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The key to success in a hypercompetitive marketplace is to create targeting plans that get ahead of tomorrow’s prescriptions, write Janardhan Vellore and Daniel Wetherill.
In today’s hypercompetitive biopharmaceutical marketplace, companies are launching innovative brands and unveiling new indications at a breathtaking pace. Prescribing behaviors are shifting rapidly, in line with these fast-paced, market-moving events. As a result, a high-volume prescriber of a drug can quickly become a low-volume prescriber and vice versa. Unfortunately, too many biopharmaceutical companies’ segmentation and targeting plans are built for the more static industry of the past. These companies base their plans on historical prescription volume, a traditional approach that makes their targeting and call plans obsolete within weeks. And it leads to three big problems for brand marketers:
• Anemic brand launches
• Inability to counter competitors’ launches
• Flatlining of brand growth.
The key to success in this hypercompetitive marketplace is to create targeting plans that get ahead of tomorrow’s prescriptions. But a backward-looking volumetric approach to targeting can inadvertently lead a commercial team to overcommit resources to reaching historically high-volume prescribers, often at the expense of middle-tier or low-tier prescribers 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 (in many cases, double-digit percentage growth in prescriptions). The traditional approach similarly can’t help a team recognize when a prescriber is on the verge of switching a significant number of her 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 commercial 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 commercial 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 techniques, brand 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 to propel brand performance.
Machine learning techniques can help a biopharmaceutical commercial 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 commercial 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 machine learning projects is poorly organized data. The commercial team must therefore create an organized repository of prescriber- and anonymized patient-level data. The team should also incorporate data on its current sales and marketing outreach to targets. And 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. We recommend having a small portion of the sales force continue to use the traditional, volumetric-based targeting plan. The majority of the sales team can use the new machine learning-based plan. The commercial team can then compare sales results between the two groups to measure and validate the effectiveness of its new approach.
Industry trends give biopharmaceutical companies little choice but to embrace machine learning-informed dynamic targeting. Some companies have already fully integrated machine learning-based insights into their call plans. Others operate in an intermediate stage on this path toward dynamic targeting. For example, a company in this stage 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 machine learning techniques would help the company identify. Additionally, it can be onerous for reps to change their call plans on short notice in response to these ad-hoc alerts.
So, the companies in this intermediate stage should seek to push beyond it, as quickly as they can. A wholehearted embrace of machine learning-driven dynamic targeting as the basis of call plans will help them navigate a rapidly changing healthcare industry and prescriber landscape with necessary agility. This forward-looking approach can help a biopharmaceutical company gain competitive advantage, achieve brand sales goals, maximize results from launches, and fend off competitors. It’s time for brands to embrace dynamic targeting and get ahead of prescriptions.