Employee Listening Technology Landscape 2018

For a number of years now, I’ve been keeping a close eye on companies working in and around the Employee Listening Technology space. Recently I shared the list with a friend and noticed how long it had become – at about 250 companies! I thought it would be worth reviewing these companies to see if it was possible to identify some high-level categories and reveal some insights into how this landscape is changing. I dare say, if you tried to do this only 10 years ago, the landscape would look very different – not only in terms of the number of categories, but in that many of the companies probably didn’t exist.

What is Employee Listening Technology?
The space I’m trying to map is the one that involves employees being actively involved in giving feedback not only about their experience of work and the organisation they work for, but also about the development of new products/services or workstreams. There are some things I haven’t included – it doesn’t include tools relating to the broader collection or analysis of quantitative HR/MI data, HR software or Business Intelligence. It doesn’t include tools for quantitative/statistical analysis or data visualisations. Nor does it include internal social networks -Yammer, Workplace and the like – sure you can have a discussion with employees on platforms like these– but it’s named and not anonymous, so not really useful for candid employee listening. Plus the internet is jam packed with discussion tools/comment systems/social networks if you’re looking for a basic discussion tool.

What criteria was used use to select these companies?
I’m regularly in touch with a lot of different people working in this space: Research-focused technology companies, research consultancies, freelancers and practitioners working in organisations and I’m always interested to hear who they’re working with and what’s new. I have various Google alerts in place, I monitor various LinkedIn discussions and groups, as well as following related conferences. Plus the various internal research we’ve conducted ourselves, including reviewing many of the SaaS Review Platforms – Capterra, Crozdesk, G2 Crowd, Product Hunt, Trust Pilot.

Consequently, this is what the landscape looks like from my perspective, sitting in London, talking to people mainly in Europe and the US. There probably are other companies who deserve to be on here somewhere, but if that’s the case, I’ve never heard of them (and I’ve been listening very carefully!) or there simply wasn’t room. There are thousands of companies out there working in this area and obviously it’s not possible to include the all. It’s probably also worth pointing out that I’m not saying anything about how good any of these companies or their products are, simply that these are the companies I see working in this space and the types of things they’re focused on. Some of them are brilliant, some of them are not – that’s for you to decide.

I should also say that I’ve only included ‘consultancies’ that build their own technology – it is a technology landscape after all. The distinction between what constitutes a ‘tech company’ and what constitutes a ‘consultancy’ is increasingly blurred nowadays and many provide a bit of both to varying degrees.

What are the categories and why categorise them like that?
I’ll be the first to admit that categorising all these companies into defined groups is problematic. There is so much overlap and complexity that it is impossible to categorise each company into one neat category. In fact, there is so much complexity that you couldn’t even draw it like a Venn diagram – there’s simply too many competing overlaps. I did try to display the companies in more of a clustered landscape, but it’s just so messy and fails to give a sense of the different categories, hence the simple categorisation you see above. I did think about having a category for AI/Machine Learning, but the reality is that AI is spreading to all areas of the landscape – especially in text analysis. In the end I went for six categories, and I’ll say a little about each in turn:

Social Collective Intelligence
I could’ve equally called this category Swarm Intelligence or Crowd-sourced Intelligence, but prefer the term Social Collective Intelligence. This is a form of insight that emerges where social processes between humans are being leveraged and enhanced by means of advanced digital and social technologies. What distinguishes this category is the fact that they are systems that are able to orchestrate the collection and analysis of human interaction. They are closed loop systems – they have one or more feedback loops between their input and their output. In this way, participants help to shape the output through their interactions within the input provided by other participants. This means that the outputs can often be much more insightful than tools where there is no such interaction between participants. Some of the companies in this category could equally be in the survey category – but I thought it was worth them having their own category so as to distinguish them from more conventional survey tools.

Organisational Network Analysis
I love Organisational Network Analysis (or Social Network Analysis if you’re conducting one with a population other than employees!). Awareness about ONA has increased rapidly over the last few years. When I first got into social network analysis in around 2010, it was relatively unheard of and many of the tools were clunky and unwieldy. There’s much more to get your head around with the various statistics used in Social Network Analysis compared to, say, a simple survey. This makes it harder for people to sell internally and prevents companies from using it.
The idea of social networks and social network analysis is nothing new. It was in the 1930s that sociologists first started to draw diagrams (called sociograms) of nodes connected to each other by lines. Yet, it is only due to recent advances in data analysis capabilities that network analysis is capturing more attention. Network analysis is all about relational data – the ties that connect employees to each other – they cannot be reduced to the properties of employees themselves. In its simplest form this can just be whether people know each other, but any type of relationship can be assessed depending on which questions are asked (e.g. How much do you communicate with this person? How much influence does this person have? How often do you turn to this person for advice on important decisions?) This is all employee listening. I should point out that most, although not all, of the tools I’ve included in this category are active rather than passive (they tend to ask people questions about relationships rather than use existing relational data).

Innovation and Ideation
It makes sense to include a category about innovation and ideation, not only because there has been an explosion in tools in this area, but also because it’s a really important element of employee listening. Listening to employee’s suggestions and ideas for the development of new products and services, as well as ideas about how to improve people’s experience of work, makes good business sense and can help make people feel more involved in what the company’s doing. Of course, all of these tools differ in their approach and functionality, but what separates them is their ability to take an idea and facilitate the validation and development of that idea through various project management and planning capability.

Text Analytics
It might seem odd to include text analysis as a category and not include a category about quantitative analysis tools. The reason for this is that sometimes the quantitative/predictive analytics conducted in employee listening is done within the tools used to collect the data themselves and, in addition, there are simply too broad a range of tools to include. Having explored this category in some depth over the years, and used a few of the tools, I’d say there’s a huge variety in how the tools work, the analysis they can do and the outputs they produce.
There’s also a big overlap between survey tools and text analysis. Some of the survey tools have varying degrees of text analysis capability. Vice versa, some of the text analysis companies have built a survey/data collection functionality and could be therefore considered survey tools. My best advice would be to get a large example data set (with qual and quant data) and get a demo of the text analysis tool using the same data set each time – then at least you can compare outputs to see which one delivers what you’re after. I’ve also included in here, as they can also be considered text-based solutions, chatbots who can talk to employees and find those who are unhappy or thinking of leaving. It will be fascinating to see where all this ends up.

Live Polling and Events
It’s also becoming more common at both physical and virtual events to engage the audience in real-time polling and commentary. Although having said that, many of the Social Collective Intelligence tools have also been designed to be used in a real time environment.

This is the biggest of the categories and probably the most diverse. I ended up attempting to plot these on a scale of whether they were primarily a consultancy building their own technology versus a technology company offering consultancy (although as I mentioned previously, the line is often quite blurred). I tried to see if there was a distinction between self-service vs managed, but many companies now offer both. Many of these companies also offer their own text or predictive analytics capability with varying degrees of sophistication. I see the main trends in this category being the increased sophistication of analysis, reporting and action planning capabilities – with a big improvement in look and feel. Some of these tools look great, providing a slick user interface for set up and reporting.

So that’s the Employee Listening Technology Landscape as I see it at the moment. If you know of any companies I should’ve included, or if you’d like to suggest a different categorisation – please get in touch!

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