Strategic EX Design with People Analytics
This article is a summary of the joint webinar by Panalyt and Wrkflow, “People Analytics: Employee Experience Design”, in which Panalyt CEO Daniel West and Wrkflow MD & EX Lead Sasha Wight covered how organizations can make EX Design more meaningful and intentional by leveraging People Analytics and Relational Analytics/ Organizational Network Analysis to go beyond traditional engagement survey measures of EX.
Panalyt thanks Sasha and the Wrkflow team for introducing us to Wrkflow’s approach to Data-Driven Employee Experience Design which we detail in the first half of this article!
Table of Contents:
- What is Employee Experience (EX) and Employee Experience Design?
- Data Driven Employee Experience Design
- Optimizing EX Design with People Analytics
- Improving EX Design By Understanding Core Demographics
- Improving EX Design By Understanding Attrition Risk
- Improving EX Design with Relational Analytics
- Improving the New Hire Digital Onboarding Experience with Relational Analytics
- Bonus: Optimizing the Return to Work Experience with Relational Analytics
What is Employee Experience and Employee Experience Design?
The Employee Experience can be defined as the sum of the experiences your employees encounter as part of your organization, such as:
- How your employees work – what tools do employees have to work effectively
- Why do they come to work – cultural elements of the organization and how employees feel like part of the culture
- Where they work – the location, physically where they are working
Employee Experience as functional usually sits within HR, but EX is a broader concept for the organization beyond HR, looking holistically at the end-to-end Employee Experience, from pre-boarding to offboarding/ alumni relationships.
At its core, Employee Experience Design is all about putting employees at the heart of the enterprise experience. Incorporating aspects of human-centered design-thinking, EX design can be seen as a creative approach to problem-solving that start with people and ends with innovative solutions that are tailor-made to suit their needs.
Historically EX programs have been defined based on what HR assumes the root cause of the problem is, rather than incorporating People Analytics and Employee Listening, leading to employees getting lost in the equation when the focus is bringing an HR initiative across the line.
By including employees in the design process and testing and validating using data to ensure that the problem we are framing is indeed the problem being faced by the organization
Data-Driven Employee Experience Design
A problem well-defined is a problem half-solved – you need the data, both quantitative and qualitative to be able to validate that you are solving for the right problem.
If you have a well-defined problem that you have researched and understood appropriately, that will make it easier for you to design an EX intervention that will actually make a difference and drive ROI for your organization. Using data from People Analytics and Relational Analytics solutions like Panalyt and other data points to ensure that what you are designing is appropriate for the organization.
Data-Driven EX Design incorporates both Quantitive Data, hard data, metrics which are a combination of leading/lagging EX data points, along with Qualitative Data, which is more about engaging with employees, active listening through surveys and focus group interviews with employees through focus groups to validate the quantitative data points.
For eg. if the hard data says there is high early tenure attrition and you suspect that something is wrong with the onboarding process, the best way to approach this would be to go out and talk to people who have just joined the organization and through this process uncover different elements of that challenge along with possible solutions.
Ideally, data-driven EX design should be a constant process of analyzing and combining both leading and lagging EX measures, along with employee listening. Leading measures shape the experiences employees would have while lagging measures provide additional context after the experience has taken place.
Currently, most companies just focus on lagging EX measures based on limitations with the data collection and analysis technologies. However, leading EX data points can provide a much richer EX measure for your organization – allowing you to monitor experiences as they are being rolled out, and designing EX interventions that reach employees before they give negative/ neutral experience results through eNPS and CSAT data.
Incorporating Leading EX measures allows you to look at how people are operating and how experiences are running in real-time to be able to make better EX Design decisions that are faster and drive value before there is a bigger issue arising.
Some examples of leading measures are:
- Relational Analytics – How are employees forming relationships within organizations
- Content Engagement – How are employees engaging with policies, content, faqs, what are they searching for
- Incident Management /Case Response times
- Pulse surveys – if done more frequently, effectively designed surveys can also be a leading indicator,
Some examples of lagging EX measures include engagement surveys, engagement scores, eNPS/CSAT, and employee attrition data, while Employee Listening validates the hard data gathered through interviews with and observing smaller focus groups and observation.
How you should start measuring EX depends on your organization’s people tech stack and how far along your organization is on its People Analytics journey.
Traditionally, designing EX initiatives has been focused on solving a particular problem, such as attrition or onboarding, in silos rather than taking a holistic view of the end-to-end Employee experience. However, integrating and analyzing people data across the employee lifecycle allow you to understand how all the steps of the employee’s journey in an organization are linked together and help managers think about the impact of their decisions on the Employee Experience – for eg. how does compensation planning impact how we recruit,how we engage people and drive performance, etc.
Optimizing EX Design with People Analytics
A common fallacy is to believe that you don’t have enough data on your Employee Experience and to delay analyzing data based on that false assumption – leading to your organization being blind to which Employee Experiences are working effectively and understanding where you need to intervene to prevent bigger issues.
A better approach is to start with baby steps, using the data you currently can collect, and make the process of collecting and analyzing people data consistent, connected, accessible and repeatable.
4 critical data sets you can look at with People Analytics to make EX Design more meaningful and intentional are:
- Core Demographics
- Attrition Risk
- Collaboration Data – Networks & Activities
- Onboarding Relationship Building
Improving EX Design by Understanding Core Demographics
Analyzing your core demographics allows you to get a snapshot of your organization based on any dimension – understand who is in which function, where are they working from, what’s the gender balance, what’s the attrition rate (split by gender), how much are employees being paid, what’s the Gender Pay Gap/ Total Pay Gap, etc.
From an EX design perspective, this allows you to have a better understanding of your employee personas ( who is it you’re designing experiences for) and ensure you’re designing for the employees by understanding nuances with culture, gender, age, tenure, and many other layers.
Understanding your core demographics helps you assess if your initiatives to improve onboarding, virtual/ hybrid work challenges, well-being, attrition, etc. should be designed as initiatives for the organization, or in a more personalized and hyper-targeted manner for different demographic groups/ employee personas.
However, EX design may prove to be less effective than hoped for if you’re designing a broad-stroke EX initiative globally for all employees without understanding the caveats of who is facing what problems and why.
For example, if you were transforming the performance management process and the majority of your employees are in one location, say Singapore, rolling out the same initiatives globally as you would in Singapore might lead to underwhelming results due to differences in the employee base in Singapore as compared to global.
You need to use the data on demographics to develop your employee personas in different locations and design based on those personas. Understanding your core demographics helps you make a business case for investing in EX design, showing that you’re being intentional and specific about the investment based on what you know about your organization.
It’s very often that HR business partners start looking at defining employees based on certain criteria, such as tenure- who’s been in the organization for a year or more, and be blinkered into only looking at that criteria when considering compensation reviews or what surveys to put out without considering nuances in the gender split, grade split, and understanding which other categories people fall into.
Analyzing a snapshot of your organization’s core demographics gives you a broad overview of your organization and allows you to kickstart your journey of understanding your employees better.
Improving EX Design by Understanding Attrition Risk
At Panalyt, we measure attrition risk by analyzing various demographic-based data points, such as age, gender, tenure, who’s the manager, how many managers you’ve had in the past year, the manager’s attrition rate, etc., in conjunction with Relational Analytics data on how connected employees are with the organization, their manager and their teams, allowing us to predict how likely someone is to leave the organization over the next six months and why.
This model is trained over our entire client dataset and then applied to a client’s historical employee data.
A deeper analysis of your attrition risk data let’s you to see employee attrition risk profile by team, by manager, all the way down to risk profile by individual.
Once you’ve identified the level of risk, you can then drill down to what are the key drivers of the risk, such as salary deviations from the norm, age, steadily decreasing relationship scores, and decrease in # of relationships – all leading indicators of the risk of people leaving. In addition, Attrition risk, as we define it at Panalyt, can also be seen as a proxy for engagement risk/ loss of engagement over the next six months.
From an EX design perspective, if you are able to see a trend in your attrition risk data that one of the most common drivers of people being at risk is managers changing: you can use this insight to investigate how do we make the transition to a new manager process more meaningful and more personal to turn the employee experience during a manager change from a problem area into an experience that employees go through that is celebrated, enjoyed and drives engagement.
Another example focussed on improving Diversity & Inclusion is if you’re seeing that your female working population is at much higher risk of leaving the org, what design decisions do you need to make and what listening do you need to go out and do.
Once you spot markers due to which female employees are at higher risk of leaving the organization, you can to your employees about factors and really understand what is it that is driving that risk, and validate that the data is accurate as well.
Having hard data points on who is at the highest level of risk, and then combining that with listening is key.
Understanding which factors are driving high attrition risk allows you to focus your time on where it’s going to produce the best results and drive discussions focussing on the right areas to validate the findings.
Rather than covering a laundry list of questions in your surveys, you can now focus your surveys on real and clearly defined problem areas to get richer insights into why people might be leaving your organization.
It is important to understand the nuances in the symptoms of attrition risk when designing EX interventions – if there is a decline in the relationship score/ or the number of relationships for an individual, is it an outcome of a change in behavior by the individual or is it a change in the behavior of their team affecting them?
Some of the data points used to drive the attrition risk calculation are just facts/ attributes, things you can’t change but help you define the population you are talking to. It might also be that you’re seeing that a lot of people are at risk, but the drivers are less about things your organization is doing to push, but rather more pull factors.
For example, at the time when people naturally progress from your organization – how can you ensure that your offboarding experience is a good one? If you’re getting to a natural churn rate of people who have joined org 5-6 years ago, how can you make sure you are involving them in the offboarding process and making sure that it is one that is positive?
Improving EX Design with Relational Analytics
The pandemic and it’s associated effects over the past year has brought about long-term changes in how we work, with remote and hybrid work models becoming the norm. This has also brought up a key issue with designing Employee Experiences – ” How do you pro-actively identify areas of risk and intervention in the Employee Experience in a virtual environment?”
Relational Analytics provides a lens into how work really gets done at your organization by mining behavioral insights from internal and external employee communication and collaboration metadata.
At the most basic level, analyzing your communications and collaboration data allows you to see how connected your employees are to the organization, their manager, their team etc., and a number of key metrics about how people are communicating such as:
- Volume of communications
- # of connections within a particular time period
- Reciprocity – are communications equally distributed or skewed one-way
- Responsiveness- how quickly are people replying; this can be seen as a measure of organizational health – a low responsiveness can indicate employees might be overworked, people might be distancing from each other, or a lack of respect within the org.
- Average non-response % – % of emails you receive which you ignore, as this number goes up it’s not good as people might be ignoring their colleagues.
A more nuanced way to analyze your communications and collaboration data is to use Relational Analytics to go beyond just communication statistics to actually measure the strength of relationships between employees in your organization, with both internal and external stakeholders.
Visualizing relationship strength over time – are relationships getting stronger or weaker, and segmented by different categorial groupings like function, location, etc. … can be used to understand how individuals are building or losing relationships within the organization, with a trend upwards being positive, and a trend downwards being negative in most cases.
Understanding digital collaboration and activity data also lets you answer key questions for your remote Employee Experience such as:
- How much time are you spending particularly during work hours/non-work hours
- How much time is being spend in meetings? How much flow time do individuals have between meetings?
- Are people time-shifting their work hours, i.e. measuring digital activity within and outside work hours? – During covid there are certain employee groups such as those with dependents or kids where the time spent in work activities during regular work hours has decreased but in balance the time working in non-work hours as increased.
Relational Analytics provides concrete data for your business case for investing into EX initiatives to improve WFH effectiveness, where and why you should be intervening by looking at how employees are connecting with each other, how has collaboration increased/decreased over time, and understanding what does this tell us about the way people are connecting with each other and building relationships in the organization.
With remote/hybrid work taking a toll on individuals – companies are also heavily investing in employee well-being and apps to drive productivity or increased collaboration. With Relational Analytics data, you can now make a more targetted investment by understanding which groups are facing issues with virtual work and why – for eg. it might be that intra-team collaboration is not an issue for you, but rather cross-functional collaboration – in which case, you might want to focus your time and money on initiatives to bring together different teams rather than investing in a new digital collaboration application.
In addition, Relational Analytics can also help you identify who are the connectors in your organization and how can you involve them in initiatives to get individuals more connected to the organization (eg. mentorship/ buddy programs).
These connectors can also be ideal for focus group interviews as they potentially have a wider visibility into issues affecting their network. Identifying internal influencers can also help you strategically involve key stakeholders for pushing out your change management and Employee Experience initiatives to ensure widespread adoption.
Improving the New Hire Digital Onboarding Experience with Relational Analytics
The accelerated shift to remote and hybrid work models has led to a consistent C-level concern: ” Are employees effectively building relationships in a virtual environment?” – and new hires and early tenure employees have been the most affected by this change in how we work.
Relational Analytics can empower line managers and HR business partners to understand how new hires are integrating into the organization (new hires who are connecting with the large network within the organization), and identify where to strategically intervene to help new hires build meaningful relationships while working remotely (people connected with a much smaller network, have much more limited access into the organization).
As a business partner, you can identify isolated new hires and then identify who they should be connecting to such as connectors who are at the center of a much larger network. You can then design practical EX interventions to introduce new hires to connectors – such as working on the same team, work on the same project, have them schedule time together, etc.
Incorporating connectors into your formal employee onboarding buddy programs is also a practical approach to ensuring your new hires are connecting into a larger network. Real-time visibility into how new hires are integrating into the organization and helping employees build those connections early ensures that employees are productive as quickly as possible and that the likelihood of them remaining with the organization and not leaving in the first six months is higher.
From an EX design perspective, Relational Analytics helps you design better onboarding experiences.
If you observe new hires joining certain teams are not integrating quickly enough or maybe not integrating at all, you can then brainstorm what can you do from an EX design perspective to ensure the onboarding process in the first 7 days, 30 days, 90 days etc. drives more connection.
Bonus: Optimizing the Return To Work Experience with Relational Analytics
The same approach can also be used for other aspects of the Employee Experience apart from onboarding- for example, lack of connection is a real issue in the Return To Work experience.
From a D&I perspective, returning to work after maternity leave is very challenging. How people connect back with an organization after an extended period of leave is a key predictor of the likelihood that they will stay and thrive within the organization, whilst also managing the challenges of parenthood. Identifying employees who are slow to integrate back into the organization allows you to intervene and ensure the Return To Work experience is being designed appropriately to ensure people are reconnected very quickly.
Drilling down into your Relational Analytics data can help you understand if you’re looking at a massive broad-stroke investment to completely overhaul the Return To Work experience or if you just need training and coaching in one function for managers.
One of the things that have always held business partners from taking action to improve Return To Work experiences – is that the data is usually collected one-time for everyone, through surveys and focus groups, rather than in an ongoing basis; and this data usually collected ends up in a graveyard of Excel spreadsheets and Powerpoint slides – the process is not repeated three months later, as HRBPs don’t have the time to do it again despite best intentions. Relational Analytics can provide a quick and repeatable means to understand how employees are able to integrate back into the organization after long periods of absence.
We hope you found this guide/ recap useful! If you felt that this article was insightful to you and your peers can benefit from it too, we’d love it if you shared this with your network ^^
In addition, we are pleased to share with you a special “the EXperience Lounge” podcast episode focussed on the Role of Relational Analytics in improving Hybrid EX Design!
Panalyt bridges the People-Data Gap, enabling real-time, uniform access to relevant people data, reports and insights for CxOs, HR and business managers.
People data, including employee interactions and connections is combined with business data empowering businesses to leapfrog to data-driven decision making, eliminating bias and improving engagement, sales effectiveness, productivity and, as a result, business performance.
Interested in a further discussion on how People Analytics and Relational Analytics can help you drive an improved employee and business outcomes? Book a 30-minute discovery call with our Panalyt co-founders to learn more!