BoomBikes, a US-based bike-sharing provider, faced a sharp revenue decline due to the COVID-19 pandemic. The project aimed to model post-quarantine bike demand using a linear model, identifying key variables influencing revenue and predicting demand. The goal was to align BoomBikes’ strategies with customer needs and market trends, ensuring profitability.

Insights:

• Seasonal Demand: Fall (Category 3) showed the highest median demand, while Spring (Category 1) had the lowest.
• Yearly Comparison: 2019 had a higher user count than 2018.
• Weekly Trends: Demand remained stable throughout weekdays, suggesting the weekday variable might not be crucial for predictions.
• Weather Impact: Clear weather saw user counts ranging from 4000 to 6000.
• Monthly Trends: Mid-month bookings often exceeded 4000, with October having the highest demand count.
• Holiday Effect: Lower user count during holidays.
• Working Days: Bookings ranged from 4000 to 6000, unaffected by whether it’s a working day.

Recommendations:

• Focus on January, July, September, November, and December due to higher demand.
• Monitor weather conditions as demand increases with warmer temperatures.
• Prepare for high demand during winter to meet customer needs.

By implementing these insights, BoomBikes can strategically plan to meet anticipated post-quarantine demand, ensuring customer satisfaction and boosting profitability.