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MS3
Based on the feedback received for MS2, the following improvements were made:
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Journey Maps: Rebuilt both journey maps (Mariana - Smart Food, and Tomás - Side-Quests) from scratch using a proper Customer Journey Map template in Canva, now including Customer Actions, Touch Points, Goals, Customer Experience (emotion curve), Pain Points, and Solutions for each stage. Both maps now follow the same consistent format.
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Interview Summary: Added a "Key Excerpts" section with direct textual quotes from each of the 6 interviews, complemented with a compiled audio file containing the corresponding original clips, to provide verifiable evidence alongside the podcast-style audio summary.
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Full Interviews: Replaced download-only zip links with direct written transcripts uploaded to the repository, so each interview can be reviewed without needing to download any files. Audio links were kept alongside the new transcripts.
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Prototype Video: Since the video was not re-recorded, added a written section explaining how each of the 6 screens ties back to a specific HMW question and voted solution from the brainstorming process, addressing the feedback that only one screen had an explicit connection.
To generate potential solutions for the problem identified during our research, the team conducted a brainstorming session using the “Yes, and…” technique. The purpose of this technique was to encourage the team to build on each other's ideas instead of immediately rejecting or criticizing them.
Methodology: The "Yes, and…" technique
- Each idea was acknowledged and expanded upon, creating chains of related concepts
- Team members built iteratively on previous suggestions, combining and evolving concepts
- No ideas were rejected during the ideation phase; criticism was deferred to evaluation
Participants: Full team (6 members) Format: Live Zoom collaborative session Duration: Single intensive session with out loud open ideation flow
Team collaborating live during the "Yes, and..." brainstorming session (Zoom call):
Some of the ideas generated during the brainstorming session included:
- Showing popular activities to do in nearby places.
- Using filters for distance, price, and date.
- Grouping activities according to interests, hobbies, and past activities.
- Recommending activities based on the user's location and frequently visited places.
- Allowing users to explore activities through a map.
- Providing reviews to help users determine whether an activity fits their profile and is worth their time.
- Giving users points for completing activities.
- Using points, rewards, and discounts to incentivize users to try different activities.
- Allowing users to broadcast activities to find people who want to do them together.
- Creating group and global leaderboards.
- Allowing users to find their friends' avatars on the city map.
- Using photos as proof of completed activities.
- Progressively upgrading the user's profile according to the side quests completed.
The brainstorming also allowed us to identify opportunities for smart features, such as recommending activities based on the user's location, interests, and previously visited places. These features could make the experience more personalized by helping users discover activities that are relevant to them.
The team used an Impact vs. Effort Matrix, where all the sticky notes generated during the "Yes, and..." brainstorming session were placed onto a four-quadrant chart, with Impact (how much value the idea brings to the user) vs. Effort (how easy the idea would be to build within the semester). Ideas landing in the High Impact / Low Effort quadrant were prioritized as the core of the solution.
Team collaborating live while building the Impact vs. Effort matrix (Zoom call):
Chosen solution: Popular activities to do in nearby places
Justification: The idea was selected because it had a high impact and low effort, and it addressed the main need identified through the interviews, journey maps, and past activities: helping people discover what they can do around them instead of having to search for activities independently and deal with the difficulties that this involves.
Additionally, it can act as a foundation for incorporating more of the ideas generated during brainstorming. For example, filters, categories, points, and reviews can be added later, but they are not essential to fulfilling the main objective.
Our proposed solution is a mobile application for side quests that transforms activity discovery from a time-consuming, multi-platform search into a gamified, location-based experience. The app aggregates nearby activities and presents them as personalized micro-quests, reducing decision friction while encouraging users to break their routines.
1. Discovery Engine
- Users receive a personalized feed of "quests" (activities) based on:
- Real-time location proximity (within user-defined radius)
- Interest categories (tagged during onboarding and profile refinement)
- Time availability (filters for duration, time of day, scheduling flexibility)
- Past activity history (machine learning-based recommendations)
2. Quest Mechanics
- Each activity is gamified as a "quest" with:
- Clear acceptance/rejection flow (low commitment entry point)
- Visual difficulty/duration indicators
- Social proof elements (reviews, completion count, average rating)
- Point rewards and streak tracking upon completion
- Photo verification option to document completion
3. Social Discovery Features (Addressing Solo Participation Anxiety)
- Buddy System: Users can view which friends are interested in the same quest or nearby quests, reducing anxiety about going alone
- Group Quests: Activities are tagged as "solo-friendly" or "group-friendly"; users can see friend recommendations or connect with other participants
- Friend Avatars on Map: See where friends are in real-time or planning to be, enabling spontaneous meetups
- Activity Broadcasting: Users can post a quest and invite others to join, converting solo activities into social opportunities
4. Personalization & Profile Growth
- User profile progressively upgrades based on completed quests
- Leaderboards (personal, friend, neighborhood) encourage healthy competition
- Achievement badges tied to specific activity types or patterns
Paola's Pain Point: "I don't want to try things alone"
- Buddy system highlights which friends are interested in the same activities
- Group quest feature enables co-participation with matched users
- Friend map visualization provides reassurance about social options nearby
- Broadcasting feature converts solo plans into group opportunities
- Gamification rewards participation (whether solo or with friends)
Mariana's Pain Point: "Decision fatigue and routine"
- Time-filtered recommendations respect busy schedules
- Quick acceptance/rejection UI reduces decision time
- Micro-quests fit between existing commitments
- Streaks and profile progression motivate behavioral change
Tomás's Pain Point: "Multi-platform searching and time constraints"
- Centralized app eliminates searching across Instagram, TikTok, and Google
- AI-powered recommendations surface relevant activities proactively
- Filters reduce manual browsing and comparison effort
Phase 1 (MVP):
- Nearby activities with location-based discovery
- Activity categories and basic filtering (distance, time)
- User profiles with interest tags
Phase 2:
- Reviews and social proof elements
- Points and streak tracking
- Friend connections and buddy system
Phase 3:
- Leaderboards and achievements
- Activity broadcasting and group matching
- Advanced personalization engine
The solution uniquely combines three elements rarely seen together:
- Gamification that motivates behavioral change (not just engagement metrics)
- Location intelligence with social discovery (reducing solo participation anxiety)
- Micro-commitment design (low-barrier entry to try new activities)