For a while, there was an interesting little method for revealing the full name of 3rd degree and group connections on LinkedIn. However, LinkedIn has changed the “get introduced” functionality and UI for most people and effectively eliminated that method (albeit unintentionally, IMO).

Oh well – it was easy and fun while it lasted.

Fortunately, I’ve recently become aware of another way of revealing the full names of 3rd degree connections on LinkedIn with a less-than-premium account that I would like to share with you.

But before we get to that, I’d like to cover some basics as well as some things I have been noticing about LinkedIn – I believe they may be tinkering with free access profile visibility.

Oh, and if you’re on the fence about attending SourceCon in Atlanta next week, it’s shaping up to be the largest in SourceCon history, and you still have time to register and get a 10% discount using my SC12GC code.

LinkedIn Public Profile Search to View Full Names

Now that the nifty “get introduced” full name visibility trick is seemingly dead, people without LinkedIn Recruiter access can of course still grab one or more unique phrases from 3rd degree and group-only LinkedIn connections and throw them into Bing or Google to find their public profile and thus their full names.

For example, I can take the headline phrase and couple it with the location phrase from a LinkedIn search result…

…and enter this into Bing: “Senior Software Development Manager, IBM” “Ottawa, Canada Area”, and here’s what I get:

You can do the same thing with Google, but Google’s first result isn’t the profile we’re looking for – that’s why I favor Bing for this technique. Google seems to index all of the nooks and crannies of LinkedIn yielding “dirty” and irrelevant results when searching for LinkedIn profile word/phrase combinations.

Is LinkedIn Tinkering with Public Profile Visibility?

I am sure I am not alone in noticing changes to the standard LinkedIn X-Ray search results.

For example, while you could see the full names of 3rd degree and group-only connections after clicking on a search result even if you were logged into LinkedIn, now I am finding that if I am logged in, once I click on a Google or Bing LinkedIn site: search result, LinkedIn recognizes that I am not connected to the person at the 1st or 2nd degree and thus only shows me the first name and last name initial.

Here is a screenshot of my Google search and the result – the full name is displayed:

However, once I click on the search result, if I am logged into LinkedIn, I only see the first name:

I know for a fact this has not always the case – I’ve been training people on this for years and I’ve done this 1,000′s of times.

Big deal?

No – but I still find it interesting to notice changes like this because it means what we have all been wondering about (fearing?) may finally be coming – evidence that LinkedIn is working to limit or close off free access to LinkedIn data.

In fact, I’ve also been running into some seemingly random funny business with LinkedIn public profile URL’s. I have encountered a number of instances in which I’m using either Chrome or IE and I click on (or cut and paste) a public profile link, LinkedIn tells me that the profile is not found, when I know it does exist because I just looked at it.

For example here’s one I found while writing this post:

When I click on that link or cut and paste it in Chrome or IE, this is what I get:

So far, there is no pattern to it that I can tell – and I have only begun to notice this in that past few weeks.

I am not sure what it is indicative of, but wondered if anyone else has encountered this and might have some insight.

Using Alumni Search to View Full Names of 3rd Degree Connections on LinkedIn

Last week, Patrick Ryan, a former colleague with whom I stay in touch, sent me an email with something he had discovered on LinkedIn. I asked him if he minded if I wrote about it and he said no, so here we go.

If you scroll down your LinkedIn home page, you’ll find the “Just joined LinkedIn” section with Colleagues and Alumni.

If you click on the school name under Alumni, you’ll be taken to an attractive dashboard with some interesting information.

From here, you can change the years attended, show alumni that don’t show a graduation date, and of course search.

When you configure your search and see some 3rd degree connections without full names, this is what they will look like – first name only:

If you click on “Connect” and you’re using either Chrome, Firefox or Safari (not IE – sorry), you’ll get a pop up that will allow you to send the person an invitation, and it will also show the person’s full name.

Okay – I am sure some of you are thinking, “Cool – but this is extremely limited because I can only search alumni from my school.”

Sure, it’s limited, but let’s just say you can similarly search through any school that’s in the education section of your LinkedIn profile.

Oh, and each school as a school ID. For example, mine is 18570.

Final Thoughts

You don’t need to have a premium LinkedIn account to view any public profile and see the full names of people who are not 1st or 2nd degree connections.

However, it’s not safe to assume that the ways in which we can currently search for and view the information on LinkedIn profiles beyond our network will remain unchanged.

In fact, I believe we can expect LinkedIn to develop ways that limit the ability to find and view certain info on LinkedIn profiles for free – and why shouldn’t we? LinkedIn isn’t a non-profit, and just as any other for-profit company, it’s their prerogative to look for ways to make money and to reasonably limit giving away too much for free.

Will LinkedIn read this post and change the ability to view full names of 3rd degree connections via alumni search?

Yes, they will read this post (Hi LinkedIn team – special shout-out to the Ninja!).

Maybe they will do something to change the appropriate functionality.

If they do, I’m happy to have helped.

Talent Connect 2011 in Las Vegas  was just as good as, if not better than, Talent Connect 2010 in San Francisco.

Nearly 2,000 people showed up, which is around 3 times as many attendees as last year’s conference, and they represented over 700 companies from 17 countries.

One thing’s for sure – LinkedIn knows how to put on a conference. The Talent Connect events have been the most well coordinated, polished and produced conferences I have ever attended.

I won’t bore you with all of the details – but I will highlight LinkedIn’s new Talent Pipeline offering, Web 3.0 (the shift from social to data), touch upon how to automatically build Boolean search strings (yes, that came up at the conference), and inform you about LinkedIn’s Recruiter Expert certification.

LinkedIn Announced Talent Pipeline

LinkedIn unveiled Talent Pipeline at the Talent Connect event, and from all indicators, it was very well received.

Perhaps it didn’t hurt that Talent Pipeline is FREE for those with the LinkedIn Recruiter corporate recruiting solution. Interestingly, it’s also available as a stand-alone offering.

Because there has already been a  ton written about LinkedIn’s Talent Pipeline, I am going to forego an overview and just let you know what I am most excited about:

• You can import candidate records (resumes, spreadsheets, notes, anything), keeping them private for your organization’s use only
• Imported resumes can be synced with LinkedIn profiles as they are updated
• With a single search, you can search across LinkedIn profiles as well as your candidate records/resumes
• You can tag LinkedIn profiles, and these tags are only visible to your team
• You can search within and across tags, notes, activities, reviews, projects, reminders, and of course all standard search fields
• You can follow candidates in Talent Pipeline and set up alerts to be notified of any public changes to the profiles
• You can add a status to people in LinkedIn’s Talent Pipeline – not contacted, contacted, screened, declined, offer, applied, etc., and you can search by these statuses

The ability to import resumes and other candidate-related information into LinkedIn, sync them with LinkedIn profiles, search across them is HUGE!

I would imagine that at some point in the near future, LinkedIn will enable the ability to mass-import entire resume databases from ATS and CRM systems into your own company’s portion of LinkedIn’s cloud, which will be brilliant. Monster already offers a similar cloud-based resume consolidation solution in their SeeMore offering, and they couple it with their 6Sense semantic search.

The introduction of candidate tagging in LinkedIn’s Talent Pipeline is a significant offering. As I’ve written before, tagging enables one of the highest forms of semantic search, because a tag is the result of a human being analyzing information and assigning meaning to the profile/resume.

A human’s analysis is more relevant than any matching algorithm, and people can “read between the lines” of resumes and profiles, while software matching solutions can only work with the text that is actually present.

When a person reviews and analyzes a LinkedIn profile and/or resume and tags them (e.g., Linux Admin), they and anyone else on their team can find that potential candidate by searching for the tag, even if the person’s profile makes no mention of the tag keywords in their profile or resume.

LinkedIn’s Human Capital Data = Power!

I could not have had a bigger grin on my face during Wednesday’s session on “Web 3.0: The New Data Opportunity,” with Reid Hoffman, Tim O’Reilly, Michael Chui and Josh Bersin.

Hearing them use the phrase “Moneyball Recruiting” several times with regard to leveraging human capital data let me know that I’m not crazy, I’ve been on to something, and I am not alone.

You see, nearly a month prior to LinkedIn Talent Connect, I wrote an article titled “Big Data, Data Science, and Moneyball Recruiting,” and I actually referenced an article from the O’Reilly Radar website, as well as a McKinsey Global Institute report which Michael Chui contributed to.

Granted, neither the O’Reilly article nor the McKinsey report were focused on specifically leveraging human capital data – just data in general.

However, it’s not a huge leap to apply the same concepts of innovating and competing with data to innovating and competing specifically with human capital data (LinkedIn profiles, resumes, Twitter& Facebook profiles and updates…), and having Tim O’Reilly and Michael Chui from McKinsey on stage with Reid Hoffman talking about Web 3.0 and the power of data shows that the leap has already been made, at least by a few people “in the know.”

I took notice that a decent number of people left during this keynote panel, which led me to believe that quite a few people simply don’t “get” the latent power of human capital data and how it can be leveraged to give a company a significant competitive advantage in the war for talent.

I fear many companies are going to be in for a rude awakening.

Automatically Building Boolean OR Searches for LinkedIn

During my sessions at Talent Connect, I demonstrated a nearly 2000 character Boolean OR company search string as a graphic example of fully leveraging all of the search space LinkedIn Recruiter gives you.

A few people approached me and asked if there was an easy way to build large Boolean OR search strings (100 to 200 search terms), and my response was, “Yes, I wrote an article on that.”

And yes, it does feel great to be able to say that.

In the article How to Automatically Build Boolean OR Strings, I’ve provided links to 3 Boolean OR string builders so anyone can download them and put them to use, as well as videos on how to use them.

If you don’t feel like reading the original article, here’s one of the Boolean OR search string builders:

Once you download the file, will need to Enable Editing…

…and you will also need to Enable Content for the macros to work.

LinkedIn Recruiter Expert Certification

I stayed at Talent Connect all the way until the end, which allowed me to attend the Recruiter Expert Certification session, and I had the honor and pleasure of taking the LinkedIn Recruiter Expert Certification assessment while sitting next to sourcing/recruiting luminaries Eric Jaquith and Jim Stroud.

Yes, we all passed.

For the record, I finished first.

When I sent out a LinkedIn update and tweet about passing the certification, a few people responded asking what it was and how to get it.

The certification comes from passing a 30 question assessment that tests your knowledge of the capabilities of LinkedIn Recruiter, specifically with regard to effective professional branding and networking, advanced talent mining, LinkedIn Recruiter collaboration tools and techniques, and daily workflow integration.

Attending LinkedIn Talent Connect and sitting in one of the certification sessions and passing the assessment is one way, but there is another way. If you have a LinkedIn Recruiter seat, I would recommend reaching out to your LinkedIn representative for more information.

If you pass, you can join a small group of professionals who can proudly claim to be LinkedIn Recruiter Experts, as well as gain access to a special LinkedIn Group reserved only for those who are certified.

Final Thoughts

Being asked to come back and present at the second annual LinkedIn Talent Connect event was an honor, and attending the conference was a great experience.

Aside from learning about Talent Pipeline and the Web 3.0 keynote panel, other highlights for me would have to include Earvin “Magic” Johnson’s keynote and learning how Jon Tait, Head of Global Attraction at BP, leveraged social media and LinkedIn to revitalize their employment brand was fantastic. Would you have been brave enough to step into his role and accept the challenge after the Deepwater Horizon oil spill?

There was so much going on Monday, Tuesday, and Wednesday on 3 floors of the MGM Grand – I felt as if I was at an all-you-can-eat buffet of recruiting knowledge, information, and best practices, with presenters from Disney, Red Hat, Deloitte, PwC, BP, Pepsico, Microsoft, FedEx, the IRS (seriously!), VMware, Pfizer, and of course, LinkedIn.

If you have or will have access to LinkedIn Recruiter by sometime next year, you won’t want to miss Talent Connect 2012.

It will be hard to top this year’s event in Vegas, but somehow I am certain they will manage.

Would you like to know more about LinkedIn’s user demographics, as well as LinkedIn’s visitor statistics broken down by country, city, and state?

If so, you’ve come to the right place!

After patiently waiting for a whole year since my last post on LinkedIn statistics, I’m excited to bring you LinkedIn’s latest user demographics and visitor statistics for 2011.

In this post, I will compare the data I presented in September 2010 to the data I just pulled from Quantcast.

Quantcast is used by 9 of the top 10 media agencies because they quite accurately quantify Internet audiences.

While some sites are not directly measured and only have estimated data at this time (such as Facebook and Twitter), LinkedIn is fully “quantified.”

In other words, Quantcast directly measures LinkedIn’s visitors – which gives us great information and some very interesting insights!

Read on to see the following LinkedIn data:

• Global monthly visitors
• Global monthly visits
• Visits per person
• Pageviews per person
• Visit frequency
• Business activity
• User demographics (gender, age, ethnicity, income, education level)
• Monthly visitors by country
• Monthly visitors by city (global)
• Monthly visitors by state (U.S.)

LinkedIn Global Monthly Visitors

This chart shows the monthly global unique visitors to LinkedIn for the past 6 months.

Note that Quantcast claims they are the only measurement service that tells you the difference between cookies and people, which is important because a single person can be represented by many cookies (home, work, phone, different browsers).

What should stand out to you is that in one year, LinkedIn has more than doubled in the number of people using their site globally on a monthly basis, from 45.8M in September 2010 to 94.3M in September 2011.

2011 Data

2010 Data

LinkedIn Global Monthly Visits

This chart shows the total number of visits to LinkedIn for the past 6 months.

Just as with the number of people using the site, LinkedIn more than doubled the number of monthly visits from global users in 2011 over 2010.

2011 Data

2010 Data

.

LinkedIn Monthly Visits Per Person

The most recent data from Quantcast shows that the average number of visits per person to LinkedIn is 2.8 per month, both for U.S. and global visitors.

This is essentially unchanged from September 2010.

2011 Data

2010 Data

LinkedIn Monthly Pageviews Per Person

Quantcast’s latest data from LinkedIn shows that monthly page-views per person has been trending down slightly, from 28 for the U.S. and 29 globally in September 2010 to 25 for the U.S. and 24 globally in September 2011.

2011 Data

2010 Data

LinkedIn Global Traffic Frequency and Business Activity

Quantcast breaks down visitor traffic into three categories:

1. Passers By, users who visit a site or interact with media once per month;
2. Regulars, those who visit a site or interact with media more than once but less than 30 times per month;
3. Addicts, those that interact with a site 30 or more times per month.

2011 Data

2010 Data

This data shows that 1% of all visitors to LinkedIn are responsible for 25% of all LinkedIn visits, that the 33% classified as “Regulars” account for 51% of all the visits, and that 66% of visitors to LinkedIn visit about once a month.

I have a hunch that the 1% “Addicts” responsible for 25% of all LinkedIn visits are recruiters and HR professionals. What do you think? That’s not bad news for LinkedIn at all, considering how they generate revenue.

With regard to where people tend to access LinkedIn from, over 60% of visitors globally and in the U.S. visit LinkedIn from home rather than from work, and the exact percentages are unchanged in 2011 vs. 2010.

2011 Data

2010 Data

LinkedIn User Demographics

As a professional social networking site, the age, income, and college graduate/post graduate figures are pretty much where most people would expect them to be.

26% of LinkedIn users are aged 18-34, and 70% are aged 35-49 and 50+.

75% of LinkedIn users are college educated, with 27% at the graduate school level. Unsurprisingly, 39% of LinkedIn users make over $100K annually.

For those who have argued that using social networking sites for recruiting purposes may be discriminatory, we can see that the male/female split is very close to 50/50 – and even closer than it was back in September 2010.

However, African Americans and Hispanics seem to be underrepresented on LinkedIn, at least with regard to Internet averages, while Asians are overrepresented based on Internet averages.

2011 Data

2010 Data

LinkedIn Monthly Visitors by Country

This chart shows the latest data (from August) for the top 48 countries by number of LinkedIn visitors monthly by country – not the number of LinkedIn profiles per country.

While the U.K. and India were battling over #2 and #3 globally back in September 2010, the United Kingdom has decidedly taken the lead over India and is solidly #2 in terms of the total number of visitors globally by country.

Brazil more than quadrupled their number of LinkedIn users visiting the site monthly, jumping impressively from #7 globally in 2010 to #4, knocking the Netherlands down to #5.

There is a tight race in the 4.7M to 4.9M monthly visitor range between #4, #5, and #6 – Brazil, Netherlands, and Canada, respectively. The next tier down is the 2.2M to 2.9M group, represented by Spain, Australia, Italy, and France.

2011 Data

2010 Data

Being the curious guy that I am, I decided to see where the lowest number of monthly visitors to LinkedIn come from – see the list below.

I have to admit that I had to look up some of these places to see where they were.

Notice that there are visits to LinkedIn coming from Vatican City (Holy See).

LinkedIn Visitors by City

This chart shows the latest data (from August) for the top 48 cities from which visitors come to LinkedIn.

I am not sure what “Unknown” represents, other than the obvious – do you have an idea?

Some folks in the United States may be surprised to see that the #1 city is (still!) London. That’s right, there are more visitors to LinkedIn from London on a monthly basis than from Mountain View, CA (LinkedIn’s headquarters and downtown Silicon Valley).

Furthermore, out of the top 25 “known” cities, only 9 are from the United States, demonstrating how truly global LinkedIn is.

I am not sure what’s going on with Council Bluffs, IA ranking #7. It’s right outside of Omaha, NE, but still – I would not have guessed they would rank #7, ahead of Toronto, Chicago, Los Angeles, and Delhi. No offense Council Bluffs!

2011 Data

2010 Data

Just for fun, I decided to see what cities had the least number of visitors to LinkedIn.

After a ridiculous amount of clicking (I had to get to page 1,379 and 1,380 of the results), here they are:

LinkedIn U.S. Monthly Visitors By State

Once again, California has the most monthly visitors to LinkedIn by a wide margin – more than double New York, jumping significantly from 3.6M monthly visitors in 2010 to 9.6M in 2011.

If you were curious, Wyoming has the least number of monthly visitors to LinkedIn out of all 50 states.

2011 Data

2010 Data

I hope you found the LinkedIn visitor demographics and data interesting!

If you haven’t already, I recommend you investigate other sites using Quantcast, even if the sites aren’t directly measured. For example – check out Quantcast’s estimated user demographics and visitor statistics for Facebook and Twitter.

Although only estimated data, the information is still quite interesting!

I’ve received a few inquiries over the past month regarding how to find active job seekers on LinkedIn.

This isn’t something I do, nor have I ever tried to do it, so I didn’t have any read-made search suggestions for these folks.

I don’t recruit people because they are looking to make a move – I recruit people based on their skills, experience and critical intangibles. I could care less if they are looking or if the thought of leaving their current employer is the furthest thing from their mind.

In my opinion and experience, everyone is a candidate and anyone can be recruited if you have a great match between their skills, experience and interests and the opportunity you’re looking to fill.

Having said all that, if you want to search LinkedIn to identify people who are highly likely to be actively seeking employment, you have a few options.

How to Find Active Job Seekers on LinkedIn

As with nearly all human capital information retrieval efforts, experimentation and pattern recognition are key.

When it comes to finding people who are likely to be active job seekers, the first thing you can do is think about what types of words and phrases these people might use on their LinkedIn profiles to signify that they are looking for a new opportunity.

I don’t want to risk losing anyone with this comparison, but I think it’s critical that you realize that any solid sourcing methodology employs the scientific method for acquiring new knowledge, or correcting and integrating previous knowledge. The foundation of the scientific method is to propose a hypothesis and to design experimental efforts to test the hypothesis via predictions which can be derived from them.

Let’s hypothesize that people on LinkedIn who are looking for new employment would actually state such on their profile, and let’s predict that they might write such things as “currently seeking,” or “seeking new.”

We can test this hypothesis by running the search to see if we get any results, and if we do, how many results are returned.

When I ran the search, I got 36,222 results for all of LinkedIn (all locations).

That struck me as low, but at least we have verified that some people do in fact mention that they are actively seeking new employment on their LinkedIn profile.

Now we need to continue to hypothesize as to other phrases people might use to denote that they are looking for employment.

Before we do, however, let’s take notice of some information returned from that first search.

Looking over at the “Current Company” facet, we hit a little jackpot of information in the form of people actually entering in active job seeker phrases in their current employer field.

One thing you can glean from this section is 2 new job seeking phrases to search for – “actively seeking” and “seeking employment.”

Another thing to notice is the companies that are in the top 10 list.

Getting back to the “actively seeking” OR “seeking new” search results, you will see that this first very basic and limited search works…

…but it also pulls false positives in the form of people who mention “seeking new” in ways not referring to employment opportunities.

When it comes to information retrieval, false positives are inevitable – the goal is to minimize them whenever possible.

LinkedIn Search String to Find Active Job Seekers

After spending about 10 minutes reviewing results, iteratively modifying my searches and looking for patterns of phrasing for seeking employment as well as hypothesizing (making educated guesses) what people might say to signify that they are pursuing employment, here is what I came up with:

(seeking OR seeker OR “looking for” OR “in search of” OR “open to” OR “new job” OR “actively pursuing” OR “pursuing new” OR “searching for” OR “new opportunity” OR “new opportunities” OR “available for”)

That search returns 2.43M people globally – quite an improvement over 36K!

You probably noticed I decided to search for “seeking” without searching for specific “_______ seeking” or “seeking ______” phrases.

That’s because searching for “seeking” is actually more effective than using it in specific phrases.

Searching for the single term “seeking” is a double edged sword – it pulls false positives because it isn’t an intrinsically specific term for pursuing employment, but if you decide to add terms to it to create specific phrases, you will actually eliminate viable results of people who are looking for a new job.

This is because there are literally 100′s of ways people can specify what they are looking for in their job search.

For example:

Because of this, I would recommend simply searching for the term “seeking” and accept a percentage of false positives because there is no way you can think of (or search for) every possible combination of “seeking _______” that someone might use to denote what kind of role they are seeking.

If you are a recruiter and you are not looking for other recruiters, you may want to add something like -(recruiter OR recruitment) to your string/query to eliminate false positives of recruiters that mention they are seeking/looking for people with specific titles and skill terms.

Of course, you can also use all of the above approaches when X-Ray searching LinkedIn through Google or another search engine, although I personally recommend starting with and exhausting LinkedIn’s search before searching LinkedIn via an Internet search engine.

LinkedIn Offers Great Job Seeker Intel!

I can’t move on before sharing one of my favorite features of LinkedIn – the ability to see the top 10 results by current company, location, industry, etc.

I find this information fascinating – and when you run your own searches by your country or metro area, you’ll get even more applicable insight.

Here are the top 10 results by location, current company, and industry for the 12 active job seeker term search:

When searching globally, it’s not surprising to see some of the largest employers in the top 10, but there are some very large employers that are not listed and a few companies in the top 10 that I didn’t expect to see there.

Here’s the global top 10 industry breakdown:

LinkedIn Jobs and Recruiting Groups

When reviewing your results, other patterns you can notice are things such as the groups that the people who mention they are actively pursuing employment belong to.

You can notice this in a couple of places, including the “Groups you share with ______”

…and the groups facet which can be expanded to show the top 10 groups:

If a medical device development project manager is in several job and recruiting-focused groups, it might be a sign that (s)he is trying to connect with recruiters in an attempt to find new employment. Hello!

Bullhorn Reach Radar

Some people have hypothesized that making numerous changes to a LinkedIn profile and/or adding multiple recommendations in a short period of time can be indicative of someone that is beginning to look for new employment.

It’s not a bad theory – but how can you be alerted when people make changes to their LinkedIn profile and rack up recommendations?

One way is to use Bullhorn Reach’s Radar.

Radar isolates key LinkedIn profile updates (and Facebook too if you connect it) so you are automatically notified of updates and recommendations within your network.

For example:

I’ve seen some profiles that have been updated 10 times and receive 5 recommendations in a month. Could that amount of profile and recommendation activity indicate that they are beginning to look for employment?

Sure, but not necessarily.

One of the sharpest recruiters I know has always expressed his interest to me in being able to find/identify people based on frequent and recent LinkedIn profile updates and recommendation stacking, and I have to agree with him that there is at least a correlation between frequent and multiple LinkedIn profile updates coupled with acquiring recommendations and job seeking status.

Bullhorn Reach’s Radar is a simple way to automate the identification of such LinkedIn profile activity.

Of course, Radar is limited to your network, so if you’re not connected to your target talent pool, you won’t gain any/much insight.

Although more tedious and manual, you could also use LinkedIn Signal to search by update type (profiles):

I wonder how many people use LinkedIn Signal – I don’t see too many people writing or talking about it, and LinkedIn all but buried it. It’s a shame – it’s quite powerful.

Final Thoughts & LinkedIn Search Challenge

I can understand why there is an interest in identifying people based on their job search status.

However, according to the BLS, only about 14% of all people are actively looking for a new job, so you’re you’re missing the deepest portion of the talent pool by only targeting them.

Also, while a percentage of people who are actively seeking employment will mention things on their profile such as “currently seeking” or similar, many people won’t.

While 2.43M results from my “active job seeker” search on LinkedIn is a big number, it’s really only around 2.4% of all LinkedIn profiles (estimating slightly over 100M profiles).

That means that not everyone who is actively seeking employment actually mentions such on their LinkedIn profile, as well as that there are many more ways to express an interest in seeking new employment opportunities than the 12 terms and phrases I turned up in 10 minutes of quick research.

To that end, I’m asking you to improve this core LinkedIn search string designed to find active job seekers:

(seeking OR seeker OR “looking for” OR “in search of” OR “open to” OR “new job” OR “actively pursuing” OR “pursuing new” OR “searching for” OR “new opportunity” OR “new opportunities” OR “available for”)

If you can think of or dig up additional search terms/phrases that reliably identify people interested in seeking new employment opportunities, please include them in a comment and I will combine them into the single most comprehensive and effective LinkedIn active job seeker search string for everyone to use.

All I ask is that you *please* test your terms/phrases before suggesting them.

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.

A couple of weeks ago I stumbled across something on LinkedIn that I am surprised I never noticed before – I’m not even certain if/when LinkedIn made the change.

Finally sitting down to write about it, I highly doubted that I could be the only person to have discovered this interesting little find, so I did some quick research and found that Gary Cozin and Cathy Ou recently noticed it as well.

What am I talking about?

I’m talking about the fact that LinkedIn has alternate location names for certain postal codes.

While some locations only have one location phrase, I’ve found many have two and some have as many as nine! If you use Internet search engines to “X-Ray” LinkedIn for public profiles and you only use one location phrase, you may be unknowingly excluding people you actually want to find!

Alternate Location Names on LinkedIn

I personally discovered that LinkedIn has multiple location phrases when I went to edit my profile a couple of weeks back.

I wasn’t changing my location, but this caught my eye:

Apparently, “Greater Atlanta Area” is the default.

Having noticed “Atlanta, Georgia” for the first time, I immediately when to Google and Bing to see how many people did not use the default “Greater Atlanta Area” location name.

I tried this search: site:linkedin.com “location * atlanta georgia” -”greater atlanta area” (inurl:pub | inurl:in) -dir

As you can see, after a few false positive results, plenty of people use the alternate location phrase/name. But don’t be fooled by Google’s estimated 100,000+ results – if you click through all of the pages you will end up on page 50 with slightly shy of 500 results.

Switching to Bing, I tried something similar: site:linkedin.com location near:8 “atlanta georgia” -”greater atlanta area” -dir

Bing claims nearly 3,000 people use the alternate LinkedIn location phrase/name, but if you click through to the last page of results, you should get slightly over 500. Interestingly, I noticed some false positive results crept in that should have been excluded by the search – but looking further into that is another post entirely.

So What’s the BIG Deal?

Although my test searches didn’t discover a ton of people using the alternate location name in Atlanta, if you don’t include the alternate location phrase(s) in your LinkedIn X-Ray searches of your target locations, you will unknowingly relegate people who do not use the default location phrase into the realm of LinkedIn’s Dark Matter – profiles that exist but your searches prevent you from discovering them.

For example, here are about 24 people in the Atlanta area who mention Java or J2EE on their LinkedIn profiles that cannot be retrieved with an X-Ray search that only targets “Greater Atlanta Area.”

What you should notice is that the public profiles of people who use the alternate location name show only the specific non-default location name that was searched for…

…however, if you click on “View Full Profile,” you will see something different – both the user-selected, non-default location name as well as the default location name:

On the full profile, you can mouse over either location listed and use them as a faceted search. On a public profile listing, you cannot click on the location name to conduct a search.

How Many LinkedIn Location Name Variants Are There?

While it appears that there is a pattern of 2 location phrase/name options for major U.S. cities (I checked New York City and a few others – you’ll have to check yours), I did stumble across the fact that some areas within certain countries have many more location names to choose from.

For example, check out this list for Postal Code AL2 1AB in the U.K.!

That probably isn’t the only postal code/area in the world that has more than a few location names, so if you rely heavily on X-Ray searching LinkedIn for sourcing talent, I highly recommend you scope out all of the location names for all of the cities/areas you source from.

On the opposite end of the spectrum, some of you don’t have to worry about multiple LinkedIn location names at all – some locations and even countries don’t have any postal codes and/or LinkedIn doesn’t recognize them, so there is only one location name (unless I am missing something):

Update Your LinkedIn X-Ray Searches

I’m not sure when LinkedIn enabled the ability for users to choose amongst multiple location names per postal code – do you know?

Regardless, I’m excited to share this on a global scale – if use Internet search engines to “X-Ray” public profiles on LinkedIn, be sure to search for all of the location names that users in your target geography have to choose from. If you don’t, you are likely to unknowingly prevent some available results from being returned.

Of course, if you have a LinkedIn Recruiter account, you don’t really have to worry about finding people via X-Ray searches…

Happy hunting!

When searching any source for potential candidates, the ability to search by years of experience can be especially helpful in that years of experience can be correlated to current/desired compensation.

If you are recruiting for a position that pays a maximum of $85,000 annually, being able to first source people who are highly likely to be qualified for the role and willing to accept that compensation is certainly more efficient than sourcing and talking to a number of people who don’t have enough experience or for whom that compensation is unacceptable.

If you know that people with 5 to 7 years of overall professional experience in a certain role with specific skills in a given industry are generally in the $70,000 to $90,000 range for annual compensation, you would simply be working smart to try and first narrow your search results down to people who have that range of years of experience if that is what the position you are recruiting for pays.

As I’ve written and spoken about many times – appropriately deep and searchable human capital data can afford sourcers and recruiters the advantage of more control over critical candidate qualification variables than any other form of candidate identification, including referrals and job postings (social or otherwise), which offer very little-to-no control over any candidate variables (years of experience, education, specific responsibilities, industry experience, etc.).

With the ability to control candidate qualification variables such as years of experience and/or likely desired compensation, sourcers and recruiters can work more efficiently with less waste, more quickly identifying and contacting prospective candidates who have a high probability of not only being qualified, but also “recruitable,” and one of the critical aspects of a “recruitable” candidate is the probability of accepting an offer at a specific compensation level.

So let’s take a quick look at how you might be able to exert some degree of control over years of experience and thus current/desired candidate compensation when searching LinkedIn for talent using LinkedIn’s filters as well as using Google and Bing to X-Ray search into LinkedIn for those of you who do not have a premium LinkedIn account.

LinkedIn Talent Filters

If you have the appropriate premium LinkedIn account, you have access to LinkedIn’s Talent Filters – one of which is Years of Experience.

I like the tight arrangement of the “Less than 1 year,” “1 to 2 years,” and “3 to 5 years,” but I find “6 to 10 years” and “More than 10 years” are a little too broad for my liking and offer a much lower level of probabilistic control over likely current/desired compensation.

The Seniority Level premium filter isn’t nearly as helpful as the Years of Experience Talent Filter, primarily because it is title-driven, and hopefully you’re well aware of how problematic and non-specific many titles can be with regard to conveying anything highly useful in accurately identifying people by years of experience or true level of management.

Also, notice the gaping hole between “Entry” and “Senior.”

Google LinkedIn X-Ray Options

So, what to do if you don’t have access to the Years of Experience Talent Filter, or if you want to try and control years of experience more specifically than 6 to 10 years or more than 10?

Well, you can do what any self-respecting sourcer or recruiter would do – which is try to hack a solution together.

As I’ve written about recently, sourcing candidates via searching human capital data isn’t about Boolean search – it’s about Information Retrieval and it’s also an investigative and iterative process.

As such, we have to first start by identifying our information need, and then try to figure out how we can construct queries to retrieve results that meet that need.

If we are trying to find people who are more likely to currently be at or willing to accept compensation in a specific compensation range without the direct ability to search by compensation data (which simply doesn’t exist on LinkedIn, but does on major online resume databases), we are left to try an influence likely compensation via years of experience.

Without the ability to use a years of experience filter, or if you need to be more specific than “6 to 10″ or “More than 10″ years of experience, you have a few options you can use to try and control years of experience.

For example, you can use Google to try and target years of post-graduate work experience.

Here is a search attempting to target a mention of a Bachelor’s degree near the mention of the year 2004:

site:linkedin.com -dir (java | j2ee) -recruiter (engineer | consultant | programmer | developer) “location * Greater Atlanta”  ”(“BA” | ” B.A.” | “BS” | “B.S.” | “Bachelor” | “Bachelors”) * * * * * * 2004″

You can see that this works to some extent, without even having to open up individual search results.

Of course, there are false positives amongst the results as well, which are to be expected given what we are trying to achieve and how we are trying to achieve it. I recommend experimenting with the number of asterisks between the degree terms and the year.

Leveraging Google’s Numrange Function

You can also leverage Google’s numrange function when targeting mentions of years. For example:

site:linkedin.com -dir (java | j2ee) -recruiter (engineer | consultant | programmer | developer) “location * Greater Atlanta”  ”(“BA” | “BS” | “B.S.” | “Bachelor” | “Bachelors”) * * * 2004..2009″

Eliminating a Range of Years on a LinkedIn Profile

You could go a step further and try to eliminate any mention of any year in the 1990′s from appearing anywhere on the LinkedIn profile, as well as any mention of 2000, 2001, 2002, or 2003:

site:linkedin.com -recruiter -dir (java | j2ee) “location * Greater Atlanta” “(“BA” | “BS” | “Bachelor” | “Bachelors”) * * * 2004..2009 ” -1990 -1991 -1992 -1993 -1994 -1995 -1996 -1997 -1998 -1999 -2000 -2001 -2002 -2003

If you’re not sure why I (or anyone) would want to eliminate mention of such a broad range of years on a LinkedIn profile – please let me know.

You may be wondering why I didn’t just try to exclude a numrange expression, such as -1990..2003. Well, that’s because it doesn’t work. I tried -(1990..2003) and -”1990..2003″ and they didn’t work either. Then I tried NOT, and then I decided to get back to writing. Let me know if you can find a way in which Google processes an exclusion of a numrange properly.

Also of note is that excluding some of those years/numbers can cause problems by eliminating some profiles we actually don’t want to because of non-year mentions such as “MS Office 2000,” etc.

Targeting Recent Grads

If you’d like to try and target recent grads, here is an example to try:

site:linkedin.com -recruiter -dir (java | j2ee) “location * Greater Atlanta”  ”(“BA” | “BS” | “Bachelor” | “Bachelors”) * * * 2011″ -1991 -1992 -1993 -1994 -1995 -1996 -1997 -1998 -1999 -2000 -2001 -2002 -2003 -2004 -2005 -2006

As you can see, this does work:

Not Targeting Education

Of course, you don’t even need to target education in an attempt to control a person’s years of professional experience by controlling what years do or do not appear on a LinkedIn profile:

site:linkedin.com -dir (java | j2ee) -recruiter -answers -jobs (engineer | consultant | programmer | developer) “location * Greater Atlanta” 2000..2011 -1990 -1991 -1992 -1993 -1994 -1995 -1996 -1997 -1998 -1999

Bing LinkedIn X-Ray Options

In my opinion, Bing’s support of configurable proximity search is better than Google’s asterisk/wildcard search. However, the major weakness of Bing’s NEAR:X search functionality is that it doesn’t play nice with OR statements, which makes it difficult and/or impossible to accomplish what we have with the above Google searches in a single Bing search.

For example – even though the logic of this query is sound, it doesn’t work:

site:linkedin.com -dir (java OR j2ee) -recruiter (“BA” OR “BS” OR “B.S.” OR “Bachelor” OR “Bachelors”) NEAR:7 (2000 OR 2001 OR 2002 OR 2003 OR 2004)

Bad Bing!

However, Bing’s NEAR:X works beautifully when targeting single terms:

site:linkedin.com -dir (java OR j2ee) -recruiter “Bachelor” NEAR:7 2004

Final Thoughts

It is important to remember that we are trying to meet a specific information need – finding people with a certain range of years of post-graduate experience – in the hopes of exerting some control over the current/desired compensation of prospective candidates, using tools (Google & Bing) that were not designed specifically for doing so.

As such, none of the example searches I demonstrated above are perfect, and please keep in mind that the searches I illustrated are just examples from which you can build upon, experiment with, and tailor to your specific needs.

While the searches I used certainly work in some cases, in others they let false positives creep into results as well as eliminate people who actually do match our information need but cannot be returned in search results with the above queries (e.g., they do not mention the dates of their educational achievements, or they simply don’t enter them at all, which as far as I know is not a requirement to complete a LinkedIn profile) – these results are what I refer to as LinkedIn’s Dark Matter.

And finally – all of this playing around with mentions of college degrees on LinkedIn profiles has got me wondering why LinkedIn doesn’t offer the ability to search by minimum education level. Seems like a no-brainer, right?

Right after they add that as a premium filter, I think they should offer LinkedIn users the option and ability to privately specify desired compensation ranges for people who are actively and “passively” looking for the next step in their career, viewable only to those with corporate and professional recruiter licenses.

What say you?

So far, I’ve launched 2 LinkedIn sourcing challenges – Ruby and X-Ray Location False Positives.

The former had very strong participation as it was a little on the easier side (for some!). The latter had fewer participants, perhaps because it was more technical – but those who did participate did so heavily.

For my 3rd Linkedin Sourcing Challenge, I think I have one that is universally appealing because it requires no technical or advanced sourcing experience to participate, nor to win the challenge!

The Inspiration for this Challenge

Just the other day I thought back to the time when LinkedIn made a change that prevented average Joes and Janes with a free LinkedIn account from running a keywordless search to reveal the total number of LinkedIn profiles in a given country.

I really enjoyed being able to select a country, enter no keywords, and hit “search” to see all of the LinkedIn profiles in a given country – it was an easy way to get accurate LinkedIn stats per country, as well as to monitor growth.

Now, at least for those using a free LinkedIn account, when searching by country or “Anywhere” (searching all of LinkedIn), you have to enter at least one keyword to be able to see people from the “3rd + Everyone Else” relationship level.

Which got me thinking – perhaps there is some all-inclusive combination of keywords that you can string together in order to find the most, if not all of the profiles in a given country…or the entire world.

LinkedIn Sourcing Challenge – All Inclusive Results

Using LinkedIn’s search interface only, create a query that returns the greatest number of total results of in all of Linkedin (location: Anywhere), and comment on this post with your search string and the number of results.

Ideally, once you’ve settled on your search, copy the entire LinkedIn URL and shorten it using a shortener (such as bit.ly) and include it in your comment. That way, anyone else participating can simply click on your shortened link and instantly be taken to your search within LinkedIn.

Are You Up To The Challenge?

I especially like this challenge for a number of reasons:

1. It’s not a technical challenge – it’s a creative, thinking, and experimental challenge
2. The results returned by any given search can be confirmed by anyone and are thus irrefutable
3. People will be able to rapidly build upon the ideas of others
4. This challenge will reveal some interesting things and raise questions about LinkedIn’s profiles and search engine
5. Someone will craft a search that returns more people than anyone else, and the “why and how” will be interesting!

By the way, because this is a pure keywords vs. keywords inclusivity challenge, even those of you with premium access to LinkedIn (e.g., LinkedIn Recruiter) can participate.

Unless, of course, you want to share your results via link or try to use a search string over 1,000 characters.

On March 22nd, LinkedIn announced that they had officially crossed the 100 Million member mark.

However, they might have actually crossed the 100 Million member milestone back in January.

How would I know?

I started seriously exploring LinkedIn Ads ever since they announced that they emerged from beta on 1/26/2011, and I noticed that once you configure an ad, LinkedIn displays the total estimated audience based on your targeting, including geography, company, job title, group, gender and age.

When I first looked into LinkedIn Ads, I noticed that the total target audience was somewhere over 99,000,000 when I did not make any targeting selections. I figured this was representative of the total estimated number of LinkedIn profiles worldwide.

Because the figure was so close to 100,000,000, I made a mental note to come back and check in to see if and when the estimated audience crossed the 100M mark.

Back on January 30th, I took a screenshot of a targeted audience of 101,382,559.

Here it is, with the file properties info so you can see when the file was created (and never modified):

I was planning on writing about this for quite some time, but I kept prioritizing other posts, so I was bummed when I saw LinkedIn’s official announcement that they crossed 100M profiles on 3/22.

In hindsight, folks at LinkedIn probably would not have been too pleased if I had announced that they had crossed 100M members before they did, so perhaps it all worked out for the best.

Of course, the target audience figure is just an estimate (as is the size of your 2nd and 3rd degree connections, not to mention your total network size!), so perhaps the 101M+ number I saw wasn’t actually representative of the real number of profiles – only LinkedIn would be privy to the exact figure.

Interestingly, when I just went back into LinkedIn Ads to see what the number had gone up to, it’s now magically locked at 100,000,000.

However, when I target North America and more specifically the United States (sorry Canada!), I see an estimated target audience of 44.6M people – so as long as as you perform some kind of targeting, you can see dynamic figures.

Explore LinkedIn Ads

If you haven’t already, I strongly encourage you to explore LinkedIn’s Ads – you can find the estimated number of LinkedIn profiles matching any combination of geography (continent & country), company (name or category), job titles (specific or category), LinkedIn group, gender and age.

While some would argue that LinkedIn Ads are not as “targetable” as Facebook’s or Google’s – LinkedIn’s professional/work related data (e.g., companies, job titles, etc.)  is much deeper and more thoroughly populated (and thus leverageable!) than Facebook or Google for employment advertising for sure!

Sourcing has a fundamental problem: All searches return results.

Yes, that is actually a problem.

Why? Because everyone’s a winner.

Type in a few keywords and BAM! – you get some good looking results. Hey, this sourcing stuff isn’t so hard!

If I’ve said it once, I’ve said it a thousand times – sourcing is easy. In fact, it’s ridiculously easy to find some people.

So if you and your company are happy with finding some people and not necessarily the best people available to be found, then you can stop reading now and go back to finding some people.

For everyone who’s still reading this, try answering these questions:

1. Can you ever be sure you’re finding everyone there is to be found?
2. How do you know you’ve found the best people available?
3. How do you know you’ve found all of the best people?
4. Are there people on LinkedIn, in your ATS, in job board resume databases that are never found?
5. How can you be aware of social media profiles and resumes that your searches can’t return in results – but are there?

Sourcing is easy, but it’s not easy to get to the point where you are sure you have found all of the best available results, nor is it easy to specifically target and find people others cannot and do not.

Most people use relatively basic, straight forward/direct keyword and title searches. There’s nothing wrong with that – they clearly “work” – anyone running those kinds of searches will find results.

However, they will also find exactly what everyone else finds when searching for the same types of people, which yields zero competitive advantage.

The fact that all searches produce results is a problem because it lulls people into thinking that sourcing is easy, and at least on the subconscious level – it leads people to believe that the results that are returned from searches represent all available matching and relevant results.

However, it is a fact that no single search can find all of the people you’re looking for, and there are many social media profiles and resumes that are never found.

Let me introduce you to the concept of Dark Matter.

The Dark Matter of Human Capital Data

People who use relatively straight forward/direct keyword and title searches find the same people as everyone else using the same keywords and titles.

What’s particularly interesting to me is the fact that everyone running basic, straight forward/direct keyword and title searches also fail to find the people who are there to be found, but cannot be found via straight forward, direct searches.

These profiles are what can be referred to as “dark matter” – which, in layman’s terms, is stuff that’s there, but you can’t see it and it isn’t directly detectable.

I’ve found that people have an especially difficult time understanding that every search they run both includes some of the right people, and excludes some of the right people.

My last LinkedIn sourcing challenge provides a good example of that. Many respondents could find matching results, but did not seem to grasp the concept of ensuring that their searches did not exclude viable, available results. To some degree, that’s understandable, because it’s difficult to be aware of what you cannot see. However, just because your searches didn’t return a result, it doesn’t mean they are not there to be found.

So what happens to the people that are excluded from your searches – the people your searches don’t return, the people you’re not even aware of?

Well, it should be obvious that very few people find them, and those that do typically do so by mistake and not through a direct effort to uncover them.

They are there for the finding if you can:

1. First realize that they do exist, even without direct evidence of such
2. Figure out how to target and retrieve them

Revealing LinkedIn’s Dark Matter Profiles

I’m going to use a few very basic examples in an attempt to clearly illustrate what I’m talking about with regard to profiles on LinkedIn that most people #1 aren’t even aware of, and #2 cannot be found via conventional search approaches.

For these examples, it is very important that you don’t get caught up on the actual search terms I use – the terms themselves are irrelevant. What is relevant is the concept behind what’s being demonstrated. It’s up to you to absorb the concept and apply it to whatever types of people you are trying to find.

LinkedIn Dark Matter Example #1

Let’s say you’re looking for a software engineer and your manager is very clear that she needs someone with strong Java/J2EE skills and experience.

Most people searching for software engineers with Java will search for “Java.” As obvious and innocent as that would seem, doing so creates dark matter profiles.

Click the image below to reveal nearly 40,000 people that cannot be found by searching with the term “Java:”

For those of you who do I.T. sourcing and recruiting, you will quickly identify that the vast majority of the people returned by the above search to be software engineers who quite obviously have Java experience, yet they do not mention “Java” – their primary development language – anywhere on their LinkedIn profile.

Why would a Java developer not explicitly mention “Java” on their LinkedIn profile?

I know why – do you?

LinkedIn Dark Matter Example #2

Let’s say you’re looking for accountants, and your manager tells you that they must be CPA’s.

Of course, you can find people by searching for all of the various ways someone can express being a CPA – (CPA OR “Certified Public Accountant”). You might even get crafty, realizing that LinkedIn sees “C.P.A.” as distinctly different than CPA and add that to the OR statement.

There are nearly 5,000 people in all of LinkedIn that cannot be found unless you specifically search for “C.P.A.” – here’s the proof.

However, searching for any variant of CPA is still a very basic and direct method of searching for people who are CPAs.

Click the image below to see over 10,000 people who are highly likely (I’d say > 90%) to be CPA’s, but cannot be found by searching directly for any mention of CPA:

After getting over 600,000 profiles returned from my initial search targeting a current or past title of “Accountant” and specifically excluding any mention of CPA, I filtered the results by people who specify that they currently work for either Deloitte, PwC, KPMG or Ernst & Young.

Do you think a “senior accountant” at PwC isn’t a CPA?

Those results represent more than 10,000 accounting professionals who work for Deloitte, PwC, KPMG or Ernst & Young that simply do not exist to anyone searching for (CPA or “C.P.A.” OR “Certified Public Accountant”).

Why would a CPA not make explicit mention of it on their LinkedIn profile?

I know why – do you?

Darkest Matter

The darkest dark matter profiles on LinkedIn are those of people who use very few, if any words that give you any real clue as to their specific skillset or experience.

These profiles are often never found, because any obvious keyword will prevent them from being returned in search results.

To reveal and target these profiles, you have to exclude the more obvious keywords that most people who actually use.

For example, if I were looking for software engineers, but did not want to see the profiles that everyone else is finding, I could target a current title of  (“software engineer” OR developer OR programmer) and try to exclude most major technologies with -(“java/j2ee” OR peoplesoft OR perl OR oracle OR SAP OR java OR j2EE OR .Net OR C# OR C++) in the keyword search field.

There are almost 1,000,000 profiles returned by that search, check them out here.

There’s nearly 13,000 of them from IBM, 12,000 from Accenture, and over 4,000 from Google.

Any sourcer or recruiter who puts Java, PeopleSoft, Perl, Oracle, SAP, J2EE, .Net, C#, or C++ in their search is simply not aware that the nearly 1,000,000 people I just found with the above search even exist because they will be excluded from their search results.

Why would you want to look at profiles of software engineers who don’t mention any particular technology on their LinkedIn profile?

I know why – do you?

Indirect Search

One way of discovering Dark Matter profiles is to conduct a form of Talent Mining I refer to as Indirect Search.

There are many applications of Indirect Search methodology, including the 2 examples I shared above, and you can also see some in action among many of the proposed solutions to my Ruby LinkedIn Sourcing Challenge.

The challenge really was an example of Dark Matter profiles on LinkedIn – I asked readers to find people on LinkedIn who had Ruby on Rails experience, but did not mention any variant of Ruby on their LinkedIn profile.

Many people who solved the challenge had to go indirectly search for LinkedIn profiles by first going outside of LinkedIn to find information on people that alluded to Ruby experience,  and then cross reference the name on LinkedIn to see if the person’s profile did or did not mention Ruby.

While that is one form of Indirect Search – finding people on LinkedIn with specific experience by finding them first elsewhere – how could you find people with Ruby experience on LinkedIn who do not make any explicit mention of it on their LinkedIn profile, AND they don’t have any other info elsewhere on the Internet to give you easy clues indicating that they have Ruby experience?

Can Semantic Search Help?

No.

While some people feel semantic search solves all sourcing problems (vendors, mostly ),  semantic search isn’t the solution to the Dark Matter challenge, for a number of reasons – not the least of which is that semantic clustering and other applications of machine learning can only work (semi)reliably with what is actually there/mentioned.

Even the most advanced NLP algorithms are limited to “analyzing” and “learning” from text.

What happens if the text isn’t there to analyze and learn from in the first place?

While there are solutions that are going to become available that claim to use semantic search that can cross reference LinkedIn profiles with other social media profiles and help identify people who may reveal some information via Twitter or other networks that they do not on LinkedIn – that too only works with information that is directly retrievable.

As you can see even from the extremely simple examples I demonstrated above, it is actually necessary to think and purposefully construct indirect searches for implied experience or qualifications in order to reveal Dark Matter profiles – those that cannot be seen/found by direct searching methods. It’s also interesting to note that more powerful forms of Indirect Search require the strategic exclusion of direct search terms – something I am not aware that any semantic search application allows. Does anyone know of one?

Before I get flamed by the semantic search vendors and fanboys/girls – I’m passionate about semantic search, and I’ve actually used most of the “big name” solutions on the market. I also perform semantic search manually at the most powerful level (sentence level verb/noun combos) to help fill job openings just about every day – ask me how if you’re interested.

Because I really understand and appreciate information retrieval – both manual and automated – I feel it’s my duty to keep vendors who are selling semantic search as the solution to all sourcing/recruiting challenges honest and to inform people who may be considering purchasing solutions that there are limitations of semantic search.

I’ll be writing more on the subject very soon, but I’d like to illustrate another issue with “semantic search” solutions that vendors won’t volunteer: If 5 companies who are constantly battling it out for top talent all use the same semantic search application, all 5 companies will find all of the same people. That means no competitive advantage. Perhaps even more importantly, they’ll also be totally unaware of the people their semantic search solution could not find – a common hidden talent pool of Dark Matter profiles that cannot be tapped by any of the companies.

Dark Matter Doesn’t Discriminate

Dark Matter profiles don’t magically appear for people who pay top dollar for a LinkedIn Recruiter account.

However, one powerful feature of LinkedIn Recruiter that can put a small dent into the Dark Matter of LinkedIn is the “All Groups” search, which allows you to target people in any group even if you are not a member. At least one person who solved my Ruby LinkedIn Sourcing Challenge did so by using this feature. However, there is a Yang for every Yin – it won’t work for the large number of people who haven’t joined groups, or haven’t joined a group that you can use to reliably identify them.

Regardless of level of account, no one searching LinkedIn, in any way or method, is immune to the Dark Matter phenomenon – it is a fundamental challenge to human capital information retrieval. Quite simply, you can’t directly search for and retrieve information that isn’t there to be retrieved. There are many great people that can only be found via Indirect Search methods.

To be sure, the Dark Matter concept certainly isn’t limited to LinkedIn – it applies to every source of human capital data, including your ATS/CRM, Monster’s resume database, the Internet, etc.

LinkedIn Dark Matter Question for You

What percent of LinkedIn do you think can be classified as Dark Matter – profiles that are there, but are never found or viewed through direct search methods?