Quantitative UX Research for an AI Health Coach: How Akraya Measured User Trust at Scale

Quantitative UX research uses numerical data to understand how people use and experience a product. It can include surveys, behavioral data, statistical analysis, and psychometric measures to answer questions at scale. For an AI product, that might mean measuring things like trust, engagement, satisfaction, or whether those signals improve as the product changes.

Trust is usually more useful when you measure it as several specific dimensions rather than relying on one question alone. A quantitative UX researcher can use psychometric methods to define those dimensions, build measures around them, and track how they change across different parts of the user experience. That gives the team a more reliable way to see where trust is strong, where it is breaking down, and how it changes as the AI product evolves.

AI products can create a different measurement problem because the experience may vary more from user to user and generate large amounts of open-ended feedback. Basic usage metrics can tell you whether people are using a feature, but they may not explain whether users trust the system, understand its behavior, or find its output useful. That means teams may need to combine behavioral data with surveys, psychometric measures, statistical analysis, and methods for analyzing large volumes of unstructured feedback.

A useful measurement framework starts with the product questions the team needs to answer. From there, it defines what should be measured, how those measures will be collected, how the data will be analyzed, and how the results will be tracked over time. That can include psychometric measures for things like trust and satisfaction, survey and behavioral data, analysis pipelines, and dashboards that make the results visible to the wider team. The goal is to give the product team a consistent way to see how the AI experience is changing from launch onward.

Qualitative UX research explains why users behave the way they do, usually through interviews, usability sessions, and open-ended observation with a small number of people. Quantitative UX research measures how many, how much, and how often across a large group, using surveys, behavioral data, and statistical analysis. Most teams need both, but quantitative research is what tells you whether a signal like trust is going up or down as your product reaches more people. Akraya focuses on the quantitative side, including the harder work of measuring things like trust and satisfaction that are easy to ask about but difficult to measure well.

Reach for quantitative research when you need to measure something across a large group, track how it changes over time, or put a number on an outcome like trust, engagement, or satisfaction. It is especially useful right before and after a launch, when you want an early read on how users are responding at scale. Qualitative research is the better fit when you are exploring a new problem and need to understand the why in depth. Many teams start qualitative to frame the questions, then use quantitative research to measure the answers.

This is where psychometrics comes in. Before a measure is used to make decisions, a quantitative researcher checks that it is valid, meaning it measures what it is supposed to, and reliable, meaning it gives consistent results. That involves designing the questions carefully, testing how they perform, and refining them so the resulting number reflects real user trust instead of noise. Without that step, a trust score can look precise while meaning very little. Akraya builds measures that hold up to that scrutiny, so the numbers your team acts on are ones you can defend to leadership.

It depends on what you are measuring and how confident you need to be, but quantitative research generally needs enough responses to detect real differences rather than random variation. A researcher can work out the right sample size up front based on the questions you are trying to answer and the size of the effect you would care about. For products already in preview or launched, there is often enough user activity and feedback to work with. Part of Akraya’s job is designing the study so the numbers you get are trustworthy.

Akraya uses a framework called the Three Pillars of AI Adoption Risk, which looks at the three things that decide whether people actually adopt an AI product: Functional Reliability (does it work correctly and consistently), Workflow Fit (does it fit how people really work), and User Trust (do people believe it enough to rely on it). Research signals in each pillar can flag where an AI product is likely to struggle before it launches. It is the lens Akraya brings to measuring AI experiences, including the trust work described in this case study.

Building in-house makes sense when you have steady, ongoing research needs and can hire the full mix of skills, which for quantitative UX research means research design, statistics, and data work together in one team. That is hard to assemble and hard to keep fully utilized. A partner makes sense when you need the capability quickly, want senior expertise without a long hiring cycle, or have a specific push like an AI launch where the measurement has to be right the first time. Many teams use a partner to stand the capability up, then decide what to bring in-house later.

A staffing firm gives you a person to manage, and the result is your responsibility. Akraya delivers the research itself: the study design, the measurement system, the analysis, and findings your team can act on. You get a working research capability and answers you can use, backed by a team rather than a single hire. The work is built to fit how large product organizations actually operate, so it plugs into your product, design, and engineering teams instead of adding overhead.

You get more than a slide deck. Depending on the engagement, that can include the measurement framework and the validated measures behind it, the analysis and findings, live dashboards your wider team can use, and the documented study designs and scripts so the research can be run again as your product changes. The goal is to leave your team with a capability they can keep using after the engagement ends.

Akraya has experience working inside strict enterprise data and privacy requirements, including on health products where how user data is collected, processed, and shared is tightly controlled. The research is designed to work within those rules from the start, so governance is built in rather than added on later. For teams in regulated or sensitive spaces, that means the measurement approach fits the constraints instead of running into them mid-project.

Akraya works with enterprise product organizations, often high-tech companies building complex or AI-powered products where getting the user experience right carries real weight. The common thread is a product team that needs rigorous, quantitative answers about how users are responding, and needs those answers to hold up to scrutiny from leadership.