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Modelling event data in look ml

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Description of how we use Looker at Snowplow Analytics, including the specific steps involved in modelling event data using LookML

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Modelling event data in look ml

  1. 1. Modeling event data in LookML London Look & Tell, Nov 19 2014
  2. 2. Modeling event data in Looker • Snowplow: what is it? • Snowplow + Looker: why? • LookML: why is it so important?
  3. 3. Snowplow is an event analytics platform 1. Trackers 2. Collectors 3. Enrich 5. Modelling 6. Analytics 2. Webhooks 4. Storage Unified log: record of every event that has occurred
  4. 4. Snowplow works great with Looker Enormous, detailed record of events Turn that data into insight
  5. 5. So what is actually happening in the data modeling step of the pipeline? 1. Trackers 2. Collectors 3. Enrich 5. Modelling 6. Analytics 2. Webhooks 4. Storage ?
  6. 6. 1. Identity stitching: identifying that groups of events belong to the same user time Page view Product summary view Transaction Product detailed view Share product Add product to basket Viewed ad … … … … … … … … … … Customer record 1. Generate single record for each user 2. Perform any behavioral segmentation based on that user’s event stream 3. Join that user record with other sources of user data e.g. CRM
  7. 7. 2. Group micro-events into macro-events time Listed video Viewed synopsis Paused video Paused video Played video Finished video User A engagement with video Y
  8. 8. 3. Group sequences of events into sessions time Session record Session record Session record Session record Session record Session record Session record
  9. 9. 4. Join Snowplow event data to data on the entities involved in the events CMS Articles Products Videos Levels … Marketing Adwords Display Social … … CRM Custome rs …
  10. 10. 5. Finally, we define a consistent set of dimensions and measures across the consolidated data set Dimensions Measures • Products • Brands • Categories • Articles • Author • Days since published • Categories • Users • User cohort • Behavioral segments • Demographic segments • Stage in funnel • … • Users count • Engagement levels • Current value • Forecast lifetime value • Number of SKUs • Number of articles • Number of upsells • Number of new users • … Accessible to the whole business
  11. 11. In summary • LookML: application of business logic to our underlying data • Data from Snowplow represents what has happened • In LookML we define how we interpret that underlying data, given our own business logic e.g. • How do we identify users? • How do we segment users? • How do we join multiple different data sets into a single source of truth? • How do we measure engagement? • We need to do this at the end of the data pipeline • Business evolve: as you get more sophisticated, your LookML model will evolve • Your data is constantly recast as your model – data never goes stale • LookML is the best framework we’ve used to manage the data modeling process required on Snowplow event data

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