Only 20% Queries Need to Be Boolean

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Sourcing includes three types of search:

  1. Research – finding info on terminology, target companies, schools, job titles, locations, and industry news
  2. Search – finding professionals with promising backgrounds
  3. Cross-referencing – finding additional qualifying professional info and contact info.

“[1] Research” and “[3] Cross-referencing” rarely require complex searching. You can accomplish most of the tasks by Googling for a few keywords and using Chrome extensions.

While “[2] Search” has a technical aspect where you create complex Boolean AND-OR-NOT searches (on LinkedIn or a job board). Advanced Google operators are highly applicable as well. However, you can accomplish quite a bit without any “Boolean complexities”.

Here are some simple – non-technical, “non-Boolean” – approaches to these search tasks. (And there are many more!)

  • Have a short question? Google it. While you can’t Google a job description and expect to see anything useful, you can Google for sites where potential candidates might be present, for example:
  • Have a lead (a perfect candidate, perhaps someone who had declined an offer, or lives in the wrong place, or is already working in a similar role)? Google his or her name along with the company name or skill keywords. Also, Google the email address in the quotation marks. You will find additional background and may find sites with other professionals “like this one”.
  • Search for qualifying phrases someone might have written such as “hired as managing director”. (Sometimes this is mistakenly called “Natural Language Search” – this term means asking your queries in English vs. some computer-oriented notation).

While complex Boolean search must remain part of any sourcing process at this time (please don’t believe that “AI is there” – it is not), you can do around 80% of searches without. Join us at the Sourcing without Boolean webinar to learn all about masterful Googling without using operators and other techniques like the one I described above.


Numrange, BERT, and Natural Intelligence

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There are two significant recent developments in Google’s algorithm.

[1] Google’s Operator Numrange is back!

Numrange seems to be working. (Knock on wood!)

[2] BERT – Search Naturally

The latest Google’s algorithm change, BERT, affects a serious 10% of all queries. Google is now paying attention to “insignificant”, short words, that it had previously ignored as “stop words”. It is noticing words like “at”, “to”, “as”, “if” where they create meaning (try sf to nyc). With BERT, Google’s search is becoming even more semantic and less formal, database-like. (For those wanting “database-like” search experience, Google keeps its Verbatim option).

What BERT tells us is to search natural-language-like, especially if we have a short question. For example, start a query with “what is”, “how many”, “top companies in”, “competitors of”, etc.

BERT (as part of other semantic-oriented changes) teaches us to be friends with working, evolving semantic search systems like Google’s. For better results on Google, search as simple as possible. It’s better to take advantage of machine-learned capabilities vs. suppress search interpretation by using long OR strings or Boolean Builders. Of course, any serious practitioner will use advanced operators, but using ORs is outdated (I mean it).

There are no textbooks on writing useful Google queries. It’s someone’s “natural intelligence” that matters in developing the “search” skill.

Join us for a webinar “Sourcing with Natural Intelligence” on Tuesday, November 12th, where we’ll share the important thinking patterns and multiple concrete examples of this (most) productive Sourcing approach.

Knowledge Graph Objects in Google CSEs (True Semantic Search!)

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As I was finishing the “Hacks” slides for my favorite conference, Sourcing Summit Europe, I stumbled across something I hadn’t seen before. Google Custom Search Engines (CSEs) got a new setting in the control panel:

We can now select Knowledge Graph Objects to restrict the search! I was intrigued; David Galley and I spent some time researching what the new option does.

We have been taking advantage of including objects in CSEs since 2016. (See an example of the technology in this post, and we use it in Social List). The new setting is quite different.

While selecting Objects rely on metadata placed there by web page creators, choosing a KG Object does not. Instead, it reflects Google’s “idea” of which KG Objects are relevant to the page. Therefore, the new option covers a much wider range of sites that can show up in the results. I say this is a true semantic search in a global search engine!

There is a just-announced API for selecting Knowledge Graph Objects, but there is not much explanation from Google how it works. (It could be quite complicated in the back-end, and is undoubtedly updated continuously.) The best way to examine what happens when we use the setting is to create CSEs and see what shows up in the results. (Get your hands dirty and your mind clear 😉).

We can restrict to or KG Objects only in Custom Search Engines, but not on, and it’s time for Sourcers to get to apply the technology. It may sound quite technical, but it’s not; everyone should able to get KG Object searches going. Please come learn all about the new CSE capacity (and everything else about CSEs) at our webinar on October 15th, 2019.

Here are a couple of details on the KG selection, and four examples.

We can select up to five KG Objects, and the search will look for an OR of the five. However, we can search for an AND of two objects with the use of the CSE refinements.

Examples of CSEs that search for:

  1. Females –  example search: java developer san francisco
  2. Jobs – java developer san francisco
  3. Curriculum Vitae – java developer san francisco
  4. OSINTmaltego

Come join us at Become A Custom Search Engines Expert class coming up this week, for an in-depth look at all the options you can set in the CSEs. Seating is limited.




New: Google College Search

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While Google has posted a blog about college search, they did not tell us how to get to that advanced college search dialog, which looks like this for me:

You can search through tons of useful info – Program, Location, Average cost. Tuition, Type, State, Acceptance rate, Size, and Campus setting.

The secret to getting to this dialog is adding this piece – &ibp=htl;splinter – to your search URL. This was my search.

The new capability doesn’t work outside of the US yet (you will see a “not supported” dialog if you are outside of the US) and shows US schools only. But I expect it will be expanded globally. If you want to use the university dialog and are located outside of the US, run one of the IP address changers such as Tunnelbear.

For other Sourcing Hacks, please check the second edition of our eBook!

Talent Pipeline Decline?

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One of the few paid Sourcing tools I use is RPS (LinkedIn Recruiter, or LIR, for Agencies). It is our highest yearly expense, but we have been choosing to stay with it for a number of years now. My favorite feature of LIR has always been “Talent Pipeline” (the name doesn’t really fit), which is, in fact, the import function. As you import Excel files into LIR, the records are matched to LinkedIn profiles, and, as import finishes, you can search across the combined info. It’s a powerful Sourcing technique and has been my frequent go-to when Sourcing. The import function has also been quite reliable.

With the just-released new UI, we still have the import function, but LinkedIn has cut off its features to the point where its usefulness sounds like a question mark to me. Import appears to be a much weaker function in the new version.

Here is a brief summary of the changes.

There is no longer a way to match imported fields with LinkedIn’s.

The file to import (it seems) must be CSV-formatted with exactly these columns:

  1. First name
  2. Last name
  3. E-mail address
  4. Phone number

We used to be able to have one column with first/last, giving us some flexibility. But what is worse, I don’t see a way to import any additional values (that used to go to Notes – and we could then search by them!) Import crashes if a column is added to the import file.

(Before the changes, I was thinking that import would become even more powerful if we could match imported columns with custom fields. Forget about that now!)

And what’s worst of all – at least in my experiments, LIR can import only so many records at once. I was able to import a file with 119 records, but anything over ~140 failed with an error message.

We used to be able to import up to 5K records at a time. Is this a bug? Unless this is fixed (or we find a workaround 😉 ), the Recruiter import feature, a.k.a. Talent Pipeline will have much less value for Sourcers.

Let’s stay optimistic and hope that we will get the full functionality back.

Don’t miss our Productivity Tools for Sourcing webinar on Wednesday, September 11th! We’ll be talking about today’s best tools for all aspects of your Sourcing life. (Most tools covered are free).

How to Restore Image Search Functions

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Quite unfortunately, Google has just removed three useful options in its Image Search:

  1. Option “Face” from “Types”,
  2. Option “Photos” from “Types”, and
  3. An ability to enter an exact image size.

Here is an example of what it used to look like until just a few days ago. This Image search looks for “Faces”, size 200×200 – with the idea to find profile pictures on LinkedIn:

… and here is what we see now (with no ability to enter an exact image size either):

(Why did Google do this?)

Here is how to compensate for the losses.

1.-2. Solution:

You will get both “Face” and “Photos” filter back by using Google Advanced Image Search URL.

3. Solution:

You will get searching by an exact size back by using the search operator imagesize: (did you know about it?)

(Note that when you press “Enter”, the operator will disappear, but the search will be filtered).

By the way, there is another Google Image Search operator with similar behavior (disappearing after “Enter” is pressed) – and that is filetype:, followed by one of the Image filetypes – JPG, GIF, PNG, BMP, SVG, WEBP, ICO, or RAW. Interestingly, after you have used the filetype: operator, you will get an extra menu for filetypes:

All three lost features can also be accessed by altering the search URL. Let’s memorize what those URL parameters spell out like for image types, sizes, and colors, and keep these strings, in case Google takes more options away from us in the menu.

  • Large images: &tbs=isz:l
  • Medium images: &tbs=isz:m
  • Icon sized images: &tba=isz:i
  • Image sized exactly 200×200: &tbs=isz:ex,iszw:200,iszh:200
  • Images in full color: &tbs=ic:color
  • Images in black and white: &tbs=ic:gray
  • Images that are red: &tbs=ic:specific,isc:red (orange, yellow, green, teal, blue, purple, pink, white, gray, black, brown)
  • Image type Face: &tbs=itp:face
  • Image type Photo: &tbs=itp:photo
  • Image type Clipart: &tbs=itp:clipart
  • Image type Line drawing: &tbs=itp:lineart
  • Image type Gif: &tbs=itp:animated
  • Show image sizes in search results: &tbs=imgo:1
  • Search for filetypes: &as_filetype=png (will get you a new filetype menu as when searching by the operator)
  • X-Ray: & (will instert a string into search)
  • Localize to country: &cr=countryNZ (a two-letter country abbreviation goes at the end)

So here, we have learned how to restore each piece of the disappeared functionality – and also about additional rather “hidden” filters.

Don’t miss the Productivity Tools for Sourcing webinar on Wednesday, September 11th!

How to Do Executive Job Title Research

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Often, especially when sourcing for executives, we need to answer questions like these):

What are possible job titles at a particular level of seniority, in a given industry (or at a company), with given functions?

Equipped with this intelligence, we can start constructing filtered people searches. Without this research upfront, we would be encountering both false positives and false negatives when searching.

While doing open-ended searches (on LinkedIn, for example) and eyeballing results is useful, at times, we want to get lists of job titles that are as full as possible. For that, we can X-Ray some sites with profiles, such as Zoominfo, and scrape search results with a tool like Instant Data Scraper. In X-Rays, we’d include keywords for job titles we are looking for, such as chief, head, director, senior vice president, etc.

For example, we can search like this: intitle:accenture “* director”

and scrape results. We will need to clean up the collected data a little, but we can get a reasonably full list of job titles, that include the word director, at Accenture. Note that if we are able to get Google to highlight the exact job titles in the results (for example, by searching for “chief * officer”), we would get a clean output of the titles in a separate column with Instant Data Scraper.

The contact-finding site has public profiles and we can X-Ray it in the same manner (then, scrape results): intitle:”credit suisse” director “united arab emirates”.

Yet another site, RocketReach, can be used for the same: intitle:walmart intitle:chief “chief * officer”.

As an example output, here are the titles of Chief Officers at Walmart found with the above string:

Chief Administrative Officer
Chief Business Development Officer
Chief Communications Officer
Chief Compliance officer
Chief Culture Diversity&Inclusion Officer
Chief Customer Officer
Chief Data Officer
Chief Ethics & Compliance Officer
Chief Ethics Compliance Officer
Chief Ethics Officer
Chief Information Officer
Chief Information Security Officer
Chief Legal Compliance Officer
Chief Marketing Officer
Chief Merchandising Officer
Chief People Officer
Chief Procurement Officer
Chief Product Officer
Chief Revenue Officer
Chief Technical Officer
Chief Technology Officer

Sure enough, we can also X-Ray LinkedIn for the same purpose. Constructing searches is straightforward since public LinkedIn profiles have both job titles and company names in the page titles.

We can get a combined job title list from each of these types of X-Ray searches, and this would inform our people searches.

Join me for a brand-new webinar Executive Sourcing Techniques on Tuesday, September 17th to learn more!

17 Custom Search Engines

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Here are some Google Custom Search Engines for Sourcing I have created:

  1. Google No-Captchas(a.k.a. Search Like a Human)
    Public link: Search Like a Human– just search for anything without being tested for not being a bot
  2. Email Formats – use to discover email patterns for any corporation
  3. LinkedIn – Countries– X-Rays LinkedIn profiles; offers a dozen refinements by country
  4. Emails in Resumes– not only looks for resumes but also pushes email addresses in the resumes to be seen in snippets
  5. Document Finder– looks for documents that are stored in one of a dozen popular document storage sites, such as Slideshare
  6. File Types– looks for certain file types such as Excel and PDF. It is helpful if you are searching for lists or resumes
  7. Software Engineers in the Bay Area– exactly what it says (created by Julia)
  8. Hidden Resumes– triggers a resume search without any search operators. It is used on the site
  9. Diversity Associations
  10. – just LinkedIn X-Ray
  11. – X-Ray LinkedIn for language proficiency (search by a language name)
  12. – Developer resumes
  13. – find Github users by programming languages
  14. – find Accountants
  15. – find Physicians
  16. – find social profiles (my most popular CSE!)
  17. – find people on the Internet

Learn about Custom Search Engines and other tools at our webinar on Wednesday, September 11th – Productivity Tools for Sourcing!

Also, learn about scraping tools in the recording Web Scraping For Recruiters and Custom Search Engines in Become A Custom Search Engines Expert.

Three LinkedIn Recruiter Sourcing Secrets

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When we talk with our sourcing and recruiting clients and our students about paid tools they invest in, we almost invariably learn that all or most Recruiters on the team have LinkedIn Recruiter (LIR) subscriptions. Our recent Facebook group poll has confirmed the same. Yet the majority of LIR users are unaware of its thee important features, that I cover below. It’s no wonder – help documents barely cover the features or not at all.

If your team uses LinkedIn Recruiter, consider getting the just-updated lecture LinkedIn Recruiter and the Talent Pipeline and learn about these and other tips about the tool, that you won’t learn elsewhere.

  1. LIR advanced search is “not what it seems to be” – and not Boolean. With pretty much every search facet, there are interpretations of your input, that are mostly unintuitive and unanticipated (and are good to know about). I have written about some in my past blog posts (for example, LIR, unknowingly to users, brings in what “it thinks” are synonyms of a selected job title).
    I have recently run into mysterious interpretations for location searches as well, applicable both to searches by a zip code/radius and by an area name. Here is, simply, a search for members in San Francisco Bay Area – yet it shows thousands of people who live outside of the Bay Area:
  2. The Import, a.k.a. “Talent Pipeline” is an incredibly powerful function included in LIR, yet about 90% of LIR users still seem to have never heard of it. (The name is misleading, too). You can import Excel spreadsheets and combine the imported data with LinkedIn’s in your work – this opens up a variety of use cases. Additionally, you would save on InMails. I wrote about it back in 2015.
  3. You can potentially reduce the number of LIR seats for your team by assigning “Hiring Managers” roles to some team members while keeping the team productivity up. Also covered in 2015.

If your team uses LinkedIn Recruiter, consider getting the just-updated lecture LinkedIn Recruiter and the Talent Pipeline and learn about these and other tips about the tool, that you won’t learn elsewhere.

How to Scrape Github Profiles in One Minute

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Here is a delightfully easy way to get an Excel spreadsheet from a search for Github users.

(See an exclusive offer for Brainfood subscribers below).

  1. Install Chome Extension AutoPagerize. It will allow appending the 2nd, 3rd, etc. search results pages to the bottom of the current page, creating one long page that contains all the results (or as many as you wish). It works in Google search results as well as in Github search results.
  2. Search for the languages and locations on Github – for example, language:java location:amsterdam:
  3. Scroll down, letting AutoPagerize create a long page containing as many results as you wish.
  4. Install and run Chome Extension Instant Data Scraper from

Voila – you can now export scraped, parsed results into Excel:

(Note that in this case there’s no need to locate the “Next” button since all the results are within the page.)


Did you miss our webinar “Web Scraping For Recruiters”? This week’s presentation was sold out, and we are repeating it on Tuesday, August 6th, followed by an optional hands-on Workshop on August 7th. We will cover scraping tool selection and multiple tools such as Data Miner, Phantombuster, ZapInfo, Outwith Hub, and more. Seating is limited.