By Dr. Maria João Cruz, PhD, Assistant Research Manager, Deloitte Centre for Health Solutions
This week marks the launch of the fifth report in our Intelligent biopharma series, which highlights the role of artificial intelligence (AI) in accelerating and driving digital transformation across the biopharma value chain. This report, Intelligent drug launch and commercial: Optimising value through AI, focuses on how companies can use AI to radically change and improve drug launches and their commercial models. The report also reflects on the challenges and disruptions caused by the COVID-19 pandemic and the response of commercial teams, including adapting new marketing and engagement channels to meet the needs of the different stakeholders.
Why launch and commercial activities need to change
Launch and commercial activities are a crucial part of the biopharma value chain, enabling patients to gain speedy access to new therapies. As in any industry focussed on meeting market needs, however, biopharma companies have to plan and execute winning launch and commercial strategies, including optimising marketing, pricing, regulatory, compliance and sales approaches (figure 1). Moreover, companies are facing increasing challenges, including escalating costs of drug research and development, growing competition and a reduction in average peak sales, mounting pressure to reduce time-to-market, new models of care and ability to pay for innovative medicines. While the right drug launch and commercial strategies have become more important than ever, planning and executing a successful launch has become even more complex and challenging.
Figure 1. The biopharma value chain and launch and commercial operations
Source: Deloitte analysis.
Biopharma companies have generally sought to identify and direct commercial activities towards the right market segment at the right time by using different communication channels based on the needs of each stakeholder (payers, providers, health care professionals (HCPs) and patients). For the past few years, the traditional ‘one-size-fits-all’ go-to-market strategy, predominantly based on physical channels, has started to shift towards the use of digital channels.
Engaging with stakeholders during and after COVID-19
Over the past 12 months, the unequivocal challenges and disruption caused by COVID-19 has led commercial teams to ask new questions, including: what channels are best suited for which stakeholder needs, how to address the needs of HCPs and patients more effectively, and what digital technologies can be leveraged to drive successful launches?
The disruptions caused by COVID-19 are likely to have a long-lasting impact. With the growing pressure to shorten time-to-market, companies increasingly need to understand what components of their sales and marketing operations, as well as other innovations, drive prescribing behaviours and expand patients’ access to new treatments. Consequently, early and efficient engagement with stakeholders is crucial to ensure companies can communicate their product’s value – this is where AI comes in.
How AI can improve launch and commercial activities
Today, biopharma companies have access to data from multiple internal and external sources. AI can enable companies to realise the power of this data, particularly real-world data (RWD), to improve their launch and commercial performance by managing tailored engagements with different stakeholders and delivering added value that meets their needs more effectively. By implementing the right AI technologies, companies can gain access to comprehensive real-world results and obtain valuable strategic insights to support key decision-making (figure 2).
Figure 2. AI applications across launch and commercial functions to develop key strategic insights and support decision-making
Source: Deloitte analysis.
The adoption of AI technologies is therefore becoming a critical commercial imperative, specifically in the following five areas:
- Making the most of RWD for commercial success: RWD provides more representative information about a therapy’s impact in a broader patient population, a more accurate view of the evolving standard of care and more comprehensively reflects routine clinical care. The evidence available suggests that by using RWD and real-world evidence (RWE) effectively, companies can understand and address proactively, in real time, evolving stakeholder needs both pre- and post-launch. Companies can only fully realise the potential of RWE through using advanced technologies which enable the continuous flow of RWD to be collected, cleaned, aggregated and analysed in a seamless and dynamic process. Such capabilities will be crucial for companies to understand their products’ value and efficacy, as well as to justify costs in a competitive landscape.
- Predictive pricing: Advanced analytics tools will help companies respond to growing scrutiny from payers and other stakeholders when making a case for new drug prices. Having confidence in these data-driven approaches is essential for biopharma to develop better-informed pricing strategies. Innovative analytical models can improve confidence and will be crucial in identifying more effective pricing opportunities, which will ultimately translate into profit and revenue.
- AI-enabled omnichannel marketing: As companies embrace the tenets of patient centricity, an effective sales and marketing approach requires companies to demonstrate that they have a deep understanding of the patient’s condition, what individuals value and need, and what is most likely to result in a positive health care outcome. AI-enabled omnichannel marketing solutions can assist by predicting behaviour and providing recommendations to biopharma marketers on next best actions, the channels to leverage and how to optimise stakeholder engagement through personalised messaging.
- AI-driven market segmentation: Understanding unmet needs and identifying different HCP/patient segments is another area in which AI can assist. AI computational algorithms, including machine learning (ML) and deep learning (DL), can be constantly updated to capture changes in behaviours and attitudes, providing robustness to strategic decision-making and tactics on sales. AI-driven market segmentation solutions can identify methods to improve commercial performance and optimise product value propositions specific to different geographies and health care systems.
- Scenario planning and intelligent forecasting: Biopharma companies are also increasingly leveraging data to build accurate forecasts and develop effective planning and long-term strategies that enable them to respond to the growing complexity and rapid changes in the market. Detailed and comprehensive scenario planning can be a crucial element to drive evidence-based decision-making about future marketing strategies. AI tools such as ML can be used for effective scenario planning to help refine the variables that provide insight into existing and future market landscapes. This can be a vital tool to implement in a ‘what-if’ forward-thinking framework to understand the potential actions and behaviours of stakeholders and competitors and how they should respond, enabling companies to optimise resource allocation and understand key performance indicators.
An AI-powered future for launch and commercial operations
While biopharma companies are already adopting AI applications in areas such as drug discovery and clinical development, marketing and sales have generally lagged compared to other parts of the value chain in digitalising systems and processes and the use of AI. However, companies are rapidly accelerating their digital transformation of launch and commercial activities, driven largely by the COVID-19 pandemic.
Going forward, biopharma leaders should create a culture that promotes commercial innovation with a focus on operational excellence, while acquiring and fostering the right skills and talents. Marketing and commercial teams should align their thinking around launch excellence and its execution, and how to integrate advanced digital technologies to foster cross-functional collaboration to enhance engagement and maximise the value from their products. The comprehensive implementation of a robust, AI-enabled commercial strategy will be a vital part of a pharma company’s armoury in the commercial future.