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This article is a condensed version of the study Workforce analytics: the gap between the rhetoric and the experience, by Aaron Sothmann and Siddharth Mehta (not peer reviewed).
Aaron Sothmann and Siddharth Mehta, authors of Mercer’s article Workforce analytics: the gap between the rhetoric and the experience, state that the vast majority of HR departments are under pressure to make better decisions, faster. As a result, they rely on intuition, personal experience and corporate belief systems rather than the analysis of data when making decisions.
Workforce analytics – and evidence that organisational performance can be improved by adopting data-driven talent management practices – have been around for the last 70 years. Yet despite this compelling promise, organisations continue to favour ‘gut feel’. The biggest challenge to embracing a more objective approach to decision-making is not the cost but the inertia of leaders when it comes to embracing a digital mindset.
That said, the use of HR analytics in South Africa is still in its early stages. The authors therefore advise HR professionals to identify high-impact opportunities, where solving a defined people problem would generate significant return on investment, to demonstrate the value of workforce analytics to business outcomes.
A manufacturing company was struggling to attract and retain employees in a manual production role. To support business growth objectives, the company would need to hire approximately 900 people in this role over the next three years. Owing to a 25% attrition rate for this role, replacement hires made up a large proportion of this target. The cost of replacement was about R129 500 per employee – R48.6 million a year.
This presented a high-impact opportunity to apply workforce analytics and demonstrate significant business value. The Mercer team summarised the premise of the problem as follows: Can we apply predictive analytics to identify high-risk attrition targets so the organisation would have enough time to determine if a high performer was worthy of a retention effort?
A common challenge to applying workforce analytics is the misapprehension of ‘not having enough data’. But the question of data sufficiency can’t be answered without actually conducting the analysis. Only then can we determine whether the results can be generalised beyond the sample.
In this case, the company had a human resource information system (HRIS) which had been storing employee transaction data since day one. The team took three years’ worth of data, such as hire and exit date, pay changes, manager changes, job changes and compensation, to conduct the analysis.
Using this data extract and additional insights gathered from employee focus-group discussions and manager interviews, they applied a disciplined data science approach to develop several predictive models of varying complexity and accuracy.
Model predicts employee churn with 83% accuracy
The best model had an 83% accuracy rate in predicting which individuals would leave two months before their actual resignation.
It’s typical to find these models have a lower predictive power in reality than in simulations. But even at a 30% accuracy rate, the organisation would be able to save close to R3 million a year through reduced attrition.
Interestingly, the model with the lowest accuracy in predicting churn revealed the most compelling features leading to an employee’s exit from the organisation. These insights could be used to update HR policies and programmes in order to address this.
Model identifies determining factors for employee attrition
The organisation’s leaders saw the business value that could be generated through solving the attrition problem. By investing a relatively small number of resources, they could solve a large business problem – all thanks to the application of workforce analysis.
Technology is providing huge opportunities to advance the way we gather, combine and analyse data about people at work. We now have exponentially more data (and, more importantly, better access to data) than ever before – both structured and unstructured. However, we don’t yet know how all this data will help us.
Workforce analytics is an experiment, and not all workforce analytics projects will lead to solutions. But failures will lead to insights and actions that will further our ability to harness value from data. An empowered workforce needs good data to drive decision making.
Just as marketing data and buyer insights are leading business transformation efforts, talent insights have the potential to deliver accelerated success on the people agenda – both to enhance the employee experience but also to drive better decisions around how to retain and engage people.
But are companies getting these insights?
Compared to last year, companies – both globally and in South Africa – didn’t move the needle an awful lot on the type of analytics that HR is able to provide. Very few report being able to translate data into predictive insights, and nearly one in three is still only able to produce basic descriptive reporting and historical trend analysis.
Companies in the financial services and logistics industries globally are ahead of the curve but still have a long way to go. Even with all of the data that is being collected, senior executives globally are not getting the kind of talent analytics they need to make business decisions.
For example, executives say that understanding the key drivers of engagement would be most value-adding, but only 35% of HR leaders globally are able to provide this information. This is especially surprising given that most companies today have at least some sort of engagement survey in place. Predictive analytics, such as identifying which employees are likely to leave in the next six to 12 months, are even less common and productivity outcomes even less likely.
HR and employees recognise that the disconnect may be due in part to a capability gap — both groups ranked data analytics and predictive modelling among the top three in-demand skills for the next 12 months.
The risk of not leveraging talent data is especially acute when there is so much organisational change on the horizon. When decisions are informed only by financial and marketing data, there can be unintended people consequences. For example, the World Economic Forum’s Future of Jobs study1 found that “women are at risk of losing out on tomorrow’s best job opportunities” as disruption and displacement are likely to occur in job families with the largest share of female employees. When HR is able to partner with the business to facilitate an evidence-based decision-making process, they help to mitigate these risks and ensure that the talent implications are being considered, especially when companies are making structural changes that will see great talent – along with the roles – leave.
The issue of increasing longevity and whether retirement ages have been set too low would seem like a low priority issue for South African employers and policymakers. Surely addressing the crisis of youth unemployment warrants greater attention? The problem here comes when we see these two concerns as mutually exclusive. By promoting the needs of youth at the expense of the aged, we ignore the interconnectedness of intergenerational dynamics. We prioritise because we believe limited resources dictate prioritisation, but occasionally we find that suppressing the interests of one group only exacerbates achieving the needs of another.
What follows is a discussion of the missing age dividend. Framing our understanding of the value of older workers should give employers pause for thought – and a deeper appreciation of what stands to be lost.
Taking age off the table when deciding who should work where
As we’ve seen, making HR policy decisions based on hunches and intuition can result in some disastrous business choices. With transformation a priority in South Africa, employers tend to assume the most obvious way to achieve it is to have older employers step aside to create opportunities for a new generation of workers. On what basis have we made that assumption? The power of HR data analytics is that we can test these assumptions empirically.
As we discussed in last year’s Benefits Barometer, studies from Scandinavia and Germany are challenging these conventions by suggesting that productivity and, by extension, job creation can actually increase when older, experienced workers are retained. But why rely on secondary information when we can do the research ourselves?
Consider the work that Mercer has done on examining this question of the productivity contribution of older workers in a range of different industry settings. Haig R. Nalbantian, Senior Partner at Mercer, provided a particularly insightful study that highlighted how effective HR data analytics can go a long way towards resolving these types of emotive policy debates.
Nalbantian argued that when traditional economic approaches for measuring productivity are applied to older workers, they typically pick up a decline in productivity. But this type of assessment may well miss a big part of the productivity story. What the analysis may miss are spillover effects, or productive ‘externalities’, that might be so significant that they more than compensate for the fall-off in productivity or performance.
For example, including older workers can add value by:
Nalbantian’s study goes on to point out that it’s only by doing the data analysis that we can really understand whether these factors are at play. His analysis showed that the results could be quite variable across different companies. For example, in a case study on a company in the professional services sector, length and dispersion of experience proved to be the strongest drivers of year-to-year sales growth. By contrast, a case study involving a retail company showed older employees performing less well. And in a natural resource company, it turned out that older workers drove productivity in one unit, whereas it was length of service that drove the performance of another unit in the company.
Nalbantian’s concluding points are as follows:
Nalbantian’s most forceful conclusion, though, is that there is simply no substitute for applying a careful, disciplined measurement of performance drivers to prevent rash decisions being formulated.
Older people can – and do – contribute to the economy
Let’s extend the discussion of older people to beyond the immediate workplace. We tend to assume that older people are an economic deficit. As the 2015 World Health Organization (WHO) study on ageing and health argues, “we need to start adapting to shifts in age structure in ways that minimise the expenditures associated with population ageing while maximising the many contributions that older people make … economic analyses of the implications of population ageing are evolving, and the models that are often used today may lead to inappropriate responses2.”
The report cites an economic indicator known as the dependency ratio, which defines anyone older than 65 as a ‘dependant’, ignoring the fact that “chronological age is only loosely associated with levels of functioning3”. As we’ve seen, there are many people over 65 who are earning incomes. More importantly, a large percentage of people in post-65 careers are engaged in mentoring, consulting and the creation of small business enterprises that are the backbone of developing a robust SMME sector for job creation.
In addition, people over 65 may have retirement savings that can be redeployed into the economy through assisting with the education needs of younger family members, the funding of ‘second-start careers’, spending on ‘grey-product’ consumption, intergenerational wealth transfers, and taxation ... to say nothing of the benefits elderly people provide through volunteer work and social care in their communities.
A 2010 study from the UK reframed the costs of caring for the aged against these economic contributions. It became apparent that older people were actually making a net contribution to society of nearly £40 billion (R683 billion). This was expected to grow to £77 billion (R 1 314 billion) by 2030.
While we don’t have comparable research for a developing economy, we do believe that South Africa’s cut-off age of 60 for being a productive economic contributor is simply nonsensical.
There is simply no substitute for applying a careful, disciplined measurement of performance drivers to prevent rash decisions being formulated.
The WHO study introduces us to a different economic model that helps us understand that investing into addressing the needs of the elderly actually has a meaningful pay off.
Figure 3: Investment in and return on investment in ageing populations
In addition to the economic benefi ts we have described, that pay-off includes:
Framing the discussion on ageing in this light allows policymakers to have a more considered view of the fair distribution of society’s resources. As the authors of the study argue, “reframing the economic questions in this way shifts debate from a singular focus on minimising the costs of population ageing to an analysis that considers the benefits that might be missed if society fails to make the appropriate adaptations and investments4.”
But let’s return to our opening discussion where we suggested that the interests of the elderly and the youth are not mutually exclusive. Intergenerational reciprocity is still the most prevalent fall-back position for caring for the elderly. So, when our institutions fail to provide the necessary support (as seems to be increasingly true in South Africa), many employees, particularly young black women, have no option but to pick up the slack for their families.
Conversations with HR directors across large and small employers alike, confirm this pressure persists. After the loss of employees to maternity or paternity obligations, the next big impediment to employment continuity is loss of an employee to family care obligations.
In 'Benefits that Matter', we believe this is one area where employers could identify potentially creative solutions. Are there not ways employees could apply for paid (or unpaid) family-care sabbaticals, much like maternity leave, so they could leave work for a period of time to provide basic care to a significant other?
Canada, for example, has recently changed its labour laws to allow for the following types of leave:
Are these not benefits that would speak directly to the heart of the care crisis in South Africa? Can we actually afford to not consider them?
Take age off the table and the decisions about how someone is best deployed in a company should be consistent with the best HR policies. These policies (discussed in detail in the section named 'Benefits that matter') argue for the benefits of diversity, non-hierarchical management structures, flexible schedules and the desperate need for continuous training and mentorship in South Africa.
HR departments and employers generally know which employees are contributing, and how much, and where they could be best deployed at any given time. Performance measurement is a well-entrenched practice. The decision to work (or continue to work) should be a win-win for both parties – continued value to be contributed and a continued desire to keep contributing.
We all work for different reasons:
The workplace will be a better place when ageism is removed and people remain because they passionately want to be there.
1 World Economic Forum. 2016. The future of jobs: employment, skills and workforce strategy for the Fourth Industrial Revolution.
2 World Health Organization. 2011. WHO Study on global AGEing and adult health (SAGE).
4 World Health Organization (2015), p. 17.
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