Realtime Learning: Using Triggers to Know What the ?$# is Going On
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Realtime Learning: Using Triggers to Know What the ?$# is Going On. By Jane Kelly, Director of Data Products, Buzzfeed
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Realtime Learning: Using Triggers to Know What the ?$# is Going On
- 1. Using Triggers to Know What the ?$# is Going On at BuzzFeed Jane Kelly - jane.kelly@buzzfeed.com Director of Data Products BuzzFeed September 2016
- 3. 5+ types of content 10+ platforms 1B+ content views “A Global, Cross-Platform Network for News & Entertainment”
- 4. How?
- 7. What posts should we translate? Translation desk focused on coordination between bureaus Opportunity for non-news content Low cost. High impact.
- 8. What did we find? # in title Over 50% visual Specific verticals What do we do? Send notification to translation team on publish.
- 9. How quickly can we identify high performing videos? Feedback loop for video can be slow Help producers iterate faster We want to push content that is starting to take off
- 10. What did we find? ● Views Shares drive growth. ● V1: 7 day shares. ● V2: 1 hour share rate. What do we do? Send alerts to social teams when video breaks benchmark.
- 11. How exactly do we build these tools?
- 12. Tech challenges from the learning cycle Shifting platforms and organizational focus Inconsistent data availability Balancing rapid experimentation with product stability A diverse set of internal consumers to support
- 13. Collection Raw O&O Events 3rd Party API Responses Publish Events Tag Events
- 14. Experiment Raw O&O Events 3rd Party API Responses Cassandra Redshift BI ToolDashboards Emails Publish Events Translation QR Notifier Slack
- 15. And repeat. Jane Kelly - jane.kelly@buzzfeed.com Director of Data Products BuzzFeed September 2016
Editor's Notes
- What does that mean? Not only BF.com, Tasty, etc Different types of content
- 1 billion plus content views a month Offices Team Types Content Types
- At BuzzFeed we talk a lot about an experimental learning cycle. Autonomous. Test in many places.
- Recently we’ve started focusing on notifications as a way: to quickly get data science work in front of users Going to go through two examples of notifications, then talk more generally about how we build.
- Messaging Bus! Means our systems need to be flexible.
- Platforms: desktop -> mobile web, web -> apps, facebook prioritizing video, rise of snapchat, app consolidation Organization focus: quiz spike, rise of video, Data availability: Balance experimentation: Diverse consumers: Means our systems need to be flexible.
- Base approach/core: Flexible messaging bus (NSQ) Collection: Pixiedust for O&O, But 3rd parties! Ferret (fix name) Storage: Cassandra , Redshift Main Delivery: BI Tool, Bespoke Dashboards, Emails
- Messaging also gives us the freedom to experiment. Listen for when something happens in a content creation system. Processing QRs: Trending, Translation