LinkedIn Recruiter Spreadsheet Hack

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Feb 3, 2020

Where is the love for LinkedIn Recruiter? As a “cool” and “modern” sourcer, I am supposed to hate corporate products. Not just because they are good or bad, but because it’s popular to hate the mainstream.

In my fledgling teen years, I turned against Top 40: pop music I had been nursed on. Some people turned me on to the wild side and quickly I gained a taste for the good stuff. I loved the loudest, most explicit, obscure, foreign, rough, artistic, free-form, creative, lucid, and most contemplative music I could find. Alternative and college radio stations played the avant-garde as a gateway to non-mall record stores with stacks of audio treasure staffed by clerks with encyclopedic knowledge. My ears were open, but my wardrobe was Zack Morris chic. I felt like a secret agent who had infiltrated the preppy crew.

When I think about LinkedIn, I envision Mr. Popular: Top-40 listening, shirt tucked with a popped collar looking suave. His user base is the envy of the startups. His gross margins are gonna make you sweat. All the features and facets stacked like trophies.

It’s easy to hate on LinkedIn. That handsome boy modeling school UI is especially rough and wrinkled. The crisp pica cotton polo shirt has pit stains from user-created data. I have always advocated for change at LinkedIn. So I’m ditching this preamble of mixed metaphors to talk about the positives of LinkedIn Recruiter.

LinkedIn has the most current employment data. Others can predict changes that may happen, but they don’t know about the change until the next time they scrape LinkedIn. Why not go to the source?


You’re Sheeting me?

Before someone calls me a heretic, let me explain. I don’t advocate ONLY sourcing on LinkedIn. In fact, I built this technique explicitly to help me match people found off LinkedIn. If you know me, I love finding people data in bulk. The goal is to quickly match people into my pseudo-CRM of LIR where the ID and outreach workflow are already defined with results I can track.

Click this link to download your copy of the sheet

Ideally, we want bulls-eye accuracy where we find exactly one result. This means we have ~100% certainty this is our person. I do this by populating the precise facets LinkedIn Recruiter offers but hide behind a link structure that changes in near real-time.

It’s also worth pointing out this approach does not break LinkedIn TOS. You are manually clicking each link. There is no script for them to detect or subsequently block. You are simply asking for exactly what you need, rather than hoping the black box algorithms interpret the request with your intentions in mind.


Data Hygiene Pointers

With all larger-scale data projects, you will need to do some clean-up. Meetup data, for example, requires that you split first and last names. Use this Google Sheets feature:

Data > Split text to columns > Spaces

On your second pass of cleanup, you may need to make educated guesses when there are more than two names.  You may wish to split the batch when you only have an initial. LinkedIn Recruiter Boolean usually requires exact accuracy with only a small number of exceptions. Some name variation logic like (Thomas, Tom, Tommy) is known. Irina Shamaeva has done the best job of anyone to document the undocumented ways LinkedIn interprets requests.

Leading and trailing spaces can break your formulas. This is a fairly new feature in Google Sheets:

Data > Trim Whitespaces


Scenario One:

We have a target company and a list of names. It’s not hard to do by hand, but is that the best use of your time? Even if not, is this an exercise that may help me in the future? Glad you are still reading.

Real-world example: On the Layoff Alerts for Talent Acquisition Professional (LATAP) Facebook group run by Alan Fluhrer, a list of who were about to be let go was shared. I want to quickly get this list to my squad. The original has some of the basic LinkedIn links, but that would take time away from the team and I would have no metrics.

Well, what do you know? Mr. Shupe is open to new opportunities!


Scenario Two:

You have searched LinkedIn Recruiter for the people with surface titles, skills, and keywords. Now let’s say you are super smart like Sofia Broberger and you have figured out how to automatically add new members of a niche Meetup group to Google Sheets. How do you make this a task you can do in seconds? Setup that Zapier sheet matching the columns. You have their name and location.

If you wanted all of the members of this NodeJS Meetup in Paris with >5700 members, you will need a plan. My plan is to map out how to get clean data, use a practical approach to time management, with the outcome being verifiable ROI. Start with a small batch. If successful go to 100 people to make a nice percentage. Does this percentage make this worthwhile? If yes keep going.

As discussed we have names, but no company name. We need at least another data point to increase certainty. This is a NodeJS group after all so add that as a keyword.

I also know their city of residence from Meetup. The sheet uses a basic OR statement with these keywords. You can’t use the location facet since it must be a perfect match. Test adding other keywords, in this case (JS OR Javascript) if you are getting a low hit ratio.

Clicking on the link you get exactly one result:

Things Fall Apart

I’ve been using this technique for over a year on medium to large scale project and I admit this is far from perfect. Don’t give up! When you get no results, click the pencil icon next to your facet to edit the field. When you click, you will see the results from the other field. This is a quick way to toggle results without changing the facet. When editing terms, keep a text editor open as a scratchpad. I use intentionally vague terms without quotes in the Boolean formula. Test using quotes when you have too many results.

Other times you may get no results. Here are a few possible reasons:

  • People who change their last name
  • People who use short or long versions of their names
  • Non-English or special characters
  • People with intentionally deceptive or no LinkedIn profile


Turbo Workflow

Once I have tested a few links and I am happy with the results you can speed this process up. I use OneTab Chrome extension to copy the links using the Import option to create a group of links.

Once Imported, rename the tab group. By default, links are removed from the group as you click on them. I click and open 10-25 tabs at a time.  Ctrl + W (or Cmd + W) closes the current tab in Chrome.

On LIR I add them to my clipboard. Then move 50 at a time to a dedicated project and remove them from my clipboard. LinkedIn if you are listening, add shortcut keys for functions like this and a way to view more than 25 on a page, or just pay to watch good sourcers operate.  You would learn so much.


ROI for the WIN

When finished, document and compare your numbers. Add custom tags “NodeJS” and change the source to “Meetup” in bulk. Then share the project with everyone on the team and explain the WHY in an email or IM.

Unfortunately, some of you may work for companies that don’t trust you to create tags or use sources. Don’t get mad. Think of ways you can segment people using project names or other approaches that allow for measurement. Then use this opportunity to put together a business case. All companies value clean and compliant data today. This approach allows you to match publicly available information the same way competitors to LinkedIn Recruiter use it. With perseverance and confidence that only a pastel sweater draped over your shoulders provides, you will do it.

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