How is HR analytics evolving your reward function?

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Andrés García Ayala
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This piece was co-authored by Alfredo Rodríguez-Muñoz, Associate Professor, Department of Social Psychology, Complutense University of Madrid, Spain.

HR Analytics is being praised as a transformational power that is shaping the present and future of HR. There even seems to be a consensus around how embracing HR Analytics leads to gain competitive advantage, as it can substantially improve the quality of decision making related to people in organisations.

There is a gap between the value that HR Analytics can provide and what companies are actually getting from it at the moment.

However, many struggle to find the way to take the most out of HR Analytics or to even get started with it.

Why do people struggle with HR Analytics?

Some of the main factors for this struggle to get value from people data are the technology available (i.e. the human resource information system in place), the data skills of the HR team or even how comfortable management feels dealing with people data.

Another important factor is the previous analytics maturity of the HR function.

These factors are some of the reasons why some companies are getting ahead than others in terms of people analytics maturity and winning the so much called ´race for predictive analytics´.

HR Analytics maturity scales: where does Reward fit in?

It's clear that there is a gap between the value that HR Analytics can provide and what companies are actually getting from it at the moment.

This is one of the main motivators of why some authors have created HR analytics maturity scales that can help to assess how well organizations are applying analytics to people data.

One example of these scales can be found in The New HR Analytics, which suggests that human capital analytics can be organized in the following five steps of increasing value for the organization:

  1. Recording our work: tracking through measurement the efficiency of processes.
  2. Relating to organization’s goals: setting targets for organizational processes and reviewing them on a regular basis.
  3. Comparing our results to others: benchmarking data with comparable groups.
  4. Understanding past behaviour and outcomes: looking for and describing relationships among data without giving meaning to the patters. In other words, descriptive analytics.
  5. Predicting future likelihood: ascribing meaning to the patterns observed in descriptive analytics. In other words, predictive analytics.

However, the Reward function has already been dealing with people data and championing for better HR technology for a very long time.

Furthermore, the Reward function tends to be comprised of data-savvy professionals that are accustomed to basing their decisions on analytics. And Reward has been this like this since before the introduction of HR Analytics.

It could be said that the Reward function was already in an advanced stage in terms of analytics maturity prior to HR Analytics becoming mainstream.

For instance, almost any Reward team has reached the third step in the previous analytics scale and is already providing consistent descriptive or predictive analytics.

Therefore, as HR embraces the analytics journey...

  • Where is Reward?
  • Is Reward getting any further with this revolution?
  • Or it is simply waiting for the rest of the HR functions to catch up with it?

Reward is a pioneer in the HR Analytics journey…

The Reward function has a long tradition of measuring several metrics and keeping track of them over time in order to make decisions.

The Reward function tends to be comprised of data-savvy professionals that are accustomed to basing their decisions on analytics.

Benchmarking analysis, scenario modelling, correlation of variables and even predictive analysis are quite common in many Reward teams. Salary surveys are a great example of this as most companies already assume that it is worthy to invest every year large amounts of money to get market data.

This data is then processed and analysed in order to get insights from it that eventually help to define and adapt the Reward strategy.

"Data-savviness"

This data-savviness is one of the main reasons that explains why recent trends in Reward are much more sophisticated that in the rest of the HR functions in terms of analytical complexity.

For instance, in the past few years it has been experienced a strong focus from companies in personalizing and tailoring the benefits package to the individual.

This has caused an increase in popularity of flexible benefits packages  and the development of employee benefits portals that allow employees to access their benefits data anytime and from anywhere.

Benchmarking analysis, scenario modelling, correlation of variables and even predictive analysis are quite common in many Reward teams.

Another example is the importance that is being currently given to do a workforce segmentation in order to adapt the benefits package. Companies are investing time and money to better understand the different interests, motivations and needs of their staff.

This data is then being utilised to analyse what sort of benefits should be offered in order to provide a benefits package that can satisfy the workforce to the highest extent possible.

Also, these analysis not only help companies to design the benefits package but also to choose the best communications channels to interact with employees about these matters.

In this way, Reward is acting as a pioneer in the HR Analytics journey as is showing the rest of the HR function how decision making is improved when people data is gathered and analysed in the right way.

…and at the same time HR Analytics is helping Reward to go even further

However, Reward is not merely waiting for the other HR functions to upgrade analytically as it is also experiencing interesting developments thanks to the HR Analytics revolution.

In the same way that HR in general struggles to support with data the idea that people is the main asset of the company, Reward has been having more than a tough time to support with evidence why wellbeing programs are important for organizations.

However, the sophistication in data analysis that the adoption of HR analytics entails and the new advancements of the data available thanks to the latest technology is bringing hope in this regard.

More specifically, wearable technology is likely to bring a whole host of new data that can be analysed in relation to wellbeing. It is expected that researchers will focus in the following years on studying the relationship that wellbeing can have with relevant organizational factors such as employee performance, engagement or absence practices.

Therefore, this expected wave of Big Data represents a huge opportunity for those that want to analyse the relationship between employee wellbeing and organizational factors and also to be able to finally produce a coherent and reliable ROI calculation for wellbeing programs.

Reward has been having more than a tough time to support with evidence why wellbeing programs are important for organizations.

There is already a strong body of research that argues that employee wellbeing can have many positive consequences on the individual and the organization.

In part as a result of this, some companies are heavily investing in employee benefits such as flexible hours, holiday trade or enhanced parental leave, in order to increase employee wellbeing. It seems clear that employee wellbeing can have a great importance in the implementation of the organizational strategy but at the same time the extent of this relationship can be blurry and benefits poise a huge investment for organizations.

This is precisely why businesses would gladly welcome a better understanding of the employee benefits value-creation.

HR Analytics can close this gap between research and practice by making the link between benefits and the reward and organizational strategy more clear.

Also, Reward analytics will just be more meaningful thanks to HR Analytics

However, the most important benefit that HR Analytics is bringing to the Reward function is an indirect one.

The fact that areas such as learning, development and talent management are starting to regularly produce metrics in a consistent and structured way means that the wider HR function is starting to be provided with high-quality data from the different HR areas and not solely from Reward. This is great news as Reward, despite its great influence in employees attitudes and behaviours, is not enough to predict in isolation effectively key organizational variables such as engagement or turnover.

As HR Analytics matures in companies, more strategic and comprehensive analysis will be made possible.

As HR Analytics matures in companies, more strategic and comprehensive analysis will be made possible. For instance, we will experience the development of more advanced algorithms that aim to predict things like the tenure of a new hire or how much budget should be allocated to leadership training.

An example based on employee relocation

One good example of this can be found in Optigrow, a big data-based solution developed by IBM that supports in the decision-making of employee relocation from soon to become obsolete areas to growth areas of the business through an analytics algorithm.

Having this sort of data available will help to produce insights that aid to improve human capital decision making and provide financial gains to the business.

As companies learn how to make the most of HR Analytics, we will witness how the gap between research and practice is reduced and how evidence-based management people practices become more widely implemented in all areas of human resource management.

Ultimately, reaching the point in which key organizational variables are predicted accurately will become a reality rather than just a wild dream in the mind of idealistic HR professionals.

The future looks bright for both Reward and HR in general, and if something can be predicted in the future, data will be at the core of this.

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