Data Gathering Methods for Ecommerce: Building Web Scrapers

Data Gathering Methods for Ecommerce: Building Web Scrapers

Data Gathering Methods for ECommerce

Web scrapers are the most commonly used tools for data extraction from the web. You will need to have programming skills to build your web scraper, but it’s easier than it may seem.

The success rate of using a web scraper as one of the data gathering methods for eCommerce doesn’t just depend on the web scraper alone. Other factors such as the target, anti-bot measures that the site uses, and others like this play a role in the success rate in the end.

To use web scrapers for long term purposes like data acquisition, or pricing intelligence requires you to constantly maintain the scraper bot and manage it properly. And so in this article, we won’t restrict ourselves to the basics of building your web scraper, but we will also talk about some challenges a newbie may face in the process.

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When data is to be acquired from the web, web scrapers play a key role in the process. They are the automated ways of extracting a huge amount of information from the web as opposed to the slow copy and paste method that was used in the past. Examples of web scraping are search engine results, eCommerce sites, and other internet resources that hold information.

Data gotten from web scraping can then be used for stock market analysis, pricing intelligence for businesses, academic researches, and other purposes that are data-dependent. Web scraping can be used in several ways as its application as a data-gathering method is boundless.


When web scrapers are used to gather data, it includes some steps: a scraping path, the data extraction script (s), browser (headless), proxies, and data parsing.



This is a very important aspect involved in web scraping and other data extraction methods. A scraping path is the library of URLs from which the data is to be extracted and even though it sounds like an easy process, building a scraping path is a very delicate process that requires utmost attention.

Sometimes it’s not as easy to create a scraping path as you may have to scrape the initial page to get the required URL. This is especially true when web scraping is used as a data-gathering method for eCommerce sites as they have URLs for each product and page. So if you want to build a scraping path for specific products in an eCommerce site, it will look like this:

1 . Scrape search page

2. Parse the product page URLs

3. Scrape the new URLs

4. Parse the data according to the selected criteria

And so in such a circumstance, it may not be as easy to build a scraping path when compared to creating one using easily accessible URLs. Developing an automated process for creating a scraping path makes it more efficient as no important URLs are missed.

The parsing and analysis that will follow depend on the collected data from the URLs in the scraping path. Insights and other inferences are only a reflection of the data acquired and so if a few key sites whose sources would make a whole difference are missing, the result gotten from the process may be inaccurate and a complete waste of time and resources.

When building a scraping path, you need to have good knowledge of the industry for which the scraper would be used, and you need to know who the competitors are. This information will allow for the careful and strategic collection of URLs.

It’s also worth noting that data storage takes place in two steps: pre-parsed (short term) and long term. For an effective data collection process, the collected data needs to be updated frequently as the best data are the fresh ones.


To build a web scraping script, you will need to have some good knowledge of programming. Basic data extraction scripts use python but this isn’t the only available option. Python is popular because it has many useful libraries that make it easier for the extraction, parsing, and analysis processes.

The web scraping script goes through various stages of development before it can be used:

  1. 1 . You need to first decide on the type of data to be extracted (pricing data or product data for example)
  2. Find out the data location and how it is nested
  3. Import the necessary libraries and install them (example of libraries are BeautifulSoup for parsing, JSON or CSV for output)
  4. Then write a data extraction script

The first step is usually the easiest and the work starts in step two. Different data is displayed in different ways and in the best case, data from various URLs in your scraping path would be stored in the same class and would not need any scripts to be displayed. You can easily find the classes and tags with the inspect element feature in modern browsers. This is not the case with pricing data most times as they are difficult to acquire.


Pricing data and some others may not be present in the initial response as they would be hidden in JavaScript elements. In such situations, you can’t scrape the data using normal data extraction methods. Python libraries for XML and HTML data scraping and parsing (BeautifulSoup, LXML, etc.) cannot be used to access JavaScript elements without the use of other tools alongside. To scrape such elements, you will need to use a headless browser.


Headless browsers are the fall to tools for scraping data in JS elements. Web drivers are other options that can satisfy that purpose too as many popular browsers have them on offer. The downside to the use of web drivers is that they are slower when compared to headless drivers as they work in similar ways to normal web browsers. So when both are used, the results may be slightly different. It may be helpful to test both methods for every project to find out which suits the need more.


Chrome and Firefox take up 68.60% and 8.17% of market share respectively and are available in headless mode, providing even more available choices. PhantomJS and Zombie.JS are also popular headless browser options among web scrapers and at this point, it is worthy to note that headless browsers need automation tools to run web scraping scripts. Selenium is a popular framework for web scraping.


In the process of data parsing, the acquired data is made intelligible and usable. Many web scraping methods extract the data and present it in a format that can’t be understood by humans hence the need for parsing. While python is one of the most popular programming languages to acquire pricing data thanks to its optimized and easily accessible libraries, BeautifulSoup and LXML are popular for parsing data.

Data parsing allows developers to easily sort through data by searching for it in specific parts of the HTML or XML files. BeautifulSoup comes with some inbuilt objects and commands to make the parsing process even easier. Most parsing libraries make it easier to move through a large chunk of data by making available a search or print command to common HTML/XML document elements.


The procedure involved in data storage would depend on the size and type of data involved. It’s necessary to build a dedicated database when storing data for continuous projects such as pricing intelligence, but it’s also good enough if you store everything for short term projects in a few CSV or JSON files.

You will find that data storage is a simple step especially in data gathering methods for eCommerce sites, but there are a few issues you will encounter. Keep in mind that the data has to be clean. If you retrieve data from an incorrectly indexed database, it will mark the beginning of a ni8htmare. Begin your extraction process the right way and maintain the same guidelines as it will help resolve many data storage problems.

In data acquisition, the long term storage is the last step. Writing the scripts, finding the target, parsing, and storing the data are all the easy parts in web scraping. The hard part is in avoiding the website’s defenses, bot detection algorithms, and also blocked IP addresses.


The above steps involved in web scraping have been pretty straightforward. Creating the scraping script, finding the right libraries, and exporting the extracted data into a CSV or JSON file have all been easy. In practice, however, website owners are not happy about large data being extracted from their sites, and so they do everything to prevent this from happening.

Many web pages have tight security put in place to detect bot activity and block the IP address. data extraction scripts work like bots as they work in loops and access the list of URLs in the scraping path. So by extension, data extraction also leads to blocked IP addresses. To prevent an IP ban as much as possible, and to ensure continuous scraping, proxies are used. Proxies are very important for a web scraping project to be completed successfully and the type of proxies used to matter a lot.

In data extraction, residential proxies are most commonly used as they allow users to send requests even to sites that would have otherwise been restricted due to geo-blocks. They are tied to a physical address, and as long as the bot activity is within normal limits, these proxies maintain normal identity and are less likely to be banned.


Using a proxy doesn’t guarantee that your IP won’t be banned as the website security also detects proxies. So using a premium proxy with features that make it difficult to detect is the key to bypassing website restrictions and bans. A good practice to prevent being banned is IP rotation. This doesn’t put an end to scraping problems as many eCommerce sites and search engines have sophisticated anti-bot measures put in place that would require different strategies of you must get past them.


To increase your chances of success in data gathering methods for eCommerce sites, IP rotation is important as well as normal human behavior if you must avoid IP blocks. There is no fixed rule on the frequency of IP changes or which type of proxies should be used as all of these depend on the target you are scraping, the frequency at which you are extracting data, etc. all of these are what makes web scraping difficult.

While every website needs a unique method to ensure success, some general guidelines have to be followed when using proxies. Top companies that are data-dependent have invested in understanding how the anti-bot algorithm works and based on their case studies, general guidelines for successful scraping have been drawn.

It's particularly important to maintain the image of a real human user when scraping and this involves how your bit carries out its activities. Residential proxies are also the best to use as they are tied to a physical location, and the website sees traffic from here as coming from a real human user. Using the right proxy from scratch will go a long way to prevent problems in the future.


Since the success of web scraping also depends on the scraper’s ability to maintain a particular identity, residential proxies are often used. Ecommerce algorithms have several algorithms that they use to calculate price and most times the prices customers get vary depending on their attributes. Some websites will block access to those they see as competitors, or worse, display the wrong information to them. So it’s sometimes important to chance location and identity.

Your IP address is the first thing that comes in contact with a target website. Since websites have anti-bot measures put in place to prevent any form of data extraction, proxies give the user another chance to change any suspicious activity that may give their identities away. Residential proxies are limited and it would be wasteful to keep switching from one to another and so to prevent this, certain strategies need to be put in place.


To successfully avoid IP blocks, you will need a strategy that will take time and experience to develop. Bear in mind that every targeted website has its parameters that are used to classify an activity as being bot-like and so for such sites, you will need to adjust your technique.

The following are basic steps involved in using proxies in data gathering methods for eCommerce:

  1. 1 . The session times should be at 10 minutes
  2. If the target has heavy traffic, it's recommended that you extend the session time
  3. You don’t need to build an IP rotator from scratch. FoxyProxy or Proxifier are third party apps that can do the job properly
  4. When scraping a site, try as much as possible to act as a normal user would
  5. To imitate human behavior even better, spend some time on the homepage, and then about 5 to 10 minutes on product pages

Note that the larger the eCommerce site, the more difficult it will be to scrape. So don’t be afraid of failure the first time as it will help you in building a strategy that works.


Data extraction is an important part of business growth as it informs decisions. While the process itself is straightforward for basic web scraping, the problem begins when you send your request and attempt to extract the data. Many websites have strong anti-bot measures that detect and block bot actions on the site. Your best chance at getting the required data successfully is to use a premium proxy.

Limeproxies gives you dedicated IP addresses that can be used in web scraping tasks without having to share your IP’s performance with another user. This ensures you get fast speeds and control the behavior you want the site to see from your IP. Using your IPs with a good proxy rotator will further improve your chances of success as IP rotation has proven to reduce the chances of your IP being banned by websites.

About the author

Rachael Chapman

A Complete Gamer and a Tech Geek. Brings out all her thoughts and Love in Writing Techie Blogs.

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