Turning Employee Feedback into Action: Putting Your Engagement Survey Data to Work
James de Souza
Minutes
28/02/2025
Employee Engagement
Employee Experience
Measurement
Research
Workplace Experience
Employee Experience
Measurement
Research
Workplace Experience
Earlier
this month, we hosted an insightful webinar with James de Souza and Phil
Carpenter from Treehouse Analytics on
cutting through the white-noise of engagement survey data. If you missed it,
you can re-watch the session here.
During the webinar, we found that 48% of the community polled struggle to
translate survey results into actionable steps that improve engagement. This
blog post from James and Phil aims to provide an overview of some tips for those
tasked with analysing survey data that enable you to take practical action
quickly.
Engagement
surveys can be powerful tools, is yours?
Employee engagement surveys are a powerful tool not only for understanding how your workforce feels about their work environment, but also for realising the benefits for your organisation from factors like increased productivity and profitability. However, the real challenge lies in interpreting this data to drive meaningful action to materialise those benefits. In this post, we'll explore the concepts of looking for connections in the results, and building up layers of understanding that can help you turn employee survey data into actionable insights. We will look at ways that you can quickly know what to do, where to prioritise your time and target investment.
Employee engagement surveys are a powerful tool not only for understanding how your workforce feels about their work environment, but also for realising the benefits for your organisation from factors like increased productivity and profitability. However, the real challenge lies in interpreting this data to drive meaningful action to materialise those benefits. In this post, we'll explore the concepts of looking for connections in the results, and building up layers of understanding that can help you turn employee survey data into actionable insights. We will look at ways that you can quickly know what to do, where to prioritise your time and target investment.
Use the connections in the data to see the bigger (hidden)
picture
A common approach to analysing survey data is to divide it by known cohorts, such as departments or roles, to identify which groups are driving up or pulling down the scores. However, a more effective method is to connect responses to different questions, identifying unseen attitude-based cohorts. This ensures actions are relevant to the needs of real distinct groups, rather than based on a best-guess at the demographic factor to split them by.
This allows HR professionals to spend less time analysing tables and move more quickly to actioning learnings.
For example: Consider an engagement survey that might aim to understand the sentiment about various EVP initiatives. Analysing the results of the questions might well show positive scores towards all the EVP initiatives but varying engagement levels between teams. As such sentiment toward the EVP initiatives on their own has no particular connection with employee engagement levels. Two things happen as a result: the EVP initiatives continue as-is and we understand nothing further about the effect of the EVP initiatives on engagement. However, by looking at the scoring patterns between the EVP questions and all other questions asked, we may see two things differently to before: being engaged is driven by how fulfilled someone feels in their role, and feeling fulfilled is related to how effective they feel the learning and development opportunities are. Now we can guide the EVP work focused on how developed people feel, through the lens of how fulfilling they feel their role is as a result. Looking at these correlations in the data means we can look at results beyond simply the rank order of scores.
This approach gives you the insight to focus on the few core experience topics that will make the most difference.
A common approach to analysing survey data is to divide it by known cohorts, such as departments or roles, to identify which groups are driving up or pulling down the scores. However, a more effective method is to connect responses to different questions, identifying unseen attitude-based cohorts. This ensures actions are relevant to the needs of real distinct groups, rather than based on a best-guess at the demographic factor to split them by.
This allows HR professionals to spend less time analysing tables and move more quickly to actioning learnings.
For example: Consider an engagement survey that might aim to understand the sentiment about various EVP initiatives. Analysing the results of the questions might well show positive scores towards all the EVP initiatives but varying engagement levels between teams. As such sentiment toward the EVP initiatives on their own has no particular connection with employee engagement levels. Two things happen as a result: the EVP initiatives continue as-is and we understand nothing further about the effect of the EVP initiatives on engagement. However, by looking at the scoring patterns between the EVP questions and all other questions asked, we may see two things differently to before: being engaged is driven by how fulfilled someone feels in their role, and feeling fulfilled is related to how effective they feel the learning and development opportunities are. Now we can guide the EVP work focused on how developed people feel, through the lens of how fulfilling they feel their role is as a result. Looking at these correlations in the data means we can look at results beyond simply the rank order of scores.
This approach gives you the insight to focus on the few core experience topics that will make the most difference.
Build understanding in layers to know what is really
going on
Engagement is an outcome of a myriad of other factors. By making the kinds of connections in the data that we have discussed, we can build up layers of understanding. In doing so we start to understand the ‘why’ behind the ‘what’ that we see in the results. This helps identify the drivers of engagement to either build on or improve.
For example: These layers could arise in a couple of ways. Firstly, It could be from the recognition that engagement is driven by emotional factors like feeling valued and heard, that in turn are driven by the day-to-day experiences such as tools, processes, reward and manager relationships. Secondly, analysing the differences in the open-text feedback between cohorts identified by making connections in the data can provide new insights, for instance, into what constitutes ‘feeling valued’ for your people beyond pay.
Combining these layers allows us to work with metrics like eNPS more effectively. For example when we have previously helped to roll out eNPS, we identified a group of “Potential Promoters” who resembled Promoters but did not score a 9 or 10. This allowed us to ‘find’ this cohort in the business, understand what differed in their experiences, and target relevant support. Focusing on helping this ‘warm-but-not-hot for the company’ group enabled a positive shift in experience and a lift in the organisation’s promoter scores.
Engagement is an outcome of a myriad of other factors. By making the kinds of connections in the data that we have discussed, we can build up layers of understanding. In doing so we start to understand the ‘why’ behind the ‘what’ that we see in the results. This helps identify the drivers of engagement to either build on or improve.
For example: These layers could arise in a couple of ways. Firstly, It could be from the recognition that engagement is driven by emotional factors like feeling valued and heard, that in turn are driven by the day-to-day experiences such as tools, processes, reward and manager relationships. Secondly, analysing the differences in the open-text feedback between cohorts identified by making connections in the data can provide new insights, for instance, into what constitutes ‘feeling valued’ for your people beyond pay.
Combining these layers allows us to work with metrics like eNPS more effectively. For example when we have previously helped to roll out eNPS, we identified a group of “Potential Promoters” who resembled Promoters but did not score a 9 or 10. This allowed us to ‘find’ this cohort in the business, understand what differed in their experiences, and target relevant support. Focusing on helping this ‘warm-but-not-hot for the company’ group enabled a positive shift in experience and a lift in the organisation’s promoter scores.
Conclusion
In this blog post, we’ve explored how looking for connections in the data allows us to focus action beyond the top and bottom performing results. We’ve looked at how different types of data collected can be layered together to give a richer picture to the results and guide the ongoing action. Used together these concepts are powerful in understanding and improving employee engagement. As a result, HR professionals can more effectively, and more swiftly, use what has been said by employees to improve the working environment and benefit from the commercial and operational advantages this brings for the business.
To make a substantial impact to engagement levels we see it being critical to move beyond the numbers and uncover the real stories driving your workplace culture. How well do you understand the root causes behind your employee engagement scores?
James & Phil, Treehouse Analytics – find out more at treehouseanalytics.com
In this blog post, we’ve explored how looking for connections in the data allows us to focus action beyond the top and bottom performing results. We’ve looked at how different types of data collected can be layered together to give a richer picture to the results and guide the ongoing action. Used together these concepts are powerful in understanding and improving employee engagement. As a result, HR professionals can more effectively, and more swiftly, use what has been said by employees to improve the working environment and benefit from the commercial and operational advantages this brings for the business.
To make a substantial impact to engagement levels we see it being critical to move beyond the numbers and uncover the real stories driving your workplace culture. How well do you understand the root causes behind your employee engagement scores?
James & Phil, Treehouse Analytics – find out more at treehouseanalytics.com
To learn more, re-watch the webinar and get in touch with James and Phil
at Treehouse Analytics.
