HR leaders and compensation managers are increasingly examining the potential business benefits of personalizing total rewards components. For smaller organizations these efforts are not overly complex, but as they grow to become mid-sized with more complex and varied compensation plans and approaches, the task of scaling personalization efforts changes. This is particularly the case as it relates to technology, data and analytics requirements, and is leading many organizations of 250 to 1,000 employees to reevaluate the compensation systems and tools they deploy.
It is worth the effort of pursuing effective rewards personalization due to its connection to improved engagement and productivity. It particularly makes sense as the seemingly ever- increasing competition for top talent impacts employee and candidate expectations. Both current and future employees expect to be treated as individuals, understood and valued, particularly in how they are compensated.
However, it’s not a straightforward task to apply total rewards personalization across the workforce to improve engagement, as it typically involves ascertaining and making decisions with respect to an array of employee preferences. Small organizations often use spreadsheets or other simple tools to produce and manage rewards personalization plans, but the use of spreadsheets does not scale very effectively, and their continued use amplifies the risk of errors. Tools that include personalization capabilities are available, and increasingly they integrate support for newer analytic methods and machine learning. These tools can help smaller organizations to maintain rewards personalization even as they grow.
Today’s compensation tools are built to comprehensively address an array of rewards factors. Once jobs and positions are priced (independent of the people occupying them), appropriate analytics or reports can be used to ensure both compensation equality across genders and pay equity. This step, addressing equal pay for equal work, is essential, and our compensation management benchmark research shows that more than four-fifths (82%) of organizations consider it important.
With equity issues addressed, the organization is ready to create comparable total compensation scenarios involving alternative base salaries and long- and short-term incentives from which employees in each compensation peer group will be able to select the compensation mix that best meets their needs and risk/reward profiles.
The next step for an organization with modest HR and compensation team resources and systems capabilities that seeks to personalize compensation is to achieve a measure of process scalability by dividing the workforce into a manageable number of segments and mapping each to what I call a “presumed compensation preference.” For example, the preference of early- career employees who did not elect to participate in the company’s 401k plan would in many cases be a compensation plan emphasizing disposable cash as priority. Conversely, a more senior-level workforce segment with fewer concerns about disposable cash could be mapped to a set of compensation packages that allow deferring or trading salary dollars for an opportunity to build investment or enhance different types of incentives.
Allocating compensation initially at the segment level makes an otherwise time-consuming exercise somewhat more manageable, and a tool designed to enable this type of strategic personalization will help keep the exercise manageable as the organization grows. Once the segmentation and assigning of default compensation plans is complete, managers can then meet with each employee to confirm or modify the default segment preference. This information can be fed back into the tool to help hone the baseline strategy over time.
Compensation plans typically are structured to incentivize employees to choose to link a large portion of their compensation to performance. This is done by constructing the plans to equate a dollar of salary to more than a dollar of more performance-based variable compensation. Of course, to maintain legal compliance and compensation equity, a compensation system must be designed with appropriate governance and controls to ensure that all employees have the same compensation opportunities.
Individual preferences will likely change over time as an employee goes through different career and life stages. How compensation options are configured should therefore be revisited periodically through employee satisfaction surveys or perhaps machine learning-enabled sentiment analysis tools to ensure the right array of choices or trade-off scenarios is being offered to benefit both employee and employer. Analysis of exit interviews can also reveal gaps in accomplishing employee satisfaction related to compensation.
In my view, for smaller organizations to remain talent-competitive they must examine how to be effective in personalizing compensation. The use of a dedicated system to support this is essential, as the use of spreadsheets will not scale as an organization grows. There are smarter ways to lessen the challenge of personalizing compensation using tools built for this purpose along with internal and external compensation data to adapt to the reality of a diverse and smarter workforce.