← Back to all products

Newsletter Analytics

$29

Track newsletter performance with open rates, click-through rates, bounces, and engagement trends.

📁 10 files

JSONMarkdownPython

📄 Product Preview

Try the interactive reader and demo tools below, or get the full product with all content unlocked.

📖 Interactive Reader (Free Preview) ⚙ Try Demo Tools 📦 Download Free Sample

📁 File Structure 10 files

newsletter-analytics/ ├── LICENSE ├── README.md ├── examples/ │ └── sample_campaigns.json ├── free-sample.zip ├── guide/ │ ├── 01_features.md │ ├── 02_quick-start.md │ ├── 03_industry-benchmarks.md │ └── 04_faq.md ├── index.html └── src/ └── newsletter_analytics.py

📖 Documentation Preview README excerpt

Newsletter Analytics

Track and analyze newsletter performance: open rates, click-through rates, bounces, subscriber engagement, and trends over time. Import from Mailchimp, ConvertKit, or SendGrid.

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


# 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"
  }
]

... continues with setup instructions, usage examples, and more.

📄 Code Sample .py preview

src/newsletter_analytics.py #!/usr/bin/env python3 """ Newsletter Analytics — Email Arsenal (DataNest) Track and analyze newsletter performance metrics: open rates, click-through rates, bounces, subscriber engagement, and trends over time. Usage: python newsletter_analytics.py import --file campaign_data.json python newsletter_analytics.py report python newsletter_analytics.py report --campaign "March Newsletter" python newsletter_analytics.py trends --last 30 Dependencies: Python 3.10+ stdlib only License: MIT """ from __future__ import annotations import argparse import json import logging import math import statistics import sys from collections import Counter, defaultdict from dataclasses import asdict, dataclass, field from datetime import datetime, timedelta, timezone from pathlib import Path from typing import Any # --------------------------------------------------------------------------- # Constants # --------------------------------------------------------------------------- logger = logging.getLogger("newsletter_analytics") DATA_FILE = Path("newsletter_data.json") # Industry benchmarks (approximate averages for tech/SaaS newsletters) BENCHMARKS = { "open_rate": 0.215, # 21.5% "click_rate": 0.025, # 2.5% "bounce_rate": 0.005, # 0.5% "unsubscribe_rate": 0.002, # 0.2% "complaint_rate": 0.0003, # 0.03% } # --------------------------------------------------------------------------- # ... 468 more lines ...

Buy Now — $29 Back to Products