Startup Expansion

<aside> 🧾

Quick Summary

Role: Senior Data Analyst & BI Developer
Industry: Startup Growth & Retail Expansion </aside>

<aside> 🚨

Live Links

🔗 Interactive Power BI Dashboard: Click Here
GitHub Repository (Data Processing & Analysis): Click Here </aside>

Executive Overview

A US-based startup approached me to support a critical expansion decision, as they lacked clarity on where to open new branches and how to allocate marketing investments effectively.

I developed a data-driven expansion analytics solution that evaluated store performance, regional demand, and marketing ROI, enabling leadership to confidently identify high-potential locations and optimize expansion strategy.

Why it mattered: Poor expansion decisions could lead to significant financial losses and wasted resources.
What changed (result): Data-driven location selection led to successful expansion, with the new branch becoming one of the top-performing locations in annual sales.

Project Summary & Scope

To support high-stakes expansion planning, I analyzed 50+ potential locations, engineering a robust regional comparison framework to evaluate revenue, profitability, regional demand, and marketing efficiency.

This multi-dimensional analysis eliminated decision ambiguity for leadership, highlighting the regions with the strongest financial potential and structural stability.

Interactive Demo

<aside> 📽️

Quick Walkthrough: From Raw Data to Business Impact ✨

</aside>

https://drive.google.com/file/d/15QV-LmI-ecoKoydLKYxAVhyuXZ376hXS/view?usp=sharing

The Business Challenge

What was broken / costly / risky?

The Solution & Strategic Approach

Methodology (thinking)

Goal definition: Identify the most profitable and high-potential locations for expansion based on performance and demand indicators.
Success metrics:
- Revenue growth in new locations
- Marketing ROI improvement
- Store performance consistency
Analytical approach: Comparative analysis between new and existing stores, regional performance evaluation, and ROI-driven decision modeling.
Validation plan: Cross-analysis of revenue, profit, and marketing spend across different locations to ensure decision reliability.

Execution (doing)

Data sources: Sales data, store performance metrics, marketing spend data across regions, cities, and stores
Modeling / transformations: Cleaned and structured multi-source data using Python and SQL to enable consistent performance comparison
Dashboard build: Developed an interactive Power BI dashboard with filters for regions, cities, and store types to support decision-making
Deployment / handover: Delivered a scalable analytics solution enabling leadership to explore expansion scenarios and optimize strategy