Pharma Competitive Intelligence: Effective Implications Assessment

Most competitive intelligence engagements in the pharmaceutical industry tend to be asset-centric. They focus on a single compound or brand and assess factors affecting the asset via so-called implications (aka so-what analysis) - a good implication assessment helps to assess the impact on value. In this article, we share how competitive intelligence (CI), with a sound understanding of enterprise-wide holistic and aligned decision-making helps assess implications more concretely and effectively.

What is Value?

Value is used rather colloquially or ubiquitously, and usually means some sort of benefit, often left unquantified. The value in the true term is the risk-adjusted net present value (rNPV), computed using a discounted cash flow analysis.

But why would one focus on this value? Because the net present value concept is aligned with all the stakeholders ranging from shareholders to decision-makers in the company. Economics-minded managers and proponents of the 'principal-agent' theory (essentially, means one has obligations to those whose money the company is using which is usually shareholders') would extend this to every employee in an organisation. Furthermore, since the capital market values a company using rNPV, adopting a market-aligned method is the most pragmatic approach for practitioners and decision-makers.

The Key Determinant of Value

Generally, organisations employ a 'bottom-up cascading' approach to forecast the sales of an asset and it is often triangulated by a variety of other methods. Other factors that materially affect the value are, inter alia, cost of capital, leverage, beta, margins, working capital, tax, (ignoring the current trend of tax haven relocation), and are reasonably constant, assuming a pharma/biotech company run on a 'business as usual' basis, without getting into oil or gas or internet, or any other unrelated sector, that would fundamentally alter the risk-return profile of the core business.

In the most rudimentary terms, the value is based on peak sales estimates, the trajectory of the sales curve, expected costs, and the probabilities of events that lead to success. Consequently, organisations tend to focus on these variables which are captured in forward-looking financials or forecasts. Thus, it is vital for CI to assess the impact on the forecast in order to enable rational decision-making.

Intelligence Impacting Forecasts and Value

Intelligence emerging from CI engagements is used to refine or modulate these variables and thus, the value added by the asset and its cognate project. Such effects might be significant enough to warrant changes in strategy and tactics and might lead to a change in the stock price. The following examples illustrate the impact on forecast and value. These should be read on a ceteris paribus basis, i.e., other variables being held constant.

• CI discovers recruitment in the trial is slower than expected > the sales curve shifts towards the late side on the timeline > a decrease in value.
• A competitor gets an FDA voucher > the sales curve shifts towards the early side of the timeline > an increase in value.
• Interim trial data yield unimpressive results > the probability of success shrinks > a decrease in value.
• Interim results are so good that the company will be filing > sales curve shifts towards the early side on the timeline, plus saving accruing from planned costs, plus an increase in the probability of success > an increase in value.
• Regulators ask for another sub-study > the sales curves shift towards the late side on the timeline, plus an increase in expenses > a decrease in value.
• Exclusive deals with the price regulators make the listed price less relevant > an increase in the peak sales estimate via volume > a decrease in the margin but an increase in value.
• A disruptive player enters and makes expensive biologic, later-line treatment > a decrease in the peak sales estimate plus sales curve shifts towards the late side on the timeline > a decrease in value.
• Slow recruitment of speciality sales reps for the state-of-the-art biologic > ramp-up time to peak sales increased > a decrease in value.
• Regulators delay their decision > the sales curve shifts towards the late side on the timeline > a decrease in value.
• Standard of care becomes generic > incremental cost-effective ratio hurdle higher for entry-seeking novel drug > shifted to later-line of treatment > a decrease in the peak sales estimate and sales curves shifts towards the late side on the timeline > a decrease in value.

Insights Applied

The examples mentioned above are routinely discovered during intelligence-seeking engagements and are usually left as a 'threat' with some qualitatively assigned grade. These illustrative scenarios are directionally correct but assume a change in one variable at a time, and are thus, technically, examples of sensitivity analysis. However, in the real world, several variables change simultaneously, with varying magnitude and impact values in a much more complex manner. Pharma organisations might want to check if their current CI engagements lead to an assessment of impacts in a meaningful fashion and leverage the insights here to finesse CI engagements.

Related Readings

• Field force and sales force competitive intelligence in the pharma industry
• Effectively understanding your competitors
• Integrated Path-to-Market Competitive Intelligence
• Ultimate Practitioner’s Guide to pharmaceutical competitive intelligence

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