data analysis SQL

BikeStore Analysis Project – NTI SQL Exploration

Over the past few weeks, I’ve been diving deep into a SQL-powered project as part of my NTI training, analyzing the comprehensive BikeStore database to extract real business insights.

Here’s a glimpse of the questions I tackled using SQL:

Which bike is the most expensive, and what might justify its premium price?

How many customers does BikeStore have, and should we count those with rejected orders?

Sales performance per store, most & least sold categories, and state-wise performance

Which brand is the most popular, and which store holds the most of it?

Tracked individual customer journeys:

‣ What did they buy?

‣ Who handled their order?

‣ When was it shipped?

Example Insight:

The Trek Domane SLR 9 Disc – 2018 topped the price chart

We found over X active customers (excluding rejected orders)

Children’s bikes were tracked for the past 8 months to measure seasonal demand

Uncovered pending orders, underperforming categories, and optimized store inventory by brand!

Tools Used:

SQL (Joins, Aggregations, Filtering, Grouping)

Business Logic Interpretation

Problem-solving with real-world datasets

This project sharpened my data analysis skills, especially in transforming raw data into clear answers and insights.

If you’re passionate about data or working on similar projects, I’d love to connect and exchange ideas!