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HR AnalyticsPresented by Melissa Asher, Director of Training &
Development and Talent AcquisitionCPS HR Consulting
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What brought you here today?
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Agenda
What does “analytics”
mean?
Why is this important for
HR?
Getting started
Analyzing the data
Presenting findings
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What does “analytics” mean anyway?
1
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Basic Terms
Data
Metrics
Analytics
What is the difference?
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Metrics
Actionable info to improve outcomes
Analytics
Data
6
In order to improve turnover
Examples ofData:Metrics:Analytics:
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IdentifyRoot Cause
ImprovementOpportunity
Why Analytics?
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Identify Need to Improve
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Why is this important for HR?
2
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HR as Business Partner
Improved Outcomes
HR & Ops as
partners
Has org-wide
orientation
Work proactively
on dept. challenges
Share credit for results
Work together on Ops and HR
issues
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Alignment with Strategy
Align people metrics with business strategy Not letting the metrics take the place of strategy Keep purpose (outcomes) in the forefront
Case Study
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Can Connect the Dots
Connecting the dots
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Getting Started
3
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• What are we trying to solve?• What are people complaining
about?• What is the business problem?
Clarify
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Plan What Data Is Needed
Understand goal or problem
Understand what types of data
Identify key HR functions
Make initial hypotheses
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For Example: Two Depts. in Same Agency
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Common Data Sources
HR Files (e.g. applications, performance reviews) HR Information System (e.g. NeoGov, Cornerstone) Financial System (e.g. salary, benefits)
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Common Metrics
HR Metric Basic FormulaTurnover (annual) # employees exiting during 12 months/average actual
# employees during same periodCost per hire Recruitment and selection costs/# new hiresTime to fill # days positions are open/number of positions filled
during same periodEngagement % of employees stating engaged with a given aspect of
the workplace on surveys or other data collection toolsWorkers’ comp cost per employee
Total workers’ comp cost for year/average number of employees
Training participation # employees participating in training opportunity/# employees eligible
Age to retirement # employees in age range/total employees
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Age to Retirement Metric Example
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Analyzing the Data
4
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Data Accuracy & Assumptions
And what do YOU mean by. . .
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Working with Qualitative Data – Basic Steps
1• Review
data vis-a-vis goals
2• Organize
data
3• Explore
& sort
4• Identify
themes
5• Analyze
& draw conclu-sions
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Example – Word Cloud
Open-ended survey question from a training effectiveness survey:
What could we have done to improve your
Risk Management Practitioner’s Program
experience?
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Working with Quantitative Data – Basic Steps
Look for missing data and errors
Look for outliers
Check quality of collection
Check possible intervening variables
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Descriptive Statistics – Two Types
Central Tendency• How data is grouped together
Spread or Variability• How different are data points within the
population
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Central Tendency – How Data is Grouped
Mean = average
Median = point with 50% data points above and 50% below
Mode = most frequent score
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Example: Means and Medians
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Variability – How Different Within Population
Range = difference of highest to lowest
Standard Deviation = defines the magnitude of deviations from the mean
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Analyze & Draw Conclusions
Look for patterns and themes
Can you explain deviations?
Are results significant &/or meaningful?
Reality check. Make sense?
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Presenting Findings
5
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Presenting Your Findings
Know your audience
Know your time
Create a story
Avoid powerpointlessness
Visualize your data
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Traditional Visualizations
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Visualize Results – Gauge Charts
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Visualize Results – Infographics
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Visualize Results – Dashboard
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How would you present this?Recruitment Date Closed
AppsReceived
Days Open
Test Date QAIHiring
InterviewJob Offer Start Date
Days to fill
Accounting & Financial Analyst II 8/11/2017 100 25 8/21/2017 8/31/2017 9/7/2017 9/20/2017 10/16/2017 91Laboratory Director 9/15/2017 41 42 9/25/2017 10/4/2017 10/19/2017 11/13/2017 101Technical Writer Intern 9/22/2017 21 32 10/2/2017 10/4/2017 10/23/2017 63Admin Specialist (FMC) 9/12/2017 106 6 9/28/2018 10/11/2018 10/25/2017 11/7/2017 11/12/2017 67Collection System Worker I 9/19/2017 1 7 10/4/2017 11/13/2017 62Admin Specialist (CIP) 9/25/2017 92 10 9/28/2018 10/12/2018 10/25/2017 11/13/2017 11/27/2017 73Casual Receptionist 10/12/2017 60 10 10/27/2017 11/2/2017 11/9/2017 11/21/2017 11/28/2017 57Engineering Technician II/III 10/24/2017 73 20 11/3/2017 11/16/2017Plant Operator III Trainee 10/31/2017 120 15 11/17/2017 11/29/2017 12/7/2017 12/27/2017 1/23/2018 99Plant Operator III 11/3/2017 26 18 11/13/2017 12/7/2017TPO Trainer 11/8/2017 7 11/15/2017 11/21/2017 1/18/2018 78Receptionist 11/13/2017 100 3 11/27/2017 12/14/2017 12/19/2017 1/4/2018 1/29/2018 80Engineering Technician I/II 1/25/2018 104 17 2/5/2018 2/13/2018 2/20/2018 3/5/2018 4/16/2018 98Purchasing Agent 3/9/2018 57 25 3/20/2018 4/3/2018 4/23/2018 70Assistant/Associate Engineer 4/26/2018 61 42 5/8/2018 5/30/2018 6/16/2018Collection Services Coach 6/1/2018 18 18 6/15/2018 6/27/2018
Averages 65.33 18.56 78.25
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A Real Example From the Data
78.25
16
21 21
2322
16
10
15
20
25
Number of Recruitments Since 2013
67
62
73
57
99
78
80
98
70
0
0
Admin Specialist (FMC)
Collection System Worker I
Admin Specialist (CIP)
Casual Receptionist
Plant Operator III Trainee
TPO Trainer
Receptionist
Engineering Technician I/II
Purchasing Agent
Assistant/Associate Engineer
Collection Services Coach
Days to fill
Average Recruitments
Per Year
19.83
AverageDays To Fill
78.25
https://www.youtube.com/watch?v=e2wDqspleNk
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Where We Went!
What does “analytics”
mean?
Why is this important for
HR?
Getting Started
Analyzing the Data
Presenting Findings