Organizations often encourage their employees to write free text comments in employee surveys. These comments can prove a gold mine as they provide the manager with context to the measurement result and encourage and facilitate the critical team dialogue. There is also great value for the employer to communicate their expectation that employees participate, reflect, and share constructive improvement suggestions and engage in the change process.
However, issues easily arise at a strategic level when analyzing the comments. Anyone who has administered a traditional employee survey knows the “wall of text” delivered after a survey has closed. A large organization can end up with thousands - sometimes even tens of thousands - of comments that are traditionally only reported as a list of comments in a web interface or as an Excel list.
Example of chaos: The “wall of text” in a traditional employee survey.
What should HR and management do with the enormous amounts of feedback that employees submit? How can you efficiently and effectively go from large volumes of input to concrete insights?
With Artificial Intelligence (AI), it is now possible to quickly analyze large amounts of free text comments in real-time - without administration work or any prerequisite of AI knowledge.
From the aggregated free text comments, AI can answer several critical strategic considerations that may otherwise be difficult to know, including:
- How well does your onboarding process work?
- Can you find out if your employees are satisfied with their salary without asking the question?
- How can you gain insights into strengthening your employer brand and gaining more employee ambassadors to increase your eNPS (Employee Net Promoter Score)?
- How can you show your employees that you are acting on their survey feedback at an overall level?
Example of organized comments: Artificial intelligence (AI) can quickly identify overarching
patterns and themes from large amounts of free text comments.
In 2022, it is unreasonable for an HR manager, CEO, or business area manager to manually process large volumes of feedback. Therefore, many leave the traditional "wall of text" and instead take advantage of smart, AI-based solutions to automatically analyze free text comments, opening up new strategic applications of employee feedback.
Four concrete examples of typical strategic applications of AI-based comment analysis are:
- You want to know if your onboarding process works well to prevent your new hires from leaving prematurely. “The onboarding experience is vital to the success of the employee and the organization.” Free text comments from new employees are difficult to capture in a traditional employee survey, as new hires often are scattered throughout the organization. Combining AI-based comment analytics with information about the employment start date (available in all modern HR and payroll systems) provides automated insights about your new hires' feedback. At a strategic level, with an overall pattern overview, you can proactively work and prevent your new hires from leaving the organization prematurely (with all the additional costs and burden on managers that attrition brings).
- You want to know if employees are satisfied with their salary without having to ask the question directly. In the employee survey, few organizations ask, "Are you satisfied with your salary?". The reason is not that the salary is unimportant for employee motivation, but instead that there is an upward pressure for change on what an employee survey measures. Therefore, measuring employees’ views on their salary may not be feasible. However, a smart AI-based comment analytics can identify salary-related notes that the employees may write to entirely different questions. With the help of AI, you keep track of what is said and can monitor that the number of negative comments does not grow to be too many.
- You want to understand how you can strengthen the employer brand, gain more ambassadors, and increase your eNPS (Employee Net Promoter Score). Many organizations measure eNPS as part of their strategy to strengthen the employer brand. An eNPS measurement provides a clear picture of the proportion of ambassadors, passive/neutral, and critics. A high percentage of ambassadors (which links to a higher eNPS) is a sound basis for ensuring a continuous and cost-effective influx of new candidates to the organization's recruitment efforts, as they often recommend new people to join the organization. While many believe that the most effective way to increase eNPS is to focus on the critics, this is difficult. Critics are not easily charmed by the employer themself. The most effective way to create more ambassadors is to understand your neutral/passive employees. You get an excellent picture of what your passive/neutral employees write about in the employee survey using AI-based comment analytics. With their feedback, you can implement accurate measures to turn them into ambassadors and boost your eNPS.
- You want to show your employees that you are acting on their feedback. While smart AI algorithms are a prerequisite for AI-based comment analytics to work, they only take you some of the ways. You also need nice packaging and user-friendly design for the insights to be used by HR and senior leaders easily. Prerequisite knowledge of AI should not be necessary. When HR and senior leaders can present nice-looking screenshots of overall comment patterns and insights, you can show the entire organization what input you are acting on in your intranet, internal newsletter, or at an internal conference. Without AI, it is almost impossible to show the complete picture of the year’s feedback accurately. However, with a smart AI system, all employees can feel listened to and understand that their voice counts.
The possibility of AI-analyzed free text comments creates excellent value for the organization at a strategic level. In addition to the significant time savings using automated AI analytics, it is possible to gain completely new insights from all submitted comments and cross-check with other data types (for example, employment time). Sleek and elegant packaging democratizes AI support and makes it practical. AI facilitates important feedback for every employee to know that their input makes a difference.
