In this revealing conversation, Campbell Brown, founder of PredictHQ, breaks down how major events drive massive revenue spikes for restaurants and retail businesses. From Taylor Swift concerts generating $3.2 million in restaurant sales in just one night to hidden impacts of youth basketball tournaments, this episode explores the untapped potential of event-driven forecasting.
The Taylor Swift Effect & Major Event Impact
- How one Taylor Swift concert at SoFi Stadium generated $5.5 million in local spend, with $3.2 million going to restaurants alone
- Why the “Taylor Swift effect” is real and measurable across entire economies
- Real examples of how major concerts, sporting events, and conferences reshape local demand patterns
Event Data Challenges & Solutions
- The complexity of aggregating 450+ event data sources globally
- Why Google failed at event forecasting and shut down their competing product
- How PredictHQ processes millions of event changes daily with 0.8% error rates
- The difference between raw event listings and actionable business intelligence
Restaurant Forecasting & Operations
- How major QSR chains like Chipotle, Domino’s, and Wingstop use event data for labor scheduling
- Why a 1,500-person youth basketball tournament can impact restaurants more than a 30,000-person baseball game
- Real stories from restaurant owners about unexpected demand surges and missed opportunities
Beyond the Obvious: Hidden Event Impacts
- How farmer’s markets boost neighboring grocery store sales
- Why Chinese New Year can be one of the worst days for certain restaurants
- The counterintuitive effects of large parades on regular customer traffic
- Gaming conventions, Comic-Cons, and emerging event categories driving demand
Technology & Implementation
- How businesses integrate event forecasting through APIs and existing systems
- The role of AI and machine learning in predicting attendance, spend, and demand patterns
- Why explainability matters more than accuracy for operational buy-in
- Moving from descriptive analytics to prescriptive actions
Industry Applications Beyond Restaurants
- How Uber uses event data across five different use cases to optimize driver positioning
- Retail chains using event intelligence for inventory management and staffing
- Travel and hospitality applications for dynamic pricing and capacity planning
- Government and insurance sector use cases
The Future of Real-World AI
- Why synthetic data isn’t enough for accurate business forecasting
- The importance of clean, real-world data for training effective AI models
- How $5.5 trillion in consumer spend data creates unprecedented economic insights
- Patent applications for predicting sports viewership and its business applications
Practical Advice for Restaurant Operators
- Why embracing AI tools personally helps operators understand business applications
- The importance of human-in-the-loop decision making vs. full automation
- How to think about event forecasting as “waves” of demand you can either ride or miss
- Starting with simple implementations before scaling to complex forecasting systems
This episode provides restaurant owners, operators, and technology professionals with concrete insights into how event intelligence is transforming demand forecasting across industries, backed by real data and customer success stories from some of the world’s largest brands.
Companies Mentioned: PredictHQ, Uber, Lyft, Chipotle, Domino’s, Wingstop, BJ’s Restaurant, Chick-fil-A, Airbnb, CVS, Toast, Google, Expedia
Keywords: Restaurant forecasting, event marketing, demand planning, AI in restaurants, labor scheduling, inventory management, QSR technology, predictive analytics, restaurant operations, hospitality technology
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