Revgrowth1 Campaign Performance Analysis
Scientific method applied to cold email campaigns. Isolate variables, measure outcomes, generate actionable recommendations.
The 5 Core Variables
| Variable | Measures | Key Question |
|---|---|---|
| Offer | Value proposition | Compelling? Solves real pain? |
| Message | Copy, subject, CTAs | Clear? Tone matches audience? |
| Segment | Who you target | Right ICP? Accurate titles? |
| Infrastructure | Email setup (Google/MS, domains, warmup) | Landing in inbox? Reputation? |
| Timing | Send times, cadence | When are opens/replies happening? |
Analysis Phases
Phase 1: Hypothesis
State expectations before analyzing (e.g., "Google infra should outperform Microsoft").
Phase 2: Data Collection
Pull 7-14 day period from your email platform. Segment by workspace, campaign name, infrastructure type. Calculate: Reply Rate, Interested Rate, Bounce Rate.
Phase 3: Analysis
Apply: Pareto (which 20% drive 80% of results?), Statistical Significance (min 500 sent, 7+ days; small samples = "TEST MORE"), Cohort Analysis (compare similar periods), Attribution (map results to 5 variables).
Phase 4: Recommendations
Prioritize: SCALE proven winners -> PAUSE underperformers -> TEST promising signals -> ITERATE specific changes.
Report Structure (6 Sections)
1. Quick Health Check
| Metric | Benchmarks |
|---|---|
| Reply Rate | >1% Healthy, 0.5-1% Attention, <0.5% Critical |
| Interested Rate | >25% of replies = Healthy |
| Bounce Rate | <3% Healthy, 3-5% Attention, >5% Critical |
2. Segment Performance Ranking
Rank all campaigns by Interested Rate.
| Verdict | Criteria |
|---|---|
| TOP PERFORMER | Highest interested rate, 500+ sent |
| SCALE | >1% reply, >25% interested |
| TEST MORE | <500 sent, promising signals |
| MONITOR | Average, needs more data |
| UNDERPERFORM | <0.5% reply OR <10% interested with 1000+ sent |
3. Infrastructure Analysis
Compare Google vs Microsoft. Key: is there 2x+ difference? If so, shift volume to winner.
4. Reply Sentiment Analysis
High replies + low interested = wrong audience. Low replies + high interested = good targeting, need volume.
5. Attribution Analysis
For top/bottom performers, identify root cause mapped to 5 variables.
6. Next Iteration Recommendations
Prioritized action table: Priority, Action, Variable, Expected Impact, Effort.
Repeatable Checklist
- Volume: 7+ days? 500+ per segment? External factors?
- Performance: >1% reply? >25% interested? Any 0% despite volume?
- Infrastructure: Google vs MS winner? Bounce spikes >5%? Domain issues?
- Attribution: What drives winners? What blocks losers? Apples-to-apples comparison?
- Next: What to scale/pause/test?
Output Format
Spreadsheet (preferred): [Client] Campaign Analysis - [Date], 6 sections, conditional formatting.
Quick Summary (Slack/chat): Winners/Losers with rates + verdicts, key insight, next actions.
Post-Analysis: Capture Learnings
After completing the analysis, always suggest the debrief step:
Analysis complete. To capture these learnings so they compound into future campaigns, run the campaign debrief workflow.
This will walk through a 5-question structured debrief and append findings to
learnings.md.
The debrief is what closes the loop - without it, analysis insights evaporate into spreadsheets and chat. The debrief writes structured entries that feed:
- Campaign ideation - Pattern Match scoring for future campaigns
- Cross-client pattern recognition
- Monthly intelligence layer updates
Integration Map
Reads From
| Source | What It Provides |
|---|---|
| Email platform API | Raw campaign data (sent, replies, interested, bounces) |
Client context.md |
Campaign strategy, ICP for attribution context |
Feeds Into
| Destination | How It Uses Analysis Output |
|---|---|
./workflows/12-campaign-debrief/ |
Analysis results flow directly into structured learning capture |
| Campaign ideation | Verdicts (SCALE/PAUSE/TEST) inform next campaign ideas |
| Campaign creation | Recommendations guide next iteration setup |