While there is much written on the subject of how to search the various talent sources available to recruiters and sourcers today, such as the Internet, LinkedIn, Facebook, Twitter, Google+, ATS/CRM systems, etc., there does not seem to be much written about their ROI as sources of talent/human capital information.

I believe that the value of any source of information is 50% based upon the actual information contained within (data depth), and 50% in the ability to extract out precisely and completely what the user needs (searchability). Information has no value if you are unable to easily access, effectively search for and find what you need and take action on it.

When it comes to leveraging information systems for talent identification and acquisition, it is critical to assess the depth of the talent/human capital data offered by the source as well as how “searchable” the source is.

Why is Data Depth and Searchability Important?

Quite simply, the deeper the data offered by and the more searchable the the source is, the higher the ROI for your sourcing efforts.

All electronic sources of talent are NOT created equal, and some offer sourcers and recruiters instrinsic advantages with regard to the ability to more quickly and precisely find more of the right people, yielding higher productivity.

I’ve created a graphic representation of a comparison of the data depth and searchability of the most common information systems used by sourcers and recruiters to find candidates.

Shallow Data Depth

The whole point of using information systems to search for candidates is to find people who have specific skills and experience, and typically people who live in a specific location.

It’s not terribly difficult to find PEOPLE, but it can be very challenging to find the RIGHT people.

As you can see from the chart above, I’ve classified Facebook, Google+, Twitter, and the Internet (non-resume results, such as press releases, company directories, etc.) as shallow sources of talent data.

This is because these sources either don’t offer much in the way of professional/occupational information (often a title and little else), and/or they have very little information as to the exact location of the potential candidates. In most cases, they contain very little information regarding critical candidate variables such as skills and responsibilities, quantity and quality of experience, career history and accomplishments, education (Facebook being the exception), precise location, etc.

Many shallow sources of candidate information simply do not provide ANY information regarding some of these details. This is because the majority of people who use sites like Twitter and Facebook often don’t include professional/employment information on their profile.

With little or no information to go on, it is extremely difficult to search for and identify candidates who have a high probability of at least meeting the minimum requirements for your opening, let alone exceeding them.

Low Searchability

While you can certainly search Facebook to find people – Facebook has significant limitations with regard to its “searchability.” Facebook simply was not designed to be highly searchable, at least not to find people you don’t know, and certainly not based on professional info beyond title and employer.

You may think I am crazy for saying that the Internet isn’t very “searchable,” but most search engines, including Google, don’t even support full Boolean logic.

Also – the Internet itself is unstructured, which makes it intrinsically difficult to find exactly what you’re looking for without drowning in a sea of false positive results. Sniffing out and following candidate leads based on shallow information and using sources that aren’t by design highly searchable comes with the territory of being a sourcer or recruiter, and the thrill of the hunt can be quite satisfying.

However, the angle of this article is ROI, or even more specifically ROTI (Return on Time Invested) – which is a good measure of productivity (Productivity + Work / Time).

Trying to search shallow data sources with limited search capability can be an incredibly slow and time consuming process, as well as result in a significantly low return on time invested. I refer to this as “low yield” sourcing, and its cause is the shallow depth of information available and poor searchability of the sources – which cripples your ability to control or even identify critical candidate variables.

Deep Data But Low Searchability

In this quadrant we have many ATS/CRM solutions, as well as Internet resumes.

In both cases, we’re dealing with resumes. Resumes are definitely deep sources of talent data – and while they are not always complete or 100% accurate – most resumes do contain significant information about the people who wrote them. Even when poorly written, most resumes contain summaries of experience, objectives that can give you insight into the types of opportunities they are interested in, a work history giving you an idea of their capabilities based on their past responsibilities and experience, and of course an addresses – which can be critical in making an educated guess at whether or not they might be open to a particular commute.

While this deep level of talent data is wonderful – it’s of little use if your ability to search for and retrieve the data is limited. Unfortunately, many ATS/CRM solutions aren’t very searchable.

In fact, some are laughably unsearchable, considering a major reason for storing human capital data is (you would think) to be able to retreive it to take action on it.

If you look closely at the chart above, you will notice it says “Most ATS’s.” That’s because there are some highly searchable ATS/CRM solutions on the market, and I am also aware of some “home-grown” systems that are also highly searchable. So while there are some highly searchable ATS/CRM solutions available, too many are unacceptably low on the “searchability” scale.

When it comes to the Internet - while you can search the Internet and find resumes, only Bing supports queries employing full Boolean logic. The irony there is that Bing limits you to 10 search terms or 150 characters (the documentation vs. realized results is sketchy).

It’s nice that Google gives you 32 search terms, but in some cases, this limits your ability to configure queries that are precise enough and/or remove all false positives.

And while finding some local resumes can be done using Internet search engines, it is difficult to be sure if you are actually finding ALL of, or even the best available, resumes.

Because the Internet is unstructured, when you search for area codes, state abbreviations, and zip ranges (as you can with Google), you often get a number of false positive results. And if a person puts their resume online but does not list an address or a phone number – good luck trying to find them as a local candidate.

To be highly “searchable” – it should not be hard to find exactly what you’re looking for, and you should not have to suffer many irrelevant results.

Highly Searchable but Shallow Data

Here we have Twitter and LinkedIn.

I’ve written about this many times before – Twitter is an extremely shallow source of talent data. The operative word in the term microblog is “micro” – 140 characters for Tweets and 160 characters for a bio.  That’s not a whole lot to go on. While some tweeps do tweet about their professional life, many do not. Also, many people don’t give away much information in their micro-bio either.

Unlike Twitter, which by design is a shallow source of talent data, LinkedIn is a deeper source of human capital data. However, while some LinkedIn profiles are as detailed as a traditional resume, there are still plenty of profiles with very little, if any, information on them. I have no doubt that over time more people will flesh out their profiles with more information and LinkedIn will move to the upper right quadrant of the chart.

Here’s where both Twitter and LinkedIn shine brightly – searchability. Twitter employs tag searching (hashtags #), supports full Boolean logic, enables location searching via geocoding ( SQL near:DC within:25mi), and some third party applications (e.g., Twellow) allow you to search specific fields such as bios (@bio developer). Click here to learn more about searching Twitter for sourcing candidates.

LinkedIn supports full Boolean logic and can accept and run insanely long and complex queries, allows for Boolean searching of structured data (current/company, current/past title, school…), has configurable location searching, supports industry and group search, as well as “hand-coding” of searches with LinkedIn’s own advanced operators (ccompany:, ptitle:, etc).

Deep Data and Highly Searchable

Here’s the sweet spot for sourcing and recruiting – sources of talent data that offer significant depth of information AND are highly searchable. In this quadrant we have the job board resume databases and something I like to refer to as Talent Warehouse solutions.

The job major job board resume databases (Monster, Careerbuilder, Dice) all have…that’s right – resumes, which as we have already seen are deep sources of talent data. Resumes offer a work history including career progression, skills and experience (at least to some extent), environment/group/division/project information, education, and precise location. In many cases, resumes will detail specific responsibilities and responsibility level, as well as accomplishments and achievements.

In addition, all of the job board resume databases are also highly searchable, supporting full Boolean logic, useful query modifiers such as the asterisk for root-word searching, structured field searching (recent experience/titles, etc.), and configurable location searching.

Sourcers and recruiters can run Boolean strings and structured queries when searching job board resume databases to precisely target specific experience, years of experience, education, certifications, environmental/project, and industry experience.

Those who are particularly adept can even achieve semantic search by crafting Boolean strings that go well beyond buzzword matching and target specific responsibilities, or in other words, what the candidates have actually done as well as what they have done it with.

The combination of deep data and high searchability affords you the ability to search for and essentially control critical candidate qualification variables enabling “high yield” e-sourcing – a high volume of more accurately and appropriately matched results in less time.

Talent Warehouse

When you saw that large yellow bubble labeled “Talent Warehouse” in the upper right hand corner of the chart, I’m sure most of your were wondering, “What the heck is a Talent Warehouse?”

Just as Business intelligence applications are typically supported by a data warehouse - which is the main repository of an organization’s historical data, also known as corporate memory (the total body of data, information and knowledge required to deliver the strategic aims and objectives of an organization) - a ”Talent Warehouse” serves as the main repository of an organization’s Human Capital data, and it would serve as the raw material for a Talent Support System (TSS) – a computerized system for helping to make Talent-related decisions, such as talent identification and acquisition.

The core of a Talent Warehouse is a relational resume database. We’ve already established that resumes are deep sources of human capital data – but you may have noticed that on the chart above, “Talent Warehouse” is actually higher on the scale of data depth than other sources of resumes. “What’s a deeper source of human capital data than a resume you ask?” A Talent Warehouse adds more depth to resumes through the use of comments/notes and tags – sourcers and recruiters can add additional information to candidate records and resumes based on phone screens, in-person interviews, references, tests and evaluations, etc.

Imagine being able to search for candidates based on information contained in their resumes AS WELL AS information gleaned from the candidates through interviews. Imagine that!

Although many Applicant Tracking Systems, HRMS/HRIS solutions and Recruiting CRM applications make lofty claims as to their capabilities and functionality, I don’t consider (m)any vendor solutions currently on the market to be a true Talent Intelligence/Talent Warehouse solution. Most are simply systems that track and organize applicants (ATS), and/or enable the management of “relationships” (CRM), and they often they lack a critical piece of the puzzle – searchability.

A true Talent Warehouse has a search interface that supports the searching of resumes as well as tags and notes using both standard and extended Boolean queries (including configurable proximity and variable term weighting) to enable effective semantic search as well include an Artificial Intelligence resume/job matching engine to cover all angles.

This kind of search interface and engine can enable sourcers and recruiters to quickly and precisely find quantities of well qualified candidates. In the hands of an adept Talent Miner, a Talent Warehouse can yield a high volume of results with a high percentage of candidates who have specific skills and experience, specific responsibilities, specific years of experience, specific environmental/project experience, and who live in a specific area.

What this essentially affords a recruiter is the ability to leverage technology to find, contact, and establish and build relationships with more of the right people more quickly – increasing effectiveness and productivity!

Final Thoughts

You can find and hire people by searching any electronic source of talent data – resume or otherwise.

However, searching shallow and less searchable sources such as Twitter, Facebook, Google+, blogs, the Internet and other similarly shallow sources of candidate data takes a higher amount of effort for a smaller return – a low ROI. This results in low yield sourcing and recruiting and ultimately lower productivity.

If you have deeper and more searchable sources of talent data, why would you go out of your way to (or allow your sourcing/recruiting team to) focus on low yield sourcing and recruiting?

Highly searchable and deeper sources of human capital data enable you find more appropriately qualified candidates more quickly, through your ability to search for, control, and quickly analyze and assess critical candidate variables such as specific roles/responsibilities, years of experience, skills/technologies, environment, education, and location.

This results in a higher return on time invested and higher productivity. While it may sound perfectly logical to start with the deepest and most searchable sources of talent data available to you, I assure you – not everyone actually does this. I continue to see and hear about sourcers and recruiters who are blinded by buzz of sources like Twitter and Facebook and who spend more time using them than their own ATS/CRM, or other deeper and/or more searchable sources available to them.

And if your private candidate database/ATS/CRM isn’t as searchable as it could be - consider doing something about it- because it should be. Make the conscious decision to focus the majority of your e-sourcing efforts on the highest ROI sources – those with deep data and those that are highly searchable.

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.

BEWARE: This post takes a contrarian (yet fact-based!) view of the Internet as a sourcing tool that may be unsuitable to some readers. If you don’t want to hear anything other than how awesome the Internet is for sourcing and recruiting, please stop reading now. 

The Internet has Free Resumes – SO WHAT?

Okay, so you can find free resumes on the Internet.  So what? What’s the big deal?  The fact that the Internet is free? While free is nice and certainly can’t be argued with, I am sure you have also heard that you get what you pay (or don’t pay) for. Or if it’s too good to be true – it is.

So let’s take a look at what you get for free on the Internet:

Not a lot of resumes

If you are a sourcer or recruiter I am sure that at some point in your career you’ve read somewhere or heard someone say how the Internet has 10X the number of candidates that can be found on the online job board resume databases. I’ve always taken that for face value because, to be honest, it’s really tough to prove or disprove such a figure/statement. However, I am a bit of a skeptic and I don’t just accept what I read or hear at face value – especially grandiose statements/claims, so I’ve taken a critical look into the matter.

While you may be able to find more names/people on the Internet than you can find on any given internal (corporate) resume database or the major online job board resume databases such as Monster, Careerbuilder, etc., the Internet does NOT have more RESUMES, even when it comes to the most common job titles and roles. 

In fact, I’ve found that the Internet has in many cases at least 10X FEWER RESUMES than you can find even on ONE major job board.

Sound crazy or impossible? Would you like some apples-to-apples comparison searches in specific locations comparing the number of resumes you can find on Monster vs. the number of resumes you can find on the Internet via Google? You’ve come to the right place – here are two posts/search exercises that illustrate this point dramatically: Monster vs. the Internet Round 1, and Monster vs. the Internet Round 2.

What’s that? You don’t need resumes to find people on the Internet? Yes, you certainly don’t need resumes to find great candidates – you can search blogs, user groups, company directories, conference attendee lists, etc., and find lots of people. Unfortunately, in most cases, without a resume we can’t reliably determine exactly where these people live (kinda helpful to know), how much experience they have (or how recent it’s been), or what they have been specifically responsibile for. For a detailed analysis – read this post on the intrinsic benefits and advantages of resume database recruiting over any other method of talent identification.

Unstructured data

A database is a structured collection of records or data. Structured data enables more accurate and targeted information retrieval. The Internet is NOT a structured collection of records or data – it’s unstructured, and as such, the Internet is by its very nature an impendiment to targeted and accurate information retrieval.

This can be especially challenging when trying to find people in a specific location, because the Internet is not designed to allow you to do so. Most people try and search for area codes, states (spelled out or abbreviated), or even get more creative and use Google’s numrange functionality and attempt to target zip codes, but any way you slice it – Internet search engines don’t know that you’re looking for area codes, zip codes, or even specific states. So you often get lots of false positive results – hits with the numbers OF the area codes or zip codes, but the numbers themselves are not actually area codes or zip codes. You can also get false positives of pages that happen to mention your target city/state, but the person referenced doesn’t actually LIVE in that city/state.

Trying to specifically target resumes is equally fraught with peril. You can try using the (intitle:resume OR inurl:resume), but then you miss people who do not title their resume as “resume.” If you DON’T use (intitle:resume OR inurl:resume), you open yourself up to a LARGE amount of false positives, even if you try and eliminate job postings and other annoying false positives. This is again due to the fact that the Internet is not a structured database, and no matter WHAT you put in your search strings, the Internet search engines don’t know what you’re looking for. In effect, sourcers and recruiters using the Internet to find resumes are actually performing “work-arounds” in that they are creatively applying search operators and symbols to make the Internet return results they’re looking for, even though neither the Internet nor the Internet search engines were designed for sourcing and recruiting.

A poor search interface

Yeah – I said it.

Yahoo, Ask, and Google don’t even support full Boolean logic. Come on. Performing text-based search without full Boolean logic is like performing surgery with a butter knife – crude and imprecise, and some things simply can’t be accomplished.

MS Live supports full Boolean logic, but has no wildcard/stemming/truncation search. And while Exalead supports full Boolean logic as well as wildcard/stemming AND fixed and configurable proximity searching (WOW!) – it indexes a small fraction of what the other large Internet search engines do. Sigh. Can’t sourcers and recruiters catch a break here?

ALL of the major job board resume databases support full Boolean logic, and Monster supports fixed proximity with the NEAR operator. While they of course have the advantage over the Internet of being designed soley as resume databases – their search interfaces are well ahead of most Internet search engines. Am I the only recruiting professional that loves to leverage the asterisk and be able to use the NOT operator on a parenthetical statement?

Anyone who has used a full-featured text search engine that supports not only standard Boolean logic, but also extended Boolean such as configurable proximity and variable term weighting (such as Lucene or dtSearch) on a structured database can tell you that in comparison – using Internet search engines is like trying to tie your shoe laces with mittens on…while handcuffed.

And of course Internet search engine interfaces don’t allow for reliable location-specific searching. This is compounded by the fact that as stated previously, the Internet does not structure any data, let alone location-specific information.

Irrelevant results

Google uses their PageRank link analysis, among other things to determine “relevance,” which has NOTHING to do with the relevance of a potential candidate’s qualifications based on a search. In fact, it’s inherently flawed in that a person who happens to have created their own website and has posted their resume on it may not have ANY links to it, so Google will interpret it as a result of low relevance.

Yahoo and other search Engines do not appear to disclose their relevance ranking/sorting methods – so who really knows what they think are relevant results? Also – am I the only one that thinks it’s ridiculous to get 2.5 million results from any search? Any guess as to how many are actually RELEVANT?

The very fact that the Internet is not a structured database of people makes it intrinsically difficult to get relevant results from any search – the Internet has tons of garbage on it. Trying to look for the term “resume” or “CV” pulls many false positives, as does any search for location-specific information such as area codes, states, or even 5 digit zip codes simply because there is so much STUFF on the Internet, and it doesn’t know what you’re looking for.

No competitive advantage

The Internet is free – anyone with an Internet connection can search it to find people and resumes. While there is no denying that some people are more proficient at searching the Internet, everyone has access to the same information – and there aren’t even that many resumes on the Internet to fight over. I don’t see that as a competitive advantage of any significance.

Even major online job board resume databases offer more competitive advantage than the Internet, because they cost money and not every person or organization pays for access to even 1 job board resume database. Plus – they all have decent search interfaces/engines.

Any recruiting or staffing organization of any significant size should have an internal resume database that affords them a TRUE competitive advantage – access to a large number of unique candidates that other people and organizations do not have (any and/or easy) access to, stored in a structured database with a powerful search engine that supports full (and ideally extended) Boolean. 

Conclusion

• It doesn’t have many resumes
• It’s unstuctured data
• It’s intrinsically difficult to find people in specific locations
• It’s intrinsically difficult to find people with specific experience and qualifications
• It has poor search interfaces
• It has TONS of irrelevant results
• It does not afford any significant competitive advantage

Before some readers cry “blasphemy!” – there is no denying that you CAN leverage the Internet to find fantastic talent – everyone knows that. But let’s not conveniently overlook the fact that using Internet search engines to locate people with specific skills and experience that live in targeted areas is intrinsically difficult due to the fact that you are using Internet search engines as well as the Internet in a manner that they were not specifically designed for (targeting people that live in specific places with specific experience and qualifications). Finding SOME people on the Internet is easy, but consistently finding a solid quantity of the RIGHT people is definitely not.

Because of this, using the Internet via Internet Search Engines for sourcing and recruiting has an intrinsically low ROTI (return on time invested). But hey – don’t blame the Internet or the Internet search engines. That’s like getting upset because your blender/food processor isn’t effective at mowing your lawn.  

If you only need to find a handful of the right people every month, perhaps the Internet alone, with all of its intrinsic limitations, can still fulfill all of your hiring needs. Regardless, there ARE and more effective methods and technologies available.

What really drives me crazy is that I feel that most people don’t even know how to effectively leverage their own internal resume database or even the online job boards if they have access to them - where they can run more precise searches and find more people who live in targeted areas that have specific skills and experience and enjoy a much higher ROTI - yet they spend lots of time searching the Internet where it’s harder to find specifically qualified people in targeted locations and there are less of them to reliably find in the first place.

I’d argue that every sourcer and recruiter leverage ALL resources available to them – but I believe they should START with the rousources that produce the highest ROTI first, and then work their way backwards from there.

I’ll end this post with an analogy:

If you were in the oil prospecting business, would you choose to specifically target and utilize technologies and methods that made it intrinsically difficult to find oil deposits?  And would you specifically target areas where you know there isn’t much oil in the first place and where everyone else has already searched and picked over?

Think about it.

Boolean Contest – come one, come all!

Irina Shamaeva and I were chatting a few weeks back and she asked me if I thought a contest focused on Booolean strings would be a good idea. You can imagine my reaction – “Of course!” She thought offering prizes of ResumeFinder or ResumeGrabber would be a great idea, and Chandra Bodapati, CEO of eGrabber, was gracious enough to offer his fantastic products FREE to the winners!

Here are the Official Rules of the Worldwide Boolean Strings Contest – 2008, sponsored by eGrabber

The contest starts on Tuesday December 9, 2008 and ends on Sunday December 21, 2008.
To participate, you need to complete three steps.

1) Post one new discussion item either on the “Boolean Strings” group on RecruitingBlogs or the “Boolean Strings” group on LinkedIn.
(Your post can be a tip, a question or a reply to somebody else’s question. Post between 12/9/08 and 12/21/08.)

2) Download and try ResumeFinder and/or ResumeGrabber.
(This step is optional but you get one bonus point for this.)

3) Answer questions in this Quiz.
(This is a multiple choice quiz on your mastery of Boolean Strings.)

The contest will have multiple winners! One person for every 25 participants will get the tool of his/her choice, ResumeFinder (a $349 value) or ResumeGrabber (a $495 value).
Plus, eGrabber will offer one month subscription to ResumeFinder to everybody who participates in the Contest! Check the box at the end of the quiz and you will receive a ResumeFinder product key.

The winners will be announced on Tuesday December 23. The top winner will get the title “Boolean Strings Master – 2008″. If you have any questions or comments please email us at contest@booleanstrings.com

Good luck, and good Boolean!

In response to my post of Resumes on the Internet: Monster vs. Google one of my readers commented that “While it may be true that Monster has more resumes than Google, using a zip code search is not a fair comparison for Google. People who post their resumes on Monster are required to enter their zip code, while people who resumes are stored online will generally only put their email and/or phone number. Also, even using the term resume can be limiting in Google. Because it was not built to only index resumes, you have to get more creative to filter out the noise. You can try the ~CV or ~Resume, you can also take that out completely and search for types of documents, .DOC, .PDF, etc. and look for words commonly found in CV’s like education, objective, etc.”

His comment inspired me to get these industry heavyweights into the ring for a second battle and experiment with not using zip code ranges or the word “resume” when searching for resumes on the Internet using Google. Let’s begin with the same searches as Monster vs. Google Round 1.

Search #1 – Java, Oracle, Sprint or Nextel, State of MD

Google #1 Zip range (original search) = 4 results

(intitle:resume | inurl:resume) java oracle (sprint | nextel) 20601..21930 (MD | Maryland) -~job -~jobs

Google #2 No zip range, using area codes instead = 3 results

(intitle:resume | inurl:resume) java oracle (sprint | nextel) (301 | 410 | 443 | 240) (MD | Maryland) -~job -~jobs

Google #3 No zip or state = 6 results, 3 are not local to MD, but we did gain 1 new resume

(intitle:resume | inurl:resume) java oracle (sprint | nextel) (301 | 410 | 443 | 240) -MD -Maryland -~job -~jobs

Because we know that not everyone actually titles or saves or refers to their resume as a “resume,” we’ll try to search for words commonly found on resumes. Note – I am not a fan of ~resume or ~cv because I tend to get lots of junk. I am not fooled by a larger quantity of results when the quality goes down – it’s not about quantity, it’s about relevance. Also, not every mention of “cv” is actually referring to a curriculum vitae – I get all kinds of strange false positives with the letters CV. “CV” is more common internationally than in the U.S., and in this case, we’re searching for U.S. based candidates. Additionally, as one observant reader pointed out, many online resumes are of people at educational institutions (.edu) – and many of those contain mention of CV. For most positions, people with only educational experience are not viable candidates. So, instead of shooting for the word “resume,” we’ll go for common resume words:

Google #4 No mention of “resume,” using zip range = 33 results, however – no resumes I can see

NOT “resume” (objective | summary) education (history | experience) java oracle (sprint | nextel) 20601..21930 (301 | 410 | 443 | 240) (MD | Maryland) -~job -~jobs

Note – for you search geeks out there, using -intitle:resume -inurl:resume did not work as the syntax suggests it should – it still includes the word “resume” in the url. For example, this search:

-intitle:resume -inurl:resume (objective | summary) education (history | experience) java oracle (sprint | nextel) 20601..21930 (301 | 410 | 443 | 240) (MD | Maryland) -~job -~jobs

Yielded results like this:

Verdict: Monster crushed Google

Search #2 Java, Oracle, State of MD (very simple and broad search)

Google #1 – Zip range (original search) = 220 results

(intitle:resume | inurl:resume) java oracle 20601..21930 (MD | Maryland) -~job -~jobs

Google #2 No zip range, using area codes instead = 218 results. HOWEVER, these results have significant overlap with Google search #1

(intitle:resume | inurl:resume) java oracle (301 | 240 | 443 | 410) (MD | Maryland) -~job -~jobs

I tried to shoot for mutually exclusive results by negating the zip code range from search #1, however, I could not negate the zip code range and get it to run properly. For example, I tried all 3 variations below to no proper effect:
-20601..21930
-(20601..21930)
NOT 20601..21930

Google #3 No mention of zip or state – just area codes = 375

(intitle:resume | inurl:resume) java oracle (301 | 240 | 443 | 410) -MD -Maryland -~job -~jobs

While this search returned 375 results – most of the results are false positives of non local candidates. See below:

As you can see, the MD-specific area code numbers come up as false positive hits in non-local resumes

I will not bother with not using (intitle:resume | inurl:resume) – as stated previously, although I concede that targeting the word “resume” is limiting, whenever you go beyond it, you get tons of garbage false positive results (see Search #1 above). I (and most sourcers and recruiters) should have better (higher yield) sources to tap for Talent rather than having to dig through large quantities of false positives to find a handful of relevant results.

Monster – 592 results @ 1 month, 1000+ @ 3 months

Java and oracle – in MD

Verdict: Monster destroys Google

Conclusion
After seeing the results of Search scenarios #1 and #2, I will not bother to repeat the other searches I executed in Monster vs. Google Round 1. I’ve seen enough to know that regardless of how you spin Booleans searching for candidates on the Internet (zip codes, area codes, resume or not resume, etc…), the quantity of candidates with resumes on the Internet is not as large as many would believe or assume, and certainly not even close to as large as even 1 of the major online job boards.

The reader who inspired this post made an excellent point – Google and other search engines are not designed to index resumes. That’s why I have never been crazy about finding resumes in the Internet – while it certainly can be done, it’s like using a screwdriver as a hammer – you’re trying to use a tool for something other than it’s original or intended design.  That certainly explains the results of this exercise.

If you’re looking for resumes – why not leverage systems specifically designed to index resumes, like the job boards (paid and free), as well as your own internal database/ATS? Which, by the way, is also free.

And if you happen to believe the hype that the job boards have poor quality candidates – please read this post on the quality of job board candidates that will provide you with a reality check.

I hope you enjoyed round 2!

If you are a sourcer or recruiter I am sure that at some point in your career you’ve read somewhere or heard someone say how the Internet has 10X the number of candidates that can be found on the online job boards. I’ve always taken that for face value because, to be honest, it’s really tough to prove or disprove such a figure/statement.

However, I am a little bit of a skeptic by nature and I tend to question everything. Socrates and I would have been fast friends. I don’t typically accept what other people say or write just because they say or write it. So that whole “there are TONS more candidates on the Internet than the job boards” thing has been slowly eating away at me and I’ve decided to take a stab at dispelling the myth by pitting The Internet (via Google) vs. Monster.

Before you jump all over the Boolean search strings I settled on for this little exercise - I’m going to keep them relatively simple for easy apples-to-apples comparisons. I am well aware that the searches you see below can be tweaked in many ways – and just so you know, I did experiment with them before settling on a particular search string format. I did not find any significant variation in the results by tweaking the approach I took to pulling resumes. For example, when I used intitle:~resume, I got a couple extra CV hits, but also a bunch of false positives that were not resumes – so I kept it pure and simple at intitle:resume.

I chose to go with 1 Internet search engine (Google) and 1 major job board (Monster). Yes – I know that there are resumes that you can only find using other search engines (hey – I do have a Black Belt in Boolean) - but I figured I would let the 800 lb gorillas of their respective niches battle it out. Plus, there are other major job boards – so we’re even.

It is important to bear in mind that I set out to just run a little experiment to see how many resumes I could find via Google for particular search terms/skills in specific locations vs. how many I could find on Monster with the same search terms and locations.  I chose the state of Maryland and a 20 mile radius of 94118 in San Francisco, CA. 

Google – are you ready? Monster – are you ready? Now, LET’S GET IT ON!!!

Search #1 – Java, Oracle, Sprint or Nextel, State of MD

Google – 4 results
(intitle:resume | inurl:resume) java oracle (sprint | nextel) 20601..21930 (MD | Maryland) -~job -~jobs
 
Monster – 123 results, ALL resumes
Java and oracle and (sprint or nextel)

Search #2 Java, Oracle, State of MD

Google – 182 results
(intitle:resume | inurl:resume) java oracle 20601..21930 (MD | Maryland) -~job -~jobs

Monster – 592 results @ 1 month, 1000+ @ 3 months
Java and oracle – in MD

And just out of curiosity – I ran a simple 1 word search of Java in the state of Maryland on Google and got 316 results. (intitle:resume | inurl:resume) java 20601..21930 (MD | Maryland) -~job -~jobs

Then, to add insult to injury, I decided to up the ante on Monster and add a whole bunch of other junk to give Monster the squeeze. But even so, with this search, I still got 120 results going with the ALL resumes date range: java and (weblogic or websphere) and apache and J2EE and XML and unix and (shell or script*) and oracle and PL/SQL
           
Let’s go to the other coast (West SIIIIIIDE!)

Search: Java, Oracle, 20 mile radius of 94118 in San Francisco, CA

Google – 575 results
(intitle:resume | inurl:resume) java oracle 94005..94979 (CA | California) -~job -~jobs
           
Monster - 301 @ 1 month, 613 @ 3 months, 911 @ 6 months
Java and oracle – 20 mile radius of 94118 in San Fran

Search: Java, Oracle, Weblogic or Websphere, Apache, J2EE, XML, UNIX, shell or script*, oracle, PL/SQL – 20 mile radius of 94118 in San Francisco, CA

Google – 12 results
(intitle:resume | inurl:resume) java (weblogic | websphere) apache J2EE XML unix (shell | ~script) oracle PL/SQL 94005..94979 (CA | California) -~job -~jobs

Monster – 81 results (date range = all) 
java and (weblogic or websphere) and apache and J2EE and XML and unix and (shell or script*) and oracle and PL/SQL
      
Okay – now that we’ve taken a swing at some simple Information Technology searches, let’s move onto Accounting.

Search: Accountant in the state of MD

Google – 19 results
(intitle:resume | inurl:resume) “accountant” 20601..21930 (MD | Maryland) -~job -~jobs
           
Monster – 711 results @ 1 month
accountant

Wow – Monster crushed Google on that one. Let’s load up and see how many CPA’s with Big 4 experience Monster can find in Maryland:
 
accountant and (CPA or “certified public”) and (“big 4″ or “big 5″ or deloitte or Andersen or Accenture or PWC or KPMG or coopers or “price waterhouse” or pricewaterhouse* or Ernst* or “E&Y” or capgemini or bearingpoint or “bearing point” or “cap gemini”)

210 results in MD, all resumes date range.

And now, let’s show some accounting love to San Fran.

Search: Accountant in 20 mile radius 94118 in San Francisco

Google – 30 results
(intitle:resume | inurl:resume) “accountant” 94005..94979 (CA | California) -~job -~jobs

Monster – 333 @ 1 month, 773 @ 3 months, 1000+ @ 6 months
Accountant
           
Monster’s not even breaking a sweat. Let’s try the CPA with Big 4 search: 
accountant and (CPA or “certified public”) and (“big 4″ or “big 5″ or deloitte or Andersen or Accenture or PWC or KPMG or coopers or “price waterhouse” or pricewaterhouse* or Ernst* or “E&Y” or capgemini or bearingpoint or “bearing point” or “cap gemini”)
           
155 @ all resumes date range.
 
I’ve tried a basic IT and F&A searches – let’s go a little more off the beaten path (for some).

Search: Chemist with degree in chemistry or biochemistry in a 20 mile radius of 94118 in San Francisco

Google – 17 results 
(intitle:resume | inurl:resume) “chemist” (chemistry | biochemistry) 94005..94979 (CA | California) -~job -~jobs

Monster - 254 results @ all resumes
Chemist and (chemistry or biochemistry)

Monster still isn’t breathing heavy. Must be all of the high altitude training.  Once again, let’s load up to try and test Monster with something more challenging:   
      
Chemist and (chemistry or biochemistry) and (HPLC or “Liquid Chromatography” or GC* or GPC or LC* or “gas chromatography” or UV* or FT* or NMR) and (USP or ICH or GMP* or cGMP* or GLP*)

84 results @ all resumes
 
Back to the East coast for chemists
 
Search: Chemist with degree in chemistry or biochemistry in the state of MD
Google – 10 results 
(intitle:resume | inurl:resume) “chemist” (chemistry | biochemistry) 20601..21930 (MD | Maryland) -~job -~jobs

Monster – 572 results @ all resumes data range 

Is anyone else seeing a trend here?

Once again, I will try and stress Monster: 
Chemist and (chemistry or biochemistry) and (HPLC or “Liquid Chromatography” or GC* or GPC or LC* or “gas chromatography” or UV* or FT* or NMR) and (USP or ICH or GMP* or cGMP* or GLP*)
           
161 results @ all resumes date range
 
Let’s go even farther off the beaten path and look for something like a senior executive of medical device product development/improvement in CA.

Google – entire state of CA – 1 result
(intitle:resume | inurl:resume) (VP | “vice president” | Director) (quality | regulatory) (“cfr 820″ | 13485) (~product | ~process) ~improve (~health | ~medical) ~device 90001..96162 (CA | California) -~job -~jobs

Monster – entire state of CA – 153 results
(VP or “vice president” or Director) and (quality or regulatory) and (“cfr 820″ or 13485) and (product or products or process or processes) and improve* and (health* or medical) and device*

Conclusion
Although this little experiment wasn’t very extensive, it does serve as a fair apples-to-apples comparison of the number of resume results you can get on the Internet (via Google) and on Monster with specific searches in specific locations.

The numbers speak for themselves. The Internet via Google didn’t stand up to well to the hype that the Internet has tons more resumes than the job boards – heck, it had sand kicked in its face by just one job board – Monster.

We also confirmed that there are more Information Technology resumes on the Internet than non-I.T. folks, such as accountants and chemists – at least in Maryland and the Bay area. Although I suspect this is true across all 50 states, and perhaps the world.

I had fun with this comparison – look for more coming soon.

I recently had a recruiter ask me if there were any way to be able to search LinkedIn for people who have worked at a specific company in the past, but who are NOT currently working for that company.

I can see why some Sourcers and Recruiters would want to specifically target people who are not currently at a company, but have worked there in the past. I’ve done a bit of digging on this, and I have yet to find a way to reliably targeting past experience while ensuring that you only get results of people who are not currently working at the target company.  When searching within your network on LinkedIn, as you may know, the only controllable option you have is to be able to search for people who are currently at target companies. If you leave the “current companies only” option unchecked, you will get results with a mix of people who are currently employed at your target company as well as those who are no longer working there. Also – when searching inside your own network – you are limited to results of people to whom you are connected up to the 3rd degree.
 
Going beyond your own LinkedIn network, you can try using Google and other Internet search engines and employ the site: command to search into LinkedIn – but we have to be aware that this is not a method that affords you precise control over current or past experience.  However, I’m going to give Google, Exalead, and AltaVista a thorough LinkedIn Boolean workout.

Let’s begin with some Google basics. For example – if you were using Google to search into LinkedIn and try to find people who have worked at Lockheed Martin at some point in their career and, for the case of this exercise, currently live in the Denver area, you could use this search and enter any additional specific search terms you might be targeting where I have SKILL/TITLE1, SKILL/TITLE2, etc:

That gets pretty clean results – I checked several pages worth. However, if you run into false positive hits like answers or jobs, you can try adding the following:

-inurl:jobid -inurl:answers

When you execute the search, you’ll see that it pulls results of people who are currently working at Lockheed Martin as well as people who have worked there in the past. Not really different from attempts made searching within your LinkedIn network  – with the exception that you will likely find more people since you are no longer limited to the size of your personal network or LinkedIn’s limit of 500 results.

Exalead
I thought of using Exalead (www.exalead.com/search) in an attempt to exploit the fact that Exalead recognizes the NEAR proximity operator. Using the NEAR operator, we can try to force certain words to be close to the word “past” or NOT in their current position in an effort to try and search people’s past experience specifically.  This is not an exact science, but it’s worth a shot.

This search is trying to find people who mention Lockheed on their profile, but who are not currently employed at Lockheed:

site:www.linkedin.com -inurl:jobid -inurl:find -intitle:directory -inurl:answers -inurl:updates “greater denver area” Lockheed NOT (current NEAR Lockheed)
 
Checking the results, you can see that it works relatively well, but not perfectly, mostly due to the structure of LinkedIn’s profile page – current and past experience are listed so close together that trying to use the NOT/- operator in conjunction with NEAR will cause problems, such as eliminating results we actually want. FYI – Exalead does not appear to recognize “-(current NEAR Lockheed)” exactly as it does “NOT (current NEAR Lockheed).”  Running searches back to back switching out the – and NOT yield slightly different results.

I tried a variation of that theme, trying to avoid profiles of people who list Lockheed as their current/most recent employer in the career history section of their profile:

site:www.linkedin.com -inurl:jobid -inurl:find -intitle:directory -inurl:answers -inurl:updates “greater denver area” Lockheed NOT (experience NEAR Lockheed)

This search seemed to work well – better than the above search, in my opinion, and it’s interesting to note that a good number of the page 1 results have profiles of people who have headers stating they are currently at Lockheed, but when you scroll down to their experience section, they are in fact, not currently working at Lockheed. So this search string worked relatively well.

Here is yet another variation on the theme – what we’re doing here is avoiding profiles of people who mention Lockheed in the current title/employer field, and it appears to work well:

site:www.linkedin.com -intitle:directory -intitle:updated “greater denver area” Lockheed NOT (“greater denver area” NEAR Lockheed)

AltaVista
Now that we’ve given Exalead a workout, I figured I would turn to an old favorite of many – AltaVista.

I first tried running the last search I just ran on Exalead:
site:www.linkedin.com -intitle:directory -intitle:updated “greater denver area” Lockheed NOT (“greater denver area” NEAR Lockheed)

It only returned 1 result – so Exalead wins this round. For anyone interested – the search executed exactly the same when I used “AND NOT.”

Then I turned to one of AltaVista’s coolest and most powerful features- configurable proximity searching.  If you’d like a refresher on AltaVista’s advanced Boolean operators, check this link out: (http://www.searchengineshowdown.com/features/av/review.html)

This next search tries to eliminate results of people with profiles that mention Lockheed within 7 words of the word “current” on their profile:

site:www.linkedin.com -intitle:directory -intitle:updated “greater denver area” Lockheed NOT (current ~~7 Lockheed)

Strangely enough – that search only returned 6 results. The first result is odd because Lockheed is definitely mentioned within 7 words of Lockheed, but the rest seem to be good results – people who are not currently at Lockheed. However, 7 results is a little on the small side.

FYI – it appears to run identically with or without parentheses. However, switching between the – and NOT where we have (current ~~7 Lockheed) does change the results – not just the number, but the actually results themselves. Odd.

Here is another search we have already run on Exalead, which attempts to avoid results of people with profiles that have Lockheed mentioned within 10 words or so of “experience:”

site:www.linkedin.com -intitle:directory -intitle:updated “greater denver area” Lockheed NOT (experience NEAR Lockheed)

That yielded only 2 results on AltaVista vs. 61 on Exalead – so Exalead maintains its lead, pun intended.

Alright – time to try and get Black Belt on AltaVista.

I attempted to invoke AltaVista’s power of proximity AND order – trying to find profiles that mention Lockheed but not Lockheed mentioned before the word “past,” which, according to the standard LinkedIn profile structure (assuming everyone uses it, of course) would mean they are currently at Lockheed. Trying to prevent “Lockheed” from showing up before the word “past” SHOULD work based on the majority of the LinkedIn profiles I have seen.

site:www.linkedin.com “greater denver area” Lockheed -intitle:directory -intitle:updated NOT (lockheed < past)

This did work – but it yielded only 12 results and some of the people do currently work at work   at Lockheed.

I then tried to prevent “Lockheed” from appearing before the location on the profile – which is a field/area where, if utilized, is where many people list where they currently work.

site:www.linkedin.com “greater denver area” Lockheed -intitle:directory -intitle:updated NOT (lockheed < “greater denver area”)

This search did run, yielding 12 results. However, it definitely let slip through some people who do mention Lockheed before “Greater Denver Area”

And now…let’s give Google another shot.

While Google does not support proximity searching in the form of the NEAR operator or configurable proximity (shame on you Google – really), we can try to approximate proximity searching by making use of Google’s single word wildcard operator – the asterisk.

What I will try to do is use multiple asterisks, representing multiple wildcard words, and combine it with the NOT operator, to attempt to prevent results of people who mention “Lockheed” within 2 – 5 words of words that would indicate that they might currently be employed there, such as “current” or “experience” or “greater denver area.”

In this search, I am trying to avoid Trying to avoid the word “Lockheed” within 2-5 words after “Current:”

site:www.linkedin.com -intitle:directory -intitle:updated “greater denver area” Lockheed -”current**Lockheed” -”current***Lockheed” -”current****Lockheed” -”current*****Lockheed”

That search ran and yielded 106, but many of the results have “Lockheed” mentioned within 5 words of the word “current.”

Out of curiosity, I tried spacing out the asterisks:

site:www.linkedin.com -intitle:directory -intitle:updated “greater denver area” Lockheed -”current * * Lockheed” -”current * * * Lockheed” -”current * * * * Lockheed” -”current * * * * * Lockheed”

That search ran and yielded what appears to be the exact same 106 results – so spacing the asterisks does not appear to have any different effect, at least in this specific case.

Next, I decided to switch out the – to NOT for the Lockheed-related statements, again, mostly out of curiosity, and neither of the below searches ran:

site:www.linkedin.com -intitle:directory -intitle:updated “greater denver area” Lockheed NOT “current * * Lockheed” NOT “current * * * Lockheed” NOT “current * * * * Lockheed” NOT “current * * * * * Lockheed”

site:www.linkedin.com -intitle:directory -intitle:updated “greater denver area” Lockheed NOT (“current**Lockheed”) NOT (“current***Lockheed”) NOT (“current****Lockheed”) NOT (“current*****Lockheed”)

Perhaps Google’s single word wildcard asterisk operator doesn’t work well when combined with the -/NOT operator.  Has anyone else experimented within combining them?  If so, please let me know.

Well, I hope you enjoyed this exercise in attempting to isolate and target profiles of people on LinkedIn who have worked for a company in the past, but are not current employees of that target company. I know I learned a few things along the way – that Exalead does a good job with proximity via the NEAR operator, that some of AltaVista’s proximity operators (NEAR and before) don’t seem to work as well as they should, and that Google’s asterisk operator doesn’t seem to play nice with the NOT operator.