Introducing Predicted Event Spend: Track the massive spending events drive
Track the massive spending events drive across industries
Live events are a powerful economic catalyst, generating billions of dollars in spending each year across the globe. In fact, live events are predicted to result in over a trillion dollars in spending across the US alone in 2024 – and our predictive analysis shows $95B of that is up for grabs for hotels, restaurants and rideshare operators near these events.
Whether it's a concert, expo, or sports game, live events attract people from all over the world who are eager to spend money on hotels, restaurants, and transportation to complete their event experience. Predictive analytics solutions help these businesses identify peak periods of event-based spending and demand, for example:
Nashville, TN saw hotel occupancy rise more than 30% and room rates increase more than 50% on concert nights for Taylor Swift’s Eras tour.
Super Bowl LVII brought 100,000+ out-of-state visitors to Glendale, AZ who spent over $221 million on local businesses while they were in town.
San Diego Comic Con attracts 135,000+ attendees over four days, producing a regional economic impact of $160M+ and generating $3M+ in hotel and sales tax revenues for the city.
You can now easily track the economic impact of these events with PredictHQ’s Predicted Event Spend – a dollar figure that represents estimated consumer spending on local restaurants, accommodation, and ground-based transportation in a specific area as a result of an event. The new predictive analytics solution empowers businesses to identify and tap into the events that are driving the most spend relevant to their business.
Predicted Event Spend powers timely data-driven decisions
The value of Predicted Event Spend extends far beyond just providing businesses with an estimate of the economic impact of events. It equips operators with reliable and actionable insights that can significantly influence their decision-making processes. For example, Taylor Swift brought in a combined $17M of spending to Buenos Aires on November 9, 11, and 12 – that same weekend, Argentinian singer Luciano Pereyra brought in an additional $920,000 combined in spending on November 10 and 11.
In Buenos Aires for Thursday, November 9 through Sunday November 12, spending based on Taylor Swift was nearly 20x that of the spending for Luciano Pereyra‘s concert – but just imagine if hotels, restaurants and transportation businesses weren’t aware of both concerts happening that weekend. This is why we stress the importance of demand forecasts that are real-world aware.
Armed with reliable real-world data indicating where and when consumer spending will be the highest, businesses across industries have the power to make informed decisions about resource allocation, marketing strategies, pricing, and much more in advance. This includes preparations to ensure they have the right amount of staff, inventory, and other resources on hand to meet fluctuating levels of customer demand:
Accommodation: Identify events driving accommodation spending in advance to set optimal room rates and develop targeted marketing campaigns.
Restaurants: Identify events most likely to boost restaurant demand, and prepare by stocking up inventory, and scheduling additional staff for event-based peaks in demand.
Transportation: Anticipate surges in demand for transportation to schedule additional vehicles and staff, develop contingency plans, and minimize disruptions such as event-based traffic.
A new era of intelligent event data
To start taking advantage of the business demand surrounding events, sign up for your free PredictHQ account today to start exploring the Predicted Event Spend of local events impacting your specific business.
You'll be able to delve into comprehensive insights into how various events, whether it's a sports championship, music festival, or conference, influence spending patterns at scale. Get started with intelligent event data today to level up your demand forecasting and boost your business growth.