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

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LinkedIn Talent Solutions
LinkedIn Talent Solutions

This document provides guidance on using adaptive search strategies and maximizing inclusion while strategically excluding irrelevant profiles on LinkedIn. It discusses using query logic and syntax like Boolean operators, phrase searching, and stemming to refine searches. It emphasizes understanding the target talent pool and competition in order to ask better questions and find overlooked candidates who may not explicitly mention search terms but have the desired skills. Facets like location, experience level, and job seeking status can help filter results. The key takeaways are that no system can determine relevance alone and users must take responsibility for search results through cognitive effort in questioning and strategies.

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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
Building a Solid Foundation with the Boolean Black Belt I Talent Connect London 2013
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
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
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
Photo: JohnEdgarPark
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?
Building a Solid Foundation with the Boolean Black Belt I Talent Connect London 2013
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
Relevance
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?
Boolean: Before and Beyond
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.
Building a Solid Foundation with the Boolean Black Belt I Talent Connect London 2013
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
Building a Solid Foundation with the Boolean Black Belt I Talent Connect London 2013
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 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 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 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 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 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 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 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 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 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 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 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 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
Facets
Facets
Joined
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")
Critical Questions
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
Building a Solid Foundation with the Boolean Black Belt I Talent Connect London 2013
Q&A
Building a Solid Foundation with the Boolean Black Belt I Talent Connect London 2013

More Related Content

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

  • 2. LinkedIn Sourcing Building a Solid Foundation Glen Cathey SVP, Strategic Talent Acquisition and Innovation Kforce Author, booleanblackbelt.com
  • 3. 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
  • 5. 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
  • 9. “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
  • 10. Title only Source: John Zappe, SourceCon
  • 11. Title only Source: Suzanne Chadwick, The Social Recruiter blog
  • 13. 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?
  • 16. You can only get what you search for Every search you run includes and excludes qualified people
  • 17. Who are you finding?  Do the best people necessarily mention your search terms?  Common search terms may be "overvalued" and yield no competitive advantage
  • 19. How many results… do you review per search? can you review per search? should you review?
  • 20. 25,000+ software engineers in London Nearly 14,000 accountants
  • 21. 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
  • 22. Understand your target talent pool…
  • 23. 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?
  • 25. 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.
  • 27. LinkedIn's working on understanding your searches…
  • 28. 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
  • 30. AND  Mandatory inclusion  Typically reduces results  Unnecessary to type in LinkedIn  Use for required skills "Objective C" Cocoa xcode iOS "software engineer"
  • 31. 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
  • 32. 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)
  • 33. 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)
  • 34. 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)
  • 35. 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
  • 36. 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 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 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 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 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 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 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 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 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 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 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 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 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)
  • 37. 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
  • 41. 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")
  • 43. 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?
  • 47. Q&A