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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Modeling event data in LookMLLondon Look & Tell, Nov 19 2014

Modeling event data in Looker

• Snowplow: what is it?

• Snowplow + Looker: why?

• LookML: why is it so important?

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

Snowplow works great with Looker

Enormous, detailed record of events

Turn that data into insight

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

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

…

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

Customer record

2. Group micro-events into macro-events

time

Listed video

Viewed synopsis

Paused video

Played video

Finished video

User A engagement with video Y

3. Group sequences of events into sessions

time

Session record

4. Join Snowplow event data to data on the entities involved in the events

CMS

Marketing

CRM

Articles Products Videos …Levels

Adwords Display Social ……

Customers

…

Page 10: Modelling event data in look ml

5. Finally, we define a consistent set of dimensions and measures across the consolidated data set

• 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

• …

Dimensions Measures

Accessible to the whole business

Page 11: Modelling event data in look ml

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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