Showing 23 exercises
| Exercise | When to use | How to run it | Outputs | Templates & tools |
|---|---|---|---|---|
| Stakeholder interviews | Project kick-off, before any user research. You're starting without knowing what the business believes about its users, or what constraints will shape the design. Surface assumptions before they become invisible design decisions. | 30–60 min semi-structured conversations with business owners, PMs and commercial leads. Surface assumptions, business constraints and success metrics. Run before user research so you can compare what the business believes against what users actually need. NN/g deep dive |
Research plan: Figma official | |
| User interviews | Discovery phase; also continuously alongside delivery. You have questions analytics can't answer: why users behave as they do, what their real goals are, or where friction lives. You're designing without enough direct evidence from real people. | Semi-structured guide around context, behaviours and pain points; never show the product. Record and transcribe. Look for patterns across sessions, not individual quotes. Debrief after each session before moving to the next. NN/g deep dive |
User interview questions: Figma official | |
| Contextual inquiry | When you need to understand users in their natural context, not a research setting. Reveals tacit knowledge, workarounds and environmental factors users can't articulate in interviews. Particularly valuable for complex workflows, fintech, healthcare or any product used in a specific physical or social context. | Visit users in their natural environment. Observe silently first, then use the master-apprentice model: ask them to teach you how they do their job. Ask "tell me why you just did that" when something unexpected happens. Photograph artefacts, workarounds and environment. Debrief immediately after each session. NN/g deep dive |
Research plan: Figma official | |
| Competitive audit | Early discovery, before defining scope. You're entering a space without knowing how the problem has already been solved, what patterns users expect, or where genuine gaps exist. You risk reinventing or replicating existing solutions. | Review 4–8 competitors or analogous products. Document onboarding, core flows, UI patterns and tone. Use a scoring matrix. Identify gaps in the market, not just what others do. Include category-adjacent products alongside direct competitors. NN/g deep dive |
Competitor analysis: Figma official | |
| Affinity mapping | Immediately after a round of interviews or testing. Research data is scattered across notes and recordings; you need to find patterns across sessions and turn raw observations into insights the whole team can act on. | Transfer observations to sticky notes (one per note). Working silently, cluster by theme. Name each cluster. Works best with everyone who attended the sessions; produces a shared understanding of patterns rather than one person's interpretation. NN/g deep dive |
Affinity diagram: Figma official | |
| Empathy mapping | After interviews, before persona synthesis; also when onboarding new team members. Your team is making decisions based on assumptions. Engineers or PMs haven't attended research sessions and lack a shared picture of who you're designing for. | 4-quadrant canvas: Says, Thinks, Does, Feels. Populate from research data as a team. Surfaces contradictions: what users say versus what they actually do. Particularly useful for aligning engineers and PMs who haven't attended research sessions. NN/g deep dive |
Empathy map: Figma official | |
| Persona creation | After synthesis, before design sprint. Decisions are being made for a vague, imagined user. You need a research-grounded reference to replace "I think users would..." with something the whole team can debate against. | Build 2–4 research-grounded personas (not marketing demographics). Include goals, frustrations, context of use and key behaviours. Anchor every attribute to a data source. Avoid fictional details; use as a decision-making shorthand throughout delivery. NN/g deep dive |
User persona: Figma official | |
| JTBD canvas | After interviews, when defining scope or positioning. You know what users do but not why. The team disagrees on what the product is fundamentally for, or you can't articulate its value in terms of user goals rather than features. | Map the functional, social and emotional jobs users hire the product to do. Use the format: "When I [situation], I want to [motivation], so I can [outcome]." Run as a team workshop; it surfaces competing interpretations of the core problem and sharpens product positioning. NN/g deep dive |
Jobs to be done: Figma official | |
| Current-state journey map | After research, before ideation. You're solving individual pain points without understanding the full journey. You don't know which moments carry the most emotional weight, or where an intervention will have the greatest impact. | Plot user steps, touchpoints, thoughts, emotions and pain points across the existing journey (not your product itself, but the task). Populate entirely from research data. The emotional curve is often more valuable than the step list; it reveals where friction is felt most acutely. NN/g deep dive |
Customer journey map: Figma official | |
| How Might We (HMW) | Transition from problem to solution space. The team keeps jumping to the same solutions or converging too early. You need to reframe pain points as opportunities and open up the solution space before moving into design. | Reframe pain points as opportunity questions: "How might we make it easier for users to...?" Generate 10–20 HMW statements, then dot-vote to prioritise. Keeps the team in problem mode long enough before jumping to solutions; prevents premature convergence on a single idea. NN/g deep dive |
Brainstorming templates: Figma official | |
| User flow mapping | After journey mapping and HMW, before wireframing. You're about to design a feature without having mapped every step, decision point and error state. You don't know where the flow might break or what edge cases to design for. | Map every step a user takes to complete a key task, including decision points, alternative paths and error states. Start from research outputs: use pain points from journey maps and opportunity statements from HMW as inputs. Keep flows task-scoped; one flow per job-to-be-done. Validate with a quick usability test before moving into detailed UI design. NN/g deep dive |
User flow: Figma official | |
| Concept testing | After ideation and before detailed prototyping. When you have a design direction but want to validate the core concept solves the right problem before investing in Figma. Also useful for comparing two competing approaches before committing to one. | Prepare deliberately rough materials: a description, a sketch, a storyboard; nothing polished. Show to 5–8 users. Ask: "Does this solve a problem you have? What would prevent you from using it? What would you expect to happen?" Don't defend the concept; listen for where it breaks down. Test 2–3 concepts side by side for comparative signal. NN/g deep dive |
Brainstorming: Figma official | |
| Prototyping | After task flows are mapped and a design direction is agreed. Also useful much earlier: rough paper sketches can validate flow logic before any screens are built. Match fidelity to the question; paper for structure, wireframes for navigation, click-through for task testing, hi-fi for stakeholder sign-off. | Paper: sketch screens on index cards, swap as participants make choices. Run 3–4 sessions in a morning and iterate between each. Figma click-through: link frames covering key task flows; build only what you need to test. Hi-fi: add visual design once structure is validated. Use Figma's AI features to scaffold repetitive screens faster. NN/g deep dive |
User flow: Figma official | |
| Card sorting | When designing navigation, IA or category structures. Users are struggling to find things, or you're building navigation based on how your team thinks rather than how users group and label content. Your IA reflects internal logic, not mental models. | Open sort: participants group items any way they like, then name groups; this reveals mental models. Closed sort: participants place items into predefined categories, testing your proposed IA. Run with 15–30 participants for quantitative signal. Use Maze or Optimal Workshop for remote sessions. NN/g deep dive |
Card sorting tool: Figma official | |
| Future-state journey map | After initial concepts, before detailed design. Your team is building features without a shared picture of the ideal end-to-end experience. Stakeholders have different visions of success and there's no agreed north star. | Map the ideal experience your product should deliver, using the same structure as the current-state map. Flag directly where the product intervenes in current-state pain points. Useful for aligning stakeholders before detailed design begins and for defining what success looks like. NN/g deep dive |
Customer journey map: Figma official | |
| Usability audit | The first activity when investigating a product with performance problems. Drop-off is frequently caused by fixable surface-level issues: confusing CTA labelling, copy that doesn't match user mental models, elements breaking on mobile, or accessibility failures. An audit surfaces these without any user recruitment. | Evaluate across four dimensions: (1) Heuristic review against Nielsen's 10 principles, severity-rated 0–4. (2) Accessibility check: WCAG 2.1 AA contrast, keyboard navigation, touch targets. (3) Responsive review at 375px, 768px and 1440px. (4) Copy review: do CTAs, headings and error messages match user language? Prioritise by severity and user exposure. NN/g: 10 Usability Heuristics |
Heuristic evaluation: Figma official | |
| First-click testing | When you need to quickly validate whether a CTA, navigation item or key action is discoverable and clearly labelled. Users who get their first click right complete tasks 87% of the time; those who don't drop to 46%. Particularly valuable for onboarding, checkout and conversion-critical screens. | Take a static screenshot of the screen to test. Write a task scenario. Upload to Maze or Optimal Workshop. Run with 20–50+ participants; the platform generates a click heat map and first-click accuracy rate. Analyse: what percentage clicked the right element? Where did wrong clicks land? Do they suggest a competing affordance or a labelling issue? NN/g deep dive |
NN/g: First-click testing guide | |
| Moderated usability testing | At any fidelity, from wireframe to live product. You have a design but don't know whether real users can complete key tasks with it. You need to find where it breaks; and why; before committing engineering time. | Give realistic task scenarios, never instructions. Observe without intervening. Use think-aloud protocol. Debrief observers immediately after each session. Identify failure points, not just opinions. Five participants will surface 85% of usability issues; run iterative rounds rather than one large study. NN/g deep dive |
FigJam UT template: Figma official | |
| Guerrilla usability testing | You need answers quickly and can't wait for formal recruitment. You have a specific hypothesis to validate, not broad discovery. Also useful as a mid-sprint check between rounds of moderated testing. | Define the one specific question you need to answer. Recruit opportunistically: customer contacts, colleagues unfamiliar with the product, or coffee shop intercepts. Run 20–30 min sessions with one or two tasks. Observe silently. Five sessions will confirm or disprove most specific hypotheses. NN/g deep dive |
FigJam UT template: Figma official | |
| Heuristic evaluation | When user testing isn't possible, or as a pre-test screen. You need a fast expert assessment of usability problems; either because testing isn't feasible right now, or you want to fix obvious issues before putting the design in front of real users. | Each evaluator independently reviews the design against Nielsen's 10 heuristics, then results are consolidated and severity-rated (0–4). Quick and cheap; it catches 75–80% of usability issues without users. Best used before usability testing, not instead of it. NN/g deep dive |
Heuristic evaluation: Figma official | |
| A/B testing | Post-launch, with sufficient traffic. You have two design approaches and a specific measurable outcome to improve. Opinion is divided and you need statistical evidence rather than seniority or gut feel to make the call. | Run controlled experiments on a single variable (copy, layout, CTA). Define the primary metric before launching; don't move the goalposts mid-test. Avoid running concurrent experiments on the same user population. Wait for statistical significance before calling a winner. NN/g deep dive |
A/B test planner (FigJam) | |
| Funnel & behavioural analytics | Continuously, especially after launch and after changes. Users are dropping off somewhere in a key flow but you don't know where, how many, or how often. You need to see what's happening at scale before deciding what to investigate qualitatively. | Track drop-off rates across key flows. Identify where users abandon tasks. Use session recordings (FullStory, Hotjar) to see specific failure moments. Analytics tells you what is happening; pair with qualitative research to find out why. Set up event tracking before launch, not after. NN/g deep dive |
Tagging map: analytics tracking visualised | |
| NPS / CSAT survey | Ongoing, after each significant release. You don't know how satisfied users are overall, or you've shipped something and want to measure its impact on sentiment. You need a trackable signal that persists between research cycles. | NPS (0–10 likelihood to recommend) gives a directional benchmark. CSAT (1–5 satisfaction per task) gives feature-level signal. Neither tells you why; always include an open text field and follow up qualitatively with outliers. Run on a fixed cadence to spot trends rather than react to individual scores. NN/g deep dive |
Likert scale: Figma official |