Who We Are
Investor RelationsNews

Tips for Empowering product launch success with AI and analytics

Through the voice of Industry Experts
25 Sep 2023
The life sciences industry is currently experiencing an exciting phase of product launches, with numerous new products poised to enter the market in the coming years. Companies, big and small, are in a race to ensure that their product launches make a significant impact, secure a substantial market share, and ultimately enhance their bottom-line growth.
The key factors that typically determine the success of these launches include crafting an effective launch strategy, perfecting the launch timing, achieving robust growth in both revenue and market share during the initial 12 months post-launch, and executing a well-coordinated series of activities within the T-12 to T+12 timeframe.
Tying all these critical launch aspects together is advanced data and analytics, a topic that industry experts, Saket Malhotra (Ipsen), Shameek Ghosh (Otsuka), and David Salmon (AstraZeneca) delved deeply into at the Indegene Digital Summit.
The panelists discussed how analytics, artificial intelligence (AI), and machine learning (ML) can contribute to the entire product launch process.
The key highlights of the session are summarized below.

Analytics has a pivotal place in today's launch excellence playbook

Analytics now holds a pivotal position in the launch excellence playbook. It drives essential decisions throughout the product launch lifecycle, influencing strategy, ideation, and implementation, enabled by numerous analytical workstreams initiated at different points in the launch timeline. This plays a significant role in understanding market landscape trends, and tracking business and brand performance.
The true power of analytics varies based on your go-to-market model, company maturity, launch size, and the complexity of the market
– Saket Malhotra

Getting Analytics teams involved early in the launch planning process is crucial

The importance of involving analytics teams early in the planning process cannot be overstated. Starting as early as 36 to 24 months before the launch, analytics prove crucial in determining clinical value, planning for elements like diagnostics and market access, understanding market potential, understanding the indication, defining the market roadmap, and defining patient behaviors.
Timing is key. The involvement of the analytics team should begin by T-36, no later than T-18 before the launch
– Shameek Ghosh

Direct analytical investments towards high-impact use cases

Once you understand the size of the market, you need to determine the field force sizing to support the launch sequencing. This includes establishing territories and alignments for field teams to balance workload. Analytics should also focus on understanding the treatment dynamics of patients in the specific therapeutic area, identifying Key Opinion Leaders (KOLs), valuing Healthcare Professionals (HCPs), and establishing the messaging arc for the launch products, customized suitably for various HCPs, patients, and caregiver profiles.
Technologies such as AI, machine learning and Gen AI play a critical role in comprehending behaviors and defining the appropriate engagement tactics and messages for your external partners
– Saket Malhotra

Keep up with the evolving functionality of AI and analytics tools

AI tools are constantly evolving, demanding a shift in our perspective of their capabilities. Precision has reached a level where we can redefine pricing models, marketing access, and resource deployment strategies. AI is no longer just about estimating numbers; it is about predicting behaviors and optimizing resources.
If we continue to view this as simply determining how many people fit in the stadium, we're missing the fact that the technical capacity now allows us to define who will be sitting in section 2E and whether they will actually show up on the day
– David Salmon

Identify your target KOLs with ML models

The panel delved into the critical metrics required for KOL identification during pre-launch, including research influence, clinical influence, digital influence, industry engagement, and leadership engagement. These metrics serve as the foundation for constructing various ML models, such as network models, influence models, and social media models. Additionally, Natural Language Processing models can be utilized to understand KOL interests. Through these diverse workstreams, KOLs can be categorized as national-level, regional-level, or emerging experts.
KOL insights are vital for optimizing medical resources, developing engagement strategies, planning events, and orchestrating peer-to-peer program strategies
– Shameek Ghosh

Generative AI can be a game-changer in the launch process

While still in its early stages, numerous applications of Gen AI models in the life sciences commercial domain are currently being explored and implemented. These models can hyper-personalize content at an individual HCP and patient level. They also have the potential to consolidate and transform unstructured and scattered data into a more digestible and streamlined format in record time. The panel recommended taking a more strategic and proactive approach to maximizing the impact of such technology.
Read through the potential of leveraging artificial intelligence (AI) to enhance product launch success, despite the associated risks and challenges.
You have to be strategic; you can't be reactive, nor can you be lured by the bright and shiny stuff. You must have some essential pieces in place, such as good governance an ongoing capability development strategy
– David Salmon


Overall, the underlying theme of the discussion was clear: life sciences companies that utilize AI tools, incorporate data analytics into their product launches, and remain adaptable in the AI-driven landscape are more likely to achieve successful launches.
Explore how Indegene accelerated major drug launches with effective omnichannel orchestration and delivered a 28% boost in open rate.

Empowering Product Launch with AI & Analytics