6 Tips for Ensuring Error-Free Incentive Compensation Results, Payments, and Reports

To drive desired behaviors, a sales organization must have confidence in the accuracy of its incentive compensation results, payments, and reports. Yet, inaccurate results are often the cause of incentive compensation management failures. These errors have both individual and organization-wide negative consequences. At the individual level, errors lead to mistrust in plan results and decreased selling efforts because of additional time spent shadow accounting, disputing results, and complaining about the plan. Continuous mistakes may also give way to a dissatisfied sales force.

Organizationally, errors result in over- or under-payments, not to mention the negative fallout for noncompliance with financial reporting regulations. Errors also result in time wasted by administrative and IT staff handling disputes, correcting systems, and reprocessing payments. Furthermore, errors often result in decreased revenue and margins because of unmotivated salespeople and higher recruiting and training costs due to increased sales force turnover.

In many cases, errors go undetected because of a lack of knowledge about what to look for and how to automate the process for uncovering and fixing them. The following six tips, derived from a long-term study of the incentive compensation management practices at 100 large companies, reflect the best cross-industry practices for eliminating and preventing incentive compensation errors.

51% lack full confidence in their organization‘s ability to manage compensation processes

Incentive Compensation Management solutions for variable sales compensation can reduce errors by more than 90% and administrative work by more than 50%.

Establish controls, checklists, and procedures

Establish controls, checklists, and procedures to ensure the process is followed and any deviations from the process are highlighted.

Incentive compensation plans—even the best of plans—change frequently. While making plan changes on a whim is not advisable, neither is holding compensation plans constant and ignoring the dynamicProcess discipline is the first step in eliminating errors. Discipline means having the right controls, checklists, and procedures in place to ensure that the process is followed and any deviations are highlighted. This rigor helps to catch errors early and reduce overall processing time.

Develop a quality assurance checklist by identifying specific Quality Control (QC) checkpoints in the process. The goal behind the checklist is for the sales operations team to conduct intensive QC to catch potential errors or problems early in the process, which results in less rework and lost time. Some logical checkpoints include: after input loads and data adjustments, and before payroll creation and report production.

To further ensure accuracy, define procedures to track any errors not caught by the standard QC process in an error log. At the end of the processing cycle, the operations team should review these errors to determine if they are indicative of any weaknesses in the existing QC process.

If necessary, update the QC documentation, implement additional QC checkpoints, or change the process to prevent errors from recurring. Without this frequent review, the QC process will rapidly become obsolete.

Ensure standards for quality and timeliness

Ensure that the standards for quality and timeliness of upstream inputs are in alignment with the needs of the incentive compensation management process.

Incentive compensation calculations often require the use of many upstream data inputs, and it is errors in these data sources that are frequently the source of errors in incentive compensation results. But the need for quality data is often overlooked or dumped onto a sales operations team, which ends up absorbing the impact for late and inaccurate payments.

One of the ways to improve data quality is to control upstream inputs through service level agreements (SLAs) with data owners. The key is to hold data owners responsible for the timely delivery of quality data. Creating a culture of accountability helps ensure that you are able to meet your processing schedule and there is no delay in commission and incentive payments.

To improve the turnaround time of upstream data, impose discipline in terms of delivering data on a set schedule, in a set format. Convey your expectations to data owners, including communicating the consequences of not adhering to standards (in terms of missed deadlines or inaccurate results).

Establish guidelines for handling exceptions

Establish guidelines for handling exceptions, outliers, and unusual circumstances to minimize the risk of errors occurring due to those.

Due to their very nature as process aberrations, exceptions and outliers often give rise to errors. Mitigate the risks of errors they can pose by having well-defined processes to handle them.

Exceptions should be made only when absolutely necessary. Establish a thorough validation process to ensure that the exception is not affecting the results. Create guidelines for situations and timelines under which exceptions will be allowed and spell out instances where they won’t.

Rather than waiting for final results to check for outliers, introduce checking at every stage of processing. Establish guidelines for dealing with and validating outliers and unusual circumstances.

Define an error management plan

Define an error management plan that captures lessons learned when errors do occur and results in actions that prevent errors from reoccurring.

Even under the best of controls, and in spite of all precautions, errors can still occur as a result of human or systemic mistakes. An error management plan helps to handle these situations. The plan should be action-oriented and include the following information:

  • how to evaluate the impact of errors
  • how to assess the impact on timelines
  • how to fix errors
  • validations to be performed
  • verification of final results
  • communication procedures

In addition to defining an error management plan, there should also be a process in place to learn from errors and prevent them from recurring. An example is to hold postmortem meetings after every processing cycle to dissect the errors and underlying root causes. Ensure that all lessons from such meetings are documented and meticulously applied.

Automate the process

Automate the process (including quality checks) as much as possible and conduct rigorous testing of changes to calculations, data, and processes.

With manual processes involving many individuals, it is virtually impossible to eliminate errors. Process automation not only eliminates the possibility of new errors being introduced while fixing old ones, but also shortens the processing cycle.

One essential, but often ignored, standard is to avoid implementing a change without testing it. Establish deadlines for accepting changes and refrain from making last-minute changes that cannot be tested.

While this may be a tough decision, consider that numerous errors creeping into the process will effectively render results ineffective. It is wiser to defer, test, and implement the change at a later date.

For unavoidable changes, establish a change management process that mandates rigorous testing, whether the change is to data, plan parameters, or the process itself. Be sure to run a robust data set through multiple periods, especially components that won’t be run early or often (e.g., a contest scheduled for the last quarter). Record and maintain the history of any changes made to data.

Include validations and verifications

Include validations and verifications at logical breakpoints in the process, with the goal of identifying and fixing errors as early in the process as possible.

While it may seem intuitive and time-saving to run the whole process and to check and verify the final result, it can actually increase the overall processing time. That’s because when there are errors, so much time can be spent finding and fixing them that results may be delayed by weeks, or even months.

It is better to identify errors early on in the process before they snowball into major disasters. Build quality assurance into your processes by including some logical breakpoints to validate and, if needed, correct calculations, such as:

  • validate incoming data to ensure that dirty upstream data does not pollute
    your system
  • check alignments and credits thoroughly
  • ensure eligibility rules are being applied correctly
  • validate payments before delivery (through automated and
    experience-based checks)
  • verify commission statements