Using AI and Machine Learning to better understand employee feedback


Many of you will already be on first name terms with Siri and Alexa. Or perhaps you’ve marvelled at how Spotify can put a playlist together for you of tunes you were listening as a teenager – mine was scarily accurate! And of course Amazon make huge amounts of money by predicting what we want to buy based on our online behaviour.

The machines are here to stay, and they are already in many areas of our lives, but what about our work-life? And how will machine learning and AI impact employee engagement?

This is a theme we’re going to be exploring over the coming months and we’re starting here by looking at the application of machine learning to analysing employee data. Luminoso Technologies, a leading AI company, use AI for data analysis. They have recently published a guide which looks at how to adopt this technology to better understand employee feedback data. Specifically they talk about the use of AI and natural language processing (NLP) to gather insights from unstructured employee feedback.

Analysis of free response data has traditionally been pretty cumbersome, involving either manual coding or resource-heavy text analytics. In addition to ensure you are avoiding bias, ideally the job involved data scientists who have been trained to analyse qualitative data of this type.

Luminoso say:

“Advancements in artificial intelligence (AI), natural language processing (NLP), and machine learning (ML) over the last ten years have dramatically changed the way employee insights can be analyzed and implemented. They have made it possible for companies to collect, analyze, and respond to employee feedback on a monthly or biweekly basis, instead of quarterly or annually.”

These new solutions can now interpret human language as it is written or spoken, enabling concepts and ideas to be understood rather than just words alone. This approach ensures every comment is taken into account, and of course the process is real time which means we have an in-the-moment view of the feedback. And of course because it’s a machine identifying the key themes and connections in the data, not a person, this eliminates the risk of bias.

‘Applying AI and NLP to text-based data enables companies to look at text as concepts and ideas instead of words…and to focus on the all-important relationships between those ideas instead of wordcounts,’ states the report. ‘Concept-based text analytics systems, for example, allow you to upload data and immediately begin to derive insights rather than having to tell a system what to look for. This has significant benefits for HR.’

Upping the Analysis

We are definitely seeing a trend in companies moving away from the annual survey approach to a more real-time feedback approach (Spotlight on Employee Engagement 2017). Technologies such as these can help with this transition and provide richer data, which adds meaning to the traditional quantitative approach to collecting insights.

Do you think AI and machine learning will have a positive impact on employee engagement? Let’s continue the conversation on Twitter, Facebook, or in the comments section below.

Liked this article? Why not try…

Using ESNs in 3 steps

Spotlight on the Employee Engagement Profession (research report)

Understanding the employee engagement gap – and what to do about it (webinar recording)


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