Measuring the impact of sales commission plans can be painful. Here’s a real world example we once observed: a company had an annual Zoom call to discuss the effectiveness of its commission structure. Nine stakeholders were attending. None of them had accurate data. Some didn’t have any data at all. Of course, all nine of them were very opinionated about the topic. The call lasted for two and a half hours, until someone thankfully had to leave. Does this type of scenario sound familiar?
It shouldn’t be that complicated. Measuring the impact of sales commission plans ultimately comes down to a pre/post analysis; seeing how performance compared before and after the changes were made. The root cause of the painful conference call was a lack of data that was up to date, granular, and consistent across systems.
All too often, there are multiple spreadsheets, sometimes interacting with a sales commissions tool or calculator, and there are issues with version control, files crashing, and inconsistencies between different data sources which need to be manually checked. There isn’t any trust in the data and the nine stakeholders are unable to start the conversation from the same place.
Getting clear data on how the new commission structure performed should turn the two and a half hour conversation into a 30 minute one. With the right data, an analysis would be granular enough to uncover what percentage of reps are meeting quota, and how average and individual quota attainment has been impacted. Digging deeper into individual quota attainment, especially for individuals with significant changes before and after changes were made, can help identify other variables that impacted overall sales performance during these periods.
All of this information should be available at a glance. A good sales commissions tool should automate all of the data collection and reconciliation involved here, and make it easy to consume. Ideally, all of the stakeholders would have a clear readout on comp plan performance with clear, updated dashboards. In our earlier example, an automation tool could have saved this team a collective 18 hours in a single afternoon. How much time is your team losing?