Tag Archives: LinkedIn Search

Free LinkedIn Sourcing Webinar Wednesday November 20 @ 2PM ET

 

LinkedIn Sourcing Webinar November 2013

When you search LinkedIn, are you finding top talent, or simply those people who are easiest to find?

Would you know the difference?

While some people firmly believe that LinkedIn is “over fished,” I can confidently tell you that nothing is further from the truth.

In fact, what if I told you that you routinely find only a fraction of the people that can be found on LinkedIn?

LinkedIn 259 Million UsersNow that LinkedIn has grown to over 259,000,000 registered users, finding people has become much easier, but finding the right people becomes increasingly more challenging, and finding all of them even harder. As such, knowing how to effectively source talent on LinkedIn is now more important than ever.

During Wednesday’s LinkedIn sourcing webinar, I’ll review advanced human capital data retrieval concepts, techniques and strategies that you can leverage in LinkedIn Recruiter, including Dark Matter, Maximum Inclusion, Adaptive Search, Strategic Exclusion, Intelligent Results Processing, and Moneyball Sourcing.

Be sure to attend this LinkedIn webinar live, because this session won’t be recorded and the slides won’t be distributed afterwards.

Also, I simply have to recognize the LinkedIn team’s graphic designer for putting this “Lord of the Strings” image together – I’m a huge LOTR fan.

Although I like the concept of Gandalf, Legolas, Gimli and Aragorn having my back in the war for talent, I think I’ll stick with my shaved head. :)

Lord-of-the-Strings 2 LinkedIn

How LinkedIn Search Actually Works

If you’re in sourcing, recruiting or HR, you no doubt search LinkedIn from time to time or perhaps even every day.

So why not gain a better understanding of how LinkedIn search actually works?

And what better way to learn how LinkedIn search works than from the Heads of Search Relevance and Query Understanding at LinkedIn?

Yes, you read that correctly – LinkedIn has folks specifically dedicated to understanding your queries in an effort to return the right set of results, emphasizing query rewriting, elaboration, and refinement.

Here is the LinkedIn Search Slideshare deck where you can learn about, among other things, LinkedIn’s contextual word sense and how LinkedIn deals with “keyword stuffers” and spammers.

[In]formation Retrieval: Search at LinkedIn from Daniel Tunkelang

If you would like to learn more about the LinkedIn search team looks at content, connections, and context, here is a fantastic Slideshare to review:

Content, Connections, and Context from Daniel Tunkelang

You may also learn a thing or two about how LinkedIn search works from their page dedicated to highlighting some of the search-related challenges they think about everyday:

Search at LinkedIn Main Page

LinkedIn’s Voltron Search: What’s New and What’s Missing

 

Voltron by wayneandwaxIn case you haven’t heard, LinkedIn is rolling out a new search interface globally over the next few weeks.

If you’d like to read the official statements and press-friendly content about LinkedIn’s new search functionality, you can find read about the changes on LinkedIn’s blog, TechCrunch, Search Engine Land, Mashable, and PCMag.com. If you’re only going to read one – read TechCrunch’s – it’s the best of the bunch in my opinion.

However, if you’d like to know what a LinkedIn power user and sourcing/information retrieval geek thinks about LinkedIn’s new search functionality, you’ve come to the right place.

I’ve had access to LinkedIn’s new search interface and functionality for a week now, and I wanted to share with you my first impressions, discoveries, disappointments, concerns, and suggestions for LinkedIn.

 

LinkedIn New Search Interface complete

 

LinkedIn Search: New, but Improved?

LinkedIn’s Smart Query Intent Algorithm

Before I had access to the new LinkedIn search, I was excited when I first read about the concept of a “smarter query intent algorithm.” LinkedIn claims that the more you search for content on LinkedIn, the more the query intent algorithm “learns and understands your intent over time to provide the most relevant results.”

Of course, I’ve only had access to LinkedIn’s new search for about a week now, so I can’t tell how “smart” is has become based on the queries I’ve been feeding it. However, the issue I have with any query intent algorithm that claims to be able to provide me with more relevant results is that only the user can determine if results are “relevant” or not.

According to Merriam Webster, relevance is defined as “the ability (as of an information retrieval system) to retrieve material that satisfies the needs of the user.”

As such, by definition, only the user can truly determine or judge relevance. A search engine cannot ever truly “know” the needs of the user.

While I appreciate and applaud the intent behind an “intelligent query algorithm,” which isn’t dissimilar to what many have been trying to do for years when it comes to search, the best way to implement such a system is to incorporate a feedback loop for the user to tell the algorithm which results the user truly finds relevant, rather than relying on supervised or unsupervised machine learning or some other method based on which profiles are clicked vs. which ones are not, and/or perhaps time spent reviewing specific profiles.

I’d love to know exactly how LinkedIn’s smarter query intent algorithm works (I’d love to make it smarter!), but something tells me that’s not something they would disclose.

I’m not a fan of black box search algorithms – I like to know exactly why I get the results I do.

LinkedIn’s Suggested Searches

I was also excited when I read about suggested searches, because my mind immediately raced to thoughts of LinkedIn being able to suggest better queries or perhaps searches other people had run for similar terms/people.

However, what LinkedIn is really referring to with regard to “suggested searches” is related to new unified search functionality in that if you type in a term or a title into the main search box on LinkedIn, you will see a list of options you can choose from, such as searching for related jobs, people, connections, groups, and skills.

 

LinkedIn New Search Product Manager

 

I’m not saying this isn’t cool functionality, it’s just that I have high expectations when someone makes a claim of “suggested searches.”

Customized LinkedIn Results

According to LinkedIn Product Manager Johnathan Podemsky, “No two professionals are alike on LinkedIn. This means even if you search for the same thing as someone else, your results will be customized to you,”  “LinkedIn’s search efforts are founded on the ability to take into account who you are, who you know, and what your network is doing to help you find what you’re looking for.”

This makes total sense based on the LinkedIn’s underlying fundamental concepts, but from a recruiting perspective – what if the best candidates aren’t within the network of the person conducting the search?

While Stephanie Mlot from PCMag claims LinkedIn’s changes put “…LinkedIn on a more level playing field with Facebook, which introduced Graph Search earlier this year as a way for users to sift through the network’s 1 trillion connections for more details about their friends,” I don’t agree. One major distinction is that a user can search for and find anyone using Graph Search – regardless of whether or not they are connected to them in any way.

Of course, LinkedIn does offer a solution for people who want the ability to search for anyone regardless of network connection – it’s called LinkedIn Recruiter.

However, if you’re searching LinkedIn for free, you’ll notice you no longer have the ability to sort all of the results of a search, which leads me to what’s missing from LinkedIn’s new search interface and functionality.

LinkedIn Signal

While LinkedIn Signal isn’t new – what IS new is that you no longer have to go to “News” on the top nav bar and click “Signal” – you can now simply click “Updates” on LinkedIn’s new search interface to instantly be taken to a Signal search for the keywords you’ve already entered.

 

LinkedIn New Search Signal with inset

 

Signal is one of LinkedIn’s most powerful and underutilized features. With the new and more prominent placement, I hope Signal will get the use and appreciation that it deserves.

What’s Missing from LinkedIn’s New Search

Curious to know what’s NOT included in LinkedIn’s new search interface and functionality?

A number of things.

Results Sorting

First and foremost, you can no longer sort your search results.

I always searched by keyword relevance when searching LinkedIn, because even with a large network, I am not so ignorant as to believe that the best people for any given position I may be sourcing and recruiting for are always going to be within my 1st or 2nd degree network, let alone my 3rd degree connections or within my LinkedIn network at all. If the best match to a search happens to be in my 3rd degree network, I’d like to see them come up on page 1 of the results.

Say goodbye to this if you’re using a free LinkedIn account:

 

LinkedIn Sort Search Results

 

LinkedIn’s sort by “relevance” option was a mix of network connection and keyword relevance. Based on my searches using LinkedIn’s new search interface, it seems that search results are sorted based on some combination of keyword relevance and relationship, as 1st and 2nd degree connections are returned early in search results and 3rd degree and group only search results come much later in ranking.

While you can still search specific layers of your LinkedIn network, there is no way to search for Group-only connections that are not also connected to you in the 1st, 2nd, or 3rd degree.

 

LinkedIn sort by connection

 

LinkedIn no ability to search group only connections

 

With a free account, the only way you can try and achieve anything close to searching solely by keyword relevance is an X-Ray search. Thankfully, you can still sort your results by keyword relevance within LinkedIn Recruiter.

The Ability to Run SUPER LONG Boolean Search Strings

I am sad to report that LinkedIn’s once-epic ability to run Boolean search strings of over 3,000 characters has come to an end.

That means you can no longer perform some of the interesting diversity sourcing searches I’ve detailed in the past, such as searching for all of the HBCU’s in a single search, or searching for the 354 most common female names in the U.S. over the past 4 decades to find 65% of all of the women on LinkedIn in a single search.

From my preliminary testing, it seems that you can get away with searches up to around 1,300 characters with spaces before you start to encounter LinkedIn just spinning and never executing your search. With a first name search, this is what 1,281 characters with spaces looks like.

Top 10 Facets

Also missing from the LinkedIn’s new search interface is the ability to see the top 10  results in each facet.

I can’t be the only person who found the ability to see the top 10 companies employing certain types of people in a given market, the top 10 markets for specific skills, or the top universities by skill to be valuable, can I?

Now free users are limited to the top 5.

 

LinkedIn Top 5

 

LinkedIn top 5 locations

 

Thankfully, you can still view the top 10 results in each facet in LinkedIn Recruiter.

 

LinkedIn top 10

 

Linkedin top 10 locations

 

Advanced Search Operators

Alas, Voltron has laid LinkedIn’s Advanced Search Operators to rest.

What? You didn’t know LinkedIn had Advanced Search Operators?

They may have been LinkedIn’s best kept secret for years, and you could do a number of interesting things with them, such as creating search agents.

Are you wondering why I referenced Voltron?

Take a look at the URL when you run a search in the main search box when using LinkedIn’s new search functionality: Voltron Federated Search

 

LinkedIn Voltron Federated Search URL

 

I’m assuming Voltron is the code name for LinkedIn’s new search and that “vsearch” also stands for Voltron Search.

 

LinkedIn Voltron Vsearch

 

Anyone care to (neither) confirm (n)or deny?

Mobile

Ingrid Lunden from TechCrunch called out the fact that mobile is missing from this LinkedIn search upgrade.

LinkedIn has claimed that extending new search functionality to their mobile apps is something that they’re looking into, but for now, the mobile apps only allow users to search people but not within other categories.

Mobile search is a big deal for LinkedIn – did you now that 19 people searches are performed and 41 profiles are viewed every second via LinkedIn mobile apps?

http://youtu.be/eO8nmRDKv2I?t=1m50s

What About 3rd Degree Connections?

While there was a bit of early buzz that users searching LinkedIn with a free account would not be able to search 3rd degree connections, you can in fact still search for them.

While some early testing showed that it appears LinkedIn’s default was to only return results from your 1st and 2nd degree network, all of my recent searches appear to default to “All,” which includes Group Members and “3rd + Everyone Else.”

 

LinkedIn Default ALL

 

Search Anomalies

Thankfully, I haven’t run across too many search anomalies yet, but I did find a few I think you (and the LinkedIn dev team) will find interesting.

I ran a basic search and took notice of the top 5 companies represented:

 

LinkedIn Top 5 Company Search Anomaly

 

I then set about to see if I could use the -/NOT functionality to eliminate results from the top 5 companies in order to find the next top 5 (thus completing the top 10).

I started entering 1 company at a time in the current company field: -Microsoft, -IBM, -Cisco, etc.

This seemed to work quite well in removing those companies from the top 5, allowing me to explore the next 5 or more. But then I noticed that when I was excluding the company names in the current company field, the company names were being returned as positive hits and highlighted as keywords in the profiles. The same thing happens if I change it to -(ibm OR microsoft OR cisco).

 

LinkedIn Search Anomaly NOT company names show up in keywords

 

Hmm. That’s not good.

The same thing happens when I try to exclude a term from the title field. As you can see below, I am excluding the term “engineer” from the title field, and while the term is excluded properly in most cases, there are a few random results where “engineer” is in the current title – as with Kevin below, the word “engineer” also shows up as a positive & highlighted keyword hit in summaries, headline phrases, etc. It doesn’t matter if I use NOT, AND NOT either – I’ve tried all 3 ways and get the same results.

 

LinkedIn New Search NOT current title shows up as highlighted keyword hit elsewhere

 

This one is pretty strange – I ran a first name search for “Abigail” and got results with “Gail” and “Abby” on the first page.

 

LinkedIn search for Abigail returns Abby and Gail

 

I don’t know how much of a fluke this is, because I’ve tried other names as well as searched for companies and various I.T. keywords to see if LinkedIn is performing some kind of fuzzy matching but have yet to run into another instance where LinkedIn gives me terms other than the one I specifically searched for. Please let me know if you find any.

Also, it seems that the ability to search within groups from the main search interface is still being listed as a premium filter with the yellow “in” icon, yet I can search within groups with my free account. Maybe it’s actually free functionality now?

 

LinkedIn Groups Premium Filter

 

LinkedIn Groups Premium Filter 2

 

What I Would Like to See from LinkedIn Search

For quite some time I’ve been thinking about writing a post specifically about what I’d like to see from LinkedIn with regard to new search functionality, but I’ve never gotten around to it.

I’ll take this opportunity to at least highlight a few things I would suggest to the LinkedIn team:

  1. The ability to specifically search within the most recent work experience listed. One word: Massive. Can I get an “amen?”
  2. Stemming/root word/wildcard search. It would certainly be nice to not always have to construct massive OR statements, e.g., (develop OR developing OR develops OR development OR developed OR developer).
  3. Not only bring back the top 10 in each facet – but enable them to be expanded to the top 25. Expanded facets yield incredible market and competitive intel/insight with the click of a mouse.
  4. Ability to sort by keyword relevance not tied to relationship. If you can’t/won’t bring this back to LinkedIn for free accounts, at the very least, never get rid of the ability to sort by keyword only relevance in premium versions.
  5. Keyword boosting – enabling users with the ability to determine which keywords are the most relevant to them.
  6. Proximity search – enabling users to search for terms within a specific distance of each other, to achieve semantic search.

If you weren’t already aware, LinkedIn used Lucene for text retrieval, and Lucene is capable of wildcard search, variable term boosting, and variable proximity matching.

I wrote a post nearly 4 years ago titled LinkedIn Search: What it COULD and SHOULD Be – I suggest you take a look and also read the comments, because one of LinkedIn’s principle software engineers working on LinkedIn’s search engine at the time weighed in with some very insightful comments here and here.

What would YOU like to see added to LinkedIn’s search functionality?

 

Diversity Sourcing: Boolean Search Strings for LinkedIn

 

 

Note: I’ve updated this post as of August, 2015 with even more inclusive and effective diversity searches for LinkedIn.

When it comes to diversity sourcing and recruiting, the first thing that comes to mind for many people is posting jobs on diversity sites and in diverse groups. However, as I have written about many times, posting jobs is an intrinsically limited talent acquisition strategy and it fails to expose you to the “deep end” of the talent pool.

At best, posting jobs can only give you access to approximately 30% of the total talent pool – those active and casual job seekers who will actually take the time to run a search for jobs and apply to an opening.

How can you access the other 70%?

Proactive sourcing, of course!

I’ve spoken at a few conferences this year (HCI, LinkedIn Talent Connect, SourceCon) in which I’ve detailed some Boolean search strings for diversity sourcing on LinkedIn, and I’ve had several requests for the specific searches I’ve demonstrated.

While the search strings I’ve used in my presentations are already posted on the conference websites, I thought it would be a good idea to create and release some new and improved diversity sourcing search strings here for quick and easy access to some “starter” queries.

However, it’s important to know that what I’m publishing is the tip of the iceberg. I have no idea what your particular diversity sourcing need might be, or even what country you’re sourcing in – it’s up to you to adapt what you see here to your specific needs.

While I know some folks will be happy to simply snag the strings, what I really want my readers to get from this post is an understanding of and appreciation for the critical underlying thought process necessary for any successful sourcing endeavor, let alone diversity sourcing.

When it comes to information retrieval, if you can conceive it, you can almost always achieve it – including diversity sourcing – and there are often many different ways to achieving your search goals.

The “magic” of search strings does not lie in the Boolean logic or site specific search syntax, nor does it exist in the keywords and phrases you search for – the true power of search lies within your own mind.

What is Your Diversity Need?

So let’s get back to basics for just a second.

When you’re creating and executing Boolean search strings for talent discovery, you’re really performing information retrieval.

Information retrieval is the activity of obtaining information resources relevant to an information need.

An information retrieval process begins when you enter a query into an information system (e.g., databases, the Internet, social networks, etc.), and queries are simply formal statements of information needs.

So when it comes to diversity sourcing, what’s your information need?

This seems like such a simple question, but I honestly don’t think many people begin their sourcing efforts with this in mind.

Gender Diversity: Women

When it comes to gender diversity recruiting and sourcing, most people tend to think of searching for women’s groups, sororities, women-only sports, and women’s colleges, including searching explicitly for the words “Women,” “Women’s,” and “female” for an exploratory search into all of the various women’s groups.

However, many of these approaches are extremely narrow in scope and low in quantity of results (e.g. “Society of Women” produces a little over 60,000 results in the U.S., and “Association of Female _____” returns just shy of 4,000 results). If you try and search for (“women OR women’s”), while you get nearly 2M results in the U.S., if you scroll through the pages, you can see that there are a fair amount of profiles of men that are returned, and that’s to be expected given that the search terms aren’t exclusive to women’s profiles – they can show up on men’s profiles too.

As an example of something that is less obvious, outside of the box, and more exclusive to women’s profiles would be something I’ve hypothesized, tested, and confirmed on LinkedIn for years – searching for (her OR she), which returns nearly 900,000 profiles.

For those who haven’t seen me present on that search before – do you know why it returns LinkedIn profiles of women?

It works because “her” and “she” can be mentioned in the summary and recommendation sections of women’s profiles.

You would not likely find many LinkedIn profiles of men mentioning “her ” or “she,” although they do exist. (sourcing challenge – do you know how to find them exclusively?)

Yes, (her OR she) is a bit clever, and yes, I’m a bit proud of the discovery, but it clearly demonstrates the fact that all anyone needs to do is *think* about what terms that could be searched for that would be relatively unique to the people you are trying to find and test any ideas you come up with to verify.

While (her OR she) “works” in that it returns predominantly women-only results,  it returns less than 1M profiles in the U.S. – so certainly not a big slice of all of the women on LinkedIn.

How could we do better?

Let’s try another more traditional search approach – women’s universities and colleges.

(“Agnes Scott College” OR “Alverno College” OR “Barnard College” OR “Bay Path College” OR “Bennett College” OR “Brenau University” OR “Brescia University College” OR “Bryn Mawr College” OR “Carlow College” OR “Cedar Crest College” OR “Chatham University” OR “College of New Rochelle, The” OR “College of Saint Benedict” OR “College of Saint Elizabeth” OR “College of Saint Mary” OR “Columbia College” OR “Converse College” OR “Cottey College” OR “Douglass Residential College of Rutgers University” OR “Hollins University” OR “Judson College” OR “Mary Baldwin College” OR “Meredith College” OR “Midway College” OR “Mills College” OR “Moore College of Art & Design” OR “Mount Holyoke College” OR “Mount Mary College” OR “Mount St. Mary’s College” OR “Notre Dame of Maryland University” OR “Pine Manor College” OR “Russell Sage College” OR “St. Catherine University” OR “Saint Joseph College” OR “Saint Mary-of-the-Woods College” OR “Saint Mary’s College” OR “Salem College” OR “Scripps College” OR “Simmons College” OR “Smith College” OR “Spelman College” OR “Stephens College” OR “Sweet Briar College” OR “Trinity Washington University” OR “Wellesley College” OR “Wesleyan College” OR “Wilson College” OR “Women’s College”)

That search returns just over 410K results in the U.S.

That’s less than my (her OR she) search, although of course you could use the -/NOT operator to make each search mutually exclusive and to eliminate overlap.

Let’s try a sorority search:

(“Alpha Chi Omega” OR “Alpha Delta Chi” OR “Alpha Delta Pi” OR “Alpha Epsilon Omega” OR “Alpha Epsilon Phi” OR “Alpha Gamma Delta” OR “Alpha Kappa Alpha” OR “alpha Kappa Delta Phi” OR “Alpha Phi Gamma” OR “Alpha Phi” OR “Alpha Pi Omega” OR “Alpha Pi Sigma” OR “Alpha Rho Lambda” OR “Alpha Sigma Alpha” OR “Alpha Sigma Kappa” OR “Alpha Sigma Omega” OR “Alpha Sigma Rho” OR “Alpha Sigma Tau” OR “Alpha Xi Delta” OR “Ceres” OR “Chi Omega” OR “Chi Upsilon Sigma” OR “Delta Chi Lambda” OR “Delta Delta Delta” OR “Delta Gamma” OR “Delta Gamma Pi” OR “Delta Kappa Delta” OR “Delta Phi Epsilon” OR “Delta Phi Lambda” OR “Delta Phi Mu” OR “Delta Phi Omega” OR “Delta Psi Delta” OR “Delta Sigma Chi” OR “Delta Sigma Theta” OR “Delta Tau Lambda” OR “Delta Xi Nu” OR “Delta Xi Phi” OR “Delta Zeta” OR “Gamma Alpha Omega” OR “Gamma Eta” OR “Gamma Phi Beta” OR “Gamma Phi Omega” OR “Gamma Rho Lambda” OR “Gamma Sigma Sigma” OR “Kappa Alpha Theta” OR “Kappa Beta Gamma” OR “Kappa Delta Chi” OR “Kappa Delta Phi” OR “Kappa Delta” OR “Kappa Kappa Gamma” OR “Kappa Phi Gamma” OR “Kappa Phi Lambda” OR “Kappa Phi Zeta” OR “Lambda Pi Chi” OR “Lambda Pi Upsilon” OR “Lambda Psi Delta” OR “Lambda Tau Omega” OR “Lambda Theta Alpha” OR “Lambda Theta Nu” OR “Mu Sigma Upsilon” OR “Omega Phi Beta” OR “Omega Phi Chi” OR “Phi Beta Chi” OR “Phi Mu” OR “Phi Sigma Rho” OR “Phi Sigma Sigma” OR “Pi Beta Phi” OR “Pi Lambda Chi” OR “Sigma Alpha Epsilon Pi” OR “Sigma Alpha Iota” OR “Sigma Delta Tau” OR “Sigma Gamma Rho” OR “Sigma Iota Alpha” OR “Sigma Kappa” OR “Sigma Lambda Alpha” OR “Sigma Lambda Gamma” OR “Sigma Lambda Upsilon” OR “Sigma Omega Nu” OR “Sigma Omega Phi” OR “Sigma Omicron Pi” OR “Sigma Phi Kappa” OR “Sigma Phi Omega” OR “Sigma Pi Alpha” OR “Sigma Psi Zeta” OR “Sigma Sigma Rho” OR “Sigma Sigma Sigma” OR “Tau Theta Pi” OR “Theta Nu Xi” OR “Theta Phi Alpha” OR “Zeta Phi Beta” OR “Zeta Tau Alpha”)

That search returns nearly 1.2M results in the U.S.

Not bad – now we’re over 1,000,000 profiles, which is actually much higher than simply searching for the term “sorority.”

However, instead of trying these more traditional search ideas, let’s try to think of the single most inclusive way of finding women on LinkedIn.

Have any ideas?

Well, what’s more inclusive than first names?

Of course you can search by groups, sororities, and sports, but you can find a larger portion of people by searching by first name.

You might be asking, “How can I possibly create and run a search by all of the female names – there must be thousands?!?!”

Yes, there are thousands, and no, we can’t practically search for all of them – and certainly not in a single search.

However, what you can do is go to a number of websites and find the most popular female names and search for those, which will statistically yield a significant portion of the women represented on LinkedIn. In the U.S., we can use the Social Security Administration website, which conveniently lets you search for the top 200 most common first names for girls and boys by decade.

Fortunately, I’ve done the heavy lifting for you. I copied the top 200 female first names from the 1950’s, 1960’s, 1970’s, 1980’s and 1990’s into Excel, sorted them alphabetically, then removed the duplicates to come up with the most popular 417 names from those 5 decades, which nearly covers the entire span of LinkedIn’s strongest representation.

Then I used those names to create a Boolean OR statement, which looks like this:

(Abigail OR Adriana OR Adrienne OR Aimee OR Alejandra OR Alexa OR Alexandra OR Alexandria OR Alexis OR Alice OR Alicia OR Alisha OR Alison OR Allison OR Alyssa OR Amanda OR Amber OR Amy OR Ana OR Andrea OR Angel OR Angela OR Angelica OR Angie OR Anita OR Ann OR Anna OR Anne OR Annette OR Annie OR April OR Ariana OR Ariel OR Arlene OR Ashlee OR Ashley OR Audrey OR Autumn OR Bailey OR Barbara OR Becky OR Belinda OR Beth OR Bethany OR Betty OR Beverly OR Bianca OR Bonnie OR Brandi OR Brandy OR Breanna OR Brenda OR Briana OR Brianna OR Bridget OR Brittany OR Brittney OR Brooke OR Caitlin OR Caitlyn OR Candace OR Candice OR Carla OR Carly OR Carmen OR Carol OR Carole OR Caroline OR Carolyn OR Carrie OR Casey OR Cassandra OR Cassidy OR Cassie OR Catherine OR Cathy OR Charlene OR Charlotte OR Chelsea OR Chelsey OR Cheryl OR Cheyenne OR Chloe OR Christie OR Christina OR Christine OR Christy OR Cindy OR Claire OR Claudia OR Colleen OR Connie OR Constance OR Courtney OR Cristina OR Crystal OR Cynthia OR Daisy OR Dana OR Danielle OR Darlene OR Dawn OR Deanna OR Debbie OR Deborah OR Debra OR Delores OR Denise OR Desiree OR Destiny OR Diamond OR Diana OR Diane OR Dianne OR Dolores OR Dominique OR Donna OR Doreen OR Doris OR Dorothy OR Ebony OR Eileen OR Elaine OR Elizabeth OR Ellen OR Emily OR Emma OR Erica OR Erika OR Erin OR Eva OR Evelyn OR Faith OR Felicia OR Frances OR Gabriela OR Gabriella OR Gabrielle OR Gail OR Gayle OR Geraldine OR Gina OR Glenda OR Gloria OR Grace OR Gwendolyn OR Hailey OR Haley OR Hannah OR Hayley OR Heather OR Heidi OR Helen OR Holly OR Irene OR Isabel OR Isabella OR Jackie OR Jaclyn OR Jacqueline OR Jade OR Jaime OR Jamie OR Jan OR Jane OR Janet OR Janice OR Janis OR Jasmin OR Jasmine OR Jean OR Jeanette OR Jeanne OR Jenna OR Jennifer OR Jenny OR Jessica OR Jill OR Jillian OR Jo OR Joan OR Joann OR Joanna OR Joanne OR Jocelyn OR Jodi OR Jody OR Jordan OR Josephine OR Joy OR Joyce OR Juanita OR Judith OR Judy OR Julia OR Julie OR June OR Kaitlin OR Kaitlyn OR Kara OR Karen OR Kari OR Karina OR Karla OR Katelyn OR Katherine OR Kathleen OR Kathryn OR Kathy OR Katie OR Katrina OR Kay OR Kayla OR Kaylee OR Kelli OR Kellie OR Kelly OR Kelsey OR Kendra OR Kerri OR Kerry OR Kiara OR Kim OR Kimberly OR Kirsten OR Krista OR Kristen OR Kristi OR Kristie OR Kristin OR Kristina OR Kristine OR Kristy OR Krystal OR Kylie OR Lacey OR Latasha OR Latoya OR Laura OR Lauren OR Laurie OR Leah OR Leslie OR Lillian OR Linda OR Lindsay OR Lindsey OR Lisa OR Lois OR Loretta OR Lori OR Lorraine OR Louise OR Lydia OR Lynda OR Lynn OR Lynne OR Mackenzie OR Madeline OR Madison OR Makayla OR Mallory OR Mandy OR Marcia OR Margaret OR Maria OR Mariah OR Marianne OR Marie OR Marilyn OR Marisa OR Marissa OR Marjorie OR Marlene OR Marsha OR Martha OR Mary OR Maureen OR Mckenzie OR Meagan OR Megan OR Meghan OR Melanie OR Melinda OR Melissa OR Melody OR Mercedes OR Meredith OR Mia OR Michaela OR Michele OR Michelle OR Mikayla OR Mildred OR Mindy OR Miranda OR Misty OR Molly OR Monica OR Monique OR Morgan OR Nancy OR Natalie OR Natasha OR Nichole OR Nicole OR Nina OR Norma OR Olivia OR Paige OR Pam OR Pamela OR Patricia OR Patsy OR Patti OR Patty OR Paula OR Peggy OR Penny OR Phyllis OR Priscilla OR Rachael OR Rachel OR Raven OR Rebecca OR Rebekah OR Regina OR Renee OR Rhonda OR Rita OR Roberta OR Robin OR Robyn OR Rosa OR Rose OR Rosemary OR Roxanne OR Ruby OR Ruth OR Sabrina OR Sally OR Samantha OR Sandra OR Sandy OR Sara OR Sarah OR Savannah OR Selena OR Shannon OR Shari OR Sharon OR Shawna OR Sheena OR Sheila OR Shelby OR Shelia OR Shelley OR Shelly OR Sheri OR Sherri OR Sherry OR Sheryl OR Shirley OR Sierra OR Sonia OR Sonya OR Sophia OR Stacey OR Stacie OR Stacy OR Stefanie OR Stephanie OR Sue OR Summer OR Susan OR Suzanne OR Sydney OR Sylvia OR Tabitha OR Tamara OR Tami OR Tammie OR Tammy OR Tanya OR Tara OR Tasha OR Taylor OR Teresa OR Terri OR Terry OR Theresa OR Tiffany OR Tina OR Toni OR Tonya OR Tracey OR Traci OR Tracie OR Tracy OR Tricia OR Valerie OR Vanessa OR Veronica OR Vicki OR Vickie OR Vicky OR Victoria OR Virginia OR Vivian OR Wanda OR Wendy OR Whitney OR Yesenia OR Yolanda OR Yvette OR Yvonne OR Zoe)

You can then take that Boolean OR statement and enter it into the first name field in LinkedIn. Unfortunately, while that search *used* to work with a free LinkedIn account, that no longer seems to be the case, as I keep getting errors.

It does appear that you can search for about half of that list at once in LinkedIn for free. Try it for yourself here, but I have to warn you, LinkedIn still appears to choke on the search when you try it with a free account. If you can get it to work, you can only view 100 results with a free account, and even then, you will likely run into LinkedIn’s commercial use limit.

The best approach would be to use a premium LinkedIn account in which you can actually fit the entire search into the first name field and view up to 1,000 results at a time.

In LinkedIn Recruiter, my search of 417 female first names returns over 38M results in the U.S. alone.

 

When it comes to finding women on LinkedIn, how big of a slice does a little over 38M represent in relation to all of the U.S. women on LinkedIn?

Let’s do a little math.

LinkedIn claims about 115M U.S. profiles.

Assuming that this Forbes article is accurate in reporting that LinkedIn has an even ratio of men and women (51%/49%), then there should be approximately 56.4 M female profiles on LinkedIn.

So the ~38.4M results from my search of the 417 first names I ran above could be capturing up to 68% of all of the U.S. women on LinkedIn (38.4M / 56.4M).

Not bad for a single search, and massively more inclusive of any other way of searching for women on LinkedIn (groups, colleges, sororities, etc.)!

If you’re looking for ways to specifically source and recruit women in engineering, I highly recommend you read this LinkedIn post on the topic – it is 1,000 times more effective than trying to hop on the #ILookLikeAnEngineer bandwagon. :)

Of course, if your diversity need is to find male candidates, you can do the exact same thing as above, using the most common male names.

LinkedIn Diversity Sourcing: Racial and Ethnic Diversity

Is your information need to find racially or ethnically diverse candidates?

Well then, all you have to do is think about what might show up predominantly, and ideally only on profiles of people representing specific racial and ethnic groups.

What comes to mind for many people includes searching for groups, fraternities and sororities, and historically black colleges and universities.

Speaking of which, here is a search for 105 HCBU’s:

(“Alabama A&M University” OR “Alabama State University” OR “Albany State University” OR “Alcorn State University” OR “Allen University” OR “University of Arkansas at Pine Bluff” OR “Arkansas Baptist College” OR “Barber-Scotia College” OR “Benedict College” OR “Bennett College” OR “Bethune-Cookman University” OR “Bishop State Community College” OR “Bluefield State College” OR “Bowie State University” OR “Central State University” OR “Cheyney University of Pennsylvania” OR “Claflin University” OR “Clark Atlanta University” OR “Clinton Junior College” OR “Coahoma Community College” OR “Concordia College, Selma” OR “Coppin State University” OR “Delaware State University” OR “Denmark Technical College” OR “Dillard University” OR “University of the District of Columbia” OR “Edward Waters College” OR “Elizabeth City State University” OR “Fayetteville State University” OR “Fisk University” OR “Florida A&M University” OR “Florida Memorial University” OR “Fort Valley State University” OR “Gadsden State Community College” OR “Grambling State University” OR “Hampton University” OR “Harris-Stowe State University” OR “Hinds Community College at Utica” OR “Howard University” OR “Huston-Tillotson University” OR “Interdenominational Theological Center” OR “J. F. Drake State Technical College” OR “Jackson State University” OR “Jarvis Christian College” OR “Johnson C. Smith University” OR “Kentucky State University” OR “Knoxville College” OR “Lane College” OR “Langston University” OR “Lawson State Community College” OR “LeMoyne-Owen College” OR “Lewis College of Business” OR “Lincoln University” OR “Lincoln University of Missouri” OR “Livingstone College” OR “University of Maryland Eastern Shore” OR “Meharry Medical College” OR “Miles College” OR “Mississippi Valley State University” OR “Morehouse College” OR “Morehouse School of Medicine” OR “Morgan State University” OR “Morris Brown College” OR “Morris College” OR “Norfolk State University” OR “North Carolina A&T State University” OR “North Carolina Central University” OR “Oakwood University” OR “Paine College” OR “Paul Quinn College” OR “Philander Smith College” OR “Prairie View A&M University” OR “Rust College” OR “Saint Paul’s College” OR “Savannah State University” OR “Selma University” OR “Shaw University” OR “Shorter College” OR “Shelton State Community College” OR “South Carolina State University” OR “Southern University at New Orleans” OR “Southern University at Shreveport” OR “Southern University and A&M College” OR “Southwestern Christian College” OR “Spelman College” OR “St. Augustine’s College” OR “St. Philip’s College” OR “Stillman College” OR “Talladega College” OR “Tennessee State University” OR “Texas College” OR “Texas Southern University” OR “Tougaloo College” OR “Trenholm State Technical College” OR “Tuskegee University” OR “University of the Virgin Islands” OR “Virginia State University” OR “Virginia Union University” OR “Virginia University of Lynchburg” OR “Voorhees College” OR “West Virginia State University” OR “Wilberforce University” OR “Wiley College” OR “Winston-Salem State University” OR “Xavier University of Louisiana”)

While people have been leveraging HCBU’s for years in their diversity sourcing efforts, unlike most (all?) ATS’s, resume databases, and Internet search engines, LinkedIn is the only place that I am aware of that can handle 3,000+ character Boolean search strings to allow you to search for all of them at once (thank you LinkedIn!).

 

 

While anyone who performs diversity sourcing in the U.S. is familiar with HCBU’s, not everyone knows that you can also search for colleges and universities that have a high percentage of other racial or ethnic groups, such as historically Native American colleges and universitieshere’s the search on LinkedIn – it all comes down to your specific need.

If you’d like to take the fraternity/sorority approach, here is a LinkedIn search for African American fraternities and sororities.

(“Sigma Pi Phi” OR “Alpha Phi Alpha” OR “Kappa Alpha Psi” OR “Omega Psi Phi” OR “Phi Beta Sigma” OR “Sigma Rhomeo” OR “Wine Psi Phi” OR “Iota Phi Theta” OR “Phi Delta Psi” OR “Delta Psi Chi” OR “Beta Phi Pi” OR “MALIK Fraternity” OR “Sigma Phi Rho” OR “Phi Rho Eta” OR “Gamma Psi Beta” OR “Alpha Kappa Alpha” OR “Delta Sigma Theta” OR “Zeta Phi Beta” OR “Sigma Gamma Rho” OR “Phi Delta Kappa” OR “Iota Phi Lambda” OR “Eta Phi Beta” OR “Gamma Phi Delta”)

Depending on need, you can also construct queries for Asian American, Latino, LGBT, and other cultural interest fraternities and sororities.

As with all information retrieval efforts, it comes down to your specific information need and discovering ways of achieving those needs.

LinkedIn Diversity Sourcing: Surname Search

Speaking of specific diversity sourcing needs, you may be able to experiment with searching for last names to achieve your diversity sourcing goals.

Just as a quick and random example, here is search for the top 100 Chinese surnames:

(Lǐ OR Wáng OR Zhāng OR Liú OR Chén OR Yáng OR Zhào OR Huáng OR Zhōu OR Wú OR Xú OR Sūn OR Hú OR Zhū OR Gāo OR Lín OR Hé OR Guō OR Mǎ OR Luó OR Liáng OR Sòng OR Zhèng OR Xiè OR Hán OR Táng OR Féng OR Yú OR Dǒng OR Xiāo OR Chéng OR Cáo OR Yuán OR Dèng OR Xǔ OR Fù OR Shěn OR Zēng OR Péng OR Lǚ OR Sū OR Lú OR Jiǎng OR Cài OR Jiǎ OR Dīng OR Wèi OR Xuē OR Yè OR Yán OR Yú OR Pān OR Dù OR Dài OR Xià OR Zhōng OR Wāng OR Tián OR Rén OR Jiāng OR Fàn OR Fāng OR Shí OR Yáo OR Tán OR Shèng OR Zōu OR Xióng OR Jīn OR Lù OR Hǎo OR Kǒng OR Bái OR Cuī OR Kāng OR Máo OR Qiū OR Qín OR Jiāng OR Shǐ OR Gù OR Hóu OR Shào OR Mèng OR Lóng OR Wàn OR Duàn OR Zhāng OR Qián OR Tāng OR Yǐn OR Lí OR Yì OR Cháng OR Wǔ OR Qiáo OR Hè OR Lài OR Gōng OR Wén)

 

 

You could of course combine this approach with one or more of the gender diversity Boolean search strings if that would help you achieve your diversity sourcing goals.

If you’re wondering if anyone actually performs these kinds of diversity-focused sourcing strategies, the answer is a resounding “yes!” I have people approach me all the time at conferences referencing how they’ve successfully leveraged the diversity sourcing strategies and tactics outlines in this post. Recently, while attending and speaking at the always awesome Talent42 technical recruiting conference in Seattle, I had someone tell me how they leveraged the most common Korean surnames to find a bilingual engineer which made short work of the otherwise seemingly impossible challenge.

Final Thoughts on LinkedIn Diversity Sourcing

To be sure, what I’ve demonstrated here has some obvious limitations and is far from perfect, but it does effectively illustrate that you do have some creative proactive sourcing options for underrepresented gender and racial/ethnic groups in your organization, allowing you to move beyond relying solely on posting jobs and hoping to get qualified (and diverse!) applicants from active candidates.

Proactive diversity sourcing has the distinct benefit of giving you access to the deeper end of the talent pool – those people (typically more than 2/3rds of any given population) who aren’t actively seeking employment and thus cannot be reached through any form of job advertisement, no matter where you post or share it.

Ultimately, what I really wanted to accomplish by writing this article was to get you thinking a little bit differently when it comes to diversity sourcing and sourcing in general. Effective sourcing, diversity or otherwise, isn’t about Boolean search strings – it’s about critical thinking and always seeking to step outside of the box to find ways to meet your information needs.

As an added bonus for reading this entire post, here is a LinkedIn search of the top 200 most popular female first names in the United States from the 1950’s, 60’s, 70’s, and 80’s de-duped to the most popular 354 names from those decades, which captures the 23-62 year old demographic, which nearly covers the entire span of LinkedIn’s strongest representation.

 

 

 

 

What’s the most effective way to X-Ray search LinkedIn?

 

I’ve recently come across some blog posts and some Boolean Strings discussions on LinkedIn that inspired me to go back and tinker with searching LinkedIn via Google and Bing.

For example, I continue to see people talk about:

  1. Whether or not you should use “pub” and/or “in” (e.g. site:linkedin.com/in | site:linkedin.com/pub)
  2. Whether or not you should use -dir
  3. Using country codes in site: searches
  4. Using different phrases to target public LinkedIn profiles – e.g., “people you know”

My first reaction when people are curious about the most effective ways of retrieving public LinkedIn profiles is to encourage them to experiment on their own first instead of looking for answers to copy and paste. Quite literally 99% of everything I know about sourcing (and recruiting!) I learned through being curious and experimenting.

People learn by doing, and more specifically by failing/struggling, and not by copying and pasting somebody else’s work. Continue reading

My SourceCon Presentation – LinkedIn: Beyond the Basics

 

I was honored to be asked to present at the Dallas 2012 SourceCon event – which turned out to be the largest SourceCon event ever!

When I was talking with Amybeth Hale back at the end of 2011 about what I’d like to present on, I asked if anyone had ever run a session solely dedicated to LinkedIn.

Now, I’ve been to every SourceCon save 2 (the first one and 2011/Santa Clara), I’ve spoken at 5 of them, and I couldn’t recall anyone delivering a LinkedIn presentation, and neither could Amybeth (for the ones I missed or sessions I did not attend).

That struck me as beyond odd, given how valuable a resource LinkedIn is for sourcing and recruiting.

What you see below is the deck from my “LinkedIn: Beyond the Basics” session, complete with YouTube videos.

 

 

8 Minute Video from my LinkedIn #InToronto Presentation

 

I’ve had the distinct honor of speaking at every event that LinkedIn has put together in the U.S. and Canada, and I will also be speaking at the third Talent Connect event on October 10-12 in Las Vegas, where they expect well over 2,000 people to attend. I’ll be running 2 sessions on effectively searching LinkedIn (one basic and one advanced). I am also looking forward to speaking at the LinkedIn Talent Connect Europe event in London on October 23rd.

While the Talent Connect events in the U.S. are strictly restricted to corporate customers only, when I presented at the #InToronto event, there was a mix of corporate customers and agency users, and over 1,200 people showed up.

I ran two 30-minute sessions on searching LinkedIn to find talent, and the LinkedIn staff filmed one of them and compiled an 8 minute video that they recently uploaded to YouTube.

In case you hadn’t seen it, I wanted to share it with you here. Granted, my U.S. Talent connect sessions are usually 45 minutes to 1 hour, and they edited out quite a bit of the “good stuff” to get a 30 minute session down to 8 minutes, but I think you’ll find the content of interest if you happen to use LinkedIn in your sourcing and recruiting efforts.

 

 

 

Full Profiles of 3rd Degree LinkedIn Network No Longer Free?

 

Do you use LinkedIn for free?

Can you still view full profiles of your 3rd degree network?

You may have read about some recent changes that have affected some LinkedIn users here and here with regard to 3rd degree profile visibility.

Prior to both of those articles, I had a recruiter who I used to work with at a past company reach out to me the other week asking me if I had seen that LinkedIn is no longer allowing free users to view full profiles of 3rd degree connections.

Now, like many people, I’ve been wondering for quite some time when LinkedIn would start making changes to limit the data available to free users, so I immediately went to LinkedIn to see what he was talking about and I was able to view full profiles of 3rd degree connections, so I asked him to send me some screenshots to see what he was seeing. Continue reading

How Would You Search for these Positions on LinkedIn?

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

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

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

I believe there may be several contributing factors:

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

What do you think?

How Would You Search for these Positions on LinkedIn?

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

How to See Full Names of 3rd Degree Connections on LinkedIn

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: Continue reading

Bing’s Semantic Search, Phonetics and Undocumented Operator

I was recently performing some searches on Bing and came across something curious that I had never noticed before.

I’m not exactly sure if what I found is new or simply something I’ve overlooked in the past. I updated Twitter with “Did you know that Bing supports the + query modifier?” on November 10th, wondering if it was something that other people knew about.

I only received a few responses, including a couple from noted sourcing luminaries, and the consensus was that I didn’t find anything because it wasn’t documented anywhere and they could not get it to work.

However, the +/Plus sign does in fact work when searching Bing – just not like it used to in Google.

It’s always a little exciting to think you are one of the first people to stumble across something most people don’t know about, although I won’t get my hopes up that I’m the only person outside of some folks at Microsoft who’s ever figured out that Bing supports the +/Plus sign in searches.

This discovery also led me to proof of Bing leveraging semantic and phonetic searchContinue reading

How I Search LinkedIn to Find and Identify Talent

Would you like to know how I search LinkedIn when sourcing for talent?

I don’t have a premium LinkedIn account, so you may be surprised to learn that I don’t X-Ray search LinkedIn all that often.

I’ll tell you why in a moment, but first I would like to share my inspiration for this post.

I recently read a great post that addressed an issue with X-Ray searching LinkedIn and that pointed out that pattern recognition is critical to effective online sourcing.

I could not agree more – truly dynamic pattern recognition is something I think is unique to humans and is something that I believe cannot be replicated by applications claiming to leverage artificial intelligence, semantic search, and Natural Language Processing (NLP). I could elaborate further on this topic, but that would unfortunately bore 98% of my readers, so I will save it for another post that they can choose not to read later. :-)

Suffice it to say I wholeheartedly agree that is it more important to have the right investigative thought process than to have any specific Boolean search string or pre-built X-Ray search.

Getting back to how I specifically search LinkedIn to find people – you first need to understand some of the significant issues associated with using Internet search engines in an attempt to find public LinkedIn profiles. In other words, you should know they “why” before the “how.”

As an added bonus, you’ll also find that I’ve discovered that Bing and LinkedIn apparently don’t play well together anymore, and I’ll issue a LinkedIn Sourcing Challenge to the international sourcing and recruiting community to crowdsource the solution. Continue reading

LinkedIn’s Undocumented Search Operator

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

Did you catch it?

If not, you’re in luck.

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

Update Your LinkedIn X-Ray Searches for Location Names

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! Continue reading

LinkedIn Search: Controlling Years of Experience & Compensation

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. Continue reading

LinkedIn Sourcing Challenge – Can You Find Everyone?

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!

Continue reading

LinkedIn’s Dark Matter – Undiscovered Profiles

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. Continue reading

LinkedIn Sourcing Challenge – Ruby

During my SourceCon NYC session, I gave an example of a sourcing challenge that can verify one’s “capacity to think logically and solve problems in novel situations, independent of acquired knowledge. It is the ability to analyze novel problems, identify patterns and relationships that underpin these problems and the extrapolation of these using logic.”

This capacity is otherwise know as fluid intelligence or fluid reasoning .

The LinkedIn Sourcing Challenge

If you and/or your team are up for a test of your fluid reasoning and sourcing capability, try solving this challenge:

  • Find a LinkedIn profile of someone who has Ruby on Rails experience, but does not mention Ruby, Ruby on Rails, Rails, or RoR in their profile, and show with a link or other evidence exactly how you are certain they have Ruby experience.

There is no single correct answer – there are many different approaches to solving this challenge.

I’m going to recognize Jeremy Langhans once again for being able to solve that challenge in about 15 minutes before I even finished my presentation, using only his iPhone. To this date, no one else has even tried to take a crack at it.

The gauntlet has been thrown down. I hope at least a few people are up to the challenge! Continue reading

Update Your Bing X-Ray Searches of LinkedIn to Target Profiles

 

Beginning early last week, I’ve had a few people reach out to me and ask about some changes LinkedIn is apparently making to the public profile listings.

In the past, I’ve written about how Bing is easier and more effective at searching LinkedIn profiles than Google.

One of my suggestions for targeting profiles an avoiding directory and job results was to search for the word “powered,” because public profiles on LinkedIn have the phrase “Public profile powered by,” and the word “powered” seemed to be unique only to profiles.

LinkedIn is Tinkering

While you can still search LinkedIn via Bing using +powered and find results, the only reason it seems to work is due to the fact that Bing has taken “snapshots” of the old LinkedIn profiles the last time Bing’s crawlers have visited them. The original (non-cached) search results don’t mention “powered.”

I’ve found that many (all?) public LinkedIn profiles no longer mention the phrase “Public profile powered by,” so adding +powered to your LinkedIn X-Ray searches via Bing will prevent some public LinkedIn profiles from being returned in your searches – and you won’t even know it.

But now you do. Continue reading

How to Use LinkedIn’s Advanced Operators as Search Agents

 

In January 2009, I wrote a feature about LinkedIn’s advanced operators. Two years later, I am still surprised that remarkably few people leverage the ability to bypass LinkedIn’s advanced search interface and “hand-code” search strings.

Before I demonstrate how you can use LinkedIn’s advanced operators as search agents, here is a quick refresher detailing the all of the advanced search operators:

 

You can use these operators in conjunction with standard keyword search terms in the people search box.

In this quick example, I am targeting profiles with a current title of engineer and a current company of Google: Continue reading