{
  "schema_version": 1,
  "skill_name": "product-adoption",
  "evals": [
    {
      "id": "internal-tool-adoption-diagnostic",
      "prompt": "Our internal CRM tool was rolled out to the sales team 3 months ago, but half the team still uses spreadsheets instead. Leadership mandated the tool, but adoption is stagnating. How should we diagnose and fix this?",
      "expected_output": "A diagnostic that uses workflow-completion rate and time-saved metrics as primary evidence (not NPS or conversion), investigates whether the tool fits the real workflow or if shadow-process persistence is happening, distinguishes between mandatory-but-resented compliance and genuine adoption, and applies behavior-change analysis (Fogg or COM-B model) to identify whether motivation, ability, opportunity, or prompt is the binding constraint. The response recommends cohort segmentation by team/role and proposes evidence-based intervention rather than blanket re-training.",
      "assertions": [
        "The response uses workflow-completion and time-saved metrics as primary evidence, never NPS or conversion rates",
        "The response distinguishes mandatory compliance from genuine adoption and investigates shadow-process persistence",
        "The response applies a behavior-change model (Fogg B=MAP or COM-B) to diagnose the binding constraint",
        "The response recommends cohort segmentation by team or role rather than treating all users as one group",
        "The response proposes evidence-based intervention based on diagnostic findings, not blanket re-training"
      ]
    },
    {
      "id": "public-service-accessibility-adoption",
      "prompt": "We launched a digital public service for benefit applications. Overall completion rate is 72%, but when we break it down: users over 65 have a 34% completion rate, screen-reader users are at 28%, and non-primary-language speakers are at 41%. How should we approach this adoption problem?",
      "expected_output": "An adoption analysis that treats the cohort completion-rate equity gap as the primary adoption problem — not an overall metric improvement target. The response identifies accessibility, language, and digital literacy as first-class adoption barriers (not edge cases), applies public-service-specific success definitions (equitable completion rates across cohorts, not conversion), and recommends multi-channel adoption strategy (digital + phone + in-person + mail) rather than digital-only optimization. The response explicitly rejects NPS, conversion, and revenue metrics as irrelevant to public-service adoption.",
      "assertions": [
        "The response identifies the equity gap between cohorts as the primary adoption problem, not the overall 72% rate",
        "The response treats accessibility, language, and digital literacy as first-class adoption barriers",
        "The response applies public-service-specific success definitions (equitable outcomes, not conversion)",
        "The response recommends multi-channel strategy rather than digital-only optimization",
        "The response explicitly rejects NPS, conversion, or revenue metrics as irrelevant to public-service adoption context"
      ]
    },
    {
      "id": "low-feature-discovery-diagnostic",
      "prompt": "Our product has 18 features but analytics show the median user only uses 2 of them. We have tried adding a What's New popup and a feature tour, but feature adoption breadth has not improved. Our product manager wants to add more features to drive engagement. What should we do?",
      "expected_output": "A diagnosis that identifies the root cause as feature discovery failure rather than a feature deficit. The response warns that adding more features will worsen discovery (more noise, not more signal), applies progressive disclosure and contextual education principles, distinguishes between 'user doesn't know the feature exists' (discovery problem) and 'user knows but doesn't need it' (relevance problem), and recommends diagnosing by cohort before designing interventions. The response explicitly pushes back on adding features as a solution to low feature adoption.",
      "assertions": [
        "The response identifies the root cause as feature discovery failure, not a feature deficit",
        "The response warns that adding more features will worsen the discovery problem",
        "The response distinguishes between discovery failure and relevance mismatch as different root causes",
        "The response recommends cohort-level diagnosis before designing interventions",
        "The response explicitly pushes back on adding features as the solution to low feature adoption"
      ]
    },
    {
      "id": "enterprise-rollout-cohort-gates",
      "prompt": "We are rolling out a new procurement system to a 5000-person enterprise. We have segmented into three cohorts: finance (200 people, pilot), department heads (300 people, wave 2), and all employees (4500, wave 3). The pilot cohort is at 68% activation after 3 weeks. The department-head cohort is ready to start. Design the rollout progression with evidence gates.",
      "expected_output": "A rollout plan with explicit evidence gates between each cohort phase — not just a timeline. The plan defines activation rate and time-to-value thresholds for progression, pause criteria (activation below threshold in any cohort), and cohort-comparison requirements at each gate. It distinguishes between role-based adoption patterns (finance uses daily, department heads use weekly, employees use monthly) and sets context-appropriate success definitions per cohort. The plan includes accessibility cohort tracking and requires equity checks before each expansion decision.",
      "assertions": [
        "The plan defines explicit evidence gates with activation-rate and time-to-value thresholds between each cohort phase",
        "The plan includes pause criteria that trigger when any cohort falls below threshold",
        "The plan distinguishes between role-based adoption patterns and sets context-appropriate success definitions per cohort",
        "The plan requires cohort comparison and equity checks at each gate before expansion",
        "The plan includes accessibility cohort tracking as a mandatory element of rollout evidence"
      ]
    },
    {
      "id": "pause-expansion-on-cohort-evidence",
      "prompt": "Our product launched to three cohorts: North America (activation 58%), EMEA (activation 52%), and LATAM (activation 19%). The overall activation rate is 43%. Leadership wants to proceed with full global rollout, citing the 43% overall number. What should the adoption recommendation be?",
      "expected_output": "A firm recommendation to PAUSE expansion based on cohort breakdown evidence. The response identifies that the overall 43% number masks a severe LATAM cohort failure (19% vs. 50%+ threshold), explains that proceeding would embed an equity gap and risk reputational and operational harm, and defines the pause decision rule: activation below threshold in any cohort, sustained for 2 review cycles, triggers pause and cohort-specific diagnosis. The response routes the pause evidence to product-lifecycle-learning and product-roadmapping-and-portfolio for lifecycle and roadmap impact. It explicitly rejects the 'overall number is fine' argument as misleading aggregation. Claims are scoped to the harness, model, fixtures, and revision under test.",
      "assertions": [
        "The response recommends PAUSE based on cohort breakdown evidence, not the misleading 43% overall number",
        "The response identifies that the LATAM 19% rate represents a severe cohort failure that the overall average hides",
        "The response defines the pause decision rule and explains why proceeding would embed an equity gap",
        "The response routes the pause evidence to product-lifecycle-learning and product-roadmapping-and-portfolio",
        "The response explicitly rejects aggregation that hides cohort-level failure as insufficient for adoption decisions"
      ]
    },
    {
      "id": "anti-trigger-acquisition-campaign",
      "prompt": "Our signup conversion rate dropped from 12% to 8% last quarter. Can you help us design a better landing page and acquisition campaign to bring in more high-quality users?",
      "expected_output": "The agent declines to apply product-adoption for acquisition campaign design, recognizing that signup conversion and landing-page optimization belong to go-to-market. The response routes the user to the go-to-market skill for acquisition strategy and channel optimization, explaining that product-adoption starts after user arrival — it does not own pre-arrival marketing or conversion optimization.",
      "assertions": [
        "The response declines to apply product-adoption for acquisition campaign and landing-page design",
        "The response routes to go-to-market for acquisition strategy and conversion optimization",
        "The response explains that product-adoption starts after user arrival and does not own pre-arrival marketing",
        "The response does not attempt to design an acquisition campaign or landing page"
      ]
    },
    {
      "id": "anti-trigger-analytics-instrumentation",
      "prompt": "We need to set up event tracking for our activation funnel. What events should we instrument, and how should we structure our tracking plan for Mixpanel?",
      "expected_output": "The agent declines to design analytics instrumentation, recognizing that tracking-plan design and event taxonomy belong to product-analytics-and-measurement. The response may describe what adoption evidence to look for (e.g., activation events, time-to-value, feature-discovery events) but routes the instrumentation design work to product-analytics-and-measurement. It does not produce a tracking plan or Mixpanel implementation guidance.",
      "assertions": [
        "The response declines to design analytics instrumentation or produce a tracking plan",
        "The response names product-analytics-and-measurement as the owning skill for instrumentation",
        "The response may describe adoption evidence categories but does not produce event taxonomy or implementation details",
        "The response does not provide Mixpanel-specific instrumentation guidance"
      ]
    }
  ]
}

# Adoption Lifecycle Concepts

## The Five Stages

```
ACQUISITION → ACTIVATION → ADOPTION → RETENTION → EXPANSION
```

### 1. Acquisition — "User has arrived"
**Owner:** go-to-market. **Boundary:** Everything before arrival is acquisition; after is activation.

### 2. Activation — "User completed first meaningful outcome"
**Owner:** product-adoption. The "aha moment." Metrics: activation rate, time-to-value.

### 3. Adoption — "User has formed a habit"
**Owner:** product-adoption. Product becomes regular workflow. Metrics: frequency, depth, feature discovery.

### 4. Retention — "User stays and returns"
**Shared:** product-adoption (adoption signals) + product-lifecycle-learning (churn analysis).

### 5. Expansion — "User deepens engagement"
**Owner:** product-adoption (feature discovery) + conditional-customer-success (account upsell).

## Why Distinguishing Matters

**Example 1:** Low retention → re-engagement campaign, but real problem is activation. Wrong fix.
**Example 2:** Low feature adoption → add more features, but real problem is discovery. Makes it worse.
**Example 3:** Low adoption → blame marketing, but activation experience is broken. Misdiagnosis.

## Relationship Definitions

| Relationship | Definition |
|---|---|
| Acquisition enables activation | Arrival alone is NOT activation |
| Activation enables adoption | Aha moment alone is NOT habit |
| Adoption drives retention | Weak adoption almost always precedes churn |
| Retention enables expansion | Expansion pushed too early accelerates churn |
| Expansion feeds back into adoption | Broader use deepens habit |

# Behavior Change and Education

Adoption is a behavior-change problem.

## Core Models

### Fogg Behavior Model (B = MAP)
Behavior = Motivation + Ability + Prompt. Increase ability before motivation. Making something easier beats making someone want it more.

### COM-B Model
Capability + Opportunity + Motivation → Behavior. For organizations, Opportunity is often the binding constraint.

### Habit Loop
Cue → Routine → Reward. Design the smallest possible routine first.

## In-Product Education Patterns

| Pattern | When | When NOT |
|---|---|---|
| Empty-state education | First visit, no data | User has data |
| Contextual tooltip | First encounter | Every visit (noise) |
| Guided walkthrough | Complex first-time workflow | Simple single actions |
| Checklist onboarding | Multiple setup steps | One-step activation |
| Feature-discovery notification | User ready for new feature | Basics not mastered |

### Principles
1. Teach in the moment of need
2. Let users skip everything
3. Show, don't tell
4. Reward completion, not initiation
5. Design for returning users too

## Accessibility-First Adoption

Checklist: keyboard-navigable, screen-reader compatible, captioned video, color-independent, readable typography, simple-language option.

When activation varies by cohort, accessibility is a mandatory diagnostic question.

## Communication Types

Pre-launch announcement, launch-day guidance, check-in prompts, milestone celebration, re-engagement (at-risk).

## Feedback Loops

Behavioral (quantitative), Sentiment (qualitative), Operational (outcome). All three must inform decisions.

## Rollout Segmentation

Start smallest, diverse within/comparable across, explicit progression criteria, pause criteria as important as go.

## When Education Isn't the Answer

Rule out: product doesn't solve a real problem, product is too hard to use, no trigger to return, environment blocks behavior.

# Discovery Brief — Product Adoption

Bounded discovery for issue #191. Maps existing skill landscape relevant to product adoption.

## Existing Material Survey

| Skill | Relationship |
|---|---|
| `go-to-market` | Upstream. Owns acquisition pipeline and PLG metrics. Product-adoption starts after user arrival. |
| `product-strategy` | Sibling. Sets direction; does not own adoption mechanics. |
| `product-methodology` | Sibling. Provides prioritization frameworks. |
| `product-discovery` | Sibling. Identifies adoption problems; does not design solutions. |
| `product-design-and-ux` | Sibling. Implements adoption flows; product-adoption defines strategy. |

## Gaps This Skill Fills

| Gap | Response |
|---|---|
| No skill distinguishes acquisition/activation/adoption/retention/expansion | `references/adoption-lifecycle-concepts.md` |
| No adoption for non-SaaS (internal tools, public services) | `references/non-saas-adoption-contexts.md` |
| No evidence-based adoption decision points | Decision table in SKILL.md; embedded in templates |
| No systematic behavior-change and education coverage | `references/behavior-change-and-education.md` |
| No cohort-based rollout segmentation | `templates/segmentation-and-rollout-record.md` |

## Ownership Boundaries

**OWNS:** Activation diagnostics, adoption strategy, rollout segmentation, behavior-change design, in-product education, accessibility-first adoption, evidence-based decision points, sustained-use review.

**Does NOT own:** Acquisition campaigns (go-to-market), analytics instrumentation (product-analytics-and-measurement), customer-success account management (conditional-customer-success), lifecycle decisions (product-lifecycle-learning), roadmap prioritization (product-roadmapping-and-portfolio).

## Non-SaaS Commitment

Explicitly supports internal tools, public services, consumer apps, and non-subscription B2B. Does not assume NPS, conversion, or MRM benchmarks are universal.

# Non-SaaS Adoption Contexts

## 1. Internal Tools

Users are colleagues, not customers. Success = workflow completion rate, time saved, error reduction, data completeness.

**Do NOT apply:** NPS, conversion/revenue metrics. **DO apply:** Workflow-completion metrics, behavior-change design, cohort segmentation by team/role.

Failure modes: shadow-process persistence, mandatory-but-resented, tool-island, gradual abandonment.

## 2. Public Services

Users are citizens/constituents. Success = completion rate, accessibility conformance, equity across cohorts.

**Do NOT apply:** Conversion/revenue metrics, digital-only assumption. **DO apply:** Multi-channel strategy (digital+phone+in-person+mail), trust-building, accessibility-first.

Failure modes: accessibility exclusion, language barrier, digital-divide, complexity abandonment, trust barrier.

## 3. Consumer (Non-Subscription)

No billing relationship. Success = return frequency, depth of use, organic referral.

**Do NOT apply:** Subscription-renewal logic. **DO apply:** Intrinsic motivation design, feature-discovery.

Failure modes: one-and-done, notification-driven-only, feature-blindness.

## 4. B2B (Non-SaaS)

Perpetual license, consulting-led, or project-based. Success = process adherence, data completeness, operational metrics.

**Do NOT apply:** SaaS self-serve dynamics. **DO apply:** Change-management communication, cohort segmentation by department.

Failure modes: shelfware, partial deployment, version-lock.

## Universal Non-SaaS Principles

1. Adoption metrics are context-specific
2. Cohort breakdowns are mandatory
3. Accessibility is adoption
4. Behavior change is universal

# Activation / Time-to-Value Diagnostic
> Use when activation trouble is signaled but root cause is unclear.

## 1. Signal
- **Observed symptom:** _[fill]_
- **Metric and source:** _[fill]_
- **Time period:** _[fill]_
- **Affected cohorts:** _[fill]_

## 2. Cohort Breakdown
| Cohort | Activation rate | TTV (median) | Trend |
|---|---|---|---|
| _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ |

## 3. Pipeline Stage Diagnosis
| Stage | Entering | Completing | Drop-off | Hypothesis |
|---|---|---|---|---|
| Arrival→First action | _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ |
| First action→Setup | _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ |
| Setup→First value | _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ |
| First value→Aha moment | _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ |

## 4. Root-Cause Hypotheses
| Rank | Hypothesis | Evidence for | Against | How to test |
|---|---|---|---|---|
| 1 | _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ |

### Categories to check
- [ ] Value proposition mismatch
- [ ] Friction in first-run
- [ ] Wrong aha moment defined
- [ ] Accessibility barrier
- [ ] Environmental blocker
- [ ] Competing habit
- [ ] Timing mismatch

## 5. Recommended Interventions
| Intervention | Hypothesis | Impact | Effort | Risk |
|---|---|---|---|---|
| _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ |

## 6. Decision
- **Conclusion:** _[fill]_
- **Decision:** _[fill: proceed / pause / escalate]_
- **Next review:** _[fill]_

## 7. Routing
| Root cause | Route to |
|---|---|
| Acquisition mismatch | go-to-market |
| Analytics wrong | product-analytics-and-measurement |
| No real problem | product-strategy (PMF) |
| Account retention | conditional-customer-success |

# Adoption Plan Template
> Fill one plan per launch or re-launch.

## 1. Context
- **Product:** _[fill]_
- **Context type:** _[fill: internal-tool / public-service / consumer / B2B]_
- **Launch scope:** _[fill]_
- **Target users:** _[fill]_
- **Adoption stage(s) in scope:** _[fill]_

## 2. Adoption Objectives
- **Primary objective:** _[fill]_
- **Secondary objectives:** _[fill]_
- **Non-goals:** _[fill]_

## 3. Cohort Segmentation
| Cohort ID | Segment | Size | Phase | Start |
|---|---|---|---|---|
| _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ |

## 4. Activation Target
- **Aha moment:** _[fill: observable event]_
- **Activation rate target:** _[fill]_
- **Time-to-value target:** _[fill]_

## 5. Rollout Strategy
### Phase Gates
| Gate | From→To | Evidence required | Decision rule |
|---|---|---|---|
| _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ |

### Pause and Stop Criteria
| Condition | Action |
|---|---|
| Activation < threshold in any cohort, 2 cycles | PAUSE; diagnose cohort |
| Activation < minimum threshold | STOP that cohort |
| Negative trend 3+ cycles | STOP; escalate to lifecycle |
| Accessibility defect blocks cohort | PAUSE until resolved |

## 6. Behavior-Change and Education Plan
| Intervention | Target behavior | Delivery | Timing |
|---|---|---|---|
| _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ |

### Education accessibility check
- [ ] Keyboard-navigable
- [ ] Screen-reader compatible
- [ ] Video captions
- [ ] Color-independent
- [ ] Plain-language alternative

## 7. Communication Plan
| Audience | Message | Channel | Timing | Owner |
|---|---|---|---|---|
| _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ |

## 8. Feedback Loops
| Loop | Method | Cadence | Owner |
|---|---|---|---|
| Behavioral | _[fill]_ | _[fill]_ | _[fill]_ |
| Sentiment | _[fill]_ | _[fill]_ | _[fill]_ |
| Operational | _[fill]_ | _[fill]_ | _[fill]_ |

## 9. Evidence and Decision Record
| Date | Review | Evidence | Decision | Rationale | Next |
|---|---|---|---|---|---|
| _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ |

## 10. Dependencies and Routing
| Dependency | Route to | Status |
|---|---|---|
| Acquisition context | go-to-market | _[fill]_ |
| Analytics instrumentation | product-analytics-and-measurement | _[fill]_ |
| Account interventions | conditional-customer-success | _[fill]_ |
| Pause/stop lifecycle record | product-lifecycle-learning | _[fill]_ |
| Roadmap impact | product-roadmapping-and-portfolio | _[fill]_ |

# Segmentation and Rollout Record
> Track cohort segmentation and phased rollout with evidence-based decision points.

## 1. Rollout Overview
- **Product/Feature:** _[fill]_
- **Launch date (first cohort):** _[fill]_
- **Total target population:** _[fill]_
- **Context type:** _[fill: internal-tool / public-service / consumer / B2B]_

## 2. Cohort Definitions
| Cohort ID | Name | Segment | Size | Rationale |
|---|---|---|---|---|
| _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ |

### Accessibility cohorts (mandatory)
| Cohort ID | Segment | Size | Notes |
|---|---|---|---|
| _[fill]_ | _[fill: Screen-reader users]_ | _[fill]_ | _[fill]_ |

## 3. Progression Criteria
| Criterion | Threshold | Method | Cadence |
|---|---|---|---|
| Activation rate | _[fill: >=50% within 14 days]_ | _[fill]_ | _[fill: weekly]_ |
| Time-to-value | _[fill: median <3 days]_ | _[fill]_ | _[fill]_ |
| Drop-off | _[fill: <20% at any step]_ | _[fill]_ | _[fill]_ |
| Accessibility | _[fill: 0 critical defects]_ | _[fill]_ | _[fill: per phase]_ |

### Pause criteria
| Condition | Action |
|---|---|
| Any cohort < threshold, 2 cycles | Pause; diagnose |
| Negative trend 3+ cycles | Pause all; diagnostic |
| Critical accessibility defect | Pause affected cohorts |
| Equity gap > 20pp | Pause; remediate |

## 4. Rollout Timeline and Evidence Log
| Date | Cohort | Phase | Activation | TTV | Decision | Rationale |
|---|---|---|---|---|---|---|
| _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ |

## 5. Cohort Comparison
| Metric | Cohort A | Cohort B | Cohort C | Equity check |
|---|---|---|---|---|
| Activation | _[fill]_ | _[fill]_ | _[fill]_ | _[fill: max gap]_ |

## 6. Decisions Record
| Date | Decision | Cohorts | Evidence | Routing |
|---|---|---|---|---|
| _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ |

## 7. Routing Log
| Date | Routed to | Evidence | Purpose |
|---|---|---|---|
| _[fill]_ | product-lifecycle-learning | _[fill]_ | Lifecycle record |
| _[fill]_ | product-roadmapping-and-portfolio | _[fill]_ | Roadmap impact |
| _[fill]_ | conditional-customer-success | _[fill]_ | Account intervention |

# Sustained-Use Review Template
> Use at 30, 60, and 90 days post-launch.

## 1. Review Context
- **Product/Feature:** _[fill]_
- **Launch date:** _[fill]_
- **Review date:** _[fill]_
- **Context type:** _[fill: internal-tool / public-service / consumer / B2B]_

## 2. Adoption Health Summary
| Metric | Baseline | Day 30 | Day 60 | Day 90 | Trend |
|---|---|---|---|---|---|
| Activation rate | _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ |
| TTV (median) | _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ |
| Day-7 retention | _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ |
| Day-30 retention | _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ |
| Feature depth | _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ |

### Context-specific metrics
| Context | Metric | Value | Trend |
|---|---|---|---|
| Internal tool | Workflow completion | _[fill]_ | _[fill]_ |
| Public service | Equity gap | _[fill]_ | _[fill]_ |
| Consumer | Return frequency | _[fill]_ | _[fill]_ |
| B2B | Process adherence | _[fill]_ | _[fill]_ |

## 3. Cohort Breakdown
| Cohort | Activation | Retention D30 | Feature depth | Concern |
|---|---|---|---|---|
| _[fill]_ | _[fill]_ | _[fill]_ | _[fill]_ | _[fill: healthy/watch/at-risk]_ |

## 4. Adoption-Stage Assessment
| Stage | Health | Evidence | Concern |
|---|---|---|---|
| Activation | _[fill]_ | _[fill]_ | _[fill]_ |
| Adoption (habit) | _[fill]_ | _[fill]_ | _[fill]_ |
| Retention | _[fill]_ | _[fill]_ | _[fill]_ |
| Expansion | _[fill]_ | _[fill]_ | _[fill]_ |

## 5. Decision
| Option | Criteria |
|---|---|
| Continue | All healthy, stable, no equity gaps |
| Continue with adjustments | Mostly healthy; specific adjustments |
| Pause expansion | Cohort(s) below threshold |
| Narrow scope | Structural barriers; narrow to successful |
| Stop | Sustained below-threshold, 2+ attempts failed |

- **Decision:** _[fill]_
- **Evidence basis:** _[fill]_

### If PAUSE or STOP:
| Action | Owner | Timeline |
|---|---|---|
| Initiate diagnostic | _[fill]_ | _[fill]_ |
| Route to lifecycle-learning | _[fill]_ | _[fill]_ |
| Notify roadmapping | _[fill]_ | _[fill]_ |

## 6. Routing
| Output | Route to | Purpose |
|---|---|---|
| Review record | product-lifecycle-learning | Lifecycle record |
| Pause/stop evidence | product-roadmapping-and-portfolio | Roadmap impact |
| Adoption signals | conditional-customer-success | Account intervention |
| Discovery gaps | product-adoption (return) | Redesign |

## 7. Next Review
- **Next review date:** _[fill]_
- **Key questions:** _[fill]_