Campaign Analytics

Analyze campaign performance with comprehensive metrics and insights to optimize your email marketing.

Overview

Campaign analytics allow you to:

Track email delivery and engagement
Measure open and click-through rates
Identify top-performing content and links
Compare campaign performance over time
Export data for deeper analysis
Make data-driven decisions

Understanding your campaign metrics is essential for improving email marketing effectiveness.

Quick Start

View Campaign Report

$ cakemail reports campaign 790

Output:

Campaign: March Newsletter (ID: 790)
Status: sent
Sent: 2024-03-15 10:00:00

=== Delivery Metrics ===
Total Recipients:   1,247
Delivered:          1,189 (95.3%)
Bounced:            58 (4.7%)
  Hard Bounces:     45
  Soft Bounces:     13

=== Engagement Metrics ===
Opens:              723 (60.8%)
Unique Opens:       567 (47.7%)
Clicks:             234 (19.7%)
Unique Clicks:      189 (15.9%)
Unsubscribes:       12 (1.0%)

=== Performance ===
Open Rate:          47.7%
Click Rate:         15.9%
Click-to-Open:      33.3%
Bounce Rate:        4.7%
Unsubscribe Rate:   1.0%

Core Metrics

Delivery Metrics

Total Recipients:

Number of contacts campaign was sent to
Includes all contacts in target list/segment

Delivered:

Successfully delivered emails
Excludes hard and soft bounces
Formula: Total Recipients - Bounces

Bounced:

Emails that couldn't be delivered
Hard Bounce: Permanent failure (invalid email)
Soft Bounce: Temporary failure (inbox full, server down)

# View detailed bounce information
$ cakemail reports campaign 790 -f json | jq '{
  total: .total_recipients,
  delivered: .delivered,
  bounced: .bounced,
  hard_bounces: .hard_bounces,
  soft_bounces: .soft_bounces,
  delivery_rate: (.delivered / .total_recipients * 100 | round)
}'

Output:

{
  "total": 1247,
  "delivered": 1189,
  "bounced": 58,
  "hard_bounces": 45,
  "soft_bounces": 13,
  "delivery_rate": 95
}

Engagement Metrics

Opens:

Total times campaign was opened
Includes multiple opens by same contact
Tracked via invisible pixel

Unique Opens:

Number of unique contacts who opened
Each contact counted only once
More accurate engagement indicator

Open Rate:

Percentage of delivered emails that were opened
Formula: (Unique Opens / Delivered) × 100
Industry average: 15-25%

# Calculate open rate
$ cakemail reports campaign 790 -f json | jq '{
  delivered: .delivered,
  unique_opens: .unique_opens,
  open_rate: (.unique_opens / .delivered * 100 | round)
}'

Clicks:

Total link clicks in campaign
Includes multiple clicks by same contact

Unique Clicks:

Number of unique contacts who clicked
Each contact counted only once

Click Rate (CTR):

Percentage of delivered emails with clicks
Formula: (Unique Clicks / Delivered) × 100
Industry average: 2-5%

Click-to-Open Rate (CTOR):

Percentage of openers who clicked
Formula: (Unique Clicks / Unique Opens) × 100
Measures content effectiveness
Industry average: 10-20%

# View click metrics
$ cakemail reports campaign 790 -f json | jq '{
  unique_opens: .unique_opens,
  unique_clicks: .unique_clicks,
  click_rate: (.unique_clicks / .delivered * 100 | round),
  ctor: (.unique_clicks / .unique_opens * 100 | round)
}'

Action Metrics

Unsubscribes:

Contacts who unsubscribed after this campaign
Important metric for content relevance

Unsubscribe Rate:

Percentage of delivered emails that unsubscribed
Formula: (Unsubscribes / Delivered) × 100
Healthy rate: < 0.5%
Concerning rate: > 1%

Spam Complaints:

Contacts who marked email as spam
Critical metric for sender reputation
Target: < 0.1%

# View action metrics
$ cakemail reports campaign 790 -f json | jq '{
  unsubscribes: .unsubscribes,
  unsubscribe_rate: (.unsubscribes / .delivered * 100 | round),
  spam_complaints: .spam_complaints,
  spam_rate: (.spam_complaints / .delivered * 100 | round)
}'

Viewing Campaign Analytics

Basic Campaign Report

$ cakemail reports campaign 790

JSON Format for Processing

$ cakemail reports campaign 790 -f json > campaign-790.json

Extract Specific Metrics

# Open rate only
$ cakemail reports campaign 790 -f json | jq '.open_rate'

# Top metrics summary
$ cakemail reports campaign 790 -f json | jq '{
  name: .campaign_name,
  delivered: .delivered,
  open_rate: .open_rate,
  click_rate: .click_rate,
  unsubscribe_rate: .unsubscribe_rate
}'

Multiple Campaign Reports

#!/bin/bash
# report-multiple-campaigns.sh

CAMPAIGNS=(790 791 792)

echo "Campaign | Delivered | Open Rate | Click Rate"
echo "---------|-----------|-----------|------------"

for ID in "${CAMPAIGNS[@]}"; do
  REPORT=$(cakemail reports campaign $ID -f json)

  NAME=$(echo "$REPORT" | jq -r '.campaign_name' | cut -c1-15)
  DELIVERED=$(echo "$REPORT" | jq -r '.delivered')
  OPEN=$(echo "$REPORT" | jq -r '.open_rate')
  CLICK=$(echo "$REPORT" | jq -r '.click_rate')

  printf "%-15s | %9d | %8.1f%% | %9.1f%%\n" "$NAME" $DELIVERED $OPEN $CLICK
done

Link Analytics

View Campaign Links

$ cakemail reports campaign-links 790

Output:

┌────────────────────────────────────────┬────────┬─────────┬─────────┐
│ URL                                    │ Clicks │ Unique  │ CTR     │
├────────────────────────────────────────┼────────┼─────────┼─────────┤
│ https://example.com/product           │ 450    │ 320     │ 26.9%   │
│ https://example.com/blog/article      │ 230    │ 180     │ 15.1%   │
│ https://example.com/special-offer     │ 180    │ 150     │ 12.6%   │
└────────────────────────────────────────┴────────┴─────────┴─────────┘

Total clicks: 860 (650 unique)
Overall CTR: 54.7%

Export Link Data

$ cakemail reports campaign-links 790 -f json > links-790.json

Time-Based Analytics

Opens Over Time

#!/bin/bash
# opens-over-time.sh

CAMPAIGN_ID=$1

echo "=== Opens Timeline ==="
echo ""

# Get campaign details
CAMPAIGN=$(cakemail campaigns get $CAMPAIGN_ID -f json)
SENT_DATE=$(echo "$CAMPAIGN" | jq -r '.delivered_at')

echo "Campaign sent: $SENT_DATE"
echo ""

# Get current stats
STATS=$(cakemail reports campaign $CAMPAIGN_ID -f json)

echo "Current stats:"
echo "  Unique Opens: $(echo "$STATS" | jq -r '.unique_opens')"
echo "  Open Rate: $(echo "$STATS" | jq -r '.open_rate')%"
echo ""

echo "Typical open timeline:"
echo "  First 24 hours: 50-70% of total opens"
echo "  First 48 hours: 70-85% of total opens"
echo "  First 7 days: 90-95% of total opens"

Daily Performance Tracking

#!/bin/bash
# track-daily-performance.sh

CAMPAIGN_ID=$1
LOG_FILE="campaign-${CAMPAIGN_ID}-tracking.log"

# Log current metrics
DATE=$(date +%Y-%m-%d-%H:%M:%S)
STATS=$(cakemail reports campaign $CAMPAIGN_ID -f json)

OPENS=$(echo "$STATS" | jq -r '.unique_opens')
CLICKS=$(echo "$STATS" | jq -r '.unique_clicks')
OPEN_RATE=$(echo "$STATS" | jq -r '.open_rate')
CLICK_RATE=$(echo "$STATS" | jq -r '.click_rate')

echo "$DATE,$OPENS,$CLICKS,$OPEN_RATE,$CLICK_RATE" >> $LOG_FILE

echo "Logged: $OPENS opens, $CLICKS clicks"

Schedule with cron:

# Track every 6 hours for first week
0 */6 * * * /path/to/track-daily-performance.sh 790

Comparative Analysis

Compare Two Campaigns

#!/bin/bash
# compare-campaigns.sh

CAMPAIGN_A=$1
CAMPAIGN_B=$2

echo "=== Campaign Comparison ==="
echo ""

# Get reports
REPORT_A=$(cakemail reports campaign $CAMPAIGN_A -f json)
REPORT_B=$(cakemail reports campaign $CAMPAIGN_B -f json)

# Campaign names
NAME_A=$(echo "$REPORT_A" | jq -r '.campaign_name')
NAME_B=$(echo "$REPORT_B" | jq -r '.campaign_name')

echo "Campaign A: $NAME_A (ID: $CAMPAIGN_A)"
echo "Campaign B: $NAME_B (ID: $CAMPAIGN_B)"
echo ""

# Compare metrics
echo "Metric              | Campaign A | Campaign B | Difference"
echo "--------------------|------------|------------|------------"

# Open Rate
OPEN_A=$(echo "$REPORT_A" | jq -r '.open_rate')
OPEN_B=$(echo "$REPORT_B" | jq -r '.open_rate')
OPEN_DIFF=$(echo "$OPEN_B - $OPEN_A" | bc)
printf "Open Rate           | %9.1f%% | %9.1f%% | %+9.1f%%\n" $OPEN_A $OPEN_B $OPEN_DIFF

# Click Rate
CLICK_A=$(echo "$REPORT_A" | jq -r '.click_rate')
CLICK_B=$(echo "$REPORT_B" | jq -r '.click_rate')
CLICK_DIFF=$(echo "$CLICK_B - $CLICK_A" | bc)
printf "Click Rate          | %9.1f%% | %9.1f%% | %+9.1f%%\n" $CLICK_A $CLICK_B $CLICK_DIFF

# Unsubscribe Rate
UNSUB_A=$(echo "$REPORT_A" | jq -r '.unsubscribe_rate')
UNSUB_B=$(echo "$REPORT_B" | jq -r '.unsubscribe_rate')
UNSUB_DIFF=$(echo "$UNSUB_B - $UNSUB_A" | bc)
printf "Unsubscribe Rate    | %9.1f%% | %9.1f%% | %+9.1f%%\n" $UNSUB_A $UNSUB_B $UNSUB_DIFF

echo ""

# Winner
if (( $(echo "$OPEN_B > $OPEN_A" | bc -l) )); then
  echo "🏆 Campaign B performed better"
else
  echo "🏆 Campaign A performed better"
fi

Historical Performance

#!/bin/bash
# historical-performance.sh

LIST_ID=123

echo "=== Historical Campaign Performance ==="
echo ""

# Get all sent campaigns
CAMPAIGNS=$(cakemail campaigns list \
  --filter "status==sent" \
  --sort "-delivered_at" \
  --limit 10 \
  -f json | jq -r '.data[].id')

echo "Campaign | Date       | Recipients | Open Rate | Click Rate"
echo "---------|------------|------------|-----------|------------"

for ID in $CAMPAIGNS; do
  CAMPAIGN=$(cakemail campaigns get $ID -f json)
  REPORT=$(cakemail reports campaign $ID -f json)

  NAME=$(echo "$CAMPAIGN" | jq -r '.name' | cut -c1-15)
  DATE=$(echo "$CAMPAIGN" | jq -r '.delivered_at' | cut -d'T' -f1)
  RECIPIENTS=$(echo "$REPORT" | jq -r '.total_recipients')
  OPEN=$(echo "$REPORT" | jq -r '.open_rate')
  CLICK=$(echo "$REPORT" | jq -r '.click_rate')

  printf "%-15s | %10s | %10d | %8.1f%% | %9.1f%%\n" \
    "$NAME" "$DATE" $RECIPIENTS $OPEN $CLICK
done

echo ""

# Calculate averages
echo "Calculating averages across all campaigns..."

Month-over-Month Trends

#!/bin/bash
# monthly-trends.sh

YEAR=2024

echo "=== Monthly Campaign Trends - $YEAR ==="
echo ""
echo "Month | Campaigns | Avg Open Rate | Avg Click Rate"
echo "------|-----------|---------------|----------------"

for MONTH in {01..12}; do
  START_DATE="$YEAR-$MONTH-01"

  # Calculate end date
  if [ $MONTH -eq 12 ]; then
    END_DATE="$((YEAR + 1))-01-01"
  else
    END_DATE="$YEAR-$(printf "%02d" $((10#$MONTH + 1)))-01"
  fi

  # Get campaigns for month
  CAMPAIGNS=$(cakemail campaigns list \
    --filter "status==sent;delivered_at>=$START_DATE;delivered_at<$END_DATE" \
    -f json | jq -r '.data[].id')

  if [ -z "$CAMPAIGNS" ]; then
    continue
  fi

  COUNT=$(echo "$CAMPAIGNS" | wc -l)
  TOTAL_OPEN=0
  TOTAL_CLICK=0

  for ID in $CAMPAIGNS; do
    REPORT=$(cakemail reports campaign $ID -f json 2>/dev/null)
    if [ -n "$REPORT" ]; then
      OPEN=$(echo "$REPORT" | jq -r '.open_rate')
      CLICK=$(echo "$REPORT" | jq -r '.click_rate')
      TOTAL_OPEN=$(echo "$TOTAL_OPEN + $OPEN" | bc)
      TOTAL_CLICK=$(echo "$TOTAL_CLICK + $CLICK" | bc)
    fi
  done

  if [ $COUNT -gt 0 ]; then
    AVG_OPEN=$(echo "scale=1; $TOTAL_OPEN / $COUNT" | bc)
    AVG_CLICK=$(echo "scale=1; $TOTAL_CLICK / $COUNT" | bc)
    printf "%5s | %9d | %12.1f%% | %13.1f%%\n" \
      "$MONTH" $COUNT $AVG_OPEN $AVG_CLICK
  fi
done

Segmentation Analysis

Performance by Segment

#!/bin/bash
# segment-performance.sh

LIST_ID=123

echo "=== Campaign Performance by Segment ==="
echo ""

# Get all segments
SEGMENTS=$(cakemail segments list $LIST_ID -f json | jq -r '.data[] | "\(.id):\(.name)"')

echo "Segment          | Campaigns | Avg Open | Avg Click"
echo "-----------------|-----------|----------|----------"

for SEG in $SEGMENTS; do
  SEG_ID=$(echo "$SEG" | cut -d: -f1)
  SEG_NAME=$(echo "$SEG" | cut -d: -f2- | cut -c1-15)

  # Find campaigns sent to this segment
  CAMPAIGNS=$(cakemail campaigns list \
    --filter "status==sent;segment_id==$SEG_ID" \
    -f json | jq -r '.data[].id')

  if [ -z "$CAMPAIGNS" ]; then
    continue
  fi

  COUNT=$(echo "$CAMPAIGNS" | wc -l)
  TOTAL_OPEN=0
  TOTAL_CLICK=0

  for ID in $CAMPAIGNS; do
    REPORT=$(cakemail reports campaign $ID -f json 2>/dev/null)
    if [ -n "$REPORT" ]; then
      OPEN=$(echo "$REPORT" | jq -r '.open_rate')
      CLICK=$(echo "$REPORT" | jq -r '.click_rate')
      TOTAL_OPEN=$(echo "$TOTAL_OPEN + $OPEN" | bc)
      TOTAL_CLICK=$(echo "$TOTAL_CLICK + $CLICK" | bc)
    fi
  done

  AVG_OPEN=$(echo "scale=1; $TOTAL_OPEN / $COUNT" | bc)
  AVG_CLICK=$(echo "scale=1; $TOTAL_CLICK / $COUNT" | bc)

  printf "%-15s | %9d | %7.1f%% | %8.1f%%\n" \
    "$SEG_NAME" $COUNT $AVG_OPEN $AVG_CLICK
done

Best Performing Segment

#!/bin/bash
# best-segment.sh

# Find segment with highest average open rate
# (Implementation from segment-performance.sh with sorting)

# Output best segment recommendation
echo "🏆 Best Performing Segment: Premium Users"
echo "   Average Open Rate: 62.3%"
echo "   Average Click Rate: 28.7%"
echo ""
echo "💡 Recommendation: Prioritize campaigns to this segment"

Key Performance Indicators (KPIs)

Email Deliverability KPIs

#!/bin/bash
# deliverability-kpis.sh

CAMPAIGN_ID=$1

REPORT=$(cakemail reports campaign $CAMPAIGN_ID -f json)

echo "=== Deliverability KPIs ==="
echo ""

# Delivery Rate
TOTAL=$(echo "$REPORT" | jq -r '.total_recipients')
DELIVERED=$(echo "$REPORT" | jq -r '.delivered')
DELIVERY_RATE=$(echo "scale=1; $DELIVERED * 100 / $TOTAL" | bc)

echo "Delivery Rate: $DELIVERY_RATE%"
if (( $(echo "$DELIVERY_RATE >= 95" | bc -l) )); then
  echo "  ✅ Excellent (target: ≥95%)"
elif (( $(echo "$DELIVERY_RATE >= 90" | bc -l) )); then
  echo "  ⚠️  Good (target: ≥95%)"
else
  echo "  ❌ Poor - Clean your list"
fi
echo ""

# Bounce Rate
BOUNCED=$(echo "$REPORT" | jq -r '.bounced')
BOUNCE_RATE=$(echo "scale=1; $BOUNCED * 100 / $TOTAL" | bc)

echo "Bounce Rate: $BOUNCE_RATE%"
if (( $(echo "$BOUNCE_RATE <= 2" | bc -l) )); then
  echo "  ✅ Excellent (target: ≤2%)"
elif (( $(echo "$BOUNCE_RATE <= 5" | bc -l) )); then
  echo "  ⚠️  Acceptable (target: ≤2%)"
else
  echo "  ❌ High - Review list quality"
fi

Engagement KPIs

#!/bin/bash
# engagement-kpis.sh

CAMPAIGN_ID=$1

REPORT=$(cakemail reports campaign $CAMPAIGN_ID -f json)

echo "=== Engagement KPIs ==="
echo ""

# Open Rate
OPEN_RATE=$(echo "$REPORT" | jq -r '.open_rate')
echo "Open Rate: $OPEN_RATE%"
if (( $(echo "$OPEN_RATE >= 20" | bc -l) )); then
  echo "  ✅ Excellent (industry avg: 15-25%)"
elif (( $(echo "$OPEN_RATE >= 15" | bc -l) )); then
  echo "  ✓ Good"
else
  echo "  ⚠️  Below average - Improve subject lines"
fi
echo ""

# Click Rate
CLICK_RATE=$(echo "$REPORT" | jq -r '.click_rate')
echo "Click Rate: $CLICK_RATE%"
if (( $(echo "$CLICK_RATE >= 3" | bc -l) )); then
  echo "  ✅ Excellent (industry avg: 2-5%)"
elif (( $(echo "$CLICK_RATE >= 2" | bc -l) )); then
  echo "  ✓ Good"
else
  echo "  ⚠️  Below average - Improve CTAs"
fi
echo ""

# CTOR
UNIQUE_OPENS=$(echo "$REPORT" | jq -r '.unique_opens')
UNIQUE_CLICKS=$(echo "$REPORT" | jq -r '.unique_clicks')
CTOR=$(echo "scale=1; $UNIQUE_CLICKS * 100 / $UNIQUE_OPENS" | bc)

echo "Click-to-Open Rate: $CTOR%"
if (( $(echo "$CTOR >= 15" | bc -l) )); then
  echo "  ✅ Excellent (industry avg: 10-20%)"
elif (( $(echo "$CTOR >= 10" | bc -l) )); then
  echo "  ✓ Good"
else
  echo "  ⚠️  Below average - Improve content relevance"
fi

List Health KPIs

#!/bin/bash
# list-health-kpis.sh

CAMPAIGN_ID=$1

REPORT=$(cakemail reports campaign $CAMPAIGN_ID -f json)

echo "=== List Health KPIs ==="
echo ""

# Unsubscribe Rate
DELIVERED=$(echo "$REPORT" | jq -r '.delivered')
UNSUBSCRIBES=$(echo "$REPORT" | jq -r '.unsubscribes')
UNSUB_RATE=$(echo "scale=2; $UNSUBSCRIBES * 100 / $DELIVERED" | bc)

echo "Unsubscribe Rate: $UNSUB_RATE%"
if (( $(echo "$UNSUB_RATE <= 0.2" | bc -l) )); then
  echo "  ✅ Excellent (target: ≤0.5%)"
elif (( $(echo "$UNSUB_RATE <= 0.5" | bc -l) )); then
  echo "  ✓ Good"
elif (( $(echo "$UNSUB_RATE <= 1.0" | bc -l) )); then
  echo "  ⚠️  Elevated - Review content relevance"
else
  echo "  ❌ High - Major content/targeting issues"
fi
echo ""

# Spam Complaint Rate
SPAM=$(echo "$REPORT" | jq -r '.spam_complaints // 0')
SPAM_RATE=$(echo "scale=3; $SPAM * 100 / $DELIVERED" | bc)

echo "Spam Complaint Rate: $SPAM_RATE%"
if (( $(echo "$SPAM_RATE <= 0.1" | bc -l) )); then
  echo "  ✅ Excellent (target: ≤0.1%)"
else
  echo "  ❌ Concerning - Review content and list source"
fi

Export and Reporting

Export Campaign Data

#!/bin/bash
# export-campaign-data.sh

CAMPAIGN_ID=$1
OUTPUT_FILE="campaign-${CAMPAIGN_ID}-report-$(date +%Y%m%d).json"

echo "Exporting campaign data..."

# Get campaign details
cakemail campaigns get $CAMPAIGN_ID -f json > campaign-details.json

# Get campaign report
cakemail reports campaign $CAMPAIGN_ID -f json > campaign-report.json

# Get link data
cakemail reports campaign-links $CAMPAIGN_ID -f json > campaign-links.json

# Combine into single file
jq -s '{campaign: .[0], report: .[1], links: .[2]}' \
  campaign-details.json \
  campaign-report.json \
  campaign-links.json > "$OUTPUT_FILE"

# Cleanup
rm campaign-details.json campaign-report.json campaign-links.json

echo "✓ Exported to: $OUTPUT_FILE"

Generate CSV Report

#!/bin/bash
# generate-csv-report.sh

echo "campaign_id,name,date,recipients,delivered,opens,clicks,open_rate,click_rate,unsubscribes" > campaigns-report.csv

# Get all sent campaigns
CAMPAIGNS=$(cakemail campaigns list --filter "status==sent" -f json | jq -r '.data[].id')

for ID in $CAMPAIGNS; do
  CAMPAIGN=$(cakemail campaigns get $ID -f json)
  REPORT=$(cakemail reports campaign $ID -f json)

  NAME=$(echo "$CAMPAIGN" | jq -r '.name' | sed 's/,/_/g')
  DATE=$(echo "$CAMPAIGN" | jq -r '.delivered_at' | cut -d'T' -f1)
  RECIPIENTS=$(echo "$REPORT" | jq -r '.total_recipients')
  DELIVERED=$(echo "$REPORT" | jq -r '.delivered')
  OPENS=$(echo "$REPORT" | jq -r '.unique_opens')
  CLICKS=$(echo "$REPORT" | jq -r '.unique_clicks')
  OPEN_RATE=$(echo "$REPORT" | jq -r '.open_rate')
  CLICK_RATE=$(echo "$REPORT" | jq -r '.click_rate')
  UNSUBSCRIBES=$(echo "$REPORT" | jq -r '.unsubscribes')

  echo "$ID,$NAME,$DATE,$RECIPIENTS,$DELIVERED,$OPENS,$CLICKS,$OPEN_RATE,$CLICK_RATE,$UNSUBSCRIBES" >> campaigns-report.csv
done

echo "✓ Report saved to: campaigns-report.csv"

Best Practices

1. Review Reports Within 24-48 Hours

Most engagement happens in first 48 hours:

# Schedule report review
$ cakemail reports campaign 790 > report-24h.txt
# Review after 24 hours
# Final review after 7 days

2. Track Metrics Over Time

# Log metrics daily for first week
0 12 * * * /path/to/track-daily-performance.sh 790

3. Compare Similar Campaigns

# Compare campaigns to same segment
$ ./compare-campaigns.sh 790 791

4. Focus on Trends, Not Single Campaigns

# Monthly trend analysis
$ ./monthly-trends.sh 2024

5. Set Benchmark Goals

# Document your benchmarks
cat > benchmarks.md << 'EOF'
# Campaign Benchmarks

## Newsletter
- Open Rate Target: 25%
- Click Rate Target: 4%
- Unsubscribe Rate Max: 0.3%

## Promotional
- Open Rate Target: 20%
- Click Rate Target: 8%
- Unsubscribe Rate Max: 0.5%
EOF

6. Investigate Anomalies

# If metrics significantly differ
# Check: Send time, subject line, content, segment, list quality

Troubleshooting

Low Open Rates

Problem: Open rate below 15%

Solutions:

Improve subject lines (A/B test)
Check send time
Verify sender reputation
Clean inactive subscribers
Test mobile optimization

Low Click Rates

Problem: Click rate below 2%

Solutions:

Improve CTA placement
Make CTAs more prominent
Verify link destinations work
Increase content relevance
Test different content formats

High Unsubscribe Rate

Problem: Unsubscribe rate above 1%

Solutions:

Review content relevance
Reduce send frequency
Improve targeting/segmentation
Provide preference center
Check for list purchase/rental

High Bounce Rate

Problem: Bounce rate above 5%

Solutions:

Clean list regularly
Remove hard bounces immediately
Use double opt-in
Verify email collection process
Check for spam traps

Overview

Quick Start

View Campaign Report

Core Metrics

Delivery Metrics

Engagement Metrics

Action Metrics

Viewing Campaign Analytics

Basic Campaign Report

JSON Format for Processing

Extract Specific Metrics

Multiple Campaign Reports

Link Analytics

View Campaign Links

Top Performing Links

Export Link Data

Time-Based Analytics

Opens Over Time

Daily Performance Tracking

Comparative Analysis

Compare Two Campaigns

Historical Performance

Month-over-Month Trends

Segmentation Analysis

Performance by Segment

Best Performing Segment

Key Performance Indicators (KPIs)

Email Deliverability KPIs

Engagement KPIs

List Health KPIs

Export and Reporting

Export Campaign Data

Generate CSV Report

Best Practices

1. Review Reports Within 24-48 Hours

2. Track Metrics Over Time

3. Compare Similar Campaigns

4. Focus on Trends, Not Single Campaigns

5. Set Benchmark Goals

6. Investigate Anomalies

Troubleshooting

Low Open Rates

Low Click Rates

High Unsubscribe Rate

High Bounce Rate