5 Tips for Eliminating Unexpected Risk from Incentive Compensation Plans

To understand how your incentive compensation plan will work before it’s rolled out to the sales force—it is important to model the plan under a range of different scenarios. Yet, most companies are unable to design and build meaningful plan models, analyze and interpret model results, and use model results to design plans and plan changes.

Failure to model, validate, and analyze the impact of new plans or plan changes before they are rolled out to the sales force exposes companies to unknown budget risks, unanticipated commission costs, and excessive mid-cycle plan modifications.

The following tips are derived from a long-term study of the incentive compensation management practices at 100 large companies and reflect the best cross-industry practices to leverage modeling to confidently launch plans and understand the impact of plan changes prior to roll-out, while eliminating financial and human risks.

31 percent of organizations analyze results and incentive program design annually

Incentive compensations plans that are rolled out correctly can increase team performance by up to 44%

Incentive Research Foundation

Proactively perform modeling

Proactively perform modeling so that you have plenty of time to adjust, fine-tune, and communicate your plan.

Begin by leaving plenty of time to do modeling (typically six to eight weeks) prior to when you need to communicate the plan to the sales force. Then, proactively model your plan well in advance of the rollout. This gives you visibility into likely results and expected commission costs so that you have enough time to act on the model results and make changes to your plan as needed.

Leaving enough time provides flexibility to conduct several iterations of your model, enabling you to analyze the assumptions behind your plan, review multiple scenarios, and evaluate the pros, cons, biases, and risks of each scenario.
With enough time, these results can be shared with other stakeholders and their feedback incorporated into the next iteration of the plan before final decisions are made.

In addition to leaving sufficient time, proactive modeling means that you have thoroughly tested all the elements of the plan—including structure, metrics, parameters, and associated business rules—prior to plan rollout. This also allows time for clear documentation to be written, training materials to be developed, and communications to be made prior to the start of the plan period.

Define your modeling goals

Define your modeling goals and evaluate your modeling effort relative to the objectives of the plan.

Use the objectives of the plan to help you define the goals you want to achieve from your modeling activity. Use the modeling goals to obtain buy-in from all stakeholders before you start modeling. Afterwards, measure the success of the exercise relative to those goals. Some examples of modeling goals include:

  • predict total payments
  • confirm expected results
  • validate plan assumptions
  • establish budgets and contingency options
  • assist with ongoing accrual process
  • understand payment distributions
  • analyze top/bottom performers and any outliers
  • predict volatility and trends over time
  • identify biases to ensure fairness and equity
  • test robustness for unusual or unexpected circumstances
  • determine sensitivity to changes in factors that impact results.

Stay focused and avoid the tendency to model

Stay focused and avoid the tendency to model every measure and every scenario.

Focus on modeling only those scenarios and metrics that provide direct visibility to the business concerns of the plan. Avoid the temptation to model every measure and every scenario. Getting distracted by the capability of the model to run any scenario and succumbing to the “let’s try this too” is a common mistake that wastes time on irrelevant or immaterial details.

Stay focused on the scenarios and parameters that significantly affect your plan, and ignore remote possibilities and metrics that are not sufficiently material to the goals of the modeling effort. Use experience with past plan results and underlying data to prioritize and choose key metrics and scenarios for modeling. Then, select scenarios that are in alignment with the goals of the modeling effort.

Conduct sensitivity analyses

Conduct sensitivity analyses to assess any weaknesses and understand how different conditions can impact the outcome.

Use sensitivity analyses for both key plan parameters and for any likely uncertainties in underlying data. The goal here is to test your assumptions for any weaknesses in the plan by understanding which factors strongly influence the outcome of the plan. Keep in mind that a model is only as strong as its assumptions. Regardless of how robust of a model you may have built, there is always a chance that reality may not be in line with your assumptions.

Understand what areas of the plan are most sensitive to changes in data and plan parameters to help you design the most efficient approach to modeling. Knowing the sensitivities of the data and plan parameters allows you to minimize that risk and know—ahead of time—what to do if actual results differ from sales predictions.

Finally, robustness of your plan can then be tested by studying the impact on the plan based on varying degrees of change in such factors such as plan parameters, performances, and market conditions.

Follow a structured, disciplined approach

Follow a structured, disciplined approach for analyzing model results to prevent people from relying on ‘gut’ or ‘instinct’.

All analyses should strike a balance between group performance (such as total payment) and individual performance (such as the person with the lowest payment). Having a clear process helps to prevent overemphasis on a single data point, which is a common tendency as managers start looking to see how their favorite person is affected by the plan change.

With a well-defined process, you can quickly get clear answers about what will or won’t work without sifting through reams of paper or being sidetracked by lengthy discussions about individuals. A structured, disciplined process for analyzing modeling results allows analysts, executives, and managers to study the impact of the changes based on empirical evidence and make decisions with a high degree of objectivity and confidence.

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