Using the right quota setting methodology is essential for increasing sales performance. According to the Aberdeen Group, pharma organizations that use quota setting tools report higher quota attainment percentages when compared to non-users. Imagine what an increase of a few percentages in quota attainment can do for the bottom line of your organization.
But how can you set quotas more accurately and enjoy lower turnover rates, a better motivated sales force, and improved strategy execution?
Setting quotas is a challenge for sales organizations year after year. 52% of companies consider setting effective quotas the biggest sales compensation challenge, while 42% have trouble differentiating top performers, according to Sales Management Association research. The stakes are high. Inaccurate quotas can lead to lack of motivation in the sales force, turnover, and failure to achieve sales objectives.
For pharma organizations, setting quotas accurately can make the difference between failing and thriving. One of the struggles is to identify the best quota setting methodology. A common choice are historic quotas, whereby goals are set by looking at past results and adding a projected increase. While historic quotas are easy to calculate and understand by the sales force, they have an important drawback: they only allow organizations to look in the rearview mirror, disregarding future opportunities.
A less common choice but one that allows for higher accuracy is regression-based quota setting. This approach is more challenging because it requires additional data and quota setting expertise. However, quota setting can make or break the ability of a pharma organization to execute the sales strategy, so being able to look at past performance as well as at future opportunities and set correct quotas is crucial.
Regression-based quota setting can be summed up in five essential steps:
- Identify factors that impact sales
These can be sales factors, such as historical product, market and share volume, and historical growth rates; or non-sales factors, such as method of payment, demographics, inventory levels and customer satisfaction.
- Build regression model and evaluate factors
At this stage, data drives results. Run the regression model and eliminate extraneous and cross-correlated variables.
- Finalize and evaluate model
Adjust the model based on business insights, market landscape and anticipated changes. Lastly, test the model to verify that it allocates goals correctly.
- Allocate quotas
Using the data at hand, use the model to predict future performance and to translate it into contributions.
- Communicate quotas to sales reps
This step is crucial to having a motivated sales force. Every sales rep should understand how quotas were calculated and why their objectives are attainable.
Regression-based quota setting is ideal for pharma organizations because it allows them to take into account complex factors and remain flexible to market opportunities and regulatory changes. It might be more challenging to implement and communicate, but with the right people, tools, and expertise, organizations stand only to gain.
Find out more about sales quota optimization for pharma companies and feel free to contact me if you have a particular situation in mind that you’d like to discuss.