Campaign Debrief

Campaign Debrief

Structured 5-question learning capture that closes the loop between campaign execution and campaign intelligence. Every debrief appends to the client's learnings.md and flags transferable insights for the cross-client intelligence layer.

This is the keystone of the intelligence engine. If debriefs don't happen, nothing compounds.

When to Use

• After performance analysis completes (you'll be prompted)
• Standalone: run debrief for any campaign with results
• Retroactively: capture learnings from past campaigns that were never debriefed

Workflow

Step 1 - Load Context

Read from ./clients/{client}/:

If learnings.md doesn't exist, create it from the template structure (see Output Format below).

Step 2 - Identify the Campaign

Ask or confirm:

Which campaign are we debriefing?

Campaign name: {name}
Client: {client}
Date range: {start} - {end}

Data sources (try in order):

1. If analysis was just completed - use those results directly
2. Pull from email platform API if workspace is known
3. Accept manual input from the user (sent count, reply rate, interested rate, meetings)

Display a quick stats summary before proceeding:

## Campaign Stats: {name}

| Metric | Value |
|--------|-------|
| Sent | {n} |
| Reply Rate | {x}% |
| Interested Rate | {y}% |
| E2L Ratio | 1:{n} |
| Meetings Booked | {n} |
| Bounce Rate | {z}% |

Step 3 - 5-Question Debrief

Walk through each question. For each, offer a suggested answer based on available data and let the user confirm or override.

Q1: What hypothesis did we test?

What did we expect to happen and why? What variable(s) were we testing?

Format: "We hypothesized that {angle/segment/timing} would {expected outcome} because {reasoning}."

Q2: What was the result?

Confirmed, partially confirmed, or rejected? One-line summary with the key metric.

Verdict	Criteria
CONFIRMED	Hypothesis validated - metrics met or exceeded expectations
PARTIAL	Directionally correct but below threshold, or worked for subset only
REJECTED	Hypothesis invalidated - metrics clearly below expectations

Q3: What worked and what didn't?

Separate the signal from the noise. What specific elements (angle, segment, timing, copy, infrastructure) drove results - positive and negative?

Q4: What surprised us?

Unexpected findings - positive or negative. These are often the most valuable learnings.

Q5: What's transferable?

Could this insight help another client? Is this vertical-specific, persona-specific, or a universal pattern?

If yes -> tag it and flag for intelligence layer update.

Step 4 - Determine Verdict

Based on the debrief, assign a campaign verdict:

Step 5 - Write the Entry

Append the structured entry to ./clients/{client}/learnings.md.

IMPORTANT: Always append. Never overwrite existing entries.

Step 6 - Tag and Flag

Apply tags from the entry:

• #vertical/{vertical} - e.g., #vertical/saas, #vertical/healthcare
• #persona/{persona} - e.g., #persona/vp-engineering, #persona/cfo-finance
• #angle/{angle} - e.g., #angle/operational-efficiency, #angle/competitive-displacement

If anything was marked transferable in Q5:

Transferable insight flagged. Update the relevant intelligence file:

• intelligence/verticals/{vertical}.md
• intelligence/personas/{persona}.md
• intelligence/angles/{angle}.md

Debrief Entry Format

This is the exact format appended to learnings.md:

---

### Campaign: {name} ({YYYY-MM-DD})
**Sent:** {n} | **Reply:** {x}% | **Interested:** {y}% | **E2L Ratio:** 1:{n} | **Meetings:** {n}
**Verdict:** SCALE / ITERATE / PAUSE / KILL

**Hypothesis:** {what we expected and why}
**Result:** {confirmed|partial|rejected} - {one-line summary}

**What Worked:**
- {angle/segment/timing} - {why it worked}

**What Didn't Work:**
- {angle/segment/timing} - {why it failed}

**Surprise:** {unexpected finding}
**Transferable:** {insight for other clients, or "None - client-specific"}
**Tags:** `#vertical/{v}` `#persona/{p}` `#angle/{a}`

Learnings.md Template

If a client has no learnings.md, create one with this structure before appending:

# Client Learnings: {Client Name}

> Cumulative learnings from all campaigns for this client.

---

## Summary Stats

| Metric | Value |
|--------|-------|
| Total Campaigns | 0 |
| Best Performer | N/A |
| Average Reply Rate | N/A |

---

## What Works

### Segments That Convert
- TBD

### Angles That Resonate
- TBD

---

## What Doesn't Work

### Segments to Avoid
- TBD

### Known Pitfalls
- TBD

---

## Campaign Log

*No campaigns logged yet*

---

## Notes

*Add ongoing observations and hypotheses to test*

---

*Last Updated: {month} {year}*

Design Constraints

• < 5 minutes to complete - suggest answers from data, user confirms or overrides
• Append-only - never rewrite history in learnings.md
• Tags are lowercase, hyphenated - #vertical/saas not #vertical/SaaS
• Verdicts are objective - based on metrics and thresholds, not feelings
• Data-first - pull from email platform when possible, fall back to manual input

Integration Map

Reads From

Feeds Into

Examples

Example 1: Post-analysis debrief

User: Run campaign debrief for Client C

Claude: Loading Client C context...
  > learnings.md - 10+ campaigns logged
  > context.md - Active campaigns found
  > profile.md - Data infrastructure vertical

Which campaign are we debriefing?
  Campaign name: Client C Google + Variant A v2
  Date range: 2025-11-15 - 2025-12-01

## Campaign Stats
| Metric | Value |
|--------|-------|
| Sent | 1,984 |
| Reply Rate | 5.7% |
| Interested Rate | 0.4% |
| E2L Ratio | 1:283 |
| Meetings | 3 |

[Walks through 5 questions with suggested answers from data]
[Appends entry to learnings.md]
[Tags: #vertical/data-infrastructure #persona/devops #angle/operational-efficiency]

Transferable insight flagged: Google infrastructure consistently
outperforms Microsoft across all segments (5.7% vs 0.4% reply).
This may apply to other clients - check intelligence/patterns.md.

Example 2: Retroactive debrief (no prior analysis)

User: Run campaign debrief for Example Client

Claude: Loading Example Client context...
  > learnings.md - 8+ campaigns logged
  > context.md - Campaign focus found

Which campaign are we debriefing?
  [User provides: Campaign v1, manual stats]

[Walks through 5 questions]
[Appends entry to learnings.md]

Related

• ./workflows/11-performance-analysis/ - Analyze performance (precedes debrief)
• Campaign ideation - Generate next campaign ideas (reads debrief output)
• Intelligence query - Query cross-client patterns
• Intelligence synthesis - Monthly pattern recognition across all debriefs