WineROI Product Requirements Document

Product Overview

WineROI is a mobile application that enables restaurant diners to make informed wine selections based on value assessment. Our vision is to democratize wine value knowledge, empowering everyday diners to make confident, informed choices without specialized expertise in the complex and often opaque world of restaurant wine pricing.

1. Foundation Layer

1.1 Product Vision

WineROI transforms the restaurant wine selection experience by providing instant, reliable value assessment that empowers diners to make confident selections without requiring wine expertise. We believe everyone deserves to enjoy good wine at fair prices, free from the intimidation and opacity that often surrounds wine selection.

Primary Value Proposition: WineROI delivers immediate, discreet assessment of wine value in restaurant settings, enabling confident decision-making without specialized knowledge.

Core Benefits:

• Financial confidence through clear value assessment relative to retail pricing
• Decision efficiency with quick, glanceable information designed for discreet use
• Collective intelligence leveraging crowdsourced data that grows more valuable with each user
• Practical knowledge focused on value rather than connoisseurship
• Social comfort by reducing anxiety in business and social dining situations

Key Differentiators:

• Exclusive focus on value assessment rather than ratings or tasting notes
• Designed specifically for discreet use in restaurant settings
• Prioritizes practical value over wine expertise or connoisseurship
• Leverages crowdsourced data to create an ever-improving value database

1.2 User Personas

Primary Persona: Business Professional (Alex)

• Demographics: 35-45, upper-middle income, urban professional
• Behaviors: Regular business meals, client entertainment, expense account dining
• Goals: Appear knowledgeable, make smart selections, avoid embarrassment
• Pain Points: Pressure to select appropriately, limited wine knowledge, concern about overpaying
• Motivations: Professional credibility, financial responsibility, practical knowledge
• Technical Sophistication: Moderate to high, comfortable with mobile apps
• Usage Scenario: Discreetly checking wine values during business dinners with clients
• Decision Factors: Speed of use, discretion, reliability of information

Secondary Persona: Value-Conscious Diner (Jamie)

• Demographics: 28-60, varied income levels, urban/suburban
• Behaviors: Occasional fine dining, special occasion celebrations
• Goals: Getting good value, enjoying quality wine without overpaying
• Pain Points: Uncertainty about markups, limited knowledge to assess value
• Motivations: Financial savvy, practical enjoyment, avoiding being taken advantage of
• Technical Sophistication: Moderate, uses apps for practical purposes
• Usage Scenario: Checking wine values during special occasion dinners
• Decision Factors: Accuracy of value assessment, ease of use, comprehensive database

Tertiary Persona: Pragmatic Regular Diner (Taylor)

• Demographics: 25-65, middle to upper-middle income
• Behaviors: Regular restaurant dining, social meals with friends/family
• Goals: Making informed choices without extensive study
• Pain Points: Overwhelmed by wine lists, intimidated by wine culture
• Motivations: Confidence in decisions, practical solutions, simplicity
• Technical Sophistication: Varies widely, prefers intuitive interfaces
• Usage Scenario: Quick check of wine values during casual dining experiences
• Decision Factors: Simplicity, quick results, practical recommendations

1.3 Market Context

Competitive Landscape:

No direct competitors currently offer a dedicated restaurant wine value assessment tool. Most diners resort to general internet searches or existing wine apps not optimized for this specific use case.

Indirect Competitors:

1. Vivino - Massive wine database with label scanning and ratings, but not focused on restaurant value assessment
2. Wine-Searcher - Comprehensive price comparison for retail, not optimized for restaurant use
3. Delectable - Social wine tracking and label recognition, focused on personal collection
4. CellarTracker - Extensive community reviews and inventory management for enthusiasts

Market Trends:

• Growing consumer interest in value-based purchasing decisions
• Increasing transparency expectations across all consumer categories
• Rising popularity of crowdsourced data platforms
• Continued growth in restaurant wine sales despite economic fluctuations
• Increasing mobile usage during dining experiences despite social taboos

Adoption Barriers:

• Social norms around phone use in dining settings
• Restaurant resistance to price transparency
• Need for critical mass of data for certain restaurants/regions
• Varying wine list formats and presentation

Market Opportunity: WineROI addresses a universal pain point in dining experiences by providing transparent, actionable information about wine value. By focusing specifically on the return on investment aspect of wine selection, the application fills a gap that larger wine applications have overlooked.

1.4 Success Criteria

Key Performance Indicators:

1. User Acquisition: 100,000 active users within first year
2. User Engagement: Average 2+ sessions per month per user
3. Database Growth: 50,000+ restaurant wine entries within first year
4. User Contribution: 25% of active users contribute pricing data
5. Retention Rate: 40% 3-month retention rate

Revenue Targets:

• Freemium model with conversion rate of 5% to premium tier
• Average revenue per paying user (ARPU) of $3.99/month

Engagement Benchmarks:

• Average session duration: 45 seconds
• Average wines assessed per session: 2-3
• Search success rate: 80%+ (users find the wine they're looking for)

Quality Indicators:

• Data accuracy rating: 90%+ verified correct
• User satisfaction score: 4.5+ out of 5
• App store rating: 4.5+ out of 5

2. Features Layer

2.1 Core Functionality

2.1.1 Wine Value Assessment

Description: Provides immediate assessment of whether specific wines represent good value relative to their retail price.

Key Features:

• Value rating system (Great Value, Fair Value, Poor Value)
• Retail price comparison with restaurant price
• Markup percentage calculation and display
• Restaurant markup comparison to industry averages
• Value history tracking for returning users

User Stories:

• As a diner, I want to quickly determine if a wine is reasonably priced so I can make an informed decision
• As a business professional, I want to see value ratings at a glance so I can discreetly choose wines during client meals
• As a value-conscious diner, I want to compare markup percentages across wines so I can select the best value option

Acceptance Criteria:

• Value assessment loads within 2 seconds of wine selection
• Clear visual indicators distinguish between value categories
• Markup percentages are calculated accurately based on verified retail pricing
• Value ratings account for regional and category-specific markup norms

2.1.2 Wine Search and Discovery

Description: Enables users to quickly find specific wines from restaurant lists through multiple search methods.

Key Features:

• Text-based search with autocomplete
• Restaurant-specific wine list browsing
• Wine label scanning (future enhancement)
• Recently viewed wines
• Search history
• Filtering by type, region, and price range

User Stories:

• As a diner, I want to search for a specific wine quickly so I can assess its value without disrupting my meal
• As a user in a dark restaurant, I want a search interface that's easy to use in low-light conditions
• As a regular at certain restaurants, I want to browse their complete wine list so I can plan selections in advance

Acceptance Criteria:

• Search results appear within 1 second of query submission
• Autocomplete suggestions appear after 3 characters
• Search functions in offline mode with previously loaded data
• Interface is optimized for low-light environments

2.1.3 Restaurant Wine List Management

Description: Manages database of restaurant wine lists and enables user contributions of new pricing data.

Key Features:

• Restaurant search and selection
• User-contributed wine pricing
• Wine list verification system
• Restaurant information and pricing trends
• Wine list update timestamps
• User contribution recognition

User Stories:

• As a user, I want to find restaurants near me so I can see their wine list before dining
• As a contributor, I want to easily add wine prices I encounter so I can help build the database
• As a user, I want to see when pricing was last updated so I can judge its reliability

Acceptance Criteria:

• Restaurant search returns results based on proximity and relevance
• Wine price contribution requires minimal steps (under 30 seconds)
• All user contributions are clearly marked with submission date
• Verification system flags potential pricing errors

2.1.4 User Profile and Preferences

Description: Manages user account, preferences, and history to personalize the experience.

Key Features:

• Optional account creation
• Wine preference settings
• Value threshold customization
• Contribution history
• Favorite restaurants
• Saved wines
• Usage statistics

User Stories:

• As a user, I want to use the app without creating an account so I can quickly assess value
• As a returning user, I want to see my search history so I can quickly find wines I've previously viewed
• As a frequent user, I want to customize my value thresholds so they align with my personal standards

Acceptance Criteria:

• Core functionality works without account creation
• All user preferences are saved locally and to account if created
• History and saved items sync across devices when signed in
• Clear benefits are shown for account creation

2.2 Data Management

2.2.1 Data Entities and Relationships

• Wines: Name, producer, vintage, region, type, varietal, retail price range
• Restaurants: Name, location, cuisine type, price tier, wine list size
• Restaurant Wine Entries: Wine ID, restaurant ID, price, date updated, contributor ID
• Users: Optional profile, preferences, contribution history, search history
• Value Assessments: Calculation logic, historical pricing data, markup norms

2.2.2 Data Collection Requirements

• Initial database seeding with retail wine pricing from partnerships
• User contribution mechanism for restaurant pricing
• Verification system for contributed data
• Optional location data for restaurant recommendations
• Search history and preferences stored locally by default

2.2.3 Data Processing Needs

• Real-time value calculation based on retail price and restaurant price
• Markup percentage calculation and comparison to industry averages
• Data normalization for wine names and vintages
• Confidence scoring for pricing data based on recency and verification
• Trend analysis for restaurant markup patterns

2.2.4 Data Retention and Privacy

• Search history retained for 90 days by default
• User contributions retained indefinitely with attribution option
• Minimal personal data collection (optional account only)
• Clear data retention and privacy policies
• GDPR and CCPA compliance built-in
• User ability to delete history and contributions

2.3 Integration Requirements

2.3.1 External Wine Database Integration

• Integration with retail wine pricing databases
• API connections to wine information services
• Regular data synchronization for pricing updates
• Fallback mechanisms for offline operation

2.3.2 Location Services Integration

• Restaurant discovery based on user location
• Proximity-based search results
• Optional location sharing controls
• Alternative manual location entry

2.3.3 Social Sharing Capabilities

• Discreet sharing of value finds with contacts
• Optional social media integration
• In-app messaging for recommendations
• Referral system for user acquisition

2.3.4 Authentication Methods

• Email/password authentication
• Social login options (Google, Apple, Facebook)
• Secure token management
• Password reset and account recovery

2.4 Administrative Functions

2.4.1 Content Management

• Wine database management dashboard
• Restaurant information management
• Pricing data verification tools
• User contribution moderation
• Content update scheduling

2.4.2 User Management

• User account administration
• Contribution tracking and recognition
• Abuse prevention and reporting
• User feedback management

2.4.3 Analytics and Reporting

• Usage statistics dashboard
• Search pattern analysis
• Contribution metrics
• Value assessment analytics
• Restaurant coverage metrics
• User retention and engagement reporting

2.4.4 System Configuration

• Value algorithm adjustment
• Markup threshold configuration
• Search relevance tuning
• Feature flag management
• A/B testing framework

3. Experience Layer

3.1 User Flows

3.1.1 First-Time User Flow

1. User downloads app from App Store/Google Play
2. App launches with brief value proposition explanation
3. User is presented with option to create account or continue without account
4. Brief tutorial highlights core features (skippable)
5. User is prompted to allow location services (optional)
6. User reaches home screen with search functionality prominent
7. Sample searches are suggested to demonstrate functionality

3.1.2 Wine Value Assessment Flow

1. User opens app in restaurant setting
2. User searches for specific wine or browses restaurant list
3. User selects wine from search results
4. App displays value assessment with retail price comparison
5. User views detailed breakdown if desired
6. User can save wine to favorites or share assessment
7. User returns to search or exits app

3.1.3 Restaurant Discovery Flow

1. User navigates to restaurant section
2. App displays nearby restaurants based on location
3. User can search for specific restaurant
4. User selects restaurant to view wine list
5. App displays available wines with value indicators
6. User can sort by value, price, or wine type
7. User selects wine for detailed assessment

3.1.4 Wine Price Contribution Flow

1. User identifies missing wine or outdated price
2. User selects "Add Wine Price" option
3. User enters wine name or selects from suggestions
4. User enters price and vintage information
5. App confirms submission and thanks user
6. User receives contribution points
7. App displays updated information immediately with "unverified" indicator

3.2 Interface Requirements

3.2.1 Core Interface Elements

• Dark Mode Default: Optimized for discreet use in restaurant settings
• High Contrast Elements: Ensuring readability in low-light environments
• Minimal Input Requirements: Reducing typing needed in restaurant settings
• Glanceable Information: Value assessments visible without scrolling
• One-Handed Operation: Critical functions accessible with thumb of holding hand
• Quick Exit: Single tap to exit to phone home screen

3.2.2 Screen Requirements

Home Screen:

• Prominent search bar
• Recent searches
• Nearby restaurants
• Quick access to saved wines
• Contribution prompt for engaged users

Search Results Screen:

• Fast-loading list view
• Clear wine identification (name, vintage, producer)
• Value indicator visible in results
• Quick filtering options
• Progressive loading for large lists

Wine Detail Screen:

• Prominent value assessment
• Retail price vs. restaurant price comparison
• Markup percentage with context
• Similar value alternatives when available
• Contribution timestamp and confidence indicator
• Quick actions (save, share, contribute)

Restaurant Screen:

• Wine list organized by category
• Value indicators for all wines
• Last updated information
• Quick contribution option
• Sorting and filtering capabilities

User Profile Screen:

• Contribution statistics
• Saved wines and restaurants
• Search history
• Preference settings
• Account management

3.2.3 Responsive Behavior

• Optimized for one-handed phone use in portrait orientation
• Limited but functional tablet layout
• Adjusts to different screen sizes and densities
• Accommodates system font size preferences
• Handles orientation changes gracefully

3.2.4 Accessibility Requirements

• VoiceOver/TalkBack compatibility
• Minimum touch target size of 44×44 points
• Color contrast ratios meeting WCAG AA standards
• Text alternatives for all non-text elements
• Keyboard navigation support
• Respects system accessibility settings

3.3 Performance Requirements

3.3.1 Response Time Targets

• App launch to usable state: < 2 seconds
• Search response time: < 1 second
• Wine detail loading: < 1.5 seconds
• Value calculation: < 0.5 seconds
• Restaurant list loading: < 2 seconds
• Contribution submission: < 1 second

3.3.2 Offline Capabilities

• Core search functionality works offline with previously accessed data
• Offline queue for user contributions
• Clear indicators of offline status
• Automatic sync when connection restored
• Cached restaurant lists and recent searches

3.3.3 Resource Efficiency

• Battery usage optimization for restaurant use
• Data usage < 5MB per typical session
• Storage requirement < 100MB for app + cache
• Memory usage < 150MB during operation
• Background activity minimized

3.3.4 Reliability Metrics

• App crash rate < 0.5%
• Search success rate > 95%
• Data accuracy > 90%
• Sync failure recovery > 99%
• Availability > 99.9%

3.4 Contextual Adaptations

3.4.1 Environmental Adaptations

• Low Light Optimization: Dark mode default with brightness adjustment
• Noisy Environment: Visual feedback prioritized over audio
• Limited Connectivity: Graceful degradation with offline functionality
• Battery Conservation: Power-saving mode for extended restaurant sessions
• Distracted Usage: Simple, glanceable interfaces requiring minimal attention

3.4.2 Device-Specific Behaviors

• iOS-Specific: Apple Pay integration, Haptic feedback, Dynamic Island support
• Android-Specific: Material Design components, Back gesture handling
• Older Devices: Performance optimizations, reduced animations
• Premium Devices: Enhanced visual effects, advanced camera integration

3.4.3 User Preference Adaptations

• Value Threshold Customization: Adjustable definitions of good/fair/poor value
• Interface Density Options: Standard or compact information display
• Notification Preferences: Customizable alerts for price changes
• Privacy Controls: Granular data sharing and history retention options
• Contribution Visibility: Public or anonymous contribution settings

4. Validation Layer

4.1 Functional Testing

4.1.1 Core Functionality Test Scenarios

• Search Accuracy: Verify search returns correct wines across various query formats
• Value Calculation: Confirm value assessments match expected results based on pricing data
• Restaurant Discovery: Verify nearby restaurants are correctly identified and displayed
• User Contributions: Ensure contributed prices are properly stored and displayed
• Profile Management: Verify all user preference settings function correctly

4.1.2 Edge Cases and Error Handling

• Poor Connectivity: Test functionality with intermittent network connection
• Unknown Wines: Verify handling of searches for wines not in database
• Conflicting Data: Test resolution of conflicting price contributions
• Extreme Pricing: Verify handling of unusually high or low prices
• High Volume Usage: Test performance under heavy search and contribution load

4.1.3 Integration Testing

• External Database Sync: Verify correct data exchange with wine pricing partners
• Location Services: Confirm accurate restaurant proximity calculations
• Authentication Systems: Test all login methods and account management functions
• Social Sharing: Verify correct content sharing across supported platforms

4.1.4 Regression Testing

• Automated test suite covering all critical user flows
• Visual regression testing for interface consistency
• Performance benchmark testing for each release
• Data integrity verification across updates

4.2 User Acceptance

4.2.1 Usability Testing Methodology

• In-context testing in actual restaurant environments
• Task completion time measurements for key functions
• Observation of discretion and social comfort factors
• Comparative testing against manual wine research methods

4.2.2 Beta Testing Approach

• Closed beta with selected users from each persona group
• Staged rollout to increasing user numbers
• Instrumented builds with enhanced analytics
• Regular feedback collection via in-app surveys

4.2.3 Success Metrics

• Task completion rates > 95%
• User satisfaction ratings > 4.5/5
• Perceived value score > 4.5/5
• Discretion comfort rating > 4.5/5
• Net Promoter Score > 40

4.2.4 Feedback Mechanisms

• In-app feedback form accessible from all screens
• Post-use micro-surveys (single question, rotating)
• Beta tester community forum
• User interviews with power users and new users
• App store review monitoring and response

4.3 Performance Verification

4.3.1 Load Testing

• Simulate concurrent users at 2x projected peak
• Test search performance with full database load
• Verify contribution handling under high volume
• Measure API response times under various loads

4.3.2 Device Testing

• Verify performance across range of iOS devices (iPhone 8 through current)
• Test on range of Android devices (various manufacturers, OS versions)
• Optimize for minimum supported specifications
• Verify battery consumption rates in typical usage scenarios

4.3.3 Network Condition Testing

• Test under various network conditions (5G, LTE, 3G, Wi-Fi)
• Verify performance with high latency connections
• Test transition between network types
• Validate offline functionality and data synchronization

4.3.4 Performance Monitoring

• Implement crash reporting and analytics
• Monitor key performance indicators in production
• Establish automated alerting for performance degradation
• Create performance dashboards for ongoing monitoring

4.4 Compliance Verification

4.4.1 Privacy Compliance

• GDPR compliance verification
• CCPA compliance verification
• Privacy policy implementation and display
• Data collection consent mechanisms
• Data access and deletion capabilities

4.4.2 Accessibility Compliance

• WCAG 2.1 AA compliance testing
• Screen reader compatibility verification
• Color contrast analysis
• Touch target size verification
• Keyboard navigation testing

4.4.3 Platform Compliance

• iOS App Store guidelines compliance
• Google Play Store policy compliance
• Apple Human Interface Guidelines adherence
• Material Design guideline adherence for Android

4.4.4 Security Verification

• Authentication security testing
• Data encryption verification
• API security assessment
• Secure storage of user credentials
• Vulnerability scanning and penetration testing

5. Implementation Considerations

5.1 Development Approach

WineROI should be developed using a phased approach, starting with a Minimum Viable Product (MVP) that delivers core value assessment functionality for a limited set of popular wines and restaurants. The development approach should prioritize:

1. Speed to Market: Focus on delivering core value quickly to begin building the user base and data collection
2. Data Foundation: Establish partnerships for initial wine pricing data to ensure value from day one
3. User Contribution: Create frictionless contribution mechanisms to accelerate database growth
4. Performance: Optimize for quick, discreet use in restaurant settings from the beginning

5.2 Technical Architecture Recommendations

Based on input from the VP Engineering, the recommended technical approach includes:

1. Cross-Platform Framework: React Native for efficient development across iOS and Android
2. Backend Services: Cloud-based microservices architecture for scalability
3. Database Strategy:
   • NoSQL database for wine and restaurant information
   • Relational database for user accounts and transactions
   • Local caching for offline functionality
4. API Strategy: RESTful APIs with GraphQL for efficient data retrieval
5. Authentication: OAuth 2.0 with social login options
6. Analytics: Custom event tracking combined with standard mobile analytics

5.3 UI/UX Considerations

Based on input from the UI/UX Lead, the following design considerations are critical:

1. Discretion-First Design: All interface elements optimized for quick, unobtrusive use
2. Information Hierarchy: Value assessment must be immediately visible and understandable
3. Reduced Cognitive Load: Minimize required decision-making during restaurant use
4. One-Handed Operation: Critical functions accessible without requiring two hands
5. Dark Mode Default: Optimized for low-light restaurant environments
6. Progressive Disclosure: Essential information first, details available on demand

5.4 Phased Implementation Plan

Phase 1: MVP (3 Months)

• Core wine value assessment functionality
• Basic search capabilities
• Limited restaurant database (top 100 restaurants in 5 major cities)
• Essential user contribution mechanisms
• Basic account functionality

Phase 2: Enhancement (3 Months Post-Launch)

• Expanded wine and restaurant database
• Improved search with autocomplete
• Enhanced contribution system with verification
• User profiles and history
• Basic social sharing

Phase 3: Expansion (6 Months Post-Launch)

• Wine label scanning capability
• Advanced filtering and personalization
• Premium features introduction
• Expanded geographic coverage
• Restaurant partnerships

5.5 Critical Success Factors

1. Data Accuracy: Ensuring wine pricing information is accurate and current
2. Database Coverage: Quickly building comprehensive coverage of popular restaurants
3. User Experience: Delivering a genuinely discreet, fast experience in restaurant settings
4. User Contribution: Creating effective incentives for user data contributions
5. Performance: Maintaining speed and reliability even as database grows

5.6 Risk Factors and Mitigation

Risk	Impact	Probability	Mitigation
Insufficient initial data	High	Medium	Secure wine database partnerships before launch
Low user contribution	High	Medium	Design gamification and recognition systems
Restaurant resistance	Medium	Medium	Emphasize consumer education rather than criticism
Social awkwardness	High	Medium	Design for extreme discretion and quick use
Competitor response	Medium	Low	Move quickly to establish user base and data advantage

6. Conclusion

WineROI addresses a universal pain point in dining experiences by providing transparent, actionable information about wine value. By focusing specifically on the return on investment aspect of wine selection, the application fills a gap in the market that larger wine applications have overlooked.

The product requirements outlined in this document provide a comprehensive framework for developing a distinctive, valuable application that will resonate deeply with its target audience. By prioritizing discretion, speed, and practical value assessment, WineROI can establish a unique position in the market and build a loyal user base of value-conscious diners.

Development should proceed with a focus on quickly delivering core functionality to begin building the critical mass of users and data that will create increasing value through network effects. Each user contribution will enhance the collective intelligence of the platform, creating a virtuous cycle of growth and increasing utility.