Revenue Protection Model For Restaurants

Executive Summary

• The Problem: High-volume "Post-Pay" workflows create payment bottlenecks, leading to customer abandonment.

• The Discovery: Saturday peak shifts were losing ₦13.3M monthly due to wait-time friction.

• The Solution: Transitioning to a "Pre-Pay" model to guarantee 100% revenue capture and eliminate exit-latency.

• The Result: A projected 28% increase in realized revenue without increasing customer acquisition costs.

The Context: When Service Speed Becomes a Financial Liability

In high-traffic environments, we often measure success by "Table Turnover" or "Order Speed." However, during my analysis of a high-performance restaurant operation, I discovered that these metrics were missing a critical factor: The Payment Bottleneck.

When a customer finishes their journey but has to wait 15+ minutes just to pay, they don't just get frustrated—they leave. In a "Post-Pay" world, that is a total loss of the order value. I built a Revenue Protection Model to quantify exactly how much money was "leaking" out of the business because of this single process flaw.

Methodology: Modeling Human Behavior with Data

To move beyond surface-level observations, I implemented a three-tier technical approach:

A. Behavioral Simulation (Python)

Using Python, I simulated a high-pressure environment. I programmed Reneging Logic, where the probability of a customer walking out increased exponentially once wait times crossed the 35-minute threshold.

B. Data Engineering (BigQuery SQL)

I moved the raw simulation data into Google BigQuery to perform a "Counterfactual Analysis." I joined transactional logs with calendar metadata to isolate "Peak" vs "Regular" performance. This allowed me to compare our current reality (Scenario A) against a theoretical optimized state (Scenario B).

C. Strategic Visualization (Power BI)

I translated the SQL outputs into a "Revenue Protection Dashboard." This wasn't just a report; it was a decision-making tool that allowed stakeholders to see the direct correlation between wait times and financial loss

.

Key Insights: The Cost of the "Leaking Bucket"

The data provided three undeniable truths that the business had previously overlooked:

• The Saturday Spike: The ₦13.3M loss wasn't a daily average; it was a "spike" phenomenon occurring during Saturday dinner rushes.

• The 35-Minute Cliff: My analysis identified 35 minutes as the "Point of No Return." Orders exceeding this wait time had an 80% higher chance of being "unrealized revenue."

• A Process Problem, Not a People Problem: By analyzing loss by staff member, I proved that the "leak" was consistent across the team. This confirmed that no amount of "harder work" could fix a fundamentally broken "Post-Pay" workflow.

Strategic Recommendations

Based on the data, I proposed a shift from Operational Response to Revenue Assurance:

1. Workflow Pivot: Move to a "Pre-Pay" model. By securing the transaction at the point of order, the business decouples "Food Preparation" from "Financial Risk."

2. Peak-Mode Staffing: Reallocate labor to order fulfillment during the Saturday 7 PM–9 PM window identified in the BigQuery audit.

3. Kiosk Deployment: Use self-service terminals to handle the "Pre-Pay" volume, ensuring the kitchen can focus on quality while the system handles the cash.

🛠 Technical Appendix: Under the Hood

The Data Architecture

The project utilized a "Star Schema" approach, joining a central Fact Table (Orders) with Dimension Tables (Calendar and Menu) to provide deep context.

The SQL Logic

The core engine of the analysis was a CASE statement that categorized risk based on simulated wait times:

SQL

CREATE OR REPLACE TABLE `restaurant-blueprint.analysis.revenue_impact` AS

SELECT

f.Order_ID,

f.Total_Wait_Mins,

f.Scenario_A_Revenue AS Actual_Revenue, -- Conditional on wait time

f.Scenario_B_Revenue AS Potential_Revenue, -- Guaranteed at start

(f.Scenario_B_Revenue - f.Scenario_A_Revenue) AS Revenue_Lost_to_Friction,

CASE

WHEN f.Total_Wait_Mins > 35 THEN 'Critical Risk'

WHEN f.Total_Wait_Mins > 20 THEN 'High Risk'

ELSE 'Safe'

END AS Risk_Category

FROM `restaurant-blueprint.data.fact_orders` AS f

JOIN `restaurant-blueprint.data.calendar` AS c ON f.Date = c.Date;

Conclusion: Data-Backed Decision Making

This project demonstrates that the most expensive problems in a business are often hidden in the "flow" of operations. By using Python to simulate, SQL to engineer, and Power BI to visualize, I was able to turn a "feeling" that the restaurant was slow into a ₦13.3 Million business case for change.

If you aren't protecting your revenue at the source, you aren't just losing time,you're losing capital.