How Can GenAI Transform the Analysis and Ingestion of PMR Reports in Pharma?

From understanding customer needs to developing effective marketing strategies- Primary Market Reasearch or PMR directly engages with the end users. By allowing researchers to tailor methods to specific objectives, the research offers a more nuanced and targeted perspective. With multiple stakeholders like HCPs, patients, caregivers, payers and industry experts in picture influencing product success it’s important to get an unbiased information from the source.

PMR is frequently outsourced to multiple specialized agencies, each employing distinct methodologies and tools, resulting in diverse data formats and varying levels of structure that complicate downstream ingestion and analysis. COMCO studies are typically quantitative in nature, leveraging structured surveys and statistical methods to support use cases such as concept testing, demand estimation, segmentation, and promotional effectiveness. The resulting datasets are generally well-structured—often delivered in spreadsheets or databases, making ingestion relatively straightforward, though schema standardization across vendors is still often necessary. In contrast, TO (tactical opportunity) reports are qualitative and draw on insights gathered from HCPs via in-depth interviews, congress interactions, and exercises like TPP assessments, positioning studies, and patient journey mapping. These studies tend to generate unstructured or semi-structured data, including interview transcripts, audio recordings, and open-text responses, which require significant manual effort or advanced NLP capabilities for processing and analysis. 

Adding to the challenge is the lack of standardized terminology and limited platform interoperability across vendors, which hampers integration efforts and increases the risk of delayed or fragmented insights in critical decision-making areas such as product positioning or go-to-market strategy. Moreover, there are instances where similar PMR studies are conducted in parallel by different teams, resulting in duplication of effort, inefficient resource utilization, and inconsistent insight delivery. 

MathCo has consistently observed—and helped clients overcome—critical challenges that hinder insight generation from PMR reports and studies, with the following being some of the most common roadblocks across engagements: 

1. Inconsistent Structure: The lack of uniform structure between these diverse data types can complicate the data ingestion process, leading to difficulties in merging and analyzing the data. Discrepancies in format can result in misaligned datasets, incomplete data integration, or inaccuracies in market insights and decision-making.
2. Lack of Schema or Schema Variability: Without a consistent schema, data from various studies or research tools cannot be easily combined. This lack of standardization can make it difficult to apply uniform analytical techniques across datasets, leading to errors in analysis or misinterpretation of insights, which could have implications for marketing strategies or drug development.
3. Data Type Inconsistencies: When the data types are inconsistent, it becomes challenging to integrate and analyze the research data. For example, numeric responses could be interpreted as text, leading to erroneous statistical analyses, or categorical data might be treated as continuous variables, skewing insights that are vital for developing marketing strategies or regulatory submissions.
4. Volume and Performance Bottlenecks: Inefficient data formats can slow down the ingestion process, affecting the speed at which research teams can access and analyze critical market insights. In the pharmaceutical industry, delayed access to research results can affect product launch timelines, marketing campaigns, and competitive intelligence efforts.
5. Error Handling Challenges: Variability in how errors are handled can lead to incomplete or inaccurate data being ingested into the system, undermining the integrity of the research findings. For example, missing patient feedback or inconsistencies in how responses are recorded can skew the results, leading to flawed insights about physician prescribing behavior, patient needs, or market dynamics.
6. Data Validation Issues: If data doesn’t pass validation during the ingestion process, it may result in inaccurate or incomplete analysis. In the pharmaceutical industry, this could lead to misleading conclusions about drug efficacy, physician preferences, or patient experiences, which could affect market access strategies, product positioning, or regulatory submissions.
7. Integration Complexity with Diverse Research Systems: The lack of seamless integration can create silos in research data, leading to delays in accessing critical insights. For example, combining quantitative data from surveys with qualitative feedback from focus groups might require significant preprocessing and manual effort. This inefficiency can slow down decision-making processes and affect the timeliness of strategic initiatives like product launches or market segmentation.
8. Data Consistency and Integrity Issues: Inconsistent or inaccurate data can lead to misinterpretation of market trends, which can significantly impact strategic decisions like targeting the right healthcare professionals, designing patient-centric marketing campaigns, or developing post-market surveillance strategies for new drugs.
9. Transformation Overhead in Primary Market Research Data: The transformation process adds overhead, requiring additional computational resources and time. In pharmaceutical industry, delays in data transformation can slow down the analysis of market trends, potentially delaying product positioning, market access decisions, or marketing campaign planning.

 
GenAI and PMR: Transforming Data into Actionable Strategy

Consider a global pharmaceutical major that sought to leverage its vast PMR data to drive faster strategic decisions. However, the data was scattered across unstructured formats—such as presentations, transcripts, and PDF reports—and structured Excel files with numerous tabs. Each study spanned multiple therapeutic areas, making consolidation a significant challenge. This fragmentation limited the ability to generate timely, consistent insights and restricted knowledge sharing across teams.

GenAI offers a transformative approach to PMR analysis through advanced techniques like text summarization, topic modeling, sentiment analysis, and data transformation. It distills long-form reports into concise summaries, identifies recurring themes and emerging market signals, and surfaces critical stakeholder insights such as side effect concerns or decision-making drivers. Sentiment analysis reveals perceptions of patients, physicians, and payers, while intelligent data extraction and terminology harmonization enable seamless integration and analysis of previously unstructured content.

MathCo deployed a GenAI-powered platform that automated the ingestion and synthesis of both structured and unstructured PMR data. The system processed 15–20 unstructured files and 6–12 structured tabs per study, intelligently routing queries between data types for contextual accuracy. A RAG framework, supported by a vector database and specialized agents like Text-to-SQL and Document Q&A, enabled semantic search and insight extraction. The solution delivered high-level summaries, identified unmet needs and KOL perspectives, and provided persona-specific responses to complex business questions—empowering teams across marketing, R&D, and strategy to make faster, data-driven decisions across therapeutic areas.

Read the full case study here.