Intelligent Event Data for Attended Events (Sports, Festivals, Expos, Conferences, Concerts) | Munich | 450+ Data Sources

This sample covers attendance-based events that impact businesses. Attended events are gatherings with a start and end date/time, where people come together in one location for entertainment or business. Companies use this information to inform their demand forecasting and planning, and update their staffing, inventory and pricing strategies to match demand. Teams at Domino’s Pizza, Uber and AccorHotels trust PredictHQ’s precise and scalable real-world events data to enhance demand forecasting models, improve workforce optimisation strategies, aid dynamic pricing efforts, and much more. Data also includes Predicted Event Spend, a dollar figure that reflects the predicted amount of retail, accommodation, and transportation spending in a specific area as a result of a major event. At the core of this data sits our market leading global event coverage, predicted event attendance, local accommodation demand, third party data and more to give you greater geographical context of the scale of the economic impact an event will have. Location: Munich, Germany Visibility Window: 1-Year Historical Categories: Sports, Festivals, Expos, Conferences, Concerts, Performing Arts, and Community. Fields Included: - Title - Category - Labels - Description - Start date and time - End date and time - Predicted end time - Country - Lat / Lon - Venue Name - Venue Address - Rank (PHQ Rank, Local Rank) - PHQ Attendance - Event status - Place Hierarchy - Created/updated timestamps - Predicted event spend total - Predicted event spend accommodation - Predicted event spend hospitality - Predicted event spend transportation Polygon information: PredictHQ's polygons enable you to see the full area impacted by an event represented as a shape, because many types of events don't occur neatly at a point on a map. That means you will get a much more accurate picture of impact. Data samples including polygons are available upon request. 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. About PredictHQ: 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. We meet customers exactly where they are, ensuring they can access our data the way that suits them best. Learn more about PredictHQ's real-world event data by visiting our Developer and Data Science Documentation: https://docs.predicthq.com/ Keywords: attended events, attendance, sports, festivals, expos, conferences, concerts, performing arts, community, polygon, consumer spending, predicted spend, location information, demand intelligence, financial data, venue location, accommodation, transportation, restaurant, demand intelligence, event intelligence, event categorisation, business insights, event tracking, historical event data, even impact analysis, event-driven decisions, predictive analytics