Scott Sandford and Abhishek Singh co-wrote this blog post with Anshul Agarwal.
The “Goldilocks principle,” which borrows its concept from the famous children’s story to describe when something is “just right,” can be applied to many situations, and the oncology landscape is no exception. Given the evolution of pharmaceutical manufacturer strategies in approaching oncology accounts and the data needed to support these strategies, manufacturers need to know what’s “just right” when it comes determining the resources needed for each account—a lesson that they can learn from Ms. Goldilocks and her friends, the bears.
First, the challenge: While they’ve always been difficult from a rep access standpoint, oncology accounts in recent years have become more sophisticated in how they work and, therefore, are complicated to work with. Be it via greater expertise and specificity in precision treatment methods or simply becoming part of larger organizations, tailoring the right message to the right oncology account is becoming an increasingly stratified and complex task. Beyond that, these accounts operate in an environment supplied by an increasingly rich menu of pharmaceutical treatment options. This makes it easier for them to carry more relative weight in cost effectiveness discussions with manufacturers.
To develop commercial strategies that meet the needs of these accounts, manufacturers in the oncology space have assembled an impressive tool set focused on account-based selling: intricate analytics, specialized field teams of nurse practitioners and key account managers, patient access programs, new GPO contracting strategies and more. Especially in increasingly crowded tumor types and mutations, these tools are integral to long-term success. However, manufacturers are finding that knowing what to use where (and when) is exceedingly hard: Resources can be expended on an account that, as part of a larger system, already has given very preferential access to a competitor. A contract can be drawn up that will never actually be utilized. A patient access program can cause more confusion than benefit.
Given the challenges laid out above, knowing what’s “just right” when resourcing different accounts is key. Traditionally, this input has been sourced from field representatives or market research. However, these methods have significant drawbacks: time away from field selling, biases and being “too high-level” to name just a few. As a more complete, objective alternative, pharmaceutical manufacturers are now looking to the ever-increasing number of secondary data sources available to help them drive these account strategies. These sources can range from very traditional sources of oncology data (such as specialty pharmacy and claims data) to newer sources such as secondary account data from large health systems or GPOs. Sales and market information is often combined with affiliations data to give an idea of the “hierarchy” that each account operates in.
Given the litany of data sources available and potential complexity in untangling affiliations, starting down the path toward a data-driven oncology account strategy can be daunting. To help begin this decision-making process, we recommend tackling the two problems separately: Decide what information is important to your strategy, and then decide how precise the affiliations hierarchy is that’s needed to take advantage of that information. Here’s a potential set of questions that an oncology manufacturer could ask to get started:
1. How can I identify the level of sales from my key accounts? This is an important baseline as performance reporting, forecasting, operations and insights will all come back to this source as a base. If possible, it’s important to identify the channel to determine how the drug is actually being distributed (for example, at a few major academic centers or via a wider pool of community oncology clinics). Specialty distributor data can help identify the level of sales showing up at individual accounts. Whichever source is used, a high capture rate is important to prevent blind spots where sales activity is missed.
2. If I know the critical channels and can capture account-level sales, can I link this sales data with other sources? This capability has to do with master data management: While we won’t go deeply into this topic, the ability to link different data sources with a high level of confidence is a key capability required to unlock deeper insights about account behavior. The goal should be a reasonably high level of accuracy and precision if basic account information (name, address) can be provided.
3. If I can link sales data with other sources, what sources line up well with my brand strategy? This should involve close coordination between brand stakeholders, medical support, operations stakeholders and data management. Specialty pharmacy data can help add information about the HCPs and payers that are impacting accounts. Here are a few additional considerations:
- Despite data capture issues, claims data can be an incredibly rich source of insights about the patient journey and can help validate strategies seen at the account level.
- Complex testing procedure for a niche indication? Consider lab data.
- Will GPO strategy be important? Reach out about the purchase of data directly from these organizations.
- Is secondary account data needed to go “a level deeper” if specialty distributor data is only showing shipments to a distributor? It can often be purchased from large accounts and health systems.
4. Now that I can inform decisions about individual accounts, how can I create something for the more holistic system? This is where affiliations come into play. A key capability of “rolling up” sales will be important for either insights or operations purposes. There are secondary data sets that can be purchased to provide a “pool” of account affiliations. (HCP to account affiliations can also be purchased.)
Field input and desk research is also a possibility, though it is challenging to collect and remain up to date over time and is likely better utilized as a validation of data-driven affiliations.
Beyond this, a set of business rules to identify the primary “one to one” or “many to one” relationships needed to create the hierarchy—that is, the pyramid from community clinics to regional health centers to a corporate parent—and the system to put them in place is required. These business rules can be tailored to your brand strategy.
5. With a good understanding of system hierarchies in place, how can I add specificity through classifications? Differing data sources can help identify how different accounts will respond to tailored strategies before significant investments are made. Starting from the broadest definition of “account,” work toward the level of detail that is needed for you. Here are a few examples of data-driven classifications:
- Inclusion of local health market information can help identify differing levels of provider vs. payer influence that could be impacting the account
- Access information can be included to help more clearly identify where strategies “beyond the rep” will be required to start conversations with the account
- Designations such as National Comprehensive Cancer Network can be included. If your organization is interested in the “top account” of a hierarchy as it drives prescribing behavior below it, including this as a classification can be helpful.
Progressing through these questions (or a similar exercise) can help to ensure that the secondary data strategy set up to support a manufacturer’s account strategy is both comprehensive and meets the brand strategy goals of the manufacturer. While the path is somewhat complex, achieving a “Goldilocks” fit will help set up the account strategies of oncology manufacturers both now and—if they revisit their strategies regularly—the long run.