Features

This chapter covers the core features and capabilities of Newsletter Analytics.

Features

• Flexible campaign import — Maps fields from Mailchimp, ConvertKit, and SendGrid exports
• Performance reports — Open rate, click rate, bounce rate, unsubscribe rate per campaign
• Industry benchmarks — Compare your metrics against tech/SaaS averages
• Engagement scoring — Score subscribers by open/click frequency
• Subject line analysis — Identify patterns in your best-performing subjects
• Trend detection — Track metric changes over time
• JSON data storage — Portable, no database required

Requirements

• Python 3.10+
• No external dependencies (stdlib only)

Quick Start

Follow this guide to get Newsletter Analytics up and running in your environment.

Quick Start

# Import campaign data
python src/newsletter_analytics.py import --file examples/sample_campaigns.json

# Generate a report for all campaigns
python src/newsletter_analytics.py report

# Report for a specific campaign
python src/newsletter_analytics.py report --campaign "March Newsletter"

# Analyze subject lines
python src/newsletter_analytics.py subjects

# View engagement scores
python src/newsletter_analytics.py engagement

# View trends over the last 30 days
python src/newsletter_analytics.py trends --last 30

Import Format

The tool accepts JSON with flexible field mapping:

[
  {
    "campaign_name": "March Newsletter",
    "sent_at": "2026-03-01T10:00:00Z",
    "total_sent": 5000,
    "opens": 1200,
    "clicks": 150,
    "bounces": 25,
    "unsubscribes": 10,
    "complaints": 1,
    "subject": "What we shipped in February"
  }
]

Field mapping also supports Mailchimp (unique_opens, recipients.recipient_count), ConvertKit (open_rate, click_rate), and SendGrid (stats.opens, stats.clicks) formats.