What Does the Future Hold for AI and Machine Learning in the Ticketing Industry?

Artificial intelligence (AI) and machine learning have yet to play a truly game-changing role in the ticketing industry. But the future is near, and the possibilities appear truly enticing. Among those most eager for that future is Martin Gammeltoft, Vice President of Commercial Operations for Activity Stream. His Europe-based company with offices in Denmark, Iceland and elsewhere offers Intelligence as a Service to the live entertainment industry, helping organizations get value from their data.

He concedes that ticketing has been slow to adopt AI compared to other industries since a lot of organizations have either built their own data platforms or are relying on their ticketing company to provide such services, with both paths ending short of advanced analytics. “But it’s picking up,” he says, “and a mainly digital industry like this is well suited to benefit. The first applications have been sales predictions, taking in a much broader range of factors than typical Excel-based models and pricing scenario evaluations, supporting key decisions. We’re also seeing some interesting chatbots, like simplif.ai, that can help ticket buyers find tickets.”

Technology has delivered numerous benefits to date. Just ask Mike Lorenc, a 16-year veteran of Google who currently heads up the company’s Ticketing, Sports & Live Events Group. Of the possibilities, he says: “The scale and real-time insight of [machine learning, or ML] has fundamentally changed the way ticket providers conduct marketing strategy. Machine learning algorithms can analyze up to 70 million signals within 100 milliseconds. Long gone are the days when ticket providers manually manage their marketing campaigns. It’s simply not possible for a human to account for all of these real-time insights for each search, each impression or each video view.”

His colleague, nine-year Google vet Luke Rodehorst, adds, “There are two main benefits we have noticed. First, by implementing ML from a search marketing perspective, profits for ticket providers have soared. Beyond sales, marketing teams now have the time to devote to strategic initiatives and other growth-focused priorities since they have more time back from when they would be making manual adjustments.”

Gammeltoft concurs. In his view, organizations incorporating predictions into their business decisions results in a much better allocation of time and marketing budget, as they can increase or decrease efforts as sales outlooks are updated. “You could say that the marketing department already does this,” he says, “but it makes it a much more continuous process, as you’re actually prompted with predictions and not just for your high-profile events.”

All agreed that AI can assist in terms of dynamic pricing. “I believe that a mix of AI and advanced real-time monitoring will be the perfect tool for pricing decisions,” Gammeltoft says. “Where pattern monitoring can alert employees of inventory changes, like price categories being close to selling out, AI can run scenarios and make recommendations. Fully automated dynamic pricing — more precisely labeled adaptive pricing — I don’t think we’ll see in the industry for a long time, as there are so many more factors to consider than in, say, hotels or airlines.”

Lorenc was quick to add his insights. “Outside of advertising and marketing,” he says, “ML is showing great promise in predicting demand for certain shows/artists in different markets. The ability to analyze — in real time — inputs such as video views on YouTube, search trends on Google, streams and social media activity along with past purchase history has the potential to impact not only optimal venue capacity, but also pricing models for yet-to-be announced shows.”

So, what does the future hold for such technology in the ticketing industry? Gammeltoft’s forecast is a measured one. “I think AI will just be one element in the range of advanced analytics being introduced by specialized partners. But the range of applications is extensive — segment suggestions for targeted campaigns, nominating likely donors from weak pattern recognition, fraud prevention and broker identification, and a long range of marketing tools assisting in cross-channel campaigning. These are just the components that I see rolling out in the coming 12 to 18 months.”

Rodehorst, who currently works on Google’s Ticketing & Live Events Team where he partners closely with Broadway shows and performing arts organizations, says: “Most of the ML impact we’ve focused on thus far has been related to marketing. In the future, how does ML interface with the fan experience — from when they research buying a ticket to making a ticket purchase to ultimately showing up to a live event? ML will have real, tangible impact across all of these stages.”

Lorenc adds: “Think about how Netflix uses ML and the vast amounts of viewership data they have access to. Anything from first-in-class marketing and advertising … to analyzing viewership trends in their show selection and greenlighting process. I can see some of these translate to performing arts, sports and music presentation as well as ticketing.”

A year from now, Gammeltoft thinks the technology “will be very developed, but adoption will be very varied, as it requires organizations to take in new technology either by third parties or developed for them. But, in 12 months, I’m sure that the results from early adopters will speak volumes to the impact.”