This post was written with Vipul Jain from BluSmart Mobility.
At BluSmart, South Asia’s first and largest zero-emission ride-hailing service and EV charging infrastructure network, we’re on a mission to Decarbonize Mobility at Scale. Since our inception in 2019, we’ve grown at an impressive year-over-year rate, with Amazon Web Services (AWS) as our trusted cloud partner. As we’ve scaled from a humble fleet of 70 electric cars to over 8,500 EVs as of October 2024, completing approximately 20 million fully electric trips, we’ve relied on data-driven insights to fuel our growth and optimize our operations.
In this post, we’ll explore how Amazon QuickSight has become an integral part of our business intelligence strategy, enabling us to make informed decisions, improve operational efficiency, and enhance customer experiences.
The challenge: Scaling sustainably with data-driven insights
As pioneers in the electric ride-hailing industry, we faced exciting opportunities to come up with solutions to manage a growing fleet, enhance driver performance, and deliver exceptional customer experiences. Each step has strengthened our resolve to innovate and excel. Our previous analytics tools provided a strong foundation, but as our operations expanded, we sought a more advanced solution to seamlessly manage the increasing volume and complexity of our data. We needed a system that could:
- Provide real-time insights into our fleet performance and customer behavior
- Scale effortlessly to accommodate our rapid growth
- Offer advanced analytics capabilities, including machine learning-powered predictions
- Integrate seamlessly with our existing AWS infrastructure
- Enable self-service analytics for team members across various departments
Choosing Amazon QuickSight: A perfect fit for BluSmart
After evaluating several business intelligence and embedded analytics tools, we chose QuickSight. The high-performance, in-memory engine (SPICE – Super-fast, Parallel, In-memory Calculation Engine) used by QuickSight enables rapid data analysis and visualization, which is essential for our real-time decision-making processes, especially during peak operational periods. The pay-per-session pricing model, with no upfront licensing costs, makes it a cost-effective alternative to traditional on-premises BI tools, allowing us to allocate resources more efficiently and invest in other critical areas of our operations.
Furthermore, as a fully managed cloud service, QuickSight allows our users to connect to various data sources, create interactive dashboards, and share insights without requiring software installation. This has been a game-changer for our teams, facilitating collaboration across departments and enhancing our overall productivity
Implementing QuickSight: A journey to data-driven decision making
Our implementation of QuickSight was swift and seamless, thanks to its native integration with our existing AWS infrastructure. We connected QuickSight to our primary data sources, including Amazon Simple Storage Service (Amazon S3), Amazon Relational Database Service (Amazon RDS), and Amazon Redshift, allowing us to use our current data ecosystem fully, as shown in the following figure.
To help ensure secure access, we integrated QuickSight with Google Workspace using SAML-based single sign-on (SSO). This approach allows our team members to access QuickSight using their Google Workspace credentials, enhancing security and convenience through centralized user management and multi-factor authentication.
Transforming operations with QuickSight dashboards
QuickSight has enabled us to create a suite of interactive visuals and dashboards that provide real-time insights into various aspects of our business. Here are some key examples:
Strategic infrastructure planning through user growth insights – By analyzing the trends in daily active user growth, shown in the preceding figure, we gain valuable insights that enable us to anticipate future user demand. We use the time-series data forecasting option in QuickSight to strategically plan and allocate infrastructure and resources ahead of time, helping to ensure that our systems are well-prepared to handle increased usage without disruptions. This proactive approach supports scalability, optimizes resource allocation, and enhances overall service reliability for our users.
Optimizing promo code distribution with seasonal and usage insights – This dashboard provides insights into the distribution of promo codes, allowing us to monitor their usage based on seasonal trends, types of rides, and availability. This helps optimize promotional efforts, making sure that promo codes align with user demand and enhance customer engagement effectively.
Optimizing fleet availability with ride demand insights – By tracking the popularity of different ride types, as shown in the preceding figure, we gain a clear understanding of customer preferences and demand patterns. This data-driven insight allows us to strategically plan for future supply, making sure that we allocate resources and vehicles more efficiently. In turn, this approach enhances our ability to meet customer needs, reduces wait times, and supports a more balanced and optimized fleet, leading to improved customer satisfaction and operational effectiveness.
Optimizing scheduling with booking insights – This detailed dashboard, shown in the preceding figure, provides insights into the timing and frequency of scheduled bookings, showing how far in advance customers are securing their slots. By understanding booking patterns, we can optimize slot availability and improve scheduling efficiency, so that peak times are adequately staffed and resources are allocated effectively to meet customer demand.
Time-based hub occupancy optimization – This analysis, shown in the preceding figure, offers detailed insights into hub occupancy across various time periods—morning, mid-shift, evening, and night—revealing patterns of over-occupancy and under-utilization at specific hubs. By identifying these trends, we can improve traffic management, optimize resource distribution, and make sure that each hub operates efficiently to meet demand at different times of the day.
The benefits: The impact of QuickSight on BluSmart
Since implementing QuickSight, we’ve observed significant improvements in our analytics capabilities and overall operational efficiency. Dashboard load times have been reduced from 40–45 seconds to just 5–8 seconds, enabling faster decision-making. Time spent on one-time analytics has decreased by approximately 40%, freeing up valuable resources for other tasks. Team productivity has increased by 30% across various departments, thanks to straightforward access to insights and improved collaboration.
“Amazon QuickSight has transformed how we leverage data at BluSmart for strategic decisions. Its interactive dashboards provide real-time insights into ride usage, customer engagement, and performance metrics, making trend analysis and decision-making seamless. The best part of QuickSight is its effortless integration with heterogenous data sources, eliminating the need for complex migrations.”
– Maulik Khanna, Product Manager- Technology @ BluSmart
These improvements have had a tangible impact on our business operations. Our operations team can now visualize real-time metrics on ride efficiency, helping them make informed decisions to improve service delivery. Our customer service department uses the embedded machine learning features in QuickSight to anticipate peak demand periods, enabling proactive resource allocation and enhancing customer satisfaction.
Looking ahead: Roadmap with QuickSight
As we continue to expand our operations through growing our EV fleet size and launching operations in other mega cities, we’re excited about the future possibilities with QuickSight. As our fleet grows, so does the volume of data we collect—from vehicle performance metrics to customer interactions and operational insights. Data is an integral part of our growth strategy, helping us optimize routes, improve customer experiences, and help ensure efficient fleet management. With QuickSight, we can transform this data into actionable insights, enabling us to make informed decisions, track progress toward our goals, and continue scaling efficiently.
Among many other key initiatives, we will be working on implementing embedded visualizations to provide even more accessible insights across our organization, using advanced machine learning capabilities—such as Amazon Q in QuickSight—to further optimize our operations and improve customer experiences, and exploring new use cases for QuickSight Embedded, such as real-time fleet management dashboards and customer insights portals.
We also plan on expanding our use of QuickSight as we enter new markets, including Dubai and other regions in the Middle East.
Conclusion: Driving sustainable mobility with data
At BluSmart, we’re not just transforming urban transportation—we’re doing it sustainably and intelligently. Amazon QuickSight is a key enabler in our growth journey, enabling us to make data-driven decisions.
By harnessing the power of QuickSight’s real-time analytics, machine learning capabilities, and seamless scalability, we’re well-equipped to continue our rapid growth while maintaining our commitment to efficiency, sustainability, and customer satisfaction. As we look to the future, we’re confident that our partnership with AWS and our use of QuickSight will play a vital role in achieving our mission of building smarter, safer, and cleaner mobility solutions for a better tomorrow.
About the authors
Vipul Jain, as the AVP of Cloud Infrastructure and DevOps, at BluSmart Mobility, drives and leads strategic cloud initiatives. He is deeply passionate about DevOps, Data Engineering, Cloud Infrastructure, and Security. With over 14 years of experience, he specializes in designing and implementing large-scale technology transformations. Outside of work, he enjoys playing sports, reading, and spending time with his family.” Avaanticka Narayan is an Analytics Specialist who has over 17 years of experience in presales, solutions consulting, and business development within the realm of business intelligence and analytics. Throughout her career, she has diligently served various industry verticals, aiding clients in defining comprehensive analytics strategies. With fervent dedication, Avaanticka eagerly imparts her expertise to the community, exuding passion and energy in her endeavors. Gaurav Malhotra is a Senior Solutions Architect in the AWS Startups segment, specializing in helping startups design and implement scalable, secure, and innovative cloud solutions. Gaurav has extensive experience in building data pipelines and delivering business intelligence solutions, enabling organizations to gain actionable insights and drive data-informed decisions. With deep expertise in Containers and Kubernetes, he has successfully guided customers through complex application development, DevOps, and large-scale cloud migration projects.
This is a companion discussion topic for the original entry at https://aws.amazon.com/blogs/business-intelligence/blusmart-revolutionized-sustainable-mobility-with-amazon-quicksight/