Accessing LinkedIn data has become crucial for diverse purposes from lead generation and competitor analysis to talent sourcing.
While copying information from LinkedIn and pasting it into a spreadsheet can be laborious and menial, tools like Sapiengraph can help streamline the data extraction process, saving valuable time and effort.
Now, all it takes to get phone numbers, email addresses, locations, jobs, and employment information are the right custom formulas and a few clicks.
In this guide, we'll walk you through the steps to scrape LinkedIn data effortlessly using Google Sheets, enabling you to cut down hours of manual copy-pasting and extract valuable insights easily and efficiently.
How to install Sapiengraph
To get LinkedIn data automatically into your Google Sheets, you will first have to download and install Sapiengraph, our Google Sheets add-on that allows you to use custom formulas to scrape public profile data from LinkedIn.
Start by setting up a new account on Sapiengraph’s website and follow the installation prompts or check out our Getting Started Guide.
Once you’re done installing both the Sapiengraph browser extension and the Sheets add-on as prompted, open the sheet with all the information that you want to enrich with data from LinkedIn.
Next, you’ll need to launch Sapiengraph. To do that, click Extensions, hover over Sapiengraph in the drop-down menu, and then click Launch Sapiengraph.
Now you’re all ready to begin using Sapiengraph’s custom Google Sheets formulas to scrape LinkedIn data. Read on to find out how.
Formulas to enrich personal data
First, let's look at the formulas you can use to enrich a person’s profile.
For demonstration’s sake, I’ll be using a spreadsheet I created after meeting a bunch of good connections at a networking event last week.
Say I want to find out more about Bill, who struck me as an important connection to keep when I met him last week. I’d want to know as much about him as possible so that I’m well-equipped with whatever information I need the next time I reach out to him.
To do that, I’ll be using the “=SG_PERSON(PROFILE_URL, PERSON_ATTRIBUTE)” formula, with ”PERSON_ATTRIBUTE” being whatever data I want to enrich.
For instance, using “=SG_PERSON(“https://www.linkedin.com/in/williamhgates”, “occupation”)” will return with Bill’s current job title and employment.
But you’re not always going to know someone’s LinkedIn URL - especially when you’re trying to find details about someone who has a common name. In such cases, it would be helpful to focus on what unique information you have on the person.
For example, I remember meeting an “Andy” from Amazon at the networking event, but aside from his first name and the company he works for, I don’t have any other information on him. To find his LinkedIn profile and learn more about Andy, I just have to use “=SG_LOOKUP_PERSON(“Andy”, “Amazon”)”.
I also recall being introduced to the founder and chief technology officer of HubSpot, but I didn’t catch his name. To find out more about this encounter and ensure I don’t lose a potentially important connection, I can use “=SG_LOOKUP_ROLE(“CTO”, “HubSpot”)”, which will return with the appropriate LinkedIn profile.
Formulas to enrich contact information data
Aside from names and current employment, you can also find someone’s contact information with Sapiengraph, which could be particularly helpful for salespeople or business development representatives trying to amp up the number of cold emails or calls they need to make.
Take a look, for example, at my lead tracker sheet. I’ve found a bunch of ideal customers, but can’t seem to find all their contact information.
If I’ve been trying to reach Neha Parikh about an opportunity with Carvana, the company she’s a board member of, but haven’t heard back from her, I might be inclined to drop her a call. To find her number, I just need to use “=SG_PERSONAL_NUMBERS(“https://www.linkedin.com/in/nehaparikh/”)”.
Separately, I’ve been trying to dial John Marty, but have never gotten him to pick up my call. If I need to find his email address, I can use “=SG_PERSONAL_EMAILS(“https://www.linkedin.com/in/johnrmarty/”)”.
But perhaps, for some reason, you have someone’s email address, and literally nothing else on them - no idea what their name is, no information on who they work for, not even their job title, but you’ve been told they could be a prospect.
In my case, I’ve been given the email address “[email protected]” and have no idea who it belongs to. All I have to do to find the LinkedIn account connected to this email - and thus find out more about this person - is use “=SG_REVERSE_EMAIL_LOOKUP(“[email protected]”)” and Sapiengraph will return with the associated LinkedIn profile.
Formulas to enrich company data
Just like how you can enrich a person’s profile with Sapiengraph, you can enrich your Google Sheets with a lot of information on companies.
To show you what I mean, let’s take a look at my company tracker spreadsheet.
I’ve been keeping tabs on a few companies that I might be interested in investing in or partnering with. Before embarking on any such endeavor, I’d want to make sure that the company is worth my resources - be it financial or otherwise.
With the “=SG_COMPANY()” function, I can find almost anything I need to know about a company, from the number of employees it has in its headcount to the year it was founded - all I need is the company’s LinkedIn profile URL.
For example, if I were interested to know what industry Apple operates in to see if there might be synergy for partnership, I’d use “=SG_COMPANY(“https://www.linkedin.com/company/apple”, “industry”)”, which should give me just that.
On the other hand, if I wanted to find a list of a certain type of company - say 10 companies in the US with between US$1 million to US$2 million in total funding, operating in the education industry, I could use “=SG_COMPANY_SEARCH(“US”, 10, , , , ,1000000, 2000000, , , , , , “education”)”.
With that, Sapiengraph will generate a list of the LinkedIn profile URLs of companies that match those parameters.
I could even use formulas to generate a list of the employees of a target company if I were feeling a bit naughty and thinking of poaching some top talent. For instance, using “=SG_EMPLOYEES(“https://www.linkedin.com/company/apple/”, 100, “software engineer”, “current”)” would give me a list of the LinkedIn profile URLs of 100 software engineers currently working at Apple.
Formulas to enrich job information data
On top of all that, you can even use Sapiengraph custom formulas to get insights into companies’ hiring activities, which could prove useful when checking out what competitors are up to.
After all, knowing that a direct competitor is on a hiring spree and buffing up its pool of developers might help you prevent your own talent from getting poached. If you notice a lot of job openings at a competing firm touting work on a new product, you could also prepare your pipeline to develop something to rival whatever they’re working on.
Check out my competitor tracking spreadsheet for this example.
All it takes to do this is the right formula. For example, with “=SG_JOBS_SEARCH(“https://www.linkedin.com/google”, 10, “software engineer”)”, Sapiengraph will return the LinkedIn URLs for 10 open jobs that Google has posted on LinkedIn looking for software engineers.
If I wanted to enrich that data further, I can use “=SG_JOB(LINKEDIN_JOB_URL, JOB_ATTRIBUTE)” to get anything from the job descriptions, title, seniority level, employment type, and job functions filled in directly into my Sheets.
Conversely, if I just wanted a simple formula to check in on how many jobs a certain company has open, I could use the “=SG_JOBS_COUNT()” function.
For instance, to get the number of open software engineering jobs at Microsoft, I’d simply use “=SG_JOBS_COUNT("https://www.linkedin.com/company/microsoft", “software engineer”)” and my Sheets would be enriched with the exact count of LinkedIn job postings matching those parameters.
What data can you scrape from LinkedIn?
Now the big question: What kind of data can you pull into your Sheets with Sapiengraph?
The short answer: It depends. Whatever information is available on public LinkedIn profiles will be scraped by Sapiengraph into your Google Sheets when you use our custom formulas.
However, if someone doesn’t update their LinkedIn regularly or has their profile set to private for whatever reason, we’re not going to magically have that information available.
That said, as long as it’s data that’s on a public LinkedIn profile, you can get it scraped directly into your Google Sheets with the right formula and a few clicks, simple and easy.
Is scraping LinkedIn data legal?
You might also be wondering whether all of this is above board. After all, it just feels too easy to get so much personal information on such a large number of people.
Well, don’t worry - as reaffirmed by US courts in 2022, scraping data from public LinkedIn profiles is perfectly legal.
This was further reinforced during the recent legal battle between Meta and Bright Data, when the social media giant alleged that the Israeli-headquartered web data firm was scraping its data illegally. Despite the allegations, the judge ruled in favor of Bright Data, and Meta ended up dropping its lawsuit.
Additionally, we here at Sapiengraph are committed to adhering to CCPA, GDPR, and SOC 2 regulations. So you can rest assured that any LinkedIn data scraping you do with Sapiengraph won’t get you in trouble with authorities and regulators.
So what are you waiting for? Stop wasting several hours a day scraping LinkedIn data and start automating spreadsheet enrichment now!
Sign up for a Sapiengraph account today to get 100 free credits and experience the difference firsthand. If you like what you see, you can get 12,500 more credits at only $49 or check out Sapiengraph’s pricing page for more options.
For further assistance or inquiries:
- Email us at [email protected]
- Chat with us directly via our homepage
- Stay informed by subscribing to the Sapiengraph newsletter
