Rover
Travel app
Rover is a community-based travel app focused on building authentic experiences through AI-powered, peer-to-peer travel recommendations and establishing meaningful user connections.
Background
As an avid traveler, I've searched for ways to not only allow me to document my travels in great detail, but also discover new an interesting routes to create an authentic and unique travel experience. My planning would typically start with identifying a general region to travel (e.g. Asia) and then beginning to research that area by way of internet searches, reading guidebooks, and seeking advice from fellow travelers. This can be an involved process and I've always thought there should be a more streamlined way to plan a trip.
Problem
Planning a trip can be an exciting, but also overwhelming for travelers who wish to get the most out of their travel experience. Once someone identifies a general region to visit, it can often be difficult to craft a travel route that will best match their travel intentions. Resources such as guidebooks, travel agents, or reading travel blogs can provide a good start to the planning process, but knowing whether this guidance will align with a particular traveler’s interests can be a big unknown. Oftentimes the best advice comes from trusted, like-minded travelers who have visited regions of interest to someone planning a trip, especially if their intentions are to explore off-the-beaten-path destinations.
Solution
My years of traveling, along with my passion for product design, have led me to explore building an app where like-minded travelers can come togehter to not only showcase their travel experiences, but also connect with fellow travelers to exchange travel recommendations.
Role
Founder and principal designer.
Design process
Research
Market viability
Interaction architecture
Sketches
User flows
Wireframes
Mobile app designs
Prototypes
Light/dark mode
Research
Research shows that age demographics play a large role in the types of resources travelers rely on when planning a trip. The following charts represent a breakdown of the resources travelers use when planning a trip by age group. This research shows an increasing reliance on peer-to-peer suggestions and online research for people planning trips.
*Demographic data provided by ChatGPT
Market viability
To further verify the market viability of the Rover travel app, I put forth a SWOT analysis to identify the strengths, weaknesses, opportunities, and threats involved in launching such an app. The major challenges lie in both the upfront lift of gaining user adoption and navigating the current competition of the myriad travel apps that current exist in the market. My conclusion is the opportunities of leveraging machine learning, along with crafting a more delightful user experience can set Rover apart from the competiton.
Uses machine learning to provide travel suggestions
Leverages increasing reliance on peep-to-peer and online research to capture growing trends in how users plan trips
Builds trust and amplifies loyalty with users
Simple and delightful user experience
Heavy upfront lift in data onboarding for alternative route suggestions
Requires increasing user adoption and usage in order for the suggestion engine to provide maximum value
Helps users discover new travel destinations to maximize their travel experience
Creates opportunities for users to challenge traditional routes and achieve more unique and authentic travel experiences
Leveraging AI and predictive analytics to enhance route planning, user engagement, and interactive experiences
Facing competition from existing route planning apps, social media, navigation tools, travel agencies, and mapping platforms that offer similar features and services
Addressing concerns related to user data privacy, security, and compliance with regulations such as GDPR and CCPA to maintain trust and user loyalty
Strategy and approach
Learnings
Given the increasing trend of travelers relying more on peer-to-peer and online research, I recognized an opportunity to lean into the organic nature of crowd-sourced travel recommendations, along with leveraing machine learning to amplify this experience. In order to capture relavent cohort data, I wanted to create a place for users to document their travels and share their unique experiences.
Objectives
To ensure maximum adoption of the Rover app, I focused on the following aspects to create a delightful user experience:
Easy-to-use route planner
AI-driven route recommendation model based on cohort data
Allow users to share their travel experiences with descriptions, photos, reviews
Interaction architecture
The interaction architecture was an important piece when thinking through the recommendation model. The following diagram illustrates how data is feed to and from the auto generation model to continuously update and train the route suggestion feature. The model is trained on each user's plotted routes and compared against cohort data. Route generator clusters other users in a specified cohort with routes that fall within the target user’s selected area of interest and creates a best route match based on similar interests and past trip experiences.
Sketching it out
Once the interaction architecture for the route planner feature was established, the next step in the process was to begin sketing out a rough interface for the feature. This allowed me to translate my thoughts to paper and begin thinking through the details of how the app would function through the lens of the user.
Getting into the flow
After initial sketches, I then turned my attention to low-fidelity wireframes and user flows to begin breathing life into the route planner feature. In this phase my goal was to start integrating the interaction architecture with a visualization of the user interface to demostrate how the data affected the outcomes of each user action.
Bringing it to life
The next phase in the design process was to dive into the visual branding of the app to establish a clean and delightful user experience. I researched various brands and apps across different industries to gain inspiration for branding and UI elements for the Rover app. I experiemented with different fonts, colors and micro-interactions to create a UI layer that was clean, unique, and easy to use in the context of the functionality previously set forth.
User creates new trip by tapping the "+" icon in the bottom menu and selecting "Create route".
User is presented with a map and search bar to begin searching for their starting location.
Search results provide auto-suggest results along with "+" icons to add locations to their route.
Once a location is selected, the user is presented with a map and roll-up showing their starting point.
User selects more places along a route. Trip planner analyzes the popularity of selected places.
Trip planner recommends additional places to include in the user’s route based on cohort data from other users.
User can add recommended places to their trip route. Recommendation engine is updated based on new route.
User can add additional recommended places based on new suggestions to further refine their trip.
User creates new trip by tapping the "+" icon in the bottom menu and selecting "Create route".
User is presented with a map and search bar to begin searching for their starting location.
Search results provide auto-suggest results along with "+" icons to add locations to their route.
Once a location is selected, the user is presented with a map and roll-up showing their starting point.
User selects more places along a route. Trip planner analyzes the popularity of selected places.
Trip planner recommends additional places to include in the user’s route based on cohort data.
User can add recommended places to their trip route. Recommendation engine is updated based on new route.
User can add additional recommended places based on new suggestions to further refine their trip.
Seeing it in action
Trip planner allows users to map their own route and intelligently assess their proposed route with recommended alternative segments the user may consider. It does this by comparing the popularity of locations within a specified region based on other users who have completed trips in and around that same region who also share similar interests. Trip planner allows users to discover other locations within the general area they plan to travel that will meet their intended travel experiences.
Starting your route
You may begin your trip by tapping the "+" in the menu and selecting "Create route". From there, you begin searching and adding locations to start building your route.
Recommendations
Refine your route by utilizing our recommenation engine to suggest additional places based on cohort data. Each time you update your route, our model will update to present you with additional suggestions.
Trip details
Build out your trip details by adding a diary of your experience, providing reviews of places you visited, and uploading photos. This will also be used to enhance our recommendation models for other users.
Light/dark mode
The next phase in the design process was to dive into the visual branding of the app to establish a clean and delightful user experience. I researched various brands and apps across different industries to gain inspiration for branding and UI elements for the Rover app. I experiemented with different fonts, colors and micro-interactions to create a UI layer that was clean, unique, and easy to use in the context of the functionality previously set forth.
Conclusions
After completion of the Rover app prototype and given the learnings through my research, I've concluded I have a viable product for launch. Much of the app's value relies on simulating a peer-to-peer experience to create a trusted source for travel planning through machine learning. I believe this is novel approach to planning trips has the potential to disrupt the travel industry.