How to understand open responses in employee surveys

Fred Gulliford

Fred Gulliford

When you’re designing employee engagement surveys, it's always important to know that free text questions will give you the richest insights. Numbers and scores do give you a sense of how things are going, but you should at least be combining them with free text. You’ll get genuine responses, plus your teams are able to express exactly how they feel.

There are a few problems to overcome though. It’s often difficult to collate all the answers, and they’re time-consuming to go through. What’s even trickier to deal with is our own bias. It sits there in our subconscious, quietly affecting the way we perceive things.

You also need to be careful not to make your surveys too long - shorter surveys make it easier to improve employee engagement. If you want some tips on how to measure employee engagement then this top 5 list is for you. Also, if you want to avoid the pitfalls of pulse surveys then read this.

With text answers in general, lots of people don’t use this data to its fullest. This is a shame because we know it’s the most valuable part of any employee engagement survey.

 

Quantitative Research vs Qualitative Research

Let’s cover the basics. When we’re talking about scores or multiple choice answers, that’s quantitative research. It’s great for general benchmarking and for getting summaries of what’s going well, or what needs improving.

With open-ended questions and free text answers, we’re getting into qualitative research. This is where you start to receive more valuable, contextual insight. You really start to understand employee experience and employee satisfaction in great detail.

What is text analysis?

Simply put, it’s a way of automatically interpreting words into useful data. It can be done in real time and at scale, and organised in lots of different ways. It’s also known as Natural Language Processing (NLP).

How does text analysis work? It’s got the human touch

Clever NLP uses algorithms and artificial intelligence (AI). It creates an automated system that understands the semantics of language. The more examples it gets exposed to, the cleverer it gets. But behind all this are humans who understand the context and sentiment of results, and guide the AI to grow correctly.

Text analysis can:

  • Sort and tag text, making it easy to classify and search.
  • Classify as positive or negative.
  • Organise the themes that people are talking about.
  • Organise by the sentiment people are feeling.

It’s far more intelligent than just searching for words in a spreadsheet and making assumptions. This technology can essentially be trained to understand semantics instead. It does use word frequency, but it’s combined with the awareness of words and phrases that often appear next to each other.

Text analysis also takes language into account, so will work across multiple regions. This is perfect if your survey results will be coming in from offices around the world – you’ll get instant reports and you won’t have to get lots of different teams to collate information.

The best NLP technology out there can even be trained to understand your own internal phrases, jargon and miss-spelling.

 

See into the future

Okay, we don’t mean that literally, but sophisticated Natural Language Processing can actually pick up on the intent of words. For instance, you might have a sentence like “if something isn’t done about this soon, I’ll be speaking to the CEO about it.” Text analysis could flag this as urgent intent that might be worth dealing with before it escalates.

 

Diversity and inclusion

A sensitive topic, and a very difficult one for machines to understand the nuances of. But when text analysis is used in the best way, it can even pick up on the underlying sentiment of very human issues.

Here are some examples:

  • “Black and white” – is this a simple metaphor to describe transparency? Or is it about discrimination? The NLP you use needs to know the difference.
  • "Women have great opportunities here...not" – it’s obviously a sarcastic comment, but could it be construed as being positive by poor text analysis?
NLP

 

Natural Language Processing for your organisation

If you’re using NLP, you should be able to quickly understand:

  • Your work culture overall.
  • What’s important to your teams.
  • What changes you need to make to actually save money.

Your employees often understand the small inefficiencies in a workplace. Give them the chance to talk openly in surveys, and when combined with sophisticated NLP, you might quickly make a business case that’ll save the company money. All while making your employees feel happier and more valued.

It also means that your employee engagement surveys will be easy to run. It’s a win-win for you. Have you ever spent so long organising surveys and collating feedback that you’ve not actually got any time to make meaningful changes for your company?

NLP gives you quick access to the areas you need to work on so you can develop a strategy around it. You’ll be happier doing what you do best, and it keeps organisations running smoothly.

Build an employee engagement survey that your employees will want to answer -  read our guide here

Qlearsite’s Natural Language Processing

We focus on giving your teams open questions that they can reply to in their own words. Once the answers have come in, we can almost instantly produce a high quality, consultancy-grade report. You also get access to the data through a dashboard and a purpose-built analytics tool. This means you can drill down into different areas and quickly make a plan to make changes.

 

Inclusion Heatmap

 

We always say that it’s so easy to get bogged down in sending, designing and collating employee engagement surveys, but really that’s just the start of the process. The key goal is to allow human resources teams to make meaningful change, so we put that at the heart of what we do. It all starts with smart questions, natural language analysis and good employee engagement.

 

Employee Engagement Survey Guide

 

 

Topics: Employee Engagement

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