High-Quality Data You Can Trust and Scale
Large scale data analysis needs high quality input.
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Many of our power-users originally tried to build their own event prediction engine before realizing how complex and costly this is. So they partnered with PredictHQ.
Every week, thousands of events impact demand globally. Whether it’s big events or multiple smaller ones creating perfect storms of demand, companies that know in advance can prepare better. Before PredictHQ, there was no central source of real-world event data. We’ve spent years building a unique, powerful event processing pipeline so your team can access trustworthy, intelligent event data in minutes.
Fetch event data from hundreds of sources
Convert raw data to a standard format
Combine duplicates into one reliable event
Our system finds a football game with 30,000 expected attendees. It finds eight listings of this game from five sources. Our unique model kicks in and deletes all but the most detailed and reliable of the listings.
Remove dirty data — such as
spam— to ensure data accuracy
30% of events are spam, add-ons or duplicates. Significant machine learning drives our spam filter, which is why it can be so accurate at such a high volume.
Pinpoint location based on latitude and longitude and tags a timezone
We add or update geocoding information on 50% of events.
Additional contextual data is added from a range of sources for deeper insight
Start and predicted end times are included in the intelligent event data
Some event data sources provide start times but none provide the end times for every event. This is critical for transport, delivery and ride sharing companies that want to ensure drivers are at a location when an event ends.
Every event is assigned a numeric value on a logarithmic scale between 0 to 100 to represent its predicted impact.
Focus on the events that will have the highest impact. Accurate ranking is complex but essential for scalable demand intelligence and forecasting.
Clean, accurate, and intelligent event data delivered directly to you