[Project] Sales & Profitability Analysis (SQL + Power BI)

Why I worked on this project

Sales data is everywhere. Revenue charts look great in presentations. But one question kept coming back while I was learning and working with commercial data:

Does high sales actually mean good performance?

In many businesses, revenue becomes the main success metric - often without fully understanding what sits underneath.. This project was my way of stepping back and asking a more uncomfortable, but far more useful question: Where does profit really come from, and where does it quietly disappear?

The problem I wanted to understand

The dataset showed strong sales across multiple global markets. On the surface, everything looked healthy.

But from a business perspective, that raised several doubts:

• Are all markets equally profitable?

• Are discounts helping growth or quietly damaging margins?

• How much do operational costs, like shipping, actually matter?

Revenue alone could not answer those questions - so I shifted the focus to profitability and cost efficiency.

How I approached the analysis

Rather than jumping straight into dashboards, I started with the fundamentals.

I used SQL to calculate and validate core business metrics such as profit margins, average discounts, and shipping cost ratios. This step mattered to me because I wanted to be confident that every number used later in visualisations actually made business sense.

Once the metrics were solid, I moved into Power BI - not to “decorate” the data, but to compare markets side by side and make patterns visible for non-technical stakeholders.

The goal was clarity, not complexity.

What stood out during the analysis

A few patterns became obvious very quickly:

Some markets with impressive sales volumes were not performing nearly as well on profit margins. At the same time, other markets generated solid margins despite much lower revenue.

Discounting turned out to be more nuanced than expected. In some cases it aligned with lower margins - but in others, the relationship was far from straightforward.

Shipping costs also played a much bigger role than I initially assumed. Differences in cost efficiency across regions had a noticeable impact on overall profitability.

What looked like “good performance” at revenue level often told a very different story once margins and costs were included.

Why this matters from a business point of view

If decisions are made purely on revenue rankings, businesses risk prioritising the wrong markets or products.

This type of analysis supports more grounded conversations around:

• which markets are actually worth scaling,

• where pricing or discount strategies should be reviewed,

• and where operational efficiency may be undermining otherwise strong sales.

It shifts the discussion from “Where do we sell the most?” to “Where do we make money - and why?”

Tools used (kept intentionally simple)

• SQL for aggregation and metric calculation

• Power BI for modelling, comparison, and insight communication

The focus was on transparent logic and business interpretation rather than advanced technical features.

Want to see the technical details?

If you’re interested in the technical side, the full project - including SQL queries and the Power BI report - is available on GitHub. That’s where the full structure, calculations, and visuals live.

Final thought

If this were a real business engagement, the next step would be to go deeper into pricing rules, logistics contracts, and discount policies to understand why certain markets underperform on margin - and which levers could realistically improve profitability.

This project reinforced something I keep seeing in analytics: good questions matter more than impressive charts.