Measuring the relationship between a product’s reimbursement position and performance begins with gathering key data points for as many products as possible.
Given the importance of reimbursement to any pharmaceutical brand, comprehensive reporting and analytics must be available for any brand to meet the two goals of reimbursement-directed marketing:
This article reviews a structural approach to guide development of the analytics and related reporting that may assist brands in meeting the above two goals. The approach, summarized in the graphic below, recommends aggregation and use of data from numerous internal and external sources to inform answers to six foundational questions related to brand reimbursement.
Organized along each of the above questions, the remainder of this article introduces an approach to answer each of the above questions through analytics and reporting.
Measuring the relationship between a product’s reimbursement position and performance at a customer begins with gathering the following two data points for as many products as possible.1
Reimbursement position should reflect how a customer applies patient cost and/or prescribing obstacles to the product relative to the costs and obstacles assigned to competitive, or alternative, agents.
Relative product performance should be a comparison of the product’s performance (e.g., market share at the customer) – compared with the product’s typical market share outside of the customer.2
Locating where products fall on these two metrics within a 2 x 2 grid called the Payer Influence Window can inform an understanding of the relationship between product position and performance at a customer.
The more products falling into the “influential” quadrants in the window, the more consistently the customer steers prescribing towards preferred agents, and away from those less preferred. Quantifying the relationship between position and performance can inform other foundational questions such as account prioritization, discussed next.
A systematic effort to prioritize customers can ensure that brand resources—both human and financial—are directed toward those customers with the greatest potential to impact brand success. A customer’s potential to impact brand success is more than just a function of the number of enrollees or class prescriptions represented by the customer.
Potential impact on brand success should be based on the number of prescriptions or revenue that a customer may steer to, or away from, a brand via application of differential patient out-of-pocket costs or prescribing obstacles.
A metric called “revenue in play”3 considers three variables to estimate this revenue and, importantly, whether the customer’s decisions may be steered by the manufacturer. These metrics are:
When considered together, these variables inform customer prioritization. Certainly, various nuances add complexity to measuring the variables above. However, a commitment to consider all three together to estimate “revenue in play” is useful in prioritizing a customer based on its potential to affect brand success.
The intent of performance measurement is not to determine winners or losers or to populate slides for a quarterly business report. Performance measurement must propel brand success via a benchmarking processthat isolates the reasons why certain customers may be performing well while others may not.
The isolated reasons driving performance—whether good or bad—can then become emulative or corrective opportunities. Fundamentally, results of performance measurement should be a primary input into customer planning, strategy development, and tactical execution.
There are two essential components of performance measurement necessary to stimulate strategy development and planning:
Given the size of rebates often paid to payers, it is imperative that analytics and reporting seek to understand what is gained from them. When evaluating gain from contracts, data and reporting need to inform answers to these three key questions—compared with answers when the brand is in a non-contracted position.
Compared with a non-contracted alternative…
Each of the first two criteria support the third—a positive return on investment (ROI).4 ROI measures the efficiency of an investment and enables comparison against other types of investment.
At its core, pull-through includes all efforts to drive incremental revenue when a brand’s performance at a payer falls short of its reimbursement position. Any improvement in reimbursement position—a new formulary position, for example—can immediately be classified as a pull-through opportunity, as performance falls short of the improved reimbursement position.
Analytics to prioritize pull-through opportunity must isolate instances in which the prescribing shortfall is greatest. Performance shortfall is the revenue value of the difference between actual performance and performance that is more consistent with the brand’s reimbursement position. As seen in the graphic below, there are two analytical methods useful in isolating pull-through opportunities.
Most of the components of this essential exploration are too complex to review in this brief. At its core, however, analytics and reporting must meet two imperatives under this question.
Changes in a brand’s reimbursement position—especially those made by a large payer or pharmacy benefit manager—may have a considerable impact on brand revenue. Therefore, a brand’s gross sales forecast should reflect the impact on revenue of changes in reimbursement expected during the forecast period.
The brand forecast, for example, should reflect the revenue impact of an expected downgrade in formulary position at a large payer. The impact of all expected changes in reimbursement should be aggregated and applied to the gross and net revenue and revenue deduction forecast.
From year to year, revenue deductions associated with rebates will likely decline or increase. Analytics must explore what drives these fluctuations—especially when the change results in a departure from a forecast.
Analytics may isolate which customers are responsible for the change. Analytics may also explain the reasons for the change by assigning the driver into one of the following categories at left in the graphic below. A waterfall chart like that at right can be applied to show how revenue deductions change over time.
Achieving the primary goals of payer-based marketing requires comprehensive use of data. This article previews a process to organize the analytics and reporting to gain reimbursement positions that maximize net sales and to enable the brand to drive sales given those positions.
About the Authors
Bill Barr, Carl Hart, and Zack Blacker, are all principals at Acuitas Analytics, a consultancy centered on payer-focused data management, reporting, and market access.