In this article, we’ll show you how to activate your proxy package and start using it.

Step 1: Add the Test Package to Your Cart

If you've already paid or received a link to a free proxy package, you're ready to proceed. Otherwise, you can find the test proxy package link on this page: https://proxycompass.com/free-trial/.

Click the link to add the test proxy package to your cart, then click the “Checkout” button.

Step 2: Register on the ProxyCompass Service

Use your Google account or enter your valid email address. Click the “Register” button to complete the registration process.

Your registration is complete. The password for your new account has been sent to the email address you provided.

Step 3: Check Your Email

In the email you received from us, you will find an automatically generated password. You can change this password later.

Step 4: Log in to the Dashboard

Go to the link:https://proxycompass.com/account/index.php?rp=/login

Enter your previously provided email address and the received password. Click the “Login” button.

Click the “Cart” button to proceed with the activation of the test proxy package.

Step 5 (The Most Important): Enter Your Own IP Address in the “Bind IP” Field

Be sure to enter the IP address of the device where you'll use the proxies. The proxies will only be accessible from the device with the IP you specified in "Bind IP".

For example:

- For a home computer, enter your home computer’s IP.

- For a remote server or VPS, enter the server's IP.

Visit https://2ip.io/ to find your current device’s IP.

In most cases, the IP will auto-fill. Click “Set” to apply settings.

Note: Activation may take 5-10 minutes. Just be patient.

Step 6: Choose the Suitable Proxy Retrieval Option

- Download an HTTP or SOCKS proxy list in the IP:PORT format.

- Download an HTTP or SOCKS proxy list in the IP:PORT:Username:Password format.

- Get a random SOCKS proxy via the link.

- Get a random HTTP proxy via the link.

- Generate and download a proxy list in a custom format.

How to Download a Proxy List (Without Username & Password)

If your program uses proxies without authentication, download the text file in the IP:PORT format. Use port 8085 for HTTP or 1085 for SOCKS, and click the "TXT" link as shown in the screenshot.

You will receive proxy lists in the following format:

How to Download a Proxy List with Username and Password

Your proxy Login and Password can be found at the top of the page. It will be displayed as follows.

In our example, our Login is USK9MFARF, and the Password is pq94v42C.

If your program requires the proxy list in the format IP:Port:Username:Password, follow these steps:

Scroll to the bottom of the page to the "Proxy list designer" section.

Enter the following code in the "Template" field:

{ip}:{port}:USK9MFARF:pq94v42C

where:

- {ip} - each IP address in the proxy list

- {port} - required port

- USK9MFARF - login

- pq94v42C - password

Select the desired option in "Proxy type" and click "Create" to generate the proxy list.

As a result, you will get a generated proxy list in the required format: IP:Port:Username:Password.

How to Get a Random Proxy from the List Without Downloading

If your program doesn’t support proxy lists and requires a direct link to a specific proxy server, you can do the following:

On the service page, find the section titled:

API for remote access to single random available proxy

You will see the following options there.

Click the “Get” link next to the option suitable for your program to obtain a random proxy from the list.

- Random HTTP Proxy without login/password:https://proxycompass.com/api/getproxy/?r=1&format=txt&type=http_ip&login=YOURLOGIN&password=YOURPASSWORD

- Random SOCKS Proxy without login/password:https://proxycompass.com/api/getproxy/?r=1&format=txt&type=socks_ip&login=YOURLOGIN&password=YOURPASSWORD

- Random HTTP Proxy with login/password:https://proxycompass.com/api/getproxy/?r=1&format=txt&type=http_auth&login=YOURLOGIN&password=YOURPASSWORD

- Random SOCKS Proxy with login/password:https://proxycompass.com/api/getproxy/?r=1&format=txt&type=socks_auth&login=YOURLOGIN&password=YOURPASSWORD

Replace YOURLOGIN and YOURPASSWORD with your actual login credentials.
https://proxycompass.com/knowledge-base/how-to-activate-a-proxy-package/

Data Scraping Legal Issues: Exploring hiQ vs LinkedIn Case

The high-profile case of hiQ Labs Inc vs LinkedIn Corporation (that took place in the US) shed light on the much-discussed data scraping legal issues.

We know you don’t want to get lost in legalese. 

So, we have prepared an easy-to-read summary of the most important points of this decision. The court sided with the scraper and established that scraping public data is not a violation of the CFAA (Computer Fraud and Abuse Act).

Let's look at the specifics of the case, and also the far-reaching consequences it left.

Is Web Scraping Legal? 

What did the web scraper say when asked about his legal strategy? "I plead the 404th."

If you're new to scraping data, you're likely concerned about the legality of your actions.

Good news is that you are not alone. Every scraper (I think?) has wondered the same. 

Bad news is that the answer is not so simple. Like dating, it just refuses to be simple.

Web scraping falls in a gray area and it can be an ambiguous practice.

Of course companies want to preserve their data, but, on the other hand, if it’s publicly available, why is it wrong to gather it? 

Now, what is the law's position on this much-debated matter? Let’s dive into the highest profile case of hiQ Labs vs LinkedIn to see if we can get some answers.

The Verdict: Scraping Data is not Unlawful

In 2022, the Ninth Circuit Court of Appeals finally made its decision and sided with hiQ Labs. The court held that scraping publicly available data does not amount to a violation of CFAA, even if it is against the terms of use of the website.

LinkedIn was attempting to prevent hiQ's bots from scraping data from its users' public profiles. But the Ninth Circuit was clear: giving a company the complete monopoly of data that it doesn’t own (as it is licensed) would be detrimental for the public interest.

A Limited Scope for the CFAA

In much simpler words, the Ninth Circuit established that companies do not have free rein over who can collect and use public data. 

One must not interpret the CFAA so broadly, as it would make almost anyone a criminal. 

Under the ruling, the CFAA only criminalizes unauthorized access to private, protected information. 

To sum up: websites can no longer use the CFAA to prevent unauthorized data collection. And they cannot employ legal tools against scrapers.  

The Public vs Private Data: Examining Legality Concerns

Data scraping legal concerns now shift towards the distinction between public-private data. 

So, for your convenience, I prepared a short cheat sheet you should follow when you are planning to scrape data:

- Is the data freely available? You are probably safe.
- Is the data only available to owners? This could lead to trouble

Easy right?

But, there are some other factors we have to consider…

Even if the scraped data is publicly available, you still have to take into account contracts, copyright, and laws, like the GDPR if you’re in the EU.

There are also ethical considerations beyond just legality like respecting robots.txt instructions and avoiding overloading servers, to name a few. Just because something is “legal” does not make it instantly right. 

A Green Light for Web Scrapers?

Although at first you may think the ruling favoring hiQ is a win for web scrapers, it doesn’t mean you have an open ticket to scraping.

This case narrows the CFAA's interpretation and affirms the right to gather public data. But, there are other data scraping legal issues we have to avoid.

For instance, if for scraping data you create a user account, then you can be in trouble as you have agreed with the terms of service. Even if the CFAA does not apply, one can be in breach of contract. What contract, you ask? Well, when you create a user account on a website, you typically have to agree to their terms of service. 

Lastly, LinkedIn obtained a permanent injunction, which in English means that it got hiQ to desist scraping as part of the agreement they’ve reached. So, it kind of was also a victory for LinkedIn too. 

PS: Keep in mind that scraping copyrighted data, like articles, videos, and images, can infringe on intellectual property rights, regardless of whether the data is publicly accessible.

Legal Implications of Web Scraping: The Bottom Line

“To scrape, or not to scrape - that is the question” as Hamlet would say - if he was born in 1998. Jokes aside, cases like hiQ vs LinkedIn helps us get some guidance on the legalities of web scraping.

It is highly improbable that scraping public data will cause you to violate the CFAA. 

However, some practices could lead you to legal repercussions, such as disregarding cease-and-desist orders, breaching user agreements, and even creating fake accounts.

The six-year-old LinkedIn vs hiQ lawsuit may be over, but the war on data scraping is still ongoing. Companies will try to protect their data, and we all know how powerful lobbyists are in the US.

In the EU, however, lobbying might not be as big of an issue. Instead, for whatever reason, they've gone all-in on privacy, and I'm pretty sure the GDPR laws might have something to say about the use of web scraping.

Despite these challenges, we all know scrapers are gonna scrape.

Disclaimer:
A) Not legal advice. This post was written for educational and entertainment purposes.
B) While the hiQ vs LinkedIn case set a precedent, it doesn't give unrestricted freedom.
C) Data protection laws like GDPR in the EU will have priority over an American case.
D) Laws in your country could be entirely different from what’s mentioned in this text.
E) I’m not a lawyer, I have no idea what I’m dooiiinng.

References:

López de Letona, Javier Torre de Silva y. “The Right to Scrape Data on the Internet: From the US Case hiQLabs, Inc. v. LinkedIn Corp. to the ChatGPT Scraping Cases: Differences Between US and EU Law.” Global Privacy Law Review (2024) https://doi.org/10.54648/gplr2024001

Sobel, Benjamin. “HiQ v. LinkedIn, Clearview AI, and a New Common Law of Web Scraping.” (2020). https://dx.doi.org/10.2139/ssrn.3581844

https://proxycompass.com/data-scraping-legal-issues-exploring-hiq-vs-linkedin-case/

Web Scraping for SEO: Don’t Waste Money on Expensive Tools

Of course, everyone wants to dominate the SERPs. It's a no-brainer!

Want to know one of my favorite ways to achieve better rankings? Yup, web scraping!

Web scraping is particularly useful for SEO; not only is it very cheap, but it allows you to access hyper-specific data that sometimes is not even visible through SEMRush's or Ahrefs' databases.

Keep in mind anyone can disallow these two bots (and any bot actually) via their robots.txt.

So maybe you want to save a few bucks on those pricey subscriptions, but it could also be that you found a website trying to hide a few things…

Most Common Web Scraping Use Cases for SEO

You already know how important it is to keep up with the competitors, so let’s jump right in!

When applied to SEO - something that not many people do - web scraping can give you the ability to identify the keywords that your competitors use and the content they produce.

You could learn what your target audience is looking for, allowing you to create content that will be both relevant and rank high. After all, content is king right? Sure, sure, they’ve been saying that since 2014, but today in a world filled with AI content, that’s starting to be true.

Also helpful for website audits to identify technical issues like broken links and duplicate content.

If we’re talking local SEO, we can scrape competitor’s GMB reviews and do sentiment analysis.

As for link building, it can help track everything your competitor is trying so hard to build. 

Who doesn’t love a bit of lazy work here and there? Let them find the opportunities!

Don’t stop, no no no, many advantages are outlined in the upcoming section.

Benefits of Web Scraping for SEO

Web scraping offers several key benefits for SEO professionals:

Tailored Data Collection: Modify the data gathering process to align with specific SEO requirements. Access unique data sets that are beyond the reach of conventional tools.

Cost-effectiveness: Once the initial setup is done, web scraping can be a cheaper option in the long run if one needs to scrape data repeatedly than paying for subscriptions for SEO tools. If you’re up for saving money, it can be your go-to option.

Real-time data: Conduct on-demand data scraping to get the latest information, which is very important, especially when the search environment is constantly shifting.

Unlimited data collection: The bigger the data, the harder it is to clean..? That’s true but I personally dislike others imposing limits on me. Call me a rebel. I want to know it all.

Expanded Data Sources: Gain access to a wider range of relevant websites and platforms compared to what is typically offered by premium SEO tools.

Scalability: It can be used to deal with a large amount of data extraction and frequent updates, only constrained by your server capability.

Comparison of Web Scraping vs. Paid SEO Tools

Web Scraping AdvantagesSEO Tools BenefitsVery specific data extraction that can be adapted to specific requirementsEasy to use and comes with templates for frequently used SEO tasksMuch less expensive in the long runProfessional set of tools for keyword research, backlink analysis, and competitor researchReal-time data on demand from the sourceCurrent, credible informationUnrestricted data collection for extensive analysisReduces time with pre-built features and connectionsAutomate data retrieval and integrationContinued customer care and information

Popular SEO Scraping Tools

Here are some of the most popular tools, I won’t cover them all because there are so many. If you’d like to see a complete list leave a comment down below and we’ll create a post for that.

Python Libraries
- Scrapy: An open-source web crawling framework that provides a powerful and flexible way to extract structured data from websites. Highly scalable and can handle large sites.

- BeautifulSoup: Parses HTML and XML documents. It creates parse trees that can be used to extract data from web pages. Can be combined with libraries like Requests.

- Selenium: A tool for automating web browsers. It can be used to scrape dynamic websites that require JavaScript rendering. Useful for more complex scraping tasks.
SaaS Tools
- ScrapingBee: A web scraping API that handles proxies, CAPTCHAs, and headless browsers. It allows you to extract data from web pages with a simple API call.

- Scraper API: Service that simplifies the process of extracting data from websites at scale, handles proxy rotation, browsers, and CAPTCHAs via  a simple interface.

- ScrapingBot: Aims to simplify and democratize web data extraction. It allows users to not get blocked by handling some of the most typical web scraping challenges.
Browser Extensions
- Web Scraper: Free Chrome and Firefox extension  for web data extraction. Benefits include a visual element selector and export data to CSV or Excel formats.

- Instant Data Scraper:: Provides a simple point-and-click interface. Key advantages are the AI-powered data selection, support for dynamic content and infinite scrolling.

- Data Miner: Free and paid plans. Allows exporting to Excel. Benefits include the ability to scrape single or multi-page sites, automate pagination, and fill web forms.

How Web Scraping Helps Optimize Your Website's SEO

Feeling the need to increase your website's ranking on the search engine results page? 

With web scraping, you can get the info necessary for your SEO delusions of grandeur.

Analyze Your Site Structure

Web scrapers can dig into the nuts and bolts of your website, examining crucial elements like:

- Page titles
- Meta descriptions
- Headings (Heading 1, Heading 2, etc.)
- Internal linking
- Image alt text
- Page load speed
Discover Your Keyword Rankings

When applied to SEO, web scraping reveals ranking terms and positions.

You can monitor your rankings moving over time and see where you should optimize. 

Web scraping also uncovers details about your backlink profile, including:

- Number of backlinks
- Quality of linking sites
- The text used in the hyperlink or anchor text
Find Content Opportunities

When you compare your content with the most popular content that is related to your targeted keywords, you can easily find out what you are missing (and also what is irrelevant).

You can use these insights to:

- Produce new and useful content that responds to the searcher's needs
- Use keywords in the existing pages in a way that will make them more effective
- Write effective meta descriptions and titles to improve the click-through rate
Spy on the Competition

Curious to know how your competitors are ranking higher? They are revealed by web scraping. 

Scraping responsibly can take you to interesting places. You can analyze rival websites to learn:

- How they organize their site and information
- What keywords they are using
- What content types and topics they use
- Which link building strategies are effective in your industry
- How they maximize their title tags and meta descriptions

Recap: Make SEO Affordable Again with Web Scraping

Cheap, cheap, cheap. That’s what comes to my mind when I think about it.

Have you seen Ahrefs’ subscriptions prices? And now they’re pretty limited as well.

No more squeezing the cheapest tier for Excel files to check later.

So if you're looking for cost effective SEO and the broad data-sets, this is for you

It can take a lot of work to set up and get used to it, so keep that in mind. 

Not for the super busy Type A, go-getter individuals.

You’ll need time, and patience. And maybe nerdiness.

So, let's wrap it up! With web scraping for SEO, you can obtain insights on what your competitors are cooking, identify long-tail keywords that may not be available on tools like SEMRush and examine websites without restrictions - think about huuuge spreadsheet files.

Start implementing it now and come back to let us know in the comments how it went.

https://proxycompass.com/web-scraping-for-seo-don-t-waste-money-on-expensive-tools/

10 Most Common Web Scraping Problems and Their Solutions

Web scraping is almost like a super-power, yet it has its own set of problems. 

If there are challenges affecting your data extraction process… Well, you're not alone. I’ve been there, and I know you too.

In this guide, we will explore the most frequent web scraping problems and how to solve them effectively. From HTML structure issues to anti-scraping measures, you will find out how to address these issues and improve your web scraping skills. 

What about you? Have you faced some challenges that we will explore in this article?

Feel free to share it in the comments!

Solving Web Scraping Challenges: Yes, There's Hope, Boys.

Web scraping is a process of extracting data from websites and it is a very useful technique (although you may already know this). However, it has several technical issues that may affect the quality of the data collected. 

Just like a miner looking for gold, you need some strategies that enable you to find your treasure. 

Continue reading to learn how to tackle challenges to improve your scraping technique.

Problem #1: HTML Structure Flaws and Lack of Data

Different HTML structures of the website pages can lead to failure of the scraper or the provision of incomplete data. It hinders the identification and retrieval of information in the right manner.

And with so many AI no-code tools about to turn every web designer into a big-brain-mega-chad out there, my guess would be that we are about to see more and more HTML incoherencies.

Solutions:

- Add error checking for the case where some elements are not present in the list.

- Employ loose selectors like XPath or regex.

- Create functions that you can use to work with different website structures.

Problem #2: Dynamic Content Loading

Most of the modern websites are built with the help of JavaScript, AJAX and Single Page Application (SPA) technologies to load the content without reloading the entire page. Did you know this is a problem for conventional scrapers?

Solutions:

- Employ headless browsers such as Puppeteer or Selenium to mimic user interactions with the website.

- Use waits to give time for the dynamic content to load.

- Poll or use WebSocket for real-time updates.

Problem #3: Anti-Scraping Measures

Websites try to control automated access through several ways, including IP blocking, rate limiting, user agent detection, and CAPTCHAs. These can greatly affect web scrapers, as I’m sure you have encountered some of them.

Solutions:

- Add some time intervals between the requests to make it look like a human is making the requests

- Use different IP addresses or proxies to prevent being blocked.

- Use user agent rotation to make the browser look like different ones

- Use CAPTCHA solving services or come up with ways of avoiding CAPTCHA.

Problem #4: Website Structure Changes

Website updates and redesigns change the HTML structure of the website and this affects the scrapers that depend on certain selectors to get data. 

Why don't they do it like me and update their sites once in a blue moon? Note to myself: improve this site more often, users will appreciate it, gotta keep the UX solid (come back later to check!).

Solutions:

- Select elements using data attributes or semantic tags as they are more reliable

- Conduct periodic checks to identify and respond to environmental shifts.

- Develop a system of tests that would help to identify the scraping failures.

- Propose to use machine learning to automatically adjust the selectors.

Problem #5: Scalability and Performance

Collecting a large amount of data from several websites is a slow and resource-consuming process that may cause performance issues. Not to mention things can get very tricky too. We know this too well, am I right?

Solutions:

- Use parallel scraping to divide workloads.

- Use rate limiting to prevent overloading of websites

- Refactor the code and use better data structures to enhance the speed of the code.

- Utilize caching and asynchronous programming

Problem #6: CAPTCHAs and Authentication

CAPTCHAs are a pain in the ass security measure that blocks bots and requires the user to complete a task that only a human can do. There are some tools to beat captchas, the auditory ones are especially easy nowadays, thanks to AI - yup, the AI listens to it and then writes the letters/words, piece of cake!

Here's a fun fact that's also a bit sad (very sad, actually): once I asked my developer what he did for the captchas, and he said there was an Indian guy solving them, I thought he was joking, but nope. Some services are using flesh to solve captchas. If that was my job, I'd go insane.

Solutions:

- Employ the services of CAPTCHA solving services or come up with own solving algorithms.

- Incorporate session management and cookie management for authentication

- Use headless browsers to handle authentication

Problem #7: Data Inconsistencies and Bias

Data collected from the web is often noisy and contains errors. This is because of the differences in the format, units, and granularity of the data across the websites. As a result, you get problems with data integration and analysis.

Solutions:

- Apply data validation and cleaning to standardize the data.

- Apply data type conversion and standardization.

- Recognize possible prejudice and use data from different sources.

Problem #8: Incomplete Data

Web scraped datasets are usually incomplete or contain some missing values. This is due to the changes that occur on the websites and the constraints of the scraping methods. So, having incomplete or missing data can affect your analysis.

That’s super annoying… I personally test something a dozen times, at least, to make sure I don’t have this type of error, that’s how much I hate it. You think everything is fine, until you open Excel or Gsheets, and realize you have to go back to the battle.

Solutions:

- Apply techniques of data imputation to predict missing values in the dataset.

- Use information from different sources to complete the missing information

- Reflect on the effects of missing data on the analysis

Problem #9: Data Preparation and Cleaning

Websites provide data in the form of text which is not organized and requires processing. It is necessary to format and clean the extracted data to use it for analysis. I know it’s the least fun part, but it needs to be done. 

If some of you guys know how to automate this part with machine learning or whatevs, please let me know! I waste so much time doing it manually like a dumbass on Excel.

Solutions:

- Develop data processing functions for formatting the data

- Use libraries such as Beautiful Soup for parsing

- Use regular expressions for pattern matching and text manipulation

- Apply data cleaning and transformation using pandas

Problem #10: Dealing with Different Types of Data

Websites display information in different formats such as HTML, JSON, XML, or even in some other specific formats. Scrapers have to manage these formats and extract the information properly.

Solutions:

- Add error control and data validation

- Utilize the right parsing libraries for each format.

- Create functions that you can use to parse the data in different formats.

Wrapping Up the Challenges in Web Scraping

Web scraping is a godsend and beautiful thing. But it can struggle with messy HTML structure, dynamic content, anti-scraping measures, and website changes, to name a few.

To improve the quality and efficiency of the scraped data, do the following:

- Use error checking
- Employ headless browsers
- Use different IP addresses
- Validate, check and clean your data
- Learn how to manage different formats
- Adopt the current and most recent tools, libraries, and practices in the field

Now it is your turn. Start following the advice we gave you and overcome the web scraping problems to be successful in your little deviant endeavors.

https://proxycompass.com/10-most-common-web-scraping-problems-and-their-solutions/

What is Web Scraping and How It Works?

Confused and want to know what in the world web scraping is and how it works?

Well you've come to the right place because we're about to lay down everything for you.

Before we dive in, I can already tell you the short version:

Web scraping is the process of extracting publicly available data from a website.

Join us to learn more about the specifics, how it works, and popular libraries that exist.

What is Web Scraping?

Basically web scraping is a procedure that allows you to extract a large volume of data from a website. For this it is necessary to make use of a "web scraper" like ParseHub or if you know how to code, use one of the many open source libraries out there.

After some time spent setting and tweaking it (stick to Python libraries or no-code tools if you're new here), your new toy will start exploring the website to locate the desired data and extract it. It will then be converted to a specific format like CSV, so you can then access, inspect and manage everything.

And how does the web scraper get the specific data of a product or a contact?

You may be wondering at this point...

Well, this is possible with a bit of html or css knowledge. You just have to right click on the page you want to scrape, select "Inspect element" and identify the ID or Class being used.

Another way is using XPath or regular expressions.

Not a coder? No worries!

Many web scraping tools offer a user-friendly interface where you can select the elements you want to scrape and specify the data you want to extract. Some of them even have built-in features that automate the process of identifying everything for you.

Continue reading, in the next section we'll talk about this in more detail.

How Does Web Scraping Work?

Suppose you have to gather data from a website, but typing it all in one by one will consume a lot of time. Well, that is where web scraping comes into the picture.

It is like having a little robot that can easily fetch the particular information you want from websites. Here's a breakdown of how this process typically works:

- Sending an HTTP request to the target website: This is the ground on which everything develops from. An HTTP request enables the web scraper to send a request to the server where the website in question is hosted. This occurs when one is typing a URL or clicking a link. The request consists of the details of the device and browser you are using.

- Parsing the HTML source code: The server sends back the HTML code of the web page consisting of the structure of the page and the content of the page including text, images, links, etc. The web scraper processes this using libraries such as BeautifulSoup if using Python or DOMParser if using JavaScript. This helps identify the required elements that contain the values of interest.

- Data Extraction: After the identified elements, the web scraper captures the required data. This involves moving through the HTML structure, choosing certain tags or attributes, and then getting the text or other data from those tags/attributes.

- Data Transformation: The extracted data might be in some format that is not preferred. This web data is cleaned and normalized and is then converted to a format such as a CSV file, JSON object, or a record in a database. This might mean erasing some of the characters that are not needed, changing the data type, or putting it in a tabular form.

- Data Storage: The data is cleaned and structured for future analysis or use before being stored. This can be achieved in several ways, for example, saving it into a file, into a database, or sending it to an API.

- Repeat for Multiple Pages: If you ask the scraper to gather data from multiple pages, it will repeat steps 1-5 for each page, navigating through links or using pagination. Some of them (not all!) can even handle dynamic content or JavaScript-rendered pages.

- Post-Processing (optional): When it's all done, you might need to do some filtering, cleaning or deduplication to be able to derive insights from the extracted information.

Applications of Web Scraping

Price monitoring and competitor analysis for e-commerce

If you have an ecommerce business, web scraping can be beneficial for you in this scenario.

That's right.

With the help of this tool you can monitor prices on an ongoing basis, and keep track of product availability and promotions offered by competitors. You can also take advantage of the data extracted with web scraping to track trends, and discover new market opportunities.

Lead generation and sales intelligence

Are you looking to build a list of potential customers but sigh deeply at the thought of the time it will take you to do this task? You can let web scraping do this for you quickly.

You just have to program this tool to scan a lot of websites and extract all the data that is of interest to your customer list such as contact information and company details. So with web scraping you can get a large volume of data to analyze, define better your sales goals and get those leads that you want so much.

Real estate listings and market research

Real estate is another scenario where the virtues of web scraping are leveraged. With this tool it is possible to explore a vast amount of real estate related websites to generate a list of properties.

This data can then be used to track market trends (study buyer preferences) and recognize which properties are undervalued. Analysis of this data can also be decisive in investment and development decisions within the sector.

Social media sentiment analysis

If you are looking to understand the sentiment of consumers towards certain brands, products or simply see what are the trends in a specific sector within social networks, the best way to do all this is with web scraping.

To achieve this put your scraper into action to collect posts, comments and reviews. The data extracted from social networks can be used along with NLP or AI to prepare marketing strategies and check a brand's reputation.

Academic and scientific research

Undoubtedly, economics, sociology and computer science are the sectors that benefit the most from web scraping.

As a researcher in any of these fields you can use the data obtained with this tool to study them or make bibliographical reviews. You can also generate large-scale datasets to create statistical models and projects focused on machine learning.

Top Web Scraping Tools and Libraries

Python

If you decide to do web scraping projects, you can't go wrong with Python!

- BeautifulSoup: this library is in charge of parsing HTML and XML documents, being also compatible with different parsers.
- Scrapy: a powerful and fast web scraping framework. For data extraction it has a high level API.
- Selenium: this tool is capable of handling websites that have a considerable JavaScript load in their source code. It can also be used for scraping dynamic content.
- Requests: through this library you can make HTTP requests in a simple and elegant interface.
- Urllib: Opens and reads URLs. Like Requests, it has an interface but with a lower level so you can only use it for basic web scraping tasks.
JavaScript

JavaScript is a very good second contender for web scraping, especially with Playwright.

- Puppeteer: thanks to this Node.js library equipped with a high-level API you can have the opportunity to manage a headless version of the Chrome or Chromium browser for web scraping.

- Cheerio: similar to jQuery, this library lets you parse and manipulate HTML. To do so, it has a syntax that is easy to get familiar with.

- Axios: this popular library gives you a simple API to perform HTTP requests. It can also be used as an alternative to the HTTP module built into Node.js.

- Playwright: Similar to Puppeteer, it's a Node.js library but newer and better. It was developed by Microsoft, and unlike Windows 11 or the Edge Browser, it doesn't suck! Offers features like cross-browser compatibility and auto-waiting.
Ruby

I have never touched a single line of Ruby code in my life, but while researching for this post, I saw some users on Reddit swear it's better than Python for scraping. Don't ask me why.

- Mechanize: besides extracting data, this Ruby library can be programmed to fill out forms and click on links. It can also be used for JavaScript page management and authentication.

- Nokogiri: a library capable of processing HTML and XML source code. It supports XPath and CSS selectors.

- HTTParty: has an intuitive interface that will make it easier for you to make HTTP requests to the server, so it can be used as a base for web scraping projects.

- Kimurai: It builds on Mechanize and Nokogiri. It has a better structure and handles tasks such as crawling multiple pages, managing cookies, and handling JavaScript.

- Wombat: A Ruby gem specifically designed for web scraping. It provides a DSL (Domain Specific Language) that makes it easier to define scraping rules.
PHP

Just listing it for the sake of having a complete article, but don’t use PHP for scraping.

- Goutte: designed on Symfony's BrowserKit and DomCrawler components. This library has an API that you can use to browse websites, click links and collect data.

- Simple HTML DOM Parser: parsing HTML and XML documents is possible with this library. Thanks to its jQuery-like syntax, it can be used to manipulate the DOM.

- Guzzle: its high-level API allows you to make HTTP requests and manage the different responses you can get back.
Java

What are the libraries that Java makes available for web scraping? Let's see:

- JSoup: analyzing and extracting elements from a web page will not be a problem with this library, which has a simple API to help you accomplish this mission.

- Selenium: allows you to manage websites with a high load of JavaScript in its source code, so you can extract all the data in this format that are of interest to you.

- Apache HttpClient: use the low-level API provided by this library to make HTTP requests.

- HtmlUnit: This library simulates a web browser without a graphical interface (aka it's headless), and allows you to interact with websites programmatically. Specially useful for JavaScript-heavy sites and to mimic user actions like clicking buttons or filling forms.

Final Thoughts on This Whole Web Scraping Thing

I hope it's clear now: web scraping is very powerful in the right hands!

Now that you know what it is, and the basics of how it works, it's time to learn how to implement it in your workflow, there are multiple ways a business could benefit from it.

Programming languages like Python, JavaScript and Ruby are the undisputed kings of web scraping. You could use PHP for it... But why? Just why!?

Seriously, don't use PHP for web-scraping, let it be on WordPress and Magento.

https://proxycompass.com/what-is-web-scraping-and-how-it-works/

Web Scraping Best Practices: Good Etiquette and Some Tricks

In this post, we'll discuss the web scraping best practices, and since I believe many of you are thinking about it, I'll address the elephant in the room right away. Is it legal? Most likely yes.

Scraping sites is generally legal, but within certain reasonable grounds (just keep reading).

Also depends on your geographical location, and since I'm not a genie, I don't know where you're at, so I can't say for sure. Check your local laws, and don't come complaining if we give some "bad advice," haha. 

Jokes apart, in most places it's okay; just don't be an a$$hole about it, and stay away from copyrighted material, personal data, and things behind a login screen.

We recommend following these web scraping best practices: 

1. Respect robots.txt

Do you want to know the secret for scraping websites peacefully? Just respect the website's robots.txt file. This file, located at the root of a website, specifies which pages are allowed to be scraped by bots and which ones are off-limits. Following robots.txt is also important as it can result in the blocking of your IP or legal consequences depending on where you’re at.

2. Set a reasonable crawl rate

To avoid overloading, freezing or crashing of the website servers, control the rate of your requests and incorporate time intervals. In much simpler words, go easy with the crawl rate. To achieve this, you can use Scrapy or Selenium and include delays in the requests.

3. Rotate user agents and IP addresses

Websites are able to identify and block scraping bots by using the user agent string or the IP address. Change the user agents and IP addresses occasionally and use a set of real browsers. Use the user agent string and mention yourself in it to some extent. Your goal is to become undetectable, so make sure to do it right.

4. Avoid scraping behind login pages

Let's just say that scraping stuff behind a login is generally wrong. Right? Okay? I know many of you will skip that section, but anyway… Try to limit the scraping to public data, and if you need to scrape behind a login, maybe ask for permission. I don't know, leave a comment on how you'd go about this. Do you scrape things behind a login?

5. Parse and clean extracted data

The data that is scraped is often unprocessed and can contain irrelevant or even unstructured information. Before the analysis, it is required to preprocess the data and clean it up with the use of regex, XPath, or CSS selectors. Do it by eliminating the redundancy, correcting the errors and handling the missing data. Take time to clean it as you need quality to avoid headaches.

6. Handle dynamic content

Most of the websites use JavaScript to generate the content of the page, and this is a problem for traditional scraping techniques. To get and scrape the data that is loaded dynamically, one can use headless browsers like Puppeteer or tools like Selenium. Focus only on the aspects that are of interest to enhance the efficiency.

7. Implement robust error handling

It is necessary to correct errors to prevent program failures caused by network issues, rate limiting, or changes in the website structure. Retry the failed requests, obey the rate limits and, if the structure of the HTML has changed, then change the parsing. Record the mistakes and follow the activities to identify the issues and how you can solve them.

8. Respect website terms of service

Before scraping a website, it is advised to go through the terms of service of the website. Some of them either do not permit scraping or have some rules and regulations to follow. If terms are ambiguous, one should contact the owner of the website to get more information.

9. Consider legal implications

Make sure that you are allowed to scrape and use the data legally, including copyright and privacy matters. It is prohibited to scrape any copyrighted material or any personal information of other people. If your business is affected by data protection laws like GDPR, then ensure that you adhere to them. 

10. Explore alternative data collection methods

It is recommended to look for other sources of the data before scraping it. There are many websites that provide APIs or datasets that can be downloaded and this is much more convenient and efficient than scraping. So, check if there are any shortcuts before taking the long road.

11. Implement data quality assurance and monitoring

Identify ways in which you can improve the quality of the scraped data. Check the scraper and the quality of the data on a daily basis to identify any abnormalities. Implement automated monitoring and quality checks to identify and avoid issues.

12. Adopt a formal data collection policy

To make sure that you are doing it right and legally, set up a data collection policy. Include in it the rules, recommendations, and legal aspects that your team should be aware of. It rules out the risk of data misuse and ensures that everyone is aware of the rules.

13. Stay informed and adapt to changes

Web scraping is an active field that is characterized by the emergence of new technologies, legal issues, and websites that are being continuously updated. Make sure that you adopt the culture of learning and flexibility so that you are on the right track. 

Wrapping it up!

If you're going to play with some of the beautiful toys at our disposal (do yourself a favor and look up some Python libraries), then… well, please have some good manners, and also be smart about it if you chose to ignore the first advice. 

Here are some of the best practices we talked about:

- Respect robots.txt
- Control crawl rate
- Rotate your identity
- Avoid private areas
- Clean and parse data
- Handle errors efficiently
- Be good, obey the rules

As data becomes increasingly valuable, web scrapers will face the choice: 

Respect the robots.txt file, yay or nay? It's up to you.

Comment below, what are your takes on that?

https://proxycompass.com/web-scraping-best-practices-good-etiquette-and-some-tricks/

How to Monitor Competitor Prices: Data-Driven Strategies to Boost Revenue

Are you struggling to match your competitors' prices in the ever-evolving environment you’re in?

Competitor price tracking is essential to remain competitive, but it is a rather tedious process.

Here, based on scientific research, you will find out how to monitor competitor’s prices like the pros do, and how to use this information to set the right pricing strategy at all times.

We will discuss the findings of a highly cited 2017 research on competition-based dynamic pricing to help you increase your revenue without having to cut your profit margins.

Read on to find out the tips that can help you get it right with competitor price monitoring.

Why Competitor Price Tracking Should be Automated

Online retail pricing transparency enables customers to easily compare prices. 

This results in keen competition because the retailers are always seeking to outdo each other.

It is a big mistake not to pay attention to the competitors, as one may end up out of business.

The so-called "competition-based dynamic pricing" is used by advanced retailers: the prices are set in response to the competitors' prices, which are monitored 24/7. Happens because with so many competitors and products, it’s impossible to do it manually even with an army of VAs.

Advanced retailers need to gather competitor data and feed it into their dynamic pricing models. Real-time market information helps the firm change prices in order to get the most revenue.

The researchers from that study we mentioned, were able to raise revenue by 11% of their test company, while at the same time maintaining margins; which is not something easy to do!

It is not enough to be a fast follower; one has to be a pioneer with data and technology.

So go ahead and track prices automatically, use our proxies to avoid getting blocked while scraping, set dynamic pricing, and out compete your competitors. No time to lose!

A Step-by-Step Guide to Track Competitors' Prices

Step 1: Define your Market Positioning

First, it is necessary to determine the positioning of the brand in the market: is it a luxury brand, an affordable option, or something in between? The “Competition-Based Dynamic Pricing in Online Retailing” study indicates that this positioning determines your pricing strategy and entails the evaluation of customer tastes and decision-making patterns. It also reveals that the value offered by a retailer in terms of quality, service or features influences consumers’ decision.

Step 2: Study the competition like a maniac

Determine your direct competitors – similar businesses offering similar products/services and targeting the same audience. Consider their pricing strategies like dynamic pricing, coupons, and loyalty programs. However, not all competitors equally influence consumer decision-making. Focus on rivals with a large market share whose prices significantly impact your demand.

Step 3: Identify the Best Competitor and Products to Track

Choose competitors and products to track based on market share, product-relatedness, and competitive effects. The research studied baby-feeding bottles because product features are tangible and measurable for comparing performance. Tools to identify competitor websites and products include web scraping, price comparison sites, and market research.

Step 4: Get Real-Time Pricing Information

Tools to track and compare competitor prices in real-time include web scraping, APIs, or price monitoring services. The study shows that flexible online retail pricing and timely information help firms set the right prices and respond to market changes.

Step 5: Test The Pricing Data For Your Products

Cross-check the collected prices with other sources and conduct controlled trials. The study shows that using a randomized price experiment with a proper design can validate pricing data findings. This enabled obtaining actual price elasticity measures.

Step 6: Identify Problems And Adjust Strategy

Price monitoring challenges include wrong data, delayed updates, and misinterpreting competitor strategies. Ensure proper validation methods and correct monitoring techniques, and adjust your approach as needed.

Step 7: Make Price Monitoring Part of Daily Operations

Treat price monitoring as a core business process assigned to specific employees. The study's successful real-business collaboration shows price monitoring should be part of daily activities. Implement it through dashboards and tools that provide the right, up-to-date data.

Benefits of Monitoring Your Competitors' Prices

1. Measure price elasticities accurately to optimize margins

This paper also describes how a randomized price test can be used to estimate price elasticities without the bias of past sales data. You will be able to find out the price levels that will enable you to reach the desired margins on revenues for each product, taking into consideration its price elasticity of demand.

2. Respond to competitor price changes based on consumer behavior

Another factor that affects the most suitable price response strategy is the level of cross-shopping by consumers. Tools that can give alerts of competitors' prices to enable you to match their prices or even beat them can speed up the process.

3. Differentiate responses based on competitor significance

Industry rivals are not the same in the level of interference with consumers’ decision making process. With the help of AI, web scraping and data mining, one can track competitor prices on a regular basis, study customers’ behavior, identify competitors’ strategies and vulnerabilities, as well as determine the proper price level for each product to increase the total revenue.

4. Incorporate competitor stock-outs into pricing decisions

Besides prices, monitoring stock-outs of competitors give valuable information on the consumers’ trends and their response to price changes. Own and competitor stock-outs are the most important source of variation used in the study to estimate its demand model. Competitors’ stock-out also reveals chances of capturing clients’ attention when rivals are out of the game.

5. Automate pricing decisions with a data-driven algorithm across channels

Compare collected prices with those from other sources and conduct controlled experiments. It is also possible to confirm pricing data conclusions with a randomized price experiment with an appropriate design, the study reveals. This made it possible to obtain actual price elasticity values.

6. Enhance competitive insights with frequent price experiments

Price reactions are more significant in the context of online retailing because consumers’ search costs and menu costs for retailers are lower than in other types of stores. According to the study, it is suggested that price experiments should be conducted on a regular basis to re-estimate the models and to adapt to the changing market conditions. During such days as Black Friday, Cyber Monday, and the like, brands go head to head in a pricing war. Monitoring tools give information on competitors’ activities and enable creation of automation rules to change prices round the clock, even when you are not physically present to do so.

7. Make a Price Monitoring Routine

It is necessary to state that price monitoring should be viewed as one of the most important business processes that can be carried out. The study we talked about so much emphasizes that pricing monitoring should be incorporated into the daily tasks of a business. It can be done easier with the help of dashboards, tools and some scraping knowledge. 

Referenced article: Fisher, M., Gallino, S., & Li, J. (2017). Competition-Based Dynamic Pricing in Online Retailing: A Methodology Validated with Field Experiments. Product Innovation eJournal. https://doi.org/10.2139/ssrn.2547793.

https://proxycompass.com/how-to-monitor-competitor-prices-data-driven-strategies-to-boost-revenue/