Agenda

• To identify the evolving needs in professional development, with a focus on younger talent entering the industry
• To examine the points at which human expertise will continue to be essential alongside AI advancement
• To provide a framework for how agencies and client-side researchers can strategically adapt to AI without compromising the human element of research.

This session, “Redefining the Rules: A Disruptive Approach to Brand Positioning,” explores how a comprehensive, multi-phased strategy rooted in behavioral sciences — spanning ideation, customer co-creation, development workshop, and rigorous testing— can redefine product launch. Attendees will gain insights into how disruptive thinking was applied to position a groundbreaking new drug, overcoming traditional barriers and resonating deeply with key stakeholders. Through this case study, learn actionable strategies to create impactful brand positions that not only meet market needs but also challenge conventions, ensuring meaningful differentiation and long-term competitive advantage.

We compiled all market research content over the last two years and used genAI to synthesize the data and build a patient journey. Once tested and validated, this formed the backbone of our digital KOL. We can now use the digital KOL to get instant answers to a range of questions that we constantly grapple with – testing of multiple TPPs, impact of new Takeda and competitor data readouts, drivers and barriers, identifying untapped opportunity areas etc. – eventually we should also be able to run sensitivity analysis on sales and marketing tactics!

The Virtual super KOL is a “live” application that will continue to learn and evolve as we layer in external publications and additional work, ensuring insights are always up to date with the latest data.

Forecasters often rely on secondary data analogs to model the effects of timing and order-of- entry but these estimates often fail to capture the unique characteristics and market situations for pipeline products. This presentation shares new tools for modeling timing and order-of-entry that combine models based on secondary data with new metrics from survey-based demand research. Accuracy of the methodology is demonstrated with several case studies.

We chose a common topic (Obesity and GLP-1 weight loss medications) in a particularly challenging methodology: the Patient Journey. This type of research yields some straightforward responses, but the majority of the output to be analyzed is narrative – respondent stories with a mix of factual details and emotional, social, and psychological impacts that we expected to challenge AI’s analysis capabilities.

To humans who have spent a lifetime decoding emotion, this task seems very simple. However, getting a computer to decode emotions like a human can is a bit more complicated than it seems. In this session, we’ll explore exactly what it means to recognize emotion, what Speech Emotion Recognition (SER) is, how computers can reliably measure emotions, and how we can use SER to better understand the thoughts, beliefs, attitudes that explain and motivate behavior.

Patient centricity is a CEO-level topic in every pharma company. All brand teams spend significant time and effort in trying to be patient centric – understanding the patient journey, improving the patient experience and communicating to patients in a compassionate way.

But do patients think that pharma brands are truly patient centric? Do they believe that pharma brands understand them, or are they doing enough to help them? How do we even measure patient centricity of a pharma brand through the lens of patients? How do we go beyond a few ATU survey questions to measure patient centricity in a more comprehensive way?

In a first-of-its-kind study, Newristics will present results of a large-scale research and analysis project to measure the centricity of dozens of brands across 8 different disease states using a simultaneous stated and derived approach:

• Derived – Newristics analyzed millions of patient conversations and “derived” the level of patient centricity embedded in them using advanced social listening algorithms.

• Stated – Newristics also analyzed patient messaging of 30+ brands using machine learning models trained to score messages on manufacturer-centricity vs. patient centricity.

Don’t miss this data-backed presentation from Newristics on the #1 strategic priority topic for the pharmaceutical industry!

We’ll examine the challenges and opportunities at the intersection of AI/ML and market research, highlighting the impact of these technologies on both individual roles and organizational strategies.

The discussion will kick off with participants sharing practical applications of AI/ML, showcasing real-world examples of how these tools are revolutionizing the field. We’ll then look ahead, considering how AI/ML will continue to reshape the role of market researchers and elevate their strategic value within organizations. Finally, we’ll explore strategies for upskilling researchers to effectively integrate advanced tools into their workflows, potentially redefining their positioning and ensuring they remain at the forefront of an evolving industry.