BOLT RIDE ANALYTICS
The online taxi industry is facing mounting pressure as drivers navigate an increasingly competitive market. Recently in Kenya, Bolt riders staged demonstrations protesting unfavorable pricing policies that they claim undermine their earnings. These protests highlight the tension between maintaining profitability for the platform and ensuring fair compensation for the drivers who keep the service running.
​
This project provides a timely and in-depth analysis of Bolt Ride operations, focusing on key factors such as ride prices, booking fees, tips, and payment methods. By examining data from January and February, I aimed to shed light on critical aspects of the business that directly impact the Bolt drivers. The analysis looked at how ride prices are structured, the variability in tips, and the distribution of revenue across different days and regions.
In light of the demonstrations, this project therefore seeks to address pressing questions like: How are pricing models affecting driver earnings? and thus help uncover potential imbalances that may have contributed to the recent protests.
​
Key insights were visualized through detailed charts, illustrating trends in ride pricing, fees, and payment preferences.
An interactive dashboard was developed to enable the Bolt driver explore these insights, empowering them to identify potential areas for improvement. By leveraging this analysis, Bolt can re-evaluate its pricing strategies, ensuring a fairer distribution of revenue that balances driver satisfaction with profitability, ultimately fostering a more sustainable and harmonious relationship with its riders.
Source of Data
MySQL - for data cleaning, formatting, and basic analysis.
Tableau - for visualizing key metrics and illustrating trends.
Tools & Files
Inquiry Questions
The project aimed to address the following operational questions regarding Bolt ride services:
-
What are the peak and off-peak hours for Bolt rides?
-
Which pickup locations are the most frequented?
-
How does revenue distribution vary by day, and what temporal trends can be identified?
Key Insights
-
Analysis revealed that peak ride activity occurs around 10 am, while off-peak hours are observed at approximately 9 pm.
-
The most frequented pickup locations included the Central Business District (CBD) of Nairobi, Jomo Kenyatta International Airport (JKIA), and Donholm.
-
In January, the driver's revenue peaked at over KShs 10,000, while the lowest recorded revenue was less than KShs 1,000. In February, the highest revenue surpassed KShs 12,000, while the lowest revenue was approximately KShs 2,500.
-
Analysis also revealed that the rides resulted in no tips for drivers, indicating a potential issue with customer satisfaction or service perception.​​​​​
Recommendations
1. Optimize Shift Schedules: Drivers should consider aligning their schedules with peak ride activity times, primarily around 10 a.m. and early afternoon, as well as late night around 11 p.m. This approach will maximize their earning potential by capturing more rides during high-demand periods.
2. Utilize High-Demand Locations: Drivers should also focus on operating in the most frequented pickup locations, such as the Central Business District (CBD), and Jomo Kenyatta International Airport (JKIA). Positioning themselves strategically in these areas during peak times can increase ride requests and reduce wait times.
​
3. Enhance Customer Engagement and Service Quality: to improve the likelihood of receiving tips, drivers should focus on delivering exceptional service by: greeting and engaging passengers in friendly and light-hearted conversations, maintaining cleanliness, providing comfort: Offering amenities such as bottled water or adjusting the temperature to passenger preferences can enhance comfort during the ride.
​
4. Increase Ride Availability and Engagement: To maximize revenue, the driver should focus on increasing ride availability by aiming to drive during peak demand hours and avoiding taking days off when ride requests are likely to be high. The driver could also ensure they stay actively engaged with the app to accept ride requests promptly.​​
THE DASHBOARD
In the Tableau dashboard, I selected a combination of KPIs and charts that provide a comprehensive view of the key performance indicators and critical operational metrics. I used:
-
Horizontal bar graphs to display the top 5 peak and off-peak hours, which are critical for understanding demand patterns.
-
A histogram of revenue by payment method that offered insights into customer preferences.
-
A treemap showing the top 5 popular pickup locations highlights key areas of demand, which is essential for targeted marketing and operational focus.
-
Line graphs for revenue growth over time that provided a clear view of financial performance trends, which is invaluable for forecasting and strategic planning.
The data visualization of the analyzed data is shown below in the Dashboard:
To filter through and explore the dashboard from different perspectives, view the Tableau visualizations here!
The analysis of the Bolt Ride operations provides valuable insights into ride activity patterns and revenue generation for drivers. By identifying significant peak times and popular pickup locations, drivers can optimize their availability to capture more rides during high-demand periods.
By adopting these targeted recommendations, drivers can improve their service efficiency, enhance customer satisfaction, and ultimately strengthen their position in the competitive online taxi market.
Conclusion
THANK YOU!
Thank you for taking the time out to view my project!
In case you would like to discuss this project further, feel free to email me at: