PredictHQ is the world’s first and only company that provides the missing context for the biggest external factor that impacts businesses demand – events.

PredictHQ’s intelligent data of verified global events enables businesses to forecast shifts in demand from events to be able to adjust their inventory, make changes to labor, dynamically price and operate more efficiently. Think conferences, sports games, college graduations, floods, and more.

PredictHQ brings all events into one place, combines it with world-first tools and intelligence to allow organizations to better predict and respond to changing customer demand created by events in an easy, reliable, and scalable way.

19 Data Categories

Attendance based events: sports games, performing arts, community, conferences, concerts, expos, festivals

Non-attendance based events: observances, public holidays, politics, daylight savings, school holidays, academic events

Unscheduled events: severe weather, airport delays, terror attacks, health warnings, disasters,

Live TV events

Customers & Use Cases

Customers such as Uber, InstaCart, Favor, and other delivery and ride-hailing providers use the data for driver retention, improved customer experience, and demand forecasting.

Transportation providers, including rail and bus operators, use the data for demand forecasting, as well as predicitng likelihood of service delays due to increased passenger demands from events.

Parking companies use the verified event data to better predict demand and as a result, more precisely match parking supply to demand.

Data Quality

PredictHQ's data quality is one of its key strengths: 

1. We have developed a set of Quality Standards for Processing Demand Causal Factors (QSPD), which are used to define the criteria for high-quality event data. By following these standards, PredictHQ ensures that their data meets the highest levels of quality.
2. We use more than 450 data sources to collect event data, including public records, social media, and ticketing websites.
3. We have built thousands of machine learning models that standardize, verify, enrich, and rank every single event.
4. On average we process 28 million events and 422,000 entities every day
5. We track the quality of our data over time and make improvements as needed.