Guest Column | July 23, 2015

Transforming Chaos Into Clarity: Launch Sequence Optimization Critical For Maximizing Revenue

Transforming Chaos Into Clarity: Launch Sequence Optimization Critical For Maximizing Revenue

By Aparna Wani, principal analyst, Alliance Life Sciences

The Launch Problem

Pharmaceutical manufacturers face significant pricing challenges during all phases of the drug life cycle. However, with literally billions of options and trade-offs in setting the time and price, product launch provides a very unique set of challenges. Simple models fail to capture the complexities and nuances of launch options. Without a sophisticated model analyzing the full range of options, firms are leaving revenue “on the table,” not just at launch but throughout the product life cycle.

Why Does Launch Order Matter?

In the struggle to optimize launch sequence by country, the main driver of complexity is international reference pricing (IRP). Reference pricing is a scheme that drives both the initial launch price in a market and how the price can change in that market through a government regulation that sets prices by referencing prices in other markets. The reference rule scheme is well-known to all pricing practitioners in pharmaceuticals, but less so to those launching a product globally for the first time. As an example, the health authorities may define the launch price in a market like Spain as no higher than the price in 10 other countries. If a firm launches first in another country at a lower price that is referenced by Spain, then the firm will no longer have the ability to price at the best price possible for Spain.

Referencing also puts many constraints on the ability to raise prices; prices generally only drop under the price-referencing scheme. This means that once Spain drops its price to match a referenced market, the potential revenue in Spain from a higher price is lost forever. This effect is further compounded as countries continue to reference each other after the initial launch price is set, further driving the price down in some cases.

The chart below shows an extremely simple example of referencing. In this example, we see that the cost of launching in Bulgaria early at the best possible price is pushing down the price in Greece and Spain. This example shows a loss of 11 percent to 13 percent of list price in those countries. Combined, these markets have almost 1,500 percent larger potential than Bulgaria. Therefore, a 10 percent-plus lower price in these markets will likely drop net revenue and margin contribution meaningfully over the full product life cycle.


Sample Rule 2

Best Price Achievable

Launch Price


Qtr 1


Qtr 2






€ 5.50

€ 5.50

€ 5.50

€ 5.50


Bulgaria 1



€ 4.00

€ 4.00

€ 4.00

€ 4.00



Lowest of N Countries, including Turkey

€ 4.50

€ 4.50

€ 4.00

€ 4.00



Average of Italy, Greece


€ 5.50

€ 5.50

€ 5.50

€ 4.75


1 For simplicity, all prices are expressed in Euros vs. local currency (e.g., lev)

2 Rules are greatly simplified for clarity of example

Why Is It Hard To Optimize Launch Order?

When a price is too low in one country at launch, the impact can cascade around the globe. The essence of launch sequence optimization (LSO) success hinges on effectively managing the collection of country-specific variables that can impact launch price, including:

  • IRP Rules, both formal and informal that vary from country to country
  • Launch date constraints, such as the earliest date, based on approval
  • Price ceilings, such as the highest perceived price to launch it
  • Product availability and supply
  • Sales volume
  • Net price and margin, such as net sales price and cost-of-goods sold
  • Opportunities for unreferenced sales

It is common for companies to plan a launch through the 50-plus top markets or countries, which means a huge number of potential sequences operating at an exponential scale. Best-in-class firms look for the potential impact out to almost 10 years, planning for close to the full patented life cycle of the product. Yet many companies still try to determine launch sequence using simple spreadsheet-based solutions, a practice that limits product revenue potential. The example below shows how the number of opportunities can grow exponentially:


Country Order


Price / Volume Variations


Total Options

# of Options

50+ Countries

11+ Currencies

3-4 Price / Volume Points

12 to 60+

> 1050

Option Details

The sequence of countries

Currencies that can fluctuate over the course of the product lifecycle

Example: 1st vs. 2nd line indication with different Price/ Reimbursement/ Volumes

Different options for month of launch


A company forecasts significant revenue for some markets, but when the time comes to finally launch in that market, price has eroded so much that the original forecast needs to be reduced by $20 million or more per annum. This level of budget impact is truly significant, particularly when avoidable. The situation calls for advanced simulation techniques and tools.

An Advanced LSO Algorithm: A 21st Century Solution

In global-level drug launches, launch sequence has a significant impact on the bottom line. An average of a one-percent higher price difference through the launch cycle could cost $15 million or more in net present value (NPV) for a product over its life cycle. The key is to quickly and accurately forecast the impact of IRP for the greatest outcome.

Advanced modeling with automation of the launch sequence through the maturity phase pays dividends. Accessible, user-friendly analytics are now available on the market. But which ones offer the best approach? Key features include:

  • Handling constraints - At its core, the best tools must use a sophisticated algorithm to search through many options and efficiently eliminate large sets that are not optimal. First, there are a number of heuristics and constraints that help prune the set of possibilities. For example, Germany is not likely to launch after Greece in a normal launch. A tool should allow capture and use of key constraints.
  • Efficient, exhaustive search - A tool should use an efficient algorithm-like simulated annealing or other tools that allows efficient searching of a large set of alternatives and narrows in on “maximum” outputs. A tool that takes too long is difficult to use, but one that finds results too quickly is likely not fully optimizing.
  • “What if” modeling - Proper “what if” modeling allows companies to plug in variables and data points to compare different situations with “what if” analysis grouped into sets to allow easy comparison. What’s more, the algorithm can be constantly updated with different variables, such as a projected reference rule change, and see how it affects the launch.
  • Launch planning and tracking - The solution should also feature the ability to gather intelligence before launch from affiliates and offer a launch-tracking dashboard for planned launch and optimal launch. This enables companies to connect effectively with the latest launch intelligence from affiliates, and to understand how they are performing against the original plan and changes in that plan.

The Best of All Possible Launches

LSO enables pharma companies to think through the constraints of a launch in terms of price, timing, order, and window-of-time based on factors in different parts of the world. The aim is to manage price over the life cycle of the product before its patent ends and get the launch price “right” to minimize price erosion.

With this in mind, companies should look for a solution that can accurately and quickly analyze the seemingly endless possible combinations of countries, dates, prices, and volumes, and simulate those possibilities, including problematic events that might change fluctuation in currency and impact the overall market price.

Launching a drug without considering the ideal order could have a very real impact on its global revenue. Given the complicated IRP environment, it is essential for companies to map international reference rules and define an optimal launch order when marketing a new medication. The traditional, simplified spreadsheet-style model analysis fails to capture the detail, complexity, and accuracy required in this sophisticated environment. A solution that supports pricing over the full product life cycle — from launch to growth and maturity to loss of exclusivity — can drive better decisions and ultimately drive value to the bottom line.  

About The Author

Aparna Wani is a Principal Analyst with Alliance Life Sciences, where she works with life science companies to understand their global pricing challenges, identify analytics objectives, and develop solutions that address those unique business needs.