DESIGNING AND IMPLEMENTING A NEW DATA STRATEGY
The Shubert Organization and Activity Stream jointly present at the Ticketing Technology Forum 2017 in Dublin how the two companies are working together to design and implement a new data strategy in an industry with pressing needs to optimize streaming sales data for shows which most run eight times a week. This blog contains an introduction to the case.
There are a few different ways of working with data. The major steps are the collection, the analysis, the visualization, the distribution and finally the reaction of those responsible for decisions based on insights learned from the data. It is common to move towards becoming a data-driven business by investing in a data warehouse and implementing complex integrations based on consolidating data, which is great when hindsight provides the necessary view on what is going on but doesn’t fully leverage forward-looking opportunities in the data.
“What information would, at this very moment, allow me to do a better job”
Organizations are increasingly realizing the perishable effect time has on operational data, and not only have an immediate need to combine data from different sources, but also view multidimensional information in real-time. As a matter of fact, with more automation of almost all aspects of operations, it is even more of a challenge to put the right information in front of the right member of a team…
…for in-time decision making.
In an effort to design a future-looking data strategy, The Shubert Organization realized how to catapult into a world of insightful, data-driven decisions and AI based operations intelligence using their existing data-structure as a jumping-off point.
For the last two decades, the largest commercial theater entity in the United States – Broadway – has been at the forefront of ticketing strategy, targeted marketing and sales forecasting in a business as unpredictable as it is reliably entertaining. But how does a whole ecosystem answer pressing questions like what makes a hit? Why do people come? How do audiences decide to pay several hundred dollars to share with 1,000 other people an experience that may or may not be to their liking?
The answer – In part – is based on a gut instinct and guesswork. But there is more to the ongoing success of an industry celebrating its best year ever with more than 13 million attendees, and revenues over $1B. Behind the scenes are departments of people conducting surveys, pouring over sales data and actively engaging with technologies designed to learn as much as possible about people sitting in the many seats of Broadway.
The last five years have seen major changes in an industry, moving toward multi-faceted reporting analysis, demographic lookalike modeling, sophisticated CRM-retargeting and prospecting strategies.
But in the last year, specifically, the Broadway of today – particularly at the Shubert Organization, Broadway’s largest venue owner – is virtually unrecognizable from that of one year ago… particularly in the area of business – and operations – intelligence.
How different, you might ask?
Just short 12 months ago venues, producers, promoters and other members of the theater ecosystem had little or no integrated data options commonly understanding their businesses through a series of non-integrated reports and dashboards. Challenges included:
Multiple data silos.
No metric for customer lifetime value.
No way to track if the same customer opened an email, bought a ticket and logged in to a theater wifi.
Industry-wide customer demographics mainly driven by survey statistics with no contact info or segmentation option.
Severe delay in data consolidation and sales data aggregation.
Limited ability to scalably cross-reference data between marketing, sales and loyalty programs.
A year later the data environment is theater ticketing is totally different as a result of the introduction of new tools, deployment of new processes and increased understanding of the importance of data. Major steps towards a truly data-driven culture include:
Tiered audience scoring based on lifetime value, and algorithmically projected lifetime value of a customer.
Marketing-spend thresholds per demographic segment based on historical and projected success.
Dynamically optimized multi-channel campaigns.
Advanced opportunity forecasting for incoming and potentially outgoing productions.
Real-time data visualization through tiered role-based access for customer service to house manager to marketing executives to producers, including machine learning contextual dashboards.
Server-query based pricing recommendations based on real-time traffic and conversion rate per marketing channel.