LinkedIn Recruiter: Not WYSIWYG

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Well-designed user interfaces follow the rule of WYSIWYG – “what you see is what you get”. Unfortunately, LinkedIn Recruiter doesn’t do the best job in this regard. Just look at the screenshot of two searches for company=Apple I have done. Which number is correct, on the right or the left?

The secret in the two different numbers displayed is that, on the right, I have added a space to the company name: Apple<space>. I’ll get the same number as on the left if I enter Apple in the quotation marks (a slight difference in large numbers of results is to be expected).

What is happening here? If we choose the company Apple from a list of company choices, the results are employees of the company Apple. But, if we enter a space after Apple or put the word in “, we get employees of companies whose name contain the word Apple, such as Apple, but also Apple and Associates, Apple Vacations, etc. Some of the companies found may be Apple’s affiliates, but not all.

To negate each of the above conditions (company equals Apple, and Company name contains Apple) in Recruiter we have two different types of UI: negating the company Apple or the word in the company name:


Take a note of it.

P.S. And here is another story on the subject.

LinkedIn Locations and Traffic in the Bay Area

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The traffic in the San Francisco Bay Area, where I live, is pretty bad. What the commute is like is a serious consideration for anyone looking for a job. Let’s take a look how LinkedIn job posts treat locations – posted and searched for.


LinkedIn has 1) “area” locations and 2) specific cities as locations. The city locations are part of the “area” locations. For example,  San Jose, California; San Francisco, California; and Berkeley, California (which are at a distance from each other) all belong to the San Francisco Bay Area.

When we post a job, we are given both “areas” and cities as location choices.

When we search for a job, the same location choices are available:

Given the commute times, we can expect job seekers to enter the city closest to where they live when searching for a job.

However, when we search for jobs, LinkedIn treats all Bay Area location the same. Even that I have entered San Francisco in this search, the second result is in San Jose (quite far from San Francisco). (This is not very helpful!)


  1. When posting jobs on LinkedIn, it is best to enter a specific city name (e.g., San Mateo, California) vs. an area name (e.g., San Francisco Bay Area. When potential applicants see the post, they will know what their commute is going to be.
  2. When people search for jobs near their locations in the Bay Area, they, in fact, see job posts from all of the Bay Area, even if they enter a city name as location. The same LinkedIn rules apply to other “areas” and cities. Quite inconvenient for job seekers! But it is how it works.

Increasing Candidate’s Response Rates

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This is a guest post from Martin Lee.

We can be as creative as we like with our sourcing, writing killer Boolean strings, utilizing the latest tools and unearthing profiles that others wouldn’t, but without a response from prospective candidates we have only done 50% of what is required.

According to a number of sources the average user spends 17 minutes on LinkedIn.

When we reach out to prospects – it’s about them, not us. Too many recruiters’ (agency & internal) messages lead with a job they are trying to fill. Often this is based purely upon a keyword search and an assumption that the person is a fit and is actually interested in the position.

If our target candidates spend 17 minutes on LinkedIn and they receive a lot of similar looking messages what chance do you have of getting a response if you do the same as everyone else ?

The purpose of the first message is always to motivate the potential candidate to want to have a conversation. It should be casual, no commitment or resume required. Internal recruiters should have ongoing interesting vacancies and should lead with a “career discussion” approach. There could well be live vacancies that this person is suitable for but unless you know their personal situation and motivations & timings to move you can not make a match. Agency recruiters have it slightly tougher but if they’re credible and market specific they too should be able to convince someone to at least have a talk with them. A conversation focused on the candidate shifts the emphasis from a job we are trying to fill to talking about them, so more people will respond.

The first message is your chance to show you have read and understand their profile(s) and to cut through other recruiters’ messages. Using personalized messaging is key whether using their name, skills, current company (and technology used), location, projects, etc., are all indications that you are being specific about them. It’s better to send fewer more personalized messages than using obvious templates.

Asking for referrals or resumes at the first message is a definite no-no.

In addition to electronic messages many recruiters are being more creative these days. Personalized videos to specific people are being used successfully now. “Hangouts” where technical people can look around the offices, ask questions to other techies are popular.

Big corporations marketing can sometimes be seen as cheesy and fluffy. We remember an example of our friend Jim Stroud who promoted working at Microsoft Canada over 10 years ago by shooting a video only on his Iphone. That video still ranks highly on YouTube as he uploaded it from multiple sources.

Check out our online class thoroughly covering the topic Improving Candidate Response Rates, and supplied with one month of support.

Learn to Search for Diversity

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We know that diversity in the workplace positively affects outcomes. Including a diverse pool of candidates in the talent  pipeline is a must for any forward-thinking recruiter and hiring manager.

When we search for diversity candidates, the same sourcing principles apply, as always – look for “what you are going to find”, “visualize success”. Here are some diversity Boolean search strings, based on that principle.

And here is a Diversity Associations Custom Search Engine –

Join us and learn how to Source for Diversity in the upcoming class – Tuesday, February 6th (lecture) and Wednesday, February 7th (practice).

Six Free Boolean Strings from e-Book

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I am excited to announce that the 3rd edition of the e-Book “300 Best Boolean Strings” has been released. To prepare the new edition, I went through the 300 strings in the previous version and removed about sixty that were no longer working. I also dug into my Google search history and added multiple strings to the book. I got carried away a bit – the new edition contains almost 400 strings!

The Boolean Strings in the Book can be considered as examples, for the reader to explore the possibilities of searching and understand what each search brings and why. It’s not a phrase book, but it will help the reader “speak” Boolean. Here is a random sampling of some Boolean Strings from the Book, particularly those that find resumes.


Easy Sourcing with NO Boolean

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Lots has been said about using the natural language to search for potential candidates. Examples would be “I am a Software Engineer at Microsoft” or “earned her MBA from Wharton”. It’s a fruitful technique.

Here is a different twist on searching in English. Suppose we wanted to find LinkedIn profiles on Google, but doing so without the operator site: or any other operators. It turns out, it is quite possible. All we need to do is to identify a phrase (or several phrases) that:

  1. Appear on every profile
  2. Don’t appear on other pages, that are not profiles.

Here is an example search, using a phrase present on LinkedIn public profiles (those that are in English, of course) :”500 million other professionals”.

linkedin “500 million other professionals” “head of localization”

Even if we drop the word linkedin from search, the results will be pretty much all LinkedIn profiles:

“500 million other professionals” “head of localization”:

We can use alternative phrases to single out LinkedIn profiles, for example: “full profile it’s free”. Here is a search:

“full profile it’s free” registered nurse ICU

For Twitter, we can search for the common element “”tweets & replies”:

“tweets & replies” biotech conference.

For Meetup and Github, “member since” is a good token to use.

Who can suggest other examples?

Check out the new online class Sourcing without Boolean.

For those who do like Boolean searches: my e-Book “300 Best Boolean Strings” is out of the press and has almost 400 search strings for all occasions.



Nine Association X-Ray Templates

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Association sites are an excellent source of professional and contact information. Are you familiar with associations in your industry? Finding them is as easy as Googling for <industry name> <location name> association.

When I research an association site, I am interested in pages with lists of members and in contact information (that will let me look up additional background).

Here are nine simple Google search templates I use when researching an association site:

  1. “”
  2. “” “”
  3. <some company email extensions>
  4. filetype:xlsx
  5. member directory
  6. search for members
  7. roster
  8. attendee list
  9. Bob Mary Lisa (or other names)

Depending on the association, these strings can unveil some treasures!

Check out the online class 300 Best Boolean Strings where we spent 90 minutes exploring a multitude Boolean Strings included in my book.

Facebook Research Hacks

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Facebook member’s groups, posts, comments, and events that we are allowed to see per member’s privacy settings can help us identify professional details on potential candidates – as well as find additional candidates. Unfortunately, this information not that easy to search for any longer, ever since Facebook retired its “official” Graph search.

Here are some simple but useful “ex-Graph” searches, that work today. Many of them use the person’s ID, which we can quickly identify by the Facebook URL.

Jim Stroud’s groups
Boolean String Group Members
Suzy Tonini’s co-workers
Shane McCusker’s events
Irina Shamaeva’s past events
Posts by Phil Tusing
Posts commented on by Randy Bailey
Pages liked by Suzy Tonini

The (dangerous!) tool Facebook Scanner provides a few more ways to run Graph searches.

The Facebook Mastery class on Tuesday, January 16th was SOLD OUT. Join us for a repeat on Tuesday, February 20th, and learn many more Facebook hacks, tips, and techniques! Seating is limited.

Least Understood Google Operator

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Arguably, the least understood Google operator is inanchor:

Google’s advanced search documentation has lost the level of detail it used to have just a few years ago. It no longer describes inanchor: and quite a few other operators.

What [ inanchor:keyword ] means, is – search for pages, links (anchors) to which from other pages have the keyword (or key phrase, as in [ inanchor:”key phrase” ]).

It’s a tricky operator! Note that while Google responds to the query, it does not tell us which pages have links responsible for the search results. For example, if somewhere is a page, pointing to your LinkedIn profile, and the link says “Top Professional of the Year”, Google will find the profile by searching for [ inanchor:”Top Professional of the Year” ] but which page said that great thing about you, we won’t know just by looking at the results. Google used to have the operator link: to look for sites linking to the given one, but it never worked well and is now gone.

Sometimes Google finds pages by keywords in “anchors.” If you don’t see your keyword on a results page, that could be the reason behind it: the word was in an anchor on a different page pointing to this results page. (Of course, there may be other reasons).

If we imagine what links to useful pages and sites can say, we can come up with some interesting use cases:

In the middle example, inanchor: finds unique pages with resumes – these pages have no word “resume” in the page title or URL:

You can find additional interesting inanchorexamples in this post.

As with the minus and all other operators (like intitle:, inurl:. etc.) Google searches for the exact word, so we’ll need to run the guesses for synonyms and variations separately.

Our Advanced Google Workshop was sold out (again!). We have scheduled another repeat; check it out.

How To Get Your Google Search History

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Google keeps your search history (and lots of other data), even if you clear the browser’s cache, and allows to download the history.

Here is how to get your Search History for the last few years in a convenient Excel format.

On your Google Take-Out, select (only) “Searches”.

Google will create an archive of your searches, which you can download as JSON files, a file per quarter of searching.

The next step is to merge the files into one Excel file, with all the searches. A genius piece of software that can help here is OpenRefine (formerly Google Refine). After installing the tool, point it to the JSON files, and get all your searches together. It’s pretty straightforward.

The export will contain:

  1. Boolean Strings,
  2. Timestamp for each String.

Now, for example, using Excel filtering, you can review your X-Ray searches by restricting the “String” column to contain the substring site: . Or, go over your OR, intitle:, or inurl:, searches. (Lots of ORs was there is the last sentence!) You can collect strings that seem worth repeating or modifying, and exchange with your peers.

This technique was most helpful while I was preparing the 3rd edition of the Boolean Book.

The above is a simplified process initially described in this article. OpenRefine was an excellent suggestion!

Curious about advanced Google Search? Please join me for a repeat of the sold-out webinar on Advanced Google.