How AI Is Transforming Pharmaceutical Sales and Marketing


November 8, 2023

In the United States, major pharmaceutical companies often allocate more funds to sales and marketing than to research and development, as reported by America’s Health Insurance Plans. In 2020, the top 10 pharmaceutical companies in the US collectively spent $137 billion on sales and marketing. This trend extends to smaller pharma, biotech, and medtech firms, resulting in substantial investments.

However, the effectiveness of this expenditure can be questionable. Sales representatives, despite high costs per representative exceeding $100,000, tend to focus on the same accounts they have always visited, sometimes waiting for extended periods to secure brief interactions with physicians. Sales leaders often face challenges in prioritising accounts, adapting messages to specific contexts, and adjusting their strategies as market dynamics evolve.

The Challenge at Hand

The situation is becoming increasingly critical as drugs become more targeted, leading to smaller patient populations, where each patient’s value grows significantly. Identifying the right patient and securing their prescription has become a pivotal battleground.

The Role of Artificial Intelligence (AI)

Can AI make a difference in addressing these challenges? Undoubtedly. The complex task of interpreting vast and ever-changing datasets to prioritise targets and customise messaging for various healthcare professionals and their employers aligns perfectly with AI’s capabilities in the healthcare sector.

One company actively tackling this issue is Verix, a firm with both Israeli and US roots. Shahar Cohen, Verix’s Chief Technology Officer, underscores the necessity for AI, stating, “To make targeting and promotion decisions, there are getting to be too many variables for humans to consider in a handcrafted way. An oncology drug, for example, should be targeted by physician specialty; geography; typical ages seen; conditions that are typically treated; the mix of insurance used; patient volumes; institutional affiliation; and whether the doctor is seen in a first, second, or third line of treatment. There are dozens or hundreds of potential combinations of factors.”

Traditionally, life science companies may have employed segmentation methods based on statistical relationships among variables. However, these approaches were designed for scenarios with fewer than a dozen variables. As complexity increases, their predictive accuracy diminishes, and they often become opaque to those without significant quantitative analysis training.

Cohen notes, “From a business perspective, it’s impossible to manage too many segments. With our AI, we don’t even try. What we do is prioritise physicians for promotions, channels, and digital engagement. Then we use a supervised learning model to understand the propensity of each physician to change his or her behaviour based on action initiated by the pharma company.”

Challenges in Implementation

Cohen acknowledges that software alone cannot solve this challenge, emphasising, “Software is never enough without a proper design of a business process to use it.” Life science companies need to entrust more decisions to these systems and align sales representatives’ incentives with the priorities embedded in the IT systems, which may necessitate significant cultural adjustments.

Another noteworthy aspect of AI implementation is that it differs from other IT platforms. In Verix’s case, Cohen asserts, “After a few weeks, we have the data integrated and ready to use. That’s a huge change from situations where IT consultants ran the whole process. It’s quick partly because AI is a platform that continues to adapt and learn. It takes in market data from vendors like IQVIA and merges different sources of information. Then the system keeps re-training. The first configuration is human, of course, but after that it’s automatic.”

To foster acceptance, avoiding the black-box nature of AI is essential. Although deep learning models seen in tools like ChatGPT could be applied to pharmaceutical sales and marketing, they are not obligatory. The structured nature of data ingested by these systems in this context reduces the need for deep learning. The behavioural recommendations generated by these systems are conducive to a more traditional, algorithmic AI approach. Through a “white box” system, companies can ensure the explainability of their models and exert more direct supervisory control.

AI’s Impact on Pharmaceutical Sales and Marketing

By enhancing targeting precision and providing more specific guidance to sales representatives, AI plays a pivotal role in making high-stakes decisions associated with personalised medicine sales and marketing. In an environment where each patient’s value can be substantial, precise targeting is imperative. Simultaneously, AI has the potential to reduce expenses related to life science sales representatives by offering more direct guidance and, potentially, by initiating automated digital actions.

Historically, only rare innovations have enabled simultaneous increases in value and reductions in costs. AI has the potential to make such innovations commonplace, revolutionising the industry.

Empowering the Future of Pharmaceutical Sales and Marketing

At Avery Fairbank, we understand the transformative power of AI in pharmaceutical sales and marketing. Our team of experts specialises in identifying top talent in the AI and healthcare sectors, ensuring that your organisation stays at the forefront of innovation. If you’re looking to enhance your team with professionals who can drive the AI revolution in pharmaceuticals, visit our website to learn more about our executive search services, or to discuss your unique requirements, don’t hesitate to get in touch. Embrace the future of pharmaceutical sales and marketing with us.

Pharma Sales AI

Published on 08-11-2023