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Listen "PredictHQ Co-Founder Campbell Brown on Forecasting the Future of Restaurant Demand"
Episode Synopsis
In this episode of the Restaurant AI Podcast, Matt Wampler, Co-Founder and CEO of ClearCOGS, sits down with Campbell Brown, Co-Founder and CEO of PredictHQ, to explore how artificial intelligence and real-world event data are transforming restaurant forecasting and operations.
Campbell shares the story of building PredictHQ from its early days in New Zealand to becoming the “demand intelligence” engine for global brands like Chipotle, Domino’s, and Wingstop. He explains how everything from Taylor Swift concerts to dental conventions can shift millions in restaurant sales—and why operators must move beyond anecdotal knowledge to data-driven foresight.
The conversation dives into the complexity of cleaning and standardizing event data, why explainability builds trust in AI-driven forecasts, and how restaurants can operationalize demand insights to optimize staffing, inventory, and guest experience. Campbell also paints a picture of what’s next: prescriptive AI that not only forecasts surges but recommends the exact actions to capture them.
From concerts and weather to strikes and sports, Campbell reveals how PredictHQ is helping restaurants and retailers embrace “real-world aware AI” to navigate demand volatility and unlock billions in value.
Episode Links:
- Connect with Campbell on LinkedIn: https://www.linkedin.com/in/campbellbrown
- Learn more about PredictHQ: https://www.predicthq.com
- Follow PredictHQ on LinkedIn: https://www.linkedin.com/company/predicthq
- Connect with Matt on LinkedIn: https://www.linkedin.com/in/matthewjwampler
- Visit ClearCOGS: https://www.clearcogs.com/
00:00 Intro
01:41 Welcome, Campbell Brown!
02:00 The Taylor Swift Effect
04:00 The major categories of PredictHQ
05:02 The origin of PredictHQ
09:21 PredictHQ's first customer
11:00 Taking on Google early on
12:55 Getting the right data is a battle itself
14:15 Retail forecasting
17:00 Explaining the business of foresight
18:30 One-off events and their impact
21:20 The benefits of foresight for the restaurant industry
22:20 Operationalizing data forecasting
24:54 Getting customers the right data
28:13 Prescriptive analytics
31:10 What it takes to build PredictHQ
33:46 The new events impacting demand
34:29 Weird events that impact demand
35:50 How much of demand can be explained
37:40 The operator's impact
40:15 PredictHQ vs LLM's
45:09 The difficulty in gathering data
46:52 Implementing data forecasting from the customer side
48:23 External features
50:34 Utilization and management
53:57 The future of PredictHQ
55:37 Real world AI
1:00:04 Why data forecasting is inevitable
1:01:54 Outro
Campbell shares the story of building PredictHQ from its early days in New Zealand to becoming the “demand intelligence” engine for global brands like Chipotle, Domino’s, and Wingstop. He explains how everything from Taylor Swift concerts to dental conventions can shift millions in restaurant sales—and why operators must move beyond anecdotal knowledge to data-driven foresight.
The conversation dives into the complexity of cleaning and standardizing event data, why explainability builds trust in AI-driven forecasts, and how restaurants can operationalize demand insights to optimize staffing, inventory, and guest experience. Campbell also paints a picture of what’s next: prescriptive AI that not only forecasts surges but recommends the exact actions to capture them.
From concerts and weather to strikes and sports, Campbell reveals how PredictHQ is helping restaurants and retailers embrace “real-world aware AI” to navigate demand volatility and unlock billions in value.
Episode Links:
- Connect with Campbell on LinkedIn: https://www.linkedin.com/in/campbellbrown
- Learn more about PredictHQ: https://www.predicthq.com
- Follow PredictHQ on LinkedIn: https://www.linkedin.com/company/predicthq
- Connect with Matt on LinkedIn: https://www.linkedin.com/in/matthewjwampler
- Visit ClearCOGS: https://www.clearcogs.com/
00:00 Intro
01:41 Welcome, Campbell Brown!
02:00 The Taylor Swift Effect
04:00 The major categories of PredictHQ
05:02 The origin of PredictHQ
09:21 PredictHQ's first customer
11:00 Taking on Google early on
12:55 Getting the right data is a battle itself
14:15 Retail forecasting
17:00 Explaining the business of foresight
18:30 One-off events and their impact
21:20 The benefits of foresight for the restaurant industry
22:20 Operationalizing data forecasting
24:54 Getting customers the right data
28:13 Prescriptive analytics
31:10 What it takes to build PredictHQ
33:46 The new events impacting demand
34:29 Weird events that impact demand
35:50 How much of demand can be explained
37:40 The operator's impact
40:15 PredictHQ vs LLM's
45:09 The difficulty in gathering data
46:52 Implementing data forecasting from the customer side
48:23 External features
50:34 Utilization and management
53:57 The future of PredictHQ
55:37 Real world AI
1:00:04 Why data forecasting is inevitable
1:01:54 Outro
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