1. HRMA Western New England March 26, 2018
HR Data Analytics
HRMA Western New England
March 26, 2018

2. HR Data Analytics Why Data Analytics? Demonstrating HR’s Value
Simplifying Data Analytics
Applications

3. Why Data Analytics?
Move from the anecdotal to the measurable to the predictive
Data
Metrics
Analytics

4. Why Data Analytics? Data –Raw Information
Metrics – Data correlated to results
Analytics – Correlated data that predicts outcomes

5. Why Data Analytics? Data – # of terms Metrics – % terms by position
Analytics – Predicted recruiting by position for upcoming year

6. Why Data Analytics? Data – # of terms Metrics – % terms by supervisor
Analytics – Predicted reduction in terms through supervisory training

7. Why Data Analytics? Data Analytics allow you to
Separate red herrings from root causes
Focus resources
Impact results
Demonstrate the value of proactive & remedial initiatives
Demonstrate the value of Human Resources

8. Why Data Analytics? Patient satisfaction issue
Anecdotal explanation – Too many contract physicians
What did the data say?

9. Why Data Analytics? Patient satisfaction issue
How likely is patient to recommend the practice?
Metric
Probability
Cleanliness
0.2132
FOA
0.0862
Wait
0.1522
Provider Explains
0.1127
Provider Listens
0.1567
Provider Time
0.5111
Provider
0.0060

10. Why Data Analytics? Patient satisfaction issue
How likely is patient to give provider a high rating?

11. Why Data Analytics? Patient satisfaction issue
How likely is patient to give provider a high rating?

12. Why Data Analytics? Patient satisfaction issue
How likely is patient to give provider a high rating?

13. Why Data Analytics? Data Analytics Impact:
Focus efforts on front office staff training and female provider recruiting
Change leadership attitudes towards employing female providers
Increase # of part-time providers creating more staff flexibility and coverage
Train male providers to be more attentive to patients
Positively impact patient satisfaction scores

14. Demonstrating HR’s Value
Recruit
Retain
Reward

15. Demonstrating HR’s Value - Recruit
Sourcing
Screening
Interviewing
Offer & Acceptance
Pre-Employment Tests and Protocols

16. Demonstrating HR’s Value - Recruit
Measurement
Metric
Analytic
# Open Reqs
Fill rate (% of positions open)
Staffing ratio to hours of work
# Applicants
Applicants / recruiting source
% Female Applicants
% Minority Applicants
# Applicants to generate to meet staffing goals
# Female & Minority Applicants to meet diversity goals
# Qualified Applicants
Qualified Applicants / Total Applicants
Qualified Applicants / Recruiting Source
% Qualified Female Applicants
% Qualified Minority Applicants
# Qualified Applicants to generate to meet staffing goals
# Qualified Female & Minority Applicants to meet diversity goals
# Offers & Hires
Hires / Recruiting Source
Avg # Days to hire
Avg cost per hire
Avg HR hours to hire
Recruiting lag to staff to set recruiting start dates
Recruiting $ per position / department
HR Staffing Model

17. Demonstrating HR’s Value - Retain
Engagement
Development
Compliance

18. Demonstrating HR’s Value - Retain
Measurement
Metric
Analytic
# EEs engaged (by survey)
% EEs engaged by position, department, supervisor
Supervisory training / recruiting needPredictive turnover by position, department, supervisor
# EEs with professional development plans
% EEs with PDPs by position, department, supervisor
HR training & development budget
HR department training hours – development & classroom
# Salary Increases
% salary increase on time by position, department, supervisor
Avg days late for salary increases by position, department, supervisor
Supervisory training / recruiting needs

19. Demonstrating HR’s Value - Retain
Measurement
Metric
Analytic
# Terminations
% Terms by positon, department, supervisor
Avg tenure by position, department, supervisor
Predicted openings by position, department, supervisor
# Lost Time Accidents
# Days Lost to Accidents
$ Due to Accidents
$ / lost time accident
OT per loss time accident
Predicted OT for Lost Time Accidents
Predicted WC stop loss coverage

20. Demonstrating HR’s Value - Reward
Recognition
Compensation
Benefits

21. Demonstrating HR’s Value - Reward
Measurement
Metric
Analytic
# Promotions
% promotions by position, department, supervisor
% promotions by gender & race by EEO group, position, department, supervisor
% achievement of AAP / diversity goals
# mentorship hours by position, department, supervisor
% positions filled by internal promotion
# employees enrolled in health plan
% employees enrolled by tier level
% eligible employees enrolled by pay level
Predictive enrollment impact of increasing opt-out stipend
Predictive enrollment impact of premium and OOP increases
# employees eligible for bonus
# employees by performance rating
Avg bonus % by gender per comparable group
Avg bonus % by performance rating
Predictive impact on pay equity remedial actions
Predictive modeling of bonus allocations

22. Simplifying Data Analytics
Build metrics & analytics into your systems
Applicant Tracking System & Payroll
Export Date to Excel powered with pivot tables
Preformatted Reports Transform Data into Metrics

23. Simplifying Data Analytics
Key data fields - ATS
Req #, Date - Opened, Application, Screen, Interview, Offer, Date Acceptance, Start
Location, Division, Department, Supervisor
Job Title, EEOC Code, Comparable Group, FLSA Status, Job Grade
Recruitment source, Advertising $, HR Hours
Disposition reason

24. Simplifying Data Analytics
Key data fields - Payroll
DOH, DOB, Emp ID#
Location, Division, Department, Supervisor
Job Title, EEOC Code, Comparable Group, FLSA Status, Job Grade
Pay rate, review date, performance rating, last inc $, bonus $
Benefit plan deductions, shift differentials

25. Applications – Workforce Planning
Position
# EES
# Terms
% Terms
Assemblers 75 15
20.00%
Technicians 32 6
18.75%
Accountants 8 2
25.00%
Engineers 27 4
14.81%

26. Applications – Workforce Planning
Position
Days to Hire
Cost /Hire
HR Hours per Hire
Assemblers 32
$1,200 25
Technicians 41
$1,800
Accountants 63
$2,600
Engineers 81
$4,250 40

27. Applications – Workforce Planning
Position
Planned EES
Adds
Expected Terms
Total Hires
Total Cost
Total HR Hours
Assemblers 80 5 15 20
$24,000
500
Technicians 30 -2 6 4
$7,200
100
Accountants 8 2
$5,200 64
Engineers 3 7
$29,750
280
Total
148 27 33
$66,150
944

28. Applications – Pay Equity
Average Pay
Supervisor F M
All
F% Avg
M% Avg
% Diff A
$64,500
$69,250
$66,875
96.4%
103.6%
7.1% B
$63,179
$66,000
$64,589
97.8%
102.2%
4.4% C
$52,500
$58,500
89.7%
110.3%
20.5%
$60,026
$66,500
$63,263
94.9%
105.1%
10.2%

29. Applications – Pay Equity
Average Service
Supervisor F M
All
F% Avg
M% Avg
% Diff A
5.00
100.0%
0.0% B
5.07
5.04
100.7%
99.3%
-1.4% C
4.92
4.96
100.8%
99.2%
-1.6%
5.03
4.97
100.5%
99.5%
-1.0%

30. Applications – Pay Equity
Pay Adjustments
Supervisor F M
All A
$54,976 $0 B
$38,637 C
$140,000
$233,613

31. Applications – Pay Equity
Adjusted Average Pay
Supervisor F M
All
F% Avg
M% Avg
% Diff A
$69,081
$69,250
$69,166
99.9%
100.1%
0.2% B
$65,938
$66,000
$65,969
100.0%
0.1% C
$63,269
$64,500
$63,885
99.0%
101.0%
1.9%
$66,016
$66,500
$66,258
99.6%
100.4%
0.7%

32. Data Analytics Evidence-based decisions Results-oriented planning
Align HR with Operations
Demonstrate HR Value

33. Thank you!
Russ Sullivan Bondcliff HR Advisors, Inc