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Hodo Travels
Hodo is an AI-based recommendation application that provides personalized recommended places to travel based on the user's travel personality.
Role
Duration
Dec 2021 - May 2022
Co-founder,
User Experience Designer,
Product Designer
Tool Kit
Figma, Miro Board,
Indesign, Adobe XD, Asana
Team
Soumya Sharma,
Nikita Gokhale,
Charmi Chovatia
Overview
Who will use it?
There are many people who want to visit exciting new places but do not have time to engage themself in the research that goes into it. Even though there are many blogs or reviews about the places, they can be misleading and full of biases. it is difficult to find a place which matches users interest which is why most of the users end up going to only well known places in the new city.
Target Audience
Solo traveler who are traveling to a new city and wants to find a new place to see.
The Challenge
There are so many places to visit in a new city. but how can users know what is best for them. for example, a person who like to party will not be able to figure our which pub is the best to visit in the city. To provide more information to the user, the app will has to collection information from the users and should also have data to present to the user.
Solution
The Hodo application aims to provide a personalized traveling experience in a new city where users can take a personality quiz which lets the app know about the kind of travel personality they are and find places based on their personality with the help of artificial intelligence. These personality quizzes will determine what kind of traveler the user is and recommend places based on this quiz result.The artificial intelligence would find the places that are collected in the datasets and provide the best-suited places for the users.
Goals
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Collect the right information from the users to help them in discovering their personalities.
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Make a simple place recommendation to the users based on their interest.
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Provide users with a wish list where they can place these places for future reference.
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Research
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Interviews and Surveys
The interview questions allowed us to gain valuable insights from potential users, helping us better understand their needs and preferences. Through these discussions, we were able to identify key pain points and requirements that guided our decision-making process.
With the impact versus efforts graph, we were able to visually represent the potential benefits of each feature against the resources and time required for implementation. This prioritization framework ensured that we focused on the most impactful features for our initial user testing phase, allowing us to deliver a more refined and user-centric product.
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Travel Personalities
There are many different reasons to travel to a new place. From the initial interviews, we figured out that we have divide the travel personalities into different sections. We combined the personalities that are similar to each other so that is it easier for us to collect the data and also provide inclusive space for the users.
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Final Personality for the app
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User Personas
After the initial interviews, we decided to focus on solo travelers who just was to visit places based on their personalities. Our focus group also included users who have very less time to plan their own trips and do the research work. We also figured out what are some important traits of the users to put them in one kind of category. The user will be someone who wants to find out different places in different cities and make their own itinerary based on the collected information from the app.
User Journey Map
User Flow
After several iterations, we decided to change this user flow, as we discovered some faults in the “plan my own trip” section. We wanted to simplify the flow and try to include less affordance in the application. After the second user testing, we changed the flow and added more features in the application that will increase the engagement of the user. These features include the details about each location in the data set.
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Rough wire frames
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These wireframes included all the necessary features that we believe could really help in enhancing the user’s experience with the application. The wireframes were low fidelity designs that were created. We provided the users with the recommended options that are very similar to how Netflix suggests their movies.
After getting the travel personality from the users, users will be provided to directly go to the recommended places on the screen, but as we researched more about this, we scrapped this idea as the development of the AI model will take an incredibly long time to provide the desired results in this format.
Design System
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Wire Frames
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Final Design screens
At the same time, it was important to provide a clean flow with all the right information on the screen.
The aim with the final design was to match the personality results of the users with the quiz flow that was created after the first user test as we changed the design flow for the third iteration.
User Testing
The design flow of the application started off looking very complicated with several of issues with the understanding of the features in the application. Over a period of further testing and iterations, we found some really impressive results. These iterations were useful to narrow down the scope and understanding of the user's mindset. The parameters to define these changes were based on the collected information, the time to make that change, and to see if it is possible to make the change in the development section.
Reflection
Having tested the validity of this project for 5 personalities, the future scope of this project would be making it work for the 12 travel personalities. Since we are only working with an ‘Italy’ dataset, in the future we would also like to incorporate other countries around the world. With the foundation of the recommendation algorithms laid, the next step would be to make both the content-based and collaborative-filtering work together.
Also as a future scope, We have decide to keep working on this project or if someone else takes over for us, we will add a Geo location feature that enables route planning. Since the app currently only offers recommendations with no follow-up options to plan the actual trip, we will in the future focus on the ‘plan a trip feature’ too, with booking functionalities.