In my last post, I discussed the process by which employee engagement increases and decreases and therefore what are the most effective interventions leaders can use to make a long-term difference. In this post, I explore the related question - what do we need to change?
As People Analysts, OrganizationView have conducted a lot of surveys over the last six years. One thing we’ve come to appreciate is how important employees’ open text comments are to driving action from management. The issue has usually been how to deal with tens of thousands of comments, provided in multiple languages. This was one of the driving forces behind our decision to develop Workometry, our employee feedback platform.
Before the advent of reliable text analytics, analysing these comments at scale was time and cost prohibitive. The way that employee surveys have developed has largely been influenced by this constraint. However, just as statistics has been changed by the availability of large, easily available computing power and cheaper acquisition costs for data, we predict that our understanding of employees will be changed by the availability of text analytics.
With text, there are two main tasks that we want to do: we want to categorise the text into one or more topics and we might want to score the text on some sort of scale, for example, by level of sentiment.
When categorizing, we want to go further than just words. We look at two dimensions - the topic (e.g., ‘career development’) and the context (e.g., ‘shortage’). This has to be more than just keyword as we’ll want to link together the multiple synonyms - it’s the meaning not the words that they’ve chosen which is important.
Doing this adds metadata to our text. We can then apply various statistical techniques to the metadata. Typically, we identify in the region of 40–60 different topics for any text question. We can think of this as equivalent to adding another 40–60 scale questions to a survey. Therefore, we can ask the short surveys that are needed to maintain response rates when you’re doing them frequently whilst capturing very rich data. We use an unsupervised learning approach meaning that the topics are suggested by the data, not precoded based on generic results.One of the reasons that we do analysis is to draw attention to the parts of the information that managers need to focus on. We’re currently doing that by combining two techniques.Read the complete blog post and much more from Andrew Marritt here.