How can you get the “best” out of Next Best Action?
With current economic pressures and tightening market conditions for life sciences companies, it is more important than ever to arm customer-facing teams – sales, marketing, medical science liaisons - with Next Best Action (NBA) recommendations to improve their engagement and coordination with healthcare professionals (HCPs) and relevant medical staff.
But what does “best” really mean?
Today’s trends: Data evolution and the demand for personalised engagement
We have a convergence of data and technology, where life sciences commercial teams can now utilise vast amounts of data to engage more meaningfully with their customers. At the same time, physicians are now expecting a greater level of personalised engagement from life sciences companies, like their everyday consumer experiences.
According to analysts at Everest Group, life sciences’ commercial function is undergoing a significant technological evolution, propelled by the need for virtual engagement support and real-time actionable insights to engage with customers in an optimized and personalised way. This necessitates seamless life sciences data management followed by cognitive analytics.
In response to these trends, life sciences companies are adopting technology that mirrors what is already ubiquitous in the consumer marketplace.
“A lot of the big disruptive players use AI-powered clustering linked to customer data to create very personalised product (think Amazon) or content (like Netflix and Spotify) recommendations,” according to Dr Andree Bates, host of the AI For Pharma Growth Podcast.
Further, pharmaceutical companies are prioritising deploying NBA solutions to deliver consumer-level personalised engagement.
“I work with a lot of multinational clients, and NBA is the dominant initiative across every single client,” said Mark Miller, MD, life sciences advertising, marketing & commerce at Deloitte Consulting. “HCPs want more nuanced messaging. But accomplishing this requires that you align data, technology, analytics, strategy, and most importantly get sales and marketing to work together.”
The same is true for medical affairs teams, particularly for medical science liaisons (MSLs) who provide scientific support to physicians about their therapeutics. Intelligence technology helps MSLs quickly gain insights into what leading physicians are interested in learning, so they can better personalise each engagement. “MSLs generate a lot of valuable data, often in the form of unstructured call notes, plus there are many sources of purchased data and they often use a generic CRM system to capture visit notes and other information, so much of that slips into a black hole,” explained Dan DeStefano, global medical affairs, digital implementation lead, at GSK. “In order to harness leading physicians’ valuable feedback and other data, we need to apply AI, identify trends, and then feed that back to MSLs in the form of next-best-action recommendations – delivered in their daily workflows.”
Too many recommendations, not enough context
Although NBA solutions are essential for personalising engagement with HCPs, life sciences companies face challenges in optimising deployment and practical use.
One common issue is that NBA systems can generate too many recommendations. Users may feel paralysed if insights aren't prioritised based on individual needs, territories, past engagement, and other real-world factors.
Moreover, many of today’s NBA recommendations are not delivered in a user-friendly format for daily workflows. Instead, they arrive in mass batches, are poorly timed, or sent without context. Consider this all-too-common scenario: a recommendation to email a physician comes two days after the sales rep has already met with that doctor in person.
Intelligence activation means getting the “best” out of NBA
Simply capturing data and feeding it into NBA solutions isn’t enough; intelligence in the activation is necessary to make the data outputs meaningful and actionable.
NBA recommendations must be contextually logical to individual users to encourage action. Advanced intelligence technology can curate NBA recommendations—ranking them based on various criteria and providing additional context.
For instance, NBA models can generate upwards of 50 different tasks for a sales representative to do in the next hour. But that individual cannot possibly do all those tasks in that time period. Maybe they can accomplish one task, and maybe that one will take two hours. In other words, there needs to be something that rationalises, synthesises, and optimises all of the different AI contributions to say, “Here is the optimal next action amongst all the signals that have come in to the system” and “Here’s how to orchestrate that across all of these different channels”.
This process requires an infrastructure component that goes beyond models and data. It connects the output of models to actions, learns from those actions, and improves future recommendations. It’s a circular, continuously self-improving process.
Integration, optimisation, and orchestration
What does that infrastructure component need to do to overcome the common limitations of NBA? AI-driven solutions that effectively bridge the gap between strategy and execution exhibit critical capabilities in three key areas: integration, optimisation, and orchestration.
- Seamless integration: A robust AI platform should seamlessly integrate with various data sources, analytics, and marketing technology, allowing organisations to fully own and manage their strategic intellectual property. This integration is critical to harness value and market differentiation from separate investments in novel data, analytics, and data sciences and customer research. Additionally, the platform should be scalable and easy to use.
- Flexible optimisation: A good AI solution should prioritise flexibility when optimising recommendations, balancing impact with constraints to provide pragmatic, adaptable suggestions that consider real-world circumstances. The ideal system will prioritise high-impact actions and offer transparency behind its logic, enabling commercial teams to make informed decisions and strategise on the go. The same process supports MSLs as they seek to provide leading physicians with the very highest level of scientific support. “The goal is to have very targeted content for leading physicians,” DeStefano explained. “A continuously improving AI engine makes it dramatically faster, easier, and more effective to send the right content, but there is also a human element that must be applied to create and select the best content for each medical expert.”
- Team orchestration: Effective AI solutions should manage cross-channel actions transparently, enabling sales and marketing teams to coordinate touchpoints within their organisation – including relevant data exchange with medical teams. This promotes behavioural change and fosters trust, ensuring every HCP outreach is purposeful and well-coordinated across teams. Moreover, it's crucial for AI solutions to adapt to internal technical and organisational changes, as well as market conditions. Finally, a dynamic feedback loop should process real-time reactions to recommendations, guaranteeing the system continuously learns from new information and refines its actions accordingly.
Overcoming the ‘last-mile’ challenge in Next Best Action
Pharmaceutical companies must overcome the ‘last-mile’ challenge of turning strategy into action to thrive in today's competitive environment. By pairing NBA strategies with a smart, scalable infrastructure that solves common challenges around integration, optimisation, and orchestration, organisations are finally able to unlock the full potential of their NBA programs. With these capabilities in place, pharmaceutical companies can transform the way they engage with HCPs, driving richer relationships to inspire better patient care.
