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The COVID-19 pandemic has caused financial uncertainty across business platforms worldwide. While specific healthcare industry sectors may have seen growth due to a heightened observance of personal health or the large-scale manufacturing of personal protective equipment (PPE), the more significant trend of the pandemic was a decrease in healthcare revenue. The leading factors are increased hospital costs due to COVID-19 hospitalizations, PPE purchasing, and canceled surgeries1.

With hospitals operating at steep losses, medical device sales were impacted as well. For a sales representative, this resulted in less opportunity to win contracts or secure purchases from surgeons. Many companies had to resort to drastic changes in their sales compensation plans to ensure their stability or satisfy the sales leadership. A lesser considered impact of the pandemic is its effect on future quota setting periods. When setting a quota, the most significant contributing factor is the sales in the baseline period, which is defined as a fixed interval where the upcoming sales should be comparable to a historical time frame. This is typically the prior year during the same period. The global pandemic necessitated further analysis into whether the impacted semester can accurately serve as a baseline period. Furthermore, the methods considered for the pandemic can and should be extended to other events that may disrupt normal buying patterns. These events may include natural disasters, military action, or political upheaval.


Various methods can be considered to adjust the baseline period. Some of these methods are described below:


Instead of using an impacted period for the baseline, this method would consider another time period outside this range. The baseline would still reflect actual sales values, but it would be based on historical sales rather than the prior year. This method loses the accuracy of the current market by venturing further into the past. Furthermore, this method replaces the entire baseline period and not just the affected weeks.


In this scenario, the latest period of sales data will be considered for the baseline setting. The idea is that the impact is not as prominent in the more recent months, and the sales would more accurately reflect a non-impacted period. The downside to this approach is that it would discount seasonal trends.


This method would still incorporate the impacted weeks but to a lesser degree. The weightage could be fine-tuned and tested before deciding on a final decision. The benefit of this method is that it may still capture the buying pattern of the impacted period and does not serve as a complete replacement.


The sales before the impacted weeks would be analyzed and projected further into the future. Various methodologies could be deployed, such as using the average sales for all remaining months or a simple linear regression model.


The baseline would consider a historical sales period for the weeks deemed impacted instead of the prior year. This method has the benefit of preserving the baseline as actual sales and limiting the adjustment to only those weeks deemed impacted. The difficulty arises from identifying and isolating the impacted weeks and issues discussed in 2.1 regarding the use of historical sales.


Sales in weeks deemed impacted would be replaced with the 26-week moving average. This method preserves the greater sales trend while limiting the fluctuations caused by the loss of sales. The advantage of a moving average is that it replaces the weeks while still accounting for a decrease in sales volume. The 26-week window, however, raises issues with periodicity. The window would only consider the prior semester and not the prior year. As with 2.2, this also raises the issue of identifying the impacted weeks.


This method is extremely similar to 2.3. However, the window is increased to 52 weeks to factor the prior year's sales into the trend. The larger window makes the series less prone to fluctuations and, therefore, less accurately resembles the actual sales trend.


The organization has the flexibility to decide which methodology to choose based on budget, timelines, or other factors. The primary basis for selecting the intended method is to maintain simplicity without compromising the effectiveness of the overall incentive plan. The "non-impacted period" and "latest semester baseline" are easy to implement because they require no additional calculations. Furthermore, they are easier to communicate to the field since they need no extra analysis and are based on actual sales performance. The accuracy of these methodologies, however, is low. Other approaches, especially the "replacement of impacted weeks with a moving average," would require more effort to calculate and implement. The time it would take to identify weeks and calculate the moving averages is only justifiable by the increased accuracy of the method.

LinkLearn More - "How To Manage Sales Incentives In Disruptive Times"

Another critical element in the compensation plan and design process is communication during roll-out. Complex incentives that are difficult to understand decrease employee morale and increase confusion. A reliable way to ensure proper delivery is to share the plan's details with every stakeholder and create an effective communication strategy between employees and the home office.

Setting sales quotas is a crucial part of the business plan and requires delicate effort. If the quota is set too low, sales will not reach their full potential. If it is set too high, reps can become frustrated and feel alienated. The balance of accuracy, implementation effort, and field communication illustrates the factors to consider during quota setting. 

Below are rankings of each factor for the methods discussed above.

Table 1: Adjustment Methodologies and Factors to Consider

  Quarter Low   half Medium full full   High  




Implementation Effort

Field Communication

Overall Assessment


Non-Impacted Period Baseline


Quarter full
  • Easy to implement and communicate to the field
  • Looses accuracy by venturing further into the past


Latest Semester Baseline

half Quarter full
  • Easy to implement and communicate to the field
  • Looses accuracy by not considering the seasonal trend


Weighted Average of Impacted and Non-Impacted Period

half half half
  • Higher complexity makes it more difficult to communicate to the field


Partial Semester Projected

Quarter full Quarter
  • Complex method that doesn’t significantly increase the accuracy
  • High effort needed to fine-tune the forecast model


Replacement of Impacted Weeks with Non-Impacted Period

half full half
  • Looses accuracy by using the historical sales
  • Isolates the impact which decreases the field communication


Replacement of Impacted Weeks with 26 Week Moving Average

full full half
  • Complex method that may decrease the field communication
  • Use of moving average decreases the influence of the impacted weeks while not totally ignoring the overall trend


Replacement of Impacted Weeks with 52 Week Moving Average

full full half
  • Same feature as the above method but also factors in seasonality

Source: Axtria, Inc. 


Of the methods discussed in section 3, three of them require isolating impacted weeks. One method to accomplish this is to calculate a flag based on deviation from weekly sales trends. Another major factor to consider is the data granularity or scope of aggregation.


When determining the impacted weeks, the most important factor is considering how egregious the sales differences were compared to the average weekly sales. Given a weekly sales trend, the average weekly sales and standard deviation can be calculated. A flag can be constructed for each week as the number of standard deviations away from the mean (EQ.1).

EquationAn impacted week must have a significantly negative flag to show serious deviation from typical trends. A helpful analogy is that the flag can be related to a z-score and, therefore, a corresponding percentile.

Table 2: Z-scores and Their Corresponding Associated Percentiles











Source: Axtria, Inc.

Ideally, the impacted period would show higher enrichment in the flag than the predicted amount using the percentile. Inspection of the flag values can be used to identify a range of weeks.


As with creating any trend, granularity and aggregation issues arise when choosing the scope of the analysis. When considering a given territory's impacted weeks, it is challenging to distinguish outside impact from normal sales trends. The reason for this is that territories may experience significant sales fluctuations throughout the semester. When the data is rolled up to higher levels, the flagged weeks will be more likely to represent actual impact due to less variation in buying patterns. However, issues arise when territories under the same aggregation have different impacted periods than the greater aggregate. This would result in incorrect replacement periods.

LinkLearn More - "Top Three Opportunities For Medical Device Sales Operations"


As the COVID-19 pandemic in the United States slows with increasing vaccination or resurges with more prominent variants, the lessons learned during this period can be extended into the future. The adjustment to a more accurate baseline period due to outside factors will result in more accurate quotas and a higher yield from sales reps. With ever-growing data capabilities, historical data should be used to reflect on prior years through reporting and serve as a valuable tool in future quota setting. The most obvious use case for the same methodologies discussed in this blog is natural disasters. With proper justification, quotas can be refined to a more accurate value.

LinkClick here to learn more about Axtria's Medical Device, Medtech, and Diagnostics solutions.



  1. American Hospital Association (2020). Hospitals and Health Systems Face Unprecedented Financial Pressures Due to COVID-19 [Internet]. Accessed on Oct 11, 2021. Available at: https://www.aha.org/system/files/media/file/2020/05/aha-covid19-financial-impact-0520-FINAL.pdf 

Written By:
Varun Arora
Varun Arora is a Manager in Commercial Excellence at Axtria with over nine years of experience in sales performance management for various pharmaceutical and medical device clients. He has extensive experience in sales force effectiveness – incentive compensation and administration, reporting, and sales crediting for pharmaceutical clients across the US and Europe. At Axtria, he has been working closely with a large medical device client and supports the client's end-to-end commercial process with a primary focus on incentive compensation.
Denis Pesacreta
Denis Pesacreta is an Analyst in Commercial Excellence at Axtria. He has been supporting a large medical device client for over a year and has assisted with many aspects of the project, including sales crediting and quota setting. His expertise is in medical devices, data analysis, and bioinformatics.
Mohit Tandon
Mohit Tandon is a Director in Commercial Excellence at Axtria with over 12 years of experience in sales performance management for various Fortune 500 clients. He has in-depth knowledge of territory alignment, roster management, call planning, incentive compensation, quota setting, and reporting. Mohit has extensive experience in the pharmaceutical, medical devices, and energy sectors, with a deep focus on project management, consulting, and change management. At Axtria, he has worked across the entire spectrum of sales performance management on the Axtria SalesIQ™ platform.

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