Strategy and evaluations part two: developing sales estimates
Jean?Louis Roux Dit Buisson
In part two of three, Jean-Louis Roux Dit Buisson discusses the challenge pharma has in estimating future sales and cash flows, and provides his opinion on how these issues can be best overcome.
(Continued from “Strategy and evaluations part one: governance &, staffing”)
In my previous article, we became the eye of the practitioner in order to propose a new look at valuations, with company governance as the key “customer”: strategic decision making must focus on hypotheses and on the likelihood of outcomes rather than single point estimates, and tracking of hypotheses is essential to the process.
This article addresses the issues of estimating future sales and cash flows. We shall review the real options approach and propose a methodology closer to the market and better suited for company governance.
“…One of the most difficult things to do in the pharmaceutical industry is to predict peak sales of a drug. The classic example is Lipitor, for which the original forecasts were for peak annual sales of $800 million, but which proved to be 16X higher…” 1
1. The case of real options
Real options account for the uncertainty embedded in any estimate and includes management flexibility in decision making, e.g. the possibility to accelerate, hold, abandon and enlarge a project, as more information becomes available in the process of development.
“Real options account for the uncertainty embedded in any estimate…”
This method has not been widely adopted in executive ranks:
• The real options’ estimates of sales are essentially a mathematical model relying on a set of rather inflexible hypotheses (see figure 1 below).
Figure 1: Peak sales estimates with real options.
• It is financially driven and as such can be perceived from being away from the realities of the field.
• The associated peak sales estimates are skewed to the upward side (estimates are limited downwards at the zero value (see figure 2 below).
Figure 2: Distribution of sales estimates with real options.
• The method does not specifically take into consideration the competitive profile of the underlying.
2. Scenario analysis
“Risk comes from not knowing what you are doing,”
Scenario planning forces managers to think their strategies in competitive terms, and to document their hypotheses:
• It takes into consideration management’s options.
• Whether as a block-buster, a me-too or a laggard, scenarii comprehend a set of discrete assumptions, with their own probabilities of occurrence.
• It accounts for the volatility of estimates.
“Scenario planning also focuses management thinking on essential value drivers… “
• It allows for ongoing updates in the hypotheses as information is collected as the project progresses.
Scenario planning also focuses management thinking on essential value drivers:
i. What is the size of the potential targeted market?
ii. What is the competitive profile of the product when introduced?
iii. Is management willing to invest marketing and sales budget beyond the identified threshold?
The method ideally delivers three to four realistic scenarios related to the expected product profile and market situation at time of launch (see figure 3 above).
This might be perceived as complicated and administrative. If it is, then your management has missed the point and you might want to revisit how you come up with valid, relevant and non-segregated information.
The next steps are assessing the probabilities of occurrence and the ranges of errors for each of the single-point market size estimates arrived at.
Figure 4: Market shares estimates.
The same process is used to assess market shares (see figure 4 above). Peak sales estimates are also easily derived (see figure 5 below).
Professional and affordable add-on programs exist that will transform these assumptions in distribution curves, as shown in the various charts.
3. Strategic impact
By systematically looking at each parameter of the sales estimates, we arrive at a powerful decision-making model, closely related to field opportunities.
Figure 5: Sales estimates per scenario.
The plot of peak sales estimates in figure 5 shows that by applying the method carefully, unexpected outcomes might surface. In our example, the highest peak sales are not correlated with the largest targeted market, because other competitive elements are accounted for in the calculation of the market share, which can be reached.
“Planning brings focus, purpose and efficiency to meetings.”
Figure 5 also shows that the use of averages for strategic decision-making purpose is not recommended. Averages are disconnected from strategic reality.
Scenario planning enables project teams to acquire an extraordinary body of knowledge on potential outcomes, which will later be of essential value for the board to come up with strategic decisions. Planning brings focus, purpose and efficiency to meetings.
Management techniques, such as Delphi, are extremely useful in building up a common database on key value drivers. Proper documentation of retained hypothesis allows for strategic decision making further down the road in a project.
Part 3 of this article will go live on 2nd August.
About the author:
Jean-Louis Roux Dit Buisson is a Professor of Entrepreneurship at the Grenoble Management School in France. He is founder of Foro Ventures, a company dedicated to provide assistance and interim management for top-line growth projects and turn-arounds.
Jean-Louis is specialized in high technology sectors (such as Bio Pharma, Medtech). He has an MSc from MIT and an MBA from INSEAD and can be reached at email@example.com.
How can pharma better predict sales estimates?