  Performing arts organizations aim to enrich their communities through the
arts. To do this, they strive to match their performance offerings to the taste
of those communities. Success relies on understanding audience preference and
predicting their behavior. Similar to most e-commerce or digital entertainment
firms, arts presenters need to recommend the right performance to the right
customer at the right time. As part of the Michigan Data Science Team (MDST),
we partnered with the University Musical Society (UMS), a non-profit performing
arts presenter housed in the University of Michigan, Ann Arbor. We are
providing UMS with analysis and business intelligence, utilizing historical
individual-level sales data. We built a recommendation system based on
collaborative filtering, gaining insights into the artistic preferences of
customers, along with the similarities between performances. To better
understand audience behavior, we used statistical methods from customer-base
analysis. We characterized customer heterogeneity via segmentation, and we
modeled customer cohorts to understand and predict ticket purchasing patterns.
Finally, we combined statistical modeling with natural language processing
(NLP) to explore the impact of wording in program descriptions. These ongoing
efforts provide a platform to launch targeted marketing campaigns, helping UMS
carry out its mission by allocating its resources more efficiently. Celebrating
its 138th season, UMS is a 2014 recipient of the National Medal of Arts, and it
continues to enrich communities by connecting world-renowned artists with
diverse audiences, especially students in their formative years. We aim to
contribute to that mission through data science and customer analytics.
