data science at Ticketmaster
summary
Ticketmaster builds a platform of concrete data products: algorithms to find unsold seats and re-engage likely buyers, personalized recommendation models that power mobile and Facebook catalogs, and abuse detection that targets reserve-and-resell scams where tickets sit in a 15-minute reserve state then hit secondary markets for $2,000–$3,000; mitigation uses machine learning and a program of 24-hour hackathons — one with six engineers from 714 Tickets Group — while work shifts toward real-time data pipelines and idempotent delivery.
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chapters (9)
- 0:00Intro
- 1:53What avenues of engagement are you finding
- 4:21Can you give an example of success
- 5:17Personalized marketing
- 5:54Obstacles
- 7:15Learnings
- 9:33Bots
- 12:06Hackathon
- 12:45Background in genetics
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