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GETTING STARTED WITH PREDICTIVE

WORKFORCE ANALYTICS

Overview of APQC Research Study Findings

The HR world is abuzz with stories about the promises and perils of predictive workforce analytics. For

a number of years, organizations have been conducting workforce analytics—using descriptive statistics

to summarize workforce events. To these capabilities, many are looking to add a predictive component.

Predictive workforce analytics involves using advanced statistical techniques to identify historical

workforce patterns in order to predict future behaviors and events. While interest in predictive

workforce analytics is strong, most organizations have not yet built predictive workforce analytics

capabilities. Luckily, there are some truly innovative organizations doing pioneering predictive work that

aspiring organizations can learn from. To help illuminate the work of these rare innovators, APQC

partnered with Talent Analytics, Corp., a globally recognized leader in predicting an individual's

performance, pre-hire. As part of a joint-research project, APQC and Talent Analytics, Corp. collected lessons learned from early adopters of predictive workforce analytics.

In summer 2015, the research team conducted structured interviews with workforce analytics leaders from Cargill, Gap, IBM, Johnson Controls, and SAS. The interviews collected information on:

why the organization conducts predictive workforce analytics,

how it staffs and structures its workforce analytics capability,

which data it uses for analyses,

what the first predictive analytics project entailed, and

how the results of early predictive analytics work were used.

This research study focused on the practices of organizations that are early adopters of predictive

workforce analytics. These organizations stressed, however, that predictive analytics is just one tool in

their workforce analytics toolboxes. The practices that these organizations use are therefore key for conducting any workforce analytics project including those that are predictive in nature.

Key Practices for Getting Started

Purpose—Articulate a vision for why your organization is adopting workforce analytics as a business tool.

The early-adopter organizations did not speak about making a large business case before getting started

with workforce analytics. However, each did talk about having clearly articulated a vision for why their

organization was adopting workforce analytics as a business tool. Essentially, each had answered the

broad, long-range question: Why conduct workforce analytics at our organization? And then they crafted specific, short-term workforce analytics plans.

Resources—Secure the specific resources necessary to carry out your organization’s short-term

workforce analytics plan.

The early-adopter organizations did not mention making large financial outlays to get started with

workforce analytics. For the most part, interviewees did not talk about supporting workforce analytics

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with significant investments in technology or new staff. However, they did underscore the importance of

securing the specific resources needed to carry out their short-term workforce analytics plans. Each

articulated and then located critical workforce analytics skills. Next, they assembled these skills into formal, dedicated workforce analytics groups.

Problems—Select workforce analytics projects in response to business challenges that your organization faces.

The early-adopter organizations do not conduct workforce analytics because they see other

organizations doing this work. Instead, their workforce analytics projects arise out of true business

problems. Moreover, their analytics projects are only predictive when predictive is the most appropriate method for answering the specific business problem at hand.

Data—Don’t wait for perfect data before getting stated with workforce analytics. Assemble and validate data according to the requirements of your short-term workforce analytics plan.

The early-adopter organizations share a common long-term goal to establish clean, organizationally

consistent, and centrally stored workforce data. Some of the early-adopter organizations started work

on this goal years back and have made significant progress. Others are still in the beginning stages of data

integration. One commonality among them all is the decision not to wait for perfect data before getting

started with workforce analytics. Instead, the early-adopter organizations assemble and validate workforce data on a per-project basis.

Education—Educate end users about the basics of workforce analytics.

The early-adopter organizations devote significant time to educating end users about workforce

analytics. During projects, they present incremental results and solicit user feedback. Post project, they

extensively socialize findings by sharing consumable amounts of information, often in the form of a story.

At all times, they provide varying levels of workforce analytics education to HR and other areas of their organizations.

Measures—Measure and share the outcomes of your organization’s workforce analytics efforts.

All of the early-adopter organizations measure the results of their workforce analytics projects. The

stories and visuals they create with data promote action, which they closely track as a key measure of

analytics success. Any positive outcomes that arise, they deliberately publicize in order to build the business case for continued investment in workforce analytics.

Early-Adopter Organizations

Cargill

Cargill provides food, agriculture, financial, and industrial products and services across the globe. Cargill

has 152,000 employees in 67 countries. For this project, APQC interviewed Michael Crespo, assessment

and selection lead, and Jeff Idle, HR business intelligence and analytics lead, at Cargill.

Cargill uses predictive workforce analytics for selecting and assessing individuals in hiring and promotion.

One of its first predictive projects aimed to improve the organization’s ability to select job candidates

who have the most potential to develop into high-performing employees. The team created a

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competency-based hiring assessment that rates how well job candidates fit with characteristics predictive of high performance at Cargill.

Gap

Gap is a leading global retailer offering clothing, accessories, and personal care products for men,

women, and children under The Gap, Banana Republic, Old Navy, Athleta, and Intermix brands. Gap has

more than 140,000 employees and stores in more than 90 countries. For this project, APQC

interviewed Anthony Walter, director of workforce analytics, and Andrew LeFevre, senior director HR

strategy and workforce analytics, at Gap.

Gap has a workforce analytics center of expertise. One of the Gap’s first predictive workforce analytics

projects aimed to identify when critical employees are at risk of leaving the organization. The workforce

analytics center of expertise uncovered drivers of turnover at the organization and used these to

project turnover for brand leaders.

IBM

IBM is a globally integrated technology and consulting company with more than 400,000 employees and

operations in more than 170 countries. For this research project, APQC interviewed N. Sadat Shami, manager of IBM’s Center for Engagement and Social Analytics.

Within IBM’s HR function is a predictive social analytics team. One of the team’s first projects was to

use social media to get a real-time understanding of employee engagement. The team created a tool

called Social Pulse, which uses IBM employees’ social media sentiment to predict if engagement is increasing or decreasing as a result of IBM’s HR initiatives.

Johnson Controls

Johnson Controls is a diversified technology and industrial company with 180,000 employees and

customers in more than 150 countries. For this project, APQC interviewed Wendy Hirsch, executive director of workforce analytics, at Johnson Controls.

Johnson Controls has a workforce analytics center of expertise within its HR function. One of the

team’s first predictive projects was to understand why voluntary turnover was slowly rising. The

workforce analytics center of expertise discovered that, at Johnson Controls, missing performance

management milestones such as yearly goal planning and performance assessment is predictive of voluntary turnover.

SAS

SAS is a business analytics and software provider with more than 13,000 employees and customers in

141 countries. For this project, APQC interviewed Jennifer Nenadic, manager of enterprise analytics services at SAS.

At SAS, HR uses workforce analytics to address human capital management issues and opportunities.

Workforce analytics is a partnership between the HR and IT functions. One of the first predictive

workforce analytics projects that SAS conducted sought opportunities to improve the organization’s

already low employee turnover rate. Out of the project arose a model that predicts whether HR process changes are likely to decrease turnover risk at SAS.

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ABOUT APQC

APQC is a member-based nonprofit and one of the leading proponents of benchmarking and best

practices business research. Working with more than 500 organizations worldwide in all industries,

APQC focuses on providing organizations with the information they need to work smarter, faster, and

with confidence. Every day we uncover the processes and practices that push organizations from good to great. Visit us at www.apqc.org, and learn how you can make best practices your practices.

ABOUT TALENT ANALYTICS, CORP.

Ta l en t Ana l y t i c s , Corp . uses data science to optimize employee performance and attrition for

high volume, individual performer roles including Call Center Reps, Insurance Agents, Sales Reps, Sales

Engineers, Bank Tellers and the like. Much of our work predicts top and bottom performers pre-

hire. Our predictive scoring algorithms are beautifully and easily deployed into the employee sourcing,

recruiting, hiring and operations processes via our award winning cloud platform, AdvisorTM.