Web scraping will scrape email addresses from random websites and also the ones that are already exhausted. It makes no sense to generate such irrelevant and less targeted leads. As a fact, publicly available emails are not checked often or are abandoned and your mail to such email id’s is likely to land up in spam folder. 

Note: Though, web scraping is possible for lead generation; the practice is not recommended.

The most popular search engine, Google can crawl only the surface web, which is a significantly smaller portion of the web. No software or bot can crawl and extract data from the entire web. Therefore, when taking up a web scraping project, it is recommended to define a set of web sources or websites that are significant and relevant to your project.

Each web scraping project has different requirements. The number of websites to be scraped, nature and code of the website are different for different projects. DIY scraping tools are made for small use cases of data extraction. There cannot be a universal web scraping tool. DIY tools have their own limitations and should be used for smaller use cases only and not for complex coded websites.

Top 10 tools for web scraping according to Guru99 are:

• Scraper API
• Octoparse
• Import.io
• Webhose.io
• Dexi intelligent
• Scraping hub
• Outwit
• PareseHub
• Diffbot
• Visual Scraper

 Outbound lead generation is one of the most promising tools in the marketing of sales. Early stages of startup Enterprise especially have a lot of reasons to embrace outbound sales. To scrape leads for Enterprise and B2B Outbound marketing the following types of tools can be used:

a. Client-based tools: These are basically chrome-based plugins that scrape what is displayed. Example-Data Miner, Scraper

b. Cloud-based tools: These are SaaS solutions to automate scraping at a higher extent. Example-Grepsr, import.io, dexi.io

c. Enrichment tools: Scapp, hunter, Clearbit

You require a functional account on the target website to scrape data behind the login. Once you log in, crawling works similar to that of a normal crawl. You can optimize the workflow by saving cookies in the task after login. The data that is available only to the registered users of a website might have some different terms and conditions and you will have to follow them too while scraping.

 A website might block you if you are scraping extensively. To save yourself from “denied access”, make your scraping look human-like and not bot-like. Adding a delay between requests can help you do this. Also, using proxy servers or using different patterns can save prevent blocking.

Nowadays there are a lot of CAPTCHA solvers that can be integrated with the scraping system. It used to be a nightmare when there were no solvers. With advanced scraping, it is possible to capture the image or text and solve the same.

Robots.txt file in a website tells crawlers or bots if a website can be scraped or how can it be scrapped. It is critical to understand the robots.txt file to prevent being blocked while web scraping. If a website is allowing you to fully crawl all the pages, then you will find this in the robots.txt file of the website:

User-agent: *

Disallow:

If a website is not allowing you to crawl and scrape data, the robot.txt file will have this:

You should stay away from such websites because scraping them could lead to legal troubles. The owner could also block you for this activity.

Python, a high-level programming language is most popularly used for web scraping and has design philosophy which emphasizes code readability. It can handle most of the web scraping requirements smoothly. Beautiful Soup is a framework in Python and is most widely used for scraping as it is a robust system to extract data from the most complicated websites.

Beautiful Soup is a Python library to get data from HTML and XML files or websites.  Beautiful Soup helps you pull particular content from a webpage, remove the HTML markup, and save the information. It is a tool for web scraping that helps you clean up and parse the documents you have pulled down from the web (https://programminghistorian.org/en/lessons/intro-to-beautiful-soup). It engages with your parser to create idiomatic ways of navigating, searching, modifying and processing the parse trees. It saves time and resources by making this process smooth for programmers.

Data from dynamic websites can be extracted by allowing the scraper to access a website at a particular frequency to mine the updated data frequently.

Dynamic websites update data frequently and hence the bots have to be quick enough to prevent missing any updated data. 

Other market sectors like Digital Marketers, Programmers and Developers and Data Scientists also use Web scraping frequently to perform SEO audits, extract data from competitor sites, look into hidden data etc. There are questions and challenges that these sectors experience too. We mention them briefly here:

Digital marketing beginners are often looking for answers to “Can you Crawl Facebook, Twitter, LinkedIn? What is Crawling in SEO?”etc 

Facebook and Twitter block automated web crawlers through their robots.txt file. LinkedIn allows gathering data only as much allowed by the user who has uploaded the data. It is unethical and unsafe to extract data from such websites. Though it is useful in market research, one should be careful in doing so.

Try to check also this web scraping guide and Web scraping for business for more learnings.