1️⃣ Objective
The objective is twofold: first, to develop a predictive model for customer satisfaction based on service, speed, and order data; and second, to apply Menu Engineering principles using cost and popularity data to optimize the menu for maximum profitability and customer appeal. The insights will drive operational improvements and strategic menu adjustments.
Key Goals:
✨ Predict Customer Satisfaction: Build a classification or regression model to predict customer ratings (e.g., 5-star rating or satisfaction binary) based on service attributes.
✨ Identify Operational Bottlenecks: Use feature importance to pinpoint service factors (e.g., wait time, accuracy) most strongly correlated with low satisfaction.
✨ Perform Menu Matrix Analysis: Categorize all menu items into four quadrants: Stars, Plow Horses, Puzzles, and Dogs based on popularity and profitability.
✨ Optimize Pricing and Placement: Recommend strategic pricing and promotional adjustments for each menu item category.
✨ Forecast Revenue Impact: Quantify the expected revenue lift from implementing the optimized menu structure.
2️⃣ Problem Statement
Restaurants often rely on aggregate sales figures and subjective customer feedback, failing to establish a clear, causal link between specific operational metrics (like server time or kitchen throughput) and customer delight, or between item popularity and true profitability. This leads to inefficient resource allocation and sub-optimal menu design.
This project addresses this by creating a data-driven framework that isolates the drivers of customer satisfaction and uses the proven methodology of Menu Engineering to restructure the offerings, ensuring that resources are focused on high-profit, high-satisfaction items.
3️⃣ Methodology
The project combines predictive modeling and prescriptive analysis:
✨ Step 1 — Data Integration & Cleaning: Merge Point-of-Sale (POS) data (sales, time stamps, server ID) with Customer Feedback data (ratings, comments). Handle noisy or sparse customer rating data.
✨ Step 2 — Customer Experience Modeling: Train a supervised learning model (e.g., Random Forest or XGBoost) to predict satisfaction score or rating bracket.
✨ Step 3 — Menu Engineering Calculation: Calculate the Contribution Margin (Selling Price – Food Cost) and Item Popularity Index for every dish.
✨ Step 4 — Menu Optimization Matrix: Plot items on the Menu Engineering Matrix (Profitability vs. Popularity) to assign strategies (Star: maintain; Dog: remove/re-evaluate). [Image of Menu Engineering Matrix]
✨ Step 5 — Prescriptive Recommendations: Based on the model insights and the Menu Matrix, deliver specific operational and menu changes.
4️⃣ Dataset
Key Process Areas:
✨ Publicly available or synthetic restaurant transactional and survey data (e.g., restaurant performance dataset, Yelp review data combined with sales data).
✨ Datasets contain thousands of individual order transactions and corresponding customer feedback records.
5️⃣ Tools and Technologies
6️⃣ Evaluation Metrics
✨ Root Mean Square Error (RMSE) / R-squared: Primary metrics if predicting numerical satisfaction rating.
✨ Accuracy / F1-Score: Primary metrics if predicting binary satisfaction (e.g., Satisfied vs. Not Satisfied).
✨ Average Contribution Margin: Business metric calculated for the entire menu and tracked for optimization success. Formula: $$Average\ Contribution\ Margin = \frac{\sum (Selling\ Price – Food\ Cost) \times Quantity\ Sold}{\sum Quantity\ Sold}$$
✨ Customer Lifetime Value (CLV) Proxy: Analyzing correlation between predicted satisfaction and repeat visits/spending.
7️⃣ Deliverables
8️⃣ System Architecture Diagram
Sales & Cost Data Ingestion (POS)
Pulls item sales volume, current pricing, raw material costs, and labor preparation time.
Customer Sentiment Feeder
Aggregates reviews (Yelp, Google), survey responses, and social media mentions.
Service Flow Metrics
Captures table turnover time, wait times, and server performance data.
Sentiment & Topic Extraction LLM
Identifies specific pain points (e.g., “slow service,” “steak was cold”) and scores urgency.
Menu Engineering Matrix Calculator
Classifies all items into Star, Plowhorse, Puzzle, or Dog based on popularity and gross margin.
Cross-Correlation Engine
Links low popularity (sales) with high negative sentiment (reviews) to isolate problematic items.
Menu Placement & Design Recommender
Suggests visual placement and descriptive copy changes to push ‘Star’ items and hide ‘Dog’ items.
Dynamic Pricing Strategy
Generates A/B test pricing based on demand elasticity, time of day, and inventory levels.
Operational Improvement Alert System
Issues immediate alerts and training needs (e.g., “Prep time for Dish X is too high, impacting service”).
Sales & Cost Data Ingestion (POS)
Pulls item sales volume, current pricing, raw material costs, and labor preparation time.
Customer Sentiment Feeder
Aggregates reviews (Yelp, Google), survey responses, and social media mentions.
Service Flow Metrics
Captures table turnover time, wait times, and server performance data.
Sentiment & Topic Extraction LLM
Identifies specific pain points (e.g., “**slow service**,” “steak was cold”) and scores urgency.
Menu Engineering Matrix Calculator
Classifies all items into **Star, Plowhorse, Puzzle, or Dog** based on popularity and gross margin.
Cross-Correlation Engine
Links low popularity (sales) with high negative sentiment (reviews) to isolate problematic items.
Menu Placement & Design Recommender
Suggests visual placement and descriptive copy changes to push ‘Star’ items and hide ‘Dog’ items.
Dynamic Pricing Strategy
Generates A/B test pricing based on **demand elasticity**, time of day, and inventory levels.
Operational Improvement Alert System
Issues immediate alerts and training needs (e.g., “Prep time for Dish X is too high, impacting service”).
9️⃣ Expected Outcome
✨ A predictive model that accurately anticipates customer satisfaction (e.g., RMSE < 0.8 on a 5-point scale).
✨ Quantitative identification of menu items that are highly profitable but under-promoted, leading to potential revenue increase.
✨ A set of data-backed operational changes (e.g., target server speed) to improve the customer experience and reduce negative feedback.
✨ Strategic recommendations for menu design, including which items to remove, which to feature, and how to price them optimally.