The Power Of ANALYTICS: HRs SECRET WEAPON Steve VanWieren Principal - - PowerPoint PPT Presentation

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

• rder 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

• r 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

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

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 % Terminated

Score 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

• n 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