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On-Market Pricing Strategies

Pharmaceutical ExecutivePharmaceutical Executive-04-01-2013
Volume 0
Issue 0

List pricing strategies that diverge from these approaches without "hitting the cliff".

Pharmaceutical executives devote significant resources to strategic decision making when launching key products—and rightfully so. Many facets of a product's optimal marketing strategy—from clinical programs, product pricing, competitive positioning and differentiation, to stakeholder value and messaging, DTC, and sales force deployment—require careful attention and investment both at launch and throughout the product lifecycle to maximize access.

In our experience, the power of pricing as a tool to maximize a brand's financial performance is often overlooked after a product is launched. While contracting strategies are typically adapted over time to differentiate net pricing for specific payer channels or customers, opportunities to improve financial performance through better list price management over the full course of the product's lifecycle often go unrealized. Decision makers may lose sight of the fact that on-market pricing is almost costless relative to other marketing investments, thereby resulting in a high ROI, if implemented effectively.

In this article, we first highlight traditional price-increase strategies for on-market products and patterns over time. Next, we describe how research and analysis can be used to identify situations in which list pricing strategies should diverge from these traditional approaches without "hitting the cliff"—that is, actions by payers that could risk a downgrade in price or restricted access to patients. We conclude with some practical observations based on our experience with pricing decision making for many on-market products.

How it's done: on-market list pricing strategies for top brands

To review the most common approaches to list price management, we examined the wholesale acquisition cost (WAC) price actions of the top 100 pharma brands over the past 10 years. We observed three distinct phases to the product lifecycle with respect to list price strategy. During the first two to three years following launch, manufacturers tend to be conservative with price increases. Over the "mid lifecycle" phase of the product, which might extend for 10 or more years, the list price strategy is most often based on annual price increases in the range of 4 to 6 percent per year, with 5 percent being the most common. The last phase, the three years prior to loss of exclusivity, is characterized by sharp increases in price in anticipation of generic competition (Figure 1).

When brands are in the mid lifecycle period, we see evidence of varied price-increase strategies: some manufacturers take multiple price increases per year, make moderate changes in price increase strategy year-over-year, or vary price increases based on market conditions. Table 1 presents the annual price increases of the Top 100 brands for the period 2002 to 2011 (cumulative percent of price increases in each year in increments up to 10 percent, and percent of price increases that exceeded 10 percent). An annual price increase in the 4 to 6 percent range is the most common approach in most years, with average annual price increases ranging from 3.4 percent in 2009 to 7.7 percent in 2010. Moreover, the majority of price increases, 63 percent over the entire period, were below 6 percent, with 85 percent of price increases below 10 percent. Recently, higher annual price increases have become more prevalent. During 2011, the most recent year we examined, the highest annual price increases among the top 100 brands were for Strattera, Zyvox, Namenda, and Copaxone, with 18.2 percent, 16.6 percent, 16.1 percent, and 14.9 percent annual increases, respectively.

One-time price increases within a calendar year have been most common, and are often taken in January or September. Recently, taking two smaller price increases during the year instead of one large price increase has become more the norm, possibly to avoid payer scrutiny and response to larger one-time price increases. For example, the 16.1 percent Namenda price increase for 2011 was spread across two price increases (8 percent and 7.5 percent). Whereas previously only 19 percent of price increases were "two per year," this practice has doubled to nearly 40 percent in the most recent three years.

The mightiest of the four Ps

The on-market list price strategies that have been used for top pharmaceuticals brands, as described in the preceding section, have generally been less than optimal. This is because insufficient research and analyses are applied to support these decisions, likely leaving money on the table. Price is clearly a marketing tool with high leverage—therefore, even small increases in list price can result in significant increases in profit margin, provided there is limited impact on volume. Pricing expert Raffi Mohammed has noted, for example, that a 1 percent price increase would result in anywhere from a 16 to 155 percent increase in operating profit, in companies across many industries with a wide range of underlying cost structures. For pharmaceuticals manufacturers, the key to realizing this leverage is to know where "the cliff" is; that is, at which point a price increase will result in a downgrade in access by key payers, and a corresponding decrease in volume utilization.


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The opportunity: the discrete effects of price on payer access

The pharmaceuticals market is distinctive with respect to the relationship between price and demand. Unlike many competitive markets in which there is a "smooth" demand curve—where a small change in price may result in corresponding decreases or increases in volume ("demand elasticity")—the pharmaceuticals market has a more discrete demand response to price. Access decisions are highly concentrated, with a relatively small number of payers controlling the bulk of pharmaceutical benefits. As prices change, payers may or may not change the access level of a product (access includes dimensions such as copayment tiers, restrictions such as prior authorizations and step-therapy requirements, and sometimes non–reimbursement of the product). Typically, payers have different thresholds at which they consider access changes—and these thresholds can vary with the characteristics of the therapeutic area, such as size of the category, severity of patient conditions, therapy options available and their cost, etc. One payer may consider restricting access when price increases by 8 percent, while another may consider such action at a somewhat higher price increase. Of course, most payers are concerned with their net costs, so price protection and other contracts that provide rebates for preferred or non-disadvantaged formulary access mitigate the impact of list price increases to some, but not all, benefit plans. Understanding the tipping points for individual payers is critical to correctly gauging how to avoid "hitting the cliff" with price actions.

Finding the cliff

Determining the threshold price increases, or "cliffs," at which key payers respond with access changes is critical for optimizing on-market pricing. This can be done through careful analysis of the historical formulary actions of payers, primary market research, and sensitivity analysis of the impact of price changes under different assumptions. We have found that a hybrid of qualitative and quantitative primary market research techniques, combined with analysis of historical list price data and formulary outcomes, has been highly effective in identifying payer access thresholds and evaluating list price strategies for specific brands and market situations.

To demonstrate the practical applicability of these methodologies, consider an example, product A, based on a real on-market pricing study. In the study, we used interviews with payers to test threshold price increases generally, to identify levels at which they might trigger a thorough review of pricing of all therapies in the category, and to determine specific responses to price increases for product A. (We found it most effective to test a variety of attributes together with price to avoid any bias that may result from focusing on price alone.) We found that increases in price resulted in no access response up to a level of 9 percent annual price increase. Figure 2 illustrates how this translates directly to increases in revenue and profit up to "the cliff." From the plan's perspective, price increases need to reach some threshold (both in absolute terms and relative to price changes of competing products) where the cost and administrative burden of changing the formulary are outweighed by savings realized from imposing restrictions intended to lower utilization of the product in favor of other, lower-cost therapy options.

Analysis of the results suggest that the manufacturer can take a price increase of 8 to 9 percent per year without hitting the cliff. It is possible to validate the research using historical data. Comparing the research results on payers' stated behavior against historical data on actual payer responses (both in the category and in similar categories) the robustness of the results can be established and the pricing strategy can be implemented with confidence.

As illustrated in Figure 2, which draws on primary market research with payers and can be validated with analysis of historical formulary changes, increases in price translate directly to increases in revenue up to the "cliff." However, above this threshold value, an access downgrade more than offsets the gains of the higher price. From the payer plan's perspective, price increases need to reach some threshold (both in absolute terms and relative to price changes of competing products) where the cost and administrative burden of changing the formulary are outweighed by savings realized from imposing restrictions intended to lower utilization of the product in favor of other, lower-cost therapy options. We have found that there are opportunities to increase price profitably based on research and analysis designed specifically to find the cliff for a particular brand. However, decision makers need to keep in mind that the optimal price increase is typically somewhat lower than what would be suggested by the "average" tipping point of payers, due to the asymmetry between risk and payoff as price increases approach the cliff. Simulation analysis that accounts for the range of uncertainty of estimated payer thresholds can be particularly valuable to ensure that hitting the cliff is avoided for even a small number of payers that may have significant impact and/or influence in the market.

Justin Works is a Vice President in Analysis Group's Menlo Park office. He can be reached at [email protected]
. Andrew Parece is Managing Principal and is based in the firm's Boston office. He can be reached at [email protected]

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