Building a Solid Foundation with the Boolean Black Belt I Talent Connect London 2013

LinkedIn Sourcing
Building a Solid Foundation
Glen Cathey
SVP, Strategic Talent Acquisition and Innovation
Kforce
Author, booleanblackbelt.com

Who is this guy?
 SVP, Talent Acquisition Strategy and Innovation
– Architect Kforce's sourcing and recruiting strategy for 800+ recruiters

 16+ years recruiting
– Professional staffing
 I.T., Engineering F&A, Healthcare I.T., Federal, Clinical Research

– Global RPO delivery

 Sourcing/Recruiting Blogger
– www.booleanblackbelt.com
– 15K+ unique visitors per week from 110+ countries

 Speaker
–
–
–
–

6X LinkedIn Talent Connect speaker (US, CA, UK)
6X SourceCon speaker
2X Australasian Talent Conference (Sydney & Melbourne)
2X TruLondon

 LinkedIn Certified, Top 25 most connected on LinkedIn globally, #5
in recruitment

Adaptive search,
maximum inclusion &
strategic exclusion
Query logic/syntax and other
search methods (e.g., faceted
search)
The nature and limitations of the text you're
interrogating, the behavior of the people
generating the text and of your
competitors, and core information retrieval

Beinecke Library, Yale University

“The flood of data means more noise (i.e., irrelevant/useless information) but not
necessarily more signal (i.e., relevant results)”
Often we expect too much of computers and not enough of ourselves. People
blame systems “when they should be asking better questions.”

Nate Silver
•
•
•

Principal, FiveThirtyEight
Author, The Signal and the Noise: Why So
Many Predictions Fail-but Some Don't
Correctly predicted 50 out of 50 states in
the 2012 U.S. presidential election

Title only

Source: John Zappe, SourceCon

Title only

Source: Suzanne Chadwick, The Social Recruiter blog

Competitive Advantage?
 LinkedIn members performed nearly 5.7 billion
professionally-oriented searches on the platform in

2012.
 LinkedIn's corporate talent solutions are used by 90
of the Fortune 100 companies.
 (How) Are your searches any different than anyone
else's?

Understanding Search and Retrieval

You can only get what you search for

Every search you run includes
and excludes qualified people

Who are you finding?

 Do the best people necessarily
mention your search terms?

 Common search terms may be
"overvalued" and yield no
competitive advantage

How many results…
do you review per search?

can you review per search?
should you review?

25,000+ software engineers in
London

Nearly 14,000 accountants

Understand your target talent pool…
Why does the average person join
LinkedIn?
What does the average person use
LinkedIn for?
Why would someone who isn't looking for
a job fill out their LinkedIn profile as they
would a resume?
The answers to these questions are
critical in developing your search
strategies

Understand your target talent pool…

Understand the competition…
Your competitors and other companies are
using LinkedIn Recruiter to look for the same
people you are
Are your searches any different than your
competitors?
Are you processing search results in the same
way as your competitors?
Are you finding and fighting over the same
talent pool?

Before & Beyond Boolean…
 Information Retrieval is the science of searching for documents, information
within documents, and searching relational databases and the Internet.

 An information retrieval process begins when a user enters a query into a
system.

 Queries are formal statements of information needs.

 As Nate Silver suggests, you should learn to ask better questions.

LinkedIn's working on understanding your searches…

However…
 No system or database:
– will ever "know" what you want or need
– can determine "relevance"

 Relevance
– is the ability (as of an information retrieval
system) to retrieve material that satisfies the
needs of the user*

When it comes to Human–Computer Information Retrieval (HCIR)*, it is
necessary for people to take responsibility for search results by expending
cognitive energy

Source: Gary Marchionini

AND
 Mandatory inclusion
 Typically reduces results
 Unnecessary to type in LinkedIn
 Use for required skills

"Objective C" Cocoa xcode iOS "software engineer"

OR
 NOT either/or!
 Inclusive – "at least one of"
 Use parentheses to group terms
 (A OR B OR C) (D OR E OR F)

 Typically increases results

OR
Use #1: searching for (un)related desired skills

(MapReduce ETL OR SAS OR SPSS OR Sqoop OR Pig
OR NoSQL OR Hive OR Hbase OR Flume)

OR
Use #2: searching for term variations and synonymous, related and relevant terms
(tax OR taxes OR taxation)
(VP OR "V.P." OR SVP OR "S.V.P." OR "Vice President")
("software engineer" OR developer OR programmer)
("Objective C" OR ObjectiveC OR "Objective-C")
(develop OR developer OR developed OR developing OR development OR
develops)

(Deloitte OR PwC OR PricewaterhouseCoopers OR "Price Waterhouse" OR
"Pricewaterhouse" OR "E&Y" OR Ernst OR KPMG)

NOT/ Exclusionary
 Almost always decreases results

 Can also use the minus sign

Use #1 Reducing/eliminating false positives

"Objective C" Cocoa xcode iOS "software engineer"
-(recruiter OR executive search OR recruiting)

Use #2 Mutually exclusive search progressions/results
Search #1 iOS "Objective C" Cocoa iPhone xcode
Search #2 iOS "Objective C" Cocoa iPhone -xcode

iOS

Search #3 iOS "Objective C" Cocoa -iPhone xcode

"Objective C"

iPhone

Cocoa

xcode

Query Modifiers

Search Field Size

 Quotation Marks " "

~3,000+ characters per field

 Exact phrase searching

 Parentheses ( )
 Grouping OR statements

Not Supported

 Asterisk *
 Root word/stemming

 Near
 Proximity search

(XXXXXXX OR XXXXXXX OR XXXXXXX OR XXXXXXX OR XXXXXXX OR XXXXXXX OR XXXXXXX OR XXXXXXX OR
XXXXXXX OR XXXXXXX OR XXXXXXX OR XXXXXXX OR XXXXXXX OR XXXXXXX OR XXXXXXX OR XXXXXXX OR
XXXXXXX OR XXXXXXX OR XXXXXXX OR XXXXXXX OR XXXXXXX OR XXXXXXX OR XXXXXXX)

Abigail OR Adrienne OR Aimee OR Alexandra 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
Arlene OR Ashlee OR Ashley OR Audrey OR Autumn OR Barbara OR Becky OR Belinda OR Beth
OR Bethany OR Betty OR Beverly OR Bonnie OR Brandi OR Brandy OR Brenda OR Brianna OR
Bridget OR Brittany OR Brittney OR Brooke OR Caitlin OR Candace OR Candice OR Carla OR
Carmen OR Carol OR Carole OR Caroline OR Carolyn OR Carrie OR Casey OR Cassandra OR
Cassie OR Catherine OR Cathy OR Charlene OR Charlotte OR Chelsea OR Cheryl OR Christie
OR Christina OR Christine OR Christy OR Cindy OR Claudia OR Colleen OR Connie OR
Constance OR Courtney OR Cristina OR Crystal OR Cynthia OR Dana OR Danielle OR Darlene
OR Dawn OR Deanna OR Debbie OR Deborah OR Debra OR Delores OR Denise OR Desiree 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 Erica OR Erika OR
Erin OR Eva OR Evelyn OR Felicia OR Frances OR Gail OR Gayle OR Geraldine OR Gina OR
Glenda OR Gloria OR Grace OR Gwendolyn OR Hannah OR Heather OR Heidi OR Helen OR
Holly OR Irene OR Jackie OR Jaclyn OR Jacqueline OR Jaime OR Jamie OR Jan OR Jane OR
Janet OR Janice OR Janis 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
Jodi OR Jody OR Jordan OR Josephine OR Joy OR Joyce OR Juanita OR Judith OR Judy OR
Julia OR Julie OR June OR Kara OR Karen OR Kari OR Karla OR Katelyn OR Katherine OR
Kathleen OR Kathryn OR Kathy OR Katie OR Katrina OR Kay OR Kayla OR Kelli OR Kellie OR
Kelly OR Kelsey OR Kendra OR Kerri OR Kerry OR Kim OR Kimberly OR Krista OR Kristen OR
Kristi OR Kristie OR Kristin OR Kristina OR Kristine OR Kristy OR Krystal 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 Lynda OR Lynn OR
Lynne OR Mallory OR Mandy OR Marcia OR Margaret OR Maria OR Marianne OR Marie OR
Marilyn OR Marissa OR Marjorie OR Marlene OR Marsha OR Martha OR Mary OR Maureen OR
Meagan OR Megan OR Meghan OR Melanie OR Melinda OR Melissa OR Melody OR Meredith
OR Michele OR Michelle 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 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 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 Shannon OR Shari OR Sharon OR Shawna OR Sheena OR Sheila
OR Shelia OR Shelley OR Shelly OR Sheri OR Sherri OR Sherry OR Sheryl OR Shirley OR Sonia
OR Sonya OR Stacey OR Stacie OR Stacy OR Stefanie OR Stephanie OR Sue OR Susan OR
Suzanne 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 Yolanda OR Yvette OR Yvonne

This search finds 49% of all U.K. women on LinkedIn

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")

Would people with the skills and experience I'm looking for use my title(s) and

explicitly mention my search terms?

How can I retrieve profiles I've previously excluded?

How can I search LinkedIn specifically to find people that no one else has?

How many ways could someone possibly express the skills and experience I
am looking for?

Am I overlooking people because I don't see the right keywords?

Building a Solid Foundation with the Boolean Black Belt I Talent Connect London 2013

Building a Solid Foundation with the Boolean Black Belt I Talent Connect London 2013

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