Get More Out of Your Employee Surveys w/ Comment & Sentiment Analysis
With almost 60% of the US workforce rethinking their career and 34% of US employees considering leaving their job, doing anything we can to improve employee relations is worth the effort. According to The Conference Board, disengaged employees in the United States cost their employers up to $500 billion each year. That’s a high cost to a fixable problem.
To better understand why your organization’s employees are disengaged, you have to understand how they are really feeling. Even if your organization has implemented an employee listening program that consists of census and pulse surveys, are you sure you understand employees’ true sentiments? Being able to read between the lines and analyze employees’ open-ended comments could make a big difference in how your organization responds to survey results, therefore improving the overall employee experience.
What is Comment and Sentiment Analysis?
By utilizing comment and sentiment analysis to better understand open-ended survey responses, your organization can learn even more about what your employees are saying, how they are feeling, as well as what they see as organizational strengths and weaknesses.
Comment and sentiment analysis, or opinion mining, is a natural language processing (NLP) and machine learning technique that survey providers use on free-response data to help determine whether employees are expressing positive, negative, or neutral opinions in their answers. It also helps identify common themes emerging amongst employees. Through use of sentiment analysis, companies gain richer insights into survey responses, and can more easily transform that data into actions that resonate with employees.
While the software and artificial intelligence (AI) is incredibly adept at gathering large volumes of information, quantifying it, and measuring employees’ perceptions of the organization and their roles, it does have some limitations. The system may struggle to correctly interpret internal jokes, company jargon, sarcasm, or complex human emotions or opinions. However, the more you use comment and sentiment functionality and modify the keywords for your organization, the better the functionality works because the artificial intelligence grows as your organization feeds it information. Therefore, it will learn to better understand your employees’ language and nuances, continuously improving your ability to easily extract useful qualitative data.
The Benefits of Comment and Sentiment Analysis for Your Organization
Access to more data and being able to better understand your employees is always a good thing – even if their comments are perceived as a negative. By using comment and sentiment analysis, your company is more aware of what is going on inside the organization, how employees feel about work-related issues, and can determine the most commonly talked about themes within your company. Extrapolating all of this extra qualitative data is beneficial for your organization in a number of ways.
It encourages transparency and open communication. When employees are able to share opinions freely and confidently, it gives them a sense of empowerment. Employees feel that they are providing useful information to make the company better, and on the flip side, the organization is getting an accurate assessment of employee opinions. It creates a mutually beneficial transparent environment.
It helps organizations make decisions that address employees’ concerns. By being able to gather employee sentiment, you can develop action plans that employees will believe in and stand behind. They will understand your organization is listening to them and truly understanding them. Plus, the decision-making can be tailored based on the sentiment of a particular region or department. It doesn’t have to be a global company initiative if those opinions aren’t shared collectively across the organization.
It leads to higher employee engagement. As mentioned above, when employees feel they are able to share honest opinions without retribution and believe their organization will act on that information, it improves trust and engagement because they feel as though their opinions and feelings really do matter.
How Perceptyx Can Help Your Organization Better Understand Employees
At Perceptyx, we know the importance of understanding your employees, their opinions, and feelings, and how those insights can help drive a data-driven culture for your organization. That is why we have been working on improvements to our comment and sentiment analysis functionality. It is now better aligned with our customers’ expectations and matches or exceeds the accuracy of human raters of comments.
The upgraded model from Perceptyx combines cutting-edge NLP and AI with survey-focused data, substantially improving the capability for understanding employee feelings expressed in comments. Our approach to comment and sentiment analysis is revolutionary because we are able to focus our analysis on full statements, rather than singular words. This gives Perceptyx the ability to detect subtle comment nuances missed by other approaches. Additionally, we consider both the question asked and the responses received, which helps put the opinions or feelings into context because some questions lead to more positive responses while others lead to negative. For instance, a question that asks, “What are we doing really well?” will have positive responses while a question that asks, “What areas need improvement?” will result in comments perceived as negative. As a result of the improvements made, our accuracy in rating sentiment improved by 25% and helps users gain richer insights into respondent feelings that they can use to pinpoint where actions are needed.
From this functionality, Perceptyx is able to deliver improved insights by providing customers more answers for "What needs to be done?" because the employees are giving that information in their responses. Users quickly learn how people feel (positive, neutral, or negative) on a topic, which clears the way for identifying actionable insights into what a good experience looks like and what needs to be improved to make poor experiences into future good ones.