Sprint 1 - Solution Context and Design

0. MS4 Corrections

Based on the feedback received for MS4, the following improvements were made:

• Business Questions: Enhanced existing questions (Q2, Q5, Q8, Q10) with measurable thresholds (e.g., 1-5 Likert scale, ≥5 quests/month, ~30% activity drop) and explicit privacy/ethics considerations for data handling and third-party sharing.

• VD Map: Rebuilt with the template with proper labeling of all transformation and aggregated nodes. Implemented distinction between SOLID cylinders (app-internal databases: User Profiles, Quest History, Interaction Logs, etc.) and DOTTED cylinders (external data sources: GPS Location). Ensured consistent connection directions, clear traceability (question → visualization → transformation → data sources), and maximum 1 visualization node per question as per requirements.

1. Problem and Solution Description

Problem

People often fall into a routine and struggle to find interesting activities to do near them. When they want to do something different, they usually have to search across multiple platforms (social media, review sites, word of mouth) and manually compare options, which takes time and effort. Because of this friction, many people end up doing nothing or repeating the same activities instead of discovering new plans nearby.

Proposed Solution

The solution is an application that helps people discover popular activities to do in nearby places. It uses the user's location to identify available activities and shows them in a simple, accessible way, so users can choose an option based on their interests and location without having to search through multiple platforms or decide on an activity from scratch.

Revenue Model

The application implements a B2B, Freemium B2C revenue model to ensure zero barrier to entry for end users while monetizing of local commerce and business analytics. It was proposed four revenue streams.

Stream 1: Sponsored Quests and Featured Placements Local businesses pay to feature their activities as Sponsored Quests or pinned recommendations within specific categories and geographic radiuses. This will be the primary revenue stream and it was raised as a B2B strategy. For the pricing mechanics will be cost-per-engagement where the business pay based on verified interactions and promoted tier packages which are monthly subscriptions that offer to local business priority placement in the nearbt feed and calendar highlights.

Stream 2: B2B Analytics and Event Insights Dashboard Event organizers and local venues pay for access to an analytics dashboard that provides anonymized, aggregate telemetry data about user demand and behavior. This stream operates as a B2B SaaS model designed to help organizers optimize their scheduling, pricing, and promotional reach. The pricing mechanic consists of a monthly or tiered subscription, giving organizers access to funnel drop-off metrics, neighborhood demand heatmaps, and category interest trends across Bogotá.

Stream 3: Merchant Partnership Commissions & Perks Local merchants partner with the platform to offer exclusive perks, discounts, or direct ticket bookings for specific activities within a quest. This stream operates as an affiliate and marketplace model that monetizes direct customer acquisition for partner venues. The pricing mechanic is based on a commission fee per transaction, where the business pays a percentage on each ticket booked or on each verified coupon redeemed in-store through the app

Stream 4: B2C Premium Tier Individual users can opt into a low-cost subscription that unlocks additional customization and convenience features while keeping the core experience entirely free. This stream functions as a B2C freemium model targeting power users who want more flexibility in how they explore plans. The pricing mechanic follows a recurring monthly subscription, granting users perks such as unlimited mystery quest re-rolls, advanced filtering options, profile badges, and weekly streak-freeze protections

Value Proposition

B2C For End Users: The app eliminates the fatigue and stress planning activities by curating contextual, low-effort, and nearby activities tailored to personal interests, schedule, and social context. Among the key differentiators provided there are context and proximity aware suggestions, gamified discovery in order to motivate the user to try new things with points, streaks and alike features, frictionless decision making using features like random quest button, energy levels based activities, etc.

B2B For Local Businesses and Event Organizers: The app provides a local, intent driven discovery channel to connect experiences, small businesses, and events with targeted local audiences at the same time they are seeking nearby plans. Among the key differentiators provided there are performance analytics and telemetry on user interest, discovery patterns, and completion rates and direct conversion from discovery to predestrian traffic without competing against noise on mass social media advertising.

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2. PAS (Problems, Alternatives, Solutions)

#	Problem	Alternative	Solution
1	People fall into a routine and struggle to find new things to do.	Searching Instagram or Facebook for plans and ideas.	A personalized feed that recommends plans based on the user's interests.
2	Small businesses that offer plans and experiences have no easy way to reach new customers.	Paying for ads on social media.	Sponsored plans and quests featured inside the app.
3	People often miss interesting activities because they do not know what is happening near them at the right time.	Checking different social media accounts or asking friends about plans.	A location-based activity feed that shows available and popular activities happening nearby.
4	People new to Bogotá have difficulty discovering activities that fit their interests, location, and schedule.	Searching across Instagram, TikTok, and Google for different plans.	An activity calendar that recommends nearby events based on the user's interests and availability.
5	Users struggle to maintain consistency in trying new activities and lose motivation over time.	Relying on willpower or friend reminders.	A gamified system with activity streaks, achievements, and progress tracking to encourage habit formation.
6	Users feel uncertain about trying unfamiliar activities without social proof or reviews.	Asking friends for recommendations or reading reviews on multiple platforms.	Built-in ratings, reviews, and user feedback directly on each activity to build trust and confidence.
7	People struggle to find activities that fit into their limited free time.	Searching manually for activities during weekends or after work.	A schedule-based recommendation system that suggests activities that fit the user's available time.
8	Users feel overwhelmed when there are too many possible activities to choose from.	Looking through different platforms to compare all available activities.	A short list of a few suitable options with a simple comparison to make the decision easier.
9	People avoid activities they would enjoy when they cannot find someone to join them.	Waiting for friends schedules to align or posting invitations in group chats.	Buddy and group quest features that show interested friends or participants and let users invite others to join.
10	Users have difficulty finding activities that fit their budget because prices are often unclear or scattered across different sources.	Opening several venue pages or contacting organizers to compare prices.	Price filters and clear cost information on each quest, including a category for free activities.
11	Users become discouraged when a suggested activity is too far away or requires too much travel time.	Checking each location manually in a separate maps application before deciding.	A map-based discovery view with distance filters that prioritizes quests within the user's preferred radius.
12	Users have no simple way to record and share the new experiences they complete.	Saving photos in their gallery or posting each activity separately on social media.	A quest history with optional photo verification that records completed activities and displays them on the user's evolving profile.

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3. Context Canvas

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4. Personas

Prospective Personas

Persona 1 - Paola Gonzalez

Persona 2 - Camila Rodriguez

Persona 3 - Andres Morales

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Analytics Persona

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5. Empathy Maps

Empathy Map 1

Empathy Map 2

Empathy Map 3

Empathy Map 4

Empathy Map 5

Empathy Map 6

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6. Business Questions

Type 1 - App's Telemetry

1. During the last 30 days, what has been our average response time for quest recommendations?
   • Justification: Monitors app performance and stability over time for developers to check.
2. Which quest step has the highest average response time?
   • Justification: Identifies which app functionalities have the longest response times allowing developers to detect performance issues.

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Type 2 - Direct User Experience Improvement

3. What activities near me match my interests based on my selected energy level (1-5 scale: low to high) right now?
   • Justification: Delivers personalized recommendations directly in the app by using explicit user input (Likert scale 1-5) combined with location data. Data sources: user input + GPS location + activity database. Privacy/Ethics: Energy level is strictly first-party user data with no third-party sharing required.
4. How is my activity streak trending compared to previous weeks (displayed as: completed activities per week)?
   • Justification: Shows user engagement trends to encourage habit formation by displaying quantifiable weekly completion metrics.
5. What quest should be recommended to me next, based on my past activity history (quests completed and categories engaged)?
   • Justification: Provides personalized quest suggestions based on user behavior patterns from completed activities.
6. Which of my favorite places or activities (bookmarked or visited >2 times) are near me right now (within 5km radius)?
   • Justification: Displays relevant locations in real-time using explicit distance threshold and user-saved preferences to improve discovery and engagement.

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Type 3 - Features Analysis

7. Which activity categories are being selected less than 3 times per week on average across all users?
   • Justification: Identifies underused features to inform decisions on improvement, removal, or repositioning.
8. At which step do users most often abandon a quest without finishing it (tracked steps: view details → accept quest → navigate to location → complete activity)?
   • Justification: A step is a phase in the quest flow. Identifying drop-off points helps optimize UX and reduce friction.
9. What characteristics do users who haven't opened the app in the last 2 weeks have in common (demographics, activity history, notification preferences)?
   • Justification: Identifies common traits of inactive users to target re-engagement campaigns through personalized outreach.
10. Which quest recommendation criteria should be updated based on the quests users choose after receiving a suggestion?
    • Justification: Helps identify which recommendation criteria should be adjusted to improve the relevance of suggested quests.

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Type 4 - Benefits from Data

11. Which neighborhoods (defined by geographical zones in Bogotá) have the most quest activity, so we can offer partnerships to local businesses there?
    • Justification: Identifies high-activity areas for possible business partnerships and monetization strategies. Data source: aggregated quest completion data by location.
12. Which quest categories are most promising for sponsored quests based on user engagement?
    • Justification: Identifies quest categories with high user engagement to determine where sponsored quests could generate value.

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Type 5 - Mixed

13. What characteristics do users classified as "highly active" (defined as: ≥5 completed quests per month) have if they haven't tried a sponsored quest yet, and how should we notify them (email, in-app, push notification)?
    • Justification: Combines UX improvement (strategic notification delivery) with data monetization (sponsored quests targeting). Privacy/Ethics: Sponsored content targeting uses only aggregated behavioral data; users can opt out of sponsored content notifications. No third-party data sharing occurs—all data remains within the app.
14. Which users whose quest activity dropped by around 30% are most likely to re-engage via personalized sponsored quests?
    • Justification: Combines user retention with sponsored content targeting to maximize engagement and revenue.

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7. VD Map

Must display all 14 business questions and their data sources. Sources must be obtainable through the designed solution or must be publicly available.

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8. Functional Scenarios

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Scenario 1: Discovering a Random Quest with Energy and Distance Filters

Field	Description
WHO	A user who is at home on a Sunday afternoon, has moderate energy, and wants to do something nearby but doesn't want to spend time deciding what
WHAT	The user taps the "Random Mystery Quest" button after setting energy level to 3 and distance to 5km
HOW	The system retrieves the user's current location, queries the activity catalog for unstarted quests within the 5km radius and energy level 3 constraints, and displays one random quest from the filtered results showing details (title, location, expected duration, distance, reward points), with options to Accept or Skip
WHY	The user wants to reduce decision fatigue by letting the app suggest a suitable activity, ensuring it matches their current energy and willingness to travel, while maintaining agency through the Skip option

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Scenario 2: Filtering Activities by Time and Duration to Find Weekend Plans

Field	Description
WHO	A working professional who has limited free time and is planning their weekend on Friday evening
WHAT	The user navigates to the Activities feed, applies two filters: "This Weekend" for time and "30-60 minutes" for duration
HOW	The system queries the quest catalog for events scheduled between Saturday and Sunday, filters results to only activities lasting between 30-60 minutes, removes fully booked or cancelled events, and displays the updated list sorted by proximity to the user's current location
WHY	The user wants to quickly surface activities that realistically fit into their weekend schedule without having to manually evaluate each option, allowing them to make time-appropriate choices without analysis paralysis

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Scenario 3: Completing a Quest and Earning Streak Continuation

Field	Description
WHO	A user who has maintained a 7-day activity streak and just finished their last quest for the day
WHAT	The user marks the quest as completed by tapping the "Mark as Complete" button in the active quest detail view
HOW	The system verifies the quest acceptance timestamp and current time to confirm the user's participation, records the completion in the user's quest history with a timestamp, increments their quest completion count, extends their activity streak by 1 day, calculates and displays earned reward points, and shows a congratulatory message highlighting the streak continuation
WHY	The user wants confirmation of their accomplishment and visual reinforcement of their consistency through streak continuation, which motivates them to maintain engagement and return tomorrow to extend the streak further

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Scenario 4: Viewing Quests on an Interactive Map to Understand Geographic Distribution

Field	Description
WHO	A user new to Bogotá who wants to understand which neighborhoods and areas have activities available, and wants to visually judge distances before committing
WHAT	The user taps the map icon on the home screen to switch from list view to a map-based discovery interface
HOW	The system displays the user's current location as a blue dot, plots nearby quest locations as markers/pins on the map with color coding by category or engagement level, and allows the user to tap individual pins to see quest previews or apply distance-based filters (e.g., show only quests within 3km radius)
WHY	The user wants spatial context to understand activity clusters and travel distances at a glance, which helps them choose activities that feel geographically accessible and discover neighborhoods they haven't explored before

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Scenario 5: Viewing Quest Details Before Making a Commitment

Field	Description
WHO	A user browsing through recommended quests who is interested in one but wants complete information before deciding to accept it
WHAT	The user taps on a quest in the activity feed or map view to open the detailed quest information page
HOW	The system displays a comprehensive quest detail view containing: quest title and description, exact location with address, scheduled date and time, expected duration, price or free indicator, difficulty/energy level, number of available spots, user ratings and review summary, reward points offered, and action buttons (Accept, Save, Share, Rate if previously completed)
WHY	The user wants all decision-relevant information centralized in one view before committing time and effort to an activity, reducing regret and ensuring the activity truly matches their expectations and constraints

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Scenario 6: Saving a Quest for Later Consideration

Field	Description
WHO	A user who finds an interesting quest but isn't ready to commit now due to schedule conflicts or needing time to discuss with friends
WHAT	The user taps the "Save" or bookmark icon while viewing a quest detail page or from a card in the activity feed
HOW	The system records the saved quest reference in the user's profile, adds it to their "Saved Quests" collection, displays a confirmation message (e.g., "Added to your list"), and the quest becomes accessible from a dedicated Saved Quests tab in the user's profile for future reference
WHY	The user wants to curate a personal collection of interesting activities without committing immediately, allowing them to revisit candidates later, compare options with friends, or wait for better timing without losing track of good activity opportunities

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Scenario 7: Rating a Completed Quest and Providing Feedback

Field	Description
WHO	A user who just completed a quest and wants to share their experience to help other users and improve the platform's recommendations
WHAT	The user opens the quest from their Quest History, taps the "Rate & Review" option, selects a star rating (1-5 stars), optionally writes a short comment describing their experience, and taps "Submit"
HOW	The system validates the user's completion of the quest, accepts the rating and comment, stores them in the quest's feedback database, updates the quest's average rating displayed to other users, archives the review on the user's public profile, and thanks the user with a confirmation message
WHY	The user wants to contribute to community trust signals and help other users make informed decisions based on real experiences, while also giving the app development team valuable qualitative feedback to improve future recommendations and understand which activities deliver real value

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Scenario 8: Sharing a Quest with Friends via Multiple Channels

Field	Description
WHO	A user who just discovered an interesting quest and wants to invite a specific friend to join them or suggest it to their social network
WHAT	While viewing a quest detail page, the user taps the "Share" button and selects a sharing method (WhatsApp, email, SMS, or direct in-app invite if the friend is also an app user)
HOW	The system generates a shareable deep link containing the quest ID and summary details, opens the device's native share menu or in-app messaging interface, pre-populates the quest information (title, location, time), and allows the user to add a personal message before sending
WHY	The user wants to lower the friction of coordinating group activities by enabling easy social sharing, turning personal discoveries into social opportunities and creating secondary discovery channels through friend recommendations, which increases both engagement and social proof

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Scenario 9: Accepting a Quest and Immediately Starting Navigation

Field	Description
WHO	A user who has decided on a quest and is ready to attend the activity, now needs to navigate to the location
WHAT	The user reviews the quest details one final time and taps the "Accept Quest" button, which immediately prompts navigation options
HOW	The system records the quest as "Active" in the user's profile, displays a confirmation with next steps, and offers two navigation options: (1) "Open in Maps" to use integrated or device mapping, or (2) "Navigate Now" to launch default navigation app. The system stores the quest location coordinates and triggers the navigation handoff with turn-by-turn directions to the activity location
WHY	The user wants a seamless transition from decision to execution, minimizing friction between committing to an activity and actually arriving, while maintaining flexibility in navigation tool choice (preferred maps app vs. integrated navigation)

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Scenario 10: Viewing Personal Activity Streak and Achievements

Field	Description
WHO	A user who has developed a habit of completing quests and wants to see visual confirmation of their progress, consistency, and unlocked rewards
WHAT	The user navigates to their Profile tab, taps the "Streak & Achievements" section to view their personal achievement dashboard
HOW	The system displays the user's current activity streak (e.g., "14 days in a row"), total quests completed (e.g., "47 quests"), earned achievement badges with unlock dates (e.g., "First 5K Runner," "Weekend Warrior"), points balance, progress bar towards the next achievement milestone, and a historical graph of weekly activity
WHY	The user wants gamified reinforcement of their consistency and accomplishment, which serves as intrinsic motivation to maintain or extend their streak, provides social proof of engagement for sharing with friends, and creates a sense of progression and investment in the app

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Scenario 11: Discovering Personalized Recommendations Based on Past Behavior

Field	Description
WHO	A user who has completed several quests in similar categories (e.g., outdoor activities, fitness classes) and returns to the app looking for more suggestions aligned with their demonstrated preferences
WHAT	The user opens the app to the Home screen where a "Recommended for You" section appears at the top of the activity feed with 3-5 personalized quest suggestions
HOW	The system analyzes the user's quest history (completed quests, saved quests, ratings), identifies patterns in categories and difficulty levels they engage with, queries the active quest catalog for matches, and ranks results by alignment with user history, proximity, and current availability. The recommendations are displayed prominently with a "Because you liked [category]" explanatory tag
WHY	The user wants the app to reduce discovery friction by proactively surfacing activities matching their demonstrated interests, creating a personalized experience that acknowledges their previous choices and saves them time browsing, while encouraging category exploration within their demonstrated comfort zone

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Scenario 12: Handling Quest Acceptance When Near Capacity

Field	Description
WHO	A user who is interested in a popular quest that is nearly full (only 2 spots remaining) and wants to secure their spot before it becomes unavailable
WHAT	The user views a quest with limited availability (shown as "2 spots left") and taps "Accept Quest" to commit before capacity is reached
HOW	The system checks real-time capacity against confirmed acceptances, locks the user into an available spot, records their acceptance timestamp, decrements the available spots counter (now showing "1 spot left" to others), sends a confirmation with attendance details and directions, and opens a cancellation window (typically 24-48 hours before the event)
WHY	The user wants the confidence that their spot is secured for a popular activity before it fills up, especially for high-demand or time-sensitive experiences, and wants clear expectations about commitment level (i.e., they can't casually abandon plans once accepted)

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9. Quality Scenarios

Quality Scenario 1: Offline Warning on Screen

Field	Description
Quality Attributes	Usability
App Status and Context	The user opens the app to see nearby activities
Changes in Context	The phone loses internet connection completely
System Reaction	The app does not crash and shows a simple message like No internet connection. Showing saved activities

Quality Scenario 2: Search Results Loading Time

Field	Description
Quality Attributes	Performance
App Status and Context	The user is on the main screen with normal internet
Changes in Context	The user taps the search button to see nearby activities
System Reaction	The list of activities loads and appears on the screen in less than 2 seconds

Quality Scenario 3: Location Update

Field	Description
Quality Attributes	Performance / Usability
App Status and Context	The user is viewing nearby quests with GPS enabled
Changes in Context	The user's location changes while browsing the map
System Reaction	The app updates the displayed quests according to the user's new location within 2 seconds

Quality Scenario 4: Quest Detail Accessibility

Field	Description
Quality Attributes	Usability
App Status and Context	The user opens a quest detail page
Changes in Context	The user needs to review the activity information before deciding whether to accept the quest
System Reaction	The app displays the key information clearly, including location, time, duration, price, and reward, without requiring unnecessary navigation

Quality Scenario 5: Battery Usage While Tracking Location

Field	Description
Quality Attributes	Performance / Energy Efficiency
App Status and Context	The user has the map open and GPS is actively tracking their location
Changes in Context	The user keeps the app open and moving for over 30 minutes
System Reaction	The app keeps battery drain low by only updating location when the user moves a meaningful distance, instead of tracking constantly

Quality Scenario 6: Protecting Location Data

Field	Description
Quality Attributes	Security / Privacy
App Status and Context	The app is using the user's GPS location to recommend nearby quests
Changes in Context	The user's location data is sent to the server to get quest recommendations
System Reaction	The app sends the location data over a secure connection and does not store more precise location history than what is needed for the recommendation

Quality Scenario 7: Filter response time

Field	Description
Quality Attributes	Performance
App Status and Context	The user is viewing the activity catalog
Changes in Context	The user applies a filter, such as duration or category
System Reaction	The app updates the displayed activities according to the selected filter within 2 seconds

Quality Scenario 8: Quest progress persistence

Field	Description
Quality Attributes	Reliability
App Status and Context	The user is completing an active quest and has completed some of its steps, but not the whole quest itself
Changes in Context	The user closes and reopens the app before finishing the quest
System Reaction	The app preserves the user quest progress and shows the completed steps correctly marked when the user returns

Quality Scenario 9: High Volume User Load - Search Performance

Field	Description
Quality Attributes	Performance / Scalability
App Status and Context	The app is running during peak hours with thousands of concurrent users searching for quests in Bogotá
Changes in Context	Multiple users simultaneously trigger search and filter requests on the activity catalog
System Reaction	The app maintains response times under 3 seconds for search queries and does not crash or become unresponsive, even with high concurrent load

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Quality Scenario 10: Poor Network Connectivity - Data Sync

Field	Description
Quality Attributes	Resilience / Reliability
App Status and Context	The user is on a slow 2G/3G network connection while trying to complete a quest
Changes in Context	The user marks a quest as completed but the network connection drops before the data is synced to the server
System Reaction	The app saves the completion locally, notifies the user that it will sync when connectivity is restored, and automatically retries the sync without requiring user intervention once the connection is re-established

Quality Scenario 11: Continuing Quest Discovery When GPS Becomes Unavailable

Field	Description
Quality Attributes	Fault Tolerance
App Status and Context	The user is browsing nearby quests on the map. The app has obtained a valid location, and the phone has an active internet connection.
Changes in Context	The user enters a tunnel or an indoor area where the phone can no longer obtain a GPS position.
System Reaction	Within 5 seconds of the location service reporting that a position is unavailable, the app informs the user that it is using their last known location. It keeps the previously displayed quests available for browsing and viewing details, and labels their distances as based on that last known location. The app remains responsive without requiring a restart. When the location service supplies a new valid position, the map and nearby quest results update within 2 seconds.

Quality Scenario 12: Navigation Handoff After the Default Maps App Changes

Field	Description
Quality Attributes	Interoperability
App Status and Context	The user has an active quest with valid destination coordinates on a device that supports choosing a default navigation app. The quest app previously handed navigation requests to the former default maps app.
Changes in Context	Outside the quest app, the user changes the device's default navigation app to another installed app that supports the operating system's navigation interface. The user then returns to the active quest and taps Navigate Now.
System Reaction	Within 2 seconds of the tap, the quest app submits a navigation request through the operating system's navigation interface, which routes it to the newly selected maps app. The receiving app recognizes the quest's destination coordinates without requiring the user to re-enter the address. The quest remains active when the user returns. The handoff works without changing or updating the quest app.

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10. Ethics Video

https://youtu.be/1Pe2XaWpcdQ

11. Sprint 1 wiki Contributions (Kanban Board)

See the board for more information about contributions on all wiki pages (some activities are not included here), descriptions and participants

Manuela Ragua’s contributions

• PAS Hypotheses
• Persona artifacts
• Complete Business Questions
• Functional and Quality Scenarios
• Ethics video

Catalina Villacris’s contributions

• Revenue model and Value proposition
• PAS Hypotheses
• Context canvas
• Empathy maps
• Functional and Quality Scenarios
• Ethics video

Daniel Reales’s contributions

• PAS Hypotheses
• Persona artifacts
• VD Map
• Functional and Quality Scenarios
• Ethics video

Juan David Gutierrez’s contributions

• PAS Hypotheses
• Persona artifacts
• Empathy maps
• Functional and Quality Scenarios
• Ethics video
• Business questions

Manuela Galarza’s contributions

• MS4 Corrections
• PAS Hypotheses
• Functional and Quality Scenarios
• Ethics video
• Business questions

Sharik Galvis’s contributions

• PAS Hypotheses
• Functional and Quality Scenarios
• Ethics video
• Analytics persona