thousands of searches per month, then the products that show up for those searches likely have a large addressable market.
Scraping search volume data from keyword tools or Google Trends can help you decide which product categories and niches to prioritize based on their total search demand. You can also identify related long-tail keywords to include in your product listing.
How to Scrape Ecommerce Data Step-by-Step with Octoparse
Now that you know what data to look for, let‘s walk through how to actually go about scraping it using a tool. For this example, we‘ll use Octoparse, which is a powerful yet user-friendly scraping tool.
Here‘s a quick step-by-step for scraping pricing, reviews, and inventory data from an Amazon product page:
Create a new task in Octoparse and enter the URL of the product you want to scrape.
Use the point-and-click interface to select the data fields you want to extract, such as price, product name, # of reviews, stock status, etc.
Customize the selected data fields if needed, such as extracting just the numeric price without the currency symbol.
Configure pagination settings to crawl through all pages of reviews if you want to get large volumes of review text.
Set up filters, such as only scraping products with at least a 4-star rating or that are below a certain price.
Choose your export format and destination, such as CSV, JSON, or sending to Google Sheets.
Run the scraper and wait for it to complete. Larger jobs may take longer.
Repeat the process for different product pages or websites.
While the exact process varies slightly by tool, most web scrapers operate in a similar fashion and don‘t require coding knowledge to configure. The key is taking the time to thoroughly test and QA the scraper on a few pages before running it on an entire site to ensure data accuracy and completeness.
Common Web Scraping Obstacles and Solutions
As you start scraping ecommerce data, you‘re likely to encounter some challenges. Here are a few of the most common obstacles and how to overcome them:
IP blocking: Many sites try to block scrapers by detecting an unusually high number of pageviews from the same IP in a short time. The solution is to throttle your scraping speed, use a pool of proxy IPs, and rotate them out if any get blocked.
CAPTCHAs: CAPTCHAs are designed to stop bots by requiring users to complete a visual challenge. Some scrapers have built-in CAPTCHA solving capabilities. You can also use a CAPTCHA solving service like 2Captcha or Death by Captcha.
Inconsistent page structures: If product info is laid out differently across a site, your scraper may fail to extract data accurately. The workaround is to either target only specific pages with a consistent structure or use more advanced techniques like XPath and regex to adapt to different page layouts.
Anti-bot tools: Ecommerce sites increasingly use dedicated anti-bot solutions that try to detect and block scrapers. To get around these, you can rotate user agents and IP addresses, introduce random click/scroll events to mimic human behavior, and avoid scraping during peak traffic times.
With some persistence, trial and error, and technical know-how, you can work around most obstacles that arise. Just be respectful of the sites you scrape and don‘t hammer them with too many requests too quickly.
Analyzing Scraped Product Data to Surface Actionable Insights
Of course, the raw data alone isn‘t enough – you need to analyze it to surface relevant insights and opportunities. Some key ways to slice and dice the data include:
Create a spreadsheet to aggregate and compare pricing data across sellers. Calculate average prices, lowest price, etc. and benchmark your products.
Use a word cloud or frequency analysis tool to visualize the most common terms used in reviews and product descriptions. Identify trends and themes to highlight in your own listings.
Plot sales ranks and inventory levels over time in a chart to see which products have staying power vs. flash-in-the-pan success.
Set up an automated alert to notify you when competitors‘ inventory drops below a certain threshold so you can take advantage of stockouts.
Use a tool like Google Data Studio or Tableau to build a dashboard that tracks competitor best seller rankings, ratings, review counts, and more in one central view.
The key is to focus your analysis on metrics that help you source in-demand products at the right price, identify weaknesses in competitors‘ offerings, and optimize your own listings to maximize conversions. Don‘t just blindly compile data without a clear use case in mind.
Putting It All Together
We‘ve covered a lot of ground in this guide to finding products to sell online with web scraping. To recap, the process boils down to:
- Identify the key types of product data to scrape based on your goals
- Choose a web scraping tool and configure it to extract the data
- Run the scraper to compile data across multiple pages and websites
- Cleanse, structure, and analyze the scraped data to identify insights
- Put those learnings into action by optimizing your ecommerce strategy
It takes some time and technical chops to get good at scraping ecommerce data effectively, but the payoff in terms of smarter product sourcing, pricing, and marketing decisions is well worth it.
To learn more about ecommerce web scraping, check out the following resources:
- Octoparse‘s library of ecommerce scraping templates
- Scraping Bot‘s guide to ecommerce web scraping
- Scrapy‘s documentation on scraping best practices
Now you‘re armed with the knowledge you need to harness the power of web scraping to find the best products to sell online. The only thing left to do is to get out there and start scraping. Your future customers will thank you for it!