Resolve conflicts between behavioral data and user research by checking what each source can answer and whom it represents. Identify the evidence to base a decision on and design one focused study to test competing explanations.
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Reconcile what the numbers say with what users say, and design the study that settles it rather than restates it. Use when behavioural data and research findings point different ways. For reading the data on its own, use `behavioural-analytics`; for synthesising interviews on their own, use `affinity-diagram`.
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You are an expert in what to do when the dashboard and the interviews disagree.
You take two accounts of the same behaviour — one measured, one reported — and work out what their disagreement means, which one answers the question actually being asked, and what the next study has to look like to settle it. The output is a decision about what to believe and one study design, not a summary of both sources.
Teams treat a conflict as a problem with one source. It is usually a signal in its own right, and the shape of it tells you where to look:
| What you see | What it usually means |
|---|---|
| Data shows abandonment; users report no difficulty | They abandoned for a reason they do not attribute to the interface — price, timing, or a decision made before arriving |
| Users report a serious problem; data shows no effect | The affected segment is small, or the problem happens before instrumentation starts |
| Data improved; users report it feels worse | You optimised a proxy. The metric moved, the experience did not |
| Users are enthusiastic; retention is flat | Stated preference, not revealed. Enthusiasm in a session is not a return visit |
| Both look fine; the business outcome does not | You are measuring the task, not the goal the task serves |
| The last two are the expensive ones, because nothing looks wrong until much later. |
Give each question to the source that can actually answer it, and stop asking the other:
A resolving study is narrower than either original. Write down, before running it: the specific claim in dispute, what result would make you drop the qualitative account, and what result would make you drop the quantitative one. If no result could change your mind, you are not resolving the conflict, you are building a case. Prefer the cheap instrument that discriminates. A session recording of the disputed step usually beats another round of interviews and another dashboard. If the dispute is about why, add measurement to the qualitative session rather than running two studies.
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Plan A/B experiments to measure the effect of a design change. Define a hypothesis, control and treatment versions, success metrics, sample size, and duration, with supporting measures to detect unintended consequences.
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Assess an existing interface against WCAG 2.2 accessibility guidelines. Identify barriers in content and interactions, rate their severity and user impact, and document affected locations, remediation steps, and code examples to guide improvements.
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Plan accessibility testing with people who use assistive technologies, such as screen readers and voice control. Define participant criteria, test environments, session procedures, coverage of WCAG accessibility guidelines, and a consistent format for reporting issues.
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Review visual consistency to understand how an interface shapes trust and perceived ease of use. Examine spacing, alignment, typography, and overlooked screen states to guide visual improvements alongside usability testing.
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Turn interview notes, observations, and survey responses into themes and actionable research insights. Group related evidence without preset categories, retain participant references, and prioritize findings by their impact on design decisions while noting patterns found in only one interview.
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Refine a specific interface animation by choosing easing, meaning changes in speed, along with duration and movement order. Use it to tune buttons, modals, transitions, or loading indicators so motion guides attention and communicates changes.
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