Firstly, let’s clarify. Web scraping is not the end-all-be-all. It is, however, a starting point for further value-adding applications such as price sensitivity or sentiment analysis, leads generation for a company looking to scale its sales process, etc. These feats can only be accomplished with the support of large enough data sets.
However, not everyone needs that large data set, and that’s okay.
Take, for instance, LinkedIn scraping. Of course, you could extract whatever profile you need manually.
The DIY Approach
As aptly put by one Redditor, a bit of ‘Google Kung-Fu’ can truly work wonders. For instance, to manually build a call list of CompanyX’s employees in analytics-related roles, one could input a Google query as follows:
site:linkedin.com +"know in common" intitle:"- CompanyX"+"Greater New York City Area" intitle:analyst OR intitle:data OR intitle:information
In fact, there are even free tools out there such as Recruitment Geek’s Linkedin Xray that can isolate profiles according to whatever you desire: title, experience, skills, country of residence, etc. Simply plug and play.
On Repetition & Scale
But what if you needed to do 1000 times? Or 10,000 times? You’d be brain dead before going through the same process for the 2549th time.
The key differentiator here is scale.
So, if you need a live, scaled data stream of people, price or product data etc, then web scraping is more than likely for you.
And you’re in the right place!
Request for a trial API key here, and good luck!