Intelligent Event Data | Consumer Spending Data at Events | Seoul, South Korea

Predicted event spend draws on: 1) verified and intelligent event data, 2) predicted attendance models which factor in venue, performers, historical attendance, and more to identify an accurate estimate of the event size, 3) anonymised customer impact data from our data lake that span across a wide range of businesses, 4) location specific information such as standard room rates and restaurant prices for thousands of locations, and 5) event categorization models which identify the category, type, and relevant labels to define events. PredictHQ created this tool because our customers requested it, but its application will vary significantly based on which industry, and which team, is using it. For example: 1) QSRs can use it to compare apples to apples i.e. the restaurant spend depending on the different kinds of events near the same store, 2) Accommodation businesses can use it to communicate to their hotel managers as to why the pricing recommendations have been increased or decreased, as well as to set appealing minimum stay discounts or similar packaging updates, 3) Event-based marketing or out-of-home marketing groups can use it to quickly describe the impact of an event for particular segments considering where to place their ads, and 4) Smaller businesses that don’t have the team capacity to use event data extensively in planning can use it as a shorthand for how impactful an event will be, and roster more staff or stock up on inventory accordingly. Predicted Event Spend demonstrates how individual or groupings of events drive massive spending on behalf of visitors and is a reflection of the total amount of additional spending within a defined area as a result of a specific event. Location: Seoul, South Korea 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, hospitality, travel, tourism, aviation, flight, ride-sharing, transportation, mobility, spending data, money, financial