Talentnet live - johnson and beygelman dallas september 13 2013
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Mike Beygelman and Lisa Johnson presented at TalentNet Live conference at Pizza Hut HQ in Dallas, TX 9/13/13 Datafication of Recruitment: Measuring and quantifying relevant information
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Talentnet live - johnson and beygelman dallas september 13 2013
- 1. Datafication of Recruitment: Measuring and quantifying relevant information
- 2. Introductions Datafication of Recruitment: Dallas September 2013 2 North America RPO President (Pontoon) • More than 100 client engagements in North America, EMEA, and APAC • Team is responsible for >73,000 hires per annum • One every 7 minutes of a business day • Prior: Executive Director, HR Outsourcing Association Director of Recruiting, North America (Gate Gourmet) • Responsible for the talent acquisition of more than 10,000 employees across North America • 16 years of Recruiting & HR leadership experience
- 3. Datafication of recruitment: some context for “big data” • Not looking for a needle in a haystack, but rather looking for a unique piece of hay in a moving field of millions of haystacks • Attributes differ from traditional data in three main ways: – Volume, Velocity, Variety • Stands apart from traditional analysis – Not organized in tables and rows – Uses data flow versus stock – Best used by process owners instead of data analysts or IT (e.g. owned by the business process/function/operations) Datafication of Recruitment: Dallas September 2013 3
- 4. • Volume – Data will be measured in Exabytes very soon (1,000 petabytes) – Wal-Mart handles a million customer transactions per hour and has c 2.5 petabytes of disparate data, Google has 24 petabytes (24,000 terabytes) • Velocity – Data is flowing in real-time, not static • Blogs, Twitter, Facebook, LinkedIn, etc. – Real-time data extremely valuable, but degrades in value very quickly – Data may be fleeting if not captured • Variety – Myriad possible sources of data – Data is unstructured, in multiple systems, in different back-end formats, etc. Datafication of Recruitment: Dallas September 2013 4 The three V’s
- 5. Datafication of Recruitment: Dallas September 2013 5 Growth of Big Data Monetary value of big data by industry
- 6. Datafication of Recruitment: Dallas September 2013 6 The promise of Big Data in everyday life
- 7. Datafication of Recruitment: Dallas September 2013 7 The promise of Big Data in recruitment?
- 8. The three V’s applied to recruitment • Variety – Internal Systems – ATS, HRIS, Performance Review Systems, POS data, Sales performance and comp systems – External Systems - Compensation, Demographics, Recruiting informatics, Traffic and transportation, Social Media , Employment Social • Volume – Up to 2,520 data points per candidate • Contact and profile information • History and status information • Each candidate dataset has a different set of data points • Velocity – Hundreds of applicants per day, different requisitions, statuses, hiring managers, locations etc. Datafication of Recruitment: Dallas September 2013 8
- 9. • With the data that sits in your current systems, right now, you could probably answer the following questions: – What is the relationship between the compensation of a position and the distance candidates are willing to commute? – What is the interaction between posted job title and number of applicants? – What is the maximum ideal number of interviews for a candidate? (btw, it’s 4) – Does using the phrase “proactive and detail oriented” drive a higher quality candidate slate? – What is the relationship between spelling errors on a resume and a candidates ultimate success on the job? • Do you think having access to this kind of information can make you more actionable around your approach? Datafication of Recruitment: Dallas September 2013 9 Example: Big Data opportunity in context of recruitment
- 10. 10 Case Study: How Gate Gourmet Datafied Recruitment in SFO Datafication of Recruitment: Dallas September 2013
- 11. • Gate Gourmet is the core business behind gategroup • World’s largest independent provider of catering and provisioning for the Airline & Railroad Industries • Gate Gourmet serves over 250 million meals per year out of our 120 airport locations around the globe • Global HQ in Zurich, Switzerland and North American HQ in Reston, VA 11Datafication of Recruitment: Dallas September 2013 About Gate Gourmet
- 12. 12Datafication of Recruitment: Dallas September 2013 A shameless infomercial about Gate Gourmet
- 13. Situation before leveraging Big Data insights and analysis • 626 employees working 24/7/365 shifts • Hourly workforce understaffed by 20% driving 15.7% OT • Wages and transportation costs barriers to staffing optimization Goal: focus sourcing to overcome these challenges • Data in three sources: – CRM (Avature) for candidate outreach, source, and location data – ATS (Taleo Business Edition) for applicant location data – HRIS (Oracle) for retention/turnover data • Big data sets: – CRM 156,022 candidates – 31,204,500 data points – ATS 108,252 candidates – 29,769,300 data points – HRIS 35,226 records – 3,522,600 data points • Implemented proprietary data visualization technology Datafication of Recruitment: Dallas September 2013 13 Case study: San Francisco International Airport (SFO)
- 14. • Red dots represent all candidates who were sourced and we connected with • Green dot in middle represents the job location (SFO) • Notice candidates pulled from all directions – North, South, East, West – Predominantly South and Southeast 14Datafication of Recruitment: Dallas September 2013 Logically we were sourcing candidates from a wide area
- 15. • Yellow dots represent all applicants who applied to the job • Notice the applicants are heavily clustered to the NORTH of SFO, much fewer willing to come from the SOUTH and across the San Mateo Bridge 15Datafication of Recruitment: Dallas September 2013 Big Data analysis of applicants revealed new insights
- 16. And when it came to actual hires, the visual is self-evident • Blue dots represent applicants who were hired for the job • Notice the applicants are heavily clustered • Most hires resulted from NORTH of the job location with heaviest concentration on the peninsula 16Datafication of Recruitment: Dallas September 2013
- 17. After launching this location with our RPO partner in 2012: • We became much more productive: effective and efficient • We stopped wasting time and money sourcing people from areas that did not bring good results – and the business results were profound! – Increased candidate attraction by 408% – Net Headcount growth increased 21% – Interview to offer ratio improved from 1.6 to 1, now is at 1.1 to 1 – Reduced overtime and temporary labor expense by over $2 Million – Achieved “fully staffed” status for first time ever Datafication of Recruitment: Dallas September 2013 17 Results
- 18. 18 Practical Advice Datafication of Recruitment: Dallas September 2013
- 19. • What business problem can you tackle and solve that might produce real bottom line impact? • What are some of the assumptions in your current recruitment and talent management process that you might validate or disprove? • Who owns your data (or access to it)? • Can you make a business case for investment? • What system are you going to normalize the data into? Datafication of Recruitment: Dallas September 2013 19 Points to consider
- 20. Datafication of Recruitment: Dallas September 2013 20 Let’s action our learning
- 21. Michael Beygelman North America RPO President, Pontoon michael.beygelman@pontoonsolutions.com Lisa Johnson Director of Recruiting, North America, Gate Gourmet Ljohnson@gategroup.com Datafication of Recruitment: Dallas September 2013 21 Questions, follow up contact
Editor's Notes
- A collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications (wikipedia).
- A collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications (wikipedia).
- 1 Petabyte = One Quadrillion Bytes (1,000,000,000,000,000 Bytes) – equivalent to 20 Million filing cabinets worth of text or a 45 mile high stack of CDs1 Exabyte = 1,000 petabytesWe’ve already come up with the next-generation of data terms – a Zettabyte is 1,000 Petabytes and a Yottabyte is 1,000 ZettabytesEvery 24 hours300 billion emails are sent22 billion text messages are sent3 billion videos are viewed on YouTube480 million people log to Facebook30 Billion pieces of facebook content added per month250 million pictures are uploaded to Facebook250 million tweets are sent1 million pictures are uploaded to Flickr58,630 websites are added to the InternetExample: The MIT Media Lab used cell phone location data to infer number of shoppers in Macy’s parking lots on Black Friday – estimating sales before Macy’s had even recorded the sale.Real-time data extremely valuable, but may degrade in value very quickly as time passesData may be fleeting if not capturedBlogs can be changed, postings can be un-postedWeb visitor data may be purgedSources of data:Social networksData from POS systemsGPS readings from cell phonesWeb browser dataData from downloaded and used smartphone appsData from club loyalty cards
- Your grocery store knows what brand of soda you buyNetflix knows what shows you watchYour credit card company knows where you shopAmazon knows what products you consider before you buy what you ultimately buyMany companies know your name, age, address, number of children, gender and average household incomeYour smartphone knows where you’ve been and who you’ve been talking withYour smartphone apps know what you ate yesterday, what movies you considered seeing and which you bought tickets for, where you got your morning coffee, and what flight you are on tomorrow…Giving anyone who can aggregate all this data a FRIGHTENINGLY accurate picture of you as a consumer, and as a human being
- Your grocery store knows what brand of soda you buyNetflix knows what shows you watchYour credit card company knows where you shopAmazon knows what products you consider before you buy what you ultimately buyMany companies know your name, age, address, number of children, gender and average household incomeYour smartphone knows where you’ve been and who you’ve been talking withYour smartphone apps know what you ate yesterday, what movies you considered seeing and which you bought tickets for, where you got your morning coffee, and what flight you are on tomorrow…Giving anyone who can aggregate all this data a FRIGHTENINGLY accurate picture of you as a consumer, and as a human being
- Per Annie: We are capturing all of the standard data points such as name, e-mail, phone, source, address, previous employers, education, etc. on each candidate. In addition, applicant tracking systems and CRM’s are tracking numerous other data points related to a single candidate. For example, the status they were given in an ATS (i.e. Schedule Phone Screen), date they were moved into an individual status, the user that moved that candidate into a status, the source status that the candidate was coming from before being moved into the current status, the e-mail that was generated when they were moved into the status, whether or not the candidate opened an e-mail that was sent to them, the date of which the applicant updated their profile, the note that was added to the candidate’s profile, the date of the note, the recruiter that added the note, etc. So, to arrive at the 2,520 number, Luke looked at the Peoplefluent and Avature database fields to determine how many data points are related to a single candidate. Although Peoplefluent is robust, it doesn’t track quite as many data points as Taleo, so we felt it was a solid representation, especially since it’s our preferred ATS.5,000 employee company hiring 500 jobs a year for the last 5 yearsEach job yields 50 applicants315 Million data points (5,000*500*5*2520)Multiple systems – ATS, HRIS, CRM, Multiple formats – Databases, paper applications, resume documents
- http://www.slate.com/articles/technology/technology/2013/01/google_people_operations_the_secrets_of_the_world_s_most_scientific_human.single.html - answer that the ideal number of interviews at Google was found to be --- 4
- 10,000 union hourly employees preparing food, 1500 people supporting
- 10,000 union hourly employees preparing food, 1500 people supporting
- Per Annie: Here are the number of candidates for slide 21. CRM – 156,022ATS – 108,252 If you want to quote in data points instead of candidates… of the 2,520 data points associated with the candidate, approximately 200 are in Avature and 2,320 are in the ATS. So the numbers for Gate would come out to (approximate):CRM – 31,204,400 data pointsATS – 251,144,640 data points
- Include discussion of what initiatives are being taken
- Problem: Traditional sourcing directly around the airport was not generating many applicants or hires.Solution: Started more passive outreach in the footprint near the airport where we weren’t getting peopleProblem: A lot of applicants from certain areas would make it to interview, but not the hireSolution: Changed screening process to be more transparent about schedules and allow people to consider transportation options (public transportation etc.)Problem: High turnover Solution: Evaluated turnover - identified specific zip codes where retention was higher by analyzing attrition data and targeted those zip codes for sourcing
- LinkedIn knows when you last updated your profile and where you’ve workedMonster knows what jobs you’ve applied for Google knows what job searches you have done and what pages you’ve visited (e.g. “How do I format my resume”)Equifax knows where you live and about what you earnSpotify knows what music you listen toYour credit card company knows how much you travelTheoretically, I could identify a Sales Manager making 80k who lives in Minneapolis, has worked in the printing industry, who is actively looking for work right now, or likely to start looking soon, who is used to traveling at least 10 days a month, and likes the Foo Fighters.