Employee Survey Analysis: Using AI To Extract More Meaningful Insights


Employee surveys are much more than a box-ticking HR requirement, they’re a means of understanding what motivates your workforce. And as we’ve recently explored, a motivated workforce means greater productivity, less absenteeism, increased sales and profitability.
So it is in your business’s interest to take employee survey analysis seriously and squeeze every last bit of valuable information from surveys to maximise their usefulness for your company.
Read on to find out how AI technology can help you extract meaningful insights from employee surveys.

Using AI for Employee Survey Analysis

Analyse open-ended answers

Since those who create employee surveys are generally also those who later have to analyse them, they have generally always favoured closed-ended or multiple-choice questions.
Analysing quantitative data has historically been much easier, but it is also much more restrictive. How can an answer to a multiple-choice question ever fully express how your employees feel?
Natural Language Processing (NLP) tools can turn unstructured qualitative data from text into measurable statistics on sentiment and topic while still allowing for nuanced answers.
As we will explore, use AI on qualitative text data to read between the lines of employee responses and get a more in-depth understanding of how they feel and what motivates them.

Pinpoint themes and patterns

Even if one person has a particular issue with, say, a new initiative in your company, it is, of course, important to take their concerns on board and discuss it with them. However, if many people express the same issue, it will need to take priority and be addressed as soon as possible.
AI technology can group answers by topic and subtopic so that you can get a high-level look at what’s affecting your workforce.


A popular method of rating sentiment in employee surveys is by asking respondents to rate on a scale of 1-10. This is a very unreliable way of gauging how people really feel and are subjective to response bias.
NLP technology can assess text to find out how someone feels (and how strongly they feel about it) based on linguistic features such as syntax and word choice.


Read between the lines of respondents’ answers to discover the emotion behind the sentiment. If someone is expressing anger it may mean that they feel frustrated, or sceptical. If they feel sad it may signify that they feel unmotivated or inferior and powerless. If they feel fear, it may signify insecurity or rejection. These nuances in emotion can help you decide what management strategy would work best to help reengage them.


Did you know that how someone speaks or writes can reveal key traits of their personality? Are they rational – driven by logic and facts, or emotional – driven by social values and relationships? AI technology can analyse written text to find the answer.
This information is useful for leadership and management to decide on the best strategy to motivate each individual on their team. It also might help you identify and establish why your latest strategy worked well on some members of your workforce, while others remained disengaged.

Beyond Employee Surveys

This same AI technology can be applied to other forms of written or transcribed employee communication.
Employee survey analysis can only go so far in reflecting the true sentiments of your workforce. Surveys do not always accurately reflect how an employee is feeling. Respondents may give answers based on what they believe is expected of them, they may exaggerate their responses or understate how they are feeling when asked directly via an employee survey.
But there are other ways to find out how an employee feels. Internal emails may help you detect whether their sentiment has changed, or the emotion they feel towards a certain project or initiative. Employee review site GlassDoor might also be a useful source to gauge employee satisfaction.
Performance reviews are another great avenue to allow your employees to express themselves. Some employees feel more comfortable expressing themselves through speech face-to-face than via a survey or questionnaire. Transcribe spoken word into text to get access to the same AI analysis.

Use AI to Give More Relevant Feedback

NLP can also be helpful when it comes to giving constructive, quality feedback to employees.

  • Leaders and managers can use AI to detect potentially toxic language. Find out what emotion you are conveying in your feedback to make sure you’re not inadvertently about to demotivate your team.
  • Is your communication style compatible with theirs? For example, you’ll want to avoid using emotional language to communicate with someone with a rational personality trait.
  • Make your communication clearer by using unequivocal wording. While it’s OK for you to read between the lines of your employees’ communication, they won’t have the same access to AI technology reciprocate. If you want to come across as positive and hopeful, make sure that is conveyed not only in what you say but how you say it. AI technology will be able to tell you if you’re using the correct tone and wording to convey your sentiment and emotion.

Get Started With Symanto

Symanto uses state of the art NLP technology to explore the true sentiment and emotion behind written communication, closing the gap between what is said and what is meant. Symanto APIs and the Symanto Insights Platform can be applied to give insights to inform almost any area of business from product innovation to marketing to HR.
We’re on hand to provide personalised solutions. To discuss how you can use Symanto technology for your company’s specific needs, get in touch today.