Access to employee details can help you pinpoint decision-makers & influencers in a company. Moreover, the employee list can help you with market research, enhance recruitment strategies, or conduct competitive analysis.
The easiest way to do so is to get on LinkedIn, type in the company name & bingo you have all the employees.
However, what if you need to extract employees from a list of companies?
Either you can have a resource dedicated to managing this activity, but then again, chances are the copy/paste method they use can produce errors.
For the above reason, and many more, you need a system that can help you with employee extraction if this is something you do regularly.
In this blog, we will understand how you can extract employees of a particular company and then enrich them with more details.
We will be using the power of Google search operator & Scrapingdog’s Google Search API & LinkedIn Profile API.
In the end, I will give you a blueprint too, for this automation.
How Our Automation Works
What will you need for this automation?
Scrapingdog’s API_KEY (1000 Free Credits on first sign up)
Make Account (1000 Operations are Free)
Since we use Google Search Operator, we can get a maximum of 100 employees per company.
The site operator we will be using is:
1site:linkedin.com/in intitle:"Company Name" ("employee" OR "at")Make sure the ‘Company name’ you use is the same as mentioned on LinkedIn.
For the sake of this tutorial, I am using the company name “PharmSight Research and Analytics” & extracting all the employees of it.
Back to our scenario, the first module we are using is an HTTP module.
In the URL, we have used the Google search APIs endpoint, you can read more about how to use the Google search API in the doc here.
All other inputs are necessary inputs, API_KEY, query, and results.
Let’s test this module only, we can see that we got 28 results i.e. 28 employees from the company.
Now all of these results are in an array, we need to iterate over this & therefore we will use an iterator module.
In the iterator, we will map this particular array:
After the Iterator module, I am using the OpenAI module, from here I will extract relevant data needed from the search result.
Here is the user prompt I am giving to it:
& here is the system prompt:
I am using the Openrouter module, this has access to all LLM models including the openAI-4o. (which we are using here). In the advanced setting of this module, we will enable JSON mode.
The output from this module is in the JSON format:
Since this data is in the JSON format, we would need a JSON parser after this, and therefore we will be using the Parse JSON module after it.
Mapping the module to the right JSON string we got.
Next, we will be using the HTTP request, here we will use Scrapingdog’s LinkedIn scraper API.
For all the input parameters read on the LinkedIn Scraper API docs here.
From the output of this module & the HTTP module used earlier, we will now paste this data in Google Sheets.
And further, the data you will get will be in the format below: -
You can extract more data from our LinkedIn scraper API, but for the sake of this tutorial, I have kept it simple.
Here is the ➡ ️blueprint for this automation.
