Organizations want to tailor total rewards programs to be successful in a fierce talent market. Yet, knowing employees’ true push and pull factors can be fraught with biased or piecemeal information that leads to ineffective rewards. Analytics help HR close the gap to design rewards programs that attract and retain the required workforce for the future.
For the typical employee the exit interview is fraught with social awkwardness. The individual is caught between conflicting imperatives: to give the honest reason for leaving, or soften the answer to keep the door open – particularly as work becomes more project-based and contract working spreads. So what do they say? Many in this scenario choose the safe option. The employee finds it far easier to blame poor pay, say, than a poor supervisor.
This is just one example of tension between what employees say drives them, and why they behave the way they do, whether done consciously or unconsciously. The mismatch makes it harder for HR to zero in on the specific factors that push employees away: a micromanaging boss, a laborious commute or an unclear career path, for example. An incomplete picture of people’s desires skews how HR views their organization’s employee value proposition. And this issue becomes even more pressing as the composition of workers evolves.
Organizations need to understand their current workforce, how it might change – for example, by pinpointing fast-growing workforce segments – and what these unique segments truly value in order to start designing total rewards strategies with future employees in mind. Tweaks to contractual elements such as pay and benefits may be in vain if providing a better employee experience or more purposeful work aligned to your values would be more effective in motivating and retaining your people.
HR is increasingly supplying data that describes the talent landscape. Mercer’s 2018 Global Talent Trends Study found 70% of executives say HR provides them with actionable analytics to improve decision-making, more than double last year (up from 27%). Still, executives believe there is more that can be done with data: the majority of the C-suite in our study intends to invest in workforce analytics in 2018.
In particular, data give HR the opportunity to bridge the employee say-do gap – intelligence that helps stave off resignations or inform more cost-effective employee rewards programs. A well-designed and applied analytics program gets people to join, stay and do their best work. It also helps HR gauge accurately which policies work best to improve retention and performance.
HR today tends to rely on generic and partial tools to design employee rewards. We polled an audience of HR practitioners at a WorldatWork webinar hosted by Mercer in December. Only 3% of the 285 attendees used predictive analytics to decide changes to rewards programs. In that small poll, the majority used benchmarking of peers’ programs to guide their own value proposition. Exit interviews and employee surveys were popular too. These techniques are valid and useful, but alone they are piecemeal, they do not provide the ROI of particular benefits in a given work environment, and – as we saw above – they are subject to employee bias.
Just as Google knows you better than you do, analytics in the workplace opens a window onto employees’ true motivations and frustrations. Executives and employees complain (but perhaps not always to HR) that today’s total rewards strategies don’t fit their needs, leading to issues of turnover or lack of engagement; indeed only two in five employees say their organization has a compelling or differentiated EVP.
Fortunately, HR already sits on a goldmine of data. In aggregate, data such as HRIS data, past pay and promotion actions, engagement surveys and social media paint a full picture of an organization’s internal labor market and can facilitate linkages between seemingly unrelated factors.
The results can be surprising. From Mercer’s experience, delving into the data of one company revealed a good salary reduced the likelihood of turnover by 3%; but promotion was linked to a 12% higher chance of turnover. People who were promoted found themselves with more responsibilities but inadequate training to manage heightened expectations. Without sufficient support, movement between jobs or departments had actually become a more critical push factor than pay levels.
Once you know the full story it is easier to design rewards that suit real human needs. By clustering employees based on attributes such as age, income, life stage, career level and certain preferences, HR can create distinct groups – or personas – that should be offered different combinations of rewards.
For example, employees identified as ‘leaders’ may be more motivated by incentive pay and health coverage, while vacation and benefits are less of a priority. On the other hand, ‘strivers’ may prize flexibility and recognition over career progression. Even HR leaders can benefit from these personas. In a spot poll of Mercer’s webinar audience, the majority of participants described themselves as ‘household managers’: multi-tasker, exhausted, juggling. More tailored rewards around flexibility, for example, could help give many of us a break.
People’s behavior is the surest sign of what works for them and what doesn’t. Analytics, in assessing that behavior, can help HR to design rewards that enable each group of workers to be their best.
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