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E-Commerce Analytics Dashboard

$49

Sales analytics, customer cohort analysis, conversion funnel tracking, revenue forecasting, and KPI dashboards.

📁 20 files
MarkdownYAMLPythonSQLPostgreSQL

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📁 File Structure 20 files

ecommerce-analytics-dashboard/ ├── LICENSE ├── README.md ├── configs/ │ └── dashboard_config.yaml ├── data/ │ └── sample_transactions.csv ├── free-sample.zip ├── guide/ │ ├── 01-overview.md │ ├── 02-sales-analytics-and-kpis.md │ └── 03-cohort-analysis-and-funnel-tracking.md ├── guides/ │ └── analytics_guide.md ├── index.html ├── sql/ │ └── queries.sql ├── src/ │ ├── __init__.py │ ├── chart_export.py │ ├── cohort_analysis.py │ ├── funnel_tracker.py │ ├── kpi_calculator.py │ ├── revenue_forecast.py │ └── sales_analytics.py └── tests/ └── test_analytics.py

📖 Documentation Preview README excerpt

E-Commerce Analytics Dashboard

A complete Python analytics toolkit for e-commerce businesses. Covers every metric that matters: sales aggregation, customer cohort analysis, conversion funnel tracking, revenue forecasting, and KPI computation — all from raw transaction data.

What You Get

  • Sales Analytics Engine — daily/weekly/monthly revenue summaries, top products, category & channel breakdowns, period-over-period growth rates
  • Customer Cohort Analysis — retention matrices, cohort revenue curves, cumulative LTV by acquisition month
  • Conversion Funnel Tracker — multi-stage funnel modeling with drop-off rates, bottleneck detection, and per-segment breakdowns
  • Revenue Forecasting — moving average, weighted MA, linear trend, and seasonal decomposition — all pure Python, no numpy required
  • KPI Calculator — AOV, LTV, CAC, conversion rate, cart abandonment, repeat purchase rate, gross margin, and industry benchmark checks
  • Chart & Export Helpers — matplotlib bar/line/funnel charts (guarded import) plus CSV and JSON export
  • SQL Query Library — 10 drop-in PostgreSQL queries for the same analytics directly against your database
  • Sample Data — 50-row transaction CSV ready to test everything immediately

Project Structure


ecommerce-analytics-dashboard/
├── README.md                        # This file
├── LICENSE                          # MIT License
├── .gitignore
├── src/
│   ├── __init__.py                  # Package imports
│   ├── sales_analytics.py          # Sales aggregation & ranking
│   ├── cohort_analysis.py          # Cohort retention & revenue
│   ├── funnel_tracker.py           # Conversion funnel analysis
│   ├── revenue_forecast.py         # Time-series forecasting
│   ├── kpi_calculator.py           # Core e-commerce KPIs
│   └── chart_export.py             # Charting & data export
├── sql/
│   └── queries.sql                  # PostgreSQL analytics queries
├── data/
│   └── sample_transactions.csv     # 50-row example dataset
├── tests/
│   └── test_analytics.py           # Comprehensive test suite
├── configs/
│   └── dashboard_config.yaml       # Configuration reference
└── guides/
    └── analytics_guide.md          # Detailed usage guide

Quick Start


# Run sales analysis on the sample data
python -m src.sales_analytics data/sample_transactions.csv

# Run cohort analysis
python -m src.cohort_analysis data/sample_transactions.csv

# Run the conversion funnel demo
python -m src.funnel_tracker

# Run revenue forecasting demo
python -m src.revenue_forecast

# Run all tests
python -m pytest tests/ -v
# or without pytest:

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

📄 Code Sample .py preview

src/chart_export.py """ Chart & Export Helpers ===================== Optional visualization and data-export module. Matplotlib is imported behind a guard so the rest of the toolkit works even if it is not installed. Provides: - Bar charts for revenue by period, category, or channel - Line charts for trend / forecast visualization - Funnel diagrams (horizontal bar approximation) - CSV and JSON export of any analytics result """ from __future__ import annotations import csv import json import logging from dataclasses import asdict, dataclass from datetime import datetime from io import StringIO from pathlib import Path from typing import Any, Dict, List, Optional, Sequence, Tuple logger = logging.getLogger(__name__) # Guard matplotlib -- it's not always available and the rest of the toolkit # should not break if it is missing. try: import matplotlib matplotlib.use("Agg") # non-interactive backend for server environments import matplotlib.pyplot as plt HAS_MATPLOTLIB = True except ImportError: HAS_MATPLOTLIB = False logger.info("matplotlib not installed -- chart functions will be unavailable") def _require_matplotlib() -> None: if not HAS_MATPLOTLIB: raise RuntimeError( "matplotlib is required for charting. Install it with: " "pip install matplotlib" ) # --------------------------------------------------------------------------- # Chart helpers # --------------------------------------------------------------------------- # ... 270 more lines ...
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