It’s common knowledge that the success of a launch sets the long-term trajectory of a pharmaceutical brand. In past decades, companies would double down on their research investments with massive sales forces, reaching every plausible potential prescriber, but in today’s environment, promotional resources often are limited. This means that targeting the right customers is increasingly important to ensuring that a new brand’s launch is successful.
For an in-market brand, targeting is generally considered a quantitative, analytical exercise. Statistical or empirical methods can be applied to identify those customer types who have shown responsiveness to the brand’s value proposition and messaging, and resources can be directed toward them and away from less promising groups.
However, for a new launch, this responsiveness often is unknown. Usually, stakeholders have a wide range of ideas about who would be the most responsive: Maybe a certain medical specialty should be the focus of promotion. Maybe early adopters with an affinity for newer products are the most important. Maybe those with early managed care access will be the first to try the product, or perhaps the company simply should target the highest-volume prescribers in the relevant therapy area. All of these ideas are intuitive and have merit, and often are supported by qualitative research, but none of them are really proven at the time of launch.
In such situations, the analytics team is usually tasked with defining a targeting approach that balances all of those good ideas that marketers have identified. Often, they respond with a simple weighted index based on secondary markers—say, 30% early adoption, 30% managed care and 40% patient volume. Usually, to appease various stakeholders, the resulting index ends up with many components.
This approach can make the stakeholders feel validated but is usually wrong because it does nothing to validate the trade-offs between the components. For example, early adoption and managed care access are almost certainly both important, but how much more important is one than another? What if they are highly cross-correlated and most early adopters had good early access, or vice versa? The weighted-index approach buries these trade-off decisions in a framework that makes people feel like they haven’t needed to make them, but in fact, the chosen weightings are themselves decisions about each factor’s relative importance. When factors are equally weighted, it’s an easy-to-spot sign that the trade-offs haven’t been given adequate consideration.
How can marketing leaders avoid this problem and develop targeting approaches that are actually effective for new product launches? Here are three critical moves that can create launch targeting success:
- Integrate primary data with secondary data to develop responsiveness expectations. Although new products, by definition, don’t have past sales behavior on which to develop quantitative models, most organizations have conducted primary research in which prospects have signaled their intent to use or not use a product (often as part of developing a forecast or a customer journey model). This data is often used for only this single purpose and then discarded for two reasons: First, this work is usually conducted in the domain of “market research” and managed by separate people from those who deal with secondary data analytics, and second, primary data is necessarily firewalled from the company’s internal data sets to maintain blinding. Some market research partners, however, are skilled in working with both primary and secondary data, and can integrate these sources while maintaining double-blinded status. Working with such a partner can help companies maximize the benefits of their data investments while staying compliant.
- Prioritize targeting metrics to avoid a “kitchen sink” approach. Internal teams who have spent years planning a launch will undoubtedly think of and justify dozens of measures that might indicate whether a potential customer might be interested in a new product, but reducing a model to the three or four most important criteria will almost always be just as effective and much easier to manage. The primary-secondary quantitative modeling can help identify those criteria and, just as importantly, the relative weightings and trade-offs among them. Sticking to a tried-and-true framework of assessing the potential and “propensity to use” of a given customer, and avoiding duplicative and distracting metrics, will provide strategic clarity and operational efficiency.
- Monitor initial results actively and be ready to adjust the targeting approach quickly after launch. In today’s environment, adjustments can and should be made in weeks, rather than months, based on the early results of a launch. Building the operational framework to gather data from day one, and setting the tone with marketers and salespeople that they can and should expect adjustments to the strategy, are critical to ensuring that the commercial team can adapt to new information and focus on the best opportunities. Good initial targeting is important, but adaptability will be required even with the best-planned efforts to ensure sustained success.
Getting launch targeting right isn’t particularly expensive or difficult, but it does require a level of collaboration across traditional functional silos, which many companies find challenging. Analytics teams need to get comfortable with relying on primary data that’s both blinded and sparser than their traditional sources; decision makers need to accept that less is more, and a few validated metrics are more powerful than a dozen gut-feel ones; and marketers and sales teams need to be comfortable with changes over time as the targeting strategy is refined. The benefits of such collaboration can be immense—for companies, their customers, and the patients who ultimately benefit from their work.