Rentomojo
Enhancing the User Experience and Redesigning the User Interface of Rentomojo's Internal Product Delivery Application to minimise cognitive load and optimise it for faster deliveries.
Discipline
Material Design Guidelines
User Experience Design
Visual Design
Team
Aditya Raj Pandey
Avinash K. Singh
Md Saif Uddin
Nayab Anwar
Timeline
Dec 2022—Early 2023
Video
0.1 Intro video
In December 2022, I had the opportunity to work on Rentomojo's Product Delivery application to enhance its user experience and optimize delivery speed. I am responsible for researching optimal solutions from both the user and business perspectives and designing products end-to-end with stakeholders.
To establish a stronger connection between delivery agents and their delivery tasks, making it easier to navigate through them. This ensures that the product delivery process becomes highly efficient, feeling seamless even when agents are faced with a high volume of new tasks.
How do we achieve our goals?
Analysis
The user analysis process starts by tracking interactions to identify patterns and preferences, guiding the creation of tailored features for a user-centric app experience.
Organisation
What factors contribute to the slow learning of delivery agents?
Prioritisation
What do delivery agents prioritise while making delivery? How so?
Constraint
What are the things slowing them down? How often?
App Design
What aspects of our app lack in terms of information architecture and design?
Interviews
We conducted interviews with delivery agents to understand their pain points and workflows.
Delivery agents struggle more during the early stages of using the app.
Organisation and efficiency
Delivery agents indicate that they strongly seek out efficient categorisation of similar tasks.
Prioritisation
Delivery agents tend to struggle with delivery tasks because they were unable to see which tasks need their attention and which do not.
Internet Problem
Delivery agents indicate that they sometimes encounter network problems in certain areas, causing significant delays.
How do we achieve our goals?
Before delving into the design phase, we start by sketching out all the features of the app using low-fidelity design. This initial step ensures that the flow is user-friendly and efficient, setting a solid foundation for the project. In our first iteration, we incorporated Map integration directly into the app and implemented categorisation by date. However, as we progressed, valuable insights emerged, prompting us to reevaluate our approach
After careful consideration, we decided to abandon the Map integration feature. Our research revealed that delivery agents were already accustomed to using Google Maps, making additional Map integration unnecessary. Moreover, implementing this feature would have increased bandwidth usage without significant benefits to users.
Similarly, we opted to remove categorisation by date from our iteration. While initially considered, we realised that delivery tasks were primarily assigned on a daily basis, rendering date-based categorisation redundant and potentially confusing for delivery agents.
Organisation & Efficiency
To enhance organisation and efficiency, our approach began with revamping the homepage layout. We conducted a thorough assessment, removing any unnecessary elements to declutter the interface. Streamlining categorisation was a key focus, resulting in the adoption of a simplified task classification system: Pending, Completed, and Incomplete tasks. Additionally, we strategically incorporated essential tasks directly onto the homepage, minimising user navigation steps and expediting task completion.
Recognising the significance of distinguishing between delivery to customers and pickup from customers, we introduced separate sections for each. This segmentation enabled delivery agents to prioritise their activities more effectively, ensuring timely and efficient deliveries.
Prioritisation
In response to the need for effective task management, we implemented a badge system to denote task priority levels. Tasks were sorted based on their importance, with high-priority items receiving prominent placement. This enhancement empowered delivery agents to prioritise their workflow efficiently, addressing critical tasks with precision.
Constraints
Addressing challenges faced by delivery agents, particularly concerning internet connectivity in remote areas, emerged as a priority. Recognising that network coverage varies across regions, hindering data upload processes, we devised a solution. We introduced a feature allowing agents to store data locally on their devices when faced with poor internet connectivity. Upon restoration of a stable connection, the system automatically uploads the stored data. Additionally, a manual upload option was integrated, providing agents with control over data transfer, ensuring seamless operations even in challenging network conditions.
App Design
The outdated design of the application, originally developed 7 to 8 years ago, demanded a modern refresh. Our focus extended beyond mere aesthetic improvements; we aimed to enhance information architecture to reduce cognitive load for users. Through a meticulous redesign process, we crafted an interface that not only appealed visually but also facilitated intuitive navigation, resulting in an enhanced user experience.
IMAGE
3.6 Before and After
With our redesign, we reduced the initial training learning curve by 30%. By removing the constraint of uploading delivery data to servers in areas with slow internet connections, we helped delivery agents perform product deliveries much faster.
Hand-Off & Final Design
