The key to success in a hypercompetitive marketplace is to create targeting plans that get ahead of tomorrow’s prescriptions.
In today’s hypercompetitive biopharmaceutical marketplace, prescribing behaviors are shifting rapidly, in line with fast-paced, market-moving events. A high-volume prescriber of a drug can quickly become a low-volume prescriber and vice versa. Unfortunately, too many biopharma segmentation and targeting plans are built for the more static industry of the past, basing plans on historical prescription volume. This leads to three big problems for brand marketers:
The key to success is to create targeting plans that get ahead of tomorrow’s prescriptions. But a backward-looking volumetric approach to targeting can inadvertently lead a brand team to overcommit resources to reaching historically high-volume prescribers, often at the expense of middle-tier or low-tier ones who may be on the cusp of becoming top prescribers in the near future. These emerging growers can represent a significant opportunity for a brand. The traditional approach similarly can’t help a team recognize when a prescriber is on the verge of switching a significant number of prescriptions for the company’s brand to a competing brand (emerging switchers). And it certainly can’t help a brand team identify those who don’t yet write the company’s drug but are ripe to be converted (emerging adopters).
If a brand team were able to identify these emerging growers, switchers, and adopters, it could use a custom mix of sales and marketing tactics to reach them with messages that resonate and convert these emerging brand opportunities. But this proactive effort is impossible when a brand team relies on historical prescription data trends to dictate its future targeting strategy. By overhauling the old framework and deploying sophisticated big data management, advanced analytics, and machine learning (ML) techniques, teams can uncover the hidden trends within large volumes of prescriber- and anonymized patient-level data, identify strategically important prescribers, and proactively increase the breadth and depth of prescriptions.
ML can help a brand team discover underlying patterns within big data sets that aren’t easy to discern using traditional analytical approaches or simple rules-based algorithms. Armed with these insights, the team can identify the distinguishing characteristics of emerging growers, switchers, and adopters. From there, it can score and rank target HCPs or accounts within each category based on the likelihood of growth, switching, and adoption in the near-term, and create a dynamic target list that drives the call plan. After that, the team can craft and deploy tailored sales and marketing tactics and messages to engage with these targets and convert them.
One of the biggest impediments to successfully executing ML projects is poorly organized data. The brand team must create an organized repository of prescriber- and anonymized patient-level data and incorporate data on its current sales and marketing outreach. It should fold in prescriber attributes, prescription data for competing drugs, as well as relevant information about patients’ treatment journeys, where applicable.
Another step in implementing dynamic targeting is to measure its effectiveness. A small portion of the sales force can continue to use the traditional, volumetric-based targeting plan, while the majority of the sales team uses the new ML-based plan. The brand team can then compare sales results to measure and validate the effectiveness of its new approach.
Industry trends give biopharma companies little choice but to embrace ML-informed dynamic targeting. Some companies have already fully integrated ML-based insights into their call plans. Others operate in an intermediate stage on this path toward dynamic targeting. For example, a firm may still use a traditional, volume-based call plan, but also leverage simple rules-based algorithms to analyze recent prescribing trends and send time-sensitive alerts to reps regarding targets in their territories. But these algorithms and alerts can miss the underlying prescribing patterns and emerging trends that ML techniques would help the company identify. It can also be onerous for reps to change their call plans on short notice in response to these ad hoc alerts.
Companies in this intermediate stage should seek to push beyond it. A wholehearted embrace of ML-driven dynamic targeting as the basis of call plans will help them navigate a rapidly changing prescriber landscape with necessary agility.
Janardhan Vellore is a Vice President and Daniel Wetherill is a Partner, both at Beghou Consulting