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The Power Of ANALYTICS: HR’s SECRET WEAPON Steve VanWieren Principal Statistician / Data Scientist October 16, 2013
The Power Of ANALYTICS:
HR’s SECRET WEAPON
Steve VanWieren
Principal Statistician / Data Scientist
October 16, 2013
Agenda
•Big Data in HR
•A case study
•Workforce trends
What is “big data”?
• The Big Data Revolution Volume
(lots of data)
Variety
(many types)
Velocity
(speed of data in/out)
Veracity
(conformity to facts)
Gartner’s formal definition
Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced
decision making, insight discovery and process optimization.
Business Intelligence vs Big Data
Business Intelligence uses descriptive statistics with data with high
information density to measure things, detect trends etc.;
Big Data uses inductive statistics and concepts from nonlinear system identification to infer laws (regressions, nonlinear
relationships, and causal effects) from large data sets to reveal relationships, dependencies, and to perform predictions of outcomes and behaviors
Source: Wikipedia
Inflated expectations for Big Data
Source: Gartner “Hype Cycle for Emerging Technologies” (July 2013)
My definition of “big data”
“Big data” is data, stored and accessed in the most up-to-date technologies
Size doesn’t matter. The knowledge gained from the data is what matters
“An analytic without action is useless.” – Steve VanWieren
What if companies had the same level of business intelligence on their human capital as they did in other disciplines like Finance,
Marketing, Sales & Manufacturing?
Why not Human Capital?
The HCM Market Response
Source: Gartner “Hype Cycle for Human Capital Management Software” (July 2013)
Internal data
Human Capital Management solutions, like UltiPro, collect employee information from Recruitment to Retirement
External data
90% of all the data ever collected has been collected in last two years
•Source: ScienceDaily
Data collected in 60 seconds (2013)
Source: Qmee
Usefulness of internal vs external
Pre-
employment
During
Employment
Post-
Employment
External data
Internal data More
predictive
Less
predictive
Some stats
74% of people would today consider finding
a new job Harris Interactive Poll
Question to consider: Do you know who the 32% are in your organization?
32% of people are actively looking for a
new job Mercer
76% of younger workers plan to find a new job as the economy improves
Harvard Business Review
More stats
2mm people voluntarily leave their
job every month US Dept of Labor Statistics
58% would take 15% pay cut in order to work for an organization
with values like their own Net Impact Survey
Question to consider: Are you hiring people with values that fit your culture?
35% of people quit their jobs within the first 6 months
Leigh Branham, “The Seven Hidden Reasons Employees Leave”
And even more stats
Question to consider: do you have any special programs for new hires?
69% are more likely to stay >3 years if they experience a well
structured onboarding program Aberdeen Group
86% know within the first 6 months if they are going to stay
or leave long term Aberdeen Group
55% of millenials say career advancement opportunities are
main thing they want in a job Bob Nelson
And still even more stats
Question to consider: does your organization have an engagement strategy?
70% are disengaged at work Gallup Poll
75% of leaders have no engagement strategy, even though 90% say engagement impacts business success
PwC
It all leads to one question…
Agenda
• Big Data in HR
• A case study: forecasting employee turnover
• Workforce trends
Forecasting at the organization level
To forecast at the macro level, you need macro level data •Ex. Industry Sales, Economy, Monthly company turnover
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
Total Active vs 12-month Retention
Total Retained Projected Retained
76.0%
78.0%
80.0%
82.0%
84.0%
86.0%
88.0%
90.0%
12-mo Retention Rate
12-mo retention rate Projected 12-mo retention
Forecasting at the employee level
With employee level data, you could develop:
Historical Reports (BI)
Predictive Scores
(ex Retention Scores)
Workforce Analytics / Planning
Organization-level summaries Actual employee level
Developing the Retention Predictor™
Retention Predictor™
Demographics
Benefits History Compensation
History
Job History
Retention Predictor Score
Retention Predictor is a score between 0 and 100, representing the probability an employee will remain with
the organization for the next 12 months.
Low Scores: lower
probability of employee
staying
High Scores: higher
probability of employee
staying
0 100
Score Distribution
Score Range # of Employees
% of All Employees
90.0 – 99.9 88,500 18.1%
75.0 – 89.9 231,915 47.3%
50.0 – 74.9 117,699 24.0%
0.0 – 49.9 51,987 10.6%
Greater than 9 in 10 chance of staying
Less than 1 in 2 chance of staying
Predictions
Model Performance – Gains Chart
On which score range does it make the most sense for managers to focus their attention?
Score Range # of Employees
% of All Employees
# Terminated % Terminated
90.0 – 99.9 88,500 18.1% 7,242 8.2
75.0 – 89.9 231,915 47.3% 37,947 16.4
50.0 – 74.9 117,699 24.0% 39,605 33.6
0.0 – 49.9 51,987 10.6% 33,779 65.0
Predictions Results
28% of terms
Model Performance – Visualization
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
0.0 – 49.9 50.0 – 74.9 75.0 – 89.9 90.0 – 99.9
% of All Employees % TerminatedScore Range
The more you know, the better
Score Range # of Employees
% of All Employees
# of High Performers*
# Terminated % Terminated
90.0 – 99.9 88,500 18.1% 12,990 7,242 8.2
75.0 – 89.9 231,915 47.3% 23,498 37,947 16.4
50.0 – 74.9 117,699 24.0% 8,188 39,605 33.6
0.0 – 49.9 51,987 10.6% 2,794 33,779 65.0
Predictions Results
* Not actual High Performer results
With additional measures, you could identify and focus on those high performers at greatest risk
Big savings!
Organizations can experience significant costs to replace an employee.1
1 – SHRM, NRF, J Douglas Philips, and Bersin studies
1.5 to 3 times the annual salary for professional salaried employees
$5,000 to $20,000 for non-salaried employees
Detail the cost savings
Separation Replacement Training
Exit interviews Communication of job availability Informational literature
Administrative functions related to the termination
Pre-employment administrative functions
New hire orientation
Separation pay Entry interviews Formal training programs
Unemployment tax Skills Testing Instruction by assignment
Staff meetings
Travel & moving expenses
Post-employment acquisition & dissemination of info
Employment medical exams
2 – “Investing in People: Financial Impact of Human Resource Initiatives” (2nd Edition), Cascio and Boudreau
Includes separation, replacement, and training costs2
Why do people leave?
31% don’t like their boss Aberdeen Group
31% do not feel empowered Aberdeen Group
35% due to internal politics/turf Aberdeen Group 43% for lack of recognition
Aberdeen Group
89% of managers believe that most employees are pulled away by better pay …but 88% of voluntary resignations happen for reasons other than pay
Leigh Branham, “The Seven Hidden Reasons Employees Leave”
>60% do not feel like they get enough feedback Gallup Poll
75% of people leave because of work relationship issues Saratoga Institute
75% of people who leave voluntarily don’t quit their jobs; they quit their boss Roger Herman
#1 reason is lack of recognition Bersin
79% of those who quit their job cite lack of appreciation as primary reason SHRM
#1 reason for millennials: not learning enough Business Insider
It is overwhelming!
31% don’t like their boss Aberdeen Group 31% do not feel empowered
Aberdeen Group
35% due to internal politics/turf Aberdeen Group
43% for lack of recognition Aberdeen Group
89% of managers believe that most employees are pulled away by better pay …but 88% of voluntary resignations happen for reasons other than pay
Leigh Branham, “The Seven Hidden Reasons Employees Leave”
>60% do not feel like they get enough feedback Gallup Poll
75% of people leave because of work relationship issues Saratoga Institute
75% of people who leave voluntarily don’t quit their jobs; they quit their boss Roger Herman
#1 reason is lack of recognition Bersin
79% of those who quit their job cite lack of appreciation as primary reason SHRM
#1 reason for millennials: not learning enough Business Insider
A change in approach
To understand what makes people stay, you have to experiment with a population who is supposed to leave
Retention scores are a great way to identify this population
CRITICAL – measure the results
Set it up like a drug trial
• Some people get the treatment
• Others do not
Compare the turnover for the two populations
• This will help you to understand which methods are most effective as well!
And then tie the results to $$$
• This will get your executives on board
The 9 Motivators
• 99% of people are motivated by at least 1 of these 9 things
Achievement and Growth
Money Teamwork
Power Approval Security
Autonomy and Freedom
Stability Equality
Create specific actions for each
• Assign Mentor/Coach
• Provide learning opportunities Achievement & Growth
• Give Spot Bonus / performance-based incentive
• Raise salary Money
• Add to a team Teamwork
• Put in charge of a team/project Power
• Recognize publicly (ex. through social media, in staff meeting in front of peers, in front of a key leader)
Approval
• Fix income (not performance or commission-based) Security
• Offer flexible working hours and location Autonomy & Freedom
• Minimize change with set schedules and daily routine Stability
• Compare duties, work hours, salary, benefits, etc to similar employees (if you don’t, they will!)
Equality
• Sometimes, you may not want to retain the person! No action
Agenda
• Big Data in HR
• A case study
• Workforce trends – copy your competition
Analytics are helping organizations…
…compete differently
…schedule the workforce differently
…prepare for the oncoming baby boomer worker gap
…manage compensation/benefits differently
…source talent differently
“The companies that
were more data-driven
were about 5 to 6
percent more
productive than their
competitors in the same
industry that had
comparable levels of
labor, capital and other
inputs, but they didn't
have that culture of data
driven decision making.”
- Erik Brynjolfsson,
Researcher &
Professor, MIT Sloan
School of Mgmt
Become data driven
Get your employees engaged
More and more research is showing that employees who are engaged outperform their competitors
43% of highly engaged employees receive weekly feedback vs 18% of low engaged
Towers Watson
Highly engaged orgs have the potential to reduce staff turnover by 87%, and improve performance by 20%
Corporate Leadership Council
Increasing investment in good workplace practices increases profits by $2,400 per employee
Accenture
Final thoughts
• Start small – keep it simple
• Tie the results to real $$$ where possible
• Repeat and optimize
