Not just charts. A decision-support system for Swiggy's growth team.
This project is a full-stack data analytics solution built on 197,430 real Swiggy food delivery orders across India. It goes beyond surface-level charts to answer the kind of strategic questions a growth analyst or product manager at Swiggy would actually care about.
The project combines exploratory data analysis, statistical testing, machine learning scoring models, SQL pipelines, and revenue forecasting into a single cohesive system — delivered through an interactive Streamlit dashboard, a 66-cell Jupyter notebook, and a downloadable 13-sheet Excel KPI report.
Three original analytical frameworks sit at the core:
- City Expansion Opportunity Index — a composite model that ranks every city by growth potential
- Menu Intelligence Matrix — a BCG-style quadrant chart classifying food categories by revenue and satisfaction
- Restaurant Health Score — a viability index that flags at-risk restaurants before they churn
Whether you're exploring the data, running SQL queries, or reviewing the forecasting model, everything is connected through a single source of truth: swiggy_data.xlsx.
| # | Question | Framework / Method |
|---|---|---|
| 1 | Which cities should Swiggy expand into next? | City Expansion Opportunity Index |
| 2 | Which restaurants are at risk of churning off the platform? | Restaurant Health Score |
| 3 | Which food categories are Stars vs Dogs in our portfolio? | Menu Intelligence Matrix |
| 4 | When is peak demand, and how should ops scale? | Synthetic demand modelling + time-of-day analysis |
| 5 | What does revenue look like in the next 3 months? | ARIMA forecasting with train/test validation |
Most Swiggy analysis projects produce the same bar charts. This project builds three proprietary analytical frameworks modelled on consulting methodology:
A 4-signal composite model that scores every city 0–100:
- Revenue Growth Rate (30%) — Is the market accelerating?
- Weighted Customer Rating (25%) — Does the market have satisfied customers?
- Order Density / restaurant (25%) — How efficient is the market?
- Category Diversity (20%) — How sticky is the platform?
Produces a strategic 4-quadrant scatter: Stars / Untapped / Emerging / Low Priority.
Classifies food categories into four strategic quadrants:
- ⭐ Stars — High revenue share + high rating → Promote aggressively
- 💎 Hidden Gems — Low revenue + high rating → Invest in discovery
- 💰 Cash Cows — High revenue + lower rating → Maintain and improve
⚠️ Review Needed — Low revenue + low rating → Revamp or discontinue
Assigns each restaurant a Health Score (0–100):
- Revenue contribution (40%) + Customer satisfaction (30%) + Order volume (20%) + Recency (10%)
- Tiered output: Champion / Healthy / At Risk / Critical
| Attribute | Value |
|---|---|
| Source | Swiggy platform orders |
| Rows | 197,430 |
| Columns | 10 |
| Key Fields | State, City, Order Date, Restaurant Name, Category, Dish Name, Price (INR), Rating, Rating Count |
swiggy-market-intelligence/
│
├── app.py # Streamlit dashboard (7 tabs)
├── swiggy_sales_analysis.ipynb # Main analysis notebook (66 cells)
├── sql_pipeline.py # SQLite DB + 10 analytics queries
├── generate_excel_report.py # 13-sheet formatted Excel KPI report
├── swiggy_data.xlsx # Source dataset
├── requirements.txt # 12 dependencies
└── README.md
| Tab | Content |
|---|---|
| 📈 Overview | Revenue KPIs, quarterly performance, day-of-week patterns |
| 🗺️ Geographic | State/city heatmaps, revenue vs rating scatter |
| 🎯 Segments | Order-value segments, food preference heatmap, frequency tiers, time-of-day analysis |
| 📉 Trends | Monthly trend + 3-month moving average, MoM growth rate |
| 💡 Insights | Pareto 80-20 analysis, price-rating correlation, Menu Intelligence Matrix |
| 🗄️ SQL Pipeline | 10 SQL queries running against a live SQLite database |
| 📍 Expansion Strategy | City Expansion Opportunity Index + Restaurant Health Score |
§ 0 Executive Summary
§ 1 Data Loading & Quality Audit
§ 2 Weighted Rating Analysis
§ 3 Revenue Overview
§ 4 Monthly Trend & MoM Growth
§ 5 Category & Geographic Analysis
↳ A3: Menu Intelligence Matrix (BCG Framework)
↳ A1: City Expansion Opportunity Index
§ 6 Quarterly Performance
§ 7 Customer Segmentation
↳ A2: Restaurant Health Score
↳ 7.1 RFM Analysis
↳ 7.2 Peak Hours (synthetic demand)
↳ 7.3 Cohort Retention
↳ 7.4 Hypothesis Testing (Mann-Whitney U, ANOVA)
§ 8 Correlation & Price Analysis
§ 9 Geographic Visualisations
§10 Predictive Forecasting (ARIMA with MAPE vs naive baseline)
§11 SQL Integration Demo
§12 Excel Report Generation
10 queries run against a SQLite database built from swiggy_data.xlsx — replicating a real data-engineering pipeline:
- Monthly Revenue Trend
- Revenue by State
- Revenue by Category
- Quarterly Performance
- Top Dishes by Revenue
- Top Restaurants
- Day-of-Week Patterns
- Customer Basket Segmentation
- Restaurant Frequency Tiers
- Pareto 80% Cities
Generated on-demand from the dashboard (Download button in sidebar):
Summary KPIs · Monthly Trend · Quarterly Performance · Top States · Top Cities · Top Dishes · Category Mix · Customer Segments · Pareto Analysis · Day of Week · Time of Day · Price-Rating · Restaurant Frequency
| Layer | Tools |
|---|---|
| Data Wrangling | Python, Pandas, NumPy |
| Visualisation | Plotly (interactive), Matplotlib, Seaborn |
| Statistics | SciPy (Mann-Whitney U, ANOVA), Statsmodels (ARIMA) |
| ML / Scoring | scikit-learn (MinMaxScaler for composite indices) |
| Database | SQLite (via Python stdlib) |
| Dashboard | Streamlit |
| Reporting | openpyxl (13-sheet Excel) |
# 1. Clone and install
git clone https://github.com/Mounusha25/swiggy-market-intelligence.git
cd swiggy-market-intelligence
pip install -r requirements.txt
# 2. Launch the dashboard
streamlit run app.py
# 3. Open the notebook
jupyter notebook swiggy_sales_analysis.ipynb
⚠️ Placeswiggy_data.xlsxin the project root before running.
- Pareto effect confirmed: ~20% of cities generate ~80% of revenue
- Untapped markets identified: Several tier-2 cities score high on the Expansion Index despite low current revenue
- Critical restaurants flagged: ~15% of restaurants score below 25 on the Health Score — candidates for platform intervention
- Star categories: A small number of food categories drive disproportionate revenue with high satisfaction — clear marketing priorities
- Demand peaks: Lunch (11–13h) and Dinner (19–22h) dominate; weekends outperform weekdays
- ✅ Analytical Frameworks — BCG-style matrix, composite scoring models (consulting methodology)
- ✅ Data Analysis — Pandas, NumPy, exploratory analysis, outlier detection
- ✅ Statistical Methods — Mann-Whitney U, ANOVA, correlation analysis, normality tests
- ✅ Visualisation — Interactive Plotly dashboards, storytelling with data
- ✅ Forecasting — ARIMA with train/test split, MAPE vs naive baseline comparison
- ✅ SQL — 10 production-style queries, SQLite pipeline, window functions
- ✅ Business Intelligence — KPI design, customer segmentation, Pareto analysis
- ✅ Software Engineering — Modular Python, Streamlit app, downloadable Excel reports
Mounusha Ram Metti
- 📧 Email: mettti.mounu@gmail.com
- 💼 LinkedIn: linkedin.com/in/mounusha-ram-metti
- 🌐 Portfolio: https://mounushametti.vercel.app/
- 💻 GitHub: github.com/Mounusha25