t=0 Software Engineering Night Product Review: Trill

curated live events from people that know what they’re doing

If you’re like me, you rely on recommendations from others to find live events. I’m definitely not an expert of music, comedy, or other kinds of shows but I love going to them. I imagine there’s a lot of people out there like me. This + being a new resident in Boston got me excited to learn about Trill at the t=0 Software Engineering night.

Trill brings the expertise of curators who know various domains of art/live shows (classical music, rock, electronic, hip-hop, jazz, theater, comedy, dance) to consumers like me who have no idea how to navigate the countless venue/artist/promoter websites to find shows. Better yet, show recommendations are from people who know what they are talking about.

Here are two thoughts I’d love to see from Trill’s curation engine:

  1. Data-driven Comedic Recommendations: You know how Pandora built the music genome project and recommends music you might like? Give me that for comedians. It doesn’t exist and that is a travesty. I’ve never done stand-up nor am I an industry expert. But at some point in almost all of the shows I go to, the comedian pokes fun at themselves talking about how being a comedian is a tough job. I believe it 100% and I want to go to those undiscovered comedians’ shows especially before they get big. At least a local level, I have to believe there is some similarity between artists that these curation experts could build a genome for comedians and then recommend the next comedian I should go see. If I like, say, Hari Kondabolu and I told Trill that, I’d want to see others that might have a similar style as he does. After all, pushing recommendations out might keep me from churning as a Trill user.
  2. Get and Use My Preferences: I am happy to give my preference information to experts assuming my data stays secure. Ask me for it! Ask me what kinds of comedians or artists I like to see, tie into my Netflix viewing behavior, or my credit cards. This could be out of scope now but the point is getting a more holistic view of a user’s preferences can bolster the recommendations. It can start with a quick survey or even a quiz to get an idea of what I like. Perhaps instead of static text or images, Trill could show me short YouTube clips of comedians and then I could rate how much I liked their material.

I’ve been a part of trying to bring data to a world that might not have standard measurements like finance or product functionality. The arts is one that could significantly benefit from codifying characteristics of performances, artists, and shows and then getting more of what people like me like without causing us the burden of searching for it.