Most humans are social creatures and will venture outside their homes to shop, dine, and be entertained. As the pandemic forced people to remain home, they turned to online shopping, meal delivery, and streaming services.
As more and more people begin to venture out in the post-vaccine world, many industries will see an influx of customers—especially the entertainment industry. The joy of experiencing an action movie and hearing others gasp or cheer is thrilling. Zoom calls are a great way to connect with others from a distance but don't give you the same connection.
Just as a roller coaster has its ups and downs, so too will theater attendance. How do companies analyze the new trends and predict attendance in this new world? So much depends on an accurate prediction, such as setting employee schedules and ordering enough supplies.
ElectrifAi’s pre-built machine learning model, Demand Forecasting, is an excellent answer to that problem. With this technology, you can predict the popularity of a show and the number of people likely to attend a specific session.
Business success depends on the number of tickets and concessions sold. Here are some use cases that this machine learning model can solve to increase success:
The Demand Forecasting machine learning model can predict attendance to help create a better schedule. The attendance forecasting is divided into two major components: Inter-Day and Intra-Day models.
Many data sources and features help the machine learning model make accurate predictions, such as:
Many types of reports are available to review the data output, such as:
The information included in these reports gives the necessary insights needed to make strategic business decisions. Using these insights, selling the most tickets is much easier to accomplish.
What’s the business impact that can be expected by implementing the Demand Forecasting machine learning model in your company?
The next step to take is to decide whether to attempt creating your own machine learning model from scratch or partnering with an experienced firm that already has a business-ready model.
Building your own machine learning model is a risky and expensive venue. There are overhead costs when hiring your own team of data scientists and it can take months if not years to produce a working model. This decision is also not guaranteed to produce actionable insights to help your business.
With ElectrifAi, we can help you get quick results that have been proven to help the entertainment industry accurately predict attendance, increase revenue, and decrease risk. Now is the time to use all the new data you’ve been accumulating during these unknown times.
If you would like to see how your data can work for you, contact us today for a custom demo!
Cluster cannibalization is the amount of demand due to the same cluster or similar shows.
Demand forecasting helps companies predict the amount of demand likely for movies and shows (e.g., plays, concerts, etc.) to help optimize schedules and supplies.
Demand shift are nearby venues with similar sessions of the same shows.
Inter-Day helps entertainment companies understand venue demand on a long-term basis (e.g., current week, next week, and longer-term).
Holdover prolongs the engagement of a show, such as extending it for another week.
Slot cannibalization is the amount of demand due to shows in very close-by slots.