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”).
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).
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”.
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:
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.
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.
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.
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.
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”.
Alan Pennington, The Customer Experience Book (Pearson, 2016)
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.
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.
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.
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)
