By Philip Burgess | UX Research Leader When I first started working on user experience (UX) projects, I often found myself reacting to problems after they had already affected users. It felt like I was always putting out fires instead of preventing them. Over time, I realized that the key to better UX was to build early-warning metrics based on solid research. These metrics help spot issues before they escalate, allowing teams to act quickly and improve the product continuous

By Philip Burgess | UX Research Leader When I first started working with user experience (UX) metrics, I believed every key performance indicator (KPI) was essential and worth tracking indefinitely. Over time, I learned that some metrics lose their value as products evolve, user behaviors shift, or business goals change. Holding on to outdated KPIs can cloud decision-making and waste resources. Knowing when to retire or "sunset" a UX metric is just as important as choosing th

By Philip Burgess | UX Research Leader When starting a new product or feature, it’s tempting to jump straight into designing screens and wireframes. But I’ve learned that measuring the right things before any design exists can save time, reduce costly mistakes, and guide the team toward building something users truly need. Early discovery is about understanding problems, behaviors, and opportunities before visuals take shape. In this post, I’ll share the key UX metrics you ca

By Philip Burgess | UX Research Leader When teams work on improving user experience, they often face a common challenge: too many metrics pulling them in different directions. This scatter can slow progress and create confusion about what really matters. I learned early in my UX career that focusing on a single, clear metric can transform how a team collaborates and delivers value. This metric is often called the North Star UX metric . It acts as a guiding light, helping ever

By Philip Burgess | UX Research Leader When launching a Minimum Viable Product (MVP), the goal is not to deliver a polished, final product but to learn quickly and validate assumptions. Many teams focus on traditional UX metrics like task completion rates or time on task, which measure performance. But for MVPs, these metrics can miss the point. Instead, tracking learning-focused UX metrics helps teams understand user behavior, uncover pain points, and iterate effectively. I’

When I first started creating presentations for UX projects, I struggled to keep my audience engaged. I had plenty of data and design ideas, but the slides felt dry and disconnected. Then I discovered the power of storytelling in UX presentations. Using storytelling slide templates transformed how I shared my work and helped me connect with stakeholders on a deeper level. If you want your UX presentations to leave a lasting impression, using storytelling templates is a game c

By Philip Burgess | UX Research Leader When I first started working in UX research, measuring user experience felt like trying to catch smoke with my bare hands. We relied heavily on surveys, interviews, and manual observation. These methods gave us valuable insights but often lacked precision and speed. Today, artificial intelligence (AI) is reshaping how we measure UX research, making the process faster, more accurate, and more insightful. I want to share how AI has transfo

By Philip Burgess | UX Research Leader I remember the first time I relied heavily on AI-generated metrics to evaluate a user experience (UX) project. The numbers looked impressive: high engagement scores, positive sentiment analysis, and smooth navigation paths. I felt confident presenting these findings to the team, convinced we had nailed the user journey. But soon, real user feedback told a different story. The AI metrics had painted an overly optimistic picture, missing c

By Philip Burgess | UX Research Leader When I first joined a UX research team, our work revolved around manual data collection, lengthy interviews, and hours spent sifting through notes. The process was slow, and the pressure to deliver actionable insights often felt overwhelming. Over the past few years, I’ve witnessed a significant shift in how AI tools have transformed our daily tasks and the expectations placed on our team. This change is not just about faster workflows b