Olist E-Commerce Sales Analytics

SQL-driven analysis of 96,470 delivered orders and R$15.4M in revenue from the Olist Brazilian e-commerce marketplace, spanning Sept 2016 – Aug 2018 (23 months / ~2 years) of transaction history. Built end-to-end with SQLite, pandas, and 19 analytical queries covering revenue trends, customer retention, delivery performance, and seller concentration — presented as an interactive dashboard.

▶ View Interactive Dashboard | View all 19 SQL queries

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Key findings

Metric	Value
Total revenue	R$15,418,394.83
Total delivered orders	96,470
Unique customers	93,350
Average order value	R$159.83
Repeat customer rate	3.0%
On-time delivery rate	92.0% (88,163 of 95,824 reviewed orders)
On-time vs late avg. review score	4.29 vs 2.57
Delivery days ↔ review score correlation	r = −0.334
Revenue from top 15% of customers	46.71%
Top 10 sellers' share of platform revenue	13.27%
Avg. days between a customer's 1st and 2nd order	81.2 days

What these numbers mean for the business:

• Retention is the biggest lever. Only 3% of customers ever order twice, yet the customers who do return account for a disproportionate share of revenue (top 15% of customers drive 46.71% of revenue). A win-back campaign targeted around the ~81-day repeat-purchase window is the highest-leverage growth opportunity in the data.
• Delivery speed is a satisfaction driver, not just an ops metric. Late orders average a 2.57 review score versus 4.29 for on-time orders — a 1.7-point gap — and delivery time is moderately correlated with review score (r = −0.334) across the full order base.
• The seller base is healthily diversified, not platform-risk-concentrated: the top 10 sellers (out of thousands) account for only 13.27% of revenue.

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Dashboard

The dashboard (/dashboard/index.html) is a single self-contained HTML file — no build step, no server required. Open it directly in a browser or host it with GitHub Pages.

It visualizes:

• Headline KPIs and 23-month revenue trend with 3-month moving average
• Cumulative revenue growth trajectory
• Month-over-month growth rate
• Delivery performance: on-time vs. late orders, review-score impact, and delivery-day decile distribution
• Revenue by customer state (top 10 of 27 states)
• Top 10 sellers by revenue
• Payment method mix
• Customer retention & concentration metrics (repeat rate, Pareto analysis, repurchase gap)

SQL techniques demonstrated

This project was built to showcase production-style SQL, not just basic SELECT statements:

• Window functions: LAG(), RANK(), DENSE_RANK(), NTILE(), ROW_NUMBER(), moving averages with ROWS BETWEEN
• Correlated subqueries (Q16 — per-customer installment comparison)
• CTEs (WITH) for multi-stage aggregation pipelines
• Reusable VIEWs (fact_orders, vw_monthly_kpis) as a single source of truth for downstream BI tools
• Data cleaning: deduplicating late-arriving review records via ROW_NUMBER() partitioning
• Query performance tuning: added indexes on join/filter columns and verified usage with EXPLAIN QUERY PLAN
• Statistical analysis in pandas: Pearson correlation between delivery time and customer satisfaction

Tech stack

SQLite · SQL (window functions, CTEs, views, indexing) · Python (pandas) · HTML/CSS/JS + Chart.js for the dashboard

Dataset

Olist Brazilian E-Commerce Public Dataset (Kaggle) — 8 relational tables covering orders, order items, payments, reviews, customers, sellers, and geolocation. Raw CSVs are not included in this repo (see .gitignore); download them from Kaggle and run the loader in notebook/ to reproduce olist.db locally.

Repository structure

olist-sql-analytics/
├── README.md
├── sql/
│   └── queries.sql          # All 19 queries, documented, with real results noted inline
├── dashboard/
│   └── index.html           # Self-contained interactive dashboard
└── notebook/
    └── (place your .ipynb / .db here — excluded from git by default)

Reproducing this locally

1. Download the Olist dataset CSVs from Kaggle into a data/ folder.
2. Run the loader queries in sql/queries.sql (Section 0) against a new SQLite database to build fact_orders.
3. Run Q1–Q19 in order — each is independent and can be run standalone against fact_orders.
4. Open dashboard/index.html in a browser to see the results visualized.

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Built as a portfolio project to demonstrate applied SQL analytics: from raw relational data to a documented, reproducible, business-relevant set of findings.