The Perils of Imprecise Forecasting

Contributed by:

Eric Levin

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Contributed by Eric Levin, Senior VP, Princeton Brand Econometrics

The ability to market products is directly related to how well a business can forecast outcomes based on alternative courses of action. Most pharmaceutical companies cannot do this very well. A forecaster whose science accurately accounts for a brand’s unique positioning, price, promotion, and competitive environment will provide extremely accurate forecasts for divergent promotional scenarios. A forecasting deviation rate of (+/-) 30% from actual sales results would likely be regarded as an abject failure by most businesses. We have observed over the last 17 years that pharmaceutical industry forecasts of Year 1 prescriptions for new brands fall within (+/-) 30% of actual only about 15% of the time. Roughly 75% of the time the forecast exceeds actual by 30% or more. Underforecasts occur only about 10% of the time. How can an industry generally regarded as sophisticated be associated with such embarrassing miscalculations? Accurate forecasting is absolutely necessary for good planning and profit maximization. Some pharmaceutical companies use forecasting as a planning tool and a guide to decision making. Many others appear to look at planning and forecasting as separate issues, rather than different sides of the same equation. The former treat forecasting and planning as a science. The latter treat them as judgment calls. For some of them, a forecast represents the company’s hopes and dreams. For others, it represents a sales goal. Abysmal forecasting can throw a monkey wrench into a company’s plans, often with devastating bottom-line results. An extreme example, Exubera, as widely noted, recently missed its forecast by about 98%, and Pfizer is still recovering from the disaster. With insufficient scientific forecasting to support their conclusions, pharma companies often start off with overly optimistic estimates of how much a new brand will sell. Many will determine the number of sales reps to put on a brand and the number of sales calls to make on a given set of doctors based on these estimates. It’s a terribly wasteful way to go about resource allocation. They probably could get away with it when new blockbuster drugs were flying through the R&D pipeline, but it’s a formula for failure in today’s climate. The Physician Equation A new brand’s sales level is a function of doctors’ responsiveness to its clinical profile, detail message, and formulary status and how this responsiveness will play out as the brand is promoted in its competitive environment. If a company can accurately account for these factors, Year 1 sales should regularly fall within (+/-) 5% to 6% of the forecast for any potential tactical plan that is implemented. A forecaster whose science accurately accounts for a brand’s unique positioning, price, promotion, and competitive environment will provide extremely accurate forecasts for divergent promotional scenarios. The company will know exactly what it will get for different levels of resources that may be allocated to the brand and will make far more money than those whose forecasts are based on judgment calls. One forecasting methodology that meets these criteria has been extensively proven in the marketplace, with virtually all validated forecasts to date falling within (+/-) 6% of actual Year 1 prescriptions. The methodology produces a unique mathematical model for each tested positioning or price. The model is used to simulate the impact of alternative promotional plans and to forecast monthly filled prescriptions for any given promotional scenario. Tactical elements that can be varied include levels of resources (details, samples, journal ads, etc.), and targeting and timing of resources. The methodology differs dramatically from the test market simulators the packaged goods giants use to forecast their new products but the basic approaches are similar. A sample of category users or potential users is exposed to a description of the new product. They then answer a battery of questions and the answers go into market-validated mathematical models that translate the raw data into an estimate of the maximum potential share the new brand could achieve, as well as its responsiveness to promotion. Over or Under? Overforecasts and underforecasts can result in equally unfavorable outcomes. Significant overforecasting of a brand’s sales wrecks a company’s credibility with the investment community and adversely affects its projected financial picture. While it may not appear so on the surface, underforecasting is just as bad and sometimes even worse. Every so often, we hear of a company bragging about one of its products exceeding sales projections by a wide margin. Assuming that the company implemented the plan on which the forecast was based, it has proven only that it is a lousy forecaster. Whether the lofty sales level shows that the company is a good marketer is open to question. The forecasting team’s enthusiasm over the higher-than-anticipated sales should be tempered with a commitment to determine why their forecasting was so far off the mark. Failure to take this step carries potential downsides. Scientifically derived forecasts are often used to determine the optimal level of promotion for a given brand, including salesforce sizing and detail allocation. If the forecast had been more accurate, it might have shown that the company could have achieved the same sales level with less extensive promotion, thus lowering costs. Furthermore, strategic and tactical plans based on an underforecast can leave money on the table by causing a company to either spend more than was necessary on promotion or to potentially sacrifice even greater sales and profits by underpromoting. Imprecise forecasting could be tolerated during the blockbuster drug era because all of the leading pharma companies as well as many others were highly profitable and misforecasting didn’t drastically affect their bottom lines. Now that times are tougher, companies cannot endure flawed forecasting and the less-than-optimal planning that accompanies it. F Princeton Brand Econometrics (PBE) is a marketing engineering consultancy. For more information, visit pbeco.com. PharmaVOICE welcomes comments about this article. E-mail us at feedback@pharmavoice.com. forecast deviation Three factors That can cause a new brand to miss its forecast by a wide margin: 1. The competitive environment differed in some important way from what was expected. Rarely do the actions of existing competitors change the environment enough to matter, but the early or late arrival of a new competitor will have an impact. Accurate forecasts are generally possible for alternative, new competitive product scenarios. 2. The strategic and/or tactical plans on which the forecast was based were not implemented. 3. Implementation of planned strategies and tactics could not produce the number of prescriptions that were forecast. This is the dominant reason why new brands miss their forecasts. And it is the area where forecasting techniques are put to their most demanding test. Methodologies that are not well-validated in the marketplace may do a poor job of forecasting how effective a given strategy or set of tactics will be.

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