Templates & Datasets
Reusable PM templates and synthetic practice data.
Reusable artefacts. Open them here or download the Markdown file and paste it into Docs, Notion, Confluence or Jira.
Hands-on starter (Module 11)
Open the Fee-Clear Cart prototype · Download the starter (.zip) — plain HTML/CSS/JS, fictional data, no real payments.
PRD (Product Requirements Document)
[Feature / initiative name] — PRD
| Field | Value |
|---|---|
| Status | Draft / In review / Approved / Shipped |
| Owner (PM) | |
| Design / Eng / Data leads | |
| Reviewers & approver (DACI) | |
| Last updated | |
| Links | Figma · Jira epic · Experiment plan · Dashboard |
1. Summary
One paragraph: the problem, the proposal, the expected impact (with a range), and the decision needed.
2. Problem and evidence
- Who: segment / persona
- Task and barrier: what they're trying to do and what gets in the way
- Evidence: analytics (numbers, dates), research (n, method), support tickets, reviews — label hypotheses
- Why now: strategy link, cost of delay
3. Goals, success metrics and guardrails
| Type | Metric (definition: numerator / denominator / window) | Baseline | Target | Source |
|---|---|---|---|---|
| Primary outcome | ||||
| Leading indicators | ||||
| Guardrails |
4. Users and use cases
Key scenarios, edge cases, accessibility needs, B2B buyer vs user (if relevant).
5. Scope
- In scope:
- Out of scope (and why):
- Assumptions:
- Dependencies (owner, date, risk):
6. Requirements
User stories (INVEST)
- As a [user], I want [capability] so that [benefit].
Acceptance criteria (Given / When / Then)
- Given … when … then …
- Include error, empty, loading and offline states.
Non-functional requirements
Performance (p95 targets) · reliability · security · privacy (no PII in analytics) · accessibility (WCAG 2.2 AA) · localisation.
7. Design
Links, key flows, open design questions.
8. Analytics / tracking plan
| Event | Trigger | Properties | Owner |
|---|---|---|---|
9. Launch plan
Rollout (flag, % stages), experiment design, support readiness, comms, kill switch and rollback owner.
10. Risks and mitigations
| Risk | Likelihood | Impact | Mitigation | Owner |
|---|---|---|---|---|
11. Open questions and decision log
| Date | Decision / question | Options considered | Decided by | Rationale |
|---|---|---|---|---|
One-pager (idea pitch)
[Idea name] — one-pager
The ask: what decision or resources you need, by when.
Problem: For [user], [task] is difficult because [barrier]. Evidence: [data + research].
Why now: [strategy link, timing, cost of delay].
Proposed approach: [2–3 sentences; what we'd test first].
Expected impact: [metric] from [baseline] to [target] (range: low–high). Assumptions: [list].
Cost: [people × weeks], [money], [opportunity cost — what we won't do].
Risks: [top 3] → mitigations.
How we'll know: [experiment / pilot, success threshold, decision date].
Not doing: [explicit exclusions].
Experiment plan and readout
Experiment plan — [name]
| Item | Detail |
|---|---|
| Hypothesis | We believe [change] for [eligible users] will [move metric] because [reason]. |
| Primary metric | Definition: numerator / denominator / window |
| Secondary metrics | |
| Guardrails | e.g., complaint rate, payment errors, AOV, latency |
| Unit of randomisation | user / session / account / geography / time (switchback) |
| Eligibility | who is included / excluded |
| Variants | control: … · treatment: … |
| Baseline & MDE | baseline …% ; minimum detectable effect … points |
| Sample size per arm | (α = 0.05, power 80%) |
| Duration | full weeks; start/end dates |
| Stop rule | fixed horizon, or a sequential method designed for peeking |
| Analysis plan | test, CI, segments (pre-registered), SRM check |
| Decision rule | ship if …; iterate if …; stop if … |
| Owners | PM, analyst, engineer |
Readout — [name]
Decision requested: … Recommendation: …
| Metric | Control | Treatment | Difference (95% CI) | p-value |
|---|---|---|---|---|
| Primary | ||||
| Guardrail 1 |
- Sample ratio check: planned vs observed split.
- Segments (exploratory): …
- Caveats: novelty, seasonality, data issues.
- Learning: what we now believe and why.
- Next steps: rollout plan, holdout, follow-up tests.
Launch checklist
Launch checklist — [feature] (Tier 1 / 2 / 3)
Product & quality
- [ ] Acceptance criteria met; QA and UAT signed off
- [ ] Accessibility check (keyboard, screen reader, contrast)
- [ ] Performance/load test against p95 targets
- [ ] Feature flag, kill switch, rollback plan and owner
Data & measurement
- [ ] Success metrics and guardrails defined with baselines
- [ ] Events validated in staging; dashboard live; alerts configured
- [ ] Experiment/holdout plan (if applicable)
Risk & compliance
- [ ] Security review · [ ] Privacy/consent review · [ ] Legal review of claims/terms · [ ] Fraud/abuse review
Go-to-market
- [ ] Positioning and messaging · [ ] Pricing/plan changes · [ ] Channels and calendar · [ ] Sales enablement / FAQ
Support & operations
- [ ] Help articles · [ ] Support macros and training · [ ] Escalation path · [ ] Ops capacity
Rollout
| Stage | Exposure | Gate criteria | Date | Owner |
|---|---|---|---|---|
| Internal | 1% | no critical errors | ||
| Canary | 5% | error rate < …; latency < … | ||
| Experiment | 50/50 | primary + guardrails | ||
| Full | 100% (+ holdout) | readout approved |
After launch
- [ ] Daily monitoring during ramp · [ ] Feedback collection · [ ] Retrospective scheduled (2–4 weeks)
Stakeholder map and DACI
Stakeholder map — [initiative]
| Stakeholder | Role | Interest (1–10) | Power (1–10) | Quadrant | What they care about | Engagement plan | Owner |
|---|---|---|---|---|---|---|---|
| Manage closely / Keep satisfied / Keep informed / Monitor |
DACI — [decision]
| Role | Name | Notes |
|---|---|---|
| Driver | Runs the process, gathers evidence | |
| Approver (one person) | Makes the call by [date] | |
| Contributors | Input, risks, estimates | |
| Informed | Told the outcome |
Options considered: … Decision: … Date: … Rationale: … Review date: …
Research kit (interview script, persona, usability test plan)
Customer interview script (45 min)
Consent: purpose, recording permission, anonymisation, right to stop.
- Warm-up (5 min): “Tell me about your typical [day/week] when [context].”
- Recent story (20 min): “Think about the last time you [did the task]. What was happening before? What did you do first? What else did you consider? Where did it get hard? What did you do next?”
- Probe friction (10 min): “You mentioned [X]. What did you expect? How did that feel? What did you do about it?”
- Current alternatives (5 min): “How do you handle this today? What does it cost you (time, money, effort)?”
- Wrap-up (5 min): “What should I have asked? Who else should I talk to?”
Avoid: pitching; “would you…?”; leading or double-barrelled questions.
Notes sheet: Participant ID · Observation (verbatim) · Interpretation · Journey stage · Tag.
Persona template
| Field | Content |
|---|---|
| Name & context | |
| Goals & motivations | |
| Behaviours (observed) | |
| Pains / frustrations | |
| Needs from the product | |
| Jobs to be done | When … I want to … so I can … |
| Evidence (n, dates) & confidence |
Usability test plan
| Item | Detail |
|---|---|
| Goals / decisions informed | |
| Prototype / build | |
| Participants (n, criteria, accessibility needs) | |
| Tasks (realistic, no hints) | 1. … 2. … 3. … |
| Metrics | task success, time on task, errors, confidence, SUS |
| Success threshold | e.g., ≥ 5 of 6 complete task 3 unaided |
| Moderator script | intro, think-aloud, tasks, debrief |
| Analysis | severity × frequency; quotes; clips |
Prioritisation sheet (RICE / WSJF) and Now–Next–Later roadmap
RICE sheet
| Feature | Problem / outcome | Reach (unit/period, source) | Impact (3/2/1/0.5/0.25) | Confidence (100/80/50%) | Effort (person-months) | RICE | Dependencies / risks | Notes |
|---|---|---|---|---|---|---|---|---|
RICE = Reach × Impact × Confidence ÷ Effort. Sensitivity check: recompute with confidence −30 points and effort +50%; if the order flips, treat as a tie.
WSJF sheet
| Job | User/business value | Time criticality | Risk reduction / opportunity | Cost of Delay | Job size | WSJF = CoD ÷ size |
|---|---|---|---|---|---|---|
Now / Next / Later roadmap
| Now (in delivery) | Next (validated, scoping) | Later (ideas) | |
|---|---|---|---|
| Outcome 1 | |||
| Outcome 2 | |||
| Reliability / tech debt |
Rules: outcomes not just features; no dates in “Later”; show confidence and dependencies; review monthly.
Metric definition, KPI tree and OKRs
Metric definition card
| Field | Value |
|---|---|
| Name | |
| Question it answers / decision it supports | |
| Numerator | |
| Denominator | |
| Time window & time zone | |
| Inclusions / exclusions (bots, test accounts, cancellations) | |
| Source table / events | |
| Owner | |
| Baseline & target | |
| Guardrails paired with it | |
| Known caveats |
KPI tree
North Star: [recurring value metric]
├── Input 1: [breadth — e.g., activated users]
│ ├── Driver 1a
│ └── Driver 1b
├── Input 2: [frequency]
└── Input 3: [quality — e.g., payment success, on-time rate]
Guardrails: [complaints] · [refunds] · [wellbeing / opt-outs]
OKRs (quarter)
| Objective | Key result (metric, baseline → target, date) | Owner | Confidence (0–10, weekly) | Initiatives |
|---|---|---|---|---|
AI PRD additions and evaluation plan
AI PRD additions (add to the standard PRD)
| Section | Content |
|---|---|
| Intended use | Who, what tasks, which channels |
| Prohibited use | e.g., financial/medical/legal advice; actions without approval |
| Non-AI baseline | What simpler solution we compared against |
| Data & sources | Approved sources, owners, freshness, permissions, retention |
| Model & architecture | Prompting / RAG / fine-tuning / tools; model choice rationale |
| Autonomy level | L0–L4 per capability; approval checkpoints |
| Guardrails | Input/output filters, refusals, escalation, kill switch |
| Evaluation | Test set composition, rubric, severity levels, launch gates |
| Quality targets | e.g., ≥ 90% correct with citation; 100% correct routing for high-risk intents |
| Safety & privacy | Injection tests, PII handling, vendor terms, logging |
| Latency & cost | p95 target; cost per request budget |
| Human-in-the-loop | Who reviews what, SLAs, feedback loop |
| Monitoring | Sampling, drift, incident process, re-eval on every change |
| Fallback & rollback | What users see when AI fails; how to disable |
Evaluation plan
| Category | # cases | Expected behaviour | Scoring | Gate |
|---|---|---|---|---|
| Answerable (single source) | answer + citation | correct/incorrect | ≥ 90% | |
| Answerable (multi-source) | answer + citations | rubric 1–5 | ≥ 4.0 avg | |
| Not covered | say so + route | pass/fail | 100% | |
| High-risk intents | route to human | pass/fail | 100% | |
| Adversarial / injection | refuse, no leak | pass/fail | 100% | |
| Unauthorised data | deny | pass/fail | 100% |
Re-run on every model, prompt, source or tool change. Review all failures by severity.
Decision log and launch retrospective
Decision log
| # | Date | Context | Options | Decision | Decided by | Evidence | Trade-offs accepted | Review date |
|---|---|---|---|---|---|---|---|---|
Launch retrospective (blameless)
Goal vs result: metric targets vs actuals (with charts).
| What went well | What didn't | Surprises |
|---|---|---|
Root causes (for what didn't): …
| Action | Owner | Due |
|---|---|---|
Keep / Stop / Start: …
Learning to share with the wider org: …
Sample datasets
All datasets are synthetic, hypothetical and generated for teaching. They are not real company data.
- ab_test_checkout.csv — 2 rows
- card_application_funnel.csv — 28 rows
- channel_creation_usability.csv — 24 rows
- cohort_retention_weekly.csv — 12 rows
- events.csv — 5096 rows
- kb_policy_snippets.csv — 6 rows
- orders.csv — 334 rows
- rice_backlog.csv — 6 rows
- saas_pricing_pilot.csv — 3 rows
- support_eval_set.csv — 60 rows
- users.csv — 800 rows