FINAL SPRINT - RouteGenie: Google Smart Routing Assistant
PM: Latifat Adeshina
Linkedin: https://www.linkedin.com/in/latifat/
Product Experience: Product Pack Write-Up
Problem Statement
How might we use AI to enhance the tools commuters use on Google Maps to plan their journey and choose a transportation method that suits their real-time needs?
Problem Background
In today’s fast-paced world, people prioritize speed and convenience when it comes to reaching their destinations. With an abundance of choices available at their fingertips, consumers expect seamless, user-friendly experiences. While Google Maps is the leading navigation app, it faces challenges in offering an optimized, integrated solution for transportation choices that combines ride-sharing, public transit, and other modes in one easy-to-use platform. Commuters often experience decision fatigue when comparing different routes, and they lack a solution that quickly identifies the best option based on real-time data, cost, and convenience. The Google Maps Smart Routing Assistant aims to simplify the decision-making process, providing users with the most efficient, personalized, and cost-effective transportation options available.
Research Insights
Through a survey, I found out that accessing transportation options on Google Maps can be cumbersome for users, as 30% of respondents report that switching between different transportation methods is either "somewhat tedious" or "very tedious." This highlights the need for a more streamlined experience that could simplify the decision-making process. Users prioritize convenience, cost, and speed when choosing transportation, but while Google Maps users emphasize convenience, ride-sharing app users focus on cost. Moreover, 40% of users prefer weather-based route adjustments, while another 40% opt for personalized route suggestions based on past behavior. This presents an opportunity for Google Maps to better cater to these preferences, especially considering that 90% of surveyed users frequently use ride-sharing apps.
Landing on the Solution
After analyzing user feedback, I realized that the current ride-sharing feature in Google Maps is underutilized due to its limited functionality. Many users find it frustrating to switch between different ride-sharing apps, which creates a significant pain point in their transportation planning. This led me to identify an opportunity to enhance the ride-share feature by incorporating AI-driven solutions. By offering personalized route suggestions and cost predictions in real-time, the feature could provide a more seamless, efficient, and personalized transportation experience, addressing both the convenience and frustration users experience when navigating multiple apps.
Explanation of Solution
My solution, the Google Maps Smart Routing Assistant, aims to address the common frustration users face when choosing the best way to get from point A to point B. By leveraging AI and real-time data, it provides personalized, optimized transportation options based on a user’s unique needs, preferences, and circumstances. The assistant considers various factors such as traffic conditions, weather, schedules, past behaviors, and transportation preferences to deliver the best possible route. This not only saves users time by eliminating the need to manually compare options but also reduces decision fatigue, making the entire process more efficient and seamless.
The Smart Routing Assistant simplifies the decision-making process by analyzing available data and presenting users with the most relevant and optimal route, whether they prioritize speed, cost, or convenience. With the integration of real-time information and AI-powered personalization, the assistant learns from user behavior and continually improves its recommendations. This solution ensures that users can make fast, confident decisions about their travel, offering both flexibility and convenience, particularly in today’s fast-paced world where people are constantly on the go.
User Flows/Mockups
User Flow:You can find a link to the user flow on Lucid Spark. In this user flow, the following assumptions were made:
- All users have former knowledge of how to navigate Google Maps
- The starting point in the app is always set to “Your location” which is the user's current location
- The user has existing accounts with ride-share apps that automatically integrate into Google Maps through the newest app store update. This means there is no need for the user to connect the apps individually, all they need to do is input their payment information.
Low Fidelity Mock-Up:
You can find a link to the Low-fi wireframes on Balsamiq’s website. This link includes wireframes for users who are new to the AI feature and users who are returning. Both these users choose ride-share as their transportation mode from the suggestions that were made.
Personalized Suggestions
Payment Options
Integrated Ride-Share Page
Pop-up
Optional Survey to Enhance AI Personalization
Pop-up
High Fidelity Mock-Up with Improvements:
Enter Route
Personalized Suggestions
Payment Options for Uber X Option
Integrated Ride-Share
Ride Completed
Optional Survey to Enhance AI Personalization
Thank you Pop-Up
You can view a live version of the high-fidelity mockups here: Lovable.dev
Future Steps
It is evident that personalization is important to commuters when using navigation apps such as Google Maps. Future development of this feature would include further personalization to refine the experience to the user. This includes building an editable user profile within the app that captures and adapts to personal preferences such as transportation modes, cost limits and time. This would offer users the ability to easily update their preferences or reset their profile to further improve their experience.
Robust data security protocols will need to be put in place since the feature would be collecting user data for personal preferences, real-time location and transportation patterns. Additionally, users may have concerns about sharing location and personal data. So we’ll have to be transparent about data usage and comply with privacy regulations for every region.
Appendix
- Recorded Pitch Presentation
- Product Pack Write-Up
- Recent PRD Spec
- Research Plan
- Research Synthesis
- User and Use-case Prioritization
- User Experience Document
- User Flow
- Mockups
- Prototype
- Prototype Website
Learnings
Product Manager Learnings:
Latifat Adeshina
Co.Lab was a very interesting experience for me because my learnings from this program revolve around the importance of iterative progress, where each step builds upon the last to refine and improve the solution continuously. I’ve learned that taking a data-driven approach is crucial for making informed decisions, ensuring that every aspect of the problem is understood thoroughly before jumping into a solution. By focusing on the customer and their needs, I’ve come to realize that understanding the problem deeply is far more important and complex than coming up with the solution itself because the solution is the easiest part. This program has reinforced the significance of deeply analyzing the user’s pain points, gathering insights, and adjusting based on feedback to create a more effective and personalized product.
Designer Learnings:
Designer Learnings:
Jo Sturdivant
- Adapting to an Established Team: Joining the team in week 6 of 8 was challenging, as I had to quickly adapt to existing workflows, dynamics, and goals. This mirrors real-world situations where you often integrate into teams mid-project, and flexibility is essential.
- Work-Blocking for Efficiency: With only two weeks to complete the project, I learned the importance of a structured work-blocking system. This approach allowed me to manage my time effectively and meet deadlines under pressure.
- Making Data-Driven Design Decisions: Unlike my past projects, I had to rely on research conducted by others. This was a valuable experience in using pre-existing data to guide design decisions, helping me focus on the core insights without starting from scratch.
Developer Learnings:
Developer Learnings:
Vanady Beard
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As the back-end developer, I learned how important it is to create efficient and reliable systems that support the entire application. This experience also taught me the importance of optimising the database and ensuring the backend is scalable and easy to maintain.
Developer Learnings:
Stephen Asiedu
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As a back-end developer, I've come to understand the importance of being familiar with various database systems and modules. This knowledge enables me to build diverse applications and maintain versatility in my work. I've also learned that the responsibility for making the right choices rests on my shoulders, guided by my best judgement.
Developer Learnings:
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Developer Learnings:
Maurquise Williams
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- Process of Creating an MVP: Developing a Minimum Viable Product (MVP) taught me how to focus on delivering core functionalities balancing between essential features and avoiding scope creep.
- Collaboration in a Real-World Tech Setting: This experience taught me how to collaborate efficiently in a fast-paced tech environment, keeping the team aligned and productive, even while working remotely across time zones.
- Sharpening Critical Thinking and Problem-Solving Skills: This experience honed my ability to think critically and solve problems efficiently. By tackling challenges and finding quick solutions, I sharpened my decision-making and troubleshooting skills in a dynamic, real-world setting.
Developer Learnings:
Jeremiah Williams
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All in all this experience was very awesome I learned that in coding with others being transparent is key
Developers Learnings:
Justin Farley
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I learned how important communication is when working with a team. Communication provides understanding, advice, ideas, and much more. While working with the product team, I’ve found that communication keeps everything flowing smoothly. Working with a team also showed me that every member brings something different to the table and we all have to work together in order to align and meet our end goal.