Employee engagement has become a major focus for organizations. Research has continued to show that engaged employees are more productive, innovative, and committed to their work. To measure and manage employee engagement, most companies rely on qualitative listening systems (for engagement survey data). However, more HR & People Operation teams are utilizing quantitative measurements through people analytics. While both methods provide valuable data and insights, they differ in both their focus and their strengths.
(More Engagement Research from Gallup at The Benefits of Employee Engagement)
Employee listening systems, such as surveys and feedback programs, are designed to collect qualitative data from employees. This data includes employees' opinions, feelings, and experiences. Qualitative data is valuable as it helps uncover the "why" behind certain work behaviors and processes. It sheds light on employees' motivations, concerns, and preferences, which can inform decision-making related to employee engagement initiatives.
For example, a company might use an employee listening system to collect feedback on employees' satisfaction with their job, their relationship with their manager, or their perception of the company's culture. This data can help identify pain points, areas of improvement, and potential risks to employee engagement. It can also highlight employees' needs and expectations. These feedback channels allow employees to share their perspectives, creating transparency and trust.
However, engagement survey's done wrong can have a negative impact on your organization. Read more at: Top 5 Problems with Employee Engagement Surveys
On the other hand, people analytics and measurements focus on quantitative data related to work behavior and processes. People analytics use data and insights to analyze how employees are performing their jobs, how work is being completed, and how processes can be optimized. This data is typically collected through HR information systems, performance metrics, and other objective sources. People analytics platforms provide a data-driven approach to understanding employee behavior, performance, and engagement.
For example, a company might use people analytics to analyze data on employee productivity, turnover rates, or performance scores. This data can help identify patterns, trends, and correlations that can inform decisions related to talent management, performance management, and learning and development programs.
The key strength of people analytics is its ability to provide objective, quantifiable data that can be easily measured and analyzed. It allows for benchmarking, trend analysis, and predictive modeling, providing insights into patterns and trends over time. This data-driven approach can help companies identify areas of improvement, set goals, and measure progress towards achieving them. People analytics also provides insights into the effectiveness of various HR initiatives, helping companies optimize their strategies and resources.
In contrast, the strength of employee listening systems is their ability to capture employees' subjective experiences and perceptions. It allows for open-ended feedback, opinions, and suggestions, giving voice to employees' opinions and concerns. This qualitative data provides a holistic understanding of the employee experience, uncovering insights that may not be apparent through quantitative data alone.
In conclusion, both engagement survey data and people analytics play important roles in measuring and managing employee engagement. Employee listening systems provide valuable qualitative data that helps understand the "why" behind employees' behaviors and perceptions, while people analytics provide quantitative data and insights into work behavior and processes. By using both types of data, companies can gain a more comprehensive understanding of employee engagement and take targeted actions to improve it.
In this guide, you will find:
- OKR principles
- Formulas & scores
- OKR methodology
- Step-by-step guide
- Free OKR templates
- Common mistakes
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