Category Archives: Boolean

Talent Mining – Unearthing Value in Human Capital Data

JIT Talent IdentificationThere are people in the HR/recruiting industry who believe that searching databases, the Internet, and social networking sites to source talent is relatively easy and that it can be automated through the use of technology.

While those people are actually right (to an extent), I am happy to say that unfortunately for them, it’s not that simple.

While anyone can manually write or automate basic searches and find some people, those searches only return a small percentage of the available talent that can be found and they also exclude qualified people. Moreover, there are actually many different levels of searching human capital data in the form of resumes, social media profiles, etc., most of which cannot be replicated or automated by software solutions available today.

In this post, I’m going to share my original slide deck from my SourceCon presentation on the 5 levels of talent mining that I delivered in DC at the Spy Museum (what an awesome venue for a sourcing conference!) and then I’ll dive deep into each distinct level, including examples. Continue reading

The Best Boolean and Semantic Search Tool

While many people are hungry for specific Boolean search strings to copy and paste and for search tools that make searching for people “easier” and even “do the thinking for you,” there simply is nothing that can come remotely close to what you can do when you think properly and ask the right questions.

Yoda Think Before You Search

That’s right – the most powerful thing you can incorporate into your people search efforts isn’t Boolean logic, a search “hack,” Chrome extension, search aggregator, semantic search solution or anything you can buy – it’s your brain. Your level of understanding of and appreciation for the unique challenges posed by human capital data in any form (social media profiles, resumes, etc.) directly correlates to your ability to extract value from any data source. The same is true of the thought processes you apply before and during your search efforts.

A little over a year ago, I presented for the 3rd time at LinkedIn’s Talent Connect event in London, and I spoke about how to leverage LinkedIn’s massive stockpile of human capital data for sourcing and recruiting. LinkedIn recorded the session and uploaded the video to YouTube, and I recently noticed the video had over 65,000 views. Now, while that is puny in comparison to the nearly 1B views Adele’s Hello video has racked up, I was surprised to see so many views given the niche content.

Although the source of human capital data that I focus on in the video happens to be LinkedIn, practically everything I talk about is equally applicable to any source you can use to find people to recruit.

So, if you use any source of human capital data to find and recruit people (e.g., your ATS/CRM, resume databases, LinkedIn, Google, Facebook, Github, etc.) and you really want to understand how to best approach your talent sourcing efforts, I recommend watching this video when you have the time.

Enjoy, and feel free to let me know your thoughts!

 

LinkedIn’s New Non-Boolean Search Functionality

I originally published this post on LinkedIn, but am reposting here to ensure my blog readers catch it.

When I attended LinkedIn’s Talent Connect 2015 conference in Anaheim, CA and I was able to take some video of Eddie Vivas, Head of Talent Solutions Product for LinkedIn, formerly the Founder and Chief Product Officer at Bright.com (acquired by LinkedIn), talking about and briefly demonstrating LinkedIn Recruiter’s new search interface and functionality.

Check it out – be sure to switch to 1080p and go full screen.

As Eddie says at 1:35 into the video, “You guys ready to see some cool shit?”

I’ve attended and spoken at every Talent Connect event, and I’ve been waiting 5 long years for LinkedIn to make some major changes to their search interface and functionality.

Whatever you think of LinkedIn, they have a ton of professional human capital data, and the value of data is directly proportional to the ability of users to quickly, easily and precisely retrieve actionable data.

Definition of Actionable

The more easily recruiters can quickly and precisely retrieve profiles of people who have a decent probability of being the right match and also likely to respond to outreach efforts, the more actionable (and thus valuable) LinkedIn’s data becomes.

Although the video and a few other assets I share below don’t show you everything that’s coming to the new Recruiter search experience, I’m going to run through a few things that will definitely make LinkedIn’s data more actionable than ever before for recruiters, and none of them involve Boolean search.

Dynamic Semantic Search Suggestions

LinkedIn claims Recruiter’s new search “learns as you go,” dynamically adjusting suggested synonymous and related search terms as you enter new terms.

Think of this as LinkedIn Skills on steroids and integrated seamlessly and practically into the search experience.

As you add search terms, Recruiter will provide you with a list of the top titles, skills, companies and schools associated with your target candidates and you can choose to incorporate the suggestions  into your search (or not).

Next-Generation-of-LinkedIn-Recruiter

I’m presuming that as you add search terms they effectively create Boolean “OR” statements whereby results will match at least one of the terms.

Historically, I’ve referred to this as conceptual search or Level 2 Talent Mining. While very effective, the challenge for most people is that they don’t know all of the various ways in which people with specific skills and experience might make mention of them, leading recruiters to craft searches that actually create Dark Matter.

Based on what I can see, this new Recruiter functionality should go a long way in reducing LinkedIn’s Dark Matter, helping people build more inclusive searches by automatically suggesting additional potentially relevant search terms to return results of people who would likely not ever be found via traditional keyword search, given the wide variety of ways people can express the same skills and experience.

LinkedIn Profile Matching

You will also be able to find potential candidates using an employee’s (and perhaps anyone’s?) profile.

Essentially using a profile to automatically build a search – Recruiter’s new functionality will:

Automatically build your search string using the job title, skills, company, and industry, listed on the employee’s profile. It will show you the terms it used to build the search string, let you add or remove terms, and instantly update the list of members who meet your search criteria – helping you quickly identify the members who are a match for your open job.

I can’t wait to get my hands on this to see how well it actually performs.

Search Spotlights

This is what I am most excited about – Recruiter’s new search will offer users the ability to quickly and easily filter results by potential candidates who (LinkedIn claims) are 2-3X more likely to engage, based on relationships and interactions on LinkedIn, including:

  • Company connections
  • Past applicants
  • People engaged with your company on LinkedIn
  • People in your competitors’ talent pool (“Who your competitors target”)
  • Who’s potentially ready for a move – people who have been in their current role for 1-5 years
  • Interested candidates – people who have indicated to LinkedIn that they are open to new opportunities

New LinkedIn Recruiter Search Spotlights

I find these last 2 to be especially interesting and particularly useful- I’ve been wondering how and when LinkedIn would allow people to show recruiters they are open to new opportunities.

Granted, 1-5 years is a HUGE window and may not be as predictive or precise as some would like, but it’s a start. Also, I am not sure why LinkedIn wouldn’t offer a spotlight showing you only people who are within 30-60 days of their work anniversary – company and/or title – as this is a time when many people think about their future and could be more open to making a change.

Eddie claims they are launching with 7 different spotlights, hinting that perhaps more spotlights are likely coming in the future.

But What About Boolean?

Don’t worry – LinkedIn claims that “Advanced recruiters can continue to use their own Boolean search strings.”

However, as I’ve always stated, effective search isn’t about Boolean logic – it’s about information retrieval, and I am excited to see LinkedIn provide users with additional, and what appear to be practically useful and effective, means of retrieving a higher quantity (through more inclusive search) of relevant results – people who have a higher probability of being the right match and more likely to respond to recruiters.

When is it Coming and What Do You Think?

Apparently LinkedIn has and/or will beta launch the new Recruiter search functionality to select customers in Q4 2015, and a general launch is planned for Q1 2016.

From a few folks who have been lucky enough to play around with the new search functionality this year, I’ve heard it’s not “fully baked” yet, but I don’t find that surprising.

What do you think about these new Recruiter search enhancements?

Boolean Strings, Semantic and Natural Language Search – Oh My!

An entertaining blog post by Matt Charney was recently brought to my attention in which he tells the world to shut up and stop talking about Boolean strings – he argues that Boolean search is a dying art and that “investing time or energy into becoming a master at Boolean is a lot like learning the fine art of calligraphy or opening a Delorean dealership.”

You can read the snippet regarding Boolean Strings below – click the image to be taken to the entire post, in which Matt addresses mobile recruiting and employer branding.

Matt Charney Boolean Strings

I enjoyed Matt’s post and his approach, but I did not find his arguments to be thoroughly sound – although I suspect he wasn’t trying to make them so (after all, his blog is titled “Snark Attack”).

I’m going to take the opportunity to address the points Matt raised – not because I am trying to stay “relevant,” as some might suggest (my blog is a not-for-profit personal passion and I don’t consult/train for a fee), and also not because I have a vested interest in “keeping Boolean search alive” (because I really don’t) – rather, because I am still amazed that a fundamental lack of understanding of search and information retrieval – both “manual” Boolean search and “automated” taxonomy driven and/or AI-powered semantic search – and I am constantly trying to help people not only understand both, but also appreciate their intrinsic limitations, as well as separate reality from hype.

So, without further ado: Continue reading

Is Boolean Search Boring and Less Effective than Semantic Search?

 

Boolean Search is Boring

Do you think Boolean search is boring, tiresome and ineffective, and that semantic search delivers faster results that count?

I was struck by the image Marc Drees used in his #SOSUEU: the day after post, which you can see above.

I would have loved to sit on that panel discussion and contribute my experience and thoughts on the subject – I was actually supposed to attend and speak but I left the sponsoring company just prior to the event.

Such is life. :)

Regardless, I am happy to weigh in here, and I believe that the majority of people simply aren’t looking at search properly in the first place.

I’ll address the statements from #SOSUEU in order.

Boolean Search is Boring

Let’s hit the reset button first and get a couple of things straight:

If Boolean search is boring, then searching the Internet, Amazon, etc.,  for anything is boring. Any time you use more than 1 term in your search on Google, Bing, Amazon, eBay, etc., you’re using Boolean search. The same is true with LinkedIn and many other sites you can search to find people. Am I alone in this simple understanding?

This may confuse some people, but “Boolean Search” isn’t about Boolean – it’s about search. Searching is about finding things you need and want, and there are many ways that you can search for and find those things. Do you find it more “exciting” to select from a list or check a box on a LinkedIn facet?

Github list and LinkedIn Facet

Whether you type in keywords, select from a list, check a box, apply a filter, etc., all you’re doing is configuring a query to get results to review.

I don’t think there is anything intrinsically “boring” about Boolean operators. I think the real issue is that some recruiters just don’t enjoy searching for people, and if you don’t enjoy something it’s common to find it boring. The same people who bash Boolean search don’t find typing terms into separate search fields, picking from lists and checking boxes exciting or particularly enjoyable.

Some people really like searching databases, social networks, the Internet, etc., for people to engage and recruit. Others would be happy to post jobs and wait for people to come to them and would rather not ever have to search for potential candidates to engage.

To say that Boolean search is boring is to say that carefully looking for and trying to find people (paraphrasing Merriam-Webster’s definition of search) is boring. Semantic search solutions alleviate the boredom of searching for those who find it tedious, because similar to posting a job and getting responses, semantic search solutions often allow you to enter minimal information and get results.

I’d be willing to bet that those same recruiters who don’t enjoy searching to find people to engage also don’t enjoy reviewing responses to job postings as many are unqualified – they would much rather be given a list of well matched people, which semantic search solutions claim to be able to do.

What do you think?

Boolean Search is Tiresome

If you think Boolean search is tiresome, I say you’re lazy.

Why?

Well, for basic Boolean search, we’re talking 2 operators and 2 of modifiers – in many search engines you don’t even need to type AND, as any old space will do.

Is typing in OR, -, ” ” and ( ) really tiresome?

Is filling out multiple search fields (e.g. Twitter below) any less tiresome than typing a couple of Boolean operators? By the way, the common elements of most “advanced search” interfaces are essentially AND’s (All of these words), OR’s (Any of these words), NOT’s (None of these words), and quotation marks (This exact phrase).  Oh wait – that’s basic Boolean, right? Snap!

Twitter Advanced Search Interface

While I love the concept of natural language queries, I actually find writing them tiresome and limiting (e.g. Facebook Graph Search).

Facebook Graph Search Tiresome

Is it less tiresome to use Facebook’s search fields? They’re all essentially linked by AND’s, btw.

Facebook Graph Search Interface

If there is anything that people really find tiresome about any non-semantic search, Boolean or otherwise, is that it requires you to think and expend mental energy.

I know – thinking is tough!

If you’re read a lot of my content over the years, you know I like to bring up the study of information retrieval techniques that bring human intelligence into the search process, otherwise known as Human–computer information retrieval (HCIR).

The term human–computer information retrieval was coined by Gary Marchionini who explained that “HCIR aims to empower people to explore large-scale information bases but demands that people also take responsibility for this control by expending cognitive and physical energy.”

I know the dream is to have computers read our minds so we don’t have to type a single search term – but until that day comes, you should be aware that experts in the field of HCIR do not believe that people should mentally “check out” of the information retrieval process and let semantic search/NLP algorithms/AI be solely responsible for the results.

Having said that, I can see why some people would see the process of building a massive OR string for all of the ways in which a person could possibly reference a certain skill or experience to ensure maximum inclusion as tiresome. It’s exhaustive work – especially if you don’t want to exclude great people who simply don’t reference their experience with the most commonly used search terms.

This is one of the main value propositions of semantic search solutions for people sourcing – through taxonomies and/or NLP-powered AI/algorithms, a user can enter in a single search term and effectively search for other related terms without requiring the user to know and search for all of the other related terms.

Sound great, right? Letting a taxonomies and/or algorithms doing the conceptual search work for you is certainly a lot less tiresome than having to perform research and pay attention to search results, looking for patterns of related and relevant terms to continually refine and improve Boolean searches.

To be sure, there are some solid semantic search offerings for talent sourcing on the market today that make sourcing talent faster and easier for people who find Boolean search boring, tiring, difficult and ineffective. However, to think that semantic search solutions don’t come with their own host of challenges and limitations would be ridiculous.

In case you haven’t see it before, you may want to quickly drive through my Slideshare on Artificial Intelligence and Black Box Semantic Search vs. Human Cognition and Sourcing derived from my 2010 SourceCon keynote to get a high-level overview of some of the challenges faced by semantic search solutions specific to talent sourcing.

If you don’t want to flip through the presentation, here’s a very brief summary:
  • Human capital data/text is often incomplete and widely varied – many people with the same job have different titles, explain their experience using different terms, and in many cases simply do not explicitly mention critical skills and experience
  • Semantic search solutions can only search for what is explicitly stated in resumes and social profiles
  • Taxonomies are difficult, if not impossible to make “complete” and thus they can exclude qualified talent
  • AI/NLP can be useful in determining related terms, but not necessarily relevant terms
  • Many semantic search solutions suffer from “once and done” query execution – there is no way to refine and improve searches or to exclude false positives/irrelevant results

Boolean Search is Ineffective

The effectiveness of Boolean search strings has more to do with the person writing the queries and the sources being searched and less to nothing to do with Boolean logic.

When used in a search, Boolean operators are essentially being used as a very basic query language, and according to Wikipedia, “an information retrieval query language attempts to find…information that is relevant to an area of inquiry.”

Any search a user conducts, whether they know it or not, is essentially a formal statement of an information need.

How effectively a user can translate their information need into a query/search string largely determines the relevance of the results – Boolean logic itself often has little to nothing to do with search relevance!

Assuming a sourcer/recruiter has a solid understanding of  what they’re looking for (a dangerous assumption, by the way – try giving 5 people the same job description and then ask them separately what they’re looking for), the effectiveness of any search they use, whether Boolean, faceted, semantic, etc., is more dependent upon the user’s ability to “explain” their needs to the system/site being searched via an effective query.

For example, let’s say you’re sourcing for a sales leader and you have a military veteran hiring initiative. Regardless of whether you decided to search your ATS (e.g. Taleo), LinkedIn, CareerBuilder, Indeed, etc., you’re essentially asking the same question, “Do you have anyone with experience leading sales teams who is also a veteran?” (among other things – just trying to keep it simple here).

How would you construct a Boolean search for a sales leader who is also a veteran?

How would (and/or could!) a semantic search engine search for a sales leader who is also a veteran?

Ultimately, it comes down to how many ways can someone who has sales leader experience could possibly express that experience on their resume or profile, and how many ways someone who is a veteran could possibly reference their veteran status.

Do you know them all?

Does any semantic search engine know them all? Some don’t know any because they simply aren’t included in their taxonomies. Others could use NLP to find some, but definitely not all. However, a person with decent sourcing skills could produce a veteran query like this one in about 5 minutes (not too tiresome) and continuously improve it:

(Army OR USAR OR “U.S.A.R.” OR “Army Reserve” OR “Army Reserves” OR Navy OR USN OR USNR OR “U.S.N.” OR “U.S.N.R.” OR “Naval Reserves” OR “Naval Reserve” OR “Air Force” OR USAF OR “U.S.A.F.” OR USFAR OR “U.S.A.F.R.” OR “Force Reserve” OR “Force Reserves” OR “Forces Reserve” OR “Forces Reserves” OR Marines OR “Marine Corp” OR “Marine Corps” OR USMC OR “U.S.M.C.” OR USMCR OR “U.S.M.C.R.” OR MARFORRES OR “Marine Expeditionary Force” OR MEF OR “Coast Guard” OR USCG OR “U.S.C.G.” OR USCGR OR “National Guard” OR Veteran OR “honorable discharge” OR “honorably discharged”)

The effectiveness of any search, Boolean or semantic, can be measured by the relevance of the results (e.g., a high percentage of the results are exactly what the searcher is looking for) and the inclusiveness of the results (how many relevant results are retrieved as a percentage of the relevant results available to be retrieved – those available but not retrieved are excluded into the abyss of Dark Matter).

Only the person conducting the search can judge the relevance of the results returned by any search, Boolean or semantic, as relevance is defined as the ability (as of an information retrieval system) to retrieve material that satisfies the needs of the user, and only the user truly knows what their needs are.

When it comes to inclusion, I am aware of some folks who are proponents of “good enough” searches and search solutions (e.g., finding some good people quickly is good enough and there is no need to find all of the best people).

Try telling your company’s executives that you really don’t care about finding the best people available to be found and that you believe that the quickest and easiest to find should be good enough for your company’s hiring needs.

Let me know how that works for you.

Okay, but what about Context and Weighting?

Some folks argue that Boolean search is ineffective due to the fact that Boolean searches are not contextual (e.g. you search for a term and it shows up not in the person’s recent experience, or in their experience at all) and that all terms in a query are given equal weight (e.g., if you search for 10 terms, some terms are likely to be more important than others, but basic Boolean logic doesn’t allow you to differentiate the value/relevance of specific terms).

Admittedly, some Boolean searches are.

However, if you have well parsed/structured data and a search interface that allows you to exploit that structured data, you can use simple Boolean logic to search contextually. For example, most recent/past employer and title, most recent/past experience, etc.

Some search engines do in fact allow you to assign different weights to terms within a single Boolean query (e.g. Lucene, dtSearch, etc.) – this functionality is sometimes referred to extended Boolean search. These same search engines allow you to search for terms in or exclude them from specific areas (e.g. top of the resume, bottom of the resume) via proximity search – functionality that also allows you to perform powerful user-specified semantic search at the verb/noun level to target people with specific responsibilities (have goosebumps yet?).

Okay, that was easy to address.

So, Is Boolean Search Boring and Less Effective than Semantic Search?

For some people, yes – Boolean search is boring, tiresome and less effective than semantic search.

For others, Boolean search is exciting, easy, and more effective than semantic search.

What do I know about any of this?

I’ve evaluated, implemented and used extended Boolean search solutions as well as semantic search solutions. In addition to using them myself on a regular basis, I help 100’s of recruiters use them effectively to find the right people for 1,000’s of real positions. From my practical hands-on experience, I can tell you that sometimes semantic search produces very good results – sometimes it doesn’t. Sounds similar to Boolean, yes?

To all of the “Boolean Bashers” out there – you’re missing the point.

The effectiveness of any Boolean search has more to do with more to do with the person writing the queries and the sources being searched and less to nothing to do with Boolean logic and search syntax

Let’s remember what the goal of sourcing is – to easily find and successfully engage people who are highly likely to be the right match for the roles being sourced/recruited for.

The ultimate sourcing solution would parse resumes and profiles into highly structured data that could be searched via semantic search (autopilot) and extended Boolean (manual control) to ensure that any user could quickly find the right people under any circumstance.

I’m honestly not sure why anyone believes sourcing solutions have to leverage semantic search and exclude Boolean/extended Boolean search capability.

Monster’s Undocumented Boolean Search Operators & Query Compression

 

Monster logo smallThe other week I came across a question regarding Monster’s search operators in the Boolean Strings group on LinkedIn and I realized that most people don’t know that Monster’s classic resume search has a few undocumented search operators as well as powerful semantic search capability.

In this article I will detail two of Monster’s undocumented search operators, how to compress your Boolean search strings by more than 30%, and remind you of Monster’s documented but seldom used NEAR search operator.

AND = & + OR = |

Although I can’t seem to find any documentation of it, Monster’s search functionality does support the & for the Boolean AND search operator as well as | for OR Boolean search operator – which can save on character space for longer queries.

While most people don’t run searches that will test Monster’s main search field limit of 500 total characters (including spaces), there are those sourcers and recruiters who extensively leverage conceptual search, employing comprehensive OR statements for each concept in their Boolean search string, which can easily exceed 500 characters, especially when searching for a number of target companies.

In cases such as these, it can be helpful to use the ampersand (&) for AND and the pipe symbol (|) for OR, effectively cutting the number of characters used for AND’s and OR’s by 60% (5 total characters down to 2).

For example, compare these two searches which return the exact same results:

  • iOS AND (ObjectiveC OR “Objective-C”) AND (cocoa OR xcode) AND (iPhone* OR iPad*) AND (“apple store” OR iTunes OR “app store”) AND (SQL* OR xib)
  • iOS & (ObjectiveC | “Objective-C”) & (cocoa | xcode) & (iPhone* | iPad*) & (“apple store” | iTunes | “app store”) & (SQL* | xib)

Even with a relatively short Boolean search string of 144 characters, you can save over 10% by using & and | (128 vs. 144 characters).

If you wanted to compress your queries further, you can actually eliminate all spaces in your Boolean search string with no negative effects.

For example – this Boolean search string returns the exact same results as the above two searches:

  • iOS&(ObjectiveC|”Objective-C”)&(cocoa|xcode)&(iPhone*|iPad*)&(“apple store”|iTunes|”app store”)&(SQL*|xib)

Sadly, Monster does not support the minus sign (-) for the NOT operator.

However, you do not have to type AND NOT, nor & NOT – a simple NOT will do.

In fact, you don’t even have to capitalize NOT or any other Boolean search operator, for that matter – lowercase not works exactly the same.

Thanks Monster!

Boolean Search: Who Needs AND Anyway?

Interestingly, most people also don’t know that you don’t have to type AND or & – similar to LinkedIn, Google, Bing, etc., any space can be an implied AND.

For example, this search runs exactly as the ones above:

  • iOS (ObjectiveC|”Objective-C”) (cocoa|xcode) (iPhone*|iPad*) (“apple store”|iTunes|”app store”) (SQL*|xib)

Furthermore, you don’t even have to use a space to leverage implied AND functionality – this search returns the exact same results:

  • iOS(ObjectiveC|”Objective-C”)(cocoa|xcode)(iPhone*|iPad*)(“apple store”|iTunes|”app store”)(SQL*|xib)

Now we’re down to 101 characters, which is nearly 30% more efficient than our original 144 character search.

How’s that for Boolean search efficiency?

If you’re wondering how I figured this stuff out, it’s actually quite simple – curiosity and experimentation.

I challenge you to be curious and to experiment – from time to time, simply ask, “I wonder what would happen if…..?” and give something a try.

Hopefully all of what I’ve shared with you today has made you curious about your other sources and how you might be able to experiment and tweak your searches for other sites to make discoveries and yield additional benefits.

If you you do – please let me know!

Monster’s NEAR Operator: Documented but Seldom Used

Although Monster’s extended Boolean NEAR search operator is documented, most people don’t use it. This is unfortunate, because proximity search is incredibly powerful and can help you zero-in on people based on what they’ve actually done vs. resumes containing search keywords.

Monster’s NEAR operator is an example of fixed proximity search, which can be used to return results with words, phrases or OR statements within 10 words of other words/phrases, or OR statements, which can enable semantic search at the sentence level.

Would you be interested in learning more about sentence level semantic search using the NEAR operator?

 

100+ Free Sourcing & Recruiting Tools, Guides, and Resources

 

It’s been a LONG time coming, but I finally got around to updating my free sourcing & recruiting tools, guides and resources page where I now keep a current list of the best of my work all in one place for easy bookmarking and reference.

You can find it here on my main page:

 

Here is where you can find all of the best of my Boolean Black belt content all in one place - free sourcing and recruiting how-to guides, tools, presentations, and videos - be sure to bookmark it, and if you're feeling  friendly, tweet it, share it on LinkedIn and/or +1 it on Google Plus.  Many thanks!

 

Additionally, I thought I might as well put all of my best work all in one blog post as well – over 110 of my articles in one place for easy referencing!

My blog is a pursuit of passion and not of profit – if you’ve ever found anything I’ve written helpful to you, all I ask is that you tweet this out, share it on LinkedIn, like it on Facebook, or give this a +1 on Google.

Many thanks for your readership and support – please pay it forward to someone who can benefit.

Big Data, Analytics and Moneyball Recruiting

Big Data, Data Science and Moneyball Recruiting

The Moneyball Recruiting Opportunity: Analytics and Big Data

Human Capital Data is Sexy – and Sourcing is the Sexiest job in HR/Recruiting! 

Is Sourcing Dead? No! Here’s the Future of Sourcing

The End of Sourcing 1.0 and the Evolution of Sourcing 2.0

How to Find Email Addresses

How to Use Gmail and Rapportive to Find Almost Anyone’s Email Address

Social Discovery

2 Very Cool and Free Social Discovery Tools: Falcon and TalentBin

Talent Communities

The Often Overlooked Problem with Talent Communities

Lean / Just-In-Time Recruiting / Talent Pipelines

What is Lean, Just-In-Time Recruiting?

Lean Recruiting & Just-In-Time Talent Acquisition Part 1

Lean Recruiting & Just-In-Time Talent Acquisition Part 2

Lean Recruiting & Just-In-Time Talent Acquisition Part 3

Lean Recruiting & Just-In-Time Talent Acquisition Part 4

The Passive Candidate Pipeline Problem

Semantic Search

What is Semantic Search and How Can it Be Used for Sourcing and Recruiting?

Sourcing and Search: Man vs. Machine/Artificial Intelligence – My SourceCon Keynote

Why Sourcers Won’t Be Replaced By Watson/Machine Learning Algorithms Any Time Soon

Diversity Sourcing

How to Perform Diversity Sourcing on LinkedIn – Including Specific Boolean Search Strings

How to Use Facebook’s Graph Search for Diversity Sourcing

Social Recruiting

How to Find People to Recruit on Twitter using Followerwonk & Google + Bing X-Ray Search

Google Plus Search Guide: How to Search and Find People on Google Plus

Facebook’s Graph Search Makes it Ridiculously Easy to Find Anyone

How to Effectively Source Talent on Social Networks – It Requires Non-Standard Search Terms!

How a Recruiter Made 3 Hires on Twitter in Six Weeks!

Twitter 101 for Sourcers and Recruiters

Anti-Social Recruiting

How Social Recruiting has NOT Changed Recruiting

Social Recruiting – Beyond the Hype

What Social Recruiting is NOT

Sourcing Social Media Requires Outside the Box Thinking

Social Networking Sites vs. Job Boards

LinkedIn Sourcing and Recruiting

Sourcing and Searching LinkedIn: Beyond the Basics – SourceCon Dallas 2012

LinkedIn’s Dark Matter – Profiles You Cannot Find

How to Get a Higher LinkedIn InMail Response Rate

The Most Effective Way to X-Ray Search LinkedIn

LinkedIn Catfish: Fake Profiles, Real People, or Just Fake Photos?

LinkedIn Search: Drive it Like you Stole It – 8 Minute Video of My LinkedIn Presentation in Toronto

How to Search LinkedIn and Control Years of Experience

How to Quickly and Effectively Grow Your LinkedIn Network

How to View the Full Profiles of our 3rd Degree Connections on LinkedIn for Free

How to Find and Identify Active Job Seekers on LinkedIn

LinkedIn Profile Search Engine Optimization

Free LinkedIn Profile Optimization and Job Seeker Advice

Do Recruiters Ruin LinkedIn?

The 50 Largest LinkedIn Groups

How to See Full Names of 3rd Degree LinkedIn Connections for Free

How I Search LinkedIn to Find People

LinkedIn’s Undocumented Search Operator

Does LinkedIn Offer Recruiters any Competitive Advantage?

Have You Analyzed the Value of Your LinkedIn Network?

Where Do YOU Rank In LinkedIn Search Results?

What is the Total Number of LinkedIn Members?

Beware When Searching LinkedIn By Company Name

LinkedIn Sourcing Challenge

How to Search for Top Students and GPA’s on LinkedIn

What’s the Best Way to Search LinkedIn for People in Specific Industries?

18 LinkedIn Apps, Tools and Resources

LinkedIn Search: What it Could be and Should be

How to Search Across Multiple Countries on LinkedIn

Private and Out of Network Search Results on LinkedIn

How to “Unlock” and view “Private” LinkedIn Profiles

Searching LinkedIn for Free – The Differences Between Internal and X-Ray Searching

Sourcing and Boolean Search

Basic Boolean Search Operators and Query Modifiers Explained

How to Find Resumes On the Internet with Google

Challenging Google Resume Search Assumptions

Don’t be a Sourcing Snob

The Top 15 Talent Sourcing Mistakes

Why Boolean Search is Such a Big Deal in Recruiting

How to Become a World Class Sourcer

Enough with the Exotic Sourcing Already – What’s Practical and What Works

Sourcing is So Much More than Tips, Tricks, Hacks, and Google

How to Find, Hire, Train, and Build a Sourcing Team – SourceCon 2013

How to Use Excel to Automatically Build Boolean Search Strings

The Current and Future State of Sourcing

Why So Many People Stink at Searching

Is your ATS a Black Hole or a Diamond Mine?

How to Find Bilingual Professionals with Boolean Search Strings

How to Best Use Resume Search Aggregators

How to Convert Quotation Marks in Microsoft Word for Boolean Search

Boolean Search, Referral Recruiting and Source of Hire

The Critical Factors Behind Sourcing ROI

What is a “Boolean Black Belt?”

Beyond Basic Boolean Search: Proximity and Weighting

Why Sourcing is Superior to Posting Jobs for Talent

The Future of Sourcing and Talent Identification

Sourcing is an Investigative and Iterative Process

Beyond Boolean Search: Human Capital Information Retrieval

Do you Speak Boolean?

Is Recruiting Top Talent Really Your Company’s Top Priority?

Sourcing is NOT an Entry Level Function

Boolean Search Beyond Google

The Internet Has Free Resumes. So What?

How to Search Spoke, Zoominfo and Jigsaw for Free

Job Boards vs. Social Networking Sites

What to Do if Google Thinks You’re Not Human: the Captcha

What if you only had One Source to Find Candidates?

Passive Recruiting is a Myth – It Doesn’t Exist

Sourcing: Separate Role or Integrated Function?

The #1 Mistake in Corporate Recruiting

How I Learned What I Know About Sourcing

Resumes Are Like Wine – They Get Better with Age!

Why Do So Many ATS Vendors Offer Such Poor Search Functionality?

Do Candidates Really Want a Relationship with their recruiter?

Recruiting: Art or Science?

What to Consider When Creating or Selecting Effective Sourcing Training – SourceCon NYC

The Sourcer’s Fallacy

Sourcing Challenge – Monster vs. Google – Round 1

Sourcing Challenge – Monster vs. Google – Round 2

Do You Have the Proper Perspective in Recruiting?

Are You a Clueless Recruiter?

Job Boards and Candidate Quality – Challenging Popular Assumptions

When it Comes to Sourcing – All Sources Are Not Created Equal

Boolean Search String Experiments

Boolean Search String Experiment #1

Boolean Search String Experiment #1 Follow Up

Boolean Search String Experiment #2

 

How Would You Search for these Positions on LinkedIn?

One of the things that has always struck me as extremely odd with regard to sourcing is the fact that there appears to be so little sharing of Boolean search strings.

While one can find basic search string examples in training materials and in various sourcing groups online, I know plenty of sourcers and recruiters that have never seen another person’s production search strings – those used to actually fill positions.

Why do you think that is? I have my ideas, and I’d like to know yours.

I believe there may be several contributing factors:

  1. Some people just don’t save their searches. If I were a betting man, from what I’ve seen over the past 15+ years, I’d wager that the majority of people don’t save their search strings. If they’re not saved anywhere – you severely limit any sharing opportunities to live, in-the-moment situations that may or may not ever present themselves.
  2. It simply never occurs to some people to share their searches with others – unless someone specifically asks, why would someone?
  3. Plain old insecurity. Some folks might not want to share their search strings with others because they are afraid theirs are somehow “wrong,” inferior or inadequate.
  4. The belief that their Boolean search strings are somehow their “secret sauce” and that in sharing their searches might somehow expose their competitive advantage.

What do you think?

How Would You Search for these Positions on LinkedIn?

Are you up to the challenge of sharing some of your searches with a global audience of talent acquisition professionals? Continue reading

Why Boolean Search is Such a Big Deal in Recruiting

In the past, I’ve explained the Boolean Black Belt concept and exposed what I feel is the real “secret” behind learning how to master the art and science of leveraging information systems for talent identification and acquisition.

Now I would like to show you precisely WHY Boolean search is such a big deal in recruiting.

There are 2 main factors:

  1. Candidate variable control
  2. Speed of qualified candidate identification.

The goal of this article is to shed significant light on the science behind talent mining, how it can lead to higher productivity levels (more and better results with less effort), why I am so passionate sourcing, and why everyone in the HR, recruiting, and staffing industry should be as well.

Control is Power

Talent identification is arguably the most critical step in recruiting life cycle – you can’t engage, recruit, acquire, hire and develop someone you haven’t found and identified in the first place.

My experience has shown me that properly leveraging deep sources of talent/candidate data (ATS/CRM’s, resume databases, LinkedIn, etc.) can enable recruiters to more quickly identify a high volume of well matched and qualified candidates than any other method of candidate identification and acquisition (e.g., cold calling, referral recruiting, job posting).

The true power of Boolean search lies in the intrinsically high degree of control over critical candidate variables that using Boolean strings to search deep data sources such as resume databases, the Internet, and social media affords sourcers and recruiters.

Applying that that high degree of control to large populations of candidates – tens of thousands (small internal ATS, niche resume database) to tens of millions (large ATS/CRM, Monster resume database, LinkedIn, etc.) enables adept sourcers to perform feats of talent identification and acquisition most would think impossible.

Continue reading

Boolean Search Strings, Referrals and Source of Hire

I read an article on ERE about the other day titled “Love Writing Boolean Instead of Recruiting? Then Don’t Read This Post.

While I happen to be pretty good at and thoroughly enjoy writing Boolean queries for talent mining, I actually love the entire recruiting life cycle. Sourcing is a means to an end, not a means in and of itself for me. Even so – with such a provocative post title (nice work John!), I had to read the article.

The article is a pretty strong pitch for Scavado, which “does the search work for you, saving hours of time otherwise spent developing Boolean search strings and applying them manually to each site searched.”

Things really got interesting when I got down to the comments on the article, as I stumbled into an interesting exchange between Amybeth Hale and Keith Halperin which covered direct sourcing, referral recruiting, and outsourcing sourcing at $6.25/hour.

Read on to learn my thoughts on all of the above. Continue reading

What is a Boolean Black Belt Anyway?

I’ve been blogging nearly 3 years now, and I realized I’ve never come out and actually defined the term “Boolean Black Belt.”

The concept seems pretty self explanatory, but there has been at least 1 person who’s taken the opportunity to point out (and gain some traffic in the process – but it’s all good!) that it could be perceived as a bit of an oxymoron to be an “expert” in something as simple as 3 Boolean operators.

Interestingly, however, I’ve found that most sourcers and recruiters don’t even fully exploit the various powers of the OR and NOT operators – not even close.

So what is a “Boolean Black Belt” anyway? Continue reading

LinkedIn’s Undocumented Search Operator

Earlier this year, I wrote an article on how to use LinkedIn’s advanced search operators as search agents in which I briefly mentioned and demonstrated an undocumented LinkedIn search operator at the very end of the post.

Did you catch it?

If not, you’re in luck.

Although it’s not an Earth-shattering discovery by any means, it is a discovery nonetheless, and because I keep encountering people who don’t know about this LinkedIn search operator, I thought it would be a good idea to dedicate a short post to the topic to ensure ensure everyone is aware of it. Continue reading

Beyond Boolean Search: Proximity and Weighting

Beyond Basic Boolean

Most sourcing, recruiting, and staffing professionals are familiar with the basic Boolean operators of AND, OR, and NOT. However, I have found that few are familiar with what some refer to as “extended” Boolean functionality, such as proximity search and term weighting.

Proximity and term weighting, where supported, are not actually logical (Boolean) operators – they are more accurately referred to as text or content operators.

Whatever you call them – extended Boolean or text operators – they offer sourcers and recruiters significantly more control, power and precision when executing searches, and in the hands of an expert, they can enable semantic search. Continue reading

Beyond Boolean: Human Capital Information Retrieval

When I recently spoke at SourceCon in New York, I showed an example Boolean search string that could be used as a challenge or an evaluation of a person’s knowledge and ability.

The search string looked something like this:

(Director or “Project Manage*” or “Program Manage*” or PM*) w/250 xfirstword and (truck* or ship* or rail* or transport* or logistic* or “supply chain*”) w/10 (manag* or project)* and (Deloitte or Ernst or “E&Y” or KPMG or PwC or PricewaterhouseCoopers or “Price Waterhouse*”)

During the presentation, an audience member asked me why there wasn’t any use of site:, inurl:, intitle:, etc. I responded by acknowledging that for many, sourcing and Boolean search seems to be synonymous with Internet search – however, this is definitely not the case. Continue reading

Are You Fluent in the Language of Information Systems?

If you traveled to a foreign country where you don’t speak the local language, you would find yourself in a situation where there are questions you would want to ask people and things you’ll need to know, and nearly everyone you run into would be able to help you – but because you can’t articulate in a manner that the locals understand, they can’t assist you and provide you with what you need.

Most people would be rightfully frustrated in this kind of scenario – knowing that nearly everyone you run into can help you with the answers or the information you need, but you just can’t express yourself in a way anyone can understand.

Some people respond to this by speaking more slowly or more loudly (or both!) – but of course this does not help one bit.  In fact, it may simply annoy the locals and make them less likely to want to try and help you.

Others might try and get a phrase or translation book to try and communicate.  Have you ever had to try and communicate with someone who does this?  It’s painful, but it’s a step better than gesticulating wildly and speaking in a different language slowly and loudly.

If you were fluent in the local language – none of this would be an issue. You’d be able to communicate quickly and effectively with nearly anyone you come into contact with and get the answers you seek or the information you need.

Working with computerized systems is no different.

Every day, most people interface with information systems of some kind – computers (tablets, laptops, smart phones, etc.), the Internet (search engines, web sites/apps, social media), and databases.

Yet most people don’t speak the “native language” of computerized systems. If you don’t speak the local language, why would you assume that the locals automatically “know” what you’re looking for and that you should be able to get you precisely the information you need?

So – what’s the “local language” of computerized systems?

Boolean.

Continue reading

Boolean Search String Experiment #2

Cyborg Sourcer

Back in November, I posted a Boolean search challenge to demonstrate that when you give a number of sourcers and recruiters the same job description/hiring profile to search for, you will get as many different searches and search strategies as you have sourcers and recruiters.

As I have said many times before, every search string “works,” provided they are syntactically correct.

However, not all search strings or strategies are created equal, nor are the results that are returned.

Because of this fact, 20 different sourcers and recruiters searching the same source (LinkedIn, the Internet, Monster, etc.) will find some of the same candidates, but each will also find some that the others do not.

The most important question to ask is anyone actually finding all of the best candidates that the particular source has to offer? Believe it or not, some of the best candidates are never found by the people who are searching for them. You can’t be aware of something your searches do not return.

Or can you?

Information Retrieval is the Key

When it comes to information retrieval– which is the science of searching for documents (e.g., resumes, press releases, etc.), for information within documents (e.g., experience and qualifications), as well as searching relational databases and the Internet – simply having access to the information does not afford a sourcer, recruiter or organization any competitive advantage.

However, human capital informational and competitive advantage can be achieved through more effective retrieval – in other words, more effective queries (i.e., Boolean search strings).

Queries are formal statements of information needs. When searching to identify talent, the more effective you are at translating your information needs (skills, experience, qualifications, etc.) into queries, the more likely you are to find all of the best candidates any particular source of talent has to offer. Continue reading

Boolean Search String Experiment Follow Up

On November 8th, 2010, I wrote a post containing a Boolean search challenge and an experiment of sorts – I asked readers to share their approach and Boolean search strings for a basic job description. The inspiration for the experiment came from the fact that very few people seem to be consciously aware of the issue that when it comes to sourcing candidates via the Internet, resume databases, LinkedIn, etc., is that all Boolean candidate searches work, provided they are syntactically correct.

This is a fundamental problem which heavily influences the perception of sourcing as a low level, non-critical function and/or role, because anyone can take the title from a job description and the required skill terms, create a basic Boolean query, and get results. This leads to the idea that finding talent is easy – slap a few search terms together and voila! – you get candidates.

Congratulations for finding the same candidates everyone else is finding with the same unsophisticated searches. All candidate queries are definitely not created equal, and you simply cannot gain any competitive advantage running the same basic taken-straight-from-the-job-description title and keyword searches that everyone else does.

The lesser-known reality is that most people who run Boolean searches on LinkedIn, job board resume databases, in their Applicant Tracking Systems (if they even support Boolean – ouch!) and the Internet only find a small fraction of the talent that is available to be found. I’ve written quite a bit on the topic so I won’t belabor that point in this post. Continue reading

Boolean Search String Experiment – Are You Game?

Cyborg SourcerOne of the most interesting yet overlooked aspects associated with sourcing candidates using the Internet, job board databases, ATS/CRM systems and social networks such as LinkedIn is that as long as your syntax is correct, every search “works.”

This fact leads (too) many people to believe that finding talent online is easy and that there is no competitive advantage to be gained in the practice of searching human capital data.

However, are all queries created equal?

Would 5 different recruiters working the same position use the same search strings and search strategy? Would they find the same people if they used the same source?

In many organizations, sourcers and recruiters do not get (or seek out) the opportunity to compare and contrast their search strategies and tactics with their peers and/or managers on a position-by-position basis. Much of the magic of talent discovery and identification, or lack thereof, happens on each person’s computer screen.

Unlike professional athletes and musicians whose skills and techniques are on display and scientists who publish their work, sourcers and recruiters responsible for talent discovery have absolutely no public basis of comparison. Continue reading

How to Automatically Build Boolean OR Strings

Writing Boolean search strings is typically a quick and simple affair, as most search engines and databases won’t let you construct anything longer than a few hundred characters.

However, if you’re not constrained to a fixed limit on search terms (such as Google’s 32 words) or characters, it’s no longer a simple matter of “this OR that.”

I wrote an article not too long ago in which I illustrated some of the serious limitations associated with using industry filters when searching LinkedIn (or any site, for that matter) for people with specific industry experience. In that post, I demonstrated that when accessing LinkedIn with a free account, there are no search string length limits, allowing you to enter long OR statements with 100’s of companies.

Building large OR strings can be very tedious and time consuming work. Thankfully, John Turnberg graciously commented on my article (thank you John!) and detailed how to use Excel to make quick work of creating large OR strings.

I am not an Excel wizard by any means, so it may have taken me longer than most to take John’s Excel advice and get it to work. If you’re not handy with Excel and would like a turn-key solution, I’ve saved you the effort of trying to build it yourself – you can download a basic Excel Boolean OR builder here: Continue reading

Basic Boolean Search Operators and Query Modifiers Explained

 

Basic Boolean Operators Explained

No, those aren’t my hands.

I never cease to be amazed by what you can find on the Internet and what people take pictures of.

Now that I have your attention, this post is going to focus on the basic Boolean search operators and search modifiers symbols and will not go into any detail of the many special Internet-only search commands/operators.

Although a great many people seem to think that Boolean = Internet search, Boolean logic and searching has been around WAY before the Internet. And here’s a quick fact: you don’t have to capitalize Boolean operators on any of the major job boards and many of the major ATS’s.

Go ahead – try it. Nothing will explode and your searches will execute.

And now, back to the Boolean basics…

Boolean Search Operator: AND

The AND operator is inclusionary and thus limits your search.

It should be used for targeting required skills, experience, technologies, or titles you would like to limit your results to. Unless you are searching for common words, with every AND you add to your Boolean query, the fewer results you will typically get.

Example: Java AND Oracle AND SQL AND AJAX

On most Internet search engines and LinkedIn, every space is an “implied AND,” and you don’t have to type it, as every blank space is interpreted as an AND operator.

Example: Java Oracle SQL AJAX

Bonus: You can use the ampersand (&) as the AND operator on Monster.

Boolean Search Operator: OR

The OR operator offers flexible inclusion, and typically broadens your search results.

Many people incorrectly think the Boolean OR operator is an either/or operator, when in fact it is not.

The OR operator is technically interpreted as “at least one is required, more than one or all can be returned.”

Although some search engines, such as Google, do not require you to encapsulate OR statements with parentheses, if you don’t on most databases and LinkedIn – your search will run but execute in a way that you probably did not intent.  As a best practice, I tell people to always use parentheses around OR statements as a matter of good search syntax.

Example: Java AND Oracle AND SQL AND AJAX AND (apache OR weblogic OR websphere)

The returned results must mention at least one of the following: apache, weblogic, websphere. However, if candidates mention 2 or all 3, they also will be returned, and most search engines will rank them as more relevant results because of such.

The best ways to use OR statements is:

  1. To think of all of the alternate ways a particular skill or technology can be expressed, e.g., (CPA OR “C.P.A” OR “Certified Public Accountant”)
  2. To search for a list of desired skills where you would be pleased if a candidate had experience with at least one, e.g., (apache OR linux OR mysql).

Bonus: You can use the pipe symbol (|) for the OR operator on Google, Bing, and Monster.

Boolean Search Operator: NOT

The NOT operator is exclusionary – it excludes specific search terms and so the query will not return any results with that term (or terms) in them.

Example: If you were searching for an I.T. Project Manager, you may want to employ the NOT operator in order to eliminate false positive results – results that mention your search terms but do not in fact match your target hiring profile.  In this case, you could run: “project manager” and not construction – this search will not return any results with “project manager” and the word “construction” contained within them.

On all of the major job board resume databases, some ATS’s and LinkedIn, you can use the NOT operator in conjunction with an OR statement.

Example: .Net AND NOT (Java OR JSP OR J2EE) – that search will not return any results with any mention of Java, JSP, and/or J2EE.

Bonus: NOT has 2 main uses

  1. Excluding words you do not want to retrieve to reduce false positive results (most common usage)
  2. Starting with a very restrictive search with many search terms, you can use the NOT operator to systematically and progressively loosen the search into mutually exclusive result sets (not so common usage, but very effective strategy)

Basic example:

  1. “Project Manager” AND SQL AND Spanish
  2. “Project Manager” AND SQL AND NOT Spanish
  3. “Project Manager” AND NOT SQL AND Spanish
  4. “Project Manager” AND NOT (SQL OR Spanish)

Bonus: You can use the minus sign as the NOT operator on many sites and search engines, including LinkedIn.

Boolean Search Modifier: ASTERISK *

The asterisk can be used on most resume databases and non-Internet search engines as a root word/stem/truncation search. In other words, the search engine will return and highlight any word that begins with the root/stem of the word truncated by the asterisk.

For example: admin* will return: administrator, administration, administer, administered, etc.

The asterisk is a time saver for search engines that recognize it (most major job boards and ATS’s) because it saves you from creating long OR statements and having to think of every way a particular word can be expressed.

LinkedIn does not support the asterisk, so you will have to construct large OR statements to search for all of the various ways someone could mention each term you’re searching for. For example: (configure OR configuring OR configured OR configures)

Boolean Search Modifier: PARENTHESES

As a best practice, use parentheses to encapsulate OR statements for the search engines to execute them properly.

Remember, the OR operator is interpreted as “I would like at least one of these terms.” Think of parentheses as your way of telling the search engine you’re looking for one of THESE: (_______________).

For example: (apache OR weblogic OR websphere)

If you don’t enclose all of your OR statements, your search may run but it will NOT run as intended.

Boolean Search Modifier: QUOTATION MARKS ” “

Quotation marks must be used when searching for exact phrases of more than one word, or else some search engines will split the phrase up into single word components.

For example: “Director of Tax” will only return “Director of Tax.” If you searched for Director of Tax without the quotation marks, on some search engines, it will split up the words Director and Tax and highlight them as relevant matches even when not mentioned as an exact phrase.

Bonus: Google auto-stems many search terms, so if you are looking specifically for the word manager, it will still return managed, management, etc. – even if you don’t want it to. If you put quotation marks on a single word in Google, it will defeat the auto-stemming feature and only return that specific word.

There you have it – Boolean basics!

If there is something you would like to see me post about with regard to Boolean logic and search tactics and strategies – let me know.

Thanks!