4 Web Scraping Challenges eCommerce Businesses Should Know About

What do we know about data?

We know that data has value, in currencies, and otherwise. It’s recognized as a force that drives businesses towards knowledgeable decisions. In fact, many eCommerce stores frequently consult web scraping services to drive continuous growth through data. 

But, do we understand what goes behind the curtains when collecting said data? 

These days, web data extraction appears a lot easier than it is. Sure, with a small focus group, things run smoothly. But, as soon as you sit down to scale, things go very wrong very quickly. 

That’s why we’re going to talk about the challenges of web scraping that, if not acknowledged timely, can raise a ruckus. 

A Brief Introduction to Web Scraping in eCommerce

Scraping the web helps find answers to a universal question that every eCommerce business often asks. 

“Where do I get new leads from?”

When finding a target demographic interested in your products, the hit-and-miss method never works well. Present-day lead generation is a highly competition-based process. You have to do it right and do it first. Plus, there is a continuously expanding sink of data in almost every industry domain. 

Businesses who want to grab a piece of this pie need to- 

• Collect data from relevant target groups
• Invest in filtering & refining it 
• Analyze the results to get actionable insights 
• Create specific marketing campaigns and modify offerings using the revealed insights to increase conversions 

It might seem too much at times. But, rest assured, it is more than critical for your eCommerce store. 

Analyzing market data helps strategize your next move and strengthens your position in the market. It saves time and cost. You get new and improved ideas to stay ahead of the competition. Tracking competitor prices and products in real-time helps your eStore improve & stay up-to-date. 

And, all of it begins with collecting the right data, i.e., web data extraction. 

4 Challenges of eCommerce Web Scraping

The internet is replete with web scraping services and tools that promise quick and easy data collection. Plus, there is a lot of desperation and urgency around accurate, cost-effective data acquisition. 

Ergo, it’s easy to lose your way and make mistakes when scraping web data at scale. Businesses often end up trading speed for data quality or the other way around. To avoid that loss, prepare in advance for these four web scraping challenges. 

1. Changing Website Formats

Web pages are structured in a widely different manner, thanks to diverse page design standards. They also get updated quite frequently, leading to changes in their structural elements. Crawling such websites can result in incomplete data collection. Or, your scraper would simply crash. 

Usually, these can be fixed with minor adjustments in the scraper code on a small scale. However, the web is filled with sloppy codes of all kinds. Be it character encoding issues or broken JavaScript; such problems can break your scraper unexpectedly. 

When you are running a web data extraction service at scale, these problems quickly add up to present a bigger issue. 

• Websites will change now and then
• Code issues will pop up more frequently than you’d like 
• Scrapers will be lost to either changing codes or broken elements 

Most reputed data extraction services use a double-check method to beat this problem. On one hand, they monitor the incoming data in real-time to point out red flags. On the other hand, they perform manual checks to weed out weak data and retouch the responsible scraper.

It’s also wise to expect a certain number of scraping bots to fail regularly. If you run your data extraction campaign with that leeway in hand, the crashing scrapers won’t affect your result. Plus, you will have enough time to look at the issues and fix them. 

But, have no doubt! This problem has no easy solutions. It almost always requires more resource commitment. You must evolve, learn, and build more robust scrapers to cope up with the changes. This need for a large and more skilled team is primarily why many businesses simply hire web scraping services.

2. Bot Access, Captcha, and Traps 

Clubbed together under the title ‘Anti-Scraping technology,’ all these factors make data collection more difficult. LinkedIn and Amazon are excellent examples of websites that use sophisticated anti-bot measures to reduce scraping. 

• Many websites refute bot access 
• Some use IP blocking if they detect an unusual number of requests from a single IP address
• Others use CAPTCHA which easily differentiates a human user from a scraper

You will also come across Honey-Pot pages while scraping. These are traps, i.e., pages that a human visitor wouldn’t need to access, but a bot would, since they open all the links on a page. 

What you need is a scraper that can work around those boundaries. 

• When scraping at scale, use proxy IPs
• Use IP rotation and session management 
• Implement blacklisting logic to avoid getting blocked

Try not to use scriptable headless browsers in excess. That’s because many anti-scraper codes use javascript to separate human users from bots. Since scriptable headless browsers render all JavaScript on a page, they can expose your scraper quickly.

3. Crawling Efficiency 

Once you deal with the top two problems and actually begin scraping at scale, efficiency becomes the next roadblock. 

When performing data extraction at a large scale, maintaining optimal performance becomes necessary. Ideally, your crawling strategy should involve minimal manual effort. It should also scrape all the required data in a limited time-frame without harming the accuracy. 

To achieve that result, every distraction, like extra data or requests, has to be eliminated. Otherwise, the request cycle will keep getting longer while your scraper’s performance will diminish. The entire crawling process would also slow down. 

In addition to optimizing requests, you can also try other methods to increase crawling efficiency-

• Instead of deploying the crawler across a single server, use multiple ones 
• Optimize database queries to reduce the time for fetching crawl stats metadata
• Use appropriate tools or hire web data extraction services to check data sets and eliminate duplication

4. Data Quality 

Data quality is the most critical aspect of any web scraping project. That becomes especially concerning when you are gathering millions of data points a day. 

All of the collected data is unstructured. It’s gathered from different sources and prone to a variety of vulnerabilities. Plus, since it’s significant in volume, manual monitoring efforts aren’t enough to detect inaccurate, duplicate, or false entries. The problems can span a wide range, including-

• Inconsistent data types
• Falsified information being returned
• Volume-based problems 
• Ambiguity in data because of website changes 
• Data and product validation issues 
• Junk data being accepted

Ensuring data quality in your web data extraction attempts involves two key steps. The first one is a quality analysis that’s performed when you’re designing the web scraper bot. The second is an automated system that can monitor data inflow and report inconsistencies. 

Note that developing such a system requires resources as well as time and money. It’s often easier to delegate data extraction services to a third-party. Not only does that save time and money, but it also ensures reliable support from experts. 

The Ethics of Web Extraction/Scraping 

In the section above, we discussed how websites implement CAPTCHA and traps to avoid being scraped. While that statement has many interpretations, a strong one is that web data extraction can be illegal. 

IT ISN’T! 

Opinions vary on the degree of scraping efforts that are considered enough. But, it is a unanimous industry belief that web data extraction hurts no one. Instead, it helps make information more accessible. 

Of course, if you abuse the server and make excessive demands, that’s simply wrong. That prevents the server from answering other requests. Or, if you pass off anybody’s data under your brand name, that’s copyright infringement, i.e., very much illegal. 

So, how can you ensure that your data collection efforts or the data extraction services you hire are all legally sound?

• Respect the website’s terms and conditions
• If there’s a confusion, ask the website owner if it okay to scrape them 
• Don’t overload any servers 
• Be transparent and use headers to identify yourself 
• Don’t do anything that comes under copyright infringement 

Go Ahead Then! Get Your Data Scraping Cap On & Get Things Done

Of course, you still need to build a scraper and overcome all the challenges you just read about. 

But, once done, your eCommerce business will get the benefit of actionable insights and targeted campaigns. Eventually, you will see its positive impact on your conversions, sales, and ROI. 

Data-Entry-India.com Can Help! We work in close association with our clients to capture relevant data from multiple sources. Our web data extraction services are engineered to ensure efficiency and accuracy. 

Let’s discuss how our web scraping services and experts can help your cause. Just drop a line at info@data-entry-india.com.