In this Radar Podcast episode, I sit down with John Carnahan, executive vice president of data science at Ticketmaster. At our recent Strata + Hadoop World Conference in San Jose, CA, Carnahan presented a session on using data science and machine learning to improve ticket sales and marketing at Ticketmaster.
I took the opportunity to chat with Carnahan about Ticketmaster’s evolving approach to data analysis, the avenues of user engagement they’re investigating, and how his genetics background is informing his work in the big data space.
When Carnahan took the job at Ticketmaster about three years ago, his strategy focused on small, concrete tasks aimed at solving distinct nagging problems: how do you address large numbers of tickets not sold at an event, how do you engage and market those undersold events to fans, and how do you stem abuse of ticket sales. This strategy has evolved, Carnahan explained, to a more holistic approach aimed at bridging the data silos within the company:
“We still want those concrete things, but we want to build a bed of data science assets that’s built on top of a company that’s been around almost 40 years and has a lot of data assets. How do we build the platform that will leverage those things into the future, beyond just those small niche products that we really want to build. We’re trying to bridge the gap between a lot of those products, too. Rather than think of each of those things as a vertical or a silo that’s trying to accomplish something, it’s how do you use something that you’ve built over here, over there to make that better?”
In an interview at the time he took the job, Carnahan noted that, “Using a website isn’t going to be the be-all-end-all of how people purchase tickets.” His initial thinking on this, he told me, was the potential in mobile, but now they’re finding that methods of engagement extend well beyond our connected devices to the fans themselves:
“The real channel that we want to tap into for getting our fans to attend more events, is how do we leverage people? How do we get other people to market for us? We’re in this business, and I use this quote all the time, ‘It’s one thing to be in a commerce industry where you’re selling something that’s very profitable, that has a good margin, that has a lot of upside to it, but it’s interesting to be in the live event space where people are really passionate about it.’
“You can sell shoes, you can sell clocks, you can sell phones, but nobody is going to be at a stage screaming and yelling for phones. They’re not going do that. We’re in that business where people care that much about what they’re purchasing that they scream and cry about it. How do we use that passion to get people to market these events for us? … I think beyond mobile channels, it’s people. It’s getting people to help get more people to attend more events and sell more tickets.”
Interestingly, Carnahan’s early background is in population genetics, and I was curious as to how his genetics experience informed his current work. He explained that it really comes down to having learned the basics of scientific discipline and to coming to the understanding that good data science is an art form:
“I argue that the field of statistics was actually invented for population genetics. It’s been around a really long time. My first venture out of grad school was to work for a small company called GoTo, which eventually became Overture, and created the sponsored search industry. One of the things I initially did was take a sequence alignment algorithm and apply that to the moneymaking industry behind search, in the very early days.
“But I think, beyond the statistics, beyond the similarities beween population genetics and what you call modern data science, the real thing I gained was scientific discipline — the ability to do an experiment, the ability to keep doing things over and over and over again, and then be the person who can recognize when something really magical is happening. Science you can get from libraries, from running code, etc., but real data science is an art form. It’s about recognizing when you see something that nobody else sees. That’s what really makes a good data scientist, it’s that art.”
Also in this Radar Podcast…
We have a special feature this week as well: Three Questions with Michael Abbott. Abbott is a general partner at VC firm Kleiner Perkins Caufield & Byers. He offers advice for working with investors and talks about how his extensive background in engineering and entrepreneurship led him to join a venture capital firm. We also get a bit of insight into the people and projects capturing his attention these days.