Dinesafe Knowledge Base
  • Welcome
  • Return to DineSafe.com
  • Real-Time Norovirus Surveillance
    • Introduction
  • Methodology
  • Data Collection & Processing
  • Metrics & Indicators
  • Implementation Guidelines
  • FAQs
  • Benchmarking
    • Introduction
      • The Importance of Food Safety
      • Introduction to Our Benchmarking System and Its Purpose
      • Why Food Safety Benchmarking Matters: A Stakeholder Overview
    • Methodology & Systems
      • The Metrics We Use
        • Reports per 100 Stores
        • Reports per $1M Revenue
        • Persons Reported Sick per 100k Customers Served
      • Data Sources & Methodology
        • How We Gather Data
        • Estimating Data When Exact Figures Aren't Available
        • Ensuring Accuracy and Transparency
      • Our Benchmark Indexing System
        • Why We Use Indexing
        • Index Composition
        • Index Calculation Process
        • Understanding Index Comparisons
    • Case Study
      • Case Study - Chipotle Mexican Grill
    • Applications
      • Global Applicability of Our Benchmarking System
      • Product-Specific Benchmarking for Food Producers
    • Service Information
      • Why Subscribe to Our Benchmarking Service
      • Limitations & Considerations
      • Conclusion
      • FAQ
        • Q1: How often are the benchmarks updated?
        • Q2: Are the food poisoning reports reviewed before being included in the benchmarks?
        • Q3: How do you account for differences in restaurant size when comparing benchmarks?
        • Q4: Can restaurants submit their own data to improve the accuracy of the benchmarks?
        • Q5: How do your benchmarks relate to official health inspections and ratings?
        • Q6: How can businesses improve their benchmark scores?
  • Data Dictionary
    • Introduction
    • Primary Fields
    • Specialized Fields
    • Delivery Methods
    • Glossary
    • Further Questions
  • IWP Reporting Widget
    • Introduction
    • Why Our Widget? Key Features & Benefits
    • Embedding the Widget
      • How to Embed the Code
      • When to Use Simple Embedding Code vs Advanced Embedding Code
        • Benefits of the Advanced Embedding Code
        • Considerations When Using the Advanced Embedding Code
      • Test the Code
      • Standalone URL Option
      • Customization Options
      • Language Support
      • Mobile-Friendly Link
    • Data Management and Access
    • Compliance with FDA’s Voluntary National Retail Food Regulatory Program Standards
    • Examples of Embedded Widgets
    • FAQ
      • Q1: Who can use the Iwaspoisoned.com reporting widget and what is it?
      • Q2: How do I embed the widget on my website?
      • Q3: Is the widget customizable?
      • Q4: How do I access the reports?
      • Q5: How does the widget support FDA compliance?
  • Email Notifications
    • Introduction
    • Widget Email Alerts
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  1. Benchmarking
  2. Methodology & Systems
  3. Data Sources & Methodology

Estimating Data When Exact Figures Aren't Available

Despite our best efforts, there are instances where exact figures are not publicly available, particularly for private companies. In these cases, we employ several estimation techniques:

  1. Imputed Revenue Calculation

    • When exact revenue figures aren't available, we calculate an imputed revenue.

    • This is done by multiplying the average sales per location by the number of US stores.

    • If average sales per location aren't available, we conduct research using reputable databases and sites, and estimate based on comparable companies in the same segment and price point.

  2. Store Count Estimation

    • For private companies that don't disclose store counts, we use a combination of press releases, news articles, and industry reports to estimate the number of locations.

    • We regularly update these estimates as new information becomes available.

  3. Average Check Size Estimation

    • When not provided in financial reports, we estimate average check size through a detailed analysis of menu prices and typical ordering patterns.

    • This process involves reviewing current menus and analyzing consumer behavior to determine the most accurate average check size:

      • We developed a spending profile for each restaurant type, taking into account whether it's a quick-service establishment where customers generally purchase a drink and meal, or a sit-down restaurant where they might order an appetizer, main course, and dessert—or just one of these options—to estimate the average check size.

  4. Comparative Analysis

    • We often use data points from similar companies in the same market segment as comparison points for when we estimate figures for private companies.

  5. Historical Data Trending

    • For companies with some historical data available, we use trend analysis to project current figures when recent data is unavailable.

    • This method takes into account past growth rates and industry trends.

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Last updated 9 months ago

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