Who Needs LinkedIn Recruiter? X-Ray vs. LinkedIn Search Comparison Chart

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LinkedIn.com LinkedIn Recruiter Google X-Ray (finds public profiles) Google Example
Name Y Y intitle:<name> site:linkedin.com/in intitle:”phil tusing”
Current Job Title Y (false positives) Y (false positives) Y – intitle:<title> site:linkedin.com/in intitle:”executive assistant”
Current Company Y (false positives) Y (false positives) Y – intitle:<company> site:linkedin.com/in intitle:seekout
Last Company (even if left) N N Y – inanchor:<company> site:linkedin.com/in inanchor:”morgan stanley”
Last School N N Y – inanchor:<school> site:linkedin.com/in inanchor:ucla
Headline N Y – headline: secret operator Y – inanchor:<headline> site:linkedin.com/in inanchor:”open to work”
Summary N Y – summary: secret operator N
Current Job Title Y Y Y site:linkedin.com/in intitle:machinist
Last Job Title  (even if left) N N Y – inanchor:title site:linkedin.com/in inanchor:”truck driver”
Job descriptions N N Y (by keywords) site:linkedin.com/in “scaled up” start-up cloud bay area
Self-Entered Skills Y – in Company Employees and School Alums search Y – skills: secret operator N
Skills and Assessments N Y but works almost like keywords
Past Company Y Y Y (by keywords) site:linkedin.com/in -intitle:chevron inanchor:chevron
Past Job Locations N N Y (by keywords) site:linkedin.com/in “united kingdom” canada AROUND(3) present
Past Job – <title at company> N N Y – use AROUND(X) site:linkedin.com/in “CFO” CFO AROUND(3) google
YOE N Y (but rounded) N
True Years at Company N Y (but rounded) Y – with AROUND(X) or Asterisks site:linkedin.com/in present AROUND “2..6 years” operations manager
Years in Position N Y (but rounded) N
Current Location Y – by Area  Name Y by Area  Name or Zip/Radius Y – by Area  Name site:linkedin.com/in present AROUND(3) “greater new york” operations manager “new york”
Profile in Language Y Y Y – secondary profiles end in /<lg> – 2-letter country abbreviation site:fr.linkedin.com/in/*/fr
Spoken Languages N Y Y – approximate site:linkedin.com/in “Native or bilingual proficiency” tagalog
Function (calculated) N Y N
Seniority (calculated) N Y N
Company Type (calculated) N Y (calculated) N
Company Size (calculated) N Y N
School Y (Boolean) Y (selection only) Y (by keywords) site:linkedin.com/in “school of arts and enterprise” -intitle:”school of arts”
Last School N N Y – inanchor:<school> site:linkedin.com/in scientist inanchor:sorbonne “sorbonne”
Field of study N Y Y (by keywords) site:linkedin.com/in “quantum physics” university Phd
Industry Y Y Y (imprecise) site:linkedin.com/in “environmental services”
Years of study N (but see school alumni search) Y (but not tied to a school) N
Degree N Y (but may be incomplete) N
Grades at School N N Y (by keywords) site:linkedin.com/in GPA AROUND(3) “4.0” accounting
Other accomplishments N N Y (by keywords)
Recommendations N N Y (by keywords)
Open to Work Status N Y N
Network Relationships Y (buggy) Y (buggy) N
Followers of Y N N
Connections Of Y N N
Group Member N Y N
Open to Volunteering Y N N
Service categories Y N Y (some)

Note: LinkedIn.com search is currently broken.

To generate target public profile lists in Excel by precise search, use our tool Social List (trial, subscription).

Advanced Sourcing School and #CPSP Exam & Catch Up on the News

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Hello Sourcers and Recruiters:

On October 25th, 2022, we are launching our most substantial live training program for Recruiters yet,

Advanced Sourcing School and CPSP Exam,

and you are invited! As hands-on Sourcers working across industries and globally, we share proven sourcing methods. Subscribers are eligible, so if you are into sourcing, consider subscribing. The School is a way to “learn it all” in less than two weeks, prove yourself by taking the certification exam, and keep the materials for future reference. Only a few seats are left. Please reach out if you have any questions. Oh, and here’s a Q&A.

Using a premium account on LinkedIn? Unfortunately, the search is broken. Consider upgrading or X-Raying. 😉

Googling? I have run into something interesting – The Behavior of the Quotes (Google Search Report). Luckily, we got unique insights from Google’s Danny Sullivan (we are told that Google’s team “had a lively discussion” over the findings, lol). It turns out that when you Google, the search engine assesses your string and gives you different results based on whether it “senses” an “open-ended” or “restrictive” search. There is still a lot of mystery left – let us explore it! This discussion affects those who want to see as many results as possible for a search.

Join our FB group for questions, answers, and shares.

Happy Sourcing!

Researchers: Run “Restrictive” Google Strings

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The post reflects my current understanding of how Google works. I hope to share extra detail with you as we do more testing or if we hear more from Google Search people.

SUMMARY

When you write a search string, Google assesses whether it is:

  • Open-ended (assuming you, the user, require assistance) or
  • Specific, or “restrictive.”

Google decides on “restrictive” in some cases with quotes or when you use advanced operators  (maybe something else). For restrictive searches, Google does a fair job of searching with less interpretation. That sometimes leads to more results.

Looking closer, it’s not one or the other; there seems to be a measure of restrictiveness (from open-ended to more and more specific, depending on input) to which Google reacts; see some examples below.

Conclusion. When you Want to Get More Google Search Results:

  • Be aware of “open-ended,” “restrictive,” and “in-between” (more and more restrictive) searches
  • Putting keywords in quotes or using operators sometimes makes a search restrictive,  showing extra results
  • When running “open-ended” searches, for different results, consider making the search more specific by adding keywords or operators.

>> Download “Quotes” PDF Summary <<

In a hurry? End of SUMMARY

Google’s Danny Sullivan‘s comments on my Quotes on Google post shed some light on the observed phenomena: words in quotes produced more results than without in my examples. I had expected words without quotes to be interpreted, therefore leading to more results, not less.

Thanks to Danny for taking the time to comment. The word therefore above was false logic. I still have many related questions and wonder whether there is a channel to ask them and interact with the team. (@Google, we would be glad to hear your thoughts.)

Evolving semantic input interpretation while allowing to keep control over search via special syntax – when everyone uses the same search box – is not easy.

NOTES for Hands-On Sourcers:

If you think that as a practical result of the “quotes” insight, you need to start putting all your keywords in quotes – perhaps you should try that. But quotes help to see more results only sometimes.

Yes in some cases:

No in other cases:

or

It does not matter in yet some other cases.

I.e., quotes around single words sometimes help find more results (perhaps working as a “restrictive” indicator). A researcher needs to know that might happen and search with and without quotes around single words.

From my tests, an advanced operator like site: makes a query restrictive.

In addition to your strings appearing to Google as “restrictive” (sophisticated!) searches, you can increase the number of combined results by repeating keywords, moving them around (as Nicolas Darcis demonstrated at #sosueu), and searching in Images.

 

 

 

 

 

 

The Behavior of the Quotes (Google Search Report)

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[Edited: apparently, there is an explanation – see Danny Sullivan‘s comments below.

Bottom line: if you are a researcher, consider using quotes!

My summary.]

To Whom It May Concern At Google Search:

There is currently a problem with Google search, specifically, quotes. It might be an unintended outcome of How we’re improving search results when you use quotes. We welcome the improvement. But here is something odd: try

What? I expect the numbers of results to be the other way around, i.e., for Google to bring in synonyms if I do not use quotes – and show more results.

Quotes even affect OR statements, e.g., “developer” OR “engineer” <keywords> finds more than developer OR engineer <keywords>  (I expect to see the same results if OR is used):

I noticed the unexpected behavior while at #sosueu in Amsterdam last week. None of us could explain it. It is as if Google works harder if you use quotation marks. And most Google users do not use them around single words.

As far as I can tell, it is a bug (unusual for Google search). Let’s see if they fix it or give us an explanation. In the meantime, keep in mind the phenomena.

I hope a fix will happen faster than it has for LinkedIn (9 months and counting).

 

Google’s Reverse Image Search Has Become a Visual Shopping Engine

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If you drag an image into Google’s reverse image search, you now arrive at Google Lens. It (often):

  1. identifies an area on your photo that looks like goods for sale and
  2. produces “visual matches” (payment links to such goods) on the right.

People and faces are ignored.

Do you want to dress like your candidate? Use their photo (Someone joked like this on my LinkedIn post).

At times, Google appears to be “creative” (and clueless) in finding goods for sale:

I am all for this way of sourcing things to buy. I like a certain style of clothing and am interested in finding more. It is just please call it a Visual Shopping Engine vs. “visual matches,” to make things transparent.
To be fair, Lens is not always about shopping. Lens will identify architecture, landscape, plants, animals, and a few more things. It is not new but is perhaps better. It can also OCR and translate.

Not that the existing Google’s reverse image is brilliant, but you can return to it either by clicking “find image source” or one of the tools like Chrome extension Search by Image.

Yandex’s Reverse Image search is superior, especially from a Russian IP address. But Yandex has not indexed LinkedIn profiles.

Who Else Wants to Overcome the Invite Limit of 100?

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Invitations from recruiters have become a standard practice on LinkedIn. So have invitations related to potentially doing any business together. If an invitation is accepted, a conversation starts.

However, LinkedIn notifications have poor deliverability, and “passive” candidates often miss our messages and invitations. It helps to invite a larger volume of potential matches and follow-up by email.

The following method meets the goal: it does not have the limit of 100 invitations per week and provides you with emails. It has somewhat changed since the UX lost the uploading a file button.

Start with a list of promising email addresses. They may come from your ATS, other social sites, X-Raying for lists, enriching LinkedIn X-Ray with SalesQL, and other sources.

STEP 1. Upload the list of emails to a Gmail account (make sure to clear out the existing list before uploading).

As a bonus, you will see non-generic pictures by the valid emails:

STEP 2. Remove existing LinkedIn Contacts.

STEP 3. Sync with Gmail Contacts.

See the identified non-connections:

You can start connecting here, but only with a limited number of members.

STEP 4. Connect to any number of members (one by one; it helps to review the profiles) from Contacts. You cannot customize the initial message, but this way is scalable.

STEP 5. You have their emails as well for follow-ups, and can personalize your outreach. (You can InMail them also.)

This technique will work for lists of thousands if desired.

STEP 6. Join me on Wednesday, September 21st, for a new webinar How to Find Hidden LinkedIn Profiles! David Galley follows with an optional practice on Thursday.

 

 

 

 

Shaping Snippets for Scraping

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In a recent post, How we’re improving search results when you use quotes, Google informed us that it would force the terms in quotation marks into snippets – as many as possible. It is a significant improvement for research; take note of it.

(Even before the announcement, repeating keywords and putting them in quotes helped.)

I am finding that in daily sourcing, as well as training, I have been increasing the number of search strings with quoted and repeated search terms. Here is an example.

Compare site:hu.linkedin.com/in “me at * * com OR hu” developer:

and site:hu.linkedin.com/in “me at” “me at * * com OR hu” developer:

While asterisks (*) and Boolean operators within quotation marks do not always help to alter snippets, a few words in quotes (“me at”) do.

In the second search, the snippets look reaffirming, displaying the desired info and the wording around it. But more importantly, you can scrape the emails into a list without visiting any result URLs; I recommend Julia’s Email Extractor.

Here is another example of manipulating snippets. Compare recruiter site:linkedin.com/in inanchor:opentowork -intitle:opentowork and “opentowork” recruiter site:linkedin.com/in inanchor:opentowork -intitle:opentowork.

While snippets are described as “the first piece of information that influences people’s decisions on what results to click or read,” we are here for not clicking results and being productive.

 

Sadly, 8 Months Later, LinkedIn.com Search Remains Broken

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We so much depend on the platform. But LinkedIn.com people search remains broken for everyone with a premium, job seeker, or a basic account. LinkedIn does not find members by keywords in the “About” section and job description. It has been over eight months. 🙁

Here are just a few examples. You will not find David Galley by the phrase in About – “blending cutting edge technology”:

Nor will you find him by the phrase in the job description – “a series of training courses” (or series of training courses david galley).

We see no rhyme or reason for what the LinkedIn.com search algorithm does, or what LinkedIn Software Developers do.

Please tweet this to @LinkedInHelp; thanks!

Invisible Developers

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No matter how long and complex Boolean you write, it will miss a significant percentage of qualified Software Developer candidates. Search on – or X-Ray – LinkedIn, Github, Gitlab, dev.to,  Stackoverflow, HackerRank, Reddit, Slack, Discord, or Twitter – and you will still miss them.

Consider this. You can search by (“preferred”) programming language and location on Github. But many Github members do not write code for a living – they are students, professors, retirees, managers who miss coding, and so on. Staying on Github, you won’t know that; user profiles do not even have a field for the job title. Meanwhile, you can search by the job title on LinkedIn, but a significant percentage of professionals with the requested programming language skills (who are popular on Github) have barely mentioned the language name on LinkedIn.

Combining knowledge and data from several sources uncovers “invisible” talent.

My followers know I have been obsessed with cross-referencing profiles, in particular, in technical recruiting, Github and LinkedIn (only because it works!). It finds prospects who did not fully reveal their professional background on any one platform. But that is not the only way to tap into that hidden talent pool.

Here is a complementary approach. Do your research and find which companies or teams use the required technology. There is a good chance all Developers on the team use it, whether they have been vocal about it or not.

As a simple example, running a recent sourcing project, I saw many North-American Developers at Shopify write in Ruby. It looked like it was the language of choice for the team. So, if I search on LinkedIn and find Developers at Shopify with no summaries and no recent job descriptions (a turn-off for Recruiters!) but Ruby in the skills or past job experience, I can safely assume that they currently use Ruby. I would also have a good guess at the years of experience with the language if they used it at past jobs. It is like reading between the lines. 😀

If you think you would benefit from sourcing ideas and practical advice on how to uncover untapped technical talent, join Master Sourcer David Galley  for a unique free webinar

10 Essential Steps for Sourcing “Invisible” IT Talent

Wednesday, August 31st at 17:00 CEST / 8 a.m. PDT

Incidentally, the webinar’s sponsor is AmazingHiring, a tool that helps to uncover technical talent based on multiple sources. If you are hiring IT talent, give it a try.

 

People Aggregators, Unique Names, and X-Ray

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When assessing a people aggregator like SeekOut, Entelo, AmazingHiring, HireEZ, and others, or even the general-purpose Zoominfo, two points are critical:

  1. How complete is the coverage (for your target audience)?
  2. How up-to-date is the data?

My idea is to run searches for unique names. If a combination <first, last> is unique (or even unique only in a given industry and location), then searching by the full name within an aggregator will reveal:

  1. No results? The person is not in the database.
  2. A match? Compare the rest of the data to see if the companies and titles match those on LinkedIn or whether they are outdated in the aggregator.

Hopefully, the aggregator will allow to mass-search for these professionals. Enter a long OR string of the names (use a copy of LinkedIn Boolean Builder; also, try the resulting search string on LinkedIn itself to verify that each name finds just one profile). Running a few sample sets will test the aggregator.

How do you find unique names? Since LinkedIn has the phrasing “see others named” on public profiles if other profiles with the same name exist, I first thought that this X-Ray would bring up unique names:

site:linkedin.com/in -“see others named”

(I came up with the search first, considered it fun but perfectly useless, and then thought of its application I am describing.)

However, there is a subtlety that may lead you to finding non-unique names with the string. The “see others named” links appear on public profiles when there is a /pub/dir directory. These directories are different if there is an addition to the last name, like a certification or degree abbreviation. For example, these are two separate directories:

  • https://www.linkedin.com/pub/dir/Jim/Smith%2C+Mba
  • https://www.linkedin.com/pub/dir/Jim/Smith

Having that in mind, by manipulating additional keywords in the above X-Ray string, you can land on lists of “truly” unique names in the locations and industries of interest. (I would be curious to hear what your strategies might be; maybe there will be another post.) Then, collect the names and proceed to test.

There is a wealth of ways to X-Ray LinkedIn! Please join Mike Santoro and me this Thursday for

The Complete LinkedIn X-Ray Masterclass (A Benefit for Ukraine)

We promise not to disappoint you.