Putting human capital analytics to work: Predicting and driving business success

• Date: 01 May 2018
• DOI: http://doi.org/10.1002/hrm.21843
• Published date: 01 May 2018

SPECIAL ISSUE ARTICLE

Putting human capital analytics to work: Predicting and driving

business success

William A. Schiemann

1

| Jerry H. Seibert

1

| Mark H. Blankenship

2

1

Metrus Group Inc., Somerville, New Jersey

2

Jack in the Box Inc., San Diego, California

Correspondence

William A. Schiemann, Principal and CEO,

Metrus Group Inc., 953 Route 202, Somerville,

NJ 08876.

Email: wschiemann@metrus.com.

Workforce analytics have evolved considerably in the past two decades and yet application in

many organizations is scant. Some organizations, however, are reaping the benefits of using

more-sophisticated, but practical, workforce tools and measures to understand, predict, and

control important business outcomes. In this article, we will share some of the work that has

been done using several human capital and business frameworks, including the service-profit

chain and People Equity, to show how such frameworks can be used to understand and predict

revenue, profit, customer satisfaction, and employee turnover. We also provide recommenda-

tions for how organizations can secure senior leader support that leads to actions and

improvements.

KEYWORDS

HR measurement issues, human capital, strategic HR, strategic decision making

1|INTRODUCTION

Talent and workforce analytics enable organizations to make better

human capital and business resource decisions, but thus far their appli-

cations in organizations is meager, and often when used, these ana-

lytics are tactical or of limited value. A number of organizations, how-

ever, are achieving considerable benefits from using strategic mea-

sures, analytics, and accompanying tools to understand, predict, and

manage performance and business results. In this article, we will share

some of the work that has been done using a variety of business and

human capital models, including the service–profit chain, people

equity, and other service models, to show how such frameworks can

be used to understand and enhance talent investments and to predict

employee turnover, customer satisfaction, revenue, and profit. We

also address the issue of supply chain excellence and obtaining senior

leader support that generates actions, decisions, and results.

For years, organizations have struggled with obtaining and utiliz-

ing information to manage their business, stay ahead of competitors,

and gain insights on effective talent resource allocation. What are the

top strategic priorities that require investment? What return will they

provide? Over the past 20 or so years, an increasing number of firms

have become savvy at using human capital analytics to both under-

stand the underlying dynamics of their business and to provide infor-

mation that drives more effective decisions and provides a

competitive advantage through people.

Analytics are not new. Over a century ago, Henry Ford knew if

he could keep the assembly lines running using good operational

measures, they would have a certain amount of throughput that

yielded calculable levels of revenue and profitability. Keep the factory

running, and accurately predict your returns.

But analytics were not all operational. Early psychology research-

ers such as Hugo Münsterberg (1913) and Elton Mayo (1945) began

experimenting in factories with human capital metrics. Over the

course of five years at the Western Electric Hawthorne plant, Mayo's

team altered working conditions and monitored the impact on work-

ers' morale and productivity. He looked at changes in working hours,

rest breaks, lighting, humidity, and temperature. For example, his

research team learned that better lighting led to high workforce pro-

ductivity in the short term.

Frederick Winslow Taylor (1911), considered the father of the

scientific management movement, and perhaps the earliest industrial

pioneer of managing variance, believed that workers could be mana-

ged by controlling different variables that led to higher productivity.

For example, by observing workers, he decided that labor should

have rest breaks so that the worker has time to recover from fatigue,

either physical (as in shoveling or lifting) or mental. Because of this,

workers were allowed to take more rests during work, and productiv-

ity increased as a result.

Later studies looked at the connection of job satisfaction and

performance (Harter, Schmidt, & Hayes, 2002; Lawler & Porter,

DOI: 10.1002/hrm.21843

Hum Resour Manage. 2018;57:795–807. wileyonlinelibrary.com/journal/hrm © 2017 Wiley Periodicals, Inc. 795