Python Web Scraping in Business: Businesses worldwide always strive to increase their revenue and profit margins. In some cases, they even aim to become market leaders in their industries. To achieve these objectives, they need to have one or several competitive advantages. There are multiple competitive advantages: customer service, distribution network, branding, product quality, pricing, and intellectual property portfolio.
Web scraping helps businesses gain and maintain one or several competitive advantages. And in this article, we explore how Python web scraping makes this possible, eventually listing the top 5 use cases.
Python Web Scraping
Whether manually undertaken or automated, web scraping entails retrieving data from the world wide web. Manual web data extraction entails copying text or numbers from a website and subsequently pasting it elsewhere. However, the steps involved are far too many, especially if you intend to collect data from multiple sites. This is where automated tools come in.
There are two main forms of automated web scraping tools, namely pre-made solutions in the form of software and ‘DIY’/tailor-made tools, that are created whenever there is a demand. The first type offers sophisticated services and offerings, which, though useful, are not tailored to specific applications.
Python web scraping is the practice of using web scrapers developed using the Python programming language to extract data from the world wide web. Python boasts numerous unique advantages that appeal to both beginners and seasoned programmers. These include its ease of use, an extensive collection of pre-written script modules known as libraries, easy-to-understand code, and flexibility. Click here to read a step-by-step Python tutorial.
Python Web Scraping Libraries
More crucially, Python web scraping libraries help improve the efficiency of the code. Examples of these libraries include:
- Selenium
- Requests
- Beautiful Soup
- lxml
- Spatula (new library)
Ideally, a code should be efficient, meaning it should use up minimal resources – computer hardware – and be executable in the least possible time. Further, the code should be reliable.
Python libraries comprise pre-written, optimized, and tested code. Thus, in addition to relieving you of the need to write everything from scratch, thereby saving time, the existing code is extremely efficient by virtue of having been tested and optimized. In this regard, Python web scraping promotes efficient data collection. That said, what are the use cases of Python web scraping?
Top 5 Use Cases of Python Web Scraping
As detailed earlier, data, when collected, analyzed, and used appropriately, offers businesses unmatched competitive advantages. The existence of several classes of competitive advantages means that there are several use cases of Python web scraping. These include:
- Lead generation
- Price comparison and competition monitoring
- Product intelligence
- Market research
- Reputation and news monitoring
Lead Generation
You can ethically collect email addresses, phone numbers, and other forms of contact information from relevant websites through Python web scraping. Being automated, the tools ensure that the data collection exercise is fast and efficient. You can utilize this data to reach out to potential customers through email marketing.
Price Comparison and Competition Monitoring
Price undercutting is a common phenomenon in the world of business. It involves pricing a product much lower than competitors in order to attract customers. In this regard, you can use Python web scraping to identify competitors who may be undercutting your business.
At the same time, you can rely on Python web data extraction tools to compare your prices with the average market price in your industry. This ensures that you come up with an informed pricing strategy.
Product Intelligence
Product intelligence involves the automated collection of product data and subsequent analysis to generate insights that are then transmitted to the design and manufacturing team to make improvements. Given that Python web scraping is an automated process, it can be used to collect product data.
Market Research
Market research is instrumental in ensuring businesses’ survival in new locations. It offers information on the viability and feasibility of a product or service. The information also helps companies come up with tailored marketing campaigns as well as products that meet the needs that had been unearthed through the research.
There are different ways through which businesses can undertake market research. These include surveys, interviews, and Python web scraping.
Reputation and News Monitoring
A business’s reputation is central to its success. You can use web scraping to collect data about your company’s products and services from social media posts, review sections in e-commerce sites, and news articles. Simply, web data harvesting helps you establish what other parties write about the business, some of which may be injurious. Then, armed with the data, you can initiate campaigns to protect your reputation.
Conclusion
Python web scraping offers an efficient and fast way of collecting data from websites. Thanks to this capability, it is increasingly used for reputation monitoring, lead generation, product comparison and competitor monitoring, product intelligence, and market research. These are the top 5 use cases of Python web scraping in business.
