Bike Sharing Data Analytics: From SQL Queries to Business Insights

One of the most exciting parts of working with data is when numbers start telling a story. Recently, I worked on a project analyzing bike-sharing data across two years. What started as a bunch of SQL tables ended up revealing clear business opportunities and strategies for growth. Let me walk you through the journey.

Bringing the Data Together

The dataset was split across two years, so the first step was to combine them. A simple SQL UNION helped me merge the two tables into one, creating a complete view of riders, seasons, hours, and weekdays. To enrich this data, I joined it with a cost table that contained details like price per ride and COGS (Cost of Goods Sold).

With this foundation, I could calculate revenue and profit for every ride record. Suddenly, it was not just about counting riders but about understanding financial performance.

Key Metrics That Matter

When you work with raw data, it is easy to get lost in millions of rows. That is why I pulled out some core KPIs that summarize the business at a glance:

• Total Riders: 3.29 million

• Total Revenue: $15.18 million

• Total Profit: $10.45 million

• Average Profit Margin: 45 percent

These numbers give a clear picture of scale and profitability. But KPIs alone are not enough. What truly matters is uncovering trends that explain why the numbers look the way they do.

What the Data Revealed

Once the basics were in place, the real insights started to emerge:

• Peak Hours: The busiest times were midday (10–15 hours) and early evening. This is when marketing or surge pricing could have the biggest impact.

• Day of Week: Wednesday and Friday consistently had the highest activity.

• Seasonal Impact: Summer was the strongest season, with revenue hitting nearly $5 million.

• Rider Behavior: Registered riders made up around 81 percent of revenue, highlighting the importance of retention over one-time casual users.

Each of these insights gives management something concrete to act on.

Should Prices Increase?

The most important question was about pricing. The current average price was around $4.49. Based on consistent year-over-year growth and strong demand, I tested what would happen with a moderate price hike.

The analysis suggested that a 5 to 10 percent increase could be sustainable without significantly hurting ridership. That would bring the average price into the $5.24 to $5.49 range.

The Business Strategy

Of course, increasing prices is not something you do overnight. My recommendation was to roll this out in stages:

1. Benchmark against competitors to ensure pricing remains competitive.

2. Run a pilot in a few locations for 4 to 8 weeks and closely monitor churn.

3. Refine pricing if signs of customer resistance appear.

4. Communicate improvements in service or sustainability alongside the increase.

This balances financial opportunity with customer experience.

Final Thoughts

What I loved about this project was how it connected pure SQL work to real-world business questions. It is one thing to write queries, but it is another to translate them into a strategy that executives can actually use.

By the end, I was not just looking at tables of riders and costs. I was answering questions about when people ride, which customers matter most, and how the company should think about pricing.

That is the power of data analytics. Numbers become decisions, and decisions drive growth.