In today’s fast-paced pharmaceutical industry, the need for timely, accurate data has never been more urgent. Real-time data use is an emerging market research approach at the forefront of this transformation. With lightning-fast insights powered by artificial intelligence (AI), real-time data use offers pharma companies powerful tools to enhance decision-making, improve market responsiveness, and drive commercial success.

What is real-time data use?

In the context of life sciences, real-time data use refers to near-instantaneous feedback gathered from healthcare professionals (HCPs). Online data collection platforms now allow direct input from HCPs, providing rapid insights into their perceptions, behavior and trends. When this feedback is combined with AI-powered analytics, the result is near real-time insights — an invaluable asset for pharmaceutical companies aiming to adapt quickly in competitive markets.

Traditional data collection methods involve numerous manual steps, e.g., gathering verbatim feedback from HCPs, cleaning the data, structuring it, and finally analyzing it for insights. The voice of the HCP “customer” can be monitored through both quantitative and qualitative feedback — although historically, qualitative data (e.g., free-text comments) has been far more difficult and time-consuming to analyze than quantitative data (e.g., multiple-choice or yes/no responses). Critical shifts in qualitative HCP sentiment may go unrecognized for some time, given the burden of manual assessment and evaluation. These time-consuming processes have always limited how rapidly commercial decisions can be made.

Using advancements in AI, particularly with large language models (LLMs), many of the requisite steps can now be automated and executed simultaneously at the point of data collection. AI-enhanced real-time data use integrates data collection and analysis into a single, continuous process. By enabling rapid sentiment analysis, categorization of free-text comments, and other forms of natural language processing, AI eliminates the delays imposed by manual handling. The shorthand term real-time data use is a figure of speech, as the data transmission and processing involved are not instantaneous. But near real-time AI analytics cuts the interval from weeks or months to minutes or hours. This drastic boost in speed and depth of insights gained is the breakthrough advantage here.

The impact of faster commercial decisions

As a result, commercial teams can instantly identify actionable insights, cutting down response times and making quicker, more informed decisions. In the pharmaceutical sector, this shift in data processing can dramatically improve market responsiveness.

Understanding how marketing messages are perceived by HCPs is of key concern. HCP perceptions and the impact of messaging on prescribing decisions become crucial. Any delays in identifying a poorly received message could lead to significant financial losses or missed opportunities in the market. Timely insights in this area can make or break a product launch or promotional campaign.

This is why life sciences companies need to focus their real-time data efforts on metrics that directly influence these decisions. Through the application of AI-powered real-time data use, marketers can glean timely insights from both quantitative and qualitative responses, enabling them to more fully grasp the “why” behind HCP behavior and quickly pivot strategies and align messaging to ensure optimal outcomes.

For example, companies can monitor how specific HCP demographics respond to marketing messages, allowing adjustments before large-scale deployment. This agility is especially crucial in competitive therapeutic areas where every moment counts.

Ensuring data reliability and trustworthiness

As pharmaceutical companies increasingly depend on real-time data, it’s a top priority to ensure that the data sources are reliable. AI systems built for healthcare, like those from IQVIA, emphasize privacy, security and data governance. With access to unparalleled volumes of medical data, domain expertise and proprietary technology, IQVIA’s Healthcare-grade AI™ ensures that the data feeding AI models is not only vast but also accurate and trustworthy.

Data validation is crucial when dealing with healthcare information, and AI brings new levels of speed, consistency and scalability to the validation process. By automating much of the data handling, AI eliminates human biases and fatigue that can often compromise the quality of analysis.

Key challenges in implementation

Even with their transformative potential, AI-enhanced real-time data strategies can pose numerous challenges for pharma companies when implementing them. Below is a quick overview of ways to address and solve these challenges.

• Data quality and integration. Many life sciences companies struggle with integrating data from various sources. Inconsistent data formats and incomplete datasets can negatively affect AI model performance. Firms must respond by investing in robust data governance frameworks to ensure proper data cleaning practices. IQVIA’s Connected Intelligence, integrated throughout all our data resources, addresses this challenge head on.
• Regulatory and ethical concerns. Navigating complex regulatory landscapes and ensuring the ethical use of patient data can be daunting. Collaborating with regulatory bodies early and establishing clear ethical guidelines for AI use are essential steps. Regular audits can ensure compliance with privacy laws, such as HIPAA. Again, our Connected Intelligence embeds these processes.
• Talent shortage. The implementation of AI in real-time data strategies requires specialized expertise. Upskilling current employees and fostering partnerships with experienced AI solution providers can help bridge the talent gap. Many pharma companies choose to partner with IQVIA to meet these needs.
• Adoption and scaling. Introducing new AI tools can be met with resistance. Companies must foster a culture of innovation, run pilot projects, and provide clear communication about the benefits of AI to ensure widespread adoption.
• Strategic alignment. Ensuring that AI initiatives align with broader business objectives can be challenging. Regularly reviewing and adjusting AI strategies based on performance metrics and market needs helps ensure that AI investments deliver tangible value.

Key trends and strategies

AI-driven real-time data use is revolutionizing promotional activities in pharma. Marketing executives may find that they can now assemble a well-informed brand plan in minutes instead of weeks. Beyond the quantum leap in speed and automation, several key trends are emerging that highlight how companies can leverage AI to drive success.

• Personalized marketing. AI allows companies to create highly personalized marketing campaigns by analyzing real-time data from various sources. Tailored messages ensure that the right information reaches the right HCP and patient audiences at the right time.¹
• Predictive analytics. AI-powered predictive analytics can forecast market trends, helping pharmaceutical companies allocate resources more effectively. Having informed predictions about which products are likely to be in demand and where, companies can allocate their marketing resources more effectively.²
• Enhanced customer engagement. AI-driven chatbots and virtual assistants can provide real-time support to HCPs and patients, enhancing engagement and customer satisfaction.²
• Optimized content creation. Generative AI can create marketing content tailored to real-time insights, promoting relevance and engagement to make positive responses more likely.³
• Real-time campaign monitoring. AI can continuously monitor the performance of marketing campaigns, allowing for data-driven adjustments and active optimization.¹
• Compliance and regulation. AI can help ensure that promotional activities adhere to regulatory standards and issue alerts for needed corrections, reducing the risk of non-compliance.³
• Market segmentation. AI can analyze real-time data to segment markets more accurately, increasing the campaign effectiveness using tailored promotional strategies.⁴

A new pathway to speed progress

The integration of AI into real-time data use is reshaping the pharmaceutical industry. By harnessing the power of real-time insights, companies can enhance decision-making, drive commercial success, and stay ahead in an increasingly competitive landscape. With the right strategies and tools in place, the future of pharmaceutical marketing is both data-driven and AI-enhanced.

References

1. “Sanofi's plai: Revolutionizing Healthcare with Artificial intelligence.” Sanofi. June 13, 2023.
2. “Harnessing AI and Data for Next-Generation Pharma Marketing and Sales.” Multiplier AI.
3. “Generative AI in the pharmaceutical industry: Moving from hype to reality.” McKinsey & Company. January 4, 2024.
4. “Generative AI for Next-Level Pharma Marketing & Sales.” Multiplier AI.