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

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

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

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

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

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

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

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

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

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

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

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

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

Demonstrating HR’s Value Recruit Retain Reward

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

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

Demonstrating HR’s Value - Retain Engagement Development Compliance

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

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

Demonstrating HR’s Value - Reward Recognition Compensation Benefits

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

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

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

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

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%

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

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

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%

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%

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

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%

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

Thank you! Russ Sullivan Bondcliff HR Advisors, Inc. rsullivan@bondcliffhr.com 800-498-6909