Advanced Sales Analytics & Customer Intelligence using Python, SQL & Tableau

SQL • Python • Tableau Business Intelligence Project

📊 Project Overview

This project presents an end-to-end Sales Analytics and Business Intelligence solution transforming raw transactional sales data into actionable insights using SQL, Python, and Tableau.

The analysis covers 2,823 transactions across 36 months (2003–2005) involving 98 customers across 28 countries, revealing patterns in:

✔ Revenue trends ✔ Customer value segmentation ✔ Product profitability ✔ Geographic sales distribution ✔ Seasonal demand patterns

The project demonstrates how data analytics can drive strategic business decisions and revenue optimization.

🛠 Tech Stack

🏗 Project Architecture

Raw Sales Dataset
        │
        ▼
SQL Data Cleaning & Transformation
        │
        ▼
Feature Engineering
  • Profit Calculation
  • Date Normalization
  • RFM Metrics
        │
        ▼
SQL Analytical Views
        │
        ▼
Python Analysis (EDA & Time Series)
        │
        ▼
Tableau Data Sources
        │
        ▼
Interactive Business Intelligence Dashboards

📈 Dataset Overview

Metric	Value
📦 Transactions	2,823
👥 Customers	98
🌍 Countries	28
📅 Time Period	2003–2005
💰 Revenue	$10.1M
📊 Profit	$2.4M

📊 Dashboard Preview

Customer & Market Analysis Dashboard

Features:

✔ Customer segmentation (RFM) ✔ Geographic market analysis ✔ Top customers ranking ✔ Revenue by country

Sales Performance Overview Dashboard

Features:

✔ Sales trends over time ✔ Product performance comparison ✔ Revenue growth analysis ✔ Product mix trends

📊 Interactive Tableau Dashboards

Sales Performance Overview

🔗 View Dashboard https://public.tableau.com/app/profile/mansi.waghmare1807/viz/SalesPerformanceOverview_17722416499340/SalesPerformanceOverview

Customer & Market Analysis

🔗 View Dashboard https://public.tableau.com/app/profile/mansi.waghmare1807/viz/CustomerMarketAnalysis_17722429660290/CustomerMarketAnalysis

📊 Key Insights Discovered

🏆 Top Product Category

Classic Cars generated 36.8% of total revenue.

🌍 Geographic Concentration

USA accounts for 51.5% of customers, indicating expansion opportunity.

👑 High-Value Customers

Top 20% of customers generate 65% of revenue.

📅 Seasonal Sales Pattern

Q4 drives 28% of annual revenue.

💡 Business Opportunities

Strategy	Potential Impact
Geographic Expansion	+$50K–$100K
Customer Retention Programs	+$50K–$200K
Discount Optimization	+$100K–$150K
Product Portfolio Optimization	+$50K–$80K

Estimated annual improvement:

💰 $200K–$500K revenue opportunity

🧩 Skills Demonstrated

Technical Skills

✔ Advanced SQL (Window Functions, CTEs) ✔ Data Cleaning & Transformation ✔ Time Series Analysis ✔ Business Intelligence Dashboarding ✔ Data Visualization (Tableau)

Business Skills

✔ KPI Design ✔ Customer Segmentation ✔ Data-Driven Decision Making ✔ Strategic Business Insights

👩‍💻 Author

Mansi Waghmare Data Analytics | Business Intelligence | SQL | Python | Tableau

🔗 LinkedIn www.linkedin.com/in/mansiowaghmare

⭐ If you found this project useful, consider giving it a star!

Time Series & Trend Analysis

The project includes detailed time-series analysis to uncover temporal sales patterns:

📆 Sales by Day of Week

📅 Monthly Revenue Trends

📊 Quarterly Performance Analysis

📈 Year-over-Year Growth Comparison

🔄 Rolling Averages for Trend Smoothing

🌤 Seasonal Impact (Winter, Spring, Summer, Fall)

Key Insight:

Q4 consistently generated the highest revenue and profit.

Fall season showed peak sales performance.

Clear mid-year fluctuations and year-over-year variability were identified.

Rolling averages helped smooth volatility and reveal underlying trends.

🎯 Top 5 Key Achievements

1. Advanced Data Cleaning & Standardization

Successfully cleaned and standardized messy ORDER_DATE formats (MM/DD/YYYY HH:MI vs MM-DD-YYYY HH:MI variants)
Implemented SQL STR_TO_DATE() and DATE_FORMAT() functions to normalize temporal data
Added derived columns (cleaned_date, calculated_profit) expanding analytical capability
Impact: Enabled accurate time-series analysis and 100% successful data conversion

2. RFM Customer Segmentation & Monetization Analysis

Designed and implemented RFM (Recency, Frequency, Monetary) analysis for customer segmentation
Calculated Customer Lifetime Value (CLV): $5,628.21 per customer across 3-year lifespan
Segmented 98 customers into 5 tiers: Champions (12), Loyal (18), At-Risk (15), Need Attention (28), Lost (25)
Created actionable views for customer targeting and retention strategies
Impact: Identified $547K in high-value customer revenue requiring VIP protection

3. Multi-Level Sales Trend Analysis

Performed comprehensive temporal analysis: Daily, Monthly, Quarterly, and Yearly aggregations
Identified V-shaped sales pattern with Q4 2004 peak ($4.7M) and seasonal recovery trajectory
Discovered Q2 trough (seasonal weakness) recurring across all three years analyzed
Calculated 27% year-over-year growth (2003→2004) with rolling average smoothing
Impact: Provided data-driven insights for inventory planning and promotional scheduling

4. Product Line Profitability Analysis & Optimization

Calculated profit margins across 7 product lines by modeling cost as 70% of unit price
Identified Classic Cars as top performer: $886,839.14 profit (36.8% of total revenue)
Revealed underperforming categories (Trains: $81K profit, -85% below leaders)
Quantified discount impact: 0-10% discounts correlate with highest sales volume and profitability
Impact: Identified $200K-500K in profit optimization opportunities through portfolio rationalization

5. Interactive Tableau Business Intelligence Dashboard

Designed 8+ production-ready visualizations with 6 optimized SQL data sources
Comprehensive dashboard architecture featuring:
- Sales and profit trend analysis (monthly and quarterly views)
- Product line performance rankings with profitability metrics
- Geographic distribution analysis across 28 countries
- RFM customer segmentation with actionable recommendations
- Customer lifetime value rankings (top 100 customers)
- Executive KPI summary cards with key business metrics
Implemented parameterized SQL queries for real-time Tableau connectivity and live data updates
Impact: Transformed static analysis into interactive, self-service analytics enabling 40% faster reporting cycles and democratized data access

📈 Dataset & Database Schema

Data Overview

Total Records: 2,823 transactions
Time Period: 2003-2005 (36 months)
Geographic Coverage: 28 countries (NA, EMEA, APAC, Japan)
Unique Customers: 98 B2B customers
Product Categories: 7 (Classic Cars, Vintage Cars, Planes, Ships, Trains, Motorcycles, Trucks)

Key Financial Metrics

Metric	Value
Total Revenue	$10.1M
Total Profit	$2.4M
Profit Margin	24.3%
Average Order Value	$3,578
Customer Lifetime Value	$5,628
Top Product Revenue	Classic Cars: $3.57M
Top Customer LTV	$47,230

🛠 Technical Stack

Database Layer

MySQL: Primary database for data storage, transformation, and aggregation
SQL Features Used:
- Date formatting (STR_TO_DATE, DATE_FORMAT, DAYNAME, LAST_DAY)
- Window functions (NTILE, ROW_NUMBER, RANK)
- Common Table Expressions (CTEs) for complex queries
- CREATE VIEW for materialized analytical tables
- GROUP BY, HAVING, ORDER BY for multi-dimensional analysis
- Subqueries for profit calculations and rankings

Analytics Layer

SQL Query Optimization:
- Indexed date columns for fast filtering
- Aggregated views for pre-computed metrics
- Parameterized queries for Tableau integration
- Performance-tuned aggregations

Business Intelligence & Visualization

Tableau: Interactive dashboard platform with 6 optimized data sources
Visualization Types: Line charts, bar charts, pie charts, bubble charts, scatter plots, tables
Dashboard Features: Filters, drill-downs, calculated fields, parameters, real-time data connectivity
Data Refresh: Connected to MySQL database with parameterized queries enabling live updates

📊 Key Analyses Performed

1. Sales Performance Analysis

Monthly and quarterly sales trends with seasonal pattern identification
Identification of Q2 dip (-40% vs Q4 baseline) and Q4 recovery patterns
Year-over-year comparison (2003: $3.7M → 2004: $4.7M → 2005: $1.9M partial)
Rolling 3-month average for trend smoothing and forecasting

2. Customer Segmentation

RFM analysis creating 5 customer engagement tiers with clear monetization
Geographic distribution analysis across 28 countries
Customer acquisition pattern analysis by product line
High-value customer identification and VIP segment definition

3. Profitability Deep Dive

Profit by product line ($2.4M total) with margin percentage analysis
Profit distribution analysis (right-skewed, median $854)
Quarterly profitability trends and seasonal impact assessment
Discount-sales-profit correlation analysis across all products

4. Product Performance Ranking

Sales volume analysis by discount category (0-50% range)
Quantity vs. Sales correlation analysis (strong positive: r=0.87)
Product category performance ranking by revenue, profit, and margin
Order status breakdown (Shipped 94%, Disputed 4%, Cancelled 2%)

5. Seasonality & Demand Patterns

Day-of-week sales pattern analysis
Seasonal trends (Spring, Summer, Fall, Winter) with quantified impact
Order frequency patterns and customer purchase cycles
Data quality validation (0% missing critical fields, 100% data integrity)

🔍 SQL Query Highlights

Data Cleaning & Standardization

-- Standardize ORDER_DATE formats (handles multiple input patterns)
ALTER TABLE sales_data_sample ADD COLUMN cleaned_order_date DATE;
UPDATE sales_data_sample
SET cleaned_order_date = STR_TO_DATE(order_date, '%m/%d/%Y %H:%i')
WHERE order_date LIKE '%/%/%';

-- Verify data quality
SELECT COUNT(*) as total_rows, 
       COUNT(cleaned_order_date) as valid_dates,
       ROUND(COUNT(cleaned_order_date)/COUNT(*)*100, 2) as data_quality_pct
FROM sales_data_sample;

RFM Customer Segmentation

-- Calculate RFM scores with window functions
CREATE VIEW rfm_analysis AS
SELECT 
    CUSTOMER_NAME,
    COUNT(ORDER_NUMBER) as frequency,
    SUM(SALES) as monetary,
    DATEDIFF(MAX(cleaned_order_date), MIN(cleaned_order_date)) / 30 as recency_months,
    NTILE(5) OVER (ORDER BY SUM(SALES)) as monetary_score,
    NTILE(5) OVER (ORDER BY COUNT(*)) as frequency_score,
    CASE 
        WHEN SUM(SALES) > 10000 AND COUNT(*) > 5 THEN 'Champion'
        WHEN SUM(SALES) > 5000 THEN 'Loyal'
        WHEN COUNT(*) = 1 THEN 'At-Risk'
        ELSE 'Need Attention'
    END as customer_segment
FROM sales_data_sample
GROUP BY CUSTOMER_NAME;

Profit Analysis by Product Line

-- Calculate profit by product with rankings
SELECT 
    PRODUCT_LINE,
    COUNT(*) as order_count,
    SUM(SALES) as total_revenue,
    SUM(SALES * 0.70) as estimated_cost,
    ROUND(SUM(SALES) - SUM(SALES * 0.70), 2) as total_profit,
    ROUND((SUM(SALES) - SUM(SALES * 0.70)) / SUM(SALES) * 100, 1) as profit_margin_pct,
    ROW_NUMBER() OVER (ORDER BY SUM(SALES) - SUM(SALES * 0.70) DESC) as profit_rank
FROM sales_data_sample
GROUP BY PRODUCT_LINE
ORDER BY total_profit DESC;

Monthly Sales Trends

-- Monthly aggregation with profit calculation
SELECT 
    DATE_FORMAT(cleaned_order_date, '%Y-%m') as month,
    MONTH(cleaned_order_date) as month_num,
    COUNT(ORDER_NUMBER) as transaction_count,
    ROUND(SUM(SALES), 2) as monthly_revenue,
    ROUND(SUM(SALES) * 0.243, 2) as estimated_profit,
    LAG(SUM(SALES)) OVER (ORDER BY DATE_FORMAT(cleaned_order_date, '%Y-%m')) as prev_month_revenue
FROM sales_data_sample
WHERE cleaned_order_date IS NOT NULL
GROUP BY DATE_FORMAT(cleaned_order_date, '%Y-%m')
ORDER BY month;

📊 Business Insights Discovered

Strategic Opportunities

Geographic Concentration Risk: 51.5% of customers in USA; EMEA represents 38% untapped growth
Product Portfolio Imbalance: Classic + Vintage Cars = 68% of profit; Trains = 3.3% (rationalization candidate)
Seasonality Window: Q4 drives 28% of annual sales; inventory planning crucial for Q3
Customer Value Concentration: Top 20% of customers = 65% of revenue (classic 80/20 Pareto principle)
Pricing Strategy Opportunity: 0-10% discounts most effective; excessive discounting erodes profitability

Financial Impact (Estimated)

Initiative	Potential Annual Impact
Geographic expansion to EMEA	+$50K-100K revenue
Portfolio optimization (SKU rationalization)	+$50K-80K profit
VIP retention program for Champions	+$50K-200K (prevent churn)
Discount strategy optimization	+$100K-150K profit
Total Estimated Annual Opportunity	$200K-500K

🎓 Skills Demonstrated

Technical Skills

✅ Advanced SQL: Window functions, CTEs, views, date manipulation, subqueries, GROUP BY analysis
✅ Database Optimization: Query optimization, indexing strategy, performance tuning
✅ Data Transformation: ETL concepts, derived columns, data standardization
✅ Tableau/BI: Dashboard design, calculated fields, filters, parameters, data blending
✅ Data Analysis: RFM segmentation, CLV calculation, trend analysis, correlation analysis
✅ Statistical Analysis: Time series decomposition, seasonal pattern identification

Business Skills

✅ Business Intelligence: KPI design, metrics definition, dashboard strategy
✅ Problem Solving: Identified real business issues and quantified opportunities
✅ Communication: Translated technical analysis into executive-friendly insights
✅ Strategic Thinking: Portfolio optimization, geographic expansion, customer segmentation
✅ Project Execution: End-to-end delivery from raw data to actionable insights

Problem Statement: "2,823 transactions from 2003-2005 in raw format lacked business structure"
Analysis Approach: "Performed SQL data cleaning, RFM segmentation, profitability analysis"
Key Insights: "Identified geographic risk (51% USA), product concentration (68% from 2 categories), seasonality (28% Q4 lift)"
Deliverable: "Created interactive Tableau dashboard with 6 data sources and 8+ visualizations"
Impact: "$200K-500K profit optimization opportunity and 40% faster reporting capability"

Business Metrics: Explain CLV ($5,628), RFM framework, segment definitions
Exploratory Analysis: Discuss how you discovered the 28% seasonal variance
Visualization: Explain chart selection rationale and design principles
Insights Communication: Demonstrate translation of technical findings to business recommendations
Trade-offs: Discuss assumptions (70% cost model, 3-year CLV window)

📈 Project Statistics

Total SQL Queries:           15+ (cleaning, analysis, Tableau preparation)
Lines of SQL Code:           500+
Documentation Pages:         6 (README, Summary, Resume, Quick Start, Setup, Assets)
Visualizations Designed:     8+ chart types
Tableau Data Sources:        6 optimized queries
Database Tables/Views:       7 (original + derived analytical views)
Query Optimization:          Yes (indexed, pre-aggregated for performance)
Data Quality Validation:     100% (0% null critical fields)
Business Opportunities ID:   5+ major optimization opportunities

📚 Learning Resources Used

MySQL Date & Time Functions Documentation
SQL Window Functions (NTILE, ROW_NUMBER, RANK)
RFM Analysis Framework (Marketing Analytics)
Customer Lifetime Value Calculation Methodology
Tableau Best Practices for Dashboard Design
Business Intelligence Concepts and KPI Design

📌 For Quick Reference

Most Important Metrics

Metric	Value	Implication
Champions Value	$547K	Focus retention strategy
Geographic Concentration	51.5% USA	Diversification needed
Top Product Profit	$886K (36.8%)	Expand Classic Cars
Seasonal Lift	28% (Q4 vs avg)	Plan inventory for Q3
Customer LTV	$5,628	Justify acquisition spend

Query Performance Tips

All queries indexed on DATE and PRODUCT_LINE
Pre-aggregated views eliminate unnecessary calculations
Parameterized queries for Tableau reduce refresh time
Consider materialized views for frequently accessed metrics Markets Covered: 28 countries

Tableau Public link

https://public.tableau.com/app/profile/mansi.waghmare1807/viz/CustomerMarketAnalysis_17722429660290/CustomerMarketAnalysis https://public.tableau.com/app/profile/mansi.waghmare1807/viz/SalesPerformanceOverview_17722416499340/SalesPerformanceOverview

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CSA in MySQL.sql		CSA in MySQL.sql
CSA.ipynb		CSA.ipynb
LICENSE		LICENSE
README.md		README.md
sales_data_sample.csv		sales_data_sample.csv

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Advanced Sales Analytics & Customer Intelligence using Python, SQL & Tableau

SQL • Python • Tableau Business Intelligence Project

📊 Project Overview

🛠 Tech Stack

🏗 Project Architecture

📈 Dataset Overview

📊 Dashboard Preview

Customer & Market Analysis Dashboard

Sales Performance Overview Dashboard

📊 Interactive Tableau Dashboards

Sales Performance Overview

Customer & Market Analysis

📊 Key Insights Discovered

🏆 Top Product Category

🌍 Geographic Concentration

👑 High-Value Customers

📅 Seasonal Sales Pattern

💡 Business Opportunities

🧩 Skills Demonstrated

Technical Skills

Business Skills

👩‍💻 Author

⭐ If you found this project useful, consider giving it a star!

Time Series & Trend Analysis

🎯 Top 5 Key Achievements

1. Advanced Data Cleaning & Standardization

2. RFM Customer Segmentation & Monetization Analysis

3. Multi-Level Sales Trend Analysis

4. Product Line Profitability Analysis & Optimization

5. Interactive Tableau Business Intelligence Dashboard

📈 Dataset & Database Schema

Data Overview

Key Financial Metrics

🛠 Technical Stack

Database Layer

Analytics Layer

Business Intelligence & Visualization

📊 Key Analyses Performed

1. Sales Performance Analysis

2. Customer Segmentation

3. Profitability Deep Dive

4. Product Performance Ranking

5. Seasonality & Demand Patterns

🔍 SQL Query Highlights

Data Cleaning & Standardization

RFM Customer Segmentation

Profit Analysis by Product Line

Monthly Sales Trends

📊 Business Insights Discovered

Strategic Opportunities

Financial Impact (Estimated)

🎓 Skills Demonstrated

Technical Skills

Business Skills

📈 Project Statistics

📚 Learning Resources Used

📌 For Quick Reference

Most Important Metrics

Query Performance Tips

Tableau Public link

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