What Triangulation Means in UX Research Triangulation refers to the practice of using more than one method, data source, or researcher to study a UX problem. The goal is to cross-check results and build a more complete picture of user behavior and needs. This approach helps avoid relying on a single perspective, which can lead to incomplete or biased conclusions. There are three main types of triangulation in UX research: Method triangulation : Using different research method

User experience (UX) research aims to understand how users interact with products and services to improve satisfaction and usability. One challenge in UX research is measuring success in a way that reflects real user needs and business goals. Google’s Heart Framework offers a clear, practical approach to this challenge. It helps teams focus on meaningful metrics that capture the full user experience, guiding design and evaluation efforts. This post explains what the Heart Fra

User experience (UX) research aims to create products that are easy and enjoyable to use. One key measure in this process is the error rate . Understanding what error rate means and why it matters can help designers and researchers improve usability and create better experiences for users. Understanding Error Rate What Is Error Rate in UX Research? Error rate refers to the number of mistakes users make while interacting with a product, divided by the total number of opportuni

When users interact with a product or service, the time they spend completing specific tasks reveals a lot about the design’s effectiveness. This measure, known as time on task , plays a crucial role in UX research. It helps designers and researchers understand how easily users can achieve their goals, identify pain points, and improve overall user satisfaction. This post explains what time on task means, how it applies to UX research, and why it deserves your attention. Unde

User experience (UX) research aims to create products that are easy and satisfying to use. One key measure in this process is the Task Success Rate (TSR) . It tells us how well users can complete specific tasks when interacting with a product or service. Understanding TSR helps designers and researchers identify usability issues and improve the overall user experience. Task success rate What Is Task Success Rate in UX Research? Task Success Rate is a metric used to evaluate t

When users interact with a product, they bring their own expectations and assumptions about how things should work. These expectations come from mental models—internal representations of how people believe systems operate. Understanding mental models is crucial for UX researchers because it helps them design experiences that align with users’ thinking, making products easier and more intuitive to use. Mental models shape how users approach tasks, solve problems, and interpret

Longitudinal studies offer a powerful way to observe how subjects change over time. Unlike one-time surveys or experiments, these studies track the same individuals or groups across months, years, or even decades. This approach reveals patterns and trends that short-term research cannot capture. If you want to understand development, cause-effect relationships, or long-term outcomes, longitudinal studies provide unique insights. Longitudinal Studies What Is a Longitudinal Stu

Creating usability questions is a critical step in understanding how users interact with a product or service. Well-crafted questions reveal insights that help improve design, functionality, and overall user experience. Poorly designed questions, on the other hand, can lead to misleading data and missed opportunities for improvement. This post explores practical strategies to develop clear, focused usability questions that generate valuable feedback. Effective Strategies for

Asking the right questions is the foundation of any successful research or survey. Yet, many struggle with crafting questions that do not lead respondents toward a particular answer. Unbiased questions help gather accurate, reliable data that truly reflects the opinions, experiences, or facts you want to understand. This post explains how to write unbiased questions, why it matters, and practical tips to improve your surveys and research projects. Crafting unbiased questions

User experience (UX) research often focuses on understanding users and improving product design. Yet, one critical factor that can determine the success of UX efforts is how well stakeholders are engaged throughout the process. Measuring stakeholder engagement is not just a nice-to-have; it directly impacts the quality of insights, decision-making, and ultimately the product’s success. This post explains why stakeholder engagement metrics matter in UX research and how to use

Research generates valuable insights that can guide product development, marketing strategies, and customer experience improvements. Yet, many organizations struggle to ensure these insights translate into real-world actions. Measuring research reuse is critical to understanding whether teams are actually applying what they learn or if valuable knowledge is left unused. This post explores how to evaluate research reuse and offers practical ways to improve the implementation o

Usability testing remains a cornerstone of creating user-friendly digital products. Yet, traditional methods can be time-consuming and sometimes miss subtle user experience issues. AI-assisted usability testing offers a fresh approach by speeding up analysis and uncovering patterns that might go unnoticed. This post provides a practical checklist and a template to help you integrate AI tools into your usability testing process effectively. AI-Assisted Usability Testing Checkl

When I first started working in UX research, the process often felt slow and repetitive. Gathering user feedback, analyzing data, and generating insights took weeks, sometimes months. I knew there had to be a better way. That’s when I began exploring agentic AI—intelligent systems that can act autonomously to support and improve UX research workflows. Since then, I’ve seen firsthand how this technology can transform the way we work, making research faster, more accurate, and

When I first stepped into a UX research leadership role, I quickly realized that guiding a team through complex projects was far more challenging than I expected. Mistakes happen, but some can slow down progress or even damage the credibility of UX research within an organization. Over time, I learned which pitfalls to watch for and how to steer clear of them. If you lead a UX research team or aspire to, this post will help you avoid common errors and build stronger, more eff

Using research effectively can transform how designers and product managers (PMs) make decisions. Yet, many teams struggle to integrate research into their daily workflows. I’ve seen firsthand how coaching can bridge this gap, helping teams not only understand research but also apply it in ways that improve products and user experiences. In this post, I’ll share practical strategies I’ve used to coach designers and PMs to get more value from research. These approaches focus o

When I first joined a product team, I quickly realized we lacked a clear way to measure how users experienced our product. We relied on gut feelings and anecdotal feedback, which made it hard to know what truly worked or where to improve. Building a UX measurement framework changed everything. It gave us clear data to guide decisions and improve user satisfaction. If you want to create a similar system in your organization, here’s how to start. UX Measurement Framework: Under

When I first started working on user experience (UX) design, I quickly realized that delivering a seamless experience across multiple channels and platforms is one of the toughest challenges. Users expect to switch from a mobile app to a website or even a physical kiosk without feeling lost or confused. But how do you measure if your UX is truly consistent? Over time, I developed a practical approach to evaluate UX consistency that I want to share with you. Measuring UX Consi

When I first started working in user experience, I was eager to quantify every insight I gathered. It seemed logical: if something is important, it should be measurable. But over time, I realized that not every UX insight fits neatly into a metric. Some insights lose their meaning or even mislead when forced into numbers. This post shares why some UX insights should remain qualitative and how turning them into metrics can sometimes do more harm than good. Why Some UX Insights

Artificial intelligence is reshaping how companies design products and understand users. As a UX researcher, I’ve seen firsthand how AI changes the tools we use and the questions we ask. To stay effective, we must develop new skills that help us work alongside AI, interpret complex data, and design for intelligent systems. Here’s what I’ve learned about the skills UX researchers need in an AI-enabled organization. A UX researcher reviewing AI-driven user insights on a laptop

When I first started working in UX research, I often felt overwhelmed by the uncertainty that came with interpreting user data. We would gather feedback, run tests, and analyze results, but there was always a lingering question: Are we really understanding what users need? Over time, I realized that the challenge wasn’t just about collecting data quickly—it was about making sense of it in a way that reduces doubt and leads to confident decisions. That’s where AI stepped in f