Performance Analysis

Campaign Performance Analysis

When to Use

• Reviewing campaign results after a send cycle completes
• Diagnosing why a campaign is underperforming
• Comparing campaign variants to identify winners
• Building performance reports for clients or leadership
• Making data-driven decisions about what to scale, pause, or kill

Framework

The Campaign Metrics Stack

Measure campaigns in layers. Each layer tells you something different about what's working and what isn't.

Layer 1: Deliverability     → Did the email reach the inbox?
Layer 2: Engagement         → Did they open and read it?
Layer 3: Response           → Did they reply?
Layer 4: Quality            → Was the reply positive?
Layer 5: Conversion         → Did the reply turn into a meeting?
Layer 6: Revenue            → Did the meeting turn into revenue?

Critical insight: Most teams obsess over Layer 2-3 (opens and replies) while ignoring Layer 1 (deliverability) and Layer 4-6 (quality and revenue). The full stack matters.

Layer 1: Deliverability Metrics

If deliverability metrics are off, STOP. Do not optimize copy, subject lines, or targeting until deliverability is fixed. You're optimizing something that never reaches the prospect.

Deliverability Diagnostic

Bounce rate > 5%?
├── YES → Verify email list. Switch to verified-only sends.
└── NO ↓

Inbox placement < 85%?
├── YES → Check domain reputation (Google Postmaster, MXToolbox)
│         → Reduce daily send volume
│         → Rotate sending domains
│         → Review email content for spam triggers
└── NO ↓

Spam complaints > 0?
├── YES → Review targeting. Are you emailing the right people?
│         → Check for spam trigger words in subject/body
│         → Ensure unsubscribe link is visible
└── NO → Deliverability is healthy. Move to Layer 2.

Layer 2: Engagement Metrics

Note on open tracking: Open tracking uses invisible pixels that can hurt deliverability. If you're tracking opens, use it for diagnostic purposes but consider turning it off for production campaigns. Low open rate with high reply rate = your emails are landing but the pixel isn't loading (common with Outlook).

Subject Line Analysis

Minimum sample size: 200 sends per variant before drawing conclusions. Below 200, the variance is too high to trust.

Layer 3: Response Metrics

Reply Rate Diagnostic

Reply rate < 5% overall?
├── Open rate > 60%?
│   ├── YES → Copy problem. Emails are being read but not compelling.
│   │         → Test new body copy variants
│   │         → Test new CTAs
│   │         → Review personalization quality
│   └── NO  → Subject line or deliverability problem.
│             → Fix opens first, then reassess replies.
│
Reply rate > 5% but meetings are low?
├── Most replies are negative ("not interested", "remove me")?
│   ├── YES → Targeting problem. You're reaching the wrong people.
│   │         → Review ICP and list quality
│   └── NO  → Qualification problem. Interested leads aren't converting.
│             → Review your meeting booking process
│             → Improve reply handling speed

Layer 4: Quality Metrics (Most Important)

Reply Classification Framework

Every reply should be classified:

The ratio that matters most:

Positive Reply Rate = (Positive replies ÷ Total emails sent) × 100

This single metric is the truest measure of campaign effectiveness. It strips out noise (opens, negative replies) and measures real demand generation.

Layer 5: Conversion Metrics

Layer 6: Revenue Metrics

The Performance Analysis Report

Campaign Summary Card

CAMPAIGN: {{campaign_name}}
Period: {{start_date}} — {{end_date}}
Total sends: {{total_sends}}

DELIVERABILITY
  Bounce rate: __% (target: <3%)
  Spam complaints: __ (target: 0)

ENGAGEMENT
  Open rate: __% (target: 60-80%)

RESPONSE
  Total reply rate: __% (target: 8-15%)
  Positive reply rate: __% (target: 2-8%)
  Negative reply rate: __%

CONVERSION
  Meetings booked: __
  Reply-to-meeting rate: __%
  Cost per meeting: $__

REVENUE
  Pipeline generated: $__
  Revenue closed: $__
  ROI: __x

Variant Comparison Table

| Metric | Variant A | Variant B | Variant C | Winner |
|--------|-----------|-----------|-----------|--------|
| Sends | __ | __ | __ | — |
| Open rate | __% | __% | __% | __ |
| Reply rate | __% | __% | __% | __ |
| Positive reply rate | __% | __% | __% | __ |
| Meetings | __ | __ | __ | __ |

Step Performance Breakdown

| Step | Sends | Opens | Open Rate | Replies | Reply Rate | Positive | Positive Rate |
|------|-------|-------|-----------|---------|------------|----------|---------------|
| 1 | __ | __ | __% | __ | __% | __ | __% |
| 2 | __ | __ | __% | __ | __% | __ | __% |
| 3 | __ | __ | __% | __ | __% | __ | __% |
| 4 | __ | __ | __% | __ | __% | __ | __% |

Decision Framework: Scale, Optimize, or Kill

Templates

Weekly Campaign Report Template

# Weekly Campaign Report
# Period: {{week}}

## Summary
- Active campaigns: __
- Total sends this week: __
- Avg positive reply rate: __%
- Meetings booked: __

## Top Performing Campaign
{{campaign_name}} — {{positive_reply_rate}}% positive reply rate

## Underperforming (Action Needed)
{{campaign_name}} — {{issue}} → {{recommended_action}}

## Key Decisions
- Scale: {{campaign_to_scale}}
- Optimize: {{campaign_to_optimize}}
- Kill: {{campaign_to_kill}}

## Next Week Plan
- {{action_1}}
- {{action_2}}

Tips

• Positive reply rate is the only metric that matters for campaign-level decisions. Everything else (opens, total replies) is diagnostic — useful for understanding WHY, but not for deciding WHAT to do.
• Always compare apples to apples. A campaign targeting VPs at enterprise companies will have different benchmarks than one targeting founders at SMBs. Segment your analysis.
• The most common analysis mistake: drawing conclusions from too-small samples. 50 sends with a 10% reply rate is not a winning campaign — it's noise. Wait for 200+ sends per variant.
• Track metrics over time, not just snapshots. A campaign with a 5% reply rate in week 1 and 2% in week 4 is degrading — the list might be exhausted or deliverability is dropping.
• Don't average across campaigns. One campaign with a 15% positive reply rate and one with 0.5% averages to 7.75% — but that hides the fact that one is amazing and one is terrible.

Progressive disclosure: load platform-specific analytics integrations and automated reporting configs only when analyzing data from a specific sending platform.