How to Use Predictive Analytics to Forecast Sales Staff Commissions

Predictive analytics is increasingly accepted as a way of improving the customer experience or optimizing supply lines, but it’s underutilized in one area: forecasting labor costs. That goes double for sales staff that work on commission. Managers need to be able to predict their commission expenses, but the qualities that make sales staff good at selling make them bad at predicting which deals will close. Their optimism is a problem for CFOs trying to forecast expenses. Enter predictive analytics, the voice of reason that brings hazy forecasts back in line with reality. Executives can use tools already at work in other areas of the company to better prepare for the future. How? To understand, start with the specific difficulties of predicting commissions and then see what a predictive analysis does differently.

Commissions as an accounting problem

Accounting for commissions is one of a CFO’s biggest headaches. Although commissions aren’t paid until a sale is made, best practices require that they be included when the cost is incurred to track profitability. There’s a surprising amount of detail involved in forecasting commission expenses. It involves predicting not only sales but also which agents will close which sales. Most companies have a variety of pay structures to account for based on who made a sale and when; a small mistake could have a large impact on the overall budget. Choosing an estimation model isn’t easy, though. There are a few common approaches:
  • Use the first months of a year to create a fixed monthly estimate for rest of the year. This method is easy to use but not very accurate. Fixed monthly estimates don’t account for season, labor fluctuations, product changes, and other factors.
  • Use the previous year’s monthly commissions as monthly estimates. Previous-year totals are as easy to manage as fixed monthly estimates. They’re also more accurate since they reflect seasonal influences and company-specific trends. What they miss are allowances for outside influences (market fluctuations, new competitors, supply problems) or internal change (new staff, commission structure changes, mergers).
  • Rely on sales staff predictions to project expenses. Good salespeople are often bad forecasters. 54% of deals predicted by sales staff never close because agents tend to be unwilling to admit defeat on a sale. In addition, staff paid through commissions have little incentive to accurately forecast since doing so takes up time they could be selling.

Getting answers with Predictive Analytics

Predictive analytics offer greater accuracy than traditional models. The process begins with feeding a machine learning algorithm reams of data on customers, market fluctuations, sales staff activity, and more. The algorithm looks for patterns and relationships between factors that may impact performance. It then uses those conclusions to produce a tailored month-by-month prediction of commission expenses. These predictive estimates are a game-changer. Companies who implement data-driven forecasting have a 82% accuracy rate on a deal by deal basis versus a 46% rate for those using other methods. In aggregate their accuracy rises to 95%, nearly 20% higher than the industry average. Using predictive in this manner is highly efficient. Much of the needed data is also beneficial elsewhere in the organization. For example:
  • Sales numbers help project revenue.
  • Staff performance data informs human resources processes.
  • Market factors are useful in optimizing the supply chain and spotting opportunities.

Bring the whole team on board

Predictive models don’t replace the sales staff in forecasting, but they do provide incentives for participation. When the data they submit is accurate salespeople are rewarded with results that identify which clients are most useful, where their time can be spent most profitably, and what commissions they can expect throughout the year. That promotes large-scale support of predictive methods within the company. Consistent internal adoption increases the ROI on technology investments. In short, extending predictive analytics into the accounting realm can positively affect overall profitability and performance. Savvy CFOs should investigate how their processes might be improved by embracing predictive analytics.

An intuitive, easy-to-navigate interface makes predictive analytics accessible to everyone, not just the CIO and IT. Contact Concepta to learn about our custom analytics dashboards!

Related Articles

No more posts to show.

A Faster Way Forward Starts Here