--- name: product-lifecycle-learning description: >- Compare intended product outcomes against observed results to close the launch-to-learning loop: collect post-launch evidence, distinguish expected from observed from uncertain from inferred claims, update assumptions, assess feature health, and choose among continue/improve/harvest/pivot/pause/retire — including retirement lifecycles with deprecation, migration, customer treatment, and retained reusable learning. Do not use for incident postmortems or root-cause analysis (routes to incident-learning or site-reliability-engineering); do not use for analytics instrumentation or metric dashboard design (routes to product-analytics-and-measurement); do not use arbitrary thresholds as universal retirement rules — decisions require human judgment and context. license: MIT metadata: tags: product-lifecycle-learning, post-launch-review, outcome-review, feature-health, assumption-update, retirement-decisions, deprecation, sunset-planning, retained-learning, evidence-ledger, epistemic-discipline, lifecycle-closure --- # Product Lifecycle Learning Close the loop from launch to learning. This skill compares what was intended against what actually happened, maintains an evidence-backed assumption ledger, assesses feature health, and makes disciplined continue/improve/harvest/pivot/pause/retire decisions — including full retirement lifecycles. It produces a durable retained learning record that feeds back into roadmap, analytics, adoption, experimentation, and future specifications. ## Loading Guide Load only the reference or template relevant to the task. Do not load every file at once. | File | Load when | |------|-----------| | [references/discovery-brief.md](references/discovery-brief.md) | You need to understand how lifecycle-learning concepts map across skills and where this skill's boundaries are | | [references/epistemic-discipline.md](references/epistemic-discipline.md) | You need the full taxonomy for classifying claims as expected, observed, uncertain, or inferred | | [references/retirement-lifecycle.md](references/retirement-lifecycle.md) | Planning a feature or product retirement, including deprecation, migration, customer treatment, and internal cleanup | | [references/feedback-destinations.md](references/feedback-destinations.md) | Routing learning outputs to the right downstream skill — roadmap, analytics, adoption, experimentation, or specification | | [templates/outcome-review.md](templates/outcome-review.md) | Conducting a structured post-launch outcome review comparing expected vs. observed | | [templates/assumption-ledger-update.md](templates/assumption-ledger-update.md) | Updating the assumption ledger with new evidence and confidence shifts | | [templates/feature-health-record.md](templates/feature-health-record.md) | Assessing feature health across multiple dimensions and surfacing signals | | [templates/retirement-decision.md](templates/retirement-decision.md) | Making and recording a justified retirement or continuation decision | | [templates/sunset-plan.md](templates/sunset-plan.md) | Planning deprecation communication, migration paths, customer treatment, and internal cleanup | | [templates/retained-learning-record.md](templates/retained-learning-record.md) | Capturing durable reusable learning that survives beyond the feature | ## Core Methodology ### The Launch-to-Learning Loop ``` LAUNCH → [OBSERVE] → [COMPARE] → [IDENTIFY GAPS] → [UPDATE ASSUMPTIONS] → [ASSESS HEALTH] → [DECIDE] → [CAPTURE LEARNING] → (feed back) | | | | | | | Collect Expected vs. Gap analysis Assumption Feature health Continue / Retained outcome observed with confidence ledger update dimensions Improve / learning data outcomes intervals Harvest / record Pivot / Pause / Retire ``` The loop starts after launch (the feature or capability is live and generating data) and ends with a durable learning artifact that feeds the next cycle of roadmap, analytics, adoption, experimentation, and specification work. ### Stage-by-Stage | Stage | Input | Activity | Output | |-------|-------|----------|--------| | **Observe** | Analytics data, adoption metrics, user feedback, support tickets, operational metrics | Collect outcome evidence from observed behavior and system data. Distinguish signal from noise. Flag missing or low-confidence data. | Collected outcome data with confidence labels | | **Compare** | Expected outcomes (from spec/roadmap), observed outcomes, confidence intervals | Compare the two; identify alignment, deviation, and surprise. Do not conflate expectation with observation. | Gap analysis: what matched, what diverged, what was ambiguous | | **Identify gaps** | Gap analysis, assumption ledger | Identify which assumptions held and which broke. Distinguish between measurement gaps (could not observe) and outcome gaps (observed deviation). | Assumption gap register with confidence | | **Update assumptions** | Assumption gap register, prior assumption ledger | Revise assumptions: strengthen confirmed ones, weaken contradicted ones, add new ones surfaced by the data. Record confidence shifts. | Updated assumption ledger. Use [templates/assumption-ledger-update.md](templates/assumption-ledger-update.md). | | **Assess health** | Updated assumptions, adoption data, operational metrics, user feedback | Evaluate feature health across adoption, technical, operational, and strategic dimensions. Do not reduce to a single score. | Feature health assessment. Use [templates/feature-health-record.md](templates/feature-health-record.md). | | **Decide** | Feature health assessment, business context, portfolio priorities | Choose one of six lifecycle decisions. The decision requires human judgment; no automated threshold. | Decision record with accountable owner. Use [templates/retirement-decision.md](templates/retirement-decision.md). | | **Capture learning** | Decision record, gap analysis, updated assumptions, context | Produce a durable retained learning record: what was learned, why, and how it should inform future work. Not a transient meeting summary. | Retained learning record. Use [templates/retained-learning-record.md](templates/retained-learning-record.md). | | **Feed back** | Retained learning record | Route learning to downstream skills: roadmap, analytics, adoption, experimentation, specifications. See [references/feedback-destinations.md](references/feedback-destinations.md). | Routed learning outputs | ### Epistemic Discipline Every claim in lifecycle-learning output is classified into exactly one of four categories. These are not conflated; a comparison is not an observation, and an inference is not a fact. | Category | Definition | Example | Source | |----------|-----------|---------|--------| | **Expected** | What was intended or predicted before launch | "We expected activation to reach 60% within 30 days" | Spec, roadmap, launch brief | | **Observed** | What actually happened, measured from data | "Activation reached 43% at 30 days (95% CI: 39-47%)" | Analytics, adoption data, operational metrics | | **Uncertain** | What is ambiguous, noisy, or contested | "Attribution is confounded by a simultaneous pricing change; cannot isolate feature effect" | Confidence intervals, conflicting signals, data-quality issues | | **Inferred** | What is concluded from evidence, with reasoning | "The gap between expected 60% and observed 43% suggests the onboarding redesign did not reduce time-to-value as hypothesized; the pricing change confound means we cannot rule out an external cause" | Reasoned implication from evidence | Full taxonomy and field guide in [references/epistemic-discipline.md](references/epistemic-discipline.md). ### Lifecycle Decisions Six outcomes are available after assessment. The choice requires human judgment informed by evidence; no numeric threshold or automated rule replaces context and accountability. | Decision | Meaning | Typical evidence profile | Follow-up | |----------|---------|--------------------------|-----------| | **Continue** | Keep as-is; feature is healthy | Outcomes match or exceed expectations; stable, low-risk | Schedule next review | | **Improve** | Invest in enhancement | Adoption gap exists but fixable; underlying need confirmed | Feed roadmap and experimentation | | **Harvest** | Reduce investment, maintain for existing users | Declining growth but stable base; not worth expanding | Monitor for retirement signals | | **Pivot** | Change direction significantly | Need confirmed but current approach failed | Feed roadmap, discovery, experimentation | | **Pause** | Temporarily halt investment | Ambiguous results, external confounds, or resource constraint | Schedule re-assessment with new evidence | | **Retire** | Deprecate and remove | Sustained non-adoption, replacement exists, or strategic misalignment | Execute retirement lifecycle | ### Retirement Lifecycle When the decision is Retire, a structured retirement lifecycle covers the full path from deprecation announcement through internal cleanup. Full detail in [references/retirement-lifecycle.md](references/retirement-lifecycle.md). | Phase | Activity | Template | |-------|----------|----------| | **Deprecation communication** | Announce retirement: timeline, rationale, alternatives. Target affected users with segmentation. | [templates/sunset-plan.md](templates/sunset-plan.md) | | **Migration path** | Provide migration tooling, documentation, and support for existing users. Define the recommended path. | [templates/sunset-plan.md](templates/sunset-plan.md) | | **Customer treatment** | Support commitments during sunset: data export, grace periods, extended support windows, SLA preservation, refund/credit policies where applicable. Coordinate with customer-success. | [templates/sunset-plan.md](templates/sunset-plan.md); route communication plans to `conditional-customer-success` | | **Internal cleanup** | Remove feature flags, archive code, update documentation, retire monitoring and alerting, reclaim infrastructure. | [templates/sunset-plan.md](templates/sunset-plan.md) | | **Learning closure** | Capture what the feature's lifecycle taught — not a postmortem, but a closure record that completes the learning loop. | [templates/retained-learning-record.md](templates/retained-learning-record.md) | ### Retained Learning Record Every lifecycle-learning cycle produces a durable retained learning record — not a transient meeting summary. The record captures: - What the feature or capability was intended to achieve (expected outcomes) - What actually happened (observed outcomes, with confidence) - What was uncertain and why - What assumptions were updated and how - What decision was made (continue/improve/harvest/pivot/pause/retire) and who made it - Why that decision was reached, with evidence - What should inform future decisions — reusable patterns, anti-patterns, assumptions to test next time - Where the learning was routed (roadmap, analytics, adoption, experimentation, specifications) This record is the durable learning artifact. It is the evidence that the launch-to-learning loop actually closed. ## When Not to Use This skill does **not** own: - **Incident postmortems, root-cause analysis, or operational incident review** — these belong to `incident-learning` (not yet landed) and [../site-reliability-engineering/SKILL.md](../site-reliability-engineering/SKILL.md). Lifecycle-learning consumes incident signals as input but does not produce postmortems. - **Analytics instrumentation, metric dashboard design, tracking-plan creation, or event taxonomy** — these belong to [../product-analytics-and-measurement/SKILL.md](../product-analytics-and-measurement/SKILL.md). Lifecycle-learning consumes analytics data as input but does not own measurement infrastructure. - **Customer-success account management, renewal decisions, or health scoring** — these belong to `conditional-customer-success` (not yet landed). Lifecycle-learning routes retirement communication plans and customer-treatment strategies there. - **Roadmap prioritization or portfolio allocation** — these belong to [../product-roadmapping-and-portfolio/SKILL.md](../product-roadmapping-and-portfolio/SKILL.md). Lifecycle-learning feeds evidence into roadmap decisions but does not make them. - **Arbitrary or automated retirement thresholds** — this skill never applies rules like "retire if DAU < 100" or "kill if NPS < 30" without context about the product, market, user base, and alternatives. Retirement decisions require human judgment and named accountability. ## Routing and Feedback ### Inputs (consumed by lifecycle-learning) | Input | Source | |-------|--------| | Expected outcomes, acceptance criteria | [../spec-driven-development/SKILL.md](../spec-driven-development/SKILL.md), roadmap briefs | | Observed outcomes, metric data, funnels, cohorts | [../product-analytics-and-measurement/SKILL.md](../product-analytics-and-measurement/SKILL.md) | | Adoption evidence, activation rates, retention signals | [../product-adoption/SKILL.md](../product-adoption/SKILL.md) | | Experiment results, readout learning entries | [../product-experimentation/SKILL.md](../product-experimentation/SKILL.md) | | Incident signals, reliability data | [../site-reliability-engineering/SKILL.md](../site-reliability-engineering/SKILL.md), `incident-learning` | | Customer feedback, support trends, health signals | `conditional-customer-success` | ### Outputs (produced by lifecycle-learning, routed to) | Output | Destination | Purpose | |--------|-------------|---------| | Revised assumptions, decision evidence | [../product-roadmapping-and-portfolio/SKILL.md](../product-roadmapping-and-portfolio/SKILL.md) | Roadmap updates, bet re-evaluation | | Metric refinement needs, measurement gaps | [../product-analytics-and-measurement/SKILL.md](../product-analytics-and-measurement/SKILL.md) | Improve instrumentation, close measurement gaps | | Adoption pattern changes, behavior insights | [../product-adoption/SKILL.md](../product-adoption/SKILL.md) | Adoption strategy adjustments | | New hypotheses, experiment ideas | [../product-experimentation/SKILL.md](../product-experimentation/SKILL.md) | Feed experimentation pipeline | | Spec improvements, acceptance-criteria refinements | [../spec-driven-development/SKILL.md](../spec-driven-development/SKILL.md) | Future specification quality | | Retirement communication plans, migration coordination, customer treatment during sunset | `conditional-customer-success` | Customer-facing retirement execution; prose reference (skill not yet landed) | | Incident-driven learning signals | `incident-learning` | Incident-driven learning loop; prose reference (skill not yet landed) | At least five feedback destinations must be updated per cycle: roadmap, analytics, adoption, experimentation, and specifications. Additional routing to customer-success and incident-learning is conditional on the decision. ## File Map | File | Purpose | Load when | |------|---------|-----------| | [references/discovery-brief.md](references/discovery-brief.md) | Maps existing lifecycle, learning, and retirement material; ownership boundaries | Understanding the skill's place in the catalog | | [references/epistemic-discipline.md](references/epistemic-discipline.md) | Full taxonomy: expected / observed / uncertain / inferred with field guide | Classifying claims in any lifecycle-learning output | | [references/retirement-lifecycle.md](references/retirement-lifecycle.md) | Complete retirement lifecycle: deprecation, migration, customer treatment, internal cleanup | Retirement decision or sunset planning | | [references/feedback-destinations.md](references/feedback-destinations.md) | Detailed routing guide for each feedback destination | Routing learning outputs to downstream skills | | [templates/outcome-review.md](templates/outcome-review.md) | Structured post-launch outcome review | Conducting an outcome review | | [templates/assumption-ledger-update.md](templates/assumption-ledger-update.md) | Assumption ledger update with confidence shifts | Updating assumptions after new evidence | | [templates/feature-health-record.md](templates/feature-health-record.md) | Multi-dimensional feature health assessment | Assessing feature health | | [templates/retirement-decision.md](templates/retirement-decision.md) | Justified retirement or continuation decision record | Making a lifecycle decision | | [templates/sunset-plan.md](templates/sunset-plan.md) | Deprecation communication, migration, customer treatment, internal cleanup plan | Planning a retirement execution | | [templates/retained-learning-record.md](templates/retained-learning-record.md) | Durable reusable learning artifact | Capturing learning that survives the feature | ## Related Skills - [../product-analytics-and-measurement/SKILL.md](../product-analytics-and-measurement/SKILL.md) — Owns instrumentation and metric definition. Lifecycle-learning consumes analytics outputs. - [../product-adoption/SKILL.md](../product-adoption/SKILL.md) — Owns adoption diagnostics and strategy. Lifecycle-learning consumes adoption evidence. - [../product-experimentation/SKILL.md](../product-experimentation/SKILL.md) — Owns experiment design and readout. Lifecycle-learning consumes experiment results. - [../product-roadmapping-and-portfolio/SKILL.md](../product-roadmapping-and-portfolio/SKILL.md) — Owns roadmap and portfolio decisions. Lifecycle-learning feeds evidence. - [../spec-driven-development/SKILL.md](../spec-driven-development/SKILL.md) — Owns specifications and acceptance criteria. Lifecycle-learning feeds spec improvements. - [../site-reliability-engineering/SKILL.md](../site-reliability-engineering/SKILL.md) — Owns operational reliability. Lifecycle-learning consumes incident signals. - `conditional-customer-success` — Consumer for retirement communication plans, customer treatment during sunset, migration support coordination. Prose reference; skill not yet landed. - `incident-learning` — Destination for incident-driven learning signals. Prose reference; skill not yet landed.