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AI in Food Service: Real Estate & Site Selection

  • Writer: James McGreggor
    James McGreggor
  • May 28
  • 4 min read

Updated: 2 days ago


Site Planner for a QSR
Site Planner for a QSR

Overview

This use case focuses on how AI can modernize QSR site selection by replacing static models with predictive simulations and hyperlocal market intelligence. The goal: better location decisions, faster, with lower risk and higher confidence.

The Challenge

Imagine expanding into a new market using outdated traffic studies or generic demographic reports. You open your next store only to realize nearby competitors, seasonal patterns, or delivery trends weren’t properly factored in—and the site underperforms.

The Opportunity

AI transforms site selection by combining traditional inputs with new data streams:

  • Delivery app activity (e.g., DoorDash, Grubhub) to surface unmet demand zones.

  • Real-time mobility, event data, and neighborhood growth trends.

  • Simulation-based modeling to test performance under multiple what-if scenarios: bad weather, competitor launches, labor shifts.

  • Automated cannibalization analysis to protect existing locations.

  • Predictive breakeven analysis based on hyperlocal costs and traffic.


The Feedback


Industry leaders have already seen success... Burger King, Inspire Brands, and Checkers use AI-powered predictive modeling platforms to analyze demographics, customer segmentation, mobile location, and traffic data—helping them make faster, smarter real estate decisions. Chick-fil-A has adopted a similarly data-rich approach, combining GIS systems like Esri’s ArcGIS with mobile analytics to guide their expansion. By leveraging billions of data points—from census and business data to GPS-driven foot traffic patterns—they identify optimal markets and reduce risk, aligning location strategy with customer behavior and brand values (TechRepublic, Buxton).

This data-backed approach allows franchisors and corporate operators to spot high-potential sites early and simulate performance under multiple real-world conditions—resulting in fewer regrets and faster ROI.

The Approach

Begin with a strategic objective: expansion into a region, rebalancing a market, or re-evaluating underperformers. Build a location intelligence charter that includes finance, ops, and marketing leaders.

You’ll need:


  • Inputs: POS data, delivery platform behavior, real estate trends, market demographics, weather and event data.


  • Expertise in: AI/ML modeling, geospatial analytics, simulation methods (e.g., Monte Carlo), GIS integrations, business strategy.


  • Supported by: Real estate teams, finance, marketing, franchise ops, data analysts.

Pilot the model in a defined metro area. Compare AI-identified sites against your current strategy, run breakeven simulations, and refine criteria for your brand’s unique cost and demand profile.

What to Watch Out For


  • Underutilized external data: Failing to incorporate delivery trends or population movement weakens predictive value.


  • Simulation bias: Poorly tuned models may overemphasize favorable outcomes.


  • Lack of scenario diversity: Skipping edge cases like supply shocks or labor scarcity can lead to overconfidence.


  • Siloed decision-making: Site selection requires cross-functional alignment to succeed.




Conceptual Architecture


To architect a robust AI-powered site selection solution for QSR and multi-location retail, you'll want to integrate geospatial intelligence, predictive modeling, and real-time mobility data into a seamless, scalable system. Every technical solution must be tailored to the needs and constraints of the business; however listed below is a breakdown of some of the best-in-class technologies and tools used across the industry today (as of the creation of this article):


Core AI & Modeling Tools: These form the brain of the system—used for simulations, predictions, and scoring.

  • Google Cloud Vertex AI / AWS SageMaker / Azure Machine Learning For training and deploying predictive models at scale (e.g., demand forecasting, site scoring).


  • H2O.ai or DataRobot AutoML platforms that speed up model experimentation and validation—useful for testing breakeven, cannibalization, or traffic models.


  • Python (scikit-learn, XGBoost, Prophet) For custom modeling and scenario simulation—like Monte Carlo models for breakeven analysis or time series forecasting for sales projections.



Geospatial & Location Intelligence Platforms: These tools handle maps, spatial queries, and visualizations.


  • Esri ArcGIS Industry leader for GIS—used by Chick-fil-A and others to overlay demographic, mobility, and business data.


  • Carto / Mapbox / Kepler.gl Modern, developer-friendly spatial data platforms for map rendering, data visualization, and analysis.


  • SafeGraph / Foursquare / Placer.ai Providers of anonymized foot traffic and mobile location data used to analyze real-world movement patterns.



Mobility, Delivery & Event Data Feeds: These offer external insights beyond static demographics.


  • Teralytics / INRIX / StreetLight Data For mobility trends and vehicle/pedestrian traffic data.


  • PredictHQ For local event data that might affect demand (e.g., concerts, conferences, weather events).


  • Gravy Analytics / Cuebiq For location-based behavioral analytics from mobile app data.



Data Integration & Pipelines: Critical for ingesting and harmonizing real estate, operational, and third-party data.


  • Snowflake / BigQuery / Redshift / Databricks Cloud data warehouses that can ingest and join POS, loyalty, real estate, and market data efficiently.


  • dbt (Data Build Tool) For transforming raw data into structured formats and creating reusable site scoring logic.


  • Apache Airflow / Prefect For scheduling and managing data pipelines and simulations.



Decision Intelligence & UX Layer: To visualize results and assist human decision-makers.


  • Power BI / Tableau / Looker For interactive dashboards that show candidate sites, predictive performance scores, and scenario simulations.


  • Streamlit / Dash / Retool For building lightweight internal apps that allow users to explore AI recommendations and tweak assumptions.



The Impact

AI-enabled site selection improves decision speed, minimizes costly missteps, and enables scalable growth. With fewer real estate regrets and faster time-to-profit, operators can grow smarter—not just bigger.


Partner With Us

Blue Forge Digital helps QSRs and restaurant operators optimize and scale. Taking an AI-forward approach, we work with you to design solutions that are dynamic and engineered to scale, while leveraging our strategic partnerships to take on large scale execution projects.




References


Burger King, Inspire Brands, and Checkers Site Selection"How Restaurants Are Leveraging Grocery Data for Strategic Site Selection." FSR Magazine, 2024, https://www.fsrmagazine.com/feature/how-restaurants-are-leveraging-grocery-data-strategic-site-selection. Accessed 27 May 2025.


Chick-fil-A and Esri GIS PlatformNorton, Tom. "Chick-fil-A's Digital Transformation Includes Data-Driven Decisions." TechRepublic, 2023, https://www.techrepublic.com/article/chick-fil-as-digital-transformation-includes-data-driven-decisions. Accessed 27 May 2025.


Chick-fil-A Mobile Analytics & Foot Traffic Patterns"Where Chick-fil-A Customers Go on Sunday." Buxton, 2022, https://www.buxtonco.com/blog/mystery-solved-where-chick-fil-a-customers-go-on-sunday. Accessed 27 May 2025.

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