AI-Enabled Benchmarking: Transforming Performance Measurement for Pharma Brands

John Clarke, Sr. Principal, Global Primary Intelligence, IQVIA

The pharmaceutical industry has witnessed a dramatic evolution in how brand performance is measured and evaluated. Today’s landscape is defined by a vast number of product launches across diverse therapeutic areas, from therapies for rare diseases to those with novel mechanisms of action. This proliferation of launches, combined with increasing market complexity, has created an urgent need for more sophisticated benchmarking approaches that can guide strategic decision-making throughout the product’s lifecycle. The investment in product launches has grown substantially, making the stakes higher than ever for achieving commercial success.

The benchmarking challenge

Traditional approaches to benchmarking brand performance have struggled to keep pace with industry dynamics. While individual companies often develop internal benchmarks based on their historical launches, these reference points may not capture the full context of current market conditions or provide actionable insights for novel therapies or categories.

The challenge is particularly acute in rare diseases and emerging therapeutic categories, where historical data may be limited or nonexistent.

Additionally, the fragmented nature of market research data—typically collected through proprietary studies that aren’t widely shared—has made it difficult to establish reliable industry-wide benchmarks. Without objective industry benchmarks, decisions often come down to gut feelings.

This lack of reliability becomes especially critical when market research indicators plateau. Comprehensive benchmark data can help determine whether plateauing represents normal market behavior or signals the need for intervention. Making decisions during these periods is particularly challenging without proper context. While it’s relatively straightforward to act when metrics are clearly trending up or down, the absence of movement requires sophisticated benchmarking to confidently interpret and appropriately respond.

AI’s transformative impact

Artificial intelligence (AI) is revolutionizing pharmaceutical benchmarking by enabling the integration and analysis of vast amounts of syndicated market research data across multiple countries, therapeutic areas, and time periods. This transformation goes beyond simple data aggregation. Without AI, the sheer scale of processing thousands of market research studies, understanding the deltas and differences, and maintaining continuous updates would make such comprehensive benchmarking difficult in the extreme.

These AI systems can analyze patterns across thousands of market research studies spanning multiple years while intelligently interpolating missing data points based on market dynamics and similar product trajectories. AI not only helps align, tag, and extract data, but it can be instrumental in designing its own programming architecture, using continuous learning to further improve reliability.

For novel or rare disease markets, the system employs a sophisticated approach to finding relevant analogues. For instance, when introducing a novel molecule to a market with two long-standing incumbents, the AI examines parallel markets within the same therapeutic area or with similar market dynamics. This might mean looking at how the first biologics entered the rheumatoid arthritis market, for example, or how biologic data for asthma might inform market predictions in chronic obstructive pulmonary diseases. The system refines these analogues into what we call a “basket of goods”—carefully selected parallel markets that match the client’s objectives and market dynamics.

Perhaps most important, AI enables the handling of nonaligned time scales in syndicated studies. Different studies may run monthly, quarterly, or at varying frequencies, creating gaps in the data. Through sophisticated interpolation techniques, AI can accurately fill these gaps by understanding the specific patterns and dynamics of each market, rather than simply applying linear progression. However, it’s important to note that the system does not extrapolate beyond the last available data point, which maintains the integrity of the benchmarking process.

Rethinking key performance indicators

The evolution of AI-enabled benchmarking has led to a refinement in how key performance indicators (KPIs) are selected and applied. Rather than tracking every possible metric, successful benchmarking focuses on universal commercial KPIs that directly correlate with launch success. These include the progression from spontaneous to prompted awareness, prescription intention, and the critical journey from trial to regular use.

The goal isn’t to create more complex metrics. Instead, we’re focusing on output measures that reflect the cumulative impact of all marketing efforts and their translation into prescribing behavior. This approach provides a clearer picture of actual market performance and enables more informed decision-making about commercial strategic adjustments.

A crucial aspect of advanced benchmarking is the concept of “performance bands” rather than single benchmark lines. Traditional benchmarking often relies on simple averages or single reference points, which can be misleading. Instead, we need to consider performance corridors that show the range of normal performance for successful products, allowing companies to understand where they truly stand.

Future of measuring brand performance

Looking ahead, AI-enabled benchmarking is poised to become even more sophisticated and accessible. The ability to process quantum syndicated data and provide dynamic benchmarks will allow companies to make more informed decisions about their commercial strategies. We’re seeing the emergence of more granular analysis capabilities for different market types, along with the enhanced ability to identify optimal measurement timing based on product and market characteristics.

Recent projects have demonstrated the system’s versatility. For instance, we’ve used benchmarking data to analyze the relationship between marketing investment and physician awareness, comparing products with varying levels of pre-launch investment and sales force deployment.

Our research has also shown that measurement timing itself can be a critical factor in launch success. Higher-performing products often require longer evaluation periods before strategic adjustments, while underperforming products benefit from earlier intervention. This kind of insight, derived from comprehensive benchmark analysis, helps companies optimize their measurement and decision-making processes.

The human element remains crucial in this technology’s deployment. While AI handles the heavy lifting of data processing and pattern recognition, experienced market researchers and data scientists validate outputs, ensure real-world applicability, and help clients interpret results in their specific context. The system continues to learn and refine itself through this collaboration between human expertise and AI.

The future of measuring pharmaceutical brand performance is not just in collecting more data but in generating more actionable insights through intelligent analysis and contextual understanding. The integration of multiple data sources will continue to enhance the comprehensiveness and accuracy of benchmarking capabilities, while maintaining human oversight helps to ensure alignment with real-world market dynamics.

Connected intelligence improves brand performance

AI-enabled benchmarking represents a significant advance in measuring pharmaceutical brand performance and providing commercial teams with more reliable, contextual, and actionable insights. As the industry continues to evolve with more specialized therapies and complex market dynamics, the ability to benchmark effectively becomes increasingly critical for commercial success.

Companies that embrace these advanced benchmarking capabilities will be better positioned to optimize their launch strategies and drive sustained market performance. The key lies in understanding not just where your brand stands currently but what that position means relative to the broader context of market dynamics. Taking a more sophisticated benchmarking approach will guide strategic decision-making across the product’s lifecycle, enabling your team to quickly identify areas of stronger or weaker performance and empowering them with the confidence, based on market data, to drive brand success.

John Clarke is Sr. Principal, Global Primary Intelligence, at IQVIA