Using HR Analytics To Improve Efficiency Of Rewards & Benefits Function

Using HR Analytics To Improve Efficiency Of Rewards & Benefits Function

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:

  • Majority of reward professionals do not consider how total reward programs affect employee engagement in the design of reward structures, policies, and programs
  • 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
  • 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. 

Before you begin your analytics journey, start with the most critical business issue facing your managers and the main people related challenges that hinder your company’s ability to execute on corporate goals and strategies. By using analytic techniques, you will find patterns and correlations that will help you better design reward and benefits packages that will attract, retain and reward your top talents. 

This article first appeared on http://jaclynlee.sg/article-empy.html

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