MarkupMeter Product Requirements Document

Product Overview

MarkupMeter is a specialized mobile application that provides transparency to restaurant wine pricing, enabling value-conscious wine enthusiasts to instantly compare restaurant wine prices against retail market rates. Our vision is to transform the restaurant wine experience by eliminating pricing opacity, empowering diners to make informed decisions, and creating market incentives for fair pricing practices within the competitive restaurant industry.

1. Foundation Layer

1.1 Product Vision

MarkupMeter exists to democratize wine pricing knowledge and restore balance to the restaurant wine experience. By bringing transparency to an intentionally opaque system, we empower consumers to make informed choices and create market incentives for fair pricing practices.

Core Value Proposition

MarkupMeter delivers instant wine pricing transparency, transforming restaurant wine selection from a moment of uncertainty to one of confidence and control.

Key Differentiators

Specialized Focus: Unlike general wine apps, we exclusively address the restaurant markup issue
Situational Design: Built specifically for discreet, in-restaurant use cases
Community Intelligence: Leveraging collective user data to create a restaurant markup index
Two-Sided Value: Creating benefits for both consumers and fairly-priced restaurants

Market Positioning

MarkupMeter positions itself as the category-defining solution for restaurant wine pricing transparency—a specialized tool that fills a significant gap between general wine information apps and restaurant review platforms.

1.2 User Personas

Primary: The Value-Conscious Wine Enthusiast

Demographics: 30-65 years old, upper-middle income ($75k-$200k), urban/suburban, college-educated professionals
Behaviors: Dines out 2+ times monthly at mid to high-end restaurants, has intermediate wine knowledge
Goals: Find fairly priced wines on restaurant lists, avoid excessive markups, make confident selections
Pain Points: Frustration with opaque pricing, social discomfort when researching prices at the table, uncertainty about value
Technical Profile: Smartphone-savvy, uses apps regularly, comfortable with search and comparison tools
Usage Scenario: Reviewing a wine list at a business dinner, wanting to order a good value bottle without appearing cheap or uninformed

Secondary: The Restaurant Wine Novice

Demographics: 25-45 years old, middle income, urban dwellers
Behaviors: Occasional fine dining, limited wine knowledge, intimidated by wine selection
Goals: Avoid overpaying, gain confidence in wine selection, learn about wine value
Pain Points: Intimidation from sommelier interactions, lack of wine knowledge, fear of making poor value choices
Technical Profile: High smartphone usage, prefers simple interfaces, uses social recommendations
Usage Scenario: On a date night, wanting to order wine confidently without appearing uninformed or spending too much

Tertiary: Fair-Pricing Restaurants

Demographics: Independent restaurants, progressive restaurant groups, wine-focused establishments
Behaviors: Offer reasonable wine markups as a competitive advantage, emphasize wine as part of experience
Goals: Attract wine-ordering customers, differentiate from competitors, build reputation for value
Pain Points: Difficulty communicating fair pricing as a value proposition, competing with establishments that subsidize food with wine markups
Technical Profile: Varies widely, from tech-savvy to traditional operations
Usage Scenario: Restaurant owner wanting recognition for fair wine pricing practices to attract value-conscious customers

1.3 Market Context

Competitive Landscape

Direct Competitors (Wine Information Apps)

Vivino: Market leader with 50M+ users, focused on wine ratings and retail pricing, limited restaurant functionality
Wine-Searcher: Comprehensive retail price database, not optimized for restaurant use
Delectable: Social wine discovery platform, limited price comparison functionality

Indirect Competitors

Restaurant Review Platforms (Yelp, TripAdvisor): Occasionally mention wine pricing in reviews
Reservation Apps (OpenTable, Resy): May include wine list information but no pricing analysis

Market Gap Analysis No current solution effectively addresses the specific pain point of restaurant wine pricing opacity, creating a clear market opportunity.

Market Trends

Increasing consumer demand for transparency across industries
Growing interest in value-based dining decisions
Rising wine consumption among millennials and Gen X
Increased smartphone usage during dining experiences
Growing acceptance of technology as part of the dining experience

Regulatory Considerations

Data privacy regulations (GDPR, CCPA) for user contribution systems
Alcohol advertising restrictions in certain markets
Restaurant industry standards and practices

1.4 Success Criteria

Business Metrics

Achieve 100,000 active users within first year of launch
Reach 250,000 wine price data points in database within 18 months
Attain 60% user retention rate after 3 months
Generate 10,000+ user-contributed wine price entries monthly by month 6

User Value Metrics

80% of users report increased confidence in wine purchasing decisions
70% of users discover at least one "good value" wine per month
Average time to find wine pricing information under 30 seconds
50% of users report ordering wine more frequently when using the app

Product Quality Metrics

95% data accuracy for wine pricing information
99% app availability during peak dining hours (6-10pm)
Average app rating of 4.5+ stars in app stores
90% of searches return relevant results within 3 seconds

2. Features Layer

2.1 Core Functionality

Wine Search & Identification

Description: Allow users to quickly find specific wines on restaurant lists
Key Capabilities:
- Text-based search with autocomplete and fuzzy matching
- Wine label scanning via camera (Phase 2)
- Voice search capability for hands-free operation (Phase 2)
- Recent search history and favorites
User Stories:
- As a diner, I want to quickly search for a specific wine by name so I can get pricing information before ordering
- As a diner in a dimly lit restaurant, I want to scan a wine label to identify it without typing
Acceptance Criteria:
- Search returns relevant results within 3 seconds
- Autocomplete suggests wines after 3 characters typed
- Search accommodates common misspellings and partial names
- Results prioritized by relevance and popularity

Price Comparison Engine

Description: Core system that compares restaurant prices to retail market rates
Key Capabilities:
- Display current average retail price alongside restaurant price
- Calculate and display markup percentage
- Show historical price trends where available
- Present price comparison visualizations
User Stories:
- As a value-conscious diner, I want to see the markup percentage on a restaurant wine so I can determine if it's reasonably priced
- As a wine enthusiast, I want to compare multiple wines on a list by their markup to find the best value
Acceptance Criteria:
- Retail prices updated at least weekly
- Markup calculations clearly explained to users
- Price comparison displayed within 2 seconds of wine selection
- Multiple pricing sources used for accuracy

Restaurant Markup Index

Description: System that rates and compares restaurants based on their wine pricing practices
Key Capabilities:
- Calculate average markup percentage for each restaurant
- Assign transparency rating based on pricing practices
- Allow users to filter and sort restaurants by markup index
- Provide trend analysis for restaurant pricing over time
User Stories:
- As a frequent diner, I want to identify restaurants with fair wine pricing so I can patronize them
- As a user planning a special dinner, I want to compare wine markup policies across several restaurants
Acceptance Criteria:
- Index calculated based on minimum of 5 wines per restaurant
- Ratings updated weekly based on new price information
- Index accounts for restaurant type and location in comparisons
- Visualizations make ratings instantly understandable

Community Contribution System

Description: Platform for users to share and update wine pricing information
Key Capabilities:
- User submission of wine prices and restaurant listings
- Verification system for user-submitted data
- Reputation system for contributors
- Flagging mechanism for inaccurate information
User Stories:
- As an active user, I want to add wine prices I discover so I can help others make informed decisions
- As a community member, I want to earn recognition for my contributions to build my reputation
Acceptance Criteria:
- Submission process completable in under 60 seconds
- Verification process prevents spam and inaccurate data
- Contributors receive acknowledgment and status benefits
- Data moderation system reviews flagged entries within 24 hours

Value Discovery Tools

Description: Features that help users identify good values on wine lists
Key Capabilities:
- "Best Value" highlighting on restaurant wine lists
- Value alerts for exceptionally good deals
- Personalized value recommendations based on preferences
- Value comparison across wine categories
User Stories:
- As a diner with limited wine knowledge, I want the app to highlight good values so I can order confidently
- As a user with specific preferences, I want personalized value recommendations
Acceptance Criteria:
- "Best Value" designation based on clear, consistent criteria
- Value alerts are accurate and relevant to user preferences
- Recommendations account for both price and quality factors
- Value comparisons are easy to understand for wine novices

2.2 Data Management

Data Entities & Relationships

Wines: Name, vintage, producer, region, varietal, category, retail price history
Restaurants: Name, location, cuisine type, price tier, markup index
Wine Lists: Restaurant association, date updated, source (official/user-contributed)
Price Points: Wine-restaurant pairing, price, date recorded, source
Users: Profile, contribution history, preferences, reputation score

Data Collection Requirements

Integration with wine retail pricing APIs and databases
User contribution system with structured data entry
Optional restaurant partnership data feeds
Web scraping of publicly available wine list information (where legal)
Historical price tracking and versioning

Data Processing & Analysis

Automated markup calculations based on average retail prices
Statistical analysis to identify outliers and potential errors
Trend analysis for price changes over time
Machine learning for price prediction and anomaly detection
Aggregation for restaurant markup index calculations

Data Privacy & Security

No personally identifiable user data shared publicly
Opt-in for location-based features
Transparency in data collection and usage policies
Compliance with GDPR, CCPA and relevant privacy regulations
Regular security audits and data protection measures

Data Retention & Archiving

Wine pricing data retained indefinitely with historical versioning
User contribution history maintained for account lifetime
Inactive restaurant listings archived after 12 months
Search history retained for 30 days unless user opts for longer retention
Regular database optimization and archiving processes

2.3 Integration Requirements

Wine Pricing Databases

Integration with Wine-Searcher API for retail pricing data
Connection to regional wine retailer databases where available
Potential partnership with Vivino for additional pricing sources
Custom API development for specialized wine retailers

Restaurant Information Systems

Integration with OpenTable, Resy, and other reservation platforms
Optional API for restaurant management systems
POS system integration for participating restaurants (future phase)
Menu management system connections for automatic updates

Mobile Device Integration

Camera access for label scanning functionality
Location services for nearby restaurant identification
Calendar integration for reservation-based reminders
Contact sharing for social features

Social Platform Integration

Share functionality with Instagram, Facebook, Twitter
Optional authentication via social platforms
Review platform (Yelp, TripAdvisor) deep linking
Social messaging platform sharing

External APIs

Maps integration for restaurant location services
Weather API for contextual recommendations
Payment processing for future premium features
Cloud storage for user data synchronization

2.4 Administrative Functions

User Management

Account creation and authentication system
Profile management and preference settings
Reputation and contribution tracking
Moderation tools for community management
User support and feedback system

Content Management

Wine database management and updates
Restaurant listing verification and management
Price data moderation and verification
Community contribution approval workflow
Notification and alert management

Analytics Dashboard

User engagement metrics and reporting
Content quality and coverage analysis
Search pattern and user behavior analysis
Community contribution analytics
Performance and error reporting

System Configuration

Feature flag management for phased rollouts
A/B testing configuration for experience optimization
Notification settings and management
API integration management
Performance optimization controls

3. Experience Layer

3.1 User Flows

First-Time User Onboarding

Download app and launch
View brief value proposition explanation (3 screens max)
Optional account creation (can be deferred)
Select wine knowledge level (beginner/intermediate/expert)
Indicate primary use case (restaurant dining, learning, collection)
Grant optional permissions (location, camera)
View quick tutorial on core features
Land on home screen with suggested actions

Restaurant Wine Search Flow

User opens app at restaurant
App suggests nearby restaurants based on location (if enabled)
User selects current restaurant or searches for it
App displays search interface for wines
User enters wine name or scans label
App displays wine details with price comparison
User can save wine to favorites, share, or view similar values
Optional: User adds current price if different from database

Adding New Wine Price Data

User selects "Add Wine Price" from restaurant view
User searches for wine or scans label to identify
App pre-fills known wine information
User enters price and confirms details
App validates entry against expected range
User submits and receives confirmation
User earns contribution points
New data becomes available to community (after verification for new users)

Restaurant Comparison Flow

User selects "Compare Restaurants" feature
App displays nearby restaurants with markup index
User can filter by cuisine, price range, or distance
App presents visual comparison of markup practices
User selects restaurant for detailed wine list view
App highlights best values at selected restaurant
User can save restaurant to favorites or share findings
Optional: Make reservation via integrated booking system

Value Discovery Flow

User selects "Find Values" feature
App presents filtering options (wine type, price range, region)
User selects preferences
App displays best value wines nearby based on markup index
User can sort by absolute price, markup percentage, or quality rating
User selects wine for detailed information
App shows restaurants offering this wine with comparative pricing
User can save findings or navigate to restaurant details

3.2 Interface Requirements

Core Interface Elements

Search Component: Prominent, accessible from all screens, with voice and camera input options
Price Comparison Display: Clear visual representation of restaurant price vs. retail price
Markup Indicator: Visual scale showing markup percentage with context (below average, average, above average)
Restaurant List View: Sortable, filterable list with markup index prominently displayed
Wine Detail View: Comprehensive information with pricing history, availability, and value assessment
Navigation System: Simple, intuitive navigation with focus on quick access to core features
Contribution Tools: Easily accessible input mechanisms for community contributions

Responsive Design Requirements

Phone Optimization: Primary design target, optimized for one-handed operation
Tablet Support: Expanded layouts with side-by-side information panels
Orientation Handling: Functional in both portrait and landscape modes
Screen Size Adaptation: Flexible layouts accommodating various device sizes
Dark Mode Support: Full dark mode implementation for discreet restaurant use
Accessibility Scaling: Text size adjustment without layout breaking

Interaction Models

Quick Actions: Swipe gestures for common actions (save, share, compare)
Stealth Mode: One-touch activation of minimal interface for discreet restaurant use
Voice Commands: Optional voice control for hands-free operation
Haptic Feedback: Subtle confirmation for key actions
Progressive Disclosure: Information layering with essential data first, details on demand
Contextual Help: In-context assistance for complex features

Accessibility Requirements

Screen Reader Compatibility: Full support for VoiceOver and TalkBack
Color Contrast: WCAG AA compliance for all text and interactive elements
Alternative Inputs: Support for external adaptive devices
Reduced Motion Option: Alternative transitions for users sensitive to motion
Keyboard Navigation: Complete functionality without touch for tablet users with keyboards
Readability: Clear typography with adjustable text size

3.3 Performance Requirements

Response Time Targets

Search results displayed within 3 seconds of query submission
Wine details page loaded within 2 seconds of selection
Price comparison calculations completed within 1 second
App launch to usable state under 4 seconds
Camera scanning to wine identification under 5 seconds
User contributions acknowledged within 1 second

Offline Capabilities

Basic wine information available offline
Recently viewed restaurants and wines cached for offline access
Queued contributions stored for submission when connectivity restored
Clear indication of offline status and limited functionality
Automatic synchronization when connection reestablished
Offline search within cached data

Resource Efficiency

Battery usage optimized for extended restaurant sessions
Data usage under 5MB per typical restaurant visit
Storage requirements under 100MB for core application
Optional expanded cache for frequent users (user-configurable)
Background processing minimized during active use
Memory footprint optimized for older devices

Reliability Standards

99.9% uptime during peak dining hours (6-10pm local time)
Graceful degradation when services are limited
Automatic recovery from common error conditions
Data preservation during unexpected shutdowns
Consistent performance across supported device types
Regular background synchronization to prevent data loss

3.4 Contextual Adaptations

Location-Based Adaptations

Automatically suggest nearby restaurants when app is opened
Adjust search radius based on urban/suburban/rural location
Highlight restaurants within walking distance in urban areas
Provide directions integration for selected restaurants
Adapt restaurant recommendations based on user location patterns
Respect location privacy with clear opt-in and temporary use options

Time-Based Adaptations

Prioritize features based on time of day (pre-dinner vs. at restaurant)
Highlight restaurants currently open
Adjust interface brightness based on ambient light detection
Surface relevant historical data (e.g., "You visited this restaurant 3 months ago")
Weekend vs. weekday optimizations for different use patterns
Seasonal wine recommendations when appropriate

User Preference Adaptations

Interface adjustments based on wine knowledge level
Personalized value recommendations based on price sensitivity
Feature visibility based on usage patterns
Detail level adapted to expertise (more technical for experts)
Language complexity adjusted to user preference
Metric/imperial unit display based on user location and preference

Device-Specific Adaptations

Camera quality adaptations for wine label scanning
Battery-saving mode for extended restaurant sessions
Performance optimizations for older devices
Enhanced visualizations for high-end displays
Reduced data mode for limited connectivity
Tablet-specific layouts for larger screens

4. Validation Layer

4.1 Functional Testing

Feature Testing Approach

Comprehensive test cases for each core feature
Edge case identification and testing for search functionality
Cross-device testing on iOS and Android platforms
Regression testing protocol for each release
Automated test suite for core functionality
Manual testing for user experience quality

Critical Test Scenarios

Wine Search Accuracy: Verify search returns correct wines with various input methods
Price Calculation Correctness: Validate markup calculations against known values
Data Contribution Flow: Ensure user contributions are properly processed and verified
Restaurant Index Accuracy: Confirm markup index calculations reflect actual pricing data
Offline Functionality: Verify critical features work without connectivity
High-Load Performance: Test system under simulated peak usage conditions

Data Validation

Verify retail price accuracy against multiple sources
Validate restaurant price data through multiple user confirmations
Test data synchronization across devices and platforms
Verify historical data retention and accessibility
Test data export and backup functionality
Validate privacy controls and data protection measures

Integration Testing

Verify all third-party API integrations function correctly
Test authentication flows with social login providers
Validate mapping and location service integration
Test sharing functionality with social platforms
Verify deep linking and app-to-app transitions
Validate notification delivery across platforms

4.2 User Acceptance

Usability Testing Methodology

Moderated usability sessions with representative users
Restaurant environment testing for real-world conditions
A/B testing of critical user flows
Heatmap and session recording analysis
Task completion rate measurement
User satisfaction surveys after key interactions

Key Acceptance Criteria

90% of users can complete wine search within 30 seconds
85% of users can understand markup information without explanation
95% of users can successfully add a new wine price
80% of users report increased confidence in wine selection
90% of users can identify "best value" wines on a list
85% of users would recommend the app to others

Beta Testing Program

Phased beta rollout to wine enthusiasts and restaurant professionals
Structured feedback collection through in-app mechanisms
Bug bounty program for critical issue identification
Feature request prioritization by beta community
Usage pattern analysis to identify improvement opportunities
Satisfaction measurement throughout beta period

Stakeholder Approval Process

Regular demos for key stakeholders throughout development
Formal approval checkpoints at feature completion
User advisory board review of major features
Restaurant partner feedback integration
Wine expert consultation on data presentation
Executive review of business metric alignment

4.3 Performance Verification

Load Testing

Simulate concurrent users at 2x projected peak levels
Test search performance under high query volume
Validate database performance with projected data volume
Measure API response times under various load conditions
Test notification system at scale
Verify content delivery network performance

Device Performance Testing

Test on range of devices from minimum spec to current flagship
Measure battery consumption during typical usage sessions
Monitor memory usage patterns across extended sessions
Verify performance in low-connectivity environments
Test performance with minimum available storage
Measure startup time across device range

Reliability Testing

Continuous operation testing over extended periods
Random shutdown and recovery testing
Connectivity interruption and restoration tests
Database consistency verification after sync operations
Error recovery from various failure conditions
Background process reliability verification

Monitoring Requirements

Real-time performance monitoring system
User-reported error tracking
Crash analytics integration
Performance metric dashboards
Alerting system for critical issues
Usage pattern analysis tools

4.4 Compliance Verification

Data Privacy Compliance

GDPR compliance verification
CCPA compliance verification
Data collection consent mechanisms
User data access and deletion capabilities
Privacy policy clarity and accessibility
Data minimization principle implementation

Accessibility Compliance

WCAG 2.1 AA compliance verification
Screen reader compatibility testing
Keyboard navigation verification
Color contrast analysis
Text scaling functionality testing
Alternative input method verification

Platform Guideline Compliance

iOS App Store guideline verification
Google Play Store policy compliance
In-app purchase implementation compliance
Notification permission handling
Camera and location permission management
Background processing compliance

Industry-Specific Compliance

Alcohol advertising regulation compliance
Restaurant industry standard adherence
Age verification where required by law
Appropriate disclaimers for user-generated content
Fair use compliance for wine information
Terms of service clarity and enforceability

5. Implementation Considerations

5.1 Technical Architecture

MarkupMeter will be built using a client-server architecture with native mobile applications (iOS and Android) communicating with cloud-based backend services. The system will employ:

Frontend: Native applications using Swift (iOS) and Kotlin (Android)
Backend: Cloud-based microservices architecture for scalability
Database: Combination of relational database for structured data and NoSQL for user-generated content
APIs: RESTful API design with GraphQL for complex data queries
Caching: Multi-level caching strategy for performance optimization
Analytics: Integrated analytics platform for user behavior tracking and performance monitoring

5.2 Development Phasing

Phase 1: MVP (Months 1-3)

Core wine search functionality
Basic price comparison engine
Initial restaurant database (top 10 markets)
Essential user accounts and profiles
Simple contribution mechanism
Basic restaurant markup index

Phase 2: Enhanced Experience (Months 4-6)

Wine label scanning capability
Expanded restaurant coverage
Advanced search filters and sorting
Enhanced user contribution system
Social sharing features
Improved visualization of markup data

Phase 3: Community & Scale (Months 7-12)

Reputation and gamification system
Restaurant partnership program
Advanced analytics and personalization
Expanded integration with reservation platforms
Premium feature exploration
International market expansion preparation

5.3 Risk Assessment

Technical Risks

Data Accuracy: Challenge of maintaining current retail pricing data
- Mitigation: Multiple data sources, user verification system, regular audits
Scaling Challenges: Handling growth in user-contributed data
- Mitigation: Scalable cloud architecture, performance monitoring, incremental capacity planning
Device Compatibility: Ensuring consistent experience across device types
- Mitigation: Comprehensive device testing matrix, progressive enhancement approach

Business Risks

Restaurant Opposition: Potential pushback from high-markup establishments
- Mitigation: Positioning as consumer tool, restaurant partnership program for transparency advocates
Data Coverage: Achieving sufficient wine and restaurant data
- Mitigation: Focused launch in key markets, incentivized contribution system, strategic data partnerships
User Adoption: Overcoming habit change for wine selection
- Mitigation: Seamless onboarding, clear value demonstration, targeted marketing to early adopters

5.4 Dependencies

Critical External Dependencies

Wine retail pricing data sources
Restaurant information databases
Mobile platform approval processes
Third-party authentication services
Payment processing for future premium features

Internal Dependencies

Design system development
Data validation methodology
Community management infrastructure
Content moderation system
Analytics implementation

5.5 Success Enablers

Key Implementation Factors

Focus on speed and simplicity for in-restaurant use
Emphasis on data accuracy and verification
Community-building from earliest stages
Restaurant relationship development
Regular user feedback incorporation
Continuous improvement based on usage patterns

6. Appendices

6.1 Glossary

Markup: The percentage increase from retail to restaurant price
Markup Index: MarkupMeter's proprietary rating of restaurant wine pricing practices
Value Rating: Assessment of a wine's price-to-quality relationship
Stealth Mode: Discreet interface option for unobtrusive restaurant use
Contribution Points: Rewards earned for adding price data to the system
Verification: Process of confirming accuracy of user-submitted data

6.2 Research References

Wine industry pricing studies
Restaurant consumer behavior analysis
Mobile app usage patterns in dining contexts
Wine enthusiast community surveys
Competitive analysis of wine and dining applications
Transparency impact studies from adjacent industries

6.3 Future Considerations

Premium subscription model for enhanced features
Expansion to spirits and craft beer pricing
Restaurant reservation integration
Sommelier recommendation integration
Personalized wine education pathways
Wine collection management features