AI is playing a growing role across the research journey. From improving fieldwork engagement and data quality to supporting analysis, moderation and reporting, we explore seven practical applications of AI that can help research teams work more efficiently while keeping people and research quality at the centre.
Discover how organisations use competitor monitoring, regulatory intelligence and market insights from discovery and clinical development through to commercialisation, illustrated with real-world case studies that demonstrate the value of evidence-based strategic intelligence.
Competitive intelligence helps life sciences teams move beyond information overload by structuring and interpreting key market signals. From ongoing monitoring to strategic support and deep-dive analysis, a structured approach enables clearer, more confident decision-making across the product lifecycle.
As we conclude our partner perspective series, we’ve arrived at a fundamental truth in healthcare market research: the ultimate differentiator isn’t just technical capability, it’s confidence.
AI is the most talked-about topic in healthcare market research, yet meaningful and compliant applications are still emerging. Here’s how partners are turning early experimentation into secure, value-driven solutions.
Explore the partner perspective on why the days of viewing facilities as the default are gone and how to navigate the virtual-first qualitative world.
The future of market research belongs to those ready for change. With insights from Neil Phillips, we explore the shifting dynamics between pharma and agencies, and why robust verification is now the essential foundation for credible healthcare insights in a transforming industry.
List feasibility isn’t just about numbers. It’s about understanding why a client list doesn’t always translate into reachable respondents. This article unpacks the key drivers behind list feasibility, from data quality and universe representation to incidence and response rates, helping you explain feasibility outcomes with confidence.
Better screeners lead to better results. In healthcare research, a well-designed screener is the difference between clean, actionable data and frustrating fieldwork challenges. This article explores common pitfalls and offers practical tips for building better screeners that set your study up for success.
In Alice’s Adventures in Wonderland, Alice falls into a world where nothing makes sense. For survey participants, a poorly designed questionnaire can feel the same. Confusing wording, unclear screening, and clunky layouts create a rabbit hole of frustration that leads to disengagement and poor data.