Monitor Your Competitors

The Information Age has created a new basis for power and control: data. Those who know how to access and analyze data most efficiently are the most competitive. So, therefore, acquiring and processing information about monitor your competitors is a wise move for any business.

Enter web scraping. Columbia University’s Mailman School of Public Health defines web scraping in this way: “a computer software technique that allows you to extract information from websites.” The process humans would take to manually copy and paste every bit of data on a website is instead automated in web scraping; the code knows how to read HTML.

The “how” involved in web scraping is a bit more complicated and covered below, but hopefully the “why” is becoming clear. Valuable data is everywhere on the internet—why not take advantage of it in the interest of gaining a competitive edge? 

How to Scrape Competitor’s Websites- monitor your competitors

Some familiarity with writing code is required to create a web scraping tool, as one might expect. But learning a language such as Python (the most popular choice of language for web scraping) is itself an invaluable skill today.

There are plenty of courses and tutorials available online, both on Python generally and on Python web scraping more specifically. See, for example, this tutorial on how to use Python for web scraping.

What exactly the web scraping tool does is dependent on context, but in essence, it will need to 1) visit a website, 2) read the HTML code, and 3) retrieve the relevant data and present it conveniently for human eyes.

Visiting a Website

This is a relatively simple component of the process. It can consist of just hardcoding the desired URL into the Python web scraping tool’s code. That’s all that’s necessary for web scraping in general, but there are certainly ways to automate the process, or giant search engines such as Google that make use of web scraping on an enormous scale would be very limited.

Reading the HTML code

Parsing the HTML code can get quite a bit more complicated. The Python web scraping tutorial mentioned above scrapes Reddit for posts matching a search term, so it needs to first find the search box on the Reddit page, then “type” a search term, hit enter, and pull the results based on the HTML encoding.  

This is just one example that doesn’t do anything extraordinarily complex. In any case, the programmer needs to know exactly what to expect on the given webpage. It’s important to note that programming languages that are convenient for web scraping, like Python or R, already have pre-made libraries to make this process significantly easier. More on this below.

Retrieving and Presenting Data

This component of web scraping can refer to a part of the code that spits out a table, or it may involve a significant amount of data analysis that will then produce graphs and the like. There is much to say on the topic of data science, but the piece essential to web scraping is that the tool must appropriately convert the raw data according to the context.

Why Use Python?

For one, Python currently ranks as most popular overall in TIOBE’s index of programming languages, which mines its data from several search engines (an example of web scraping). It has a list of popular frameworks like Django. Any Django development agency will confirm that python and its framework is often a wise choice for web app development. Popularity is an important factor in the decision because code that is more readily understood is easier to revise in the future.

But Python is also especially useful for anything data science-related. Using Python is recommended over R, for instance, because it has so many libraries and frameworks specifically made for handling a lot of data. 

Some popular tools for Python web scraping are Selenium, Scrapy, and BeautifulSoup. Selenium and Scrapy are Python frameworks, and BeautifulSoup is a Python library; frameworks contain less complex code. Any of them are highly useful regardless.

Legal Concerns with Web Scraping

A word of caution: web scraping falls into a legal gray area in some cases. This is because it consumes a site’s bandwidth and could potentially be accessing data considered private. Columbia University has this advice regarding ethical web scraping: respect the law, the website’s bandwidth, and any terms and conditions of the site.

This means that using Python web scraping to monitor competitors carries a certain degree of risk that is not to be overlooked. So-called “bad bots” use web scraping for nefarious purposes, like clogging access to Covid-19 vaccine appointments or scalping basic necessities.

That said, web scraping in itself is legal. Carefully and respectfully implemented web scraping can be more “necessary” than “necessary evil” in today’s day and age.

A Final Word

While using Python for web scraping can present some ethical challenges, it is ultimately just a tool that is neither good nor bad for monitor your competitors. It can be legally and responsibly used, and arguably should be used by those looking to get a leg up on the competition.

Implementing a web scraping tool takes some coding know-how but has been made significantly more doable by open-source libraries and frameworks as well as online tutorials on how to use them. The information it yields could be invaluable for a business or webpage.