Rethink benchmarking and use data to understand your global reward and benefits strategy
A reward strategy that is right for one organisation will be suboptimal for another, while one reward package will meet the personal requirements for one employee but be wholly unsuitable for the next person.
That means total reward strategies and the way organisations look to remain competitive with their benchmarking outcomes is pivoting too.
Organisations have traditionally looked towards sector peers to compare and benchmark salaries within the same industry. However, increasingly most roles are becoming industry-agnostic, with the term ‘transferable skills’ never being more relevant.
Are you comparing the right roles?
Take certain job profiles and the traditional approach of comparing the roles and skills required within a DevOps team as an example, within the fast moving consumer goods (FMCG) sector, to those roles in similar software teams against an FMCG competitor. Is it not more relevant to instead compare and benchmark them to an enterprise tech software-as-a-service business? Or a leading professional services software development team? It’s the rate for skills that are comparable in this example, rather than what the norm is within the industry sector.
If employees are themselves looking across various industries when they compare the market-rate available to them, perhaps the corporate benchmarking process could ensure a skills-based comparison approach is a central part of the strategy.
With increasing prevalence of portfolio careers and people moving roles more frequently from gig to gig, it’s the skills and competency with those skills that are relevant drivers of personal reward. And so an organisation’s reward strategy based on skills would likely provide a positive impact on talent attraction and retention, as well as optimising the reward investment being made.
Seeing the full picture
However, total reward teams across the globe are working with one arm behind their back. They rarely get the full picture of total reward investments being made and so their own internal understanding of ROI on the spend is fragmented, incomplete and inaccurate.
We are, of course, in a transition period on this subject, where skills-based reward is in its infancy. That new world of benchmarking across skills is not yet mature enough to fully drive the reward strategy. The more traditional approach to benchmarking will be around for a while yet.
Sort the fundamentals first
Therefore, one immediate, practical step that we have outlined previously revolves around getting some fundamentals sorted first.
If your business is global, the benchmarking season can be a challenging and stressful time, leaving you managing multiple activities and decisions at once. In one country, data can be blended from two or three of the main vendors, as well as from local role-specific data providers. There will be a myriad of new and old roles, with various definitions that all need to map to a global norm.
Along with salary benchmarks, managers need to gather other internal and external market data, such as market inflation, attrition, unemployment, local GDP, and many more.
How do you align your global reward strategy against so many variables? How do you ensure you act fairly and in the best interest of so many countries, each jostling to get the best for their employees?
This is especially true when finance teams leave little wiggle room for final budget allocation and rarely see the effort required from reward teams to get there.
Data is the answer
1. Start by taking the time to understand all your data points from start to end. Take note of how they interact and where data flows. Ensure that this is not just done with your reward teams, but other stakeholders like Finance, HR and IT, and anyone else who touches the process. Ensure you understand the level of friction at each data point, and what drives it.
The problem, unsurprisingly, is a data one. There is no easy method to set these budgets without measurable data. Good data science can mean the difference between being competitive or non-competitive, and under or over budget.
Therefore, one of the most impactful interventions you can make is to remove data-friction wherever you can — particularly in the most impactful areas.
2. Create a list of all the data flows you find, and rate the complexity of each one, and its impact. Taking estimates from several different stakeholders and averaging these out is a great way to improve the accuracy of your ratings.
High data-friction can be driven by:
• The transfer of data internally and externally, or between technology systems that cannot easily talk to one another, and maybe use different date cut-offs, currencies, or file formats.
• High levels of normalisation or transformation of data required, such as the effort needed to translate data into a common format – is it daily, monthly, annual, annualised?
• Wide variations of human input, so the underlying systems allow data entry according to definitions set by the individual author rather than agreed global standards.
• And everyone’s friend, spreadsheet errors. Mixing up cell content, over-complication, lack of peer review, lack of skills and training and more account for this.
3. Then start with the easiest and most impactful items. Work through an identified area of friction one-by-one. Perform regular retrospectives to measure the impact it has on the project. Ideally, this is after each intervention, but you may need to wait until the end of the benchmarking season.
There is no magic fix for all data-friction issues.
However, organisations would see the benefits of finishing the digitisation of total rewards across its entire enterprise. Total rewards is one of the last remaining analogue functions where spreadsheets and local technology instances are the main source of both transactional data and simulated data for modelling and analysing.
Becoming a requirement
This trend of building a digital total reward platform to underpin and automate the benchmarking process, and the total rewards strategy as a whole, is growing fast and becoming a requirement.
That will continue until data friction for vital reward processes is no longer a consideration for total rewards teams, who are relied upon by CHROs, CFOs, and CEOs to keep the organisation attracting and retaining the best talent through the right levels of reward and to use data-driven evidence to optimise and understand ROI in that vast reward investment.
In partnership with uFlexReward
uFlexReward is an HR Technology spin out from Unilever HR and the first of its kind.