Product Delivery System

"The goal isn't shipping. The goal is learning whether your bet was right."

This skill covers the Delivery System — how we ship, measure, and learn. It runs discovery and delivery in parallel (dual-track), ships with staged rollouts, measures with a clear hierarchy, reflects through retrospectives, and executes GTM with precision.

Related skills: product-strategy, product-discovery, product-architecture, ai-native-product, product-leadership

When to Use This Skill

Use this skill when:

Planning how to roll out a new feature or product
Designing a metrics hierarchy for a bet or product
Running bet retrospectives after shipping
Executing GTM launches
Setting up dual-track development rhythm
Deciding when to scale, iterate, or kill a bet

Cadence: Continuous | Owner: Product Trio + GTM Team

The Problem This Solves

Most teams either:

Ship features and never measure impact
Measure vanity metrics that don't connect to outcomes
Do "big bang" launches that create risk
Never officially conclude bets—zombies live forever
Treat GTM as marketing's problem after PM ships

The Delivery System ensures shipping is the beginning of learning, not the end.

Philosophy

Core Beliefs

Discovery and delivery run in parallel — Don't pause discovery to deliver
Staged rollouts are the default — Ship to 10% before 100%
Metrics exist in hierarchy — Leading → Core → Lagging
Every bet gets a retrospective — Explicit scale/iterate/kill decision
GTM is a product responsibility — PM owns adoption, not just availability

What This Framework Rejects

Ship and forget (no measurement)
Big bang launches (maximum risk)
Vanity metrics (activity without outcome)
Zombie bets (never concluded, never killed)
Throwing features over the wall to marketing

Progress Tracking

Display progress during delivery planning:

[████░░░░░░░░░░░░░░░░] 25% — Phase 1/4: Rollout Planning & Risk Assessment
[████████░░░░░░░░░░░░] 50% — Phase 2/4: Metrics Hierarchy & GTM Coordination
[████████████░░░░░░░░] 75% — Phase 3/4: Launch Execution & Monitoring
[████████████████████] 100% — Phase 4/4: Bet Retrospective & Learning Loop

Framework Components

1. Dual-Track Development

The Core Idea: Discovery and delivery happen simultaneously. While one bet is being built, the next bet is being shaped.

Week 1    Week 2    Week 3    Week 4    Week 5    Week 6
─────────────────────────────────────────────────────────
[  Discover Bet B  ][  Shape Bet B  ][  Discover Bet C  ]
         [    Build Bet A    ][   Build Bet B   ]
                    [ Ship A ]        [ Ship B ]

How It Works:

Track	Activities	Who
Discovery Track	Interviews, OST updates, solution exploration, assumption tests	Full trio (PM heavy)
Delivery Track	Building, testing, shipping, measuring	Full trio (Eng heavy)

Time Allocation (Example):

Role	Discovery	Delivery
PM	60%	40%
Designer	50%	50%
Tech Lead	30%	70%

Coordination Points:

Weekly sync: What's in flight on each track
Handoff moment: When a bet moves from "shaped" to "building"
Learning moment: When shipped bet results inform discovery

0→1 Mode: Tracks may blur. Everyone does everything. Speed > separation.

Scaling Mode: Clear separation. Dedicated discovery time. Research ops support.

2. Staged Rollout

The Core Idea: Never ship to everyone at once. Start small, learn, expand.

Default Rollout Stages:

Stage	Audience	Duration	Purpose
Stage 0: Internal	Team dogfooding	1-3 days	Find obvious bugs
Stage 1: Alpha	5-10 friendly customers	1 week	Qualitative feedback
Stage 2: Beta	10% of users	1-2 weeks	Quantitative signal
Stage 3: GA	100% of users	Ongoing	Full measurement

Progression Criteria:

From	To	Criteria
Internal → Alpha	Ready for external	No P0 bugs, core flow works
Alpha → Beta	Validated experience	Positive qualitative feedback, no major usability issues
Beta → GA	Metrics acceptable	Leading metrics trending right, no guardrail breaches

Feature Flags:

Every significant feature ships behind a flag
Flags enable instant rollback
Flags enable % rollout control
Flags are cleaned up after GA (don't accumulate debt)

Rollback Triggers:

Guardrail metric breached
Error rate > threshold
Customer-reported critical issue
Leading metrics trending wrong

0→1 Mode: Stages can be compressed. Alpha might be 3 customers for 2 days.

Scaling Mode: Formal stage gates. Release management. Beta programs.

3. Metrics Hierarchy

The Three-Tier Model:

┌─────────────────────────────────────────────────────┐
│                 LAGGING METRICS                     │
│         (Revenue, Retention, NPS)                   │
│         Move slowly, hard to attribute              │
├─────────────────────────────────────────────────────┤
│                  CORE METRICS                       │
│       (Activation, Engagement, Conversion)          │
│       The outcomes your bets target                 │
├─────────────────────────────────────────────────────┤
│                LEADING METRICS                      │
│         (Feature adoption, Task completion)         │
│         Move fast, early signal                     │
└─────────────────────────────────────────────────────┘

Metric Types:

Type	Definition	Example	Use For
Leading	Early signal, fast-moving, directly influenced by feature	Feature adoption rate, task completion rate	Weekly decisions, rollout gates
Core	Primary outcome you're targeting	Activation rate, conversion rate, engagement score	Bet success criteria
Lagging	Business results, slow-moving, influenced by many factors	Revenue, retention, NPS	Quarterly/annual planning
Guardrail	Metrics you won't let degrade	Performance, error rate, support tickets	Rollout gates, rollback triggers

Hierarchy Example (Activation Bet):

Lagging:    Revenue growth (quarterly)
               ↑
Core:       Activation rate (weekly)
               ↑
Leading:    Onboarding completion (daily)
            First value action (daily)
               ↑
Guardrail:  Support tickets (daily)
            Error rate (real-time)

Metric Selection Criteria:

Criterion	Question
Measurable	Can we actually track this?
Actionable	Can we influence it with our work?
Attributable	Can we connect changes to our bet?
Timely	Will we see signal fast enough to decide?

Dashboard Design:

Leading metrics: Real-time or daily
Core metrics: Weekly view with trend
Lagging metrics: Monthly/quarterly view
Guardrails: Alerting, not just reporting

4. Bet Retrospectives

The Core Idea: Every bet concludes with an explicit decision: Scale, Iterate, or Kill.

Retrospective Format:

BET RETROSPECTIVE: [Name]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Timebox: [Duration] | Shipped: [Date]

HYPOTHESIS REVIEW
Original: "We believed [X] would result in [Y]"
Result: [ ] Confirmed  [ ] Disproved  [ ] Inconclusive

METRICS REVIEW
| Metric    | Target | Actual | Verdict |
|-----------|--------|--------|---------|
| Primary   | [X]    | [Y]    | ✅ / ❌  |
| Secondary | [X]    | [Y]    | ✅ / ❌  |
| Guardrail | [X]    | [Y]    | ✅ / ❌  |

KEY LEARNINGS
• [Learning 1]
• [Learning 2]
• [Learning 3]

DECISION: [ ] SCALE  [ ] ITERATE  [ ] KILL

NEXT STEPS
• [Action 1]
• [Action 2]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Decision Framework:

Outcome	Criteria	Action
SCALE	Primary metric hit, no guardrail issues	Expand rollout, invest more
ITERATE	Signal positive but not at target	Refine and re-test (one more cycle)
KILL	Hypothesis disproved or too costly	Stop investment, document learning

Iteration Limits:

Maximum 2-3 iteration cycles before forcing Scale or Kill
Each iteration must have a new hypothesis
Time-bound iterations (don't let them drag)

Retrospective Cadence:

Run at timebox end, regardless of "completion"
Include full trio + relevant stakeholders
30-60 minutes maximum
Document in central repository

0→1 Mode: Informal but still explicit. Even a Slack message: "Bet X result: [Y]. Decision: [Z]."

Scaling Mode: Formal retrospective meetings. Learning database. Cross-team sharing.

5. GTM Execution

The Core Idea: PM owns adoption, not just availability. GTM is part of delivery, not an afterthought.

GTM Components:

Component	Owner	Timing
Positioning & messaging	PMM / PM	During solution shaping
Sales enablement	PMM / PM	Before beta
Documentation	PM / Tech Writer	Before beta
Support training	PM / Support Lead	Before GA
Launch communications	PMM / Marketing	At GA
Customer success playbook	CS / PM	Before GA

Launch Tiers:

Tier	Definition	GTM Effort
Tier 1	Major new capability, strategic priority	Full GTM: press, event, sales training, campaign
Tier 2	Significant improvement, notable value	Moderate GTM: blog, email, sales brief
Tier 3	Incremental improvement, quality of life	Light GTM: changelog, in-app, support brief
Tier 4	Bug fix, minor enhancement	No GTM: release notes only

Launch Checklist (Tier 1/2):

Phase	Tasks
Pre-launch	Positioning defined, Sales trained, Support trained, Docs ready, Success playbook ready
Launch	Announcement sent, In-app messaging live, Sales notified, Support notified
Post-launch	Feedback monitored, Metrics tracked, Issues triaged, Iteration planned

Adoption Metrics (PM Responsibility):

Metric	Definition
Awareness	% of target users who know feature exists
Trial	% of aware users who try feature
Adoption	% of trial users who continue using
Habit	% of adopters using regularly

0→1 Mode: PM does GTM. Scrappy launches. Focus on learning, not polish.

Scaling Mode: PMM partnership. Launch playbooks. Integrated marketing calendar.

Key Outputs

Output	Description	Update Cadence
Rollout plan	Staged rollout with progression criteria	Per bet
Metrics dashboard	Leading, core, lagging, guardrail metrics	Continuous
Bet retrospective	Scale/Iterate/Kill decision with learnings	At timebox end
GTM checklist	Launch activities by tier	Per launch
Learning repository	Archive of bet results and learnings	After each retrospective

Templates

This skill includes templates in the templates/ directory:

rollout-plan.md — Staged rollout checklist
metrics-hierarchy.md — Metrics design template
bet-retrospective.md — Retrospective format and decision framework
launch-checklist.md — GTM execution checklist by tier

Using This Skill with Claude

Ask Claude to:

Design rollout plan: "Create a staged rollout plan for [feature]"
Build metrics hierarchy: "Design a metrics hierarchy for [bet/product]"
Set success criteria: "What metrics should determine success for [bet]?"
Plan retrospective: "Create a retrospective agenda for [bet] that shipped [X]"
Analyze results: "Given these metrics, should we scale, iterate, or kill?"
Design launch tier: "What launch tier should [feature] be? What GTM is needed?"
Create launch checklist: "Build a GTM checklist for [Tier X] launch"
Identify guardrails: "What guardrail metrics should we watch for [bet]?"
Plan dual-track: "Help me design a dual-track rhythm for [team size]"
Write changelog: "Write release notes for [feature] at [tier]"

Connection to Other Skills

When you need to...	Use skill
Define what metrics ladder up to	`product-strategy`
Validate before delivery	`product-discovery`
Define what you're delivering	`product-architecture`
Adapt delivery for AI products	`ai-native-product`
Scale delivery across teams	`product-leadership`

Anti-Patterns to Avoid

Anti-Pattern	Why It Fails	Instead
Ship and forget	No learning, no improvement	Measure and retrospect
Big bang launch	Maximum risk, no learning	Staged rollout
Vanity metrics	Activity ≠ outcome	Hierarchical metrics
Zombie bets	Resources trapped, no clarity	Explicit Scale/Iterate/Kill
GTM afterthought	Features available but not adopted	GTM as part of delivery
Perfectionism	Never ships, never learns	Timebox and ship

Quick Reference: Delivery Quality Checklist

Before GA:

Rollout staged — Not shipping to 100% first
Feature flagged — Can roll back instantly
Metrics instrumented — Can measure leading/core/lagging
Guardrails defined — Know what triggers rollback
GTM prepared — Docs, training, comms ready
Retrospective scheduled — Date on calendar at timebox end
Success criteria agreed — Team knows what Scale/Iterate/Kill means

Sources & Influences

Marty Cagan — INSPIRED, EMPOWERED
Eric Ries — The Lean Startup
Teresa Torres — Continuous Discovery Habits
April Dunford — Obviously Awesome (for GTM)