Issue Trees

Full Markdown demo

# Issue Tree: Activation Drop Diagnosis

## Input Summary
Signups have stayed roughly flat over the past 8 weeks, but activation fell from 41% to 28%. Sales blames lead quality, product blames longer onboarding, support says users do not understand permissions, and data shows mobile signup share is rising. Use an issue tree to structure the problem, prioritize branches, list data needs, and create a 7-day analysis plan.

## Classic Case Context
Signups have stayed roughly flat over the past 8 weeks, but activation fell from 41% to 28%. Sales blames lead quality, product blames longer onboarding, support says users do not understand permissions, and data shows mobile signup share is rising. Use an issue tree to structure the problem, prioritize branches, list data needs, and create a 7-day analysis plan.

## Skill Used
- Issue Trees
- Break a broad problem into structured branches that can be tested.
- Best for: Growth stalls, cost increases, churn, and broad business diagnostics.
- Can generate: Issue tree, top branches to validate, data needs, and analysis plan.

## Situation Judgment
Signups are stable, but activation fell from 41% to 28%, and the team has scattered theories.

## Executive Summary
Prioritize the combined hypothesis: mobile share increased, permissions are confusing, and onboarding got longer. Lead quality may matter, but should not be the first branch.

## Framework Analysis
| Branch | Sub-question | Priority | Evidence |
| --- | --- | --- | --- |
| Traffic mix | Device or channel changed | High | Channel cohorts, device activation |
| Product path | Onboarding became longer or blocked | High | Step conversion, recordings, event funnel |
| Value clarity | Permissions/team model unclear | High | Tickets, interviews, help searches |
| Lead quality | Sales sources changed | Medium | Source quality, ICP fit |

## Reusable Diagram
This is a Markdown-only output. Switch to diagram or PDF-ready output to generate Mermaid.

## Recommendation
Within 48 hours, pull device, channel, and step-level funnels to locate the drop by cohort. Then interview 5 users to validate the permissions-confusion branch.

## Risks And Unknowns
- Blended activation can mix channel shifts with product friction.
- Lead quality may matter, but it must be separated from device and channel effects.

## Next Actions
1. Pull 8 weeks of cohort funnels.
2. Compare mobile and desktop drop-offs step by step.
3. Review permission-related support tickets and interview 5 users.

Full Markdown demo

# Issue Tree: Activation Drop Diagnosis

## Input Summary
Signups have stayed roughly flat over the past 8 weeks, but activation fell from 41% to 28%. Sales blames lead quality, product blames longer onboarding, support says users do not understand permissions, and data shows mobile signup share is rising. Use an issue tree to structure the problem, prioritize branches, list data needs, and create a 7-day analysis plan.

## Classic Case Context
Signups have stayed roughly flat over the past 8 weeks, but activation fell from 41% to 28%. Sales blames lead quality, product blames longer onboarding, support says users do not understand permissions, and data shows mobile signup share is rising. Use an issue tree to structure the problem, prioritize branches, list data needs, and create a 7-day analysis plan.

## Skill Used
- Issue Trees
- Break a broad problem into structured branches that can be tested.
- Best for: Growth stalls, cost increases, churn, and broad business diagnostics.
- Can generate: Issue tree, top branches to validate, data needs, and analysis plan.

## Situation Judgment
Signups are stable, but activation fell from 41% to 28%, and the team has scattered theories.

## Executive Summary
Prioritize the combined hypothesis: mobile share increased, permissions are confusing, and onboarding got longer. Lead quality may matter, but should not be the first branch.

## Framework Analysis
| Branch | Sub-question | Priority | Evidence |
| --- | --- | --- | --- |
| Traffic mix | Device or channel changed | High | Channel cohorts, device activation |
| Product path | Onboarding became longer or blocked | High | Step conversion, recordings, event funnel |
| Value clarity | Permissions/team model unclear | High | Tickets, interviews, help searches |
| Lead quality | Sales sources changed | Medium | Source quality, ICP fit |

## Reusable Diagram
```mermaid
flowchart TD
  A["Activation fell 41% to 28%"] --> B["Traffic mix changed?"]
  A --> C["Onboarding path changed?"]
  A --> D["Value or permissions unclear?"]
  A --> E["Lead quality changed?"]
  B --> B1["Mobile share up"]
  B --> B2["Channel cohort shift"]
  C --> C1["More steps"]
  C --> C2["Drop-off after workspace setup"]
  D --> D1["Permission model confusion"]
  D --> D2["Support tickets"]
  E --> E1["ICP mismatch"]
  E --> E2["Sales source mix"]
```

## Recommendation
Within 48 hours, pull device, channel, and step-level funnels to locate the drop by cohort. Then interview 5 users to validate the permissions-confusion branch.

## Risks And Unknowns
- Blended activation can mix channel shifts with product friction.
- Lead quality may matter, but it must be separated from device and channel effects.

## Next Actions
1. Pull 8 weeks of cohort funnels.
2. Compare mobile and desktop drop-offs step by step.
3. Review permission-related support tickets and interview 5 users.

Mermaid demo

flowchart TD
  A["Activation fell 41% to 28%"] --> B["Traffic mix changed?"]
  A --> C["Onboarding path changed?"]
  A --> D["Value or permissions unclear?"]
  A --> E["Lead quality changed?"]
  B --> B1["Mobile share up"]
  B --> B2["Channel cohort shift"]
  C --> C1["More steps"]
  C --> C2["Drop-off after workspace setup"]
  D --> D1["Permission model confusion"]
  D --> D2["Support tickets"]
  E --> E1["ICP mismatch"]
  E --> E2["Sales source mix"]

Full Markdown demo

# Issue Tree: Activation Drop Diagnosis

## Input Summary
Signups have stayed roughly flat over the past 8 weeks, but activation fell from 41% to 28%. Sales blames lead quality, product blames longer onboarding, support says users do not understand permissions, and data shows mobile signup share is rising. Use an issue tree to structure the problem, prioritize branches, list data needs, and create a 7-day analysis plan.

## Classic Case Context
Signups have stayed roughly flat over the past 8 weeks, but activation fell from 41% to 28%. Sales blames lead quality, product blames longer onboarding, support says users do not understand permissions, and data shows mobile signup share is rising. Use an issue tree to structure the problem, prioritize branches, list data needs, and create a 7-day analysis plan.

## Skill Used
- Issue Trees
- Break a broad problem into structured branches that can be tested.
- Best for: Growth stalls, cost increases, churn, and broad business diagnostics.
- Can generate: Issue tree, top branches to validate, data needs, and analysis plan.

## Situation Judgment
Signups are stable, but activation fell from 41% to 28%, and the team has scattered theories.

## Executive Summary
Prioritize the combined hypothesis: mobile share increased, permissions are confusing, and onboarding got longer. Lead quality may matter, but should not be the first branch.

## Framework Analysis
| Branch | Sub-question | Priority | Evidence |
| --- | --- | --- | --- |
| Traffic mix | Device or channel changed | High | Channel cohorts, device activation |
| Product path | Onboarding became longer or blocked | High | Step conversion, recordings, event funnel |
| Value clarity | Permissions/team model unclear | High | Tickets, interviews, help searches |
| Lead quality | Sales sources changed | Medium | Source quality, ICP fit |

## Reusable Diagram
```mermaid
flowchart TD
  A["Activation fell 41% to 28%"] --> B["Traffic mix changed?"]
  A --> C["Onboarding path changed?"]
  A --> D["Value or permissions unclear?"]
  A --> E["Lead quality changed?"]
  B --> B1["Mobile share up"]
  B --> B2["Channel cohort shift"]
  C --> C1["More steps"]
  C --> C2["Drop-off after workspace setup"]
  D --> D1["Permission model confusion"]
  D --> D2["Support tickets"]
  E --> E1["ICP mismatch"]
  E --> E2["Sales source mix"]
```

## Recommendation
Within 48 hours, pull device, channel, and step-level funnels to locate the drop by cohort. Then interview 5 users to validate the permissions-confusion branch.

## Risks And Unknowns
- Blended activation can mix channel shifts with product friction.
- Lead quality may matter, but it must be separated from device and channel effects.

## Next Actions
1. Pull 8 weeks of cohort funnels.
2. Compare mobile and desktop drop-offs step by step.
3. Review permission-related support tickets and interview 5 users.

Mermaid demo

flowchart TD
  A["Activation fell 41% to 28%"] --> B["Traffic mix changed?"]
  A --> C["Onboarding path changed?"]
  A --> D["Value or permissions unclear?"]
  A --> E["Lead quality changed?"]
  B --> B1["Mobile share up"]
  B --> B2["Channel cohort shift"]
  C --> C1["More steps"]
  C --> C2["Drop-off after workspace setup"]
  D --> D1["Permission model confusion"]
  D --> D2["Support tickets"]
  E --> E1["ICP mismatch"]
  E --> E2["Sales source mix"]

PDF-ready HTML demo

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  <title>Issue Tree: Activation Drop Diagnosis</title>
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    h1 { font-size: 34px; line-height: 1.1; margin: 0 0 18px; }
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    <p class="meta">ThinkOps AI PDF-ready output</p>
    <h1>Issue Tree: Activation Drop Diagnosis</h1>
    <pre># Issue Tree: Activation Drop Diagnosis

## Input Summary
Signups have stayed roughly flat over the past 8 weeks, but activation fell from 41% to 28%. Sales blames lead quality, product blames longer onboarding, support says users do not understand permissions, and data shows mobile signup share is rising. Use an issue tree to structure the problem, prioritize branches, list data needs, and create a 7-day analysis plan.

## Classic Case Context
Signups have stayed roughly flat over the past 8 weeks, but activation fell from 41% to 28%. Sales blames lead quality, product blames longer onboarding, support says users do not understand permissions, and data shows mobile signup share is rising. Use an issue tree to structure the problem, prioritize branches, list data needs, and create a 7-day analysis plan.

## Skill Used
- Issue Trees
- Break a broad problem into structured branches that can be tested.
- Best for: Growth stalls, cost increases, churn, and broad business diagnostics.
- Can generate: Issue tree, top branches to validate, data needs, and analysis plan.

## Situation Judgment
Signups are stable, but activation fell from 41% to 28%, and the team has scattered theories.

## Executive Summary
Prioritize the combined hypothesis: mobile share increased, permissions are confusing, and onboarding got longer. Lead quality may matter, but should not be the first branch.

## Framework Analysis
| Branch | Sub-question | Priority | Evidence |
| --- | --- | --- | --- |
| Traffic mix | Device or channel changed | High | Channel cohorts, device activation |
| Product path | Onboarding became longer or blocked | High | Step conversion, recordings, event funnel |
| Value clarity | Permissions/team model unclear | High | Tickets, interviews, help searches |
| Lead quality | Sales sources changed | Medium | Source quality, ICP fit |

## Reusable Diagram
```mermaid
flowchart TD
  A["Activation fell 41% to 28%"] --&gt; B["Traffic mix changed?"]
  A --&gt; C["Onboarding path changed?"]
  A --&gt; D["Value or permissions unclear?"]
  A --&gt; E["Lead quality changed?"]
  B --&gt; B1["Mobile share up"]
  B --&gt; B2["Channel cohort shift"]
  C --&gt; C1["More steps"]
  C --&gt; C2["Drop-off after workspace setup"]
  D --&gt; D1["Permission model confusion"]
  D --&gt; D2["Support tickets"]
  E --&gt; E1["ICP mismatch"]
  E --&gt; E2["Sales source mix"]
```

## Recommendation
Within 48 hours, pull device, channel, and step-level funnels to locate the drop by cohort. Then interview 5 users to validate the permissions-confusion branch.

## Risks And Unknowns
- Blended activation can mix channel shifts with product friction.
- Lead quality may matter, but it must be separated from device and channel effects.

## Next Actions
1. Pull 8 weeks of cohort funnels.
2. Compare mobile and desktop drop-offs step by step.
3. Review permission-related support tickets and interview 5 users.
</pre>
    <h2>Mermaid diagram source</h2><pre>flowchart TD
  A["Activation fell 41% to 28%"] --&gt; B["Traffic mix changed?"]
  A --&gt; C["Onboarding path changed?"]
  A --&gt; D["Value or permissions unclear?"]
  A --&gt; E["Lead quality changed?"]
  B --&gt; B1["Mobile share up"]
  B --&gt; B2["Channel cohort shift"]
  C --&gt; C1["More steps"]
  C --&gt; C2["Drop-off after workspace setup"]
  D --&gt; D1["Permission model confusion"]
  D --&gt; D2["Support tickets"]
  E --&gt; E1["ICP mismatch"]
  E --&gt; E2["Sales source mix"]</pre>
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