Tag Archives: People analytics

Utilising People Analytics To Provide Deeper Insights

The biggest challenge facing organisations today is inflation and the cost-of-living crisis. What role can people analytics play in helping leaders and employees tackle these issues?

The biggest challenge facing organisations today is inflation and the cost-of-living crisis. In this presentation, I will ask a simple question: What role can people analytics play in helping leaders and employees tackle these issues?

Let’s start at the beginning:

HR has been doing analytics for a long time, but it’s fair to say that, in the main, HR analytics (or “people analytics”) has involved a lot of reporting.

That means counting things and measuring the effectiveness of programmes. A good deal of time has been spent on simply getting data about the workforce into shape (agreeing common definitions, cleaning data, and automating reporting).

So in 2016 Janet Marler and John Boudreau were correct, when they did a meta analysis of the field, in stating that people analytics was primarily focused on providing data for better decisions within HR.

The good news is that since then things have started to change. That relatively narrow description has been expanded. The variety of papers in this conference amply demonstrates that people analytics is evolving to address issues that provide far more value.

This shift is partly due to the pandemic. Covid gave organisations a fundamental employee experience “stress test”. HR has been asked to provide better analytics in order to help. Leaders want to know how employee experience is being affected and what this means for people management, performance development, teamwork, collaboration, and productivity.

Another important shift relates to how HR thinks about its own purpose and mission. The function is gradually evolving from an internally-focused and compliance-driven mindset. It’s becoming a more strategic function.

So Jonathan Ferrar and David Green are right to point out, in their recent book, that people analytics now is more focused on providing value. It’s about addressing business challenges and surfacing solutions. Those things need to help the business perform better, they need to support leaders with their priorities, and they should have value for employees too.

So what I want to do in this session is put that to the test. I want to think about how people analytics can provide value when it comes to addressing the biggest challenge facing, organisations, leaders and employees right now.

For sure, the biggest organisational and people challenge at the moment is rising inflation and the cost-of-living of crisis.

Employers are facing a very tough set of problems. You know the headlines. Inflation is the highest it’s been in 40 years. At the same time growth is low. And on top of that the labour market is tricky. There’s a lot of turnover and companies are finding it hard to attract people with the skills they need.

In our research, we see that three-quarters of companies are finding it hard to retain and recruit employees with digital skills and 63% of employers say the same about professional staff in general. In order to fill gaps, most companies have stepped up their recruitment efforts.

In addition, employers are facing pressure from unions and other stakeholders. In the UK, the number of strikes is already at a decades-long high.

For all these reasons, “inflation and financial/market instability” is the Number 1 concern for CEOs (according to the Summer 2022 Fortune/Deloitte CEO Survey.)

From an employee perspective, of course, things are also very difficult:

Employees are under pressure. As an example, again according to our research, 36% of employees say they’re living from pay cheque to pay cheque. One-on-three say financial problems are having a negative impact.

36% of employees are living pay cheque to pay cheque

There’s a cost for individuals and businesses to this. At an individual level, financial wellbeing and anxiety impacts people’s physical, social, and emotional wellbeing too. For businesses, there are operational costs, such as absence and presenteeism (and perhaps “Quiet Quitting”). Of course, people are also more likely to move jobs if they can pick up even a small pay increase.

If you look at the data that sits behind the headline of a “Great Resignation” then it appears there are a lot of people making sideways moves. Some of this may be because people are re-evaluating their job after lockdown. It’s also very likely due to this urgent need to earn more as living costs rise.

In response, employers are having to react. These are some of the changes companies are making:

They’re targeting salary increases and offering one-off payments and they’re investing in broader support and assistance for employees.

These changes are potentially expensive and in some cases they’re being made quickly as an urgent response to events. Rather than being evidence-based, they’re sometimes driven by anecdote and leaders’ instinct. What’s often missing is an understanding of motivation that’s rooted in behavioural science.

What’s often missing is an understanding of motivation that’s rooted in behavioural science

Surely there’s a role for great analytics here?

Let’s begin by trying to break things down a little and to consider the kinds of questions we might want to answer:

At the risk of sounding like Donald Rumsfeld, what are our known-knowns and known-unknowns? More specifically, when it comes to total-rewards, what are we spending money on and are these the things that employees really value?

By total rewards, I mean three things:

  1. Foundational elements like pay and benefits
  2. Performance-based components like merit-increases and incentives
  3. Plus career and environmental dimensions, such as learning and development

Take just benefits, for a moment. In my experience, established companies often have programmes that are designed for their legacy workforce rather than the needs of their future and up-and-coming talent. As such, it’s possible that you’re spending money on things that your mission-critical talent doesn’t really value. This is obviously a waste.

Benefits needs and preferences aren’t static; they change with the times.

One example is how employees’ views about private health in the UK have changed following the pandemic. Health insurance has been seen as a benefit for senior leaders. But there’s growing interest generally in health plans as people struggle to see their GP and get appointments on the NHS. By being more innovative when it comes to health plans both employees and employers can “win”.

Another example is “green benefits”. These can include things like supporting people to buy electric cars or introducing cycling-to-work schemes or investing pensions in sustainably-focused funds. These can help with your overall Net Zero commitments and they’re a way of tapping into the energy that your employees have about climate action.

As well as understanding needs and costs we should also ask ourselves if we’re incentivising the right behaviours. Specifically, are we driving high engagement and motivation? Are we encouraging collaboration and innovation? Are we doing enough to encourage people (that we want to stay) to grow a career here?

And there are other important considerations, such as ensuring we’re building an organisation that’s inclusive and where rewards and recognition are aligned to our values.

What these questions show is that you need to think systemically about total rewards. The danger is that you might “fix” one problem over here, just to create another problem somewhere else.

That’s enough theory.

How can you operationalise this kind of thinking?

The first question we should try to answer is what it is that people really need and want when it comes to their total rewards (so that we’re not simply responding reflexively to problems as and when they occur).

It makes sense to begin with, “what do we already know?” It might be a lot. For example, you know take up rates for benefits and you have feedback on providers, and you can look at data from your Employee Assistance Programmes, and so on.

But even with all that, you may still not be able to answer the question fully.

So you can ask people — through surveys and focus groups, for example. Surveys can be of various degrees of sophistication. We sometimes use Max-Diff surveys in order to force choices.

I find focus groups valuable. We often run them virtually (using a chat-based tool). They provide rich insights and a deep understanding of preferences. However, it’s hard to connect that feedback to specific cost data.

In terms of what you may or may not know already, here’s an example: I’m currently working with a very large tech company that has vast quantities of information about and from their people. I mean “enormous”. They are super data rich. They run very frequent pulse surveys using a survey widget tool. They run lots of polls. They have masses of open feedback on internal social media. They have input from Employee Resource Groups. They get insights from team huddles and other events. But having sifted through it none of it is specific enough to help them answer the exam question for total rewards: “What do employees really need and want?” It’s all noise and no signal.

So if this is the case for you too, how do you get a clear signal — what’s another option?

The approach that we find most useful is conjoint, which you may or may not be familiar with.

In case you’re not, then adaptive conjoint was first used in the 1980s in market research. It’s a way of testing different product attributes to estimate a preference score. It yields better information than asking people about each attribute individually.

That’s because a conjoint survey asks you to make trade offs between different sets of choices. The software is adaptive in that it tries to be efficient by testing the most relevant choices based on earlier responses.

There are two elements. You have to design a Reward Matrix with different rewards and options (typically decrease, stay the same, and increase). You can, of course, also calculate the cost of these options.

And then a conjoint survey tests those in combination. It’s a very different experience from an opinion survey. You have to make some tough choices. And the communication of the survey is important since you’re talking about people’s reward and you’re presenting options where it can decrease.

After running a conjoint survey, you can then use the results to understand the perceived value of different reward elements. This is the second step in what we call Total Rewards Optimization (TRO).

You can run a simulation of the impact on the perceived value of potential reward changes and the implications for costs. Effectively, these are “What-If Analyses”.

For example, are there simple changes that don’t cost much that would be greatly valued? Are there expensive programmes you’re paying for that people don’t actually value very much?

Are there simple changes that don’t cost much that would be greatly valued? Are there expensive programmes you’re paying for that people don’t actually value very much?

The graph on the right (above) shows the change in value for different reward elements and the associated cost

You can also segment the results for different talent and demographic groups. Are you offering rewards that are valued by your long-tenured employees, but not so much by new, younger hires? Can you build a better understanding of the needs and preferences of those critical talent groups you’re struggling to retain or attract?

As a third step, you can then build a model of all the potential options. In this graph, the cost or investment is shown on the horizontal / x-axis and the perceived value on the vertical / y-axis. It also shows an efficient frontier — based on plotting the most efficient allocations at various cost levels.

This means you can find your total rewards “sweet spot”.

For example, that could be spending the same amount of money, but increasing value. Or it could be spending less, because you need to cut cost, but doing so in a way that preserves value. Or perhaps you want to increase value and don’t mind spending a bit more in order to maximise talent retention. The point is that these are options based on analytics rather than instinct.

And typically we run an online simulation that’s used to build different portfolios as an aid to decision-making in a workshop setting:

It’s worth reflecting that traditionally, decisions on rewards have been set from the top-down (think budgets cascading through a hierarchy). The approach I’m outlining here to TRO is about building-in an understanding of employee needs to make sure you’re aware of the impact of those decisions on engagement and behaviour. It’s about understanding and mitigating risks. It’s also about involving people and capturing employee voice.

TRO is about building-in an understanding of employee needs to make sure you’re aware of the impact of those decisions on engagement and behaviour

So that’s the analytics. What happens to those findings? How are they turned into actions?

Here are a couple of examples.

In this first case (below) the analysis was turned into programme changes. This is a large global organisation that was struggling with turnover. Through the analysis they realised they had been too concerned by those foundational elements of total rewards (like pay) and that they weren’t paying sufficient attention to elements like Learning & Development. The analysis was able to quantify the impact of focusing more on L&D which meant there was a business case for investing in some of the programmes shown here. And there was indeed a payoff. Turnover dropped and engagement increased and they saved some money.

This second example is a recent and topical one. It’s from another large organisation that had a focus on recruitment, especially graduates and experienced hires:

In this case, the review of total rewards highlighted a need for more than programme changes. The top priority was actually financial wellbeing — things like assistance, education and tools on topics like clearing debt, building up savings, and applying for a mortgage.

They built a microsite to deliver employees with a semi-personalised experience. Effectively, as an employee, after answering some questions, you’re given a “score” and directed to particular resources that are relevant for your situation.

Their curriculum was based around four life stages. It mainly delivers financial education (as opposed to advice — although you can imagine “robo-advice” being deployed in the future).

It’s proven to be very popular. The resources are all “bite size”, so the average user visit only lasts around 15 minutes. And the peak time of day for accessing the portal is between 5pm and 6pm, in other words as people are transitioning from core working hours. Of course, everything is mobile-first.

The tool and the curriculum has continued to evolve based on feedback.

It’s also part of a broader shift in communications that the company is building out over time, whereby employees are delivered a personalised employee experience. In other words, rather than employees hunting for information on an intranet, as is typical in most places, it’s about delivering content that’s relevant to you, on your phone, in the moments that matter for you.

It’s worth building this point out a little. If people analytics is about providing value to the business and employees, then at heart it’s about improving employee experience (EX).

That might be a bit of a controversial statement here and it certainly requires a longer discussion. But if you ask me “what is people analytics for?” then my answer is “transforming employee experience”.

If you ask me “what is people analytics for?” then my answer is “transforming employee experience”

When I talk about employee experience, I’m including total rewards as one of the core dimensions of EX alongside the work itself, the sense of purpose you get from your job, and the culture and climate of the organisation you work for.

When it comes to thinking this way, there are always three steps to take when “Utilising People Analytics To Provide Deeper Insights”:

  1. The first is to begin by really understanding employee needs; this is also a great way to start involving people of course
  2. The second is to use those insights to help prioritise and plan changes; your “organisational compass points” here are your people strategy, your values, and your EVP
  3. The third is to spark behaviour change through people leadership capability and technology; again involvement is key through design-thinking, testing and iteration

Of course, this all needs to be aligned to some kind of ambition or “blueprint”.

When you start thinking this way, you can broaden your horizons further.

Organisations have traditionally considered people’s experience at work quite narrowly. The focus is on roles, levels, training, compensation, benefits, etc. But it’s just as important to think about what those things mean in human terms.

The goal is to translate ”employee experience” and “the deal” into how I think about “my health, my wealth, and my career”.

When providing value to employees (as well as the business) it’s really about helping me achieve success at work and in life (however I define that.)

So, I’ve covered an awful lot of ground in this talk. But this is where some of my clients that are using people analytics to transform EX are heading.

They’re using people analytics to transform EX through human-centred design that’s based on a deep understanding of needs and wants.

Using people analytics to transform EX through human-centred design that’s based on a deep understanding of needs and wants

This presentation — or at least a version of it, as I skipped around a bit — was given to the HR Analytics Summit in London on the 8th of September 2022.

EX Newsletter April 2022

@nickl4

Here’s the latest version of my informal newsletter, containing a short selection of the very best EX articles I’ve come across over the last few months (so you don’t have to slog through LI or Twitter).

First up is a terrific HBR article by Diane Gherson and Lynda Gratton on how overwhelmed many managers are and what to do about it. In our data we’re seeing more and more evidence of manager burnout. It’s often a systemic problem that’s fixed by rethinking the role of people leader. There is some great advice in this piece: building people leadership skills, simplifying work, and job redesign. Related to this, I am working on a number of “Manager 180s” for clients at the moment that provide tailored developmental feedback to people leaders at all levels. It’s a great use of our listening platform (and often not part of a traditional “listening strategy”).

https://hbr.org/2022/03/managers-cant-do-it-all

I’m a long-time fan of Joe Pine and Jim Gilmore, the authors of The Experience Economy, one of my favourite books. I really like their latest article on transforming jobs to create more compelling employee experiences. Too much of the discussion about the Future of Work focuses on automation, cost-saving, and efficiency (the transactional side of work). It’s good to be reminded of the opportunity to invest in people, engagement, and trust (by transforming jobs).

Employment as transformation

This is an interesting article by Ayelet Fishbach on how moderate emotional discomfort can be a signal that you’re developing as a person. It often happens before you can actually detect the benefits of self-growth. In other words, short-term discomfort can be a sign you’re making progress towards long-term gains. Ayelet is author of the book “Get It Done: Surprising Lessons from the Science of Motivation”.

Get Comfortable with Feeling Uncomfortable

The final pair of articles are both reflections on what has happened over the last 2-3 years:

Here, Eric McNulty focuses on leadership. He sets out a simple process of “sensing-responding-adapting” in order to be agile enough to respond to uncertainty and shocks. I think it’s a very powerful (and simple) framework:

https://www.strategy-business.com/blog/The-best-way-to-lead-in-uncertain-times-may-be-to-throw-out-the-playbook

And here Gethin Nadin asks how you design employee experiences starting from the premise of needing “more conscious and compassionate workplaces”:

https://www.hrzone.com/engage/employees/employee-experience-and-the-rise-of-compassionate-capitalism

As always, let me know what you think!

Nick

Including Rewards In People Analytics

@nickl4

The field of of people analytics has seen rapid growth over the last years. As someone who has explored the links between people and performance throughout my career, it’s been great to see this explosion of interest.

There are various definitions of people analytics, which is sometimes called HR analytics or workforce analytics. Janet Maher and John Boudreau call it “An evidence-based approach for making better decisions on the people side of the business; it consists of an array of tools and technologies, ranging from simple reporting of HR metrics all the way up to predictive modelling.”

Jonathan Ferrar and David Green in their book Excellence in People Analytics emphasise the importance of using people data to provide business value. They describe different ages in the evolution of the field with most companies now focusing on supporting leaders to navigate key challenges: “People analytics is an absolute must-have for any Chief Executive Officer or Chief Human Resources Officer.”

There is a potential hitch in all this progress, however. HR has long been a siloed function and it strikes me that this characteristic is being reflected in the work that is now published and shared in the people analytics community. Recent people analytics books, collections, conferences, and articles all seem to have a glaring gap; hardly any of them make any mention of rewards.

Recent people analytics books, collections, conferences, and articles all seem to have a glaring gap; hardly any of them make any mention of rewards.

There are a few notable exceptions. There has been some great work looking at gender and ethnicity pay gaps, for example. It’s also true that, because of the nature of rewards work, it can be harder to share the outputs in public. But in the main, reward analytics operates as a separate field from people analytics, just as rewards is usually a separate sub-function from talent. Even though rewards is full of data-savvy and analytically-minded people. This feels like a missed opportunity.

That’s because decisions about rewards are important business ones. Payroll is a significant percentage of revenue. Companies source and offer a complicated mix of pay, incentives and benefits. It’s an area where smart analytics can provide a lot of business value and generate a return on investment.

Reward design choices are also important human decisions. Rewards carry emotional as well as practical weight. A lot of organisational energy is spent discussing them. And incentives affect behaviour, often in oblique ways.

Getting total rewards right can mean the difference between competing effectively in the global talent marketplace and being left behind. A consumer-grade total rewards portfolio of pay, benefits, wellbeing and career programmes serves as a catalyst, driving attraction, retention and engagement of talent essential to business success. Yet, in many organisations, total rewards are not evolving quickly enough to keep pace with changes in the world of work.

All of this underlines that when it comes to employee experience (EX), rewards obviously matter. It’s why in our work we include total rewards as one of the four key dimensions of a High-Performance EX, as shown below:

Let me expand on this point about employee experience. I have written quite a bit about EX, and one of the things I believe strongly is that EX requires a shift in perspective. In essence, it means moving away from a traditional and top-down view of organisations towards a messy, conversational, and more personal view of life at work.

From an EX point of view, therefore, all the following things are super-interconnected: jobs, work, performance, skills, careers, learning, pay, benefits, inclusion, engagement, well-being, communications, culture…

It’s a blatantly obvious point, but worth stating — employees don’t experience life at work through a HR lens. Rather, HR needs to think about organisational effectiveness from an employee perspective. That’s the fundamental trick in making EX work.

The same logic applies to people analytics. Talent metrics only address part of the humans and work equation. If you exclude rewards, you’re not capturing the whole picture and you’re not thinking systemically.

Talent metrics only address part of the humans and work equation. If you exclude rewards, you’re not capturing the whole picture and you’re not thinking systemically.

So what does it look like to bring reward into people analytics? One example is the work we do around optimisation. Specifically, Total Rewards Optimisation (TRO) allows you to align reward investments with the employee experience. We talk about finding the “sweet spot” — the intersection that aligns what and how much you spend on total rewards with what your employees value most and least across what you offer — while uncovering how reward changes affect employee behaviour and performance on the job.

There are four key parts to TRO:

  • Understand which rewards employees value most and least using conjoint analysis, a survey methodology used in market research to understand customer preferences. You can also pull in selection data from your flex and benefit programmes to understand actual employee choices and trade offs. We also bring in employee engagement, retention, and performance data in order to analyse the linkages.
  • Assess the return on your total rewards investment, as well as the impact of the programme on your workforce, by combining employee preferences with financial data. We model various investment scenarios in order to help leaders decide how much to spend and where to get the best possible results for the right size of investment.
Modelling total reward options along an efficient frontier
  • Use data to understand what employees see as the most and least valuable components of their reward packages. We help leaders make investment decisions and deliver a talent value proposition that is likely to foster desired attitudes and behaviours at a cost the organisation can afford.
Heat map of perceived value by talent segments
  • Use segmentation to understand the different priorities and attitudes of a diverse and multigenerational workforce towards benefits, cash and work/life balance and build a competitive edge in attracting, retaining and engaging top talent.
Modelling the impact of total reward changes on the engagement of key talent segments

To my mind, TRO is a great piece of people analytics. It’s got interesting and important data, cool maths, fun modelling, nice data visualisation. More importantly, you’re linking together employee preferences and behaviours to business and financial data in order to understand trade-offs and ROI. And those scenarios are typically explored interactively with leaders as different hypotheses are tested and analysed.

My broader argument, however, is that this is an example of how it’s possible to include rewards in people analytics and that this is an important thing to do. It’s one thing to look at engagement, retention and performance drivers for your key talent, and to make decisions based on that data. It’s another to look at those things alongside what you spend on total rewards and how you shape, customise and communicate your value proposition. The latter is taking a step towards thinking holistically about employee experience.

This is especially important right now as leaders are acutely aware of the importance of retaining and attracting key talent in the midst of a period of high turnover. Rather than throwing money at a problem, finding the sweet spot matters more than ever.

There’s also a point here about the state of people analytics at the current time. It’s possible to see the recent rapid growth in people analytics as a transitory moment. A point when data became more available and HR began to explore how it can be used for improving decision making. At first, growth in analytics has mostly occurred with a traditional HR mindset, within HR silos, reflecting long-held budgets and distinct backgrounds and skillsets. But in the near future, the picture might look quite different. As datafication continues apace, the current people analytics community may merge with others to become the analytics engine of an EX function or even an EX analytics team within a business intelligence function. In some of my clients, this shift is already happening.

As datafication continues apace, the current people analytics community may merge with others to become the analytics engine of an EX function.

Jonathan Ferrar and David Green also refer to this kind of transition in their recent book, as they herald a new Age of Excellence in people analytics where “the human resources function itself becomes even more data literate.”

A key question for me is whether that means “business as usual” (such as continuing to think in HR terms) or taking a leap and embedding analytics and design thinking within a truly EX mindset.

Social science careers, the future of work & evidence-based management

This week I did a podcast with King’s College London on business careers for social science researchers.

You can listen to the podcast here:

No alt text provided for this image

Postgraduate students are not always aware of the different career paths that are available to them, which is something King’s is trying to tackle. I believe there are some great opportunities that people should think about, especially now.

Social scientists clearly have some important business-relevant skills. These include working with data, producing insights, presenting findings and arguing your case.

In the podcast, I suggest that these kinds of skills are going to become even more important in the future. Technology is already having a big impact on work. Routine elements of jobs are being automated. There are new and emerging cognitive technologies. As a result, what companies need more of is people who can exercise judgement and reasoning. In fact, in an age of AI and machine algorithms, human judgement, critical thinking and problem solving become even more important.

On top of this, what companies have more and more of is data – and not just numbers, but increasingly text, images and video. People who can develop insights from all these data are already in high demand. That’s especially true if you can also communicate those insights through storytelling.

According to the World Economic Forum, the top business skills are soon going to be things like analytics, critical thinking and complex problem-solving. On top of that, workers will need to be a life-long learners. They will also need to be able to teach others new skills in turn.

No alt text provided for this image

There are potential derailers. I have seen social science PhDs who are too rigid in their thinking and not practical enough. You have to be prepared to try things out. “Good enough” is sometimes an important principle.

But there is a growing movement of people who are interested in evidence-based practice in business, and this plays to many social science researchers’ strengths.

According to Rob Briner, evidence-based management means making “a conscientious, explicit and judicious use of the best available evidence.” This means using multiple sources and adopting a structured approach of inquiry and appraisal. In other words, it’s about applying social science rigour to business data and decision making.

Copyright The Center for Evidence-Based Management (CEBMa) - the leading authority on evidence-based practice in the field of management and leadership.

I hope to see this movement grow over time. In the realm of people analytics, which I am particular interested in, for example, there are immediate benefits from evidence-based practice. When it comes to people and performance, it’s far better to explore the evidence than to rely on intuition and gut feeling.

Let me know what you think of these points and feel free to connect with me here on LinkedIn or on twitter @nickl4. I’m always happy to link up and to offer advice if I can.

Please note: The image above is Copyright of The Center for Evidence-Based Management. CEBMa is the leading authority on evidence-based practice in the field of management and leadership.

Tags: #PeopleAnalytics #SocialScience

This article was first published on LinkedIn on December 9, 2019.

The Employee Experience Maturity Curve

One of my favourite books is Alan Pennington’s “The Customer Experience Book”. The reason is in the subtitle: “How to design, measure and improve customer experience in your business”. It’s a very practical guide to putting customer experience into action. I use ideas from it all the time.

One idea that I’ve found especially useful is assessing the maturity of an organisation in terms of its approach to customer experience. Alan talks about moving from being customer centric to being customer intelligent. In a customer-intelligent organisation “all staff know the experience they are required to deliver”. Moreover, there’s an understanding of “the precise points in the customer journey where value is either created or destroyed”. Above all, “a customer-intelligent company is making small adjustments every day to improve the experience”.

There is an obvious parallel with employee experience (EX) where many companies are looking to make a similar leap in maturity. I typically think about this in two dimensions: insights and activation.

Organisations who are just starting to build EX capability probably collect insights through an annual engagement survey. However, engagement survey results are likely to be looked at in isolation from other human capital data, even the results of other surveys. Key results from an engagement survey may be included in the company’s annual report and some engagement insights may be included in recruitment materials. These can help with building a consistent approach to how the organisation markets itself to potential recruits on LinkedIn and elsewhere.

More mature organisations supplement their engagement survey with agile pulse surveys. This means they can track sentiment on an ongoing basis. Connections are made between the findings of the engagement and pulse surveys, as well as automated joiner and exit surveys. This allows them to identify expectation gaps and misalignment. Insights are used to develop a broader employment brand, which is linked to organisational values and leadership behaviours.

A key tool is integrated people analytics that uses a broad range of connected data. These can include unstructured qualitative data, survey results, network data, human capital data, operational and business measures, and customer feedback such as NPS. Insights are used to personalise communications. The employment brand is translated into a differentiated employee value proposition (EVP) which is customised for key talent groups.

In mature, employee-intelligent organisations, data are translated from moment-in-time insights into employee journey maps and personas. They focus on a deep understanding of cohorts and critical talent and the employee life cycle. HR takes a design-thinking approach to employee experience. This means maximising the value of key episodes and moments, such as on-boarding, anniversaries, performance reviews, development discussions, and so on. They do this through prototyping and testing, from learning what’s working well and what’s not, and through rapid iteration. All people managers understand their role in delivering experiences that build trust in the future.

One problem with maturity curves like this is that they are seen as a sequential progression when it’s my experience that in practice things are typically messy and uneven. But by assessing where you fall in terms of your current EX capability you can identify where you need to focus and how to prioritise your efforts. To come back to Alan’s book again, he argues that it’s best to focus on lots of small changes rather than major programmes: “Your mantra for change is 100s and then 1000s of tiny changes”.

Tags: #EmployeeExperience #PeopleAnalytics #DesignThinking

This article was first published on LinkedIn on March 8, 2018

People Analytics and Employee Experience

This week I attended the People Analytics World conference #PAWorld17. It’s my third year and attendance has gone from 100 to 350, which indicates a real growth in interest. It’s always an event I enjoy. There are great case studies and there’s a lot of enthusiasm among attendees. It’s also personally satisfying, as I have a career-long interest in helping organisations use data and insights to make better decisions about people.

There was a lot of discussion this year on how the field should develop both inside organisations (how do you build an effective people analytics team?) and as a profession (including a call to establish a professional body). And I think this is a good discussion to be having as it feels like people analytics is not having as much impact as it could.

In fact, there’s evidence to show its impact is really quite limited. From an HR perspective, a 2016 report by the New Talent Management Network (NTMN) concluded that “Only basic people analytics are being performed by most organizations, undercutting the popular narrative that companies are rapidly advancing in this space.” The study also found that in those organisations which are doing people analytics, the most common focus areas by far are still turnover and acquisition. From an academic perspective, Janet Marler and John Boudreau conducted an evidence-based review of HR Analytics in 2016 and concluded that “despite evidence linking the adoption of HR Analytics to organizational performance, the adoption of HR Analytics is very low.” Even in the latest Deloitte Human Capital Trends report, people analytics is well down the priority list (a lowly 8th) and the authors note that “Readiness to capitalize on people analytics remains a challenge… Only 8 percent of organizations report they have usable data, while only 9 percent believe they have a good understanding of the talent factors that drive performance”. At PAW itself Alec Levenson, who kicked things off, talked about “amazing potential” but highlighted how “much analysis is disjointed and uncoordinated”.

This lack of impact is a problem, and it’s worth digging into in more detail.

Broadly, I think there are three domains within people analytics.

  1. The first is human capital measurement, workforce planning and reporting. In large organisations this is being automated by technologies whose analytical capabilities are increasingly impressive. They are able to provide leaders across an organisation with near-enough real-time updates on key measures. For a central team the manual workload is reduced and attention can shift to thinking about what information to highlight because of its impact on business strategy and performance. Smart algorithms, machine learning and artificial intelligence which are being embedded within these tools will simplify even this task and help drive further efficiency. The other role of the central team is to help leaders use this information to make better decisions regarding org. design and capabilities, which is often a storytelling and consulting role. In smaller organisations that cannot afford these smart systems, this whole area can remain a problem. In fact, the NTMN report revealed that many smaller companies still rely on Excel and have problems with basic data quality.
  2. The second domain is what you might call “classic” people analytics. Take a business and talent problem, such as “Why do so many of our engineers leave after three years?” Calculate the business cost (in terms of lost experience, cost to replace, etc.) and dig into the data to understand the drivers of turnover for that particular group and to identify possible fixes. These are often small projects dealing with specific “fires”. Sometimes the question might be broader, such as “Are our assessment tools actually predicting performance?” or “Does our performance management process reward collaboration and innovation?” I’ve called this domain “classic” because it’s been around for a long time. For example, in 1999 we produced a service-profit-chain analysis for a major retail bank. It was based on structural equation modelling and several years of data and it showed the business impact in terms of sales (penetration) of improving the onboarding of new staff. It led to a revised induction and training programme, which helped to improve performance. I felt I could have presented that client story at this year’s PAW and not felt too out of place. There have been advances in using more creative statistics (e.g. machine learning, cluster, segmentation, survival analyses), there have been improvements in data visualisation, and there has been a notable advance in qualitative data analysis, but these feel like refinements rather than major shifts.
  3. The third domain is “big data science” and it is the least defined and the most hyped. Data science outside of people analytics has grown quickly due to easy access to massive, open data sets which can be explored and tested. When you have a huge amount of data you can take a different approach to analytics, namely using lots of computing power to explore, test and refine different kinds of models to see what’s out there that might be useful to know. The challenge here for people analytics is really a data challenge. For obvious reasons, companies are reluctant to provide access to integrated data at an individual level. This is why approaches are often conducted at a very aggregate level on external data (e.g. tools like Joberate) or very specifically on topics like workspace and collaboration (e.g. tools like Humanyze). At PAW Randy Knaflic described how Jawbone, who you would expect to be ahead in this, has used biometric data to help some of its teams improve. With these kinds of data the focus for the people analytics profession is to build trust in and to demonstrate value through these kinds of approaches. I think the real future for big data science in people analytics is when large organisations fully embrace social platforms like Workplace (i.e. Facebook at work) and whatever MS/Yammer/LinkedIn evolves into, and when employees are producing data that social media-style analytics can explore (more on this below).

All three domains face challenges then. But I think there is also an overall problem for people analytics, in that it is missing a clear “hook” (or perhaps several good hooks). By this, I mean a way of communicating to business leaders and others what it is all about – not so much a unifying theme, but a clear message or set of messages (in marketing terms a hook is a simple way of creating interest). And I would suggest that one very good “hook” is “Employee Experience”.

Employee Experience is a term that is being used more and more. Sometimes it is used in a narrow sense to describe the user experience of the digital tools that employees have access to. But others define it more broadly, which I think is better, such as: “The intersection between employee expectations, needs, and wants and the organizational design of those expectations, needs and wants” (Jacob Morgan) and “The sum of perceptions employees have about their interactions with the organization in which they work” (Tracy Maylett and Matthew Wride). In this sense, it’s a nod to the field of Customer Experience and reflects an overall trend towards approaching employees in the same way as you would consumers and customers. The big driver of this change in perspective is the digital transformation of business and the trends associated with the future of work, such as a more flexible workforce, greater numbers of contingent workers, more diverse teams, dependence on social media, a focus on projects rather than jobs, etc. (I have written more about this here).

Personally, I really like Maylett and Wride’s description of thinking of employee experience as “creating an operating environment that inspires your people to do great things”. To my mind this means identifying the key interactions that employees have with the organisation throughout the life cycle and then applying design thinking to improve performance. It is a joined-up approach to org. design and capabilities, jobs, teams, rewards and the way people work. It includes understanding employee journeys and maximising the value of key episodes. It also means improving the digital tools employees use and reviewing the physical workspace in order to increase collaboration and productivity. A key focus is “inspiring people to do great things” – it’s about removing obstacles, simplifying processes, building trust and allowing people to do their best work and to contribute to the organisation’s mission and purpose.

Each of the domains of people analytics has a role in improving employee experience. “Classic” people analytics can highlight which aspects require optimising and for whom. One key contribution here is to break employee experience down into actionable parts through approaches such as micro-segmentation, personas, journeys, episodes, moments, networks, etc. As more data are integrated at the individual level, it’s possible to tell a far more holistic story about the “operating environment”. In terms of reporting, once you have identified the elements of employee experience that are key to your business strategy, it’s vital to help leaders review the progress that’s being made. One way that we are already doing this for our clients is through Employee Experience Scorecards, which pull in data and insights from a variety of HR, business and external sources. And data science perhaps offers some of the most exciting opportunities for improving employee experience through social media analytics. This can take the form of using design thinking to create shared digital experiences, testing different approaches through analysing real-time feedback, learning about your workforce based on their online behaviours and adjusting your plans as a result. The future of “business-to-employee marketing” is this direction.

To return to the start of this piece then, one of the things that strikes me attending PAW is the passion that people have to use data and analytics to make a positive difference. That’s why the feedback about limited impact is a bit disheartening. As well as debating the need for a professional body, I wonder if the lack of impact is partly because there’s a need for a better “hook” in order to tell a more complete story about the contribution the specific domains of people analytics make. If that’s the case, then Employee Experience is one possible hook and a powerful and positive one as we consider the trends associated with the future of work.

References:

  • New Talent Management Network “Still Under Construction: The State of HR Analytics 2016”
  • Janet H. Marler & John W. Boudreau (2016) “An evidence-based review of HR Analytics” in The International Journal of Human Resource Management
  • Tracy Maylett and Matthew Wride “The Employee Experience: How to Attract Talent, Retain Top Performers, and Drive Results” (Wiley, 2017)
  • Jacob Morgan “The Employee Experience Advantage” (Wiley, 2017)
  • Willis Towers Watson “Under pressure to remain relevant, employers look to modernize the employee value proposition” (2016)

Tags: #PeopleAnalytics #EmployeeExperience #FutureOfWork

This article was first published on LinkedIn on April 26, 2017

Employee experience: analytics and design thinking

Back in 1998 Joseph Pine and James Gilmore described the rise of the experience economy. They highlighted how successful companies stage engaging experiences through mass customisation.  One of the key points they made was that while services provide a value, experiences are memorable.  With the shift to digital, companies have put a focus on designing customer experiences and using customer insights effectively.

Many organisations are realising that they also need to focus on employee experience. In part, this is being forced on them.  As traditional elements of the employment deal have fallen away, organisations are looking for other ways to differentiate themselves, so they can attract and engage the best people.  What’s left are the more experiential elements, such as values, culture, the physical environment and the work itself. At the same time, research has shown that these factors, along with purpose, are vital for motivating people to perform at their best.  It’s also the case that the digital natives companies want to retain have very different expectations as consumers than previous cohorts.  As a result, many organisations are having to play catch up.

All this is leading to a growing interest in measuring and managing employee experience.  This takes a number of different threads:

  1. First, there is an emphasis on integrated people analytics.  The aim is to link together all the touch points that impact employees and to identify the key moments that matter.  In customer terms this is akin to understanding customer journeys (indeed, employee journeys can be one output).  Or in technology terms, this is like shaping user experience.  There is also a focus on micro-segmentation.  This means getting far more specific in how you identify employee groups.  This might include critical roles and talent, or life-stage and values-based clusters.  By digging into micro-segments, and playing back the findings through tools like employee personas, you can help design meaningful strategies for communication and engagement.
  2. Second, there are capabilities that require attention.  For example, it becomes important to have an overall listening strategy, which deploys a range of tools and approaches.  And these insights need to be collected in a way that means they can all be connected to tell a holistic story.  Manager capability is another crucial area.  It goes without saying that managers have an impact on a lot of the touch points that impact employees.  The requirement for managers to deliver the value proposition well makes their selection and learning especially important.  In addition, it’s vital to personalise communications through social technology and portals.  And the physical work space must allow collaboration and conversation.
  3. Third, some organisations are establishing an oversight role, a Director of Employee Experience.  This role can encompass insights, employer branding, people analytics and aspects of learning, capability and rewards.  And sometimes they are filled by people with a background in customer experience rather than HR.  This reflects the fact that employee experience is a business (and customer) priority.  Another success factor is the ability to apply design thinking in this role.  This means developing models to make sense of connections, valuing emotional as well as practical concerns, and finding solutions through experiments and pilots.

What’s the impact of all these elements?  Our research indicates there is a performance premium for companies that get it right.  They have higher levels of employee engagement.  They find it easier to attract key talent. They face fewer regrettable losses. And they report stronger business performance.  This is especially the case when a focus on employee experience is part of a broader modernisation agenda.

References:

Tags: #EmployeeExperience #EmployeeSurveys #PeopleAnalytics

This article was first published on LinkedIn on September 26, 2016.

Employee surveys and people analytics

Although there has been speculation about their demise, I see employee opinion surveys, and the insights you gain from them, as being at the intersection of two exciting trends:

  1. People analytics: HR and business leaders want to make evidence-based decisions about people. Data science and predictive modelling provide new options for understanding how people impact performance. And insights from employee surveys are a crucial part of the people analytics mix. You can use employee survey insights to help answer key business questions: How do we attract, retain and engage the digital talent we need to achieve our strategy? What are the barriers to more effective collaboration and how can we overcome them? How do we build a performance management system that encourages innovation? How do we create leadership roles that are of interest to our next generation of leaders?
  2. Treating employees like consumers: The consumer experience has changed radically over the last 5 – 10 years. Employees are increasingly sophisticated consumers of their company’s employee value proposition. Understanding and managing employee experience is becoming a core focus for HR. Employee surveys are again an important means for answering key questions, especially when you spotlight key talent segments: Is this a company where I feel I can make a difference? Is it somewhere where I believe I can have an impact? Do I feel supported by my manager and encouraged to try new things? Can I see a future for myself here? Do I feel recognised and rewarded?

Employee survey data are a critical part of the people analytics picture, especially as it comes to measuring the employee experience and understanding how people impact performance. In fact, there has never been a better time in which to conduct creative and value-add employee research, as new tools provide new ways of listening and making sense of data, including unstructured qualitative data.  It’s a good time to be doing more with employee surveys.

Tags: #EmployeeSurveys #PeopleAnlytics #EmployeeExperience

First published on LinkedIn on February 11, 2016