"Data doesn't speak to everyone.
It speaks to those who've trained their eyes to see beyond the numbers." ✨
It began with a single question from a retail boardroom —
"We have mountains of transaction data. But where do we grow next?"
150,000 transactions. 12,000 customers. 4 regions. 36 months of business reality.
The data existed. But the intelligence didn't — until now.
Enter THE PARTH SHAH — analyst, architect, storyteller.
What followed wasn't just a data project.
It was a corporate intelligence operation — built from raw CSV chaos into a boardroom-ready command center where every query answered a question, every chart told a story, and every insight became a decision.
This isn't a school assignment.
This is retail strategy, engineered.
Six metrics. Six stories. One business truth — this company has everything it needs to grow.
Transform 150,000 raw transactions into a Fortune-500-grade analytics system
capable of guiding a multi-region retail company's Q3 strategy.
The mission was clear — and uncompromising:
- 🔍 Extract the truth buried in ₹2.37 Billion of transaction data
- 🗺️ Identify which regions win, which need help, and which are untapped
- 📈 Decode which categories are rising and which are silently dying
- 👤 Find the loyal customers and protect them before the competition does
- 📊 Build a dashboard that a CEO would actually use on a Monday morning
Mission accepted. Mission completed. ✅
| Dimension | Specification |
|---|---|
| Total Transactions | 1,50,000 rows |
| Date Range | January 2023 — December 2025 (3 full years) |
| Total Revenue | ₹ 2,37,65,84,898 (~₹237.7 Crore) |
| Unique Customers | 12,000 |
| Regions | East · West · North · South |
| Categories | 10 (Electronics, Fashion, Grocery, Furniture, Sports, Beauty, Appliances, Stationery, Toys, Accessories) |
| Products | 90+ realistic SKUs across all price bands |
| Sales Channels | Online · Offline (digital growth embedded) |
| Payment Modes | UPI · Credit Card · Cash · Net Banking |
| File | Role |
|---|---|
RetailTransactions.csv |
The foundation — 150,000 rows of raw business truth |
RetailAnalysis.sql |
7 SQL queries + full DB setup + executive commentary |
RetailAnalysis_Dashboard.xlsx |
10-sheet Excel dashboard — charts, KPIs, insights |
DAX_Measures.txt |
30 Power BI DAX measures — every metric engineered |
Insights.txt |
7 strategic business findings, boardroom-ready |
Executive_Report.txt |
Full consulting report — findings, strategy, action plan |
Data_Dictionary.txt |
Column definitions, business context, data lineage |
images/ |
8 premium dark-theme visualization screenshots |
README.md |
This legendary document |
36 months of business rhythm captured in one chart.
Notice the October peaks every year — Diwali doesn't lie.
And the steady climb in Average Order Value — premiumisation is real and accelerating.
West owns the crown. 35.5% revenue share. Highest AOV at ₹18,750.
North is the white space. 13.1% share — an underpenetrated market with ₹47M+ upside.
The donut tells the concentration story. The bar tells the AOV story.
Together, they tell the strategy.
Each query isn't just a SQL statement.
It's a business question answered with mathematical precision.
| # | Query Mission | Business Revelation |
|---|---|---|
| 1 | Total sales per region — last quarter | Who ruled Q4 2025? |
| 2 | Top 5 best-selling products by revenue | Which SKUs are carrying the company? |
| 3 | Monthly sales trend across all regions | When does the business breathe hardest? |
| 4 | Region-wise contribution % to total | Where is the concentration risk hiding? |
| 5 | Online vs Offline — month-by-month | How fast is the digital shift moving? |
| 6 | Category trend — rising vs falling | Which categories are the future? Which are the past? |
| 7 | Customers with 10+ purchases | Who are the loyal soldiers driving the empire? |
Electronics dominates. All 5 top revenue generators are electronics SKUs.
iPhone 15 Pro alone drives ₹240M — from just 1,843 orders.
These 5 SKUs = 40.4% of all company revenue.
Stockout any one of these? The P&L feels it the same day.
Beauty (+9.1%). Fashion (+8.8%). Electronics (+7.8%) — The growth engines. Feed them.
Appliances (-7.7%). Stationery (-7.4%) — The declining verticals. Prune them.
The arrows above each bar don't lie.
This chart is your portfolio reallocation decision, visualized.
The golden dashed line marks the inflection point — June 2025.
Online revenue crossed offline for the first time in company history.
From 35.3% online share in Jan 2023 to 54.9% by Dec 2025.
This isn't a trend. This is a structural transformation.
The bottom panel tracks the online % in real time — watch it climb past 50%.
6,251 customers made 10+ purchases — a 52% repeat rate that most retail businesses dream of.
The top customer (CUST00406) placed 67 orders worth ₹17.3 Lakhs over 3 years.
Gold bars = Platinum tier (₹5L+ spend). These are not customers — they are assets.
A VIP loyalty programme for this cohort alone can unlock ₹35M+ incremental revenue.
Left: UPI dominates at 35% — India's payment revolution reflected in the data.
Credit Card at 28% signals high-AOV transactions perfect for co-brand reward schemes.
Right: Q4 2025 region performance — West leads at ₹72M, North trails at ₹26M.
That gap between West and North is your next growth campaign.
What a McKinsey partner would tell the CEO on page 1.
| Priority | Initiative | Revenue Impact | Timeline |
|---|---|---|---|
| 🔴 HIGH | North region targeted marketing | +₹47M | Q1–Q2 2026 |
| 🔴 HIGH | VIP Loyalty Programme launch | +₹35M | Q2 2026 |
| 🔴 HIGH | Beauty & Fashion assortment expansion | +₹28M | Q1 2026 |
| 🟡 MED | Online UX & checkout optimization | +₹22M | Q2 2026 |
| 🟡 MED | Diwali early campaign (Oct 1 launch) | +₹18M | Sep 2026 |
| 🟡 MED | Appliances/Stationery SKU pruning | +₹12M margin | Q1 2026 |
| 🟢 LOW | Credit card co-brand programme | +₹15M LTV | Q3 2026 |
Total Identified Revenue Opportunity: ₹177M+ over 12 months
Power BI Desktop → Get Data → Text/CSV → RetailTransactions.csv
Transform: Date (Date type) | TotalAmount (Decimal) → Load
DateTable = ADDCOLUMNS(CALENDARAUTO(),
"Year", YEAR([Date]),
"Month", FORMAT([Date], "MMMM"),
"Quarter", "Q" & QUARTER([Date]),
"Year-Month", FORMAT([Date], "YYYY-MM"))
{
"name": "RetailDark",
"dataColors": ["#4361EE","#4CC9F0","#06D6A0","#E94560","#F5A623","#7209B7"],
"background": "#1A1A2E",
"foreground": "#FFFFFF",
"tableAccent": "#4361EE"
}30 professional DAX measures. Every metric the C-suite could ever need.
// Total Sales
Total Sales = SUM(RetailTransactions[TotalAmount])
// Average Order Value
Avg Order Value = DIVIDE([Total Sales], [Total Transactions], 0)
// Online Share %
Online Sales % = DIVIDE([Online Sales], [Total Sales], 0)
// Month-over-Month Growth
MoM Growth % = DIVIDE([Total Sales] - [Sales Previous Month], [Sales Previous Month], BLANK())
// Region Contribution
Region Contribution % = DIVIDE([Total Sales],
CALCULATE([Total Sales], ALL(RetailTransactions[Region])), 0)
// Loyal Customers
Loyal Customers Count = COUNTROWS(
FILTER(SUMMARIZE(RetailTransactions, RetailTransactions[CustomerID],
"PurchaseCount", COUNTROWS(RetailTransactions)), [PurchaseCount] > 10))
→ Full 30-measure library in DAX_Measures.txt
CREATE DATABASE RetailAnalyticsDB;
USE RetailAnalyticsDB;
LOAD DATA INFILE 'RetailTransactions.csv'
INTO TABLE RetailTransactions
FIELDS TERMINATED BY ',' ENCLOSED BY '"'
LINES TERMINATED BY '\n' IGNORE 1 ROWS;sqlite3 retail.db
.mode csv
.import RetailTransactions.csv RetailTransactions
.read RetailAnalysis.sqlCREATE DATABASE retail_analytics;
\c retail_analytics
\i RetailAnalysis.sql
\COPY RetailTransactions FROM 'RetailTransactions.csv' CSV HEADER;Because I don't analyze data — I interrogate it.
🎯 Every query has a business purpose — not just a syntax.
📊 Every chart earns its pixel — not just decoration.
🧭 Every insight demands action — not just observation.
💡 Every file breathes intelligence — not just information.
This isn't a submission.
This is a standard.
╔════════════════════════════════════════════════════════════╗
║ "Don't just read the data. ║
║ Make the data read the business — and confess." ║
║ — THE PARTH SHAH ⚡ ║
╚════════════════════════════════════════════════════════════╝
- 🗂️ Start with
RetailTransactions.csv— understand the raw material. - 🔍 Open
RetailAnalysis.sql— run the 7 queries, read the business commentary. - 📊 Explore
RetailAnalysis_Dashboard.xlsx— let the visuals do the talking. - 🧮 Copy
DAX_Measures.txtinto Power BI — build the live intelligence layer. - 📄 Read
Executive_Report.txt— present with the confidence of a consultant. - 💡 Summarize with
Insights.txt— 7 bullets that win any boardroom.
💬 Inspired by this creation?
Let's collaborate, build, and redefine how data is understood.
👉 Connect with me on LinkedIn 🔗
⭐ Crafted by THE PARTH SHAH —
Because even retail data deserves a masterpiece.
Red & White Skill Education | Practical Exam | Business Case Study