Hybrid AI-Led Post-Launch Study with Pharmacists to Inform Further Investments

This case study explores an AI-led post-launch study conducted with pharmacists to inform further investment decisions.

Table of Contents

The Problem Statement

After launch, the key questions for insights, brand, and marketing teams are no longer about readiness, but about real in-market performance: how the proposition is perceived, whether it is landing as intended, and whether it should be optimized, expanded, or stopped before further investment.

In this case, Reckitt had launched a new product range in Poland, a highly competitive category spanning both pharmacies and broader retail. Internal stakeholders needed to understand what was happening at the point of recommendation and whether the key product benefits, communicated the key messages effectively.

To answer this, it was essential to understand the perceptions of pharmacists, the people communicating about the product in real life: what they knew about the range, how they interpreted its role in the category, how they reacted to the relatively new key point of difference, and how they evaluated the supporting communication materials. At the same time, the business needed structured evidence at scale.

Traditional research with pharmacists often relies on quantitative surveys, as large-scale qualitative interviewing is typically too expensive and pharmacists are difficult to recruit for in-depth research. This provides metrics, but only limited qualitative context. We therefore needed a more efficient hybrid approach that could combine quantitative and qualitative elements in one study and generate learning that was both decision-ready and deep enough to support product refinement and future expansion planning.

Methodology

To address this challenge, Yasna was selected to run a hybrid conversational research study with pharmacists in Poland. AI-led conversational research was chosen both for its methodological novelty and for the new opportunities it offers in studying healthcare professionals.

At a comparable cost, this approach made it possible to combine qualitative depth with quantitative confidence in one study. Instead of asking respondents to complete a static survey or attend scheduled interviews, we engaged them in AI-moderated conversations tailored to a professional B2B audience.

This format gave respondents far greater flexibility. Pharmacists could answer, pause, return, and continue at a time that fit naturally into their working day and even beyond it. This was especially important for a busy audience whose day is shaped by interruptions, short attention windows, and constant switching between tasks.

Although automated, the study took place in a conversational, messenger-style format with substantial probing depth. The interview design was adapted to the audience and category, with moderation logic built to maintain a professional tone, ensure category relevance, and ask targeted follow-up questions. Pharmacists knew they were interacting with an AI agent, yet still felt heard and understood. Overall satisfaction reached 87%, showing that the method can work effectively even with a hard-to-reach B2B audience.

The methodology also included closed-ended questions to measure awareness and other key indicators. With a sample of 60 respondents, the study generated useful directional quantitative learning alongside richer qualitative feedback, making it possible to capture both the “how many” and the “why” within one integrated experience.

Results

First, the study provided deep qualitative insight into how pharmacists spontaneously perceived the product, including key drivers, barriers, use cases, and their understanding of its uniqueness and efficacy.

Second, within the same study, we generated a quantified view of pharmacist awareness, reported use cases, competitive context, product understanding, and perceived relevance. Pharmacists also evaluated communication materials, helping identify what supported understanding and what required refinement before broader rollout.

Third, the research captured how consumer needs are expressed at the pharmacy counter through pharmacists’ real-world experience. This provided valuable language for refining future communication.

Most importantly, the study supported a broader business decision: whether the Polish launch should remain a local initiative, be further optimized, or serve as a basis for expansion into additional markets.

Conclusion

This case demonstrates that post-launch research should no longer be treated as a narrow quantitative exercise. In uncertain markets, companies need approaches that do more than measure. They need research that can quantify, explain, and guide the next decision within one connected process.

At a cost comparable to a quantitative survey, hybrid AI-powered conversational research offers a practical alternative to traditional approaches. By combining structured measurement with AI-led probing in a flexible respondent experience, it becomes possible to generate evidence that is both scalable and diagnostically rich.

This project also showed that efficient AI-powered methods can be applied even with expert B2B audiences such as pharmacists. When designed around the realities of their workflow, AI-moderated research can make studies faster, more cost-efficient, and more engaging, while still producing the depth of feedback needed for valuable business decisions.

Key Takeaways

Post-launch research is a source of strategic insight, not just a tracking exercise.
Hybrid AI-powered conversational research – a method effectively combining quantitative scale with qualitative depth in one study.
AI-moderated research has proven it can work for busy B2B audiences such as pharmacists for faster and deeper insights.

From AI-Led Post-Launch Research to Strategic Decisions at MRMW Europe 2026

An AI-led post-launch study can do more than track performance—it can combine qualitative depth with quantitative scale to guide major investment and expansion decisions. See how Reckitt and Yasna.ai are applying this hybrid approach to research with busy healthcare professionals at MRMW Europe 2026.

Join Irina Kalugina, Global I&A Senior Manager at Reckitt, and Tanya Berlina, Research Director at Yasna.ai, for “Hybrid AI-Led Post-Launch Study with Pharmacists to Inform Further Investments”.

The session will explore how messenger-style AI research can engage pharmacists around their professional workflows, capture spontaneous category language and combine rich qualitative feedback with quantitative evidence—all within a single, cost-efficient methodology.

📅 MRMW Europe 2026 | October 21–22 | Berlin, Germany

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