Introduce the concept of value architecture — a model to connect team actions to measurable outcomes. Most design teams have this gap; this lecture names it.

• What is the 'value gap' and why design teams fall into it

• Value architecture: company actions → customer value → business outcomes

• How to map your design decisions to this chain

• Outcome laddering: from feature → benefit → outcome

• The Double Loop model for tracking strategic assumptions

One of the most practical distinctions in the course. Leading indicators are forward-looking signals; lagging indicators confirm what already happened.

• Leading indicators: feature adoption, onboarding completion, rage clicks, support volume

• Lagging indicators: retention, churn, NPS, revenue, MRR

• Why leading indicators matter more to design teams

• How leading and lagging indicators are causally related

• Mapping indicators to your customer journey touchpoints

• 4 UX snapshot states: Baseline / Current / Target / Industry benchmark

Honest diagnostic lecture. Bottlenecks are the most disruptive parts of any company — usually visible to employees, invisible to senior managers.

• 10 most common organisational bottlenecks (meetings, dependencies, decision speed, debt)

• How bottlenecks show up as design symptoms (delayed delivery, broken flow, high turnover)

• How to identify the bottleneck in your own organisation

• Using design to surface bottlenecks — not just work around them

• When to raise bottlenecks with stakeholders and how

Without stakeholder alignment, metrics mean nothing. This lecture teaches the political side of measurement — how to get buy-in before you start.

• Why stakeholders disagree with design (3 root causes — Julie Zhuo framework)

• Mapping stakeholders: interest, influence, RACI

• How to frame measurement conversations with business language

• The 12 diagnostic questions to ask your organization before setting KPIs

• Building trust before building dashboards

Side-by-side comparison of business KPIs and design KPIs. Students often don't know the overlap — this unlocks collaboration with PMs and finance.

• Business metrics: NPS, MAU, CSAT, ARPU, MRR, CAC, CLTV, churn rate

• Behavioral metrics: CTR, bounce rate, session duration, drop-off rate

• Design metrics: task success rate, error rate, SUS, rage clicks, time to first success

• Attitudinal metrics: CSAT, EAC, feedback scoring

• Diagnostic metrics: accessibility coverage, feature utilisation, ticket resolution time

• How to build a healthy mix — not one number

NPS is the most widely misused metric in product. This lecture is the honest breakdown — cover its flaws and give students three better alternatives.

• What NPS is and what it was designed to measure (brand loyalty / growth predictor)

• Why NPS fails: 11-point scale reduced to 3, high sample size needed (1000+), high margin of error

• The 52% problem: people who discourage and recommend the same brand

• Only 55% of promoters actually recommend a product

• 5 better replacement questions (actionable alternatives)

• When NPS is unavoidable: how to frame it usefully for your team

• B2B vs. B2C NPS — completely different dynamics

TARS (Target, Adoption, Retention, Satisfaction) is the most practical feature-level metric in the course. Walk through the full calculation with a real example.

• Why business metrics alone don't capture UX performance

• T — Target Audience: what % of users have the problem this feature solves?

• A — Adoption: what % of target users actually use the feature?

• R — Retention: what % of adopters come back to use it again?

• S — Satisfaction: how easy was it to solve the problem? (CES-style question)

• The S÷T Score: calculating composite feature performance (example: 0.8 × 0.5 × 0.7 × 0.9 = 0.252)

• Building a feature matrix comparison chart using TARS

• 96% of high-effort users become disloyal vs. 9% of low-effort users (Gartner)

A 3-step system to turn qualitative feedback into quantitative data, then map gaps between expectations and delivery on a radial chart.

• Step 1: Write 5 survey statements — have users score 1–5 on agree/disagree

• Step 2: Average scores per statement, plot on radial (spider) chart

• Step 3: Set goal scores, track sentiment over time

• How to write good statements (customer support, integrations, UX, security, documentation)

• Segmenting results by cohort: free vs. paid, new vs. mature, team size

• Randomising questions — don't ask everything at once (max 6 per survey)

• Making sure Customer Success has a live feed of responses

B2B UX measurement is fundamentally different. Competitive analysis may be restricted, you often can't interview users, and employee UX metrics matter as much as customer ones.

• Things you can't do in B2B UX research and viable alternatives

• Customer UX metrics: CSAT, compliance, software quality (Sigrid), reliability

• Employee UX metrics: failure frequency, application processing time, training effectiveness

• Usability in B2B: CES, SEQ, SUPR-Q, UMUX-Lite

• Standardised assessment frameworks: HEART, UEQ, NASA-TLX

• eNPS and employee engagement surveys as a starting point

Walk through the full KPI target cheat sheet with context for each number. Students often don't know what good looks like — this lecture fixes that.

• SUS > 78, NPS > 50, CSAT > 80%, Customer Effort Score < 3

• Task success rate > 80%, error frequency < 1%, bounce rate < 40%

• Time to first success < 30s, time to complete < 35s, time to value < 30s

• User retention rate > 90%, activation rate > 60%, abandonment rate < 20%

• CLV/CAC ratio > 3:1, OKRs completion rate > 80%

• Core Web Vitals ~100%, WCAG AA ~100%, environmental impact < 0.3g/page

• How to choose the right 2–4 KPIs for your team (local + global)

• Reporting cadence: monthly reports, quarterly reviews

KPI trees are how design earns its seat at the table. Show how a design metric connects upwards to a business KPI — and downwards to specific design actions.

• What a KPI tree is and why vertical teams need one

• How to start: pick a business KPI you can influence

• How to traverse the KPI tree across departments

• Setting OKRs and targets tied to KPI tree nodes

• Example KPI tree: time-to-first-success → onboarding completion → activation → MRR

• Client project design KPI tree example — walkthrough

Before measuring anything, you need to understand how your organisation currently works. These 12 questions surface the gaps.

• How are design priorities defined? (by whom — PM, UXR, or design?)

• How do we translate business goals into design tasks?

• How do we decide what to work on in the next sprint?

• How do we define design success for our team?

• How do we measure the impact of our design system?

• What KPIs does the business already track?

• How to use answers to build your measurement baseline

Introduce TPI — a stable, repeatable metric for testing top tasks over time. Only 15–18 participants needed for statistical reliability.

• Why most usability metrics are one-off snapshots — TPI builds continuous insight

• The 6-step TPI process overview

• What 'top tasks' means and why they're the right unit to measure

• 15–18 participants is all you need for statistical reliability

• Gerry McGovern on TPI: stable, reliable, repeatable

Walk through how to define what success looks like before running any tests, and how to build representative user segments.

• Building the baseline: success rate, time on task, failure rate, recovery rate

• Why success rates and completion times stabilise at 15–18 participants

• Defining 2–3 representative segments (experience, role, usage level)

• Recruitment: aim for 30–40 participants (allow for drop-outs and no-shows)

• How to document your baseline for ongoing comparison

Task question quality makes or breaks TPI. Walk through the rules for writing valid task questions with real examples from OECD and EU Commission.

• Test 10–12 tasks per session — each must be doable by all segments

• One task = one unique correct answer

• Task questions: 30 words or less, specific, scenario-based

• OECD examples: 'Was Vietnam on the list of countries receiving ODA in 2018?'

• EU Commission examples: 'Find the opening date for biotech proposals in Horizon 2020'

• Common mistakes: ambiguous tasks, multiple valid answers, leading questions

The moderation rules for TPI sessions and how to interpret and visualise results across the organisation.

• Remote testing works best — same rules as moderated usability testing

• No talk-aloud, no hints, no guidance, no background noise

• 1 hour per person for 10–12 task questions

• How to create and read task completion time charts (0–30s to failure buckets)

• Average success time as the primary headline metric

• Making results visible throughout the organization — not just the UX team

TPI only works if it's run consistently. This lecture covers cadence, ownership, and how to link TPI results to OKRs.

• Measuring TPI every 6–12 months depending on team velocity

• Vertical teams own specific tasks — set OKR targets for each

• Linking TPI to the KPI tree across departments

• TPI setup timeline: 1 day choosing metrics → 2–4 days setup → 10 days measurement → 10 days analysis → presentation

UX strategy is the bridge between user needs and business goals. Without it, you're measuring the wrong things.

• UX strategy defined: vision + goals + principles + measurement

• The difference between UX strategy and UX process

• Why the Double Diamond isn't enough

• How to connect UX strategy to your organisation's business strategy

• The Designer's Growth Model — where you are and where you're going

Measurement tells you what's happening. Prioritisation tells you what to fix first. The Kano model is one of the most practical tools for this.

• What the Kano model measures: must-haves vs. delighters vs. performance features

• How to run a Kano survey and analyse results

• Critical assessment: when the Kano model misleads you

• Effort-value curves (John Cutler) for prioritisation decisions

• How to combine Kano + effort-value with your TARS scores

• Feature prioritization guide — the decision framework

Measurement reveals debt. This lecture teaches students how to identify, communicate, and manage design debt and UX risk — especially in legacy environments.

• What design debt is and how it accumulates invisibly

• Design risk management: identifying, sizing, and mitigating UX risk

• Designing with legacy: constraints and strategies

• UX storyboarding as a communication tool for legacy change

• How lean UX differs from traditional process in high-debt environments

• Legal considerations: tracking, consent, and data collection flowcharts

Synthesise the course into 9 actionable principles students can put on their desk. This is the takeaway slide expanded into a full lecture.

• Every product needs a healthy mix of metrics — no single number tells the whole story

• Design KPIs must align with business KPIs — not run in parallel

• Avoid NPS if possible — or frame it to tap into your actual goals

• UX strategy delivers real value only when it speaks to users AND business

• Use feedback scoring + gap analysis for actionable, segmented insights

• Identify top tasks per user segment — this is your measurement anchor

• Set up 2–4 local and global design KPIs and report monthly

• Track feature-level performance with TARS

• Measure AI features with AI evals

Students need a concrete starting point. Give them a week-by-week 30-day plan they can personalise.

• Week 1: Run the 12 diagnostic questions in your organisation

• Week 2: Map your top 3 user segments and identify top tasks

• Week 3: Baseline one metric (SUS or task success rate)

• Week 4: Draft your KPI tree and present one metric to a stakeholder

• How to adapt this plan for IC vs. lead vs. manager roles