3. • BA math.
• Took programming and simulation classes.
• First job – programming at Royal Institute of Technology in
Stockholm.
• Taught math, computer science in Swedish public school.
• MBA University of Michigan.
• CSHRP for 20+ years.
• Consulting work – Jackson Hole Group and Woolcock
Consulting.
• Discovered analytical work is what I love.
5. • Problem definition:
– “We have an aging workforce problem.
Could you please do a 5-year staffing
forecast?”
6. • What else is going on?
• What about turnover?
--“Not a problem. Lower than the national
average. We don’t need to look at that.”
• What about that new facility I see you are
building?
--“We have no idea what the staffing requirements
might be for that.”
My Response
7. My Approach
• Made assumptions about retirement
• Included turnover data
• Did interviews of various function heads to
get their estimates of staffing requirements
for new facility
--Some new hiring required
--Some current employees moving over
8. • My client, the Director of Staffing, had
trouble getting the data
• Needed to work with HRIS specialist
• He was not familiar with the database
• Got data as an excel file
9. Data Analysis
• Hired a programmer
• Data set big enough and complex enough
that it couldn’t be done “manually”
• If we wanted to change the assumptions, it
would be easy to re-run the data
10. Explicit Assumptions
• Turnover rate does not change
• Voluntary turnover percentage is the same
• “Avoidable” turnover includes other employment,
resignation – personal and working hours
• No new hires will retire in the next 5 years
• Retirement – 5% age 55, 4% age 56, 3% age 57 4% age
58-59, 7% age 60-61, 18% age 62, 12% age 63-64, 40%
age 65, 25% age 66, 30% age 67, 35% age 68, 40% age
69, 100% age 70+
• Growth – simplified as 2% growth for outpatient year
over year, 1.7% growth for inpatient for 2006, then flat
11. Notes
• Data used in the analysis represents a snapshot in time
and may vary from current totals
• The number of openings indicated includes active
requisitions only and may not reflect all vacancies
• Growth data is an approximation. More detailed
analysis can be done with a variety of growth scenarios.
• Growth for laboratory positions was not included in this
project. Analysis shown is based only on turnover data.
• Turnover data covered the period of 5/2005 – 4/2006
• Requisitions shown are as of April 2006.
• All positions at X were included.
12. Staffing Projections Review
Shown by position or group of similar
positions
Turnover data from HR
SHC
Entire Population
Current
Openings
Current Head
Count
Percentage
openings of
total (Openings
+ Headcount)
Total 460 5506 7.7%
2007 2008 2009 2010 2011 Total Impact
Growth 105 79 81 82 84 431 Turnover
Replace
Turnover 708 718 718 719 719 3582 12.9%
Replace
Retirement 94 25 26 34 38 217
Total
Recruiting
Requirement 907 822 825 835 841 4230
13. Detailed data tables exist for the following “slices”
of the employee data:
• Vice Presidents – by name
• Department Group – cardio, clinic, etc.
• EEO Group – professionals, clerical, technical,
etc.
• General Group – admin, diagnostic, etc.
• Job Group – by job code in HRIS
• Super Group – support services, patient care,
etc.
14. Overall turnover is high ( > 20%):
• Super Group – Mgmt
• Dept Group – Amb Surg Ctr, Audiology, Comm/Gov
Relations, Lab Support, Legal, Nutrition, Occ Health,
Ortho, Planning, Sleep Clinic, Transport
• Job Group – Assistant, Nurse – Exempt, Nurse – Relief,
PA, Rlf Cyto Tech, Rlf Sonographer, Rlf Technician, Rlf
Therapist, Service Rep
• VP Group
15. “Avoidable” Turnover
• Almost 50% of all terms are “avoidable” in all periods
studied – probationary, less than 2 yrs, greater than 2
yrs
• “Avoidable” Turnover is high:
– Super Group – Clinical Services
– Dept Group – Admin, Amb Surg Ctr, ED, OR, Outreach Lab,
Pharmacy, Sleep Clinic, Transplant
– Job Group – Lab Asst, Staff Nurse, NA, Professional, Rlf
Technician, Technician
– EEO Group – Office and Clerical, Professionals, and Service
Workers
– VP Group
16. Retirement Statistics are high:
• Super Group – General Services
• Dept Group – Dietary, Plant Operations,
Social Services
• Job Group – Courier, Housekeeper, Mgr -
People
• VP Group
17. Findings
• For the current employee headcount, X will need to
recruit 4,230 new hires between 2007 and 2011.
– 10% of the total is the result of growth
– 85% is the result of turnover
– 5% is the result of retirement
• 2007 and 2011 are the years identified with the
highest recruiting requirements.
• When viewed in isolation, the turnover rate of
12.9% is not alarming. However the greatest
impact on achieving staffing requirements can be
accomplished by reducing “avoidable” turnover.
18. Findings, p.2
• 47% of all resignations occur in the first two years of
employment.
• 47% of all voluntary terminations were “avoidable.” More
detailed study, tracking and analysis are recommended
into causes for seeking other employment.
• Several job categories did not project staff growth (Lab,
for example). More detailed projection is recommended.
• Data collection and projection is difficult and internal
systems are not integrated (Business Development,
Finance, HR).
19. Recruitment
Identifying, selecting and
capturing the talent required.
Retention
Creating a culture of sustained
commitment
HR Systems
Providing support processes to
facilitate effective recruitment
•Expand recruiting sources, including
school relationships, internships, etc.
•Increase image advertising, media
recognition, visibility on selected
campuses
•Enhance HR recruiting systems to
simplify job applications and to speed
up response to applicants
•Enhance employee referral bonuses
for selected positions; consider
campaign related to specific growth
initiatives
•Implement standardized interview and
selection process to ensure high-quality
hiring. Train managers and monitor.
•Establish retention as a management
responsibility, hold managers
accountable, set goals, track and
reward.
•Conduct analysis of voluntary
terminations and identify more specific
causes for voluntary quits
•Establish mentoring, support and
retention initiatives directed at new
hires
• Develop 1-day retention management
workshop for all managers.
•Explore use of scholarships or loan
payback incentives tied to length of
employment.
•Streamline application, interviewing
and job offer processes to accelerate
hiring of identified candidates.
•Implement applicant and requisition
tracking systems. Monitor “time to
close,” establish goals, identify causes
for failure to achieve.
•Expand employee referral awards
•Evaluate implementation of sign-on
bonuses, tiered housing , commute
allowances, housing allowances (or
housing) for long-distance commuters.
•Increase exit interview and post-
termination surveying to determine
true reasons for resignations.
Recommendations
20. High Tech Company
• Problem definition: “Can you interview
former employees who have been gone for 6
months to find out why they really left?”
• Global company, 13,000 employees, would
need to do interviews globally
• Hard to contact 6 months later, hard to get
them to agree to talk, schedule interviews
21. My Response
• Happy to do the interviews
• Want to first learn what the
organization collectively “knows” so
that I can ask smarter questions
22. Data Collection
• Started looking at various sources of
internal data:
–HRIS
–Exit data from vendor
–“Great places to work” data
23. • Difficult to get access to data
• Unwillingness to share data
• Data for HR dept was incomplete
24. • Worked with internal team:
–2 HR VP’s
–One OE consultant
–One intern
25. • Team wanted to tell me what the issues were
• I wanted to go where the data took me.
• Assumed there were “hot spots” in different
parts of the company, for different reasons.
26. • Looked at all the variables I could based
on the data
• Split by BU, geography, level, job family,
etc.
• Prepared report (detailed and high
level) for CEO and Staff, and VPs.
Data analysis
27. Discovery-Based Study
• Assumptions
– While there may be some common themes across the
Company, there are likely “hot spots.”
– Need to confirm what we already know.
– Some turnover is good – look at desirable and undesirable.
– COMPANY turnover should be better (lower) than the market.
• Questions
– What are the top reasons for leaving?
– Is turnover different among Business Units, Locations, Jobs,
Tenure, Age, Ethnicity, Gender?
– What is going on that is not obvious?
28. High Level Findings
• Comparable attrition trends exist between COMPANY and the industry
within the US and internationally
• Job market and social trends increasing impact on the employment
dynamic
• Better job opportunities is cited as the main reason for leaving in general
and across several cuts of the data
• Significant number of employees who leave have under 3 years of tenure
– 14.5 % turnover rate among that demographic (52.5% of total terms)
• Employees under the age of 30 are leaving at an overall turnover rate
(14%*) exceeding average
• There are hot spots among jobs in various job families; turnover varies by
BU
• Sunnyvale and India have highest attrition in tech centers; Turnover in
RTP is better than top quartile in turnover among valued employees (rated
1, 2 or 3)
• V, S, and A have highest attrition in Field Ops sites
31. 4.7%
3.6%
4.2% 4.1%
4.4%
9.0%
10.1%
11.1%
9.7% 9.6%
3.12
3.1
3.13
3.15
3.18
3.06
3.08
3.1
3.12
3.14
3.16
3.18
3.2
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
Q1FY11 Q2FY11 Q3FY11 Q4FY11 Q1FY12 Jobsinmillions
%ofpeople
Bay Area Unemployment Rates National Turnover Rate Bay Area Job Gains
(in millions)
Trends are pointing to a buyer’s
market.
Source: Employment Development Department, Dice.com and Radford
Trends
• Escalation for talent predicted in
high tech firms like Facebook,
Apple, Google, Twitter and
Zynga
• Turnover in high demand
occupations predicted to rise by
25%
*ere.net (Recruiting Intelligence)
• In San Jose, there is just one
person available for every job
posted; ratio 1:1
** Indeed.com job competition trends
• Sourcing passive candidates and
social professional networking
are top recruiting trends in 2012
*** Linked in Global Recruiting Trends Survey
• According to a 2011 SHRM study
42% of satisfied employees said
that they are “likely to look”
32. Market trends show move to new/cool
companies for traditional reasons.
32
5%
11%
17%
23%
0% 5% 10% 15% 20% 25%
Promotion or new title
Flexible work hours
For better compensation
More challenging job roles
Reasons for Leaving in Silicon Valley
Source: Linked in – most common reasons for employees leaving
Source: Dice.com – most common reasons for employees leaving
Source: Forbes – job migration trend
35. 35
- Employees rated 1, 2 and 3 = Valued and Voluntary
- Source: Exit Check Data (N = 479 Valued Employees)
- Bullet Points in descending order of frequency
Other Reasons
• COMPANY Strategy and
Processes
• Lack of clarity about strategy
• Due to changes in strategy
• Due to work culture
• Due to bureaucratic processes
• Management Behavior
• Lack of strong management skills
• Due to conflict with manager
• Lack of management support /
mentoring
• Natural Progression
• Managed out due to performance
• Had been in the company long
enough
… And analysis of qualitative data provides
more insight.
COMPANY Confidential - Internal Use Only
Top Reasons Valued
Employees Depart
• Better and More Challenging Jobs (25%)
• Career Advancement (12%)
• Better Job Fit / Alternate Domains or
Careers (11%)
• Start Ups (6%)
• Personal Reasons – Relocation or Family (8%)
• Work-Life Balance (3%)
• Approached externally (rated as “1”) (3%)
• Further Education (“1s and 2s” in India) (3%)
• Compensation (“1s and 2s”) (3%)
36. Over half of voluntary turnover is employees with less than 3
years of service.
36 COMPANY Confidential - Internal Use Only
8.1%
12.6%
10.5% 10.1%
7.8%
6.3% 6.4%
3.8%
3.0%
6.3%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
0-1
Years
1-2
Years
2-3
Years
3-5
years
5-7
Years
7-10
Years
10-15
Years
15-20
Years
20-30
Years
30+
Years
%HCasofFY13Q1
Actual Turnover by Tenure
15.7%
24.4%
12.4%
19.0%
15.4%
7.0% 5.6%
0.3% 0.1% 0.3%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
0-1
Years
1-2
Years
2-3
Years
3-5
years
5-7
Years
7-10
Years
10-15
Years
15-20
Years
20-30
Years
30+
Years
%ofTerms
% of Terms by Tenure
Top Reasons <3 yrs.*
• Better and more challenging
job roles (58%)
• Personal reasons (i.e. work-life
balance, relocation) (31%)
• For further education (6%)
• For better compensation (2%)
HRIS data
* Voluntary Turnover
COMPANY Confidential - Internal Use Only
37. We are losing our funnel for the future at a rate
exceeding Company Average.
37 COMPANY Confidential - Internal Use Only
Top Reasons <30 yrs*
N=79 N=247 N=445 N=398 N=179 N=45N=168
• Career Opportunity (35%)
• Personal Reasons (19%)
• Return to School (13%)
• Relocation (8%)
• Compensation (4%)
16.5%
13.1%
14.0%
10.3% 9.8%
9.0%
14.0%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
18.0%
Under 25 25-29 Under 30 30-39 40-49 50-59 Over 60
ActualAttritionRate
14.0%
11.2%
11.9%
9.4%
8.4%
7.1%
11.5%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
Under 25 25-29 Under 30 30-39 40-49 50-59 Over 60
ActualAttritionRate
N=67 N=211 N=404 N=341 N=140 N=37N=144
OVERALLVOLUNTARY
HRIS data
*Voluntary Turnover
COMPANY Confidential - Internal Use Only
38. Field Operations
• Better job opportunities
• Compensation
• Work-life balance
G & A Functions
• Work-life balance
• Frequent strategy change
• Conflict with managers
• Better job opportunities
Reasons for valued employees leaving
varies by BU…
38 COMPANY Confidential - Internal Use Only
Customer Advocacy
• Alternate careers or domains
• Better job fit
- Rated 1, 2 and 3 = Valued and Voluntary
- Source: Exit Check Data
- Bullet points in descending order of frequency
Product Operations
• Better job opportunities
• Start-ups
• More challenging jobs
• Alternate domains
• Career advancement
• Further education
COMPANY Confidential - Internal Use Only
39. Lessons Learned
• Need access to data
• Data needs to be “clean”
• Look beyond what is being asked - what they
need may be different than what they want
• Tie the results to the business – ask “so what?”
• Document all assumptions and steps
• Someone, either internal or consultant, needs to
be able to do this for your company
