From the Super Bowl to your local production of As You Like It, sports and entertainment events bring in a lot of attention—and a lot of revenue. For that reason, fans, businesses, and governments collect sports and entertainment event data—that is, all data about events in the entertainment industry.
This data comes from online sources, usually the official league, club, or venue sites. Additional sources include social media, ticket broker, and betting and player stats websites.
All entertainment event datasets note the type of event, the headliners, the venue, and the start date and time. Additional information may include the duration (especially if recurring), door time, sponsor, language, director, or manager.
Relatedly, fans use datasets about player stats for betting and fantasy sports leagues.
Sports fans use this data to plan their attendance at these events, place bets online, play fantasy sports leagues, or engage in discussions online. On the other hand, businesses located near event venues (or just in the same city of a large enough event) use the data to market to attendees, promote specific products, make pricing changes, and plan inventory and workforce schedules. Finally, municipalities and transportation companies use this data to upgrade infrastructure, promote tourism, and update transportation schedules.
Entertainment event data sources are very reliable but subject to frequent changes—for example, weather, accidents, pandemics, changes to tournament eligibility. For this reason, your entertainment dataset must update in real time. Equally important, you must keep it clean and free of duplicates or outdated information.
It is also very important to standardize your event date and time format: different countries use different date formats and the times updated on official sites may give the hours in local time or GMT.
What we learned is these virtual events can be very successful if they are run well. You cannot treat them like a Zoom call. You have to treat them like an event with a run of show and gifting and all the elements that make live events important.
The Jinni Entertainment Genome powers Jinni’s entertainment recommendation engine and marketing campaign platform. The Genome tracks TV and movie content as well as viewer data, ratings, reviews, and much more; they can even apply up to sixty meta-tags to each content title.
In addition, Jinni provides movie ticket purchases, viewer scheduling data, and other information across devices. All this data goes on to drive their targeted marketing campaign platforms.
AthleteMonitoring Data primarily consists of internal data from user biometrics and questionnaire feedback. Users can also import data from other sources, such as EMR documents.
AthleteMonitoring also stays up-to-date on disease, injury prevention and management, and sport data and best practices.
PredictHQ Demand Intelligence combines a company’s historical transaction data with external events data that impacts customer demand