Table of contents

• Description of the problems and the proposed solution
• Personas
• Problems-Alternative-Solutions (PAS)
• Context Canvas
• Business Questions
• VD Map

Description of the problems and the proposed solution

Problem Description

Universidad de los Andes and its surrounding area offer wide variety of restaurants, cafés food courts, and other places where students and university staff can eat or buy snacks. However, this gastronomic offer is highly fragmented, and there is currently no centralized platform where users can easily discover, compare, and evaluate the different available options. As identified during the previous Design Thinking stages, users usually rely on different sources such as Instagram, TikTok, Google Maps, recommendations from friends, or simply the restaurants they already know. This makes finding a suitable place more time-consuming and limits the discovery of alternatives that may better match their needs.

The problem becomes more relevant because choosing where to eat around the university usually involves several constraints at the same time. Students often have limited time between classes, different budgets, dietary restrictions, food preferences, and different tolerance for waiting times or crowded locations. Information about these characteristics is not always available, updated, or presented in a single place. As a result, users may spend unnecessary time searching for options, repeatedly choose the same places, arrive at restaurants that are too crowded, or select alternatives that do not adequately match their preferences.

The main problems identified can therefore be summarized as:

• Fragmented information about the gastronomic offer around the university.
• Difficulty discovering restaurants beyond the places users already know.
• Limited time to compare alternatives between classes or university activities.
• Lack of centralized information about prices, food categories, dietary options, opening hours, and location.
• Difficulty estimating crowd levels or waiting times before visiting a restaurant.
• Lack of personalized recommendations according to each user's preferences and constraints.

Proposed Solution

Our proposed solution is a mobile application that centralizes the gastronomic offer available around Universidad de los Andes and supports users throughout the process of discovering, comparing, and selecting where to eat. Instead of requiring users to search through multiple social networks, maps, or recommendations from other people, the application provides a single platform where relevant information about nearby restaurants and food options can be explored in a structured and accessible way.

The application allows users to discover restaurants from a personalized home screen, search for specific places or food categories, and explore nearby options through an interactive campus map. Each restaurant can provide relevant information such as its location, type of food, estimated price range, opening hours, dietary alternatives, and other characteristics that can influence the user's decision. Users can also save their favorite places and complete a preference survey that helps the application understand factors such as preferred cuisines, budget, and dietary restrictions.

Additionally, the proposed solution seeks to reduce uncertainty during the decision-making process by incorporating information such as estimated crowd levels and waiting times. By combining restaurant information, user preferences, location, and interaction data, the application can provide more relevant recommendations and help users quickly identify alternatives that fit their current needs.

The main capabilities of the proposed solution include:

• A Home Discover section with restaurant suggestions and relevant food options.
• A Search functionality to find restaurants based on specific criteria.
• An interactive Campus Map displaying nearby gastronomic options.
• Detailed restaurant information to support comparison between alternatives.
• A Preference Survey to identify user interests, budget, dietary restrictions, and food preferences.
• Personalized recommendations based on user preferences and interactions with the application.
• Information about estimated crowd levels and waiting times to support faster dining decisions.
• The possibility of saving and revisiting favorite restaurants.

Analytics persona

Problems-Alternative-Solutions (PAS)

The following Problems-Alternative-Solutions (PAS) were defined based on the needs and pain points identified for the prospective users and the analytics persona. For each problem, we considered how the persona currently addresses the issue and proposed a solution that improves the current alternative through the application's functionality and analytics capabilities.

ID	Problem	Alternative	Solution
1	Students and university staff have difficulty discovering the variety of restaurants and food options available around Universidad de los Andes because the information is fragmented across multiple sources.	Users rely on Instagram, TikTok, Google Maps, recommendations from friends, or the restaurants they already know.	The app centralizes the gastronomic offer in a single platform where users can discover and explore nearby restaurants and food options.
2	Users have limited time between classes or university activities and may spend too much time comparing different places before deciding where to eat.	Users manually search different platforms, ask friends, or choose a familiar restaurant to avoid spending additional time deciding.	The app provides search, filters, restaurant details, and personalized recommendations that allow users to quickly identify options that match their current needs.
3	A student with only 30 minutes between classes may choose a restaurant without knowing whether the current waiting time will allow them to eat and return to class on time.	The student walks to the restaurant, checks the line in person, or asks friends nearby before deciding whether to stay or look for another option.	The app displays estimated waiting times and crowd levels for nearby restaurants, allowing the student to compare alternatives before leaving campus.
4	Users may arrive at a restaurant without knowing that it is crowded or that the expected waiting time is too long for the time they have available.	Users walk to the restaurant and check the situation in person, ask friends who are nearby, or choose another place after arriving.	The app provides estimated crowd levels and waiting times so users can compare options before going to a restaurant.
5	A user with a vegetarian or vegan diet may find a restaurant that looks suitable but later discover that it has very few options that match their dietary restriction.	The user checks restaurant menus individually, searches social media posts, or asks restaurant staff before deciding where to eat.	The app stores dietary attributes for each restaurant and allows users to filter the gastronomic offer specifically by vegetarian, vegan, or other dietary options.
6	Users have different budgets, dietary restrictions, and food preferences, making it difficult to identify restaurants that are suitable for them.	Users manually inspect menus, social media posts, restaurant information, or ask other people before deciding whether a place fits their needs.	The app allows users to provide their preferences and constraints and uses this information to filter options and generate more personalized restaurant recommendations.
7	A student with a fixed lunch budget may spend time opening several restaurant profiles just to determine which places are affordable.	The student checks menus one by one, asks friends about prices, or repeatedly visits restaurants whose prices are already familiar.	The app allows users to define a preferred price range and filters or prioritizes restaurants whose estimated prices fall within that budget.
8	The Product Manager lacks a clear understanding of which restaurants, food categories, and application features generate the most interest among users.	She relies on user interviews, informal feedback, and manual observations to infer what users prefer and how they interact with the platform.	The platform aggregates interaction data such as searches, restaurant views, favorites, and feature usage to identify user behavior patterns and support evidence-based product decisions.
9	Users may repeatedly choose the same restaurants because they do not know which less familiar places offer food similar to what they already enjoy.	They rely on recommendations from friends, social media posts, or continue visiting the same known restaurants.	The app uses the user's selected cuisine preferences, favorites, and previous interactions to recommend less familiar restaurants with similar characteristics.
10	The Product Manager cannot easily identify whether users abandon the restaurant discovery process after performing a search without opening any restaurant.	She reviews general usage metrics or user feedback, but cannot determine at which specific point users stop interacting with the search flow.	The platform records search events, result impressions, restaurant-detail openings, and subsequent actions so the Product Manager can identify searches that frequently end without meaningful interaction.
11	The Product Manager cannot easily determine whether personalized restaurant recommendations are relevant and useful to users.	She relies on occasional feedback or general application usage without directly measuring how users interact with the recommendations they receive.	The platform tracks interactions with personalized recommendations, such as recommendation views, restaurant detail visits, and favorites, allowing the Product Manager to evaluate their effectiveness and identify opportunities to improve personalization.
12	The Product Manager does not know which filters actually help users narrow down restaurant options and which filters are rarely used or provide little value.	She relies on assumptions, interviews, or occasional feedback when deciding whether a filter should be maintained, changed, or removed.	The analytics system tracks how frequently each filter is selected and whether filtered searches lead to restaurant-detail views or favorites, allowing the Product Manager to evaluate the usefulness of each filter.

Context Canvas

Business Questions

Type 1: App’s telemetry

• On avarage, how many times does a user open the app during a typical academic week?

Type 2: Direct user experience improvement

• Which filters (diet, budget, distance, available time) are most commonly used when searching for a place to eat?
• How long does it take, on average, for a user to go from opening the app to selecting a place to eat?
• What percentage of users complete the food preferences survey during onboarding?
• How often do users check or inquire about a restaurant’s occupancy level before deciding to go?

Type 3: New or deprecated/updated features

• What categories of food or restaurants do users search for without getting any results (gaps in the listed offerings)?
• What places are saved as favorites but rarely viewed in detail again?
• Which sections of the prototype (map, filters, details, favorites) receive the least interaction and could be redesigned or removed?

Type 4: Benefits from data

• What patterns of traffic by time slot could be shared with partner restaurants to help them better manage their peak hours?

Type 5: combination of two or more types

• How does the use of the “free time between classes” filter relate to the likelihood that the user will actually visit the recommended location?

VD Map