Against this background, it’s perhaps not surprising that corporate executives and Compensation & Benefits managers have begun to ask two questions: 1) does our current sales commission plan work effectively with the sales team; and 2) are there any other effective ways to motivate our sales people?
With both questions in mind, we conducted a quantitative analysis based on the variable sales compensation data of 5130 sales professionals collected from 42 pharmaceutical or health sciences companies from 2014 to 2015. Incumbents studied came from four countries – UK, Germany, Russia and Romania. By dividing each individual’s actual sales incentive by their target sales incentive, we were able to get a rough performance score for each individual and track the variation of performance scores from 2014 to 2015.
Sales performance curve
In order to analyse performance data with more granularity, we divided sales professionals under the study into three groups (Exhibit 1):
||% of total sample
||1.2>performance score >=0.6
*Individuals with performance scores above 1.8 or equal to zero are considered as outliers, hence are excluded from the analysis.
We then looked at each performance group along two dimensions: 1) how many individuals have moved out of/stayed within the group during these two years, and 2) how the average performance score of the group has evolved from 2014 to 2015. Upon completing data analysis of each group, we also took into account the latest research findings on how to motivate each type of performance group most effectively.
Data analysis and findings
The average performance score of the total sample increased from 0.91 in 2014 to 0.95 in 2015, showing a slight increase of 4%. Exhibit 2 shows how the impact varied according to our three performance segments. At first sight, based on the change in nominal performance, lower performers appear to have made significant improvements while the strong performers’ score slumped from 2014 to 2015 (see nominal performance line in Exhibit 2). However, the nominal impact is looking at performance scores of the same incumbents from year 1 to year 2 without considering the impact of external random factors. This perspective, however can be skewed. For example, many parents might have noticed that when their children did poorly in one maths exam, they would often make improvements in the next exam even if no extra efforts were made by the child between the two exams. The reason behind this is that a student’s poor score in the one exam could often derive from random factors like he/she made a careless mistake during the exam or he/she missed the teacher’s lecture on an important maths principle. As these random factors don’t often repeat themselves in the next exam, the student has a good chance to get a better score in the next exam.
For a similar reason, a salesperson’s performance in a particular year can be impacted by random factors like a poor economy or a sudden large order from a new client. To control the impact of these random factors, we used a regression formula to calculate the real performance evolution within the two years (see real performance score bars in Exhibit 2). On this basis, we found that the strong performer group was the only group showing an improvement (with the real score increasing by 0.03) across the years. No significant improvement was identified with lower performers while the real score of core performers decreased by 0.03 between the years. This can be seen in Exhibit 2; further assessment of the impact on each performance segment is described in more detail below.
Sales performance evolution.