Improve your forecast with “bottom-up” methodologies 

Forecasting in the pharmaceutical industry can feel a lot like gambling at cards. With cards, you take into account the players at the table, the cards that have been played, the current stakes, your experience at the game, and then consider if you are feeling lucky before you risk placing that bet. In pharmaceutical forecasting, you measure current market conditions: competition, potential patient population, physicians in the therapeutic area most likely to prescribe, and payer attitudes. Do you offer a product better than what’s currently available, and if so, how does that convert to TRx’s? At times, the resulting forecast can feel as risky as that card bet. However, unlike cards, you have ways to improve the odds that can keep you safely in your comfort zone and your business model on track.

The standard, strategic forecasting approach

The need to maintain your brand forecast is critical from pre-launch planning through loss of exclusivity (LOE). The forecast provides a target for sales and manufacturing as well as the roadmap to understanding whether you’re achieving your full potential or simply coasting along. It’s a critical tool, but needs to be grounded in integrated real-world evidence and insights to avoid becoming disconnected with the operational realities for a brand.

As a product moves through its lifecycle from clinical development to pre-launch planning to operations and LOE, the complexity and inputs must evolve as the questions the forecast seeks to answer evolve. In clinical development phases, forecasting is long-term and strategic, addressing questions about how big a brand can be. For these questions, we would rely on EPI data, analog product comparisons and market research to estimate market potential. As the product nears launch, and throughout its post-launch period, forecasting shifts to considering how we can accelerate launch trajectory. In this case, operational planning, using real-world data such as the sales trends, prescription and claims data, and time-series models within the marketplace are critical to answering these questions. As a product nears LOE, the forecast further evolves to focus on balancing investments with rate of product decline.

Across all stages, the pharmaceutical industry uses a mix of patient flow models, analog analysis, and scenario planning, with forecasting responsibilities shared between strategy, commercial, and supply chain teams depending on the product phase.

Three standard, strategic ways to approach forecasting throughout the product life cycle are:

• Strategic forecasting, which looks at the long-term, working to pinpoint the brand’s peak year and identify the national level resourcing necessary to achieve the full brand potential post-launch. It is typically used from 36 months pre-launch up to 24 months pre-launch for overall strategic planning and such organizational business decisions as investments, capacity expansion and market entry. It typically forecasts at a high-level national aggregate.
• Quantified strategic forecasting is similar to strategic forecasting, but will rely on data sources with finer detail to address long-term revenue with a focus on maximizing long tail, life-cycle management objectives. It builds its forecast from the middle up using HCP reported considerations; broader promotion outlook such as national level HCP reach, writer conversion, and payer coverage; and higher order preference share assumptions. Again, it works on a national level.
• Operational forecasting is an approach that utilizes HCP-based metrics such as new writer trends, re-writing rates and average writing trends. The operational forecast helps quantify and understand the brand’s true potential at the local level, helping to validate the feasibility of projected outcomes and outlining what is needed from the market to meet those goals. Depending on where the brand sits in its life cycle, taking these operational granular impacts into account decreases risk.

Since there is no constant in the greater healthcare landscape, these forecasts should be built with the understanding that changes will be needed, if not right after completion, soon after.

Minimizing brand risk with “Most Probable Forecasting” methods

As brands near their launch and begin to fine tune their operational and tactical planning, employing a “bottom-up” approach can increase confidence in the forecast. Known as a Most Probable Forecast (MPF), this methodology provides the best estimate of future demand under current assumptions, and is used most frequently for short- to mid-term planning periods of six to 60 months. It provides a rationalization of the strategic demand forecast based on the bottom-up details available.

The MPF leverages operational realities such as the field force size, DTC investments, bridge and co-pay investments and anticipated local market access coverage to assess whether the brand can achieve its full potential. While working to optimize short and mid-range profits, the MPF will ensure that the full, long-range revenue position of the brand is not compromised.

By working in a bottom-up approach, we are able to change the conversation with commercial leaders from “if a brand can achieve its ambition” to “what the business must do to meet its ambition.” Further, the MPF provides a finer level of detail against which to measure and track results, making diagnosing trends and necessary course correction much more efficient and effective. Forecasting risk is minimized.

“An MPF allows us to quantify the true potential of a brand based on all marketing, sales, and payer strategies and assumptions, and does so on a more granular, territorial level, therefore generating a forecast firmly based on real-world assumptions and actualities.”

—Jonathan Howard, Decision Science Leader, Propensity 4

Quite often brands complete strategic forecasting activities and won’t progress to the MPF during launch tactical planning, thus disconnecting the forecast from the day-to-day realities the commercial team must face. By taking the extra step of combining strategic long-term forecasting with MPFs addressing shorter periods and more granularity, management is protected by getting the tools it needs to best plan for the future and pivot to course correct when necessary – far less of a gamble for all.

A case in point: A Rare Disease Bottom-Up Forecast

In a recent client engagement with a large life sciences organization preparing to launch a rare disease treatment into a new, crowded market, we partnered with them to prepare an MPF and demonstrate a peak year share for their new medicine. Within 12 weeks, using our integrated optimizer tools we interrogated the data and identified strategies to address data gaps. We filled gaps in available data caused by a lack of specialty pharmacy distribution reporting including predicting competitor uptake and share, based on publicly available information. We built patient flow models to understand the speed and intensity with which patients would be diagnosed and treated to better identify the true patient population. Ultimately, we integrated these unique data assets to deliver a monthly forecast from launch through LOE and then partnered with the brand leader to understand the levers they could use to meet their ambitious expectations for year one and peak year forecasts.

Click here to read the full case study: Market Landscape and Most Probable Forecast

Learn more about how our forecasting and integrated data and insights capability can improve your organization’s performance: Propensity4.com | Product Lifecycle Optimization