Dr. Jaclyn Lee, PhD and IHRP-MP //
Articles
Using HR Analytics To Improve Efficiency Of Rewards & Benefits Function
By Dr. Jaclyn Lee, PhD and IHRP-MPPublished on August 16, 2016
(A short extract from my recent lecture at Employee Benefits Asia)
How do we ensure that our reward strategy is motivating our best-performing employees? How do
we determine critical drivers of employee retention? Is it pay, benefits or the environment? How do we
tailor our reward strategies to adjust to changing demographics or generational trends or even identify
critical talent segments in the workforce? These are critical questions that Reward Professionals, as
well as CEOs of companies, constantly ask themselves when managing a large and complex workforce.
To answer all these questions and more, we need to understand the growing role of big data in
reward strategies and how the use of such analytic techniques can help uncover stable correlations
within particular populations. These will, in turn, help us to refine reward design to better achieve its
objectives. Reward Strategies have a big impact on organizations as they influence who we recruit,
how they behave and thus impact the talent strategy, which in turn impacts business results. How
do we move from reporting and benchmarking to correlations and projections? Here are four simple
methods to start your journey.
We begin by looking at cause and effects. These simple types of analysis can help you begin to uncover
correlations and help you to focus your reward strategy in the right direction. The analysis includes:
1. Engagement Driver Analysis – What is the effect of pay and other rewards on employee engagement?
In a study done by (worldatwork) with 6,300 reward professionals from across United States,
Canada, and Western Europe, it was discovered that:
a. Majority of reward professionals do not consider how total reward programs affect employee
engagement in the design of reward structures, policies, and programs
b. They also discover that base pay and benefits had the overall weakest relationship with the
organization’s ability to foster high levels of employee engagement
c. In fact, quality of leadership had the strongest relationship.
This study shows us how important the role of data analytics play in helping us to fine-tune pay
packages to the right kind of drivers. For example, from the study, we know that using pay packages
to attract leaders who have demonstrated their ability to engage employees might be more important
than just adjusting base pay to keep up with the competition.
2. Reward Analysis – Do we understand the type of employee mix and talents that will drive profitability?
Are we able to calculate the value of top performers? Google executives have calculated the
performance differential between an exceptional technologist and an average one (as much as 300
times higher). Proving the value of top performers convinces executives to provide the resources
necessary to hire, retain, and develop extraordinary talent.
3. Turnover Driver Analysis – How can we slice and dice data to review turnover for critical jobs or
groups of employees? Are we able to use data analytics to understand employee preferences for
the types of benefits or reward structures that will lead to retention?
4. Employee Benefits Analysis - In this type of analysis, data-driven metrics such as engagement
measures, attrition, absenteeism and employee surveys are monitored together with benefit
take-up rates over time so that the impact of benefits can be monitored and benefit spend justified.
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