Blinkit Sales Insight

PROBLEM STATEMENT

As India’s quick commerce platforms race for growth, understanding customer behavior isn’t just helpful — it’s essential. Blinkit’s massive order volumes create a goldmine of insights, but much of it goes untapped.

That’s when I thought: 👉 “What if we could break down every sale, outlet, and product into a story — one that helps business leaders take action, fast?”

And that’s how this dashboard came to life — blending business KPIs with user behavior, product trends, and outlet performance in one interactive Power BI experienc

MY APPROACH

Using real Blinkit-style data and core analytics tools, I built an end-to-end dashboard that answers not just what is happening, but why. Here's how it was done:

Data Wrangling & Cleaning: Processed large CSVs using Python (Pandas, NumPy) and cleaned noisy data for accurate analysis.
Data Modeling: Connected MySQL data to Power BI, creating relational models for smooth cross-filtering.
Data Visualization: Built interactive charts to break down:
- Sales by Category, Outlet Type, and Year
- Average Order Value
- Customer Ratings
- Low-performing vs. high-performing SKUs
Insights Layer: Embedded recommendations into the dashboard using DAX, storytelling visuals, and tooltip interactions.
Design & Usability: Focused on clean layout, emoji-coded insights, and dark theme visuals optimized for storytelling.

WHY IT MATTERS?

This dashboard isn’t just about pretty charts — it’s a strategic tool that helps Blinkit answer questions like:

What are our top revenue-generating outlets and why?
Which categories are slipping — and how can we recover?
What’s the average value per transaction and how can we raise it? Are newer stores underperforming compared to established ones?

With these insights, decision-makers can prioritize growth channels, optimize inventory, and run targeted marketing campaigns — all powered by data.

Key Features At a Glance

📊 Sales by Product Category & Outlet Type

💰 Average Order Value (AOV): $141

⭐ Customer Ratings integrated

🔎 SKU-level performance metrics

🧊 Underperforming categories (Dairy, Frozen Foods) highlighted

📈 Growth opportunities in Tier-3 cities + new outlets

📌 Strategic suggestions based on data, not assumptions

LESSONS LEARNED

This project helped me:

Apply business-oriented thinking to data analysis.
Build custom KPIs and calculated metrics using DAX.
Craft actionable stories from complex retail data.
Identify underperforming categories and propose turnaround strategies. Use Python + Power BI + SQL together in a unified project pipeline.

Recommendations & Business Impact

✅ Expand Supermarket Type 1 Stores – These outlets generate the highest revenue, making them a key focus for expansion. More stores of this type can boost overall profitability.

✅ Strengthen Presence in Tier 3 Cities – 39% of total sales come from small cities, proving high demand in these areas. Investing in logistics and local marketing can further increase revenue potential.

✅ Stock More Low-Fat Products – With 65% of customers preferring low-fat options, ensuring a steady supply of these products can drive higher sales and customer retention.

✅ Boost Sales for Dairy & Frozen Foods – These categories are underperforming despite their importance. Targeted promotions, discounts, and improved inventory placement can enhance their visibility and demand.

✅ Improve Marketing for New Outlets (2020 & 2022) – Newly established outlets show lower sales compared to older ones. Stronger promotional campaigns, introductory offers, and customer engagement strategies can help increase footfall and revenue.