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 percentage points in quota attainment would do for the bottom line of your organization.
But how can you set quotas more accurately to engender lower turnover rates, a better motivated sales force, and improved strategy execution?
Quotas are a constant issue for sales organizations. According to the Sales Management Association, 52% of companies consider setting effective quotas to be their biggest sales compensation challenge, while 42% have trouble differentiating top performers. The stakes are high – after all, inaccurate quotas can lead to a demotivated sales force, high turnover, and failure to achieve sales objectives.
For pharma organizations, accurate quotas can be the difference between thriving and failing. That accuracy begins with identifying the optimal quota-setting methodology. Unfortunately, historic quotas – whereby goals are set by looking at past results and adding a projected increase – are still the most common approach. Although historic quotas are easy to calculate and communicate to the sales force, they have a major 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, since this process can make or break a pharma organization’s ability to execute on its sales strategy, it’s crucial that quotas take into account not only past performance but also future opportunities.
The process for 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, as well as historical growth rates; they can also include non-sales factors, such as method of payment, demographics, inventory levels, and customer satisfaction.
- Build the 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 the model
Adjust the model based on business insights, the market landscape, and anticipated changes. Then 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 translate it into contributions.
- Communicate quotas to sales reps
This step is crucial to ensuring that your sales force stays motivated. 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 complex factors into account while maintaining the flexibility to adjust in response to market opportunities and regulatory changes. It might be more challenging to implement and communicate, but with the right people, tools, and expertise in place, your organization will reap both short- and long-term benefits.