Mobile Sales Analytics Dashboard

I built an interactive Power BI dashboard for analyzing mobile phone sales data across geographies, channels, and customer demographics to derive actionable business insights.

Data Cleaning, Data Modeling, DAX, Data Visualization.

This project was built using a semi-structured dataset from Onyx Data of Mobile Sales 2024. The dataset included a flat sales table, along with Dim_Product Dim_Location tables. However, it didn’t include a proper Date table or Customer table, which meant I had to handle data modeling from the ground up.

I started by loading the data into Power Query and doing some exploratory analysis to get a sense of the structure and spot any issues. From there, I cleaned and transformed the data directly in Power BI to make it usable for reporting.

Since key dimensions were missing, I created a custom Date table and Customer table to set up a proper star schema by linking the fact and dimension tables through physical relationships. This structure made it possible to write reliable DAX measures and use time-based functions for insights like monthly trends, running totals, and cumulative revenue.

Everything I built, from data modeling and DAX calculations to visuals, was designed to answer real stakeholder questions, such as:

Which phone models are top performers?

Which countries and sales channels drive the most revenue?

What sales trends emerge over time?

For the first time in my journey, I used a Pareto Chart to apply the 80/20 rule and discovered that just 7 mobile models contribute over 70% of total sales. This insight confirmed that a small portion of the product lineup delivers most business value. As a result, stakeholders could prioritize inventory, marketing, and distribution strategies around these top performers and reallocate resources from low-impact models.

Stakeholders needed a clear understanding of which mobile products, regions, and sales channels were driving revenue to optimize inventory, marketing, and sales strategies.

Only 7 mobile models accounted for over 70% of total revenue.

A Pareto Chart revealed that the top 20% of brands generated 80% of revenue.

Certain countries consistently outperformed others, despite similar customer counts.

Online channels showed higher average order values than physical stores.

Sales peaked in Q3, suggesting seasonal demand patterns.

Focus marketing and stock on high-performing models to maximize ROI.

Reallocate resources away from underperforming models.

Increase promotion in top-performing regions and optimize logistics.

Boost investment in online channels to capitalize on higher order values.

Plan seasonal campaigns around Q3 to match peak buying behavior.

Github Repository Link :