Measurement error is the error we introduce when we measure or observe something about our users. It can come from different sources, such as the number of participants, individual variation between participants, testing environment, or other outside factors. This video helps understand and communicate such measurement errors.
The product-led growth model enables users to try a product or service before paying. This video offers three tips for UX professionals to support a product-led user experience: Connect changes to metrics, interview/survey users, and compare behavior and feedback.
False positives and negatives are common errors in quantitative studies that can lead to harmful business decisions. To avoid these mistakes recruit large enough sample sizes, representative participants, and control for confounding variables.
Avoid creating personas from analytics data alone. Personas are artifacts that aim to capture users' attitudes, goals, and pain points, aspects which analytics alone can't provide.
Confounding variables interfere with quantitative studies, leading to inaccurate results. Avoid introducing such variables by randomizing your study’s conditions and keeping your research questions focused.
Engagement is an abstract, complex concept used to understand how much people interact with our products. Choosing the right engagement metrics goes beyond time spent.
The long tail refers to the data points at the trailing end of a power-law distribution. A long-tail strategy involves efficiently exploiting these low-impact data points for an aggregated benefit.
Confounding variables can affect the validity of data collected during research studies. It's important for researchers to know what they are and how to identify them.
The Net Promoter Score (NPS) can be gamed, and its definition loses information and precision by treating fairly dissimilar responses in the same way. It should be used together with other UX metrics rather than in isolation.
Product instrumentation tells us when and how much people use different aspects of our product. This insight can inform feature prioritization, validate our assumptions, and identify potential user problems.
A small share of all items produce most of the impact: a few features, a few usability problems, a few customers, and your key persona. Focusing design efforts on those high-impact areas leads to higher success rather than broadly (and thinly) distributed UX work.
The HEART framework is great for B2C products but is lacking for workplace applications where users cannot choose the product. CASTLE offers a complementary assessment framework for UX that focuses on the needs of internal product teams.
