The movie theater industry has been shrinking because of better home-viewing experiences and the reduction in time for movies to become available with online providers such as Netflix, HULU, Amazon etc. With all this competition, movie theaters are challenged to focus on the customer experience to maintain their market presence.
With a substantial number of movies to choose from each week, our client needed the ability to send personalized and relevant communications to each customer, suggesting the movie that would most appeal to them individually. Sending generic communications to their 10,000,000+ loyalty members was creating customer disappoint rather than encouraging folks to head to the movies.
To top it off, our client needed clear KPIs for measurement, and a plan to optimize their return on marketing investment (ROMI).
With its subject matter expertise and strong data science capabilities, Sutherland designed and implemented a customer behavior-based recommendation engine, which blended collaborative and content filtering. Collaborative filtering is a method of making recommendations for a user by collecting preferences from many users (i.e., Netflix), and content filtering makes recommendations for a user by identifying items that are like those that a user has liked in the past (i.e., Pandora). The recommendation engine leverages all this filtering to then provide unique recommendations for each customer in the loyalty program, based on their historic movie watching behavior and choice of movie genre, ratings, franchises, formats, etc.
The recommendation engine helped the client accurately predict time of next visit, choice of movies, choice of format, and even the preferred theater for each of their customers, every week. And, customers were communicated with via their preferred channel, inclusive of email, SMS, mobile app notifications, and the website landing page. This strategy significantly improved customer engagement across channels.
