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.

Sourcing vs. Recruiting?

I’m not exactly sure where the whole concept of sourcing vs. recruiting comes from, but I do find it interesting that some people think that people who source spend more time writing Booelan search strings than they do recruiting.

I think it comes mostly from people who either don’t know how to source candidates via ATS/CRM systems, resume databases, social media and the Internet, or just aren’t that good at it.

For anyone who is remotely adept at sourcing, the actual process of creating and refining Boolean (and Faceted search on LinkedIn) takes less than 10% of their time. For me, that number is less than 5%. On an average recruiting day, I might spend 15 – 20 minutes or so per day working specifically on building and refining Boolean search strings.

That’s about 3.1% to 4.2% of an 8 hour day.

Care to guess what I’m doing the other 95.8% – 96.9% of the time? If you guessed recruiting and NOT writing Boolean search strings, you’d be right. The author of the ”Love Writing Boolean Instead of Recruiting? Then Don’t Read This Post” article mentioned that the creator of Scavado “got tired of spending more time writing search strings than calling prospects.”

If someone is spending more time writing Boolean search strings than calling potential candidates, something is seriously wrong.

Of course, if you’re dealing with low quality/shallow data (the Internet) and poor search interfaces/capability (many ATS’s and some search aggregators), that time might expand somewhat – but it should never be a significant chunk of any given day. No one should be asked to be fast, efficient and highly productive if they are stuck with using only free sources and a practically unsearchable ATS – but that’s a topic for a future post.

Oh, and you did know that there are more recruiters and HR professionals who source candidates than sourcers who source candidates, didn’t you? Just one more reason I am confused by the sourcing vs. recruiting mentality.

Ultimately, the whole point of becoming more facile with information retrieval (Boolean search, faceted search, semantic search, talent mining, etc.) is to be able to more quickly identify and engage people to assess their potential as candidates and/or get referrals.

More on that later.

Outsourcing Sourcing

There is nothing intrinsically wrong with outsourcing sourcing, but I love to hear of people using resume and lead sourcing services at rates as low as $6.25/hour.

When you pay $6.25 – $15/hour for sourcing, you’re essentially getting a resume scraping service, which literally scrapes the surface of the talent pool in the sources being searched. This level of sourcing is what I refer to as Level 1 Talent Mining (with perhaps a sprinkle of Level 2), which essentially finds what everyone else finds with basic and imprecise searches. The proverbial tip of the iceberg, offering no competitive advantage.

If anyone can hire all of the people they need to, at the level of candidate quality and at the speed needed using this level of sourcing, then more power to them.

One thing to think about, however, is that you may be paying $6.25 per hour and be billed for an hour that was really 5-10 minutes of someone’s time. Some of these folks may be really making $37.50 –  $62.50 an hour for their services.

Think about it.

Boolean Search Strings vs. Referrals

I’m going to take this post as an opportunity to clear the air with regard to sourcing (Boolean et al) vs. referrals.

They are not mutually exclusive.

When searching internal ATS/CRM systems, job board resume databases (e.g., Monster, Dice, Careerbuilder, Indeed), LinkedIn, the Internet, etc. – the results returned by the searches are merely first-order results, and only represent a fraction of the talent that can ultimately be reached and actualized.

Any sourcer/recruiter worth their salt is not only looking to potentially recruit the people directly returned by their searches (first-order results),  but also tap into the networks of those people (second-order results, third-order results, etc.).

In that sense, any source that can be searched can be viewed similarly to LinkedIn, as each person directly retrieved via any search method or source knows people who know other people, and so on.

Here’s the bottom line: Sourcing via Boolean search strings or any other method of information retrieval affords referral recruiting opportunities.

In fact, the more effective and efficient you are at sourcing, the faster you can crowdsource your hiring need. Yes, strong sourcing actually accelerates and multiplies any sourcer’s/recruiter’s referral recruiting opportunities.

If you’re searching ANY site/source and your’re not tapping into the second- and third-order+ results available (each person’s direct and extended network), leveraging everyone you contact for networking and referrals, you’re not doing your job as effectively as you could.

Are Referrals Really the #1 Source of Hire?

Gerry Crispin’s CareerXroads fantastic Source of Hire data was mentioned and linked to in the comment exchange between Amybeth and Keith, so I decided to take a peek (again).

Notice that 27.5% of hires came from referrals. No surprise there, right?

Stick with me.

If you keep moving through the report, you’ll find that 45% of the respondents attributed all of their referral hiring from employee referrals.

That means a good chunk of referrals come from non-employees.

Where am I going with this?

You’ll notice that the #2 source of hire was job boards, at 24.9%, nipping on the heels of referrals (27.5%) as a source of hire.

Yeah, I know – I thought job boards had been killed years ago by social media and referrals. Who knew?

Humor aside, if you keep moving forward to Figure 15, you can see that 52.8% of firms said that their job board hires were predominantly from postings and not resume searches.

That still leaves a good percentage of job board hires coming from resume searches.

So let’s say you search Monster (via Boolean queries) for potential candidates, contact someone who turns out to not be available to consider making a move at this time, you sell your opportunity to them and ask who they could recommend for the role, and they refer you someone.

Let’s say that the person referred to you is a great fit and eventually gets the job.

What’s the source of the hired candidate?

Monster?

Referral?

What’s the probability that it’s coded properly in the ATS?

Who knows – if every referral hired that came from people sourced through job board, ATS, Internet or social media searches was actually coded properly and specifically, referrals may not actually be the #1 source of hire.

What is Direct Sourcing Anyway?

In the exchange between Keith and Amybeth, it was asserted that direct sourcing represents only 5% of hires, and it seemed to me that Boolean search was somehow being tied to the concept of “direct sourcing.”

I don’t consider searching my ATS or a job board resume database such as Monster or Dice for potential candidates to engage and hire to be direct sourcing, and I am not alone – take a look at the CareerXroads data regarding direct sourcing:

So a solid chunk of the searching (via Boolean queries or otherwise) of internal ATS/CRM systems and job board resume databases to identify and engage potential candidates isn’t really a part of the 5% of direct sourcing, and could in fact be a significant contributor to the “Job Boards” source of hire. Which, I might remind you, is 24.9%.

Also, it is interesting that “ATS” isn’t its own source of hire in the survey – could it be lumped into the “Career Site” source?

With regard to ATS search (Boolean queries or otherwise), we could easily run into source of hire coding accuracy issues.

For example, let’s say you search your ATS for potential candidates and find an old resume from someone who responded to a Monster ad over a year ago. Let’s say you make contact with this person, recruit and hire them.

What’s the source of the hired candidate?

Monster?

Your ATS?

See the source of hire coding challenge?

Either way, the hire came from a search. Likely Boolean.

Final Thoughts

I completely understand and appreciate the sell and positioning of products and services that “perform your searches for you” – not everyone wants or needs to know how to leverage information systems for talent identification, nor is everyone capable of doing so effectively.

However, as I have written and spoken about many times before, any attempt to automate information retrieval without human influence in the querying process has significant limitations and issues. Of course, it certainly doesn’t help that the people who are drawn most to automated solutions are the least equipped to be able to test the claims made by those who are selling automated search solutions.

I hope I’ve made a dent in the sourcing vs. recruiting issue – it’s not an either/or relationship. Sourcing is a critical part of recruiting – you can’t engage, recruit and hire someone you haven’t identified in the first place.

Posting jobs only attracts active candidates, and referrals only account for 27.5% of external hires – so if you’re going to try and recruit people who haven’t found your job and can’t be reached through employee referrals, you can find and target passive candidates (and even those who aren’t looking but can be recruited!) by searching ATS/CRM systems, job board resume databases, LinkedIn, Facebook, Twitter, the Internet, etc.

Additionally, searching information systems for potential candidates affords you the opportunity to tap into second- and third-order results – the networks and connections of the people you find directly from your searches, increasing and accelerating your referral recruiting opportunities.

Not everyone has to be interested in or capable of searching databases, social media and the Internet to source potential candidates, but there is no denying that the volume of and speed at which human capital data is being generated poses a huge opportunity and need.

For example, some really smart people have been talking for quite some time about the latent power of data, and more specifically human capital data.

Here’s the thing about data – data requires analysis for insights, intelligence, and decision making, but data can’t be analyzed until it’s retrieved.

And the simplest form of information retrieval involves Boolean logic, whether it’s in your face or behind the interface.

Oh – and one last thing…if you’re spending more time creating and refining Boolean search strings than engaging candidate prospects, I’d advise you to get a mentor and perform some deliberate practice to get better and faster at information retrieval, perhaps invest in some training, or if you have no desire to get better at sourcing – outsource your sourcing to specialists if that’s a viable option.

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?

Black Belt

I use the term ”Black Belt” in reference to the widely known way of describing an expert in martial arts, where the black belt is commonly the highest belt color used and denotes a high degree of competence.

That’s the easy part; the “Boolean” part isn’t so simple to define.

Boolean

I’d like to take the opportunity to clear up some misconceptions about, and disambiguate my use of “Boolean” in “Boolean Black Belt,” and pretty much any article in which I refer to Boolean.

When I refer to “Boolean,” I am not refering only to the basic Boolean operators of AND, OR, and NOT. I’m actually referring to the entire process of:

1. Analyzing, understanding, and interpreting job opening/position requirements
2. Taking that understanding and intelligently selecting titles, skills, technologies, companies, responsibilities, terms, etc. to include (or purposefully exclude!) in a query employing appropriate Boolean operators and query modifiers
3. Reviewing the results of the initial search to assess relevance as well as scanning the results for additional and alternate relevant search terms, phrases, and companies
4. Based upon the observed relevance of and intel gained from the search results, modifying the search string appropriately and running it again
5. Repeat steps 3 and 4 until an acceptably large volume of highly relevant results is achieved

Instead of trying to put all of that into a domain name and a concise catch phrase, hopefully you can appreciate why I chose to summarize that entire process as ”Boolean.”

Beyond Boolean Logic

Admittedly, the basic Boolean operators are easy to learn – after all, there’s only 3 of them!

However, anyone who’s adept at leveraging databases and information systems for talent identification knows that the “magic” does not lie in the operators themselves, but in all of the steps detailed above.

The “real” work of creating effective Boolean search strings lies in the interpretive analysis of the need, determining what terms to include and exclude from searches and in what specific combination, in the analysis of the relevance of the initial search results, and the adaptive process of learning from the results to further refine the Booleans to find a large quantity of highly relevant results – people who are highly likely to be (or know!) the right match for your hiring needs.

What I just described is actually the process of Information Retrieval (IR), but no matter how much I write on the subject, people still cling to “Boolean.”

Sourcing isn’t so Simple

While learning about the concepts of basic Boolean logic is easy, there is nothing inherently easy about creating Boolean search strings for talent identification.

To say that searching databases and information systems to identify talent is “easy” because it’s defined only by 3 simple Boolean operators is to admit that you have little to no understanding or appreciation of online, database, or social network sourcing.

That would be like saying that a challenging math-based brain teaser is simple because everyone understands addition, subtraction, division, and multiplication.

For example, this classic puzzle should be easy for anyone who understands basic math, right?

“My grandson is about as many days as my son is weeks, and my grandson is as many months as I am in years. My grandson, my son and I together are 100 years. Can you tell me my age in years?”

After all, it only requires 3 basic and simple mathematical operations: addition, multiplication, and division. If that one is too “easy” for you, give this brain teaser a try – it too only requires basic math to solve.

It should be obvious that the real challenge of math-based problems comes from being able to understand the puzzle in the first place, and then determining precisely what types of equations and operations are required to solve the problem.

The analysis and understanding is primary, the mathematical operators secondary, as they are useless without the proper understanding of the required and specific application of them.

It’s the same thing with Boolean search strings.

Extended Boolean

Beyond the 3 “standard” Boolean operators, there lies extended Boolean, which typically includes proximity operators and term weighting/boosting.

While not every search engine supports extended Boolean, those that do afford users the ability to dramatically increase the relevance of search results, effectively enabling user-defined semantic search.

Semantic Search

Semantic search can be defined as search techniques that leverage the actual meaning in words and phrases and can return results that more closely match the “meaning”  or intent of the search rather than simply returning results that match the words of the search.

The whole goal of searching databases, the Internet, social media, or other information systems is ostensibly to find people who have a high likelihood of being (or knowing!) a potential match for a hiring need that you have now, or will have in the future.

The more skill and ability you have in being able to craft and execute Boolean and extended Boolean search strings that find more of the right people more quickly, the more effective you can be as a Sourcer or Recruiter.

By “effective” I mean filling more positions with high quality talent while reducing time-to-fill.

More. Faster. Better.

Whenever I refer to “Boolean” in articles or even in the name of this blog, I’m actually referring to extended Boolean and user-defined semantic search as well as the basic Boolean operators.

Query Modifiers

Boolean search strings are often comprised of more than just search terms and Boolean operators.

There are also query modifiers, and depending on the search engine, they can include: *, ” “, inurl:, ~, ( ), w/, and many more.

Anyone hoping or claiming to have a high degree of competence with sourcing not only has to have a solid command of the basic Boolean operators, but also how to leverage the available and appropriate query modifiers.

Final Thoughts

I use the term “Boolean Black Belt” to describe someone with a high degree of competence in the entire process of interpreting and understanding a specific talent need, determining what terms to include and/or exclude from searches and in what specific combination, crafting search strings making effective and appropriate use of Boolean operators, query modifiers, search terms, and semantic search techniques, the analysis of the relevance of the initial search results, and the adaptive process of learning from the results to further refine the Booleans to find a large quantity of highly relevant results – people who are highly likely to be (or know!) the right match for their hiring need.

I believe that when most people in sourcing and recruiting roles refer to “Boolean,” they are not simply referring to AND, OR, and NOT.

To say that mastering the use of Boolean search strings for talent identification is limited to the understanding of the functions of 3 Boolean operators would be ridiculous and an obvious sign of ignorance.

Most people would agree that Barack Obama is an excellent orator, yet he does not use words most people do not understand. For the most part, he uses common words that everyone is familiar with. But his ability as an orator cannot be defined by or limited to the common words he uses - it lies in how he organizes his thoughts and how he arranges and delivers his sentences to convey his indended meaning.

Most sculptors, golfers, jiu jitsu practitioners, and orators use the same tools, clubs, moves, and words. However, mastery does not come from the specific tools, clubs, movements, or words - it’s in the appropriate and effective APPLICATION of them, typically in response to a challenge or to achieve a specific goal.

Knowing what golf clubs are and how to swing them does not make you a world-class golfer. Having a good vocabulary does not make you an excellent public speaker. Knowing how to punch and kick will not ensure you can win any martial arts/MMA competitions. Owning a hammer and chisel does not make you a world-renowned sculptor.

Similarly, having a command of 3 Boolean operators does not ensure that you can understand the positions you are sourcing or recruiting for and effectively leverage electronic sources of human capital data (databases, ATS/CRM’s, social media, the Internet, job boards, etc.) to find more of the best candidates available for your hiring needs more quickly.

Nor does it define a Boolean Black Belt, if such a thing can or should exist.

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.

LinkedIn’s Search Documentation

If you take a look at LinkedIn’s Advanced Search tips or in LinkedIn’s Help section, you will see the standard Boolean operator explanations.

When you get to the NOT operator, you will see a mysterious reference – “note: we also have a NOT operator that does the same.”

Okay, maybe that’s not so mysterious, but at least it could be interpreted a number of ways.

In any event, I haven’t found any LinkedIn documentation that actually specifies their NOT operator other than “NOT.” If you find some, please let me know.

I do, however, think I found their NOT operator…

LinkedIn Supports the Minus Sign

One day I was curious to see if I could use the minus sign inside LinkedIn, and lo and behold, it worked.

I’ve found that you don’t actually have to use the NOT operator when searching LinkedIn – neither in the Advanced Search interface nor in the Quick Search bar.

That’s because LinkedIn supports the minus sign (-) as the Boolean NOT operator, just as Google does.

This works for single search terms, quoted phrases, parenthetical OR statments, LinkedIn’s advanced operators, and it also works in every search field I’ve tested – Keywords, Title, Company, etc.

Here’s an example of a query using LinkedIn’s advanced search operators to look for current employees of Facebook that mention Hadoop on their profiles that are not in recruiting, marketing, or sales:

ccompany:facebook hadoop country:”united states” -(recruiting OR recruiter OR marketing OR sales OR “business development”)

The advanced LinkedIn search operators, as well as the minus sign, populate the left search rail:

As I was working on this post, I spent a little time looking for some kind of reference to LinkedIn supporting the minus sign, and before I got bored of finding nothing, I did discover this little gem in LinkedIn Answers from back in 2007.

I found it interesting that the suggestion to try the minus sign was proffered by non-recruiters/sourcers (one was a UNIX admin).

What’s the Big Deal?

It’s not really a big deal, but I like discovering things, and I also like sharing what I discover.

I also have to say that finding 1 undocumented search operator makes me wonder if there are more. Have you found any?

As someone who gets annoyed that he has to capitalize Boolean operators on Internet search engines and LinkedIn (I don’t have to in my ATS, nor on any major job board resume database), I’m quite happy that I don’t have to type AND, nor do I have to type out NOT when searching LinkedIn.

I’m a fan of efficient and concise query syntax, so I owe LinkedIn thanks for supporting the minus sign as the NOT operator. I’d also suggest that LinkedIn include this in their documentation so other people don’t have to learn about it from my site or stumble into it out of curiosity like I did.

While being able to use the minus sign as the NOT operator in your LinkedIn searches isn’t a huge deal, the NOT operator/functionality itself IS a big deal.

In fact, I believe the NOT operator is the most powerful and least utilized of the 3 main Boolean search operators.

Stay tuned in to my blog if you’d like to learn why.

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.

Relevance is Everything!

When it comes to search – relevance rules.

Ultimately, any sourcing or recruiting professional knows that what’s most critical in running Boolean searches on LinkedIn, the Internet, a job board, or in an internal resume database is getting relevant results.

However, few people talk about exactly what determines relevance – and I think I know why.

According to Wikipedia, “relevance” denotes how well a retrieved set of documents (or a single document) meets the information need of the user.

The problem is that no search engine, social networking site, or database can “know” what is relevant to you – only you can determine how relevant results are because only you know what you were looking for in the first place!

For sourcing and recruiting, relevant results are typically defined as resumes or profiles of (or information about) potential candidates whose experience and capabilities closely match the hiring profile or job opening that the sourcer or recruiter is trying to find candidates for.

I’d argue that the value of any source of information (LinkedIn, a resume database, the Internet, etc.) lies less in the information contained within, and more in the ability of a user to extract out precisely and completely what the user needs – finding and retrieving any and all appropriately qualified candidates.

Information has no value to you if you are unable to find it and take action on it.

So how can extended Boolean help sourcers and recruiters find more relevant results?

Let’s take a look at proximity first.

Proximity Search

Proximity search functionality enables a user to search for specific terms that are mentioned within a certain distance of other specific terms.

Being able to control how close search terms are to each other can be especially helpful when leveraging the structure of certain websites and pages – I’ll demonstrate this later in the post using LinkedIn and Twitter as examples.

In my opinion, the more powerful application of proximity search lies in the ability to perform natural language or semantic search.

Semantic search uses the science of meaning in language to produce highly relevant search results rather than have a user sort through a list of loosely related keyword results. Words that are close together are often in the same sentence, and when you can search for meaning at the sentence level, you can target people based on what they actually do/what their responsibilities have been.

Being able to target sentences in which people detail their specific responsibilities and level of responsibility is absurdly more powerful than basic keyword search (Level 1 Talent Mining), which is prone to low levels of relevance and false positives.

There are 3 main types of proximity searching: fixed proximity, variable proximity, and adjacency. For the purposes of this post – I will focus only on fixed and variable proximity.

Fixed Proximity Search

Fixed proximity is most commonly represented by the NEAR operator. The search engines that do recognize and support the NEAR operator typically define NEAR proximity as within 1 to 10 words (specific search engines can differ – check their documentation). Monster’s resume database supports the NEAR operator (which doesn’t have to be capitalized, btw) at a fixed distance of up to 10 words.

How could you leverage fixed proximity to find more relevant search results?

If you were looking for a Windows and Exchange administrator, any basic keyword and title search can pull tons of results of resumes that mention all of the search terms, as well as a high percentage of false positive results. False positive results in this example would be of resumes that mention all of the search terms and titles, but the people have never been primarily responsible for administering windows and exchange servers. A 1 year helpdesk professional can show up in these results because all they have to do is mention the keywords somewhere in their resume.

Leveraging fixed proximity, you could craft this (purposefully basic) search using the NEAR operator: Windows and Exchange NEAR admin* and server*.

That search will ONLY return results of resumes/profiles that mention Exchange within 1 to 10 words of any word starting with the root of admin (administrator, administration, administer, administered, etc.).

Being able to control the fact that Exchange MUST be mentioned within close proximity to admin* will dramatically affect and improve the relevance of the search results, typically returning results of candidates who either have a title using both terms and/or candidates that talk about being responsible for Exchange administration.

• Managed & administered more than 300 Exchange Servers
• Provisioned & administer multiple Exchange 5.5/2003 servers
• Not only are there administration duties for Exchange and Blackberry…
• Exchange/RightFax administrator
• Installing, Configuring, and Administering Microsoft Exchange 2000 Server
• Administer a Microsoft Exchange 2003/2007 environment
• 8+ years of expertise as a System Administrator in Windows 2003 family, Windows 2000 family, MS Exchange 5.5, MS Exchange 2000, and Exchange 2003
• I am proficient with the following skills; planning, installation and administration of Windows Active Directory, Windows Servers, Exchange Server
• Windows Server Support, Active Directory,Exchange Server 2000, 2003 administration and Blackberry Server administration
• Administer Exchange 2003 Server for corporate email

As you can see, being able to control the proximity of specific search terms essentially increases the likelihood of returning results of candidates who have had administrative responsibility for Exchange servers, effectively increasing the relevance of the results, because that’s what we were actually trying to find and identify!

Configurable Proximity

A search engine that supports configurable proximity affords users the ability to precisely control the distance between specific search terms.

This can produce even more relevant results than the NEAR operator, because the NEAR operator’s maximum range of 10 can allow for some non-relevant results to be returned. The farther words are mentioned apart from each other, the less likely it is that they are semantically related. In fact, at a distance over 10 words, each word could easily be mentioned in separate bullet points or in separate sentences on a resume and be completely unrelated.

However, with configurable proximity, a sourcer or recruiter can choose the maximum distance between search terms.

Instead of being limited to a distance of 10 or fewer words, a search engine that allows for configurable proximity allows you to create searches that force terms to be quite close together – as close as you like.

For example, you could choose to search for only people who mention Exchange within 5 words of any word starting with the root of admin (administrator, administration, administer, administered, etc.), regardless of order. A maximum distance of 5 words will dramatically increase the relevance of the search results because mentioning those 2 search terms at such a close range makes it more likely that they are mentioned in the same bullet point or sentence and thus more likely to be semantically related.

Essentially, this search will only return results of people who specifically mention something about being responsible for administering Exchange at least once in their resume. By employing this kind of search, a sourcer is actually performing a semantic search, targeting sentence-level meaning, as they are looking specifically for people who talk about having a particular responsibility – not just looking for documents that happen to contain the search terms.

Leveraging Website and Page Structure with Proximity Search

Once you have noticed a consistent pattern to the structure of certain websites and pages, you can use Internet search engines that support proximity search to target the distance between search terms to yield highly relevant search results.

Although Google supposedly supports proximity search with their undocumented AROUND(x) search operator, I have found its reliability to be suspect. Perhaps that’s why it’s not officially documented?

The good news is that Bing’s configurable proximity search functionality of NEAR:x seems to work quite well and consistently.

To leverage the structure of certain websites such as LinkedIn, here is a quick example of how you can target current titles and companies when using Bing.

site:linkedin.com current near:3 “engineer at Google” “san francisco bay area”

In this query, all of the results must have the phrase “engineer at Google” within 3 words of the word “Current,” which is on every LinkedIn profile.

If you click on any of the cached results, you can see how Bing happily returned results of people who have the phrase “engineer at Google” in their current title field:

With Bing’s NEAR:x functionality, it is remarkably simple to X-Ray Twitter and target people in specific locations who mention specific titles and/or skill terms in their bios.
For example, let’s say you wanted to find Twitter profiles of user experience professionals who live in the New York area. You could run a search like this on Bing to force the search engine to return only results that mention UX within 15 words of “Bio” and “New York” within 3 words of “Location:”

site:twitter.com bio near:15 UX location near:3 new york

You can see how Bing’s proximity search helps you target terms in Twitter bios and location text:

Viewing a cached result displays Bing’s NEAR:x flawless execution:

How’s that for a relevant result?

Basically as good as it gets – I wanted someone who lives in the NY area who is a User Experience professional, and that’s exactly what I got! That is relevance!

Of course, when searching Twitter, it is especially important to realize that people can be very creative in how they may describe themselves (titles, skills, etc.), their experience, and their location – they can enter whatever they want.

As such, you could not find the above Twitter bio by searching only for “Drupal.”

Performing Semantic Search with Configurable Proximity

You can perform basic semantic search by targeting sentence-level meaning using Bing’s support of configurable proximity.

For example, let’s say you were searching for resumes on the Internet and wanted to find people who have had a specific responsibility, such as configuring juniper routers.

You could run a basic search like this: (inurl:resume OR intitle:resume) configuring near:5 juniper juniper near:5 routers

And see results like this:

Of course, there are many different ways to run that search – I only wanted to demonstrate the power of being able to control how close search terms are to each other, especially when targeting responsibilities, typically stated in verb/noun combinations. This allows you to perform semantic search at the sentence level.

Now that we’ve played around a bit with proximity search, let’s move onto the other half of extended Boolean – variable term weighting.

Variable Term Weighting

Talented sourcers and recruiters know that not all terms are equally important in a query.

In most queries and searches, certain search terms are more important than others. When running standard Boolean queries, all search terms are considered/weighted equally – and this is the stone that the makers of so-called semantic search applications often throw at Boolean search.

Unfortunately, many search engines and database search interfaces simply assign relevance to results by the number of search term “hits” in each document. In most cases, the simple frequency of search terms does not correlate to relevant results. This is where the derisive description “buzzword bingo” comes from, most often used to denote that there is little skill involved in running Boolean searches counting matched keywords.

Using an Information technology hiring profile as an example – if a sourcer was looking for candidates who have significant experience administering Windows servers and Exchange email servers they might create a simple Boolean query such as this: Windows AND Exchange AND server* and admin*.

That search is highly likely to return and rank candidates who are Windows systems administrators who mention Windows many times in their resume/profile and happen to mention Exchange once or twice as highly relevant because of the number of “hits” for Windows – which is by nature a very common term in resumes.

This would leave the sourcer with having to sort through a large volume of false positive results (that contain the keywords, but are not of people who have been primarily responsible for administering Windows and Exchange servers) to find the candidates who actually have been primarily responsible for administering Exchange servers as well as Windows servers.

Search engines that offer users the ability to assign different weights to each search term enable sourcers and recruiters to move beyond simple buzzword matching and take control of the relevance of the results. Essentially, with variable term weighting you can assign a number value to words to increase their weight when ranking retrieved documents – which does not change the total number of results, but the ORDER of the results.

Using the same example as above, a sourcer using a search engine that supports variable term weighting could create a Boolean search string to more heavily weight the term “Exchange.” That Boolean query would pull the same number of results as the first search that had no term weighting – however, it would sort and rank the results heavily favoring resumes/profiles that mention Exchange more often in relation to the other search terms, increasing the likelihood that the sourcer can quickly identify candidates who have had experience being responsible for administering and supporting Exchange servers.

By employing variable term weighting, you can positively affect the relevance of the search results.

Final Thoughts

Hopefully I’ve shed some light on how being able to control the proximity of two search terms can yield results that are FAR more relevant than results that simply mention the two terms anywhere in a document or form – this is the critical difference between the semantic similarity between a search and its results vs. the lexical similarity between a search and its results.

There are countless ways you can apply extended Boolean functionality such as variable term weighting and proximity searching to nearly any industry/hiring profile to create searches that return highly relevant results - results that are more relevant than those that can be achieved with standard Boolean logic.

Using a search engine that supports both variable proximity and variable term weighting can empower sourcers and recruiters to quickly find large volumes of highly relevant results, increasing productivity and achieving Just-In-Time sourcing and recruiting.

I wish the makers of search engines would seek less to “dummy-down” search interfaces and functionality and incorporate more powerful search capability that allows users to take significant control over the relevance of their search results.

There are a few search engines and ATS/CRM systems that support both configurable proximity search and variable term weighting.

Does yours?

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.

Boolean Logic is Simply the Simplest Way to Search

Some (but I hope not too many!) sourcing and recruiting professionals may be surprised to learn that Boolean logic significantly predates the Internet as well as computers – by over a century!

I still run into sourcers and recruiters that are not aware that the word “Boolean” comes from the man who invented Boolean Logic in the 19th century – George Boole. Boolean Logic is the basis of modern computer logic, and George Boole is regarded in hindsight as one of the founders of the field of computer science.

With Boolean logic being created in the 1800′s – it’s pretty obvious that Boolean logic is not just for searching for people and information on the Internet.

Practically any information system from which you need to search and retrieve information from “speaks” Boolean.

This is understandable, because using Boolean logic is the simplest way to construct a search. When you want a combination of terms/phrases you use AND, when you want at least one of a group of terms/phrases you use OR, and when you don’t want something you use NOT. It really doesn’t get any easier than that.

When anyone types more than a single word or phrase into Google, Bing, LinkedIn, Amazon, eBay, etc., they’re performing Boolean search, because spaces are automatically converted to ANDs. Billions of people across the globe are running basic Boolean strings whether they are aware of this or not, which is a testament to how easy Boolean search is.

Sourcing isn’t about Boolean Search Strings

Sourcing candidates is much more than Boolean search strings – they are but one aspect of sourcing.

Sourcing talent is more accurately and completely defined and described as human capital information retrieval.

Information retrieval (IR) is “the science of searching for documents, for information within documents, and for metadata about documents, as well as that of searching relational databases and the World Wide Web.”

Leveraging information systems for talent discovery and identification is about searching documents, for information within documents, and for metadata about documents, as well as that of searching relational databases and the Internet for human capital information, including titles, companies, responsibilities, skills, technologies, social network updates, blog posts, resume information, event and association lists, etc.

With IR, an information retrieval process begins when a user enters a query into an interface.

Queries are simply formal statements of information needs. For a sourcer or recruiter, their information need is typically to find information that will lead them to discover and identify people with specific skills, experience, capabilities, education, etc.

While using Boolean operators is arguably the easiest way to construct a query, IR queries do not have to be limited solely to Boolean logic, as can be seen in the various non-Boolean query modifiers of Internet search engines (here are some of Google’s and Bing’s), LinkedIn’s advanced search operators, faceted search (e.g., LinkedIn’s filters), etc.

The “hard” part of creating queries for human capital information retrieval isn’t deciding which Boolean operators to use. AND/OR/NOT is the easy part. In fact, my daughter learned about Boolean logic last year, including constructing Venn diagrams – in her 1st grade public school class!

The hard part of creating queries is intelligently selecting a combination of words and phrases, and in some cases strategically excluding some words and phrases, that will return highly relevant results – people who are not only likely to be qualified for the position being sourced for, but also highly likely to be interested in the opportunity (i.e., “recruitable”).

Yes – you actually have to think in order to create effective queries that return highly relevant results.

Human-Computer Information Retrieval

Human–computer information retrieval (HCIR) is “the study of information retrieval techniques that bring human intelligence into the search process.”

According to Wikipedia, which IBM’s Watson used heavily to compete in Jeopardy, “The fields of human–computer interaction (HCI) and information retrieval (IR) have both developed innovative techniques to address the challenge of navigating complex information spaces…[and] Human–computer information retrieval has emerged in academic research and industry practice to bring together research in the fields of IR and HCI, in order to create new kinds of search systems that depend on continuous human control of the search process.” (emphasis mine)

The term human–computer information retrieval was coined by Gary Marchionini whose main thesis is 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.” (emphasis mine again)

For those who simply want information systems to magically provide them with the most relevant results at the click of a button, you should take special note of the fact that experts in the field of HCIR do not believe that people should step out of the information retrieval process and let semantic search/NLP algorithms/AI be solely responsible for the search process.

If you’re interested in learning more about HCIR, I suggest you read this blog – you may be surprised and interested to see who the author is, where he’s been, what he’s done, where he is now, and what’s on his mind.

Talent Mining

In my opinion and experience, Boolean search neither adequately describes nor gives proper credit to what sourcers and recruiters are really doing when they leverage the Internet, resume databases, ATS/CRM applications and social networking sites such as LinkedIn to find candidates, and to what some very talented and highly skilled professionals are able to accomplish with human capital information.

At SourceCon 2010, I spoke about a specialized form of HCIR which I call talent mining, which is essentially human capital information retrieval – a specialized form of IR involving querying and analyzing human capital data (resumes, social network profiles and updates, blogs, etc.) for talent discovery, identification, and ultimately acquisition.

I believe there are at least five distinct levels of Talent Mining:

1. Skill/Title Search
2. Concept Search
3. Implicit Search
4. Semantic/Natural Language Search
5. Indirect Search

Talent Mining is not defined by nor limited to Boolean search – any and all information retrieval methods that can be leveraged to discover and return human capital data are applicable and should be used.

At the strategic level, talent mining is the process of transforming human capital data into an informational and competitive advantage, which is much more than simply writing Boolean search strings.

Only the simplest and most basic level 1 talent mining can be performed without much thought – slapping titles and keywords taken directly from a job description into a Boolean search string and hitting “search.”

Beyond that, more advanced level 1 and most certainly levels 2 through 5 talent mining require significant “cognitive energy,” as well as involve continual improvement.

In fact, effective sourcing can and should be an iterative process.

Beyond Boolean & Internet Search

I believe that those who equate sourcing with basic Boolean Internet search don’t fully understand or appreciate the power of human capital data, its many forms and sources, and the many ways that it can be leveraged.

While the Internet has a lot of information, it is also full of garbage (others would call it “noise”) and it does not hold as many “findable” resumes as you may have been led to believe.

There is no denying that non-resume human capital data is valuable, but searching the Internet for non-resume information can easily spiral into an exercise in low ROI, time consuming garbage-sifting. Many don’t realize (or want to recognize) that non-resume data offers shallow information at best and thus has less qualitative and predictive value.

Additionally, the Internet isn’t a database – it’s a network of networks and the information stored on those networks is largely unstructured.

Structured data is an order of magnitude (it could easily be argued many orders of magnitude) more valuable and searchable than unstructured data, if for no other reason than it’s intrinsically high predictive value.

LinkedIn offers a good example of the power of structured human capital data, although a large percentage of LinkedIn profiles are information-anemic. Even so, all profiles are required to have employer and title information, and both are structured, fully searchable fields.

Additionally, corporate ATS’s and major job board resume databases have hundreds of thousands to tens of millions of candidate records – with deep and sometimes well-structured data. I’m perpetually confused as to why there is so much written on Internet sourcing and why I don’t see more people writing and speaking about mining all of the rich human capital data hiding in resume databases and applicant tracking systems.

Perhaps one of the reasons why the sourcing function and role isn’t highly regarded or respected by some is because those people equate sourcing with basic Boolean search. If all they think sourcers and recruiters are doing is directly searching for keywords and titles from job descriptions, then I can actually understand why some people would think of sourcing as an entry level role or function.

However, sourcing isn’t just about Boolean search, it’s about human capital information retrieval.

While Boolean logic is the simplest way to construct an IR query and practically all information systems accept basic Boolean operators, the real “magic” and work of sourcing talent is the iterative, intelligent, and cognitively challenging process of selecting a combination of words and phrases, and in some cases strategically excluding others, analyzing the results returned, making changes to the query based on observed relevance, and repeating the process until an acceptable quantity of highly qualified and matched candidates are identified.

I would personally like to see more sourcing, recruiting and HR conferences and blogs to address human capital information retrieval, specifically with regard to focusing on the sourcing process, as well as deep and structured human capital data. If this happens, I don’t think it will be long before companies start to realize that sourcing can offer a serious strategic competitive advantage, and perhaps invest more in technologies and talented people to achieve a competitive advantage based on human capital data for talent discovery, identification, acquisition, and retention.

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.

Public Basis of Search Strategy Comparison

In many organizations, sourcers and recruiters do not often get (or even seek out) the opportunity to compare and contrast their search strategies and tactics with a large community of their peers on a position-by-position basis. Much of the “magic” of sourcing (or lack thereof!) happens on each person’s computer screen for no one else to see, appreciate, or learn from.

Unlike professional athletes and musicians whose skills and techniques are on display and scientists who publish their work for others to analyze, the skills and techniques of sourcers and recruiters responsible for talent discovery are not widely publicly available.

A powerful and effective way to demonstrate the fact that not all searches are created equal (nor do they return the same number and quality of results!) is to offer the global sourcing and recruiting community the opportunity to share and view the search strings and strategies of many sourcers and recruiters all crafting searches for the same hiring profile.

You Can Contribute to the Global Sourcing Community!

It’s easy!

All you need to do is comment on this post with your search string(s) and include a brief overview of your analysis and approach to searching for the hiring profile below.

In the first Boolean search string experiment, I used a relatively basic and brief job description. I also specified that participants could craft searches using the source of their choice – the Internet, LinkedIn, Monster, an ATS, etc.

For this experiment, I am going in the opposite direction.

The job description and hiring profile I pulled from Indeed is quite long and appears more complex. I would also like to limit you to creating search strings that would run on a source that allows for longer strings and supports full Boolean logic as well as stemming (e.g., config* to return configure, configured, configuration, etc.). That excludes Internet search engines and LinkedIn.

When you leave a comment with your search(es), you have a choice:

1. You can let the world who you are and what you can do, or…
2. You can choose to remain anonymous and not identify yourself when leaving your comment and your search strings/strategy. Feel free to use a fake name (e.g., Recruiter1, Yoda, etc.), and you can even use a fake email address so there is absolutely no way anyone, including me, can identify you.

Either way – don’t be shy, and please contribute! If sourcing isn’t something you’re responsible for - please forward this to someone who would like to participate.

How Would You Search for Candidates for this Job?

Senior Financial Analyst

We are seeking a Senior Financial Analyst to support our Global eCommerce organization. The main responsibilities include working with cross functional groups in Strategy, Product Management and Engineering to understand and financially evaluate new business strategies; building financial models to understand business case and ROI of potential investments; budgeting, forecasting and analyses in support of the businesses; identifying opportunities and risk as well as driving business results. Potential areas of focus include Mobile and Digital Strategy, Multi-channel Initiatives, and Capital Planning. You will be expected to utilize your financial background to provide financial advice to your business partners.

Description
1. Analysis

• Provide input for business decisions by producing detailed financial models and P&Ls incorporating assumptions, expectations, and known risks
• Prepare presentations and other supporting materials for recommendations; participate in business case presentations to senior and executive management; clearly articulate merits of case under consideration
• Proactively identify opportunities to improve efficiency in core Finance processes, to analyze the business from new value-added perspectives, and to enhance the strategic role of Finance in the organization

2. Forecasting

• For current business and new projects, develop bi-monthly financial forecasts that incorporate current business trends and business strategies
• Quantify and articulate risks and opportunities in achieving forecasts and prepare monthly “cause of change” reports related to these forecasts
• Understand the key tools available for research and reporting and actively incorporate them into tracking and forecasting process
• Create ad-hoc forecasts as needed during key periods to provide insights into areas of risk and opportunity and influence business strategies
• Lead automation of models and processes which result in streamlining forecasting, month-end and other on-going reporting

3. Budgeting/ Planning

• Create annual operating, overhead and capital budgets in partnership with the Global eCommerce organizations
• Support monthly P&L review process by recapping actual sales and margin results versus plan, preparing monthly variance reports for business owners, and analyzing key performance drivers
• Participate in long range financial planning process
• Present findings to corporate management that focus on Year-over-year changes, key operating metrics, and any key points or drivers that were influential in the creation of the plan

4. Accounting Support

• Monitor P&L lines during monthly financial close; be able to discuss variances to forecast
• Conduct research as needed during monthly financial close
• Prepare, reconcile monthly revenue recognition

Functional Competencies:

• Demonstrated analytical and quantitative skills
• Expertise in working with large data sets and concepts to develop models and reports
• Solid understanding of accounting principles
• MUST have substantial experience with MS Excel and PowerPoint, Tableau, Essbase, SAP experience desired; a good understanding of database systems such as MS Access, SQL and Business Objects a plus
• Understanding of technology and retail industry preferred

Experience:

• 3-5 years of post MBA experience or 5-7 years of post undergrad work experience in a finance-related field (including management consulting, investment banking, corporate finance within Fortune 500)
• Technology, Retail and e-commerce experience a plus

Preferred educational level:

• Bachelor’s degree required; B.S. in business administration, economics, finance, mathematics preferred
• MBA preferred

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.

The Boolean Search String Experiment Succeeded AND Failed

Unfortunately, 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 using Boolean queries for talent discovery and identification, or lack thereof, happens on each person’s computer screen and thus a great number of people have absolutely no public basis of comparison.

As someone who has trained several hundred recruiters and sourcers, I know that even for basic positions and hiring requirements, 10 different recruiters/sourcers will come up with 10 different search approaches, and while they will find some of the same people, each is likely to find some that none of the others will.

I wanted to show the world that point – to give people a public basis of comparison.

To date, the Boolean search string experiment has had 1,106 unique views with an average “time on page” of over 5 minutes, which is pretty solid for a short post. I was sincerely hoping I would get at least 100 responses to the Boolean search string experiment.

So far, only 36 intrepid people (thank you!) have shared their approach to analyzing the job description I used for the experiment and the Boolean search strings they would use to try and find qualified candidates – a whopping 3% of the number of people who have viewed the post. It’s also worth noting that 9 of the 36 work for the same company.

As I and a few people have pointed out – there is no single best Boolean string or approach. However, I did see a number of  intelligently and logically constructed Boolean strings and well thought out analyses and search strategies.

Why the Low Response Rate?

I have several ideas as to why there were so few responses.

I’m only going to share one of them – I think most sourcers and recruiters aren’t truly confident in their Boolean search string skills.

I’ll put it this way – nearly everyone is comfortable dancing and singing when no one is looking and they are alone, but that all changes in a public forum.

However, if you know you’re a really good dancer or singer, most people have no problems dancing or singing in front of others. In fact – many enjoy performing because they take pride in their ability and they enjoy what they do.

The issue is that the only way someone can know they’re really good at  using Boolean queries for talent discovery and identification is if they have a basis of comparison. Sure – they may get good results, but how do they really know their skill level unless they have a chance to see what others would do if they were working the same positions?

If you were raised on a desert island in complete isolation and you taught yourself how to play the guitar – how would/could you know if you were actually any good at playing the guitar?

The reason why I won’t share any more of my theories as to why the experiment didn’t get more responses is that I’m actually more curious about your thoughts as to why there was such a low response rate. If you have any ideas, please share them.

My Analysis and Search Strategy

I was going to hold out for more responses, but I don’t think I would be accomplishing anything by doing such.

So, without further ado, here is the job description from the experiment:

Business Analyst

• This mission critical role will involve you working with the inventory team to provide data analysis, reporting and technical expertise to meet business objectives. You will work directly with the inventory control group to provide the technical needs as driven by the business, and you will be required to provide business analysis support to the eCommerce and retail groups.

Required:

• A minimum of 3 years of experience as a Business Analyst
• Strong data analysis skills
• Crystal Reports experience preferred
• BSCS or related degree and/or experience
• Experience with enterprise systems

My Analysis/Observations

While I don’t typically prefer to search by title, “Business Analyst” is a fairly common title used to describe this kind of work. However, I am aware that other companies may call people performing this role many things, including “business systems analyst,” “system analyst,” “systems analyst,” “requirements analyst,” “functional analyst,” “IT analyst,” or simply “analyst” or “consultant.”

You may be surprised to learn that my testing on this position specifically, 50% of the results mentioned “business analyst” and none of these (“systems analyst” or “system analyst” or “data analyst” or “requirements analyst” or “functional analyst”). The other 50% mentioned one of these (“systems analyst” or “system analyst” or “data analyst” or “requirements analyst” or “functional analyst”), and NOT “business analyst.”

The even 50/50 split threw me off, but I checked it 3 times. That means that if you only searched for the title “business analyst,” you would be missing 50% of the available results, and you wouldn’t even know it.

From the job description, I can see this person will be primarily responsible for data analysis and reporting, and likely using Crystal Reports for the reporting, otherwise it wouldn’t likely be listed as preferred experience. Also of note is that this person will be working with the inventory control group as well as supporting the eCommerce and retail groups.

While experience with enterprise systems is listed as a requirement, I would not likely including it in my search efforts without speaking with the hiring manager/team for more clarification. There is no way to know if the reference to “enterprise systems” means something like experience with PeopleSoft, Oracle E-Business, SAP, etc., or something else. Outside of the major ERP systems, “enterprise systems” isn’t something many people will explicitly state in their resume or LinkedIn profile, and searching for it (or simply “enterprise”) can also result in many false positives. I’ll assess this qualification on the phone with the people I find.

(Some of) My Searches

I would choose to search my ATS and Monster first, given that I can take advantage of proximity search, the sheer volume of the sample size (millions to 10′s of millions – and there aren’t as many resumes on the Internet as you might think) and data/information depth, which is a weakness of LinkedIn.

For my first searches, I would not use any date range – I would search all resumes.

Search #1

(“business analyst” or “systems analyst” or “system analyst” or “data analyst” or “requirements analyst” or “functional analyst”) and crystal and report* and analy* and data near analy* and inventory and retail and (ecommerce or “e-commerce” or b2b or b2c)

Search Notes:

I thought about searching for the common title denominator of “analyst,” but my experience and some testing has shown me that that results in many false positives (e.g., programmer analyst).

Even though the job description doesn’t require or even desire it, the ideal candidate would have some retail experience. However, this is very tricky to isolate, as most people won’t explicitly mention “retail,” and any retail experience they have will often represented in the companies they have worked for (e.g., Nordstrom’s, which has online retail, aka e-commerce, btw).

You can’t practically search for every retail company in existence, and using a “retail” industry filter on any site will only yield a small fraction of all people who actually do have retail industry experience.

Similar to the retail experience, even though it is not required or even preferred, I chose to search for (ecommerce or “e-commerce” or b2b or b2c), simply because if that’s the kind of group that this person will be responsible for supporting, why not try first to find people who have done it before? While those are some of the most common ways to express e-commerce experience, I could have added EDI, e-tail*, e-business, electronic funds tranfer, online transaction processing, credit card payments, etc., to the “or” statement.

I chose to search directly for the term “inventory” first, with the realization that some people may simply mention supply chain management/SCM as the only hint in their resume/profile that they may have experience with inventory systems. As such, I would plan on taking this approach if necessary after I exhaust all “inventory” searches. However, I also know that just because someone mentions supply chain management, it does not automatically mean they have inventory systems experience – there are plenty of other aspects/stages of SCM than just inventory.

I did not search for “Crystal Reports” because some people will only make reference to “Crystal Enterprise,” Crystal v.X, SAP Crystal, or Crystal Xcelsius. Thus I chose to work with the common denominator of “Crystal” at the expense of a few false positive hits of people with the name Crystal (who will, by the way, have everything else I am searching for). Crystal Reports is fairly common and widely used, however, if I didn’t find enough people with Crystal Reports, I would likely search for other popular reporting applications (“business objects,” “reporting services,” SSRS, Cognos, Hypersion Essbase, etc.)

This search only returns a very small number of results (which is exactly what I wanted – the bulls eye) – so once I have cherry-picked all of the people I want to contact that have obvious clues that they have everything my manager/client could possibly ask for (even though they didn’t explicitly ask for some – but I aim to exceed expectations by always searching for “maximum qualifications” first).

After I’ve gone through the first search’s results, I begin to systematically NOT out the non-required skills and experience, never seeing the same results twice, until my last search is limited to the minimum qualifications.

Search #2

(“business analyst” or “systems analyst” or “system analyst” or “data analyst” or “requirements analyst” or “functional analyst”) and crystal and report* and analy* and data near analy* and inventory and not retail and (ecommerce or “e-commerce” or b2b or b2c)

Search #3

(“business analyst” or “systems analyst” or “system analyst” or “data analyst” or “requirements analyst” or “functional analyst”) and crystal and report* and analy* and data near analy* and not inventory and retail and (ecommerce or “e-commerce” or b2b or b2c)

Search #4

(“business analyst” or “systems analyst” or “system analyst” or “data analyst” or “requirements analyst” or “functional analyst”) and crystal and report* and analy* and data near analy* and inventory and retail and not (ecommerce or “e-commerce” or b2b or b2c)

Search #5

(“business analyst” or “systems analyst” or “system analyst” or “data analyst” or “requirements analyst” or “functional analyst”) and crystal and report* and analy* and data near analy* and not inventory and not retail and (ecommerce or “e-commerce” or b2b or b2c)

Search #6

(“business analyst” or “systems analyst” or “system analyst” or “data analyst” or “requirements analyst” or “functional analyst”) and crystal and report* and analy* and data near analy* and not inventory and retail and not (ecommerce or “e-commerce” or b2b or b2c)

Search #7

(“business analyst” or “systems analyst” or “system analyst” or “data analyst” or “requirements analyst” or “functional analyst”) and crystal and report* and analy* and data near analy* and inventory and not retail and not (ecommerce or “e-commerce” or b2b or b2c)

Search #8

(“business analyst” or “systems analyst” or “system analyst” or “data analyst” or “requirements analyst” or “functional analyst”) and crystal and report* and analy* and data near analy* and not inventory and not retail and not (ecommerce or “e-commerce” or b2b or b2c)

Search Progression

After searching sources that allow me to leverage proximity search and have a huge volume of deep data, I would then go to other resume databases (no proximity and the same or smaller volume of deep data), then LinkedIn (large volume of mostly shallow data), then the Internet.

However, I have to say I likely would not even have to leave my own database and go to a job board, LinkedIn, or the Internet because I would have found a decent quantity of very nice matches that I would begin messaging, contacting, assessing, matching and recruiting.

I also would not likely get past search #3 or #4 to find 20-30+ people to call. This is important because every person I would contact would have at least 2 out of the 3 implicitly desired skills (implied by the job description, but not explicitly stated in the requirements).

Final Thoughts

There is no single “best” search string for any given position, although one could argue that some search strategies and approaches are better than others, in terms of more quickly getting to and uncovering people are are highly likely to be a very strong match for a given role, taking required and desired (explicitly mentioned as well as implicit) skills and experience into consideration.

You should always wonder about whether or not your search strategy and specific queries are uncovering the strongest potential candidates that you have access to. The fact that every search “works” is a significant yet oft-overlooked issue, giving many the false perception that whomever is returned from the queries is all that is available to be found.

Perception is reality for most, but subjective reality isn’t objective reality – there are always more people to be found than are actually found by sourcers and recruiters. Always.

I will likely post another Boolean search string experiment in the future – please share your searches and encourage others to do the same.

Thanks!

One 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.

Let’s Prove a Point to the Recruiting World

This leads me to an experiment and a demonstration of sorts and I sincerely hope you will participate.

I would like you to comment on this post with your search string(s) and a brief overview of your analysis and approach to searching your source(s) of choice if you were responsible for finding and hiring someone for the position detailed below. If sourcing isn’t something you’re responsible for – please forward this to someone who would like to participate.

You have a choice:

1. You can let the world who you are and what you can do, or…
2. You can choose to remain anonymous and not identify yourself when leaving your comment and your search strings/strategy. Feel free to use a fake name (e.g., Recruiter1, Yoda, etc.), and you can even use a fake email address so there is absolutely no way anyone, including me, can identify you.

I am interested in collecting a decent number of responses to demonstrate to the HR, sourcing and recruiting world that many people can look at the same straightforward job description and they will come up with a surprising variety and number of different search strings and approaches.

I’ve long contended that if 30 people were given the same position to source and recruit for, you would see 30 different search strings and approaches which would lead to some overlap and as well as a lack thereof.

How Would You Search for Candidates for this Job?

Business Analyst

This mission critical role will involve you working with the inventory team to provide data analysis, reporting and technical expertise to meet business objectives. You will work directly with the inventory control group to provide the technical needs as driven by the business, and you will be required to provide business analysis support to the eCommerce and retail groups.

Required:
A minimum of 3 years of experience as a Business Analyst
Strong data analysis skills
Crystal Reports experience preferred
BSCS or related degree and/or experience
Experience with enterprise systems

Location:
Washington, DC

Let the Boolean String Slinging Begin!

You can use any single source or a combination of online sources – that’s up to you. Just be sure to detail what sites the searches are for. It’s perfectly acceptable for you to use and specify your own ATS/CRM if it offers you any syntactical and/or search advantage.

Show us what you can do!

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:

Here’s a quick video clip of how to use it.

Concatenation

During my research into how to use Excel to accomplish what I wanted to do, I found out that while it’s easy to add/combine a range of numbers together in Excel (e.g., A1:A100), you can’t concatenate a range of text cells without manually specifying all of the cells in the range (e.g., A1&A2&A3…).

I’m using Excel 2010, and I’m still dumbfounded that you can’t concatenate a range of cells with text values – it seems like something you should be able to accomplish with a click of a button much as you can with AutoSum. Then again, maybe this functionality exists and I just haven’t found it yet?

Knowing that I could not possibly be the only person annoyed by this lack of simple functionality, some quick Internet searching led me to a few sites that claimed you actually could concatenate a range of cells, and one site showed how to do it with a VBA function.

Now, I’ve never used VBA, but I am a bit of a hacker (as much as a non-technical person can be) and I managed to get it working. You can download it here:

Once you download the file, you should notice this warning:

Be sure to “Enable Content,” otherwise the macros won’t work.

Here’s a quick video clip of how to use it:

Using Company Lists to Build OR Strings

There are many sites you can use to find lists of target companies by size and by industry. If you’re searching for Fortune 500 companies, you could of course go straight to the source. However, when copying and pasting from various websites, you may pick up some unwanted and unseen garbage formatting, even if you copy to Notepad first before entering the list into Excel.

When copying and pasting lists from Notepad to Excel, it is safest to be sure to right-click, select “Paste Special” and then select Text. I’ve had a few instances of trying to build OR strings from Notepad and an extra space was hiding at the end of every company name after pasting into Excel (e.g., “Lockheed Martin “), which ruined the integrity of the string.

Using LinkedIn to Build Company/Industry Boolean OR Strings

LinkedIn is a fantastic source of industry and company information – here’s a short video clip of how you can build industry-specific company lists using LinkedIn and one of the above Excel OR builders:

Cleansing and Improving your Company Strings

Ensure your data is clean by using Notepad to strip unwanted formatting before copying into Excel to build your OR strings, paying attention to lookout for hidden spaces.

Additionally, look over your list to identify opportunities to improve your string by adding variations of the company names that people are likely to use (abbreviations, adding/removing spaces between two or more names, etc.).

For example, although the company is listed as “J.P. Morgan Chase & Co.” on the Fortune 500 list, people are more likely to enter it as JPMC, or JPMorganChase, JP Morgan Chase, etc. Essentially, be sure to examine all of the companies in your list with a keen eye for any and all possible variations that people may use, even if they are quite wrong in doing so, as you cannot find what you don’t search for.

This is true in less obvious cases, such as Lowes vs. Lowe’s and Walgreen vs. Walgreens. To test whether or not people use a possible variant, you can isolate a test variant in LinkedIn like so, using the company search field: Lowes NOT Lowe’s, Walgreen NOT Walgreens. The force is strong with the NOT operator.

Also be on the lookout for common denominators of many/all potential variations. For example, a good number of companies have more than 1 word in their name, such as Honeywell International, Lockheed Martin, Publix Super Markets, Kraft Foods, Medco Health Solutions, Verizon Communications, etc. In those cases, there is only a very small risk of yielding false positive results if you simply searched for Honeywell, Lockheed, Publix, Kraft, Medco, and Verizon.

Here is a cleansed and modified Fortune 100 list I put together with 129 terms:

Once you download the file, you should notice this warning:

Be sure to “Enable Content,” otherwise the macros won’t work.

When you enter that string into LinkedIn’s company field and execute the search, you may notice a few results that seem to defy the logic of the string. If you’re up for a challenge – can you tell me why these exceptions happen?

Using Word to Automatically Build Boolean OR Strings

As long as you are not building strings with multi-word phrases requiring the use of quotation marks, you don’t have to use Excel to build long OR statements – you can actually use Word to quickly build Boolean OR strings.

For example, if you are interested in using LinkedIn for diversity sourcing, you can use Word to build first name-based OR strings, which would consist of a string of single word search terms which don’t require the use of quotation marks. In fact, one of the only reasons for using Excel instead of Word to build Boolean OR strings is because most search engines (including LinkedIn) don’t play nice with Word’s quotation marks. More on that in a moment.

Watch this quick video demonstrating how easy it is to use Word to create a Boolean OR string:

Wow – that was easy!

You could of course find ^p and replace all with ” OR “, but you will discover that LinkedIn and other search interfaces/engines won’t like the quotes generated by Word.

Feedback and Suggestions

Let me know if you find the Excel Boolean OR builders and the video overviews helpful, and don’t hesitate to let me know if I missed anything, if they weren’t clear and easy to understand and use, or if you have suggestions for improvements – I’d be happy to make them better!