Menu Optimization

Urban Bistro Group struggled with an oversized, unprofitable menu that led to high food costs and inconsistent sales across their locations. Deepiom's AI platform analyzed their sales, cost, and customer preference data to scientifically re-engineer their offerings, boosting profitability and streamlining kitchen operations.
Menu Optimization

Project Overview

Industry: Hospitality (Hotels & Resorts, F&B Outlets)

Property Size: 12 hotels, 20+ restaurants and bars

Project Duration: 6 months

Team Size: 1 Data Scientist, 1 F&B Manager, 1 POS Systems Specialist, 1 UX Designer

Business Challenge

The hotel chain’s F&B outlets were struggling with inefficient menus and inconsistent profitability. Key issues included:

  • Overly complex menus with low-performing dishes cluttering the selection
  • Limited visibility into which items drove profitability vs. guest preference
  • High food waste due to poorly forecasted demand
  • Missed opportunities for cross-selling and upselling
  • Lack of alignment between guest expectations and menu offerings

As a result, F&B revenue growth stagnated, and guest satisfaction with dining options declined.

Our Approach

We deployed a data-driven menu optimization system combining POS data, guest feedback, and profitability analysis.

Key reasons for this approach:

  • Profitability Focus: Identify and promote high-margin items.
  • Guest Insights: Tailor menus to match preferences and trends.
  • Sustainability: Reduce food waste through demand forecasting.
  • Consistency: Standardize best-performing menus across properties.

Menu Optimization Features

  • AI-powered analysis of POS transaction data to identify top- and under-performing items
  • Profitability heatmaps to highlight high-margin vs. low-margin dishes
  • Guest preference analysis from surveys and mobile app reviews
  • Seasonal and regional customization for better local alignment
  • Smart menu design with recommended pricing adjustments and item placement
  • Upsell prompts for sides, beverages, and premium add-ons

Implementation Process

  • Phase 1: Data collection from POS, guest feedback, and F&B cost sheets
  • Phase 2: Profitability analysis and menu item categorization (stars, dogs, plowhorses, puzzles)
  • Phase 3: Redesign of digital and physical menus with AI-driven recommendations
  • Phase 4: Pilot launch in 3 restaurants with A/B testing of menu versions
  • Phase 5: Full rollout with standardized reporting and staff training

Quality Assurance

  • Continuous monitoring of menu item performance
  • Guest feedback integration into iterative design
  • Menu usability testing with both staff and guests
  • Food cost and margin tracking to ensure profitability

Results

Productivity & Efficiency Improvements

  • Reduced food waste by 25% through better demand forecasting
  • Menu complexity reduced by 30% with streamlined offerings
  • Improved staff efficiency in recommending upsell pairings

Guest Experience

  • 92% of guests reported menus were easier to navigate
  • Increased satisfaction with menu variety and pricing
  • Enhanced perception of value for money

Business Impact

  • 18% increase in average check value per guest
  • 12% uplift in F&B profitability within 6 months
  • Improved alignment between guest demand and supply chain ordering
  • Enabled data-driven seasonal promotions that boosted sales by 20%

Technical Implementation

  • Data Sources: POS transaction logs, guest surveys, mobile app reviews
  • AI Analytics: Demand forecasting, item categorization, and price elasticity modeling
  • Menu Design: Digital menu boards, in-app menus, and updated physical designs
  • Reporting Dashboard: Real-time profitability tracking and guest preference analytics

Key Features

  • AI-driven profitability analysis
  • Guest preference alignment
  • Demand forecasting to reduce waste
  • Smart menu design with upsell integration


Client Feedback

“”

By redesigning our menus with data insights, we’ve cut costs, increased margins, and guests are happier with the choices. The results exceeded expectations in both profitability and guest satisfaction.

Implementation Timeline

Before Implementation

  • Complex menus with underperforming dishes
  • High food waste from poor demand forecasting
  • Low visibility into profitability by item
  • Flat F&B revenue growth

After Implementation

  • 25% reduction in food waste
  • 18% increase in average check value
  • 12% increase in overall F&B profitability
  • Streamlined, guest-friendly menus aligned with preferences

Quality Control Process

  • Monthly review of item performance and profitability
  • Guest feedback surveys integrated into menu updates
  • A/B testing of menu layouts for optimal impact
  • Ongoing analysis of food cost fluctuations to adjust pricing

Implementation Challenges

  • Consolidating data across different POS systems
  • Managing chef and staff resistance to removing long-standing menu items
  • Balancing profitability optimization with maintaining guest favorites
  • Updating physical menus across multiple outlets efficiently

Continuous Improvement

  • Seasonal updates informed by demand forecasting models
  • Personalized in-app menu recommendations based on guest profiles
  • Integration with supply chain systems for predictive ordering
  • Iterative design refreshes based on guest engagement data


Future Enhancements

  • Dynamic, digital menus with real-time price adjustments based on demand
  • AI-driven personalized meal recommendations for loyalty members
  • Integration with nutrition and sustainability data for eco-conscious guests
  • Multi-language digital menus for international travelers

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