Intelligent drug launch and commercial

Optimising value through AI

This is the fifth report in our Intelligent biopharma series, which explores the current and future potential of artificial intelligence (AI) across the biopharma value chain. This report focuses on how companies can use AI to improve drug launches and their commercial models.

Why launch and commercial activities need to change

In the biopharma value chain, launch and commercial activities enable patients to gain access to new therapies. However, companies are facing increasing challenges in achieving a successful launch, including the escalating costs of drug development, growing competition, mounting pressure to reduce time-to-market, new models of care and ability to pay for new, innovative medicines.

As in any industry focused on meeting market needs, biopharma companies have to plan and execute winning launch and commercial strategies, including optimising marketing, pricing, regulatory, compliance and sales approaches (figure 1). Biopharma companies seek to identify and direct commercial activities towards the right market segment at the right time by leveraging 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.

Intelligent drug launch and commercial

Over the past 12 months, the unequivocal challenges and disruption caused by COVID-19 has led commercial teams to ask new questions, including which channels are best suited for stakeholder needs, how to address the needs of HCPs and patients more effectively, and what digital technologies can be leveraged to drive successful launches? Furthermore, 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

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 effectively 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).

The adoption of AI technologies is therefore becoming a critical commercial imperative, specifically in the following five areas.

  1. 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 proactively address, 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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

While biopharma companies are already adopting AI applications in a number of areas, such as drug discovery and clinical development, marketing and sales have generally lagged other parts of the pharma value chain in digitalising systems and processes and the use of AI. However, companies have recently accelerated their digital transformation of launch and commercial activities, driven largely by the COVID-19 pandemic. Biopharma leaders should create a culture that promotes commercial innovation with a focus on operational excellence; having a clear view of what they can anticipate from investments in data, AI and other advanced technologies.

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. By breaking down data silos and interconnecting the right technologies across the product life cycle, performance can be monitored from end-to-end using key metrics to ensure business activities support product value and lead to commercial success. Biopharma companies should therefore build a robust, end-to-end market access strategy framework to understand what matters most to market access stakeholders and develop products and approaches that meet their priorities.

This end-to-end visibility across commercialisation will provide significant benefits. Utilising AI technologies, biopharma companies can coordinate product launches better, establish proof of value to support reimbursement models for new curative therapies and services and improve patient engagement. However, before adopting and upscaling AI solutions across their commercial operations, there are a number of questions companies need to consider (figure 3).

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