Seamless Group Trip Planning : Connecting Personal Inspirations to Unified Decisions

Dhanvi Patel

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I embarked on researching about trip planning, recognising the intricacies of group decision-making and the potential for design innovation. During this journey I mapped out three primary areas of concern : lack of relevant recommendation, navigating decision making as a group and lack of collaborative features. For each, I conceptualised user-centric potential features aimed at addressing these issues.

Research Outcomes
User centric AI Features
High Fidelity Application Mockup

My Role
UX Researcher
Product Designer

Haig Armen

The Rollercoaster of Group Trip Planning

If you’ve ever tried coordinating a trip with friends across different time zones, you know it’s may not be just a matter of a few quick phone calls. It could be going through waves of conflicting preferences, procrastinating the decision-making, days of silence and finally booking some compromised accommodation in a hurry.

How can one be expected to find the perfect stay or plan the ideal itinerary, considering everyone’s preferences all while juggling work, multiple part-time commitments, and daily responsibilities? 

What was the Process and Methods at Play?

  • Informal Interviews
  • Literature Review
  • Participatory Research
  • Auto-ethnographic Observations
  • Comparative Analysis
  • Experimenting with ChatGPT
  • Sketching
  • Storyboarding
  • Wireframes
  • Prototype
  • Usability Testing
  • Think Aloud Testing


Users need hyper personalised recommendations

Asynchronous collaboration

Respect for individual and diverse preferences

Balanced & effective decision-making process

Dynamic mental models

I recognised potential areas for improvement and reframed them into actionable

“How Might We” questions.

• How might we make recommendations relevant? 

• How might we enhance the collaborative trip planning experience ?

• How might we ease the decision-making process?

Conceptual Solution

How might we make recommendations relevant? 

Let’s assume Rachel downloads the app Zway upon receiving an invite from a friend. As she is building her profile on the platform she is asked to import her travel inspirations from social media channels. She decides to go with instagram and adds only posts that are relevant to her current trip. She can go back to this inspiration folder to modify later as well. The algorithm analyses the preferences of the group and creates a relevant itinerary which can serve as a starting point.

How might we enhance the collaborative trip planning experience ? 

After everyone submits their preferences, the app crafts a group itinerary while allowing space for individual pursuits. However, Rachel isn’t satisfied with the proposed accommodation. So she decides to add alternative options. In the search section she is impressed by one of Zeelo’s recommendations, and immediately adds it into the itinerary. These recommendations are made based on users initially shared Instagram inspirations. Desiring a beach view, Rachel searches for more suitable options, and Zeelo presents her with the perfect spot within her budget. Without hesitation, she adds that as well to the itinerary. Now, To streamline decision-making, she sets a three-day voting period for everyone to choose between these options.

A quick way to swipe and add alternative options
Personalized recommendations and search results that can be easily added
Setting a voting deadline for selecting among options
Cropped image of 4. Nudge members to vote and find tips to compare optionsNudge members to vote and find tips to compare options
Nudge members to vote and find tips to compare options
Multiple options consideration and viewed on map for diverse preferences

How might we ease the decision-making process? 

Despite numerous nudges and multiple options for voting, only 2 participants responded, and the group remained undecided about the accommodation. Four more days passed in silence until Timothy, another member of the group, inquires about the final accommodation on the chat. Here, Zeelo an AI powered chatbot facilitates their discussion.

Zeelo provides relevant recommendations in chat and maintains transparency about the way it operates
Zeelo detects conflicting signals based on the conversation, then offers assistance accordingly.
Group members rank their preferences in private
Promotes equal participation by prompting less active members to foster inclusive conversation
Summarises the discussion so far to align everyone’s understanding. Suggests optimal approach to make a decision.
Zeelo analyses the groups responses and proposes options that align with the collective interests of the group.

Looking Back

This project gave me a chance to think strategically about how to best place AI into a service and create a convincing prototype that helped to test the hypothesis. If I were to do it again, I would simulate a scenario with participants interacting in a group chat with AI bot to plan a trip. This would help to understand user behaviour and preferences in group decision-making processes, informing further enhancements.

Dhanvi Patel

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Dhanvi is a Product Designer and Researcher from India, now based in Vancouver. Driven by a passion for understanding user behavior and needs, she embraces a user-centric approach in her design methodology, while persistently seeking to innovate UI designs. Her profound interest lies in exploring the intricacies of human engagement with AI technology, propelling her to delve deeper into this realm.

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