A product launch is a defining moment. While "spray and pray" marketing and postmortems once worked for blockbuster launches, success now requires an adaptive approach. The industry must embrace real-time data and continuous feedback loops to pivot quickly. And as AI upends customer behavior patterns, pharma must evolve now to compete in the race for Share of Answer.

A Challenging Launch Environment

In the era of specialized medicines, biopharma cannot afford to wait to identify what is and isn’t working. Over half of launches miss sales forecasts: 56% of oncology and 67% of infectious disease products, for example.¹ These failures stem from limited insights, poor cross-functional collaboration, and reactive planning.

Marketers must also navigate tighter budgets while meeting rising consumer expectations. Customers demand timely, hyper-personalized information delivered through preferred channels, mirroring their experiences with non-health brands.

And today, they often ask AI first when seeking health-related information. 81% of US HCPs use AI within practice, and 70% will use it to find medical research and care standards by the end of 2026.³ Patient use is growing too: 32% of Americans now turn to AI chatbots for health advice.⁴ When AI agents answer HCP and patient questions before industry content reaches them, share of voice no longer effectively measures investment impact.

Share of Answer is the new metric for success, quantifying a brand's ability to be the definitive, cited source of information as AI answer engines respond to HCP or patient queries. And winning it––to win launches––requires transformational shifts.

Omnichannel marketing gained popularity with the promise of coordinated customer engagement, but it lacked the hyper-targeting to maximize spend, relevance and efficiency. Organizational barriers also hindered impact, resulting in wasted resources, diluted messages and missed opportunities to reach HCPs where they’re most likely to engage.

Despite investments, 77% of pharma leaders say omnichannel failed to deliver results, and half struggle to use data for personalization.² As AI answer engines emerge, many teams risk extending these inefficiencies to them in an attempt to just keep up.

To overcome these challenges, pharma companies need a deep understanding of customer behaviors, preferences and platform usage, informed by real-time data.

Optimizing Campaigns in Real Time

Optichannel marketing is an agile, data-driven approach based on selecting the optimal channels for an audience based on dynamic customer affinity and platform performance data. The approach offers the ability to seize opportunities when something is working––and adapt when it is not. Unlike conventional buying models, which incentivize broader, costlier campaigns, optichannel prioritizes precision and creates a personalized experience based on what an HCP wants to receive. And 86% of biopharma leaders believe optichannel could help improve customer engagement.

By instantly incorporating channel performance data and granular customer insights, optichannel creates agility and maximizes budget. Direct-from-source data pipelines and machine learning, a core component of applied AI, inform individualized next best action (NBA) sequences to optimize engagement without locking in expensive channel buys.

Use of data triggers based on real-world signals also means content can be deployed to HCPs within 48 hours of seeing an eligible patient. Crucially, this deploys to the channels and platforms the HCP already uses. Focusing reach where HCPs are now, rather than outdated data, means content lands with a higher chance of action. This includes touchpoints on the AI answer engine like ChatGPT and Open Evidence that an individual customer prefers most.

Dynamic affinity data also helps identify HCPs who prefer sales engagement, allowing marketers to deploy reps more effectively. If an HCP searches for relevant keywords, field teams can be alerted within 1-2 days to respond rapidly. Transparency is key to eliminating "black box" engagement and providing a clear picture of the customer journey across all channels.

Targeted engagement is vital as "push" marketing ends, and responding to real-world signals in near real-time is critical as customers shift to AI engines and away from traditional ad placement sites. Winning Share of Answer will in part require optimizing content for AI consumption through clear data structure and scientific authority. It will also necessitate investments in tools with the integrations needed to connect data sources, link to AI assistants, and deliver the right context in real time.

Data and recommendations should be shared throughout the campaign to optimize performance and improve business outcomes. Reporting quarters later is no longer acceptable, and speed from insight to action must be a top priority for leaders preparing for launch to ensure agility and a stronger competitive position.

Continuous feedback loops in optichannel approaches offer several benefits. Brands can avoid months of wasted budget on inappropriate or ineffective platforms, and it’s easier to track investments on a narrower channel mix. Two-way data integrations, combined with NPI-level tracking, offer further insights by connecting specific field and digital deployments to concrete outcomes in real time.

Case Study: Optimizing a Rare Disease Launch

Rare disease launches can be particularly challenging, with small patient populations that mean trying to reach a limited number of highly specialized HCPs. Complex or variable treatment windows can also make it more difficult to identify key moments to optimize outreach.

A rare tumor therapy launch successfully replaced traditional planning with an optichannel approach. Despite a variable treatment window, small patient pool, and lack of a single ICD code, the team used multiple data sets to identify patients and inform channel decisions.

Instead of broad segment data for the target list, an optichannel approach provided real-time, first-party data so the team could make informed decisions on which channels and tactics to use – a paradigm shift to proactive and rapid targeting. Real-world claims data was used to identify key moments for HCPs treating eligible patients, along with behavioral triggers such as HCPs who engaged with disease education or searched for competitor information. By augmenting claims data in this way, the team identified more relevant providers and delivered valuable education over time. The campaign was further enhanced with a holistic approach to content strategy which combined promotional messages and KOL-driven content to ensure a balanced and comprehensive flow of information.

Within a year as the launch smashed analyst forecasts, 60% of new prescribers were traced to the campaign, with 30% driven solely by digital optichannel efforts. This was transformative for a small company with a lean field team that was under pressure during the launch of their first commercial brand. The importance of personalization was also underscored by the fact that there was an average of six engagements per prescription, highlighting the need for sustained communication.

Conclusion

Successful launches now rely on optimizing campaigns in real time. With live audience and signal data, pharma companies can deploy personalized content at the exact moment of opportunity. This is not just keeping pace with AI, but leveraging it to future-proof the brand. Successful teams that make this shift will be positioned to win Share of Answer and market share in this new AI-enabled landscape.

References

1. https://www.ey.com/en_gl/insights/life-sciences/how-biopharma-can-get-the-right-mix-of-people-and-tech-for-launch-success
2. https://www.graphitedigital.com/insights/disconnected-pharma
3. https://www.ama-assn.org/system/files/physician-ai-sentiment-report.pdf
4. https://www.kff.org/public-opinion/kff-tracking-poll-on-health-information-and-trust-use-of-ai-for-health-information-and-advice/
5. PFIQ/Biopharma Dive Survey, Feb-March 2026, n=151