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

Steve VanWieren

Steve_VanWieren@UltimateSoftware.com